@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 {132, title = {Disentangling selection on genetically correlated polygenic traits via whole-genome genealogies.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 02 04}, pages = {219-239}, abstract = {

We present a full-likelihood method to infer polygenic adaptation from DNA sequence variation and GWAS summary statistics to quantify recent transient directional selection acting on a complex trait. Through simulations of polygenic trait architecture evolution and GWASs, we show the method substantially improves power over current methods. We examine the robustness of the method under stratification, uncertainty and bias in marginal effects, uncertainty in the causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, controlling for pleiotropy even among traits with strong genetic correlation (|r|=80\%) while retaining high power to attribute selection to the causal trait. When the causal trait is excluded from analysis, selection is attributed to its closest proxy. We discuss limitations of the method, cautioning against strongly causal interpretations of the results, and the possibility of undetectable gene-by-environment (GxE) interactions. We apply the method to 56 human polygenic traits, revealing signals of directional selection on pigmentation, life history, glycated hemoglobin (HbA1c), and other traits. We also conduct joint testing of 137 pairs of genetically correlated traits, revealing widespread correlated response acting on these traits (2.6-fold enrichment, p = 1.5~{\texttimes} 10). Signs of selection on some traits previously reported as adaptive (e.g., educational attainment and hair color) are largely attributable to correlated response (p = 2.9~{\texttimes} 10 and 1.7~{\texttimes} 10, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5~{\texttimes} 10).

}, keywords = {Computer Simulation, Diabetes Mellitus, Type 2, Evolution, Molecular, Gene-Environment Interaction, Genetic Heterogeneity, Genetic Pleiotropy, Genome, Human, Genome-Wide Association Study, Glycated Hemoglobin A, Humans, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Sample Size, Selection, Genetic}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.12.005}, author = {Stern, Aaron J and Speidel, Leo and Zaitlen, Noah A and Nielsen, Rasmus} } @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 {136, title = {Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians.}, journal = {Genet Epidemiol}, year = {2021}, month = {2021 Mar 19}, abstract = {

Previous genome-wide association studies (GWASs) have been largely focused on European (EUR) populations. However, polygenic risk scores (PRSs) derived from EUR have been shown to perform worse in non-EURs compared with EURs. In this study, we aim to improve PRS prediction in East Asians (EASs). We introduce a rescaled meta-analysis framework to combine both EUR (N = 122,175) and EAS (N = 30,801) GWAS summary statistics. To improve PRS prediction in EASs, we use a scaling factor to up-weight the EAS data, such that the resulting effect size estimates are more relevant to EASs. We then derive PRSs for EAS from the rescaled meta-analysis results of EAS and EUR data. Evaluated in an independent EAS validation data set, this approach increases the prediction liability-adjusted Nagelkerke{\textquoteright}s pseudo R by 40\%, 41\%, and 5\%, respectively, compared with PRSs derived from an EAS GWAS only, EUR GWAS only, and conventional fixed-effects meta-analysis of EAS and EUR data. The PRS derived from the rescaled meta-analysis approach achieved an area under the receiver operating characteristic curve (AUC)~of 0.6059, higher than AUC = 0.5782, 0.5809, 0.6008 for EAS, EUR, and conventional meta-analysis of EAS and EUR. We further compare PRSs constructed by single-nucleotide polymorphisms that have different linkage disequilibrium (LD) scores and minor allele frequencies (MAFs) between EUR and EAS, and observe that lower LD scores or MAF in EAS correspond to poorer PRS performance (AUC = 0.5677,~0.5530, respectively) than higher LD scores or MAF (AUC = 0.589, 0.5993, respectively). We finally build a PRS stratified by LD score differences in EUR and EAS using rescaled meta-analysis, and obtain an AUC of 0.6096, with improvement over other strategies investigated.

}, issn = {1098-2272}, doi = {10.1002/gepi.22382}, author = {Ji, Ying and Long, Jirong and Kweon, Sun-Seog and Kang, Daehee and Kubo, Michiaki and Park, Boyoung and Shu, Xiao-Ou and Zheng, Wei and Tao, Ran and Li, Bingshan} } @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 {133, title = {RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.}, journal = {PLoS Genet}, volume = {17}, year = {2021}, month = {2021 01}, pages = {e1009315}, abstract = {

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

}, keywords = {Biological Specimen Banks, Genome, Human, Genome-Wide Association Study, Genotyping Techniques, Haplotypes, Humans, Models, Genetic, Pedigree, Polymorphism, Single Nucleotide}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009315}, author = {Naseri, Ardalan and Shi, Junjie and Lin, Xihong and Zhang, Shaojie and Zhi, Degui} } @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 {112, title = {Whole exome sequencing analyses reveal gene-microbiota interactions in the context of IBD.}, journal = {Gut}, volume = {70}, year = {2021}, month = {2021 Feb}, pages = {285-296}, abstract = {

OBJECTIVE: Both the gut microbiome and host genetics are known to play significant roles in the pathogenesis of IBD. However, the interaction between these two factors and its implications in the aetiology of IBD remain underexplored. Here, we report on the influence of host genetics on the gut microbiome in IBD.

