@article {138, title = {Comprehensive cell type decomposition of circulating cell-free DNA with CelFiE.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 05 11}, pages = {2717}, abstract = {

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising noninvasive biomarker for cell death. Here, we propose an algorithm, CelFiE, to accurately estimate the relative abundances of cell types and tissues contributing to cfDNA from epigenetic cfDNA sequencing. In contrast to previous work, CelFiE accommodates low coverage data, does not require CpG site curation, and estimates contributions from multiple unknown cell types that are not available in external reference data. In simulations, CelFiE accurately estimates known and unknown cell type proportions from low coverage and noisy cfDNA mixtures, including from cell types composing less than 1\% of the total mixture. When used in two clinically-relevant situations, CelFiE correctly estimates a large placenta component in pregnant women, and an elevated skeletal muscle component in amyotrophic lateral sclerosis (ALS) patients, consistent with the occurrence of muscle wasting typical in these patients. Together, these results show how CelFiE could be a useful tool for biomarker discovery and monitoring the progression of degenerative disease.

}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, B-Lymphocytes, Biomarkers, Case-Control Studies, Cell-Free Nucleic Acids, DNA Methylation, Epigenesis, Genetic, Female, Humans, Macrophages, Male, Monocytes, Muscle, Skeletal, Neutrophils, Organ Specificity, Pregnancy, Pregnancy Trimesters, T-Lymphocytes}, issn = {2041-1723}, doi = {10.1038/s41467-021-22901-x}, author = {Caggiano, Christa and Celona, Barbara and Garton, Fleur and Mefford, Joel and Black, Brian L and Henderson, Robert and Lomen-Hoerth, Catherine and Dahl, Andrew and Zaitlen, Noah} } @article {144, title = {A cross-population atlas of genetic associations for 220 human phenotypes.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 10}, pages = {1415-1424}, abstract = {

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.

}, keywords = {ABO Blood-Group System, Biological Specimen Banks, Genetic Association Studies, Genetic Loci, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Major Histocompatibility Complex, Meta-Analysis as Topic, Mutation, Phenotype}, issn = {1546-1718}, doi = {10.1038/s41588-021-00931-x}, author = {Sakaue, Saori and Kanai, Masahiro and Tanigawa, Yosuke and Karjalainen, Juha and Kurki, Mitja and Koshiba, Seizo and Narita, Akira and Konuma, Takahiro and Yamamoto, Kenichi and Akiyama, Masato and Ishigaki, Kazuyoshi and Suzuki, Akari and Suzuki, Ken and Obara, Wataru and Yamaji, Ken and Takahashi, Kazuhisa and Asai, Satoshi and Takahashi, Yasuo and Suzuki, Takao and Shinozaki, Nobuaki and Yamaguchi, Hiroki and Minami, Shiro and Murayama, Shigeo and Yoshimori, Kozo and Nagayama, Satoshi and Obata, Daisuke and Higashiyama, Masahiko and Masumoto, Akihide and Koretsune, Yukihiro and Ito, Kaoru and Terao, Chikashi and Yamauchi, Toshimasa and Komuro, Issei and Kadowaki, Takashi and Tamiya, Gen and Yamamoto, Masayuki and Nakamura, Yusuke and Kubo, Michiaki and Murakami, Yoshinori and Yamamoto, Kazuhiko and Kamatani, Yoichiro and Palotie, Aarno and Rivas, Manuel A and Daly, Mark J and Matsuda, Koichi and Okada, Yukinori} } @article {130, title = {Elevated Polygenic Burden for Autism Spectrum Disorder Is Associated With the Broad Autism Phenotype in Mothers of Individuals With Autism Spectrum Disorder.}, journal = {Biol Psychiatry}, volume = {89}, year = {2021}, month = {2021 03 01}, pages = {476-485}, abstract = {

BACKGROUND: Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder that encompasses a complex and heterogeneous set of traits. Subclinical traits that mirror the core features of ASD, referred to as the broad autism phenotype (BAP), have been documented repeatedly in unaffected relatives and are believed to reflect underlying genetic liability to ASD. The BAP may help inform the etiology of ASD by allowing the stratification of families into more phenotypically and etiologically homogeneous subgroups. This study explores polygenic scores related to the BAP.

METHODS: Phenotypic and genotypic information were obtained from 2614 trios from the Simons Simplex Collection. Polygenic scores of ASD (ASD-PGSs) were generated across the sample to determine the shared genetic overlap between the BAP and ASD. Maternal and paternal ASD-PGSs were explored in relation to BAP traits and their child{\textquoteright}s ASD symptomatology.

RESULTS: Maternal pragmatic language was related to child{\textquoteright}s social communicative atypicalities. In fathers, rigid personality was related to increased repetitive behaviors in children. Maternal (but not paternal) ASD-PGSs were related to the pragmatic language and rigid BAP domains.

CONCLUSIONS: Associations emerged between parent and child phenotypes, with more associations emerging in mothers than in fathers. ASD-PGS associations emerged with BAP in mothers only, highlighting the potential for a female protective factor, and implicating the polygenic etiology of ASD-related phenotypes in the BAP.

}, keywords = {Autism Spectrum Disorder, Autistic Disorder, Child, Fathers, Female, Humans, Male, Mothers, Phenotype}, issn = {1873-2402}, doi = {10.1016/j.biopsych.2020.08.029}, author = {Nayar, Kritika and Sealock, Julia M and Maltman, Nell and Bush, Lauren and Cook, Edwin H and Davis, Lea K and Losh, Molly} } @article {114, title = {Genetics of 35 blood and urine biomarkers in the UK Biobank.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 02}, pages = {185-194}, abstract = {

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build {\textquoteright}multi-PRS{\textquoteright} models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.

}, keywords = {Biological Specimen Banks, Biomarkers, Cardiovascular Diseases, Diabetes Mellitus, Type 2, DNA Copy Number Variations, Genetic Pleiotropy, HLA Antigens, Humans, Linkage Disequilibrium, Liver-Specific Organic Anion Transporter 1, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, Proteins, Renal Insufficiency, Chronic, Serine Endopeptidases, United Kingdom}, issn = {1546-1718}, doi = {10.1038/s41588-020-00757-z}, author = {Sinnott-Armstrong, Nasa and Tanigawa, Yosuke and Amar, David and Mars, Nina and Benner, Christian and Aguirre, Matthew and Venkataraman, Guhan Ram and Wainberg, Michael and Ollila, Hanna M and Kiiskinen, Tuomo and Havulinna, Aki S and Pirruccello, James P and Qian, Junyang and Shcherbina, Anna and Rodriguez, Fatima and Assimes, Themistocles L and Agarwala, Vineeta and Tibshirani, Robert and Hastie, Trevor and Ripatti, Samuli and Pritchard, Jonathan K and Daly, Mark J and Rivas, Manuel A} } @article {142, title = {Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 03}, pages = {313-321}, abstract = {

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less well known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of new colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.

}, keywords = {Bardet-Biedl Syndrome, Calcium Channels, Cell Line, Cerebellar Ataxia, DNA Methylation, Gene Expression, Genetic Variation, Humans, Induced Pluripotent Stem Cells, Polymorphism, Single Nucleotide, Proteins, Quantitative Trait Loci, Rare Diseases, Regulatory Sequences, Nucleic Acid, Sequence Analysis, RNA, Whole Genome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-021-00800-7}, author = {Bonder, Marc Jan and Smail, Craig and Gloudemans, Michael J and Fr{\'e}sard, Laure and Jakubosky, David and D{\textquoteright}Antonio, Matteo and Li, Xin and Ferraro, Nicole M and Carcamo-Orive, Ivan and Mirauta, Bogdan and Seaton, Daniel D and Cai, Na and Vakili, Dara and Horta, Danilo and Zhao, Chunli and Zastrow, Diane B and Bonner, Devon E and Wheeler, Matthew T and Kilpinen, Helena and Knowles, Joshua W and Smith, Erin N and Frazer, Kelly A and Montgomery, Stephen B and Stegle, Oliver} } @article {129, title = {Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.}, journal = {Genet Epidemiol}, volume = {45}, year = {2021}, month = {2021 Feb}, pages = {99-114}, abstract = {

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

}, issn = {1098-2272}, doi = {10.1002/gepi.22358}, author = {Sun, Ryan and Xu, Miao and Li, Xihao and Gaynor, Sheila and Zhou, Hufeng and Li, Zilin and Boss{\'e}, Yohan and Lam, Stephen and Tsao, Ming-Sound and Tardon, Adonina and Chen, Chu and Doherty, Jennifer and Goodman, Gary and Bojesen, Stig E and Landi, Maria T and Johansson, Mattias and Field, John K and Bickeb{\"o}ller, Heike and Wichmann, H-Erich and Risch, Angela and Rennert, Gadi and Arnold, Suzanne and Wu, Xifeng and Melander, Olle and Brunnstr{\"o}m, Hans and Le Marchand, Loic and Liu, Geoffrey and Andrew, Angeline and Duell, Eric and Kiemeney, Lambertus A and Shen, Hongbing and Haugen, Aage and Johansson, Mikael and Grankvist, Kjell and Caporaso, Neil and Woll, Penella and Dawn Teare, M and Scelo, Ghislaine and Hong, Yun-Chul and Yuan, Jian-Min and Lazarus, Philip and Schabath, Matthew B and Aldrich, Melinda C and Albanes, Demetrios and Mak, Raymond and Barbie, David and Brennan, Paul and Hung, Rayjean J and Amos, Christopher I and Christiani, David C and Lin, Xihong} } @article {140, title = {Native American Ancestry and Air Pollution Interact to Impact Bronchodilator Response in Puerto Rican Children with Asthma.}, journal = {Ethn Dis}, volume = {31}, year = {2021}, month = {2021 Winter}, pages = {77-88}, abstract = {

Objective: Asthma is the most common chronic disease in children. Short-acting bronchodilator medications are the most commonly prescribed asthma treatment worldwide, regardless of disease severity. Puerto Rican children display the highest asthma morbidity and mortality of any US population. Alarmingly, Puerto Rican children with asthma display poor bronchodilator drug response (BDR). Reduced BDR may explain, in part, the increased asthma morbidity and mortality observed in Puerto Rican children with asthma. Gene-environment interactions may explain a portion of the heritability of BDR. We aimed to identify gene-environment interactions associated with BDR in Puerto Rican children with asthma.

Setting: Genetic, environmental, and psycho-social data from the Genes-environments and Admixture in Latino Americans (GALA II) case-control study.

Participants: Our discovery dataset consisted of 658 Puerto Rican children with asthma; our replication dataset consisted of 514 Mexican American children with asthma.

Main Outcome Measures: We assessed the association of pairwise interaction models with BDR using ViSEN (Visualization of Statistical Epistasis Networks).

Results: We identified a non-linear interaction between Native American genetic ancestry and air pollution significantly associated with BDR in Puerto Rican children with asthma. This interaction was robust to adjustment for age and sex but was not significantly associated with BDR in our replication population.

Conclusions: Decreased Native American ancestry coupled with increased air pollution exposure was associated with increased BDR in Puerto Rican children with asthma. Our study acknowledges BDR{\textquoteright}s phenotypic complexity, and emphasizes the importance of integrating social, environmental, and biological data to further our understanding of complex disease.

}, issn = {1945-0826}, doi = {10.18865/ed.31.1.77}, author = {Contreras, Mar{\'\i}a G and Keys, Kevin and Maga{\~n}a, Joaquin and Goddard, Pag{\'e} C and Risse-Adams, Oona and Zeiger, Andrew M and Mak, Angel C Y and Samedy-Bates, Lesly-Anne and Neophytou, Andreas M and Lee, Eunice and Thakur, Neeta and Elhawary, Jennifer R and Hu, Donglei and Huntsman, Scott and Eng, Celeste and Hu, Ting and Burchard, Esteban G and White, Marquitta J} } @article {145, title = {Population sequencing data reveal a compendium of mutational processes in the human germ line.}, journal = {Science}, volume = {373}, year = {2021}, month = {2021 08 27}, pages = {1030-1035}, abstract = {

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.

}, keywords = {Algorithms, CpG Islands, DNA Damage, DNA Demethylation, DNA Mutational Analysis, DNA Replication, Genetic Variation, Genome, Human, Germ Cells, Germ-Line Mutation, Humans, Long Interspersed Nucleotide Elements, Mutagenesis, Oocytes, Transcription, Genetic}, issn = {1095-9203}, doi = {10.1126/science.aba7408}, author = {Seplyarskiy, Vladimir B and Soldatov, Ruslan A and Koch, Evan and McGinty, Ryan J and Goldmann, Jakob M and Hernandez, Ryan D and Barnes, Kathleen and Correa, Adolfo and Burchard, Esteban G and Ellinor, Patrick T and McGarvey, Stephen T and Mitchell, Braxton D and Vasan, Ramachandran S and Redline, Susan and Silverman, Edwin and Weiss, Scott T and Arnett, Donna K and Blangero, John and Boerwinkle, Eric and He, Jiang and Montgomery, Courtney and Rao, D C and Rotter, Jerome I and Taylor, Kent D and Brody, Jennifer A and Chen, Yii-Der Ida and de Las Fuentes, Lisa and Hwu, Chii-Min and Rich, Stephen S and Manichaikul, Ani W and Mychaleckyj, Josyf C and Palmer, Nicholette D and Smith, Jennifer A and Kardia, Sharon L R and Peyser, Patricia A and Bielak, Lawrence F and O{\textquoteright}Connor, Timothy D and Emery, Leslie S and Gilissen, Christian and Wong, Wendy S W and Kharchenko, Peter V and Sunyaev, Shamil} } @article {143, title = {Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 05 13}, pages = {2633-2648.e19}, abstract = {

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

}, keywords = {Coronary Artery Disease, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Disease, Gene Expression Profiling, Genetic Variation, Humans, Inflammatory Bowel Diseases, Multifactorial Inheritance, Organ Specificity, Population, Quantitative Trait Loci, RNA, Long Noncoding, Transcriptome}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.03.050}, author = {de Goede, Olivia M and Nachun, Daniel C and Ferraro, Nicole M and Gloudemans, Michael J and Rao, Abhiram S and Smail, Craig and Eulalio, Tiffany Y and Aguet, Francois and Ng, Bernard and Xu, Jishu and Barbeira, Alvaro N and Castel, Stephane E and Kim-Hellmuth, Sarah and Park, YoSon and Scott, Alexandra J and Strober, Benjamin J and Brown, Christopher D and Wen, Xiaoquan and Hall, Ira M and Battle, Alexis and Lappalainen, Tuuli and Im, Hae Kyung and Ardlie, Kristin G and Mostafavi, Sara and Quertermous, Thomas and Kirkegaard, Karla and Montgomery, Stephen B} } @article {134, title = {Regulatory genomic circuitry of human disease loci by integrative epigenomics.}, journal = {Nature}, volume = {590}, year = {2021}, month = {2021 02}, pages = {300-307}, abstract = {

Annotating the molecular basis of human disease remains an unsolved challenge, as 93\% of disease loci are non-coding and gene-regulatory annotations are highly incomplete. Here we present EpiMap, a compendium comprising 10,000~epigenomic maps across 800~samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 30,000~genetic loci that were associated with 540~traits, predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.

}, keywords = {Chromatin, Disease, Enhancer Elements, Genetic, Epigenesis, Genetic, Epigenomics, Female, Gene Regulatory Networks, Genetic Loci, Genome-Wide Association Study, Humans, Male, Multifactorial Inheritance, Organ Specificity, Reproducibility of Results}, issn = {1476-4687}, doi = {10.1038/s41586-020-03145-z}, author = {Boix, Carles A and James, Benjamin T and Park, Yongjin P and Meuleman, Wouter and Kellis, Manolis} } @article {118, title = {Synaptic processes and immune-related pathways implicated in Tourette syndrome.}, journal = {Transl Psychiatry}, volume = {11}, year = {2021}, month = {2021 01 18}, pages = {56}, abstract = {

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS.

}, issn = {2158-3188}, doi = {10.1038/s41398-020-01082-z}, author = {Tsetsos, Fotis and Yu, Dongmei and Sul, Jae Hoon and Huang, Alden Y and Illmann, Cornelia and Osiecki, Lisa and Darrow, Sabrina M and Hirschtritt, Matthew E and Greenberg, Erica and Muller-Vahl, Kirsten R and Stuhrmann, Manfred and Dion, Yves and Rouleau, Guy A and Aschauer, Harald and Stamenkovic, Mara and Schl{\"o}gelhofer, Monika and Sandor, Paul and Barr, Cathy L and Grados, Marco A and Singer, Harvey S and N{\"o}then, Markus M and Hebebrand, Johannes and Hinney, Anke and King, Robert A and Fernandez, Thomas V and Barta, Csaba and Tarnok, Zsanett and Nagy, Peter and Depienne, Christel and Worbe, Yulia and Hartmann, Andreas and Budman, Cathy L and Rizzo, Renata and Lyon, Gholson J and McMahon, William M and Batterson, James R and Cath, Danielle C and Malaty, Irene A and Okun, Michael S and Berlin, Cheston and Woods, Douglas W and Lee, Paul C and Jankovic, Joseph and Robertson, Mary M and Gilbert, Donald L and Brown, Lawrence W and Coffey, Barbara J and Dietrich, Andrea and Hoekstra, Pieter J and Kuperman, Samuel and Zinner, Samuel H and Wagner, Michael and Knowles, James A and Jeremy Willsey, A and Tischfield, Jay A and Heiman, Gary A and Cox, Nancy J and Freimer, Nelson B and Neale, Benjamin M and Davis, Lea K and Coppola, Giovanni and Mathews, Carol A and Scharf, Jeremiah M and Paschou, Peristera and Barr, Cathy L and Batterson, James R and Berlin, Cheston and Budman, Cathy L and Cath, Danielle C and Coppola, Giovanni and Cox, Nancy J and Darrow, Sabrina and Davis, Lea K and Dion, Yves and Freimer, Nelson B and Grados, Marco A and Greenberg, Erica and Hirschtritt, Matthew E and Huang, Alden Y and Illmann, Cornelia and King, Robert A and Kurlan, Roger and Leckman, James F and Lyon, Gholson J and Malaty, Irene A and Mathews, Carol A and McMahon, William M and Neale, Benjamin M and Okun, Michael S and Osiecki, Lisa and Robertson, Mary M and Rouleau, Guy A and Sandor, Paul and Scharf, Jeremiah M and Singer, Harvey S and Smit, Jan H and Sul, Jae Hoon and Yu, Dongmei and Aschauer, Harald Aschauer Harald and Barta, Csaba and Budman, Cathy L and Cath, Danielle C and Depienne, Christel and Hartmann, Andreas and Hebebrand, Johannes and Konstantinidis, Anastasios and Mathews, Carol A and M{\"u}ller-Vahl, Kirsten and Nagy, Peter and N{\"o}then, Markus M and Paschou, Peristera and Rizzo, Renata and Rouleau, Guy A and Sandor, Paul and Scharf, Jeremiah M and Schl{\"o}gelhofer, Monika and Stamenkovic, Mara and Stuhrmann, Manfred and Tsetsos, Fotis and Tarnok, Zsanett and Wolanczyk, Tomasz and Worbe, Yulia and Brown, Lawrence and Cheon, Keun-Ah and Coffey, Barbara J and Dietrich, Andrea and Fernandez, Thomas V and Garcia-Delgar, Blanca and Gilbert, Donald and Grice, Dorothy E and Hagstr{\o}m, Julie and Hedderly, Tammy and Heiman, Gary A and Heyman, Isobel and Hoekstra, Pieter J and Huyser, Chaim and Kim, Young Key and Kim, Young-Shin and King, Robert A and Koh, Yun-Joo and Kook, Sodahm and Kuperman, Samuel and Leventhal, Bennett L and Madruga-Garrido, Marcos and Mir, Pablo and Morer, Astrid and M{\"u}nchau, Alexander and Plessen, Kerstin J and Roessner, Veit and Shin, Eun-Young and Song, Dong-Ho and Song, Jungeun and Tischfield, Jay A and Willsey, A Jeremy and Zinner, Samuel and Aschauer, Harald and Barr, Cathy L and Barta, Csaba and Batterson, James R and Berlin, Cheston and Brown, Lawrence and Budman, Cathy L and Cath, Danielle C and Coffey, Barbara J and Coppola, Giovanni and Cox, Nancy J and Darrow, Sabrina and Davis, Lea K and Depienne, Christel and Dietrich, Andrea and Dion, Yves and Fernandez, Thomas and Freimer, Nelson B and Gilbert, Donald and Grados, Marco A and Greenberg, Erica and Hartmann, Andreas and Hebebrand, Johannes and Heiman, Gary and Hirschtritt, Matthew E and Hoekstra, Pieter and Huang, Alden Y and Illmann, Cornelia and Jankovic, Joseph and King, Robert A and Kuperman, Samuel and Lee, Paul C and Lyon, Gholson J and Malaty, Irene A and Mathews, Carol A and McMahon, William M and M{\"u}ller-Vahl, Kirsten and Nagy, Peter and Neale, Benjamin M and N{\"o}then, Markus M and Okun, Michael S and Osiecki, Lisa and Paschou, Peristera and Rizzo, Renata and Robertson, Mary M and Rouleau, Guy A and Sandor, Paul and Scharf, Jeremiah M and Schl{\"o}gelhofer, Monika and Singer, Harvey S and Stamenkovic, Mara and Stuhrmann, Manfred and Sul, Jae Hoon and Tarnok, Zsanett and Tischfield, Jay and Tsetsos, Fotis and Willsey, A Jeremy and Woods, Douglas and Worbe, Yulia and Yu, Dongmei and Zinner, Samuel} } @article {87, title = {Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 01 02}, pages = {112-120}, abstract = {

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (\~{}38{\texttimes} coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (\~{}10\% and \~{}1\% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in~vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23~mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97~mg/L and major allele homozygotes with mean CRP of 4.11~mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.