DESIGN: To evaluate the impact of host genetics on the gut microbiota of patients with IBD, we combined whole exome sequencing of the host genome and whole genome shotgun sequencing of 1464 faecal samples from 525 patients with IBD and 939 population-based controls. We followed a four-step analysis: (1) exome-wide microbial quantitative trait loci (mbQTL) analyses, (2) a targeted approach focusing on IBD-associated genomic regions and protein truncating variants (PTVs, minor allele frequency (MAF) >5\%), (3) gene-based burden tests on PTVs with MAF <5\% and exome copy number variations (CNVs) with site frequency <1\%, (4) joint analysis of both cohorts to identify the interactions between disease and host genetics.

RESULTS: We identified 12 mbQTLs, including variants in the IBD-associated genes , , and . For example, the decrease of the pathway acetyl-coenzyme A biosynthesis, which is involved in short chain fatty acids production, was associated with variants in the gene (false discovery rate <0.05). Changes in functional pathways involved in the metabolic potential were also observed in participants carrying rare PTVs or CNVs in , and genes. These genes are known for their function in the immune system. Moreover, interaction analyses confirmed previously known IBD disease-specific mbQTLs in .

CONCLUSION: This study highlights that both common and rare genetic variants affecting the immune system are key factors in shaping the gut microbiota in the context of IBD and pinpoints towards potential mechanisms for disease treatment.

}, issn = {1468-3288}, doi = {10.1136/gutjnl-2019-319706}, author = {Hu, Shixian and Vich Vila, Arnau and Gacesa, Ranko and Collij, Valerie and Stevens, Christine and Fu, Jack M and Wong, Isaac and Talkowski, Michael E and Rivas, Manuel A and Imhann, Floris and Bolte, Laura and van Dullemen, Hendrik and Dijkstra, Gerard and Visschedijk, Marijn C and Festen, Eleonora A and Xavier, Ramnik J and Fu, Jingyuan and Daly, Mark J and Wijmenga, Cisca and Zhernakova, Alexandra and Kurilshikov, Alexander and Weersma, Rinse K} } @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 {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 {82, title = {Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis.}, journal = {Genet Med}, volume = {22}, year = {2020}, month = {2020 07}, pages = {1191-1200}, abstract = {

PURPOSE: The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease.

METHODS: We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort.

RESULTS: GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan.

CONCLUSION: Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.

}, keywords = {Adult, Cystic Fibrosis, Cystic Fibrosis Transmembrane Conductance Regulator, Electronic Health Records, Humans, Mutation, Phenotype}, issn = {1530-0366}, doi = {10.1038/s41436-020-0786-5}, author = {Zhong, Xue and Yin, Zhijun and Jia, Gengjie and Zhou, Dan and Wei, Qiang and Faucon, Annika and Evans, Patrick and Gamazon, Eric R and Li, Bingshan and Tao, Ran and Rzhetsky, Andrey and Bastarache, Lisa and Cox, Nancy J} } @article {104, title = {FasTag: Automatic text classification of unstructured medical narratives.}, journal = {PLoS One}, volume = {15}, year = {2020}, month = {2020}, pages = {e0234647}, abstract = {

Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes. Despite these efforts, however, label availability and accuracy are both suboptimal. In this retrospective study, we aimed to automate the assignment of top-level International Classification of Diseases version 9 (ICD-9) codes to clinical records from human and veterinary data stores using minimal manual labor and feature curation. Automating top-level annotations could in turn enable rapid cohort identification, especially in a veterinary setting. To this end, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baseline classification performances by training Decision Trees (DT) and Random Forests (RF). We also investigated whether transforming the data using MetaMap Lite, a clinical natural language processing tool, affected classification performance. We showed that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the "neoplasia" category, the model trained on veterinary data had a high validation accuracy in veterinary data and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from and inform one another.