}, keywords = {African Continental Ancestry Group, Asian Continental Ancestry Group, C-Reactive Protein, Cohort Studies, European Continental Ancestry Group, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Whole Genome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.12.002}, author = {Raffield, Laura M and Iyengar, Apoorva K and Wang, Biqi and Gaynor, Sheila M and Spracklen, Cassandra N and Zhong, Xue and Kowalski, Madeline H and Salimi, Shabnam and Polfus, Linda M and Benjamin, Emelia J and Bis, Joshua C and Bowler, Russell and Cade, Brian E and Choi, Won Jung and Comellas, Alejandro P and Correa, Adolfo and Cruz, Pedro and Doddapaneni, Harsha and Durda, Peter and Gogarten, Stephanie M and Jain, Deepti and Kim, Ryan W and Kral, Brian G and Lange, Leslie A and Larson, Martin G and Laurie, Cecelia and Lee, Jiwon and Lee, Seonwook and Lewis, Joshua P and Metcalf, Ginger A and Mitchell, Braxton D and Momin, Zeineen and Muzny, Donna M and Pankratz, Nathan and Park, Cheol Joo and Rich, Stephen S and Rotter, Jerome I and Ryan, Kathleen and Seo, Daekwan and Tracy, Russell P and Viaud-Martinez, Karine A and Yanek, Lisa R and Zhao, Lue Ping and Lin, Xihong and Li, Bingshan and Li, Yun and Dupuis, Jos{\'e}e and Reiner, Alexander P and Mohlke, Karen L and Auer, Paul L} } @article {91, title = {Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 05 07}, pages = {611-622}, abstract = {

Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters, including genetic correlation, to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family history GWAS using cases ascertained on family history of disease agrees with combined hospital record and questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.

}, keywords = {Asthma, Databases, Factual, Disease, Female, Genetics, Medical, Genome-Wide Association Study, Genotype, Humans, Male, Neoplasms, Phenotype, United Kingdom}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.03.007}, author = {DeBoever, Christopher and Tanigawa, Yosuke and Aguirre, Matthew and McInnes, Greg and Lavertu, Adam and Rivas, Manuel A} } @article {85, title = {A brief history of human disease genetics.}, journal = {Nature}, volume = {577}, year = {2020}, month = {2020 01}, pages = {179-189}, abstract = {

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved~our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

}, keywords = {Animals, Genetic Testing, Genetic Variation, Genomics, Genotype, Humans, Phenotype, Rare Diseases}, issn = {1476-4687}, doi = {10.1038/s41586-019-1879-7}, author = {Claussnitzer, Melina and Cho, Judy H and Collins, Rory and Cox, Nancy J and Dermitzakis, Emmanouil T and Hurles, Matthew E and Kathiresan, Sekar and Kenny, Eimear E and Lindgren, Cecilia M and MacArthur, Daniel G and North, Kathryn N and Plon, Sharon E and Rehm, Heidi L and Risch, Neil and Rotimi, Charles N and Shendure, Jay and Soranzo, Nicole and McCarthy, Mark I} } @article {126, title = {A common variant in PNPLA3 is associated with age at diagnosis of NAFLD in patients from a multi-ethnic biobank.}, journal = {J Hepatol}, volume = {72}, year = {2020}, month = {2020 06}, pages = {1070-1081}, abstract = {

BACKGROUND \& AIMS: The Ile138Met variant (rs738409) in the PNPLA3 gene has the largest effect on non-alcoholic fatty liver disease (NAFLD), increasing the risk of progression to severe forms of liver disease. It remains unknown if the variant plays a role in age of NAFLD onset. We aimed to determine if rs738409 impacts on the age of NAFLD diagnosis.

METHODS: We applied a novel natural language processing (NLP) algorithm to a longitudinal electronic health records (EHR) dataset of >27,000 individuals with genetic data from a multi-ethnic biobank, defining NAFLD cases (n~= 1,703) and confirming controls (n~= 8,119). We conducted i) a survival analysis to determine if age at diagnosis differed by rs738409 genotype, ii) a receiver operating characteristics analysis to assess the utility of the rs738409 genotype in discriminating NAFLD cases from controls, and iii) a phenome-wide association study (PheWAS) between rs738409 and 10,095 EHR-derived disease diagnoses.

RESULTS: The PNPLA3 G risk allele was associated with: i) earlier age of NAFLD diagnosis, with the strongest effect in Hispanics (hazard ratio 1.33; 95\% CI 1.15-1.53; p <0.0001) among whom a NAFLD diagnosis was 15\% more likely in risk allele carriers vs. non-carriers; ii) increased NAFLD risk (odds ratio 1.61; 95\% CI 1.349-1.73; p <0.0001), with the strongest effect among Hispanics (odds ratio 1.43; 95\% CI 1.28-1.59; p <0.0001); iii) additional liver diseases in a PheWAS (p <4.95~{\texttimes} 10) where the risk variant also associated with earlier age of diagnosis.

CONCLUSION: Given the role of the rs738409 in NAFLD diagnosis age, our results suggest that stratifying risk within populations known to have an enhanced risk of liver disease, such as Hispanic carriers of the rs738409 variant, would be effective in earlier identification of those who would benefit most from early NAFLD prevention and treatment strategies.

LAY SUMMARY: Despite clear associations between the PNPLA3 rs738409 variant and elevated risk of progression from non-alcoholic fatty liver disease (NAFLD) to more severe forms of liver disease, it remains unknown if PNPLA3 rs738409 plays a role in the age of NAFLD onset. Herein, we found that this risk variant is associated with an earlier age of NAFLD and other liver disease diagnoses; an observation most pronounced in Hispanic Americans. We conclude that PNPLA3 rs738409 could be used to better understand liver disease risk within vulnerable populations and identify patients that may benefit from early prevention strategies.

}, issn = {1600-0641}, doi = {10.1016/j.jhep.2020.01.029}, author = {Walker, Ryan W and Belbin, Gillian M and Sorokin, Elena P and Van Vleck, Tielman and Wojcik, Genevieve L and Moscati, Arden and Gignoux, Christopher R and Cho, Judy and Abul-Husn, Noura S and Nadkarni, Girish and Kenny, Eimear E and Loos, Ruth J F} } @article {100, title = {On the cross-population generalizability of gene expression prediction models.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 08}, pages = {e1008927}, abstract = {

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

}, keywords = {African Americans, Gene Expression Profiling, Genome-Wide Association Study, Humans, Models, Genetic, Quantitative Trait Loci, Reference Standards, RNA-Seq, Transcriptome}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008927}, author = {Keys, Kevin L and Mak, Angel C Y and White, Marquitta J and Eckalbar, Walter L and Dahl, Andrew W and Mefford, Joel and Mikhaylova, Anna V and Contreras, Mar{\'\i}a G and Elhawary, Jennifer R and Eng, Celeste and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and LeNoir, Michael A and Ye, Jimmie C and Thornton, Timothy A and Zaitlen, Noah and Burchard, Esteban G and Gignoux, Christopher R} } @article {99, title = {Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 09}, pages = {969-983}, abstract = {

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce {\textquoteright}annotation principal components{\textquoteright}, multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

}, keywords = {Cholesterol, LDL, Computer Simulation, Genetic Predisposition to Disease, Genetic Variation, Genome, Genome-Wide Association Study, Humans, Models, Genetic, Molecular Sequence Annotation, Phenotype, Whole Genome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-020-0676-4}, author = {Li, Xihao and Li, Zilin and Zhou, Hufeng and Gaynor, Sheila M and Liu, Yaowu and Chen, Han and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Aslibekyan, Stella and Ballantyne, Christie M and Bielak, Lawrence F and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Broome, Jai G and Conomos, Matthew P and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and Freedman, Barry I and Guo, Xiuqing and Hindy, George and Irvin, Marguerite R and Kardia, Sharon L R and Kathiresan, Sekar and Khan, Alyna T and Kooperberg, Charles L and Laurie, Cathy C and Liu, X Shirley and Mahaney, Michael C and Manichaikul, Ani W and Martin, Lisa W and Mathias, Rasika A and McGarvey, Stephen T and Mitchell, Braxton D and Montasser, May E and Moore, Jill E and Morrison, Alanna C and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Pampana, Akhil and Peralta, Juan M and Peyser, Patricia A and Psaty, Bruce M and Redline, Susan and Rice, Kenneth M and Rich, Stephen S and Smith, Jennifer A and Tiwari, Hemant K and Tsai, Michael Y and Vasan, Ramachandran S and Wang, Fei Fei and Weeks, Daniel E and Weng, Zhiping and Wilson, James G and Yanek, Lisa R and Neale, Benjamin M and Sunyaev, Shamil R and Abecasis, Gon{\c c}alo R and Rotter, Jerome I and Willer, Cristen J and Peloso, Gina M and Natarajan, Pradeep and Lin, Xihong} } @article {123, title = {Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models.}, journal = {J Comput Biol}, volume = {27}, year = {2020}, month = {2020 04}, pages = {599-612}, abstract = {

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

}, issn = {1557-8666}, doi = {10.1089/cmb.2019.0325}, author = {Mefford, Joel and Park, Danny and Zheng, Zhili and Ko, Arthur and Ala-Korpela, Mika and Laakso, Markku and Pajukanta, P{\"a}ivi and Yang, Jian and Witte, John and Zaitlen, Noah} } @article {120, title = {Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 11}, pages = {1145-1150}, abstract = {

The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet-Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes-a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders.

}, keywords = {Alleles, Bardet-Biedl Syndrome, Cohort Studies, Exome, Genetic Variation, Humans}, issn = {1546-1718}, doi = {10.1038/s41588-020-0707-1}, author = {Kousi, Maria and S{\"o}ylemez, Onuralp and Ozanturk, Ayseg{\"u}l and Mourtzi, Niki and Akle, Sebastian and Jungreis, Irwin and Muller, Jean and Cassa, Christopher A and Brand, Harrison and Mokry, Jill Anne and Wolf, Maxim Y and Sadeghpour, Azita and McFadden, Kelsey and Lewis, Richard A and Talkowski, Michael E and Dollfus, H{\'e}l{\`e}ne and Kellis, Manolis and Davis, Erica E and Sunyaev, Shamil R and Katsanis, Nicholas} } @article {75, title = {Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort.}, journal = {Epilepsia}, volume = {61}, year = {2020}, month = {2020 02}, pages = {249-258}, abstract = {

OBJECTIVE: We evaluated the yield of systematic analysis and/or reanalysis of whole exome sequencing (WES) data from a cohort of well-phenotyped pediatric patients with epilepsy and suspected but previously undetermined genetic etiology.

METHODS: We identified and phenotyped 125 participants with pediatric epilepsy. Etiology was unexplained at the time of enrollment despite clinical testing, which included chromosomal microarray (57 patients), epilepsy gene panel (n = 48), both (n = 28), or WES (n = 8). Clinical epilepsy diagnoses included developmental and epileptic encephalopathy (DEE), febrile infection-related epilepsy syndrome, Rasmussen encephalitis, and other focal and generalized epilepsies. We analyzed WES data and compared the yield in participants with and without prior clinical genetic testing.

RESULTS: Overall, we identified pathogenic or likely pathogenic variants in 40\% (50/125) of our study participants. Nine patients with DEE had genetic variants in recently published genes that had not been recognized as epilepsy-related at the time of clinical testing (FGF12, GABBR1, GABBR2, ITPA, KAT6A, PTPN23, RHOBTB2, SATB2), and eight patients had genetic variants in candidate epilepsy genes (CAMTA1, FAT3, GABRA6, HUWE1, PTCHD1). Ninety participants had concomitant or subsequent clinical genetic testing, which was ultimately explanatory for 26\% (23/90). Of the 67 participants whose molecular diagnoses were "unsolved" through clinical genetic testing, we identified pathogenic or likely pathogenic variants in 17 (25\%).

SIGNIFICANCE: Our data argue for early consideration of WES with iterative reanalysis for patients with epilepsy, particularly those with DEE or epilepsy with intellectual disability. Rigorous analysis of WES data of well-phenotyped patients with epilepsy leads to a broader understanding of gene-specific phenotypic spectra as well as candidate disease gene identification. We illustrate the dynamic nature of genetic diagnosis over time, with analysis and in some cases reanalysis of exome data leading to the identification of disease-associated variants among participants with previously nondiagnostic results from a variety of clinical testing strategies.

}, keywords = {Adolescent, Adult, Age of Onset, Brain Diseases, Child, Child, Preschool, Chromosomes, Human, Cohort Studies, Epilepsy, Epilepsy, Generalized, Exome, Female, Genetic Testing, Genetic Variation, Humans, Infant, Male, Microarray Analysis, Phenotype, Whole Exome Sequencing, Young Adult}, issn = {1528-1167}, doi = {10.1111/epi.16427}, author = {Rochtus, Anne and Olson, Heather E and Smith, Lacey and Keith, Louisa G and El Achkar, Christelle and Taylor, Alan and Mahida, Sonal and Park, Meredith and Kelly, McKenna and Shain, Catherine and Rockowitz, Shira and Rosen Sheidley, Beth and Poduri, Annapurna} } @article {107, title = {Genome-Wide Analysis Reveals Mucociliary Remodeling of the Nasal Airway Epithelium Induced by Urban PM.}, journal = {Am J Respir Cell Mol Biol}, volume = {63}, year = {2020}, month = {2020 08}, pages = {172-184}, abstract = {

Air pollution particulate matter <2.5 μm (PM) exposure is associated with poor respiratory outcomes. Mechanisms underlying PM-induced lung pathobiology are poorly understood but likely involve cellular and molecular changes to the airway epithelium. We extracted and chemically characterized the organic and water-soluble components of air pollution PM samples, then determined the whole transcriptome response of human nasal mucociliary airway epithelial cultures to a dose series of PM extracts. We found that PM organic extract (OE), but not water-soluble extract, elicited a potent, dose-dependent transcriptomic response from the mucociliary epithelium. Exposure to a moderate OE dose modified the expression of 424 genes, including activation of aryl hydrocarbon receptor signaling and an IL-1 inflammatory program. We generated an OE-response gene network defined by eight functional enrichment groups, which exhibited high connectivity through , , and . This OE exposure also robustly activated a mucus secretory expression program (>100 genes), which included transcriptional drivers of mucus metaplasia ( and ). Exposure to a higher OE dose modified the expression of 1,240 genes and further exacerbated expression responses observed at the moderate dose, including the mucus secretory program. Moreover, the higher OE dose significantly increased the / gel-forming mucin expression ratio and strongly downregulated ciliated cell expression programs, including key ciliating cell transcription factors (e.g., and ). Chronic OE stimulation induced mucus metaplasia-like remodeling characterized by increases in MUC5AC secretory cells and MUC5AC mucus secretions. This epithelial remodeling may underlie poor respiratory outcomes associated with high PM exposure.

}, issn = {1535-4989}, doi = {10.1165/rcmb.2019-0454OC}, author = {Montgomery, Michael T and Sajuthi, Satria P and Cho, Seung-Hyun and Everman, Jamie L and Rios, Cydney L and Goldfarbmuren, Katherine C and Jackson, Nathan D and Saef, Benjamin and Cromie, Meghan and Eng, Celeste and Medina, Vivian and Elhawary, Jennifer R and Oh, Sam S and Rodriguez-Santana, Jose and Vladar, Eszter K and Burchard, Esteban G and Seibold, Max A} } @article {122, title = {Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations.}, journal = {Am J Hum Genet}, volume = {107}, year = {2020}, month = {2020 10 01}, pages = {788-789}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.08.020}, author = {Martin, Alicia R and Gignoux, Christopher R and Walters, Raymond K and Wojcik, Genevieve L and Neale, Benjamin M and Gravel, Simon and Daly, Mark J and Bustamante, Carlos D and Kenny, Eimear E} } @article {74, title = {Identifying causal variants and genes using functional genomics in specialized cell types and contexts.}, journal = {Hum Genet}, volume = {139}, year = {2020}, month = {2020 Jan}, pages = {95-102}, abstract = {

A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.

}, keywords = {Cell Lineage, Genes, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1432-1203}, doi = {10.1007/s00439-019-02044-2}, author = {Liu, Boxiang and Montgomery, Stephen B} } @article {95, title = {Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx.}, journal = {Genome Biol}, volume = {21}, year = {2020}, month = {2020 09 11}, pages = {233}, abstract = {

BACKGROUND: Population structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15\% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization.

RESULTS: Here, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02\%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up.

CONCLUSIONS: We provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

}, issn = {1474-760X}, doi = {10.1186/s13059-020-02113-0}, author = {Gay, Nicole R and Gloudemans, Michael and Antonio, Margaret L and Abell, Nathan S and Balliu, Brunilda and Park, YoSon and Martin, Alicia R and Musharoff, Shaila and Rao, Abhiram S and Aguet, Francois and Barbeira, Alvaro N and Bonazzola, Rodrigo and Hormozdiari, Farhad and Ardlie, Kristin G and Brown, Christopher D and Im, Hae Kyung and Lappalainen, Tuuli and Wen, Xiaoquan and Montgomery, Stephen B} } @article {113, title = {Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 12}, pages = {1346-1354}, abstract = {

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9\% mean relative increase in R). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.

}, keywords = {Asian Continental Ancestry Group, Base Sequence, Computational Biology, Enhancer Elements, Genetic, European Continental Ancestry Group, Gene Expression Regulation, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Genetic, Molecular Sequence Annotation, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {1546-1718}, doi = {10.1038/s41588-020-00740-8}, author = {Amariuta, Tiffany and Ishigaki, Kazuyoshi and Sugishita, Hiroki and Ohta, Tazro and Koido, Masaru and Dey, Kushal K and Matsuda, Koichi and Murakami, Yoshinori and Price, Alkes L and Kawakami, Eiryo and Terao, Chikashi and Raychaudhuri, Soumya} } @article {105, title = {Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand and Gene-By-Air-Pollution Interaction.}, journal = {Genetics}, volume = {215}, year = {2020}, month = {2020 07}, pages = {869-886}, abstract = {

Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole-genome sequencing data from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine project, we identified a novel genetic association with FEV on chromosome 12 in 867 African American children with asthma ( = 1.26 {\texttimes} 10, β = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded nine variants as the most likely candidates responsible for the association with FEV Hi-C data and expression QTL analysis demonstrated that these variants physically interacted with (KIT ligand, also known as ), and their minor alleles were associated with increased expression of the gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year ambient sulfur dioxide exposure ( = 0.003, β = 0.32). This study identified a novel protective genetic association with FEV, possibly mediated through , in African American children with asthma. This is the first study that has identified a genetic association between lung function and , which has established a role in orchestrating allergic inflammation in asthma.

}, issn = {1943-2631}, doi = {10.1534/genetics.120.303231}, author = {Mak, Angel C Y and Sajuthi, Satria and Joo, Jaehyun and Xiao, Shujie and Sleiman, Patrick M and White, Marquitta J and Lee, Eunice Y and Saef, Benjamin and Hu, Donglei and Gui, Hongsheng and Keys, Kevin L and Lurmann, Fred and Jain, Deepti and Abecasis, Goncalo and Kang, Hyun Min and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Winterkorn, Lara and Reeves, Catherine and Huntsman, Scott and Eng, Celeste and Salazar, Sandra and Oh, Sam S and Gilliland, Frank D and Chen, Zhanghua and Kumar, Rajesh and Mart{\'\i}nez, Fernando D and Wu, Ann Chen and Ziv, Elad and Hakonarson, Hakon and Himes, Blanca E and Williams, L Keoki and Seibold, Max A and Burchard, Esteban G} } @article {125, title = {Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies.}, journal = {Biometrics}, volume = {76}, year = {2020}, month = {2020 12}, pages = {1262-1272}, abstract = {

Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonnormally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of nonnormally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for nonnormally distributed traits, INT-based tests outperform the standard untransformed association test, both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on~CRAN.

}, issn = {1541-0420}, doi = {10.1111/biom.13214}, author = {McCaw, Zachary R and Lane, Jacqueline M and Saxena, Richa and Redline, Susan and Lin, Xihong} } @article {76, title = {Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies.}, journal = {Nat Med}, volume = {26}, year = {2020}, month = {2020 01}, pages = {98-109}, abstract = {

Discovery of genotype-phenotype relationships remains a major challenge in clinical medicine. Here, we combined three sources of phenotypic data to uncover a new mechanism for rare and common diseases resulting from collagen secretion deficits. Using a zebrafish genetic screen, we identified the ric1 gene as being essential for skeletal biology. Using a gene-based phenome-wide association study (PheWAS) in the EHR-linked BioVU biobank, we show that reduced genetically determined expression of RIC1 is associated with musculoskeletal and dental conditions. Whole-exome sequencing identified individuals homozygous-by-descent for a rare variant in RIC1 and, through a guided clinical re-evaluation, it was discovered that they share signs with the BioVU-associated phenome. We named this new Mendelian syndrome CATIFA (cleft lip, cataract, tooth abnormality, intellectual disability, facial dysmorphism, attention-deficit hyperactivity disorder) and revealed further disease mechanisms. This gene-based, PheWAS-guided approach can accelerate the discovery of clinically relevant disease phenome and associated biological mechanisms.