}, keywords = {Animals, Automation, Data Mining, Databases as Topic, Humans, Narrative Medicine, Reproducibility of Results, Software, Species Specificity}, issn = {1932-6203}, doi = {10.1371/journal.pone.0234647}, author = {Venkataraman, Guhan Ram and Pineda, Arturo Lopez and Bear Don{\textquoteright}t Walk Iv, Oliver J and Zehnder, Ashley M and Ayyar, Sandeep and Page, Rodney L and Bustamante, Carlos D and Rivas, Manuel A} } @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 {124, title = {Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis".}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 10}, pages = {e1009165}, abstract = {

BACKGROUND: The majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.

RESULTS: We applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.

CONCLUSIONS: Unlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.

}, keywords = {Alleles, Animals, Epistasis, Genetic, Evolution, Molecular, Genotype, Humans, Mice, Models, Genetic, Multifactorial Inheritance, Phenotype, Quantitative Trait Loci, Saccharomyces cerevisiae, Selection, Genetic}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009165}, author = {Rau, Christoph D and Gonzales, Natalia M and Bloom, Joshua S and Park, Danny and Ayroles, Julien and Palmer, Abraham A and Lusis, Aldons J and Zaitlen, Noah} } @article {86, title = {A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 01 02}, pages = {71-91}, abstract = {

Gene-environment interactions (GxE) can be fundamental in applications ranging from functional genomics to precision medicine and is a conjectured source of substantial heritability. However, unbiased methods to profile GxE genome-wide are nascent and, as we show, cannot accommodate general environment variables, modest sample sizes, heterogeneous noise, and binary traits. To address this gap, we propose a simple, unifying mixed model for gene-environment interaction (GxEMM). In simulations and theory, we show that GxEMM can dramatically improve estimates and eliminate false positives when the assumptions of existing methods fail. We apply GxEMM to a range of human and model organism datasets and find broad evidence of context-specific genetic effects, including GxSex, GxAdversity, and GxDisease interactions across thousands of clinical and molecular phenotypes. Overall, GxEMM is broadly applicable for testing and quantifying polygenic interactions, which can be useful for explaining heritability and invaluable for determining biologically relevant environments.

}, keywords = {Adult, Animals, Computer Simulation, Female, Gene-Environment Interaction, Genetic Markers, Genome-Wide Association Study, Humans, Male, Mental Disorders, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenomics, Phenotype, Rats}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.11.015}, author = {Dahl, Andy and Nguyen, Khiem and Cai, Na and Gandal, Michael J and Flint, Jonathan and Zaitlen, Noah} } @article {110, title = {A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk.}, journal = {Cancer Res}, volume = {80}, year = {2020}, month = {2020 10 15}, pages = {4346-4354}, abstract = {

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR <= 0.05, including seven previously reported genes (, and ) and six novel genes not yet reported for pancreatic cancer risk [6q27: OR (95\% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: OR (95\% CI), 0.78 (0.70-0.88); 14q24.3: OR (95\% CI), 1.35 (1.17-1.56); 17q12: OR (95\% CI), 6.49 (2.96-14.27); 17q21.1: OR (95\% CI), 1.94 (1.45-2.58); and 20p13: OR (95\% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (, and ) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.

}, keywords = {Age Factors, Case-Control Studies, European Continental Ancestry Group, Female, Gene Expression Regulation, Neoplastic, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Models, Genetic, Pancreatic Neoplasms, Polymorphism, Single Nucleotide}, issn = {1538-7445}, doi = {10.1158/0008-5472.CAN-20-1353}, author = {Liu, Duo and Zhou, Dan and Sun, Yanfa and Zhu, Jingjing and Ghoneim, Dalia and Wu, Chong and Yao, Qizhi and Gamazon, Eric R and Cox, Nancy J and Wu, Lang} } @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 {111, title = {A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 11}, pages = {1239-1246}, abstract = {

Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.

}, keywords = {Animals, Gene Expression Profiling, Genetic Association Studies, Humans, Lipoproteins, LDL, Mendelian Randomization Analysis, Mice, Models, Genetic, Multifactorial Inheritance, Predictive Value of Tests}, issn = {1546-1718}, doi = {10.1038/s41588-020-0706-2}, author = {Zhou, Dan and Jiang, Yi and Zhong, Xue and Cox, Nancy J and Liu, Chunyu and Gamazon, Eric R} } @article {88, title = {Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex.}, journal = {Cell Rep}, volume = {31}, year = {2020}, month = {2020 04 07}, pages = {107489}, abstract = {

Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.