}, keywords = {Abnormalities, Multiple, Animals, Behavior, Animal, Biological Specimen Banks, Chondrocytes, Disease Models, Animal, Extracellular Matrix, Fibroblasts, Guanine Nucleotide Exchange Factors, Humans, Models, Biological, Musculoskeletal System, Osteogenesis, Phenomics, Phenotype, Procollagen, Protein Transport, Secretory Pathway, Syndrome, Zebrafish, Zebrafish Proteins}, issn = {1546-170X}, doi = {10.1038/s41591-019-0705-y}, author = {Unlu, Gokhan and Qi, Xinzi and Gamazon, Eric R and Melville, David B and Patel, Nisha and Rushing, Amy R and Hashem, Mais and Al-Faifi, Abdullah and Chen, Rui and Li, Bingshan and Cox, Nancy J and Alkuraya, Fowzan S and Knapik, Ela W} } @article {89, title = {A positively selected FBN1 missense variant reduces height in Peruvian individuals.}, journal = {Nature}, volume = {582}, year = {2020}, month = {2020 06}, pages = {234-239}, abstract = {

On average, Peruvian individuals are among the shortest in the world. Here we show that Native American ancestry is associated with reduced height in an ethnically diverse group of Peruvian individuals, and identify a population-specific, missense variant in the FBN1 gene (E1297G) that is significantly associated with lower height. Each copy of the minor allele (frequency of 4.7\%) reduces height by 2.2~cm (4.4~cm in homozygous individuals). To our knowledge, this is the largest effect size known for a common height-associated variant. FBN1 encodes the extracellular matrix protein fibrillin~1, which is a major structural component of microfibrils. We observed less densely packed fibrillin-1-rich microfibrils with irregular edges in the skin of individuals who were homozygous for G1297 compared with individuals who were homozygous for E1297. Moreover, we show that the E1297G locus is under positive selection in non-African populations, and that the E1297 variant shows subtle evidence of positive selection specifically within the Peruvian population. This variant is also significantly more frequent in coastal Peruvian populations than in populations from the Andes or the Amazon, which suggests that short stature might be the result of adaptation to factors that are associated with the coastal environment in Peru.

}, keywords = {Body Height, Female, Fibrillin-1, Gene Frequency, Genome-Wide Association Study, Heredity, Humans, Indians, South American, Male, Microfibrils, Mutation, Missense, Peru, Selection, Genetic}, issn = {1476-4687}, doi = {10.1038/s41586-020-2302-0}, author = {Asgari, Samira and Luo, Yang and Akbari, Ali and Belbin, Gillian M and Li, Xinyi and Harris, Daniel N and Selig, Martin and Bartell, Eric and Calderon, Roger and Slowikowski, Kamil and Contreras, Carmen and Yataco, Rosa and Galea, Jerome T and Jimenez, Judith and Coit, Julia M and Farro{\~n}ay, Chandel and Nazarian, Rosalynn M and O{\textquoteright}Connor, Timothy D and Dietz, Harry C and Hirschhorn, Joel N and Guio, Heinner and Lecca, Leonid and Kenny, Eimear E and Freeman, Esther E and Murray, Megan B and Raychaudhuri, Soumya} } @article {94, title = {Transcriptomic signatures across human tissues identify functional rare genetic variation.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

}, keywords = {Genetic Variation, Genome, Human, Humans, Multifactorial Inheritance, Organ Specificity, Transcriptome}, issn = {1095-9203}, doi = {10.1126/science.aaz5900}, author = {Ferraro, Nicole M and Strober, Benjamin J and Einson, Jonah and Abell, Nathan S and Aguet, Francois and Barbeira, Alvaro N and Brandt, Margot and Bucan, Maja and Castel, Stephane E and Davis, Joe R and Greenwald, Emily and Hess, Gaelen T and Hilliard, Austin T and Kember, Rachel L and Kotis, Bence and Park, YoSon and Peloso, Gina and Ramdas, Shweta and Scott, Alexandra J and Smail, Craig and Tsang, Emily K and Zekavat, Seyedeh M and Ziosi, Marcello and Ardlie, Kristin G and Assimes, Themistocles L and Bassik, Michael C and Brown, Christopher D and Correa, Adolfo and Hall, Ira and Im, Hae Kyung and Li, Xin and Natarajan, Pradeep and Lappalainen, Tuuli and Mohammadi, Pejman and Montgomery, Stephen B and Battle, Alexis} } @article {109, title = {Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.}, journal = {Nat Commun}, volume = {11}, year = {2020}, month = {2020 10 12}, pages = {5139}, abstract = {

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

}, keywords = {Angiotensin-Converting Enzyme 2, Betacoronavirus, Child, Coronavirus Infections, COVID-19, Epithelial Cells, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Host-Pathogen Interactions, Humans, Inflammation, Interferons, Interleukin-13, Middle Aged, Nasal Mucosa, Pandemics, Peptidyl-Dipeptidase A, Pneumonia, Viral, SARS-CoV-2, Serine Endopeptidases, Virus Internalization}, issn = {2041-1723}, doi = {10.1038/s41467-020-18781-2}, author = {Sajuthi, Satria P and DeFord, Peter and Li, Yingchun and Jackson, Nathan D and Montgomery, Michael T and Everman, Jamie L and Rios, Cydney L and Pruesse, Elmar and Nolin, James D and Plender, Elizabeth G and Wechsler, Michael E and Mak, Angel C Y and Eng, Celeste and Salazar, Sandra and Medina, Vivian and Wohlford, Eric M and Huntsman, Scott and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Abecasis, Goncalo and Kang, Hyun Min and Rice, Kenneth M and Kumar, Rajesh and Oh, Sam and Rodriguez-Santana, Jose and Burchard, Esteban G and Seibold, Max A} } @article {115, title = {Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium.}, journal = {bioRxiv}, year = {2020}, month = {2020 Apr 10}, abstract = {

Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both and , that vary in frequency across world populations. Importantly, we find is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

}, doi = {10.1101/2020.04.09.034454}, author = {Sajuthi, Satria P and DeFord, Peter and Jackson, Nathan D and Montgomery, Michael T and Everman, Jamie L and Rios, Cydney L and Pruesse, Elmar and Nolin, James D and Plender, Elizabeth G and Wechsler, Michael E and Mak, Angel Cy and Eng, Celeste and Salazar, Sandra and Medina, Vivian and Wohlford, Eric M and Huntsman, Scott and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Abecasis, Goncalo and Kang, Hyun Min and Rice, Kenneth M and Kumar, Rajesh and Oh, Sam and Rodriguez-Santana, Jose and Burchard, Esteban G and Seibold, Max A} } @article {101, title = {Whole-exome sequencing in adult patients with developmental and epileptic encephalopathy: It is never too late.}, journal = {Clin Genet}, volume = {98}, year = {2020}, month = {2020 11}, pages = {477-485}, abstract = {

Developmental and epileptic encephalopathies (DEE) encompass rare, sporadic neurodevelopmental disorders and usually with pediatric onset. As these conditions are characterized by marked clinical and genetic heterogeneity, whole-exome sequencing (WES) represents the strategy of choice for the molecular diagnosis. While its usefulness is well established in pediatric DEE cohorts, our study is aimed at assessing the WES feasibility in adult DEE patients who experienced a diagnostic odyssey prior to the advent of this technique. We analyzed exomes from 71 unrelated adult DEE patients, consecutively recruited from an Italian cohort for the EPI25 Project. All patients underwent accurate clinical and electrophysiological characterization. An overwhelming percentage (90.1\%) had already undergone negative genetic testing. Variants were classified according to the American College of Medical Genetics and Genomics guidelines. WES disclosed 24 (likely) pathogenic variants among 18 patients in epilepsy-related genes with either autosomal dominant, recessive or X-linked inheritance. Ten of these were novel. We obtained a diagnostic yield of 25.3\%, higher among patients with brain malformations, early-onset epilepsy and dysmorphisms. Despite a median diagnostic delay of 38.7 years, WES analysis provided the long-awaited diagnosis for 18 adult patients, which also had an impact on the clinical management of 50\% of them.

}, issn = {1399-0004}, doi = {10.1111/cge.13823}, author = {Minardi, Raffaella and Licchetta, Laura and Baroni, Maria Chiara and Pippucci, Tommaso and Stipa, Carlotta and Mostacci, Barbara and Severi, Giulia and Toni, Francesco and Bergonzini, Luca and Carelli, Valerio and Seri, Marco and Tinuper, Paolo and Bisulli, Francesca} } @article {54, title = {ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies.}, journal = {Am J Hum Genet}, volume = {104}, year = {2019}, month = {2019 Mar 07}, pages = {410-421}, abstract = {

Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.01.002}, author = {Liu, Yaowu and Chen, Sixing and Li, Zilin and Morrison, Alanna C and Boerwinkle, Eric and Lin, Xihong} } @article {59, title = {Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives.}, journal = {Genetics}, volume = {211}, year = {2019}, month = {2019 04}, pages = {1179-1189}, abstract = {

High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.

}, keywords = {Animals, Genome-Wide Association Study, Humans, Models, Genetic, Phenotype, Principal Component Analysis, Quantitative Trait Loci, Reproducibility of Results}, issn = {1943-2631}, doi = {10.1534/genetics.118.301768}, author = {Dahl, Andy and Guillemot, Vincent and Mefford, Joel and Aschard, Hugues and Zaitlen, Noah} } @article {71, title = {Drug-Resistant Juvenile Myoclonic Epilepsy: Misdiagnosis of Progressive Myoclonus Epilepsy.}, journal = {Front Neurol}, volume = {10}, year = {2019}, month = {2019}, pages = {946}, abstract = {

Juvenile myoclonic epilepsy (JME) is a common epilepsy syndrome characterized by bilateral myoclonic and tonic-clonic seizures typically starting in adolescence and responding well to medication. Misdiagnosis of a more severe progressive myoclonus epilepsy (PME) as JME has been suggested as a cause of drug-resistance. Medical records of the Epilepsy Center Hessen-Marburg between 2005 and 2014 were automatically selected using keywords and manually reviewed regarding the presence of a JME diagnosis at any timepoint. The identified patients were evaluated regarding seizure outcome and drug resistance according to ILAE criteria. 87/168 identified JME patients were seizure-free at last follow-up including 61 drug-responsive patients (group NDR). Seventy-eight patients were not seizure-free including 26 drug-resistant patients (group DR). Valproate was the most efficacious AED. The JME diagnosis was revised in 7 patients of group DR including 6 in whom the diagnosis had already been questioned or revised during clinical follow-up. One of these was finally diagnosed with PME (genetically confirmed Lafora disease) based on genetic testing. She was initially reviewed at age 29 yrs and considered to be inconsistent with PME. Intellectual disability ( = 0.025), cognitive impairment ( < 0.001), febrile seizures in first-degree relatives ( = 0.023) and prominent dialeptic seizures ( = 0.009) where significantly more frequent in group DR. Individuals with PME are rarely found among drug-resistant alleged JME patients in a tertiary epilepsy center. Even a very detailed review by experienced epileptologists may not identify the presence of PME before the typical features evolve underpinning the need for early genetic testing in drug-resistant JME patients.