}, keywords = {Base Sequence, Brain, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genomics, Humans, Phenotype, Polymorphism, Single Nucleotide, Prefrontal Cortex, Quantitative Trait Loci, Sequence Analysis, RNA, Transcriptome, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {2211-1247}, doi = {10.1016/j.celrep.2020.03.053}, author = {Werling, Donna M and Pochareddy, Sirisha and Choi, Jinmyung and An, Joon-Yong and Sheppard, Brooke and Peng, Minshi and Li, Zhen and Dastmalchi, Claudia and Santpere, Gabriel and Sousa, Andr{\'e} M M and Tebbenkamp, Andrew T N and Kaur, Navjot and Gulden, Forrest O and Breen, Michael S and Liang, Lindsay and Gilson, Michael C and Zhao, Xuefang and Dong, Shan and Klei, Lambertus and Cicek, A Ercument and Buxbaum, Joseph D and Adle-Biassette, Homa and Thomas, Jean-Leon and Aldinger, Kimberly A and O{\textquoteright}Day, Diana R and Glass, Ian A and Zaitlen, Noah A and Talkowski, Michael E and Roeder, Kathryn and State, Matthew W and Devlin, Bernie and Sanders, Stephan J and Sestan, Nenad} } @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 {64, title = {A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data.}, journal = {Nat Neurosci}, volume = {22}, year = {2019}, month = {2019 05}, pages = {691-699}, abstract = {

Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.

}, keywords = {Animals, Bayes Theorem, Disease Models, Animal, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Mice, Risk Factors, Schizophrenia}, issn = {1546-1726}, doi = {10.1038/s41593-019-0382-7}, author = {Wang, Quan and Chen, Rui and Cheng, Feixiong and Wei, Qiang and Ji, Ying and Yang, Hai and Zhong, Xue and Tao, Ran and Wen, Zhexing and Sutcliffe, James S and Liu, Chunyu and Cook, Edwin H and Cox, Nancy J and Li, Bingshan} } @article {60, title = {De novo pattern discovery enables robust assessment of functional consequences of non-coding variants.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 May 01}, pages = {1453-1460}, abstract = {

MOTIVATION: Given the complexity of genome regions, prioritize the functional effects of non-coding variants remains a challenge. Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used {\textquoteright}black boxes{\textquoteright} methods that simplify the task as the pathogenicity/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously.

RESULTS: DVAR performed de novo pattern discovery in high-dimensional data and identified five regulatory clusters of non-coding variants. Leveraging the new insights into the multiple functional patterns, it measures both the between-class and the within-class functional implication of the variants to achieve accurate prioritization. Compared to other two-class learning methods, it showed improved performance in identification of clinically significant variants, fine-mapped GWAS variants, eQTLs and expression-modulating variants. Moreover, it has superior performance on disease causal variants verified by genome-editing (like CRISPR-Cas9), which could provide a pre-selection strategy for genome-editing technologies across the whole genome. Finally, evaluated in BioVU and UK Biobank, two large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effectiveness in prioritizing non-coding variants associated with medical phenotypes.

AVAILABILITY AND IMPLEMENTATION: The C++ and Python source codes, the pre-computed DVAR-cluster labels and DVAR-scores across the whole genome are available at https://www.vumc.org/cgg/dvar.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bty826}, author = {Yang, Hai and Chen, Rui and Wang, Quan and Wei, Qiang and Ji, Ying and Zheng, Guangze and Zhong, Xue and Cox, Nancy J and Li, Bingshan} } @article {65, title = {Diagnostic Algorithms to Study Post-Concussion Syndrome Using Electronic Health Records: Validating a Method to Capture an Important Patient Population.}, journal = {J Neurotrauma}, volume = {36}, year = {2019}, month = {2019 Jul 15}, pages = {2167-2177}, abstract = {