}, issn = {1664-2295}, doi = {10.3389/fneur.2019.00946}, author = {Martin, Sarah and Strzelczyk, Adam and Lindlar, Silvia and Krause, Kristina and Reif, Philipp S and Menzler, Katja and Chiocchetti, Andreas G and Rosenow, Felix and Knake, Susanne and Klein, Karl Martin} } @article {50, title = {Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.}, journal = {Am J Hum Genet}, volume = {104}, year = {2019}, month = {2019 Feb 07}, pages = {260-274}, abstract = {

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute{\textquoteright}s Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

}, keywords = {Chromosomes, Human, Pair 4, Cloud Computing, Female, Fibrinogen, Genetic Association Studies, Genetics, Population, Humans, Male, Models, Genetic, National Heart, Lung, and Blood Institute (U.S.), Precision Medicine, Research Design, Time Factors, United States, Whole Genome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2018.12.012}, author = {Chen, Han and Huffman, Jennifer E and Brody, Jennifer A and Wang, Chaolong and Lee, Seunggeun and Li, Zilin and Gogarten, Stephanie M and Sofer, Tamar and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Bowler, Russell P and Cade, Brian E and Cho, Michael H and Correa, Adolfo and Curran, Joanne E and de Vries, Paul S and Glahn, David C and Guo, Xiuqing and Johnson, Andrew D and Kardia, Sharon and Kooperberg, Charles and Lewis, Joshua P and Liu, Xiaoming and Mathias, Rasika A and Mitchell, Braxton D and O{\textquoteright}Connell, Jeffrey R and Peyser, Patricia A and Post, Wendy S and Reiner, Alex P and Rich, Stephen S and Rotter, Jerome I and Silverman, Edwin K and Smith, Jennifer A and Vasan, Ramachandran S and Wilson, James G and Yanek, Lisa R and Redline, Susan and Smith, Nicholas L and Boerwinkle, Eric and Borecki, Ingrid B and Cupples, L Adrienne and Laurie, Cathy C and Morrison, Alanna C and Rice, Kenneth M and Lin, Xihong} } @article {73, title = {Exome sequencing reveals a high prevalence of BRCA1 and BRCA2 founder variants in a diverse population-based biobank.}, journal = {Genome Med}, volume = {12}, year = {2019}, month = {2019 Dec 31}, pages = {2}, abstract = {

BACKGROUND: Pathogenic variants in BRCA1 and BRCA2 (BRCA1/2) lead to increased risk of breast, ovarian, and other cancers, but most variant-positive individuals in the general population are unaware of their risk, and little is known about prevalence in non-European populations. We investigated BRCA1/2 prevalence and impact in the electronic health record (EHR)-linked BioMe Biobank in New York City.

METHODS: Exome sequence data from 30,223 adult BioMe participants were evaluated for pathogenic variants in BRCA1/2. Prevalence estimates were made in population groups defined by genetic ancestry and self-report. EHR data were used to evaluate clinical characteristics of variant-positive individuals.

RESULTS: There were 218 (0.7\%) individuals harboring expected pathogenic variants, resulting in an overall prevalence of 1 in 139. The highest prevalence was in individuals with Ashkenazi Jewish (AJ; 1 in 49), Filipino and other~Southeast Asian (1 in 81), and non-AJ European (1 in 103) ancestry. Among 218 variant-positive individuals, 112 (51.4\%) harbored known founder variants: 80 had AJ founder variants (BRCA1 c.5266dupC and c.68_69delAG, and BRCA2 c.5946delT), 8 had a Puerto Rican founder variant (BRCA2 c.3922G>T), and 24 had one of 19 other founder variants. Non-European populations were more likely to harbor BRCA1/2 variants that were not classified in ClinVar or that had uncertain or conflicting evidence for pathogenicity (uncertain/conflicting). Within mixed ancestry populations, such as Hispanic/Latinos with genetic ancestry from Africa, Europe, and the Americas, there was a strong correlation between the proportion of African genetic ancestry and the likelihood of harboring an uncertain/conflicting variant. Approximately 28\% of variant-positive individuals had a personal history, and 45\% had a personal or family history of BRCA1/2-associated cancers. Approximately 27\% of variant-positive individuals had prior clinical genetic testing for BRCA1/2. However, individuals with AJ founder variants were twice as likely to have had a clinical test (39\%) than those with other pathogenic variants (20\%).

CONCLUSIONS: These findings deepen our knowledge about BRCA1/2 variants and associated cancer risk in diverse populations, indicate a gap in knowledge about potential cancer-related variants in non-European populations, and suggest that genomic screening in diverse patient populations may be an effective tool to identify at-risk individuals.

}, issn = {1756-994X}, doi = {10.1186/s13073-019-0691-1}, author = {Abul-Husn, Noura S and Soper, Emily R and Odgis, Jacqueline A and Cullina, Sinead and Bobo, Dean and Moscati, Arden and Rodriguez, Jessica E and Loos, Ruth J F and Cho, Judy H and Belbin, Gillian M and Suckiel, Sabrina A and Kenny, Eimear E} } @article {41, title = {Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 Jul 15}, pages = {2495-2497}, abstract = {

SUMMARY: Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here, we present Global Biobank Engine (GBE), a web-based tool that enables exploration of the relationship between genotype and phenotype in biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities.

AVAILABILITY AND IMPLEMENTATION: GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

}, keywords = {Biological Specimen Banks, Genome-Wide Association Study, Genotype, Humans, Phenomics, Phenotype}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bty999}, author = {McInnes, Gregory and Tanigawa, Yosuke and DeBoever, Chris and Lavertu, Adam and Olivieri, Julia Eve and Aguirre, Matthew and Rivas, Manuel A} } @article {56, title = {GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish.}, journal = {Am J Hum Genet}, volume = {104}, year = {2019}, month = {2019 Mar 07}, pages = {503-519}, abstract = {

Although the use of model systems for studying the mechanism of mutations that have a large effect is common, we highlight here the ways that zebrafish-model-system studies of a gene, GRIK5, that contributes to the polygenic liability to develop eye diseases have helped to illuminate a mechanism that implicates vascular biology in eye disease. A gene-expression prediction derived from a reference transcriptome panel applied to BioVU, a large electronic health record (EHR)-linked biobank at Vanderbilt University Medical Center, implicated reduced GRIK5 expression in diverse eye diseases. We tested the function of GRIK5 by depletion of its ortholog in zebrafish, and we observed reduced blood vessel numbers and integrity in the eye and increased vascular permeability. Analyses of EHRs in >2.6 million Vanderbilt subjects revealed significant comorbidity of eye and vascular diseases (relative risks 2-15); this comorbidity was confirmed in 150 million individuals from a large insurance claims dataset. Subsequent studies in >60,000 genotyped BioVU participants confirmed the association of reduced genetically predicted expression of GRIK5 with comorbid vascular and eye diseases. Our studies pioneer an approach that allows a rapid iteration of the discovery of gene-phenotype relationships to the primary genetic mechanism contributing to the pathophysiology of human disease. Our findings also add dimension to the understanding of the biology driven by glutamate receptors such as GRIK5 (also referred to as GLUK5 in protein form) and to mechanisms contributing to human eye diseases.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.01.017}, author = {Unlu, Gokhan and Gamazon, Eric R and Qi, Xinzi and Levic, Daniel S and Bastarache, Lisa and Denny, Joshua C and Roden, Dan M and Mayzus, Ilya and Breyer, Max and Zhong, Xue and Konkashbaev, Anuar I and Rzhetsky, Andrey and Knapik, Ela W and Cox, Nancy J} } @article {63, title = {Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.}, journal = {Nat Med}, volume = {25}, year = {2019}, month = {2019 06}, pages = {911-919}, abstract = {

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene. The current molecular diagnostic rate is estimated at 50\%, with whole-exome sequencing (WES) among the most successful approaches. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases. This includes muscle biopsies from patients with undiagnosed rare muscle disorders, and cultured fibroblasts from patients with mitochondrial disorders. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5\% diagnostic rate, and an additional 16.7\% with improved candidate gene resolution.

}, keywords = {Acid Ceramidase, Case-Control Studies, Child, Child, Preschool, Cohort Studies, Female, Genetic Variation, Humans, Male, Models, Genetic, Mutation, Oxidoreductases Acting on CH-CH Group Donors, Potassium Channels, Rare Diseases, RNA, RNA Splicing, Sequence Analysis, RNA, Whole Exome Sequencing}, issn = {1546-170X}, doi = {10.1038/s41591-019-0457-8}, author = {Fr{\'e}sard, Laure and Smail, Craig and Ferraro, Nicole M and Teran, Nicole A and Li, Xin and Smith, Kevin S and Bonner, Devon and Kernohan, Kristin D and Marwaha, Shruti and Zappala, Zachary and Balliu, Brunilda and Davis, Joe R and Liu, Boxiang and Prybol, Cameron J and Kohler, Jennefer N and Zastrow, Diane B and Reuter, Chloe M and Fisk, Dianna G and Grove, Megan E and Davidson, Jean M and Hartley, Taila and Joshi, Ruchi and Strober, Benjamin J and Utiramerur, Sowmithri and Lind, Lars and Ingelsson, Erik and Battle, Alexis and Bejerano, Gill and Bernstein, Jonathan A and Ashley, Euan A and Boycott, Kym M and Merker, Jason D and Wheeler, Matthew T and Montgomery, Stephen B} } @article {57, title = {Opportunities and challenges for transcriptome-wide association studies.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 04}, pages = {592-599}, abstract = {

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn{\textquoteright}s disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.

}, keywords = {Crohn Disease, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Lipoproteins, LDL, Quantitative Trait Loci, Schizophrenia, Transcriptome}, issn = {1546-1718}, doi = {10.1038/s41588-019-0385-z}, author = {Wainberg, Michael and Sinnott-Armstrong, Nasa and Mancuso, Nicholas and Barbeira, Alvaro N and Knowles, David A and Golan, David and Ermel, Raili and Ruusalepp, Arno and Quertermous, Thomas and Hao, Ke and Bj{\"o}rkegren, Johan L M and Im, Hae Kyung and Pasaniuc, Bogdan and Rivas, Manuel A and Kundaje, Anshul} } @article {53, title = {Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies.}, journal = {Elife}, volume = {8}, year = {2019}, month = {2019 Mar 21}, abstract = {

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor{\textquoteright}s assessment is that all the issues have been addressed (see decision letter).

}, issn = {2050-084X}, doi = {10.7554/eLife.39702}, author = {Sohail, Mashaal and Maier, Robert M and Ganna, Andrea and Bloemendal, Alex and Martin, Alicia R and Turchin, Michael C and Chiang, Charleston Wk and Hirschhorn, Joel and Daly, Mark J and Patterson, Nick and Neale, Benjamin and Mathieson, Iain and Reich, David and Sunyaev, Shamil R} } @article {35, title = {A common loss-of-function variant is associated with lower vitamin B concentration in African Americans.}, journal = {Blood}, volume = {131}, year = {2018}, month = {2018 Jun 21}, pages = {2859-2863}, keywords = {Black or African American, Female, Genome-Wide Association Study, Humans, Loss of Function Mutation, Male, Polymorphism, Single Nucleotide, Transcobalamins, Vitamin B 12, Vitamin B Deficiency}, issn = {1528-0020}, doi = {10.1182/blood-2018-03-841023}, author = {Hu, Yao and Raffield, Laura M and Polfus, Linda M and Moscati, Arden and Nadkarni, Girish and Preuss, Michael H and Zhong, Xue and Wei, Qiang and Rich, Stephen S and Li, Yun and Wilson, James G and Correa, Adolfo and Loos, Ruth J F and Li, Bingshan and Auer, Paul L and Reiner, Alex P} } @article {44, title = {Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 Oct}, pages = {1366-1374}, abstract = {

To define potentially causal variants for autoimmune disease, we fine-mapped 76 rheumatoid arthritis (11,475 cases, 15,870 controls) and type 1 diabetes loci (9,334 cases, 11,111 controls). After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89\% of common variants. We defined credible sets of <=5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.