Post-concussion syndrome (PCS) is characterized by persistent cognitive, somatic, and emotional symptoms after a mild traumatic brain injury (mTBI). Genetic and other biological variables may contribute to PCS etiology, and the emergence of biobanks linked to electronic health records (EHRs) offers new opportunities for research on PCS. We sought to validate the EHR data of PCS patients by comparing two diagnostic algorithms deployed in the Vanderbilt University Medical Center de-identified database of 2.8 million patient EHRs. The algorithms identified individuals with PCS by: 1) natural language processing (NLP) of narrative text in the EHR combined with structured demographic, diagnostic, and encounter data; or 2) coded billing and procedure data. The predictive value of each algorithm was assessed, and cases and controls identified by each approach were compared on demographic and medical characteristics. The NLP algorithm identified 507 cases and 10,857 controls. The negative predictive value in controls was 78\% and the positive predictive value (PPV) in cases was 82\%. Conversely, the coded algorithm identified 1142 patients with two or more PCS billing codes and had a PPV of 76\%. Comparisons of PCS controls to both case groups recovered known epidemiology of PCS: cases were more likely than controls to be female and to have pre-morbid diagnoses of anxiety, migraine, and post-traumatic stress disorder. In contrast, controls and cases were equally likely to have attention deficit hyperactive disorder and learning disabilities, in accordance with the findings of recent systematic reviews of PCS risk factors. We conclude that EHRs are a valuable research tool for PCS. Ascertainment based on coded data alone had a predictive value comparable to an NLP algorithm, recovered known PCS risk factors, and maximized the number of included patients.

}, issn = {1557-9042}, doi = {10.1089/neu.2018.5916}, author = {Dennis, Jessica and Yengo-Kahn, Aaron M and Kirby, Paul and Solomon, Gary S and Cox, Nancy J and Zuckerman, Scott L} } @article {52, title = {Differential NOVA2-Mediated Splicing in Excitatory and Inhibitory Neurons Regulates Cortical Development and Cerebellar Function.}, journal = {Neuron}, volume = {101}, year = {2019}, month = {2019 Feb 20}, pages = {707-720.e5}, abstract = {

RNA-binding proteins (RBPs) regulate genetic diversity, but the degree to which they do so in individual cell types in~vivo is unknown. We developed NOVA2 cTag-crosslinking and immunoprecipitation (CLIP) to generate functional RBP-RNA maps from different neuronal populations in the mouse brain. Combining cell type datasets from Nova2-cTag and Nova2 conditional knockout mice revealed differential NOVA2 regulatory actions on alternative splicing (AS) on the same transcripts expressed in different neurons. This includes functional differences in transcripts expressed in cortical and cerebellar excitatory versus inhibitory neurons, where we find NOVA2 is required for, respectively, development of laminar structure, motor coordination, and synapse formation. We also find that NOVA2-regulated AS is coupled to NOVA2 regulation of intron retention in hundreds of transcripts, which can sequester the trans-acting splicing factor PTBP2. In summary, cTag-CLIP complements single-cell RNA sequencing (RNA-seq) studies by providing a means for understanding RNA regulation of functional cell diversity.

}, keywords = {Alternative Splicing, Animals, Antigens, Neoplasm, Cells, Cultured, Cerebellum, Cerebral Cortex, Excitatory Postsynaptic Potentials, Female, Inhibitory Postsynaptic Potentials, Male, Mice, Mice, Inbred C57BL, Nerve Tissue Proteins, Neuro-Oncological Ventral Antigen, Neurogenesis, Neurons, Polypyrimidine Tract-Binding Protein, RNA-Binding Proteins}, issn = {1097-4199}, doi = {10.1016/j.neuron.2018.12.019}, author = {Saito, Yuhki and Yuan, Yuan and Zucker-Scharff, Ilana and Fak, John J and Jereb, Sa{\v s}a and Tajima, Yoko and Licatalosi, Donny D and Darnell, Robert B} } @article {49, title = {An evolutionary compass for detecting signals of polygenic selection and mutational bias.}, journal = {Evol Lett}, volume = {3}, year = {2019}, month = {2019 Feb}, pages = {69-79}, abstract = {

Selection and mutation shape the genetic variation underlying~human traits, but the specific evolutionary mechanisms driving~complex trait variation are largely unknown. We developed a statistical method that uses polarized genome-wide association study (GWAS) summary statistics from a single population to detect signals of mutational bias and selection. We found evidence for nonneutral signals on variation underlying several traits (body mass index [BMI], schizophrenia, Crohn{\textquoteright}s disease, educational attainment, and height). We then used simulations that incorporate simultaneous negative and positive selection to show that these signals are consistent with mutational bias and shifts in the fitness-phenotype relationship, but not stabilizing selection or mutational bias alone. We additionally replicate two of our top three signals (BMI and educational attainment) in an external cohort, and show that population stratification may have confounded GWAS summary statistics for height in the GIANT cohort. Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other species, and offer insights into the evolutionary mechanisms driving variation in human polygenic~traits.