}, keywords = {Alleles, Arthritis, Rheumatoid, Case-Control Studies, CD28 Antigens, Chromosome Mapping, CTLA-4 Antigen, Diabetes Mellitus, Type 1, Gene Frequency, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Jurkat Cells, Mutation, Polymorphism, Single Nucleotide, Quantitative Trait Loci, RNA, Long Noncoding, Tumor Necrosis Factor alpha-Induced Protein 3}, issn = {1546-1718}, doi = {10.1038/s41588-018-0216-7}, author = {Westra, Harm-Jan and Mart{\'\i}nez-Bonet, Marta and Onengut-Gumuscu, Suna and Lee, Annette and Luo, Yang and Teslovich, Nikola and Worthington, Jane and Martin, Javier and Huizinga, Tom and Klareskog, Lars and Rantapaa-Dahlqvist, Solbritt and Chen, Wei-Min and Quinlan, Aaron and Todd, John A and Eyre, Steve and Nigrovic, Peter A and Gregersen, Peter K and Rich, Stephen S and Raychaudhuri, Soumya} } @article {43, title = {Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Oct 02}, pages = {4038}, abstract = {

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.

}, keywords = {Genome, Human, Human Genetics, Humans, Whole Genome Sequencing}, issn = {2041-1723}, doi = {10.1038/s41467-018-06159-4}, author = {Regier, Allison A and Farjoun, Yossi and Larson, David E and Krasheninina, Olga and Kang, Hyun Min and Howrigan, Daniel P and Chen, Bo-Juen and Kher, Manisha and Banks, Eric and Ames, Darren C and English, Adam C and Li, Heng and Xing, Jinchuan and Zhang, Yeting and Matise, Tara and Abecasis, Gon{\c c}alo R and Salerno, Will and Zody, Michael C and Neale, Benjamin M and Hall, Ira M} } @article {61, title = {Genetic diversity in populations across Latin America: implications for population and medical genetic studies.}, journal = {Curr Opin Genet Dev}, volume = {53}, year = {2018}, month = {2018 12}, pages = {98-104}, abstract = {

Hispanic/Latino (H/L) populations, although linked by culture and aspects of shared history, reflect the complexity of history and migration influencing the Americas. The original settlement by indigenous Americans, followed by postcolonial admixture from multiple continents, has yielded localized genetic patterns. In addition, numerous H/L populations appear to have signatures of pre-colonization and post-colonization bottlenecks, indicating that tens of millions of H/Ls may harbor signatures of founder effects today. Based on both population and medical genetic findings we highlight the extreme differentiation across the Americas, providing evidence for why H/Ls should not be considered a single population in modern human genetics. We highlight the need for additional sampling of understudied H/L groups, and ramifications of these findings for genomic medicine in one-tenth of the world{\textquoteright}s population.

}, keywords = {African Continental Ancestry Group, European Continental Ancestry Group, Genetic Variation, Genetics, Medical, Genetics, Population, Genome, Human, Genomics, Humans, Latin America}, issn = {1879-0380}, doi = {10.1016/j.gde.2018.07.006}, author = {Belbin, Gillian M and Nieves-Col{\'o}n, Maria A and Kenny, Eimear E and Moreno-Estrada, Andres and Gignoux, Christopher R} } @article {39, title = {Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies.}, journal = {G3 (Bethesda)}, volume = {8}, year = {2018}, month = {2018 Oct 03}, pages = {3255-3267}, abstract = {

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance mean imputed r at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1\%) by 0.5-3.1\% for an array of one million sites and 0.7-7.1\% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30\% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

}, keywords = {Computational Biology, Databases, Nucleic Acid, Ethnicity, Genetic Association Studies, Genetics, Population, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Models, Genetic, Polymorphism, Single Nucleotide, Reproducibility of Results, Selection, Genetic}, issn = {2160-1836}, doi = {10.1534/g3.118.200502}, author = {Wojcik, Genevieve L and Fuchsberger, Christian and Taliun, Daniel and Welch, Ryan and Martin, Alicia R and Shringarpure, Suyash and Carlson, Christopher S and Abecasis, Goncalo and Kang, Hyun Min and Boehnke, Michael and Bustamante, Carlos D and Gignoux, Christopher R and Kenny, Eimear E} } @article {28, title = {Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Apr 24}, pages = {1612}, abstract = {

Protein-truncating variants can have profound effects on gene function and are critical for clinical genome interpretation and generating therapeutic hypotheses, but their relevance to medical phenotypes has not been systematically assessed. Here, we characterize the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and find 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We perform phenome-wide analyses and directly measure the effect in homozygous carriers, commonly referred to as "human knockouts," across medical phenotypes for genes implicated as being protective against disease or associated with at least one phenotype in our study. We find several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.

}, keywords = {Databases, Nucleic Acid, Genome-Wide Association Study, Humans, Phenotype, Proteins, Sequence Deletion, United Kingdom}, issn = {2041-1723}, doi = {10.1038/s41467-018-03910-9}, author = {DeBoever, Christopher and Tanigawa, Yosuke and Lindholm, Malene E and McInnes, Greg and Lavertu, Adam and Ingelsson, Erik and Chang, Chris and Ashley, Euan A and Bustamante, Carlos D and Daly, Mark J and Rivas, Manuel A} } @article {29, title = {Phenotype risk scores identify patients with unrecognized Mendelian disease patterns.}, journal = {Science}, volume = {359}, year = {2018}, month = {2018 Mar 16}, pages = {1233-1239}, abstract = {

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.

}, keywords = {Databases, Genetic, DNA Mutational Analysis, Electronic Health Records, Exome, Genetic Association Studies, Genetic Diseases, Inborn, Genetic Predisposition to Disease, Genetic Variation, Humans, Phenotype, Risk Factors}, issn = {1095-9203}, doi = {10.1126/science.aal4043}, author = {Bastarache, Lisa and Hughey, Jacob J and Hebbring, Scott and Marlo, Joy and Zhao, Wanke and Ho, Wanting T and Van Driest, Sara L and McGregor, Tracy L and Mosley, Jonathan D and Wells, Quinn S and Temple, Michael and Ramirez, Andrea H and Carroll, Robert and Osterman, Travis and Edwards, Todd and Ruderfer, Douglas and Velez Edwards, Digna R and Hamid, Rizwan and Cogan, Joy and Glazer, Andrew and Wei, Wei-Qi and Feng, QiPing and Brilliant, Murray and Zhao, Zhizhuang J and Cox, Nancy J and Roden, Dan M and Denny, Joshua C} } @article {34, title = {A synthetic-diploid benchmark for accurate variant-calling evaluation.}, journal = {Nat Methods}, volume = {15}, year = {2018}, month = {2018 Aug}, pages = {595-597}, abstract = {

Existing benchmark datasets for use in evaluating variant-calling accuracy are constructed from a consensus of known short-variant callers, and they are thus biased toward easy regions that are accessible by these algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two fully homozygous human cell lines, which provides a relatively more accurate and less biased estimate of small-variant-calling error rates in a realistic context.

}, keywords = {Algorithms, Benchmarking, Cell Line, Tumor, Databases, Genetic, Diploidy, Female, Genetic Variation, Genome, Human, Homozygote, Humans, Hydatidiform Mole, Pregnancy, Synthetic Biology, Uterine Neoplasms, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-018-0054-7}, author = {Li, Heng and Bloom, Jonathan M and Farjoun, Yossi and Fleharty, Mark and Gauthier, Laura and Neale, Benjamin and MacArthur, Daniel} } @article {15, title = {Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.}, journal = {Genome Med}, volume = {9}, year = {2017}, month = {2017 Nov 24}, pages = {98}, abstract = {

BACKGROUND: Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects.

METHODS: Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.

RESULTS: We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.

CONCLUSIONS: Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

}, keywords = {Anticoagulants, Black or African American, Female, Gene Expression, Genome-Wide Association Study, Humans, Male, Tissue Distribution, Warfarin}, issn = {1756-994X}, doi = {10.1186/s13073-017-0495-0}, author = {Gottlieb, Assaf and Daneshjou, Roxana and DeGorter, Marianne and Bourgeois, Stephane and Svensson, Peter J and Wadelius, Mia and Deloukas, Panos and Montgomery, Stephen B and Altman, Russ B} } @article {22, title = {Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {753-761}, abstract = {

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G{\texttimes}Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G{\texttimes}E discovery. This paper highlights current and critical issues and themes in G{\texttimes}E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

}, keywords = {Disease, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Software}, issn = {1476-6256}, doi = {10.1093/aje/kwx227}, author = {McAllister, Kimberly and Mechanic, Leah E and Amos, Christopher and Aschard, Hugues and Blair, Ian A and Chatterjee, Nilanjan and Conti, David and Gauderman, W James and Hsu, Li and Hutter, Carolyn M and Jankowska, Marta M and Kerr, Jacqueline and Kraft, Peter and Montgomery, Stephen B and Mukherjee, Bhramar and Papanicolaou, George J and Patel, Chirag J and Ritchie, Marylyn D and Ritz, Beate R and Thomas, Duncan C and Wei, Peng and Witte, John S} } @article {7, title = {The Effects of Migration and Assortative Mating on Admixture Linkage Disequilibrium.}, journal = {Genetics}, volume = {205}, year = {2017}, month = {2017 Jan}, pages = {375-383}, abstract = {

Statistical models in medical and population genetics typically assume that individuals assort randomly in a population. While this simplifies model complexity, it contradicts an increasing body of evidence of nonrandom mating in human populations. Specifically, it has been shown that assortative mating is significantly affected by genomic ancestry. In this work, we examine the effects of ancestry-assortative mating on the linkage disequilibrium between local ancestry tracks of individuals in an admixed population. To accomplish this, we develop an extension to the Wright-Fisher model that allows for ancestry-based assortative mating. We show that ancestry-assortment perturbs the distribution of local ancestry linkage disequilibrium (LAD) and the variance of ancestry in a population as a function of the number of generations since admixture. This assortment effect can induce errors in demographic inference of admixed populations when methods assume random mating. We derive closed form formulae for LAD under an assortative-mating model with and without migration. We observe that LAD depends on the correlation of global ancestry of couples in each generation, the migration rate of each of the ancestral populations, the initial proportions of ancestral populations, and the number of generations since admixture. We also present the first direct evidence of ancestry-assortment in African Americans and examine LAD in simulated and real admixed population data of African Americans. We find that demographic inference under the assumption of random mating significantly underestimates the number of generations since admixture, and that accounting for assortative mating using the patterns of LAD results in estimates that more closely agrees with the historical narrative.