}, issn = {2056-3744}, doi = {10.1002/evl3.97}, author = {Uricchio, Lawrence H and Kitano, Hugo C and Gusev, Alexander and Zaitlen, Noah A} } @article {62, title = {Genome sequencing analysis identifies Epstein-Barr virus subtypes associated with high risk of nasopharyngeal carcinoma.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 Jul}, pages = {1131-1136}, abstract = {

Epstein-Barr virus (EBV) infection is ubiquitous worldwide and is associated with multiple cancers, including nasopharyngeal carcinoma (NPC). The importance of EBV viral genomic variation in NPC development and its striking epidemic in southern China has been poorly explored. Through large-scale genome sequencing of 270 EBV isolates and two-stage association study of EBV isolates from China, we identify two non-synonymous EBV variants within BALF2 that are strongly associated with the risk of NPC (odds ratio (OR) = 8.69, P = 9.69 {\texttimes} 10 for SNP 162476_C; OR = 6.14, P = 2.40 {\texttimes} 10 for SNP 163364_T). The cumulative effects of these variants contribute to 83\% of the overall risk of NPC in southern China. Phylogenetic analysis of the risk variants reveals a unique origin in Asia, followed by clonal expansion in NPC-endemic regions. Our results provide novel insights into the NPC endemic in southern China and also enable the identification of high-risk individuals for NPC prevention.

}, issn = {1546-1718}, doi = {10.1038/s41588-019-0436-5}, author = {Xu, Miao and Yao, Youyuan and Chen, Hui and Zhang, Shanshan and Cao, Su-Mei and Zhang, Zhe and Luo, Bing and Liu, Zhiwei and Li, Zilin and Xiang, Tong and He, Guiping and Feng, Qi-Sheng and Chen, Li-Zhen and Guo, Xiang and Jia, Wei-Hua and Chen, Ming-Yuan and Zhang, Xiao and Xie, Shang-Hang and Peng, Roujun and Chang, Ellen T and Pedergnana, Vincent and Feng, Lin and Bei, Jin-Xin and Xu, Rui-Hua and Zeng, Mu-Sheng and Ye, Weimin and Adami, Hans-Olov and Lin, Xihong and Zhai, Weiwei and Zeng, Yi-Xin and Liu, Jianjun} } @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 {37, title = {An ancestry-based approach for detecting interactions.}, journal = {Genet Epidemiol}, volume = {42}, year = {2018}, month = {2018 Feb}, pages = {49-63}, abstract = {

BACKGROUND: Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.

RESULTS: In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P<5{\texttimes}10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8{\texttimes}10-6).

CONCLUSION: We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

}, keywords = {Black or African American, Black People, DNA Methylation, Epistasis, Genetic, Gene-Environment Interaction, Hispanic or Latino, Humans, Models, Genetic, Phenotype, White People}, issn = {1098-2272}, doi = {10.1002/gepi.22087}, author = {Park, Danny S and Eskin, Itamar and Kang, Eun Yong and Gamazon, Eric R and Eng, Celeste and Gignoux, Christopher R and Galanter, Joshua M and Burchard, Esteban and Ye, Chun J and Aschard, Hugues and Eskin, Eleazar and Halperin, Eran and Zaitlen, Noah} } @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 {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 {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 {21, title = {Covariate selection for association screening in multiphenotype genetic studies.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Dec}, pages = {1789-1795}, abstract = {

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

}, keywords = {Algorithms, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Multivariate Analysis, Phenotype, Reproducibility of Results, Sample Size}, issn = {1546-1718}, doi = {10.1038/ng.3975}, author = {Aschard, Hugues and Guillemot, Vincent and Vilhjalmsson, Bjarni and Patel, Chirag J and Skurnik, David and Ye, Chun J and Wolpin, Brian and Kraft, Peter and Zaitlen, Noah} } @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 {19, title = {A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.}, journal = {Genet Epidemiol}, volume = {41}, year = {2017}, month = {2017 Nov}, pages = {620-635}, abstract = {

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 {\texttimes} 10 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 {\texttimes} 10 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

}, keywords = {Biomarkers, Body Mass Index, Computational Biology, Diabetes Mellitus, Type 2, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide, Risk, Stochastic Processes}, issn = {1098-2272}, doi = {10.1002/gepi.22058}, author = {He, Liang and Zhbannikov, Ilya and Arbeev, Konstantin G and Yashin, Anatoliy I and Kulminski, Alexander M} } @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 {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} }