}, keywords = {Alleles, Black or African American, Datasets as Topic, Gene Frequency, Genetics, Population, Genomics, Human Migration, Humans, Linkage Disequilibrium, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide}, issn = {1943-2631}, doi = {10.1534/genetics.116.192138}, author = {Zaitlen, Noah and Huntsman, Scott and Hu, Donglei and Spear, Melissa and Eng, Celeste and Oh, Sam S and White, Marquitta J and Mak, Angel and Davis, Adam and Meade, Kelly and Brigino-Buenaventura, Emerita and LeNoir, Michael A and Bibbins-Domingo, Kirsten and Burchard, Esteban G and Halperin, Eran} } @article {11, title = {Estimating the selective effects of heterozygous protein-truncating variants from human exome data.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 May}, pages = {806-810}, abstract = {

The evolutionary cost of gene loss is a central question in genetics and has been investigated in model organisms and human cell lines. In humans, tolerance of the loss of one or both functional copies of a gene is related to the gene{\textquoteright}s causal role in disease. However, estimates of the selection and dominance coefficients in humans have been elusive. Here we analyze exome sequence data from 60,706 individuals to make genome-wide estimates of selection against heterozygous loss of gene function. Using this distribution of selection coefficients for heterozygous protein-truncating variants (PTVs), we provide corresponding Bayesian estimates for individual genes. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, Mendelian disease-associated genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that are likely to have crucial functions but have not yet been thoroughly characterized.

}, keywords = {Algorithms, Animals, Bayes Theorem, Exome, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotype, Heterozygote, Humans, Mice, Knockout, Models, Genetic, Mutation, Selection, Genetic, Sequence Analysis, DNA}, issn = {1546-1718}, doi = {10.1038/ng.3831}, author = {Cassa, Christopher A and Weghorn, Donate and Balick, Daniel J and Jordan, Daniel M and Nusinow, David and Samocha, Kaitlin E and O{\textquoteright}Donnell-Luria, Anne and MacArthur, Daniel G and Daly, Mark J and Beier, David R and Sunyaev, Shamil R} } @article {46, title = {The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies.}, journal = {J Am Stat Assoc}, volume = {112}, year = {2017}, month = {2017}, pages = {64-76}, abstract = {

It is of substantial interest to study the effects of genes, genetic pathways, and networks on the risk of complex diseases. These genetic constructs each contain multiple SNPs, which are often correlated and function jointly, and might be large in number. However, only a sparse subset of SNPs in a genetic construct is generally associated with the disease of interest. In this article, we propose the generalized higher criticism (GHC) to test for the association between an SNP set and a disease outcome. The higher criticism is a test traditionally used in high-dimensional signal detection settings when marginal test statistics are independent and the number of parameters is very large. However, these assumptions do not always hold in genetic association studies, due to linkage disequilibrium among SNPs and the finite number of SNPs in an SNP set in each genetic construct. The proposed GHC overcomes the limitations of the higher criticism by allowing for arbitrary correlation structures among the SNPs in an SNP-set, while performing accurate analytic -value calculations for any finite number of SNPs in the SNP-set. We obtain the detection boundary of the GHC test. We compared empirically using simulations the power of the GHC method with existing SNP-set tests over a range of genetic regions with varied correlation structures and signal sparsity. We apply the proposed methods to analyze the CGEM breast cancer genome-wide association study. Supplementary materials for this article are available online.

}, issn = {0162-1459}, doi = {10.1080/01621459.2016.1192039}, author = {Barnett, Ian and Mukherjee, Rajarshi and Lin, Xihong} } @article {18, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

}, keywords = {Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci}, issn = {1476-4687}, doi = {10.1038/nature24277}, author = {Battle, Alexis and Brown, Christopher D and Engelhardt, Barbara E and Montgomery, Stephen B} } @article {32, title = {Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system.}, journal = {Elife}, volume = {6}, year = {2017}, month = {2017 Sep 12}, abstract = {

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic Bio biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, , with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2\% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.

}, keywords = {Adolescent, Adult, Aged, Child, Collagen Diseases, Female, Fibrillar Collagens, Genotype, Heterozygote, Hispanic or Latino, Homozygote, Humans, Male, Middle Aged, Molecular Epidemiology, Multigene Family, Musculoskeletal Diseases, New York City, Pedigree, Whole Genome Sequencing, Young Adult}, issn = {2050-084X}, doi = {10.7554/eLife.25060}, author = {Belbin, Gillian Morven and Odgis, Jacqueline and Sorokin, Elena P and Yee, Muh-Ching and Kohli, Sumita and Glicksberg, Benjamin S and Gignoux, Christopher R and Wojcik, Genevieve L and Van Vleck, Tielman and Jeff, Janina M and Linderman, Michael and Schurmann, Claudia and Ruderfer, Douglas and Cai, Xiaoqiang and Merkelson, Amanda and Justice, Anne E and Young, Kristin L and Graff, Misa and North, Kari E and Peters, Ulrike and James, Regina and Hindorff, Lucia and Kornreich, Ruth and Edelmann, Lisa and Gottesman, Omri and Stahl, Eli Ea and Cho, Judy H and Loos, Ruth Jf and Bottinger, Erwin P and Nadkarni, Girish N and Abul-Husn, Noura S and Kenny, Eimear E} } @article {14, title = {Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations.}, journal = {Am J Hum Genet}, volume = {100}, year = {2017}, month = {2017 Apr 06}, pages = {635-649}, abstract = {

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

}, keywords = {Americas, Genetic Predisposition to Disease, Genetics, Medical, Genetics, Population, Haplotypes, Human Genome Project, Humans, Multifactorial Inheritance, Racial Groups}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2017.03.004}, author = {Martin, Alicia R and Gignoux, Christopher R and Walters, Raymond K and Wojcik, Genevieve L and Neale, Benjamin M and Gravel, Simon and Daly, Mark J and Bustamante, Carlos D and Kenny, Eimear E} } @article {30, title = {The impact of rare variation on gene expression across tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {239-243}, abstract = {

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58\% of underexpression and 28\% of overexpression outliers have nearby conserved rare variants compared to 8\% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

}, keywords = {Bayes Theorem, Female, Gene Expression Profiling, Genetic Variation, Genome, Human, Genomics, Genotype, Humans, Male, Models, Genetic, Organ Specificity, Sequence Analysis, RNA}, issn = {1476-4687}, doi = {10.1038/nature24267}, author = {Li, Xin and Kim, Yungil and Tsang, Emily K and Davis, Joe R and Damani, Farhan N and Chiang, Colby and Hess, Gaelen T and Zappala, Zachary and Strober, Benjamin J and Scott, Alexandra J and Li, Amy and Ganna, Andrea and Bassik, Michael C and Merker, Jason D and Hall, Ira M and Battle, Alexis and Montgomery, Stephen B} } @article {23, title = {Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {771-777}, abstract = {

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.

}, keywords = {Animals, Disease, Gene-Environment Interaction, Genome-Wide Association Study, Genomics, Humans, Models, Animal, Sequence Analysis, RNA}, issn = {1476-6256}, doi = {10.1093/aje/kwx229}, author = {Ritchie, Marylyn D and Davis, Joe R and Aschard, Hugues and Battle, Alexis and Conti, David and Du, Mengmeng and Eskin, Eleazar and Fallin, M Daniele and Hsu, Li and Kraft, Peter and Moore, Jason H and Pierce, Brandon L and Bien, Stephanie A and Thomas, Duncan C and Wei, Peng and Montgomery, Stephen B} } @article {24, title = {Lessons Learned From Past Gene-Environment Interaction Successes.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {778-786}, abstract = {

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G{\texttimes}E) has been a focus of research for several years. In this article, select examples of G{\texttimes}E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G{\texttimes}E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G{\texttimes}Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G{\texttimes}E in several examples. Future studies may benefit from these lessons learned.

}, keywords = {Biomedical Research, Disease, Environmental Exposure, Gene-Environment Interaction, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Models, Biological}, issn = {1476-6256}, doi = {10.1093/aje/kwx230}, author = {Ritz, Beate R and Chatterjee, Nilanjan and Garcia-Closas, Montserrat and Gauderman, W James and Pierce, Brandon L and Kraft, Peter and Tanner, Caroline M and Mechanic, Leah E and McAllister, Kimberly} } @article {9, title = {Population- and individual-specific regulatory variation in Sardinia.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 May}, pages = {700-707}, abstract = {

Genetic studies of complex traits have mainly identified associations with noncoding variants. To further determine the contribution of regulatory variation, we combined whole-genome and transcriptome data for 624 individuals from Sardinia to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 new QTLs. We identified high-frequency QTLs and found evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.

}, keywords = {Alternative Splicing, Chromosome Mapping, Family Health, Female, Gene Expression Profiling, Genetic Predisposition to Disease, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Genotype, Humans, Italy, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Transcription Initiation Site}, issn = {1546-1718}, doi = {10.1038/ng.3840}, author = {Pala, Mauro and Zappala, Zachary and Marongiu, Mara and Li, Xin and Davis, Joe R and Cusano, Roberto and Crobu, Francesca and Kukurba, Kimberly R and Gloudemans, Michael J and Reinier, Frederic and Berutti, Riccardo and Piras, Maria G and Mulas, Antonella and Zoledziewska, Magdalena and Marongiu, Michele and Sorokin, Elena P and Hess, Gaelen T and Smith, Kevin S and Busonero, Fabio and Maschio, Andrea and Steri, Maristella and Sidore, Carlo and Sanna, Serena and Fiorillo, Edoardo and Bassik, Michael C and Sawcer, Stephen J and Battle, Alexis and Novembre, John and Jones, Chris and Angius, Andrea and Abecasis, Gon{\c c}alo R and Schlessinger, David and Cucca, Francesco and Montgomery, Stephen B} } @article {33, title = {Whole-transcriptome sequencing in blood provides a diagnosis of spinal muscular atrophy with progressive myoclonic epilepsy.}, journal = {Hum Mutat}, volume = {38}, year = {2017}, month = {2017 Jun}, pages = {611-614}, abstract = {

At least 15\% of the disease-causing mutations affect mRNA splicing. Many splicing mutations are missed in a clinical setting due to limitations of in silico prediction algorithms or their location in noncoding regions. Whole-transcriptome sequencing is a promising new tool to identify these mutations; however, it will be a challenge to obtain disease-relevant tissue for RNA. Here, we describe an individual with a sporadic atypical spinal muscular atrophy, in whom clinical DNA sequencing reported one pathogenic ASAH1 mutation (c.458A>G;p.Tyr153Cys). Transcriptome sequencing on patient leukocytes identified a highly significant and atypical ASAH1 isoform not explained by c.458A>G(p<10 ). Subsequent Sanger-sequencing identified the splice mutation responsible for the isoform (c.504A>C;p.Lys168Asn) and provided a molecular diagnosis of autosomal-recessive spinal muscular atrophy with progressive myoclonic epilepsy. Our findings demonstrate the utility of RNA sequencing from blood to identify splice-impacting disease mutations for nonhematological conditions, providing a diagnosis for these otherwise unsolved patients.

}, keywords = {Acid Ceramidase, Child, Preschool, Humans, Male, Muscular Atrophy, Spinal, Mutation, Myoclonic Epilepsies, Progressive, Pathology, Molecular, RNA Splicing, Sequence Analysis, DNA, Transcriptome}, issn = {1098-1004}, doi = {10.1002/humu.23211}, author = {Kernohan, Kristin D and Fr{\'e}sard, Laure and Zappala, Zachary and Hartley, Taila and Smith, Kevin S and Wagner, Justin and Xu, Hongbin and McBride, Arran and Bourque, Pierre R and Consortium, Care Rare Canada and Bennett, Steffany A L and Dyment, David A and Boycott, Kym M and Montgomery, Stephen B and Warman Chardon, Jodi} }