@article {131, title = {Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 01 13}, pages = {350}, abstract = {

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30\%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

}, keywords = {Biological Specimen Banks, Cardiovascular Diseases, Causality, Computer Simulation, Gene Regulatory Networks, Genetic Pleiotropy, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Mendelian Randomization Analysis, Models, Theoretical, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-020-20516-2}, author = {Amar, David and Sinnott-Armstrong, Nasa and Ashley, Euan A and Rivas, Manuel A} } @article {142, title = {Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 03}, pages = {313-321}, abstract = {

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

}, keywords = {Bardet-Biedl Syndrome, Calcium Channels, Cell Line, Cerebellar Ataxia, DNA Methylation, Gene Expression, Genetic Variation, Humans, Induced Pluripotent Stem Cells, Polymorphism, Single Nucleotide, Proteins, Quantitative Trait Loci, Rare Diseases, Regulatory Sequences, Nucleic Acid, Sequence Analysis, RNA, Whole Genome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-021-00800-7}, author = {Bonder, Marc Jan and Smail, Craig and Gloudemans, Michael J and Fr{\'e}sard, Laure and Jakubosky, David and D{\textquoteright}Antonio, Matteo and Li, Xin and Ferraro, Nicole M and Carcamo-Orive, Ivan and Mirauta, Bogdan and Seaton, Daniel D and Cai, Na and Vakili, Dara and Horta, Danilo and Zhao, Chunli and Zastrow, Diane B and Bonner, Devon E and Wheeler, Matthew T and Kilpinen, Helena and Knowles, Joshua W and Smith, Erin N and Frazer, Kelly A and Montgomery, Stephen B and Stegle, Oliver} } @article {145, title = {Population sequencing data reveal a compendium of mutational processes in the human germ line.}, journal = {Science}, volume = {373}, year = {2021}, month = {2021 08 27}, pages = {1030-1035}, abstract = {

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

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

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

}, keywords = {Coronary Artery Disease, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Disease, Gene Expression Profiling, Genetic Variation, Humans, Inflammatory Bowel Diseases, Multifactorial Inheritance, Organ Specificity, Population, Quantitative Trait Loci, RNA, Long Noncoding, Transcriptome}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.03.050}, author = {de Goede, Olivia M and Nachun, Daniel C and Ferraro, Nicole M and Gloudemans, Michael J and Rao, Abhiram S and Smail, Craig and Eulalio, Tiffany Y and Aguet, Francois and Ng, Bernard and Xu, Jishu and Barbeira, Alvaro N and Castel, Stephane E and Kim-Hellmuth, Sarah and Park, YoSon and Scott, Alexandra J and Strober, Benjamin J and Brown, Christopher D and Wen, Xiaoquan and Hall, Ira M and Battle, Alexis and Lappalainen, Tuuli and Im, Hae Kyung and Ardlie, Kristin G and Mostafavi, Sara and Quertermous, Thomas and Kirkegaard, Karla and Montgomery, Stephen B} } @article {146, title = {Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 06 03}, pages = {965-982}, abstract = {

Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.

}, keywords = {Case-Control Studies, Cohort Studies, Epilepsy, Exome, Genetic Markers, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Humans, Phenotype, Whole Exome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.04.009} } @article {85, title = {A brief history of human disease genetics.}, journal = {Nature}, volume = {577}, year = {2020}, month = {2020 01}, pages = {179-189}, abstract = {

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

}, keywords = {Animals, Genetic Testing, Genetic Variation, Genomics, Genotype, Humans, Phenotype, Rare Diseases}, issn = {1476-4687}, doi = {10.1038/s41586-019-1879-7}, author = {Claussnitzer, Melina and Cho, Judy H and Collins, Rory and Cox, Nancy J and Dermitzakis, Emmanouil T and Hurles, Matthew E and Kathiresan, Sekar and Kenny, Eimear E and Lindgren, Cecilia M and MacArthur, Daniel G and North, Kathryn N and Plon, Sharon E and Rehm, Heidi L and Risch, Neil and Rotimi, Charles N and Shendure, Jay and Soranzo, Nicole and McCarthy, Mark I} } @article {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 {120, title = {Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 11}, pages = {1145-1150}, abstract = {

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

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

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

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

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

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

}, keywords = {Adolescent, Adult, Age of Onset, Brain Diseases, Child, Child, Preschool, Chromosomes, Human, Cohort Studies, Epilepsy, Epilepsy, Generalized, Exome, Female, Genetic Testing, Genetic Variation, Humans, Infant, Male, Microarray Analysis, Phenotype, Whole Exome Sequencing, Young Adult}, issn = {1528-1167}, doi = {10.1111/epi.16427}, author = {Rochtus, Anne and Olson, Heather E and Smith, Lacey and Keith, Louisa G and El Achkar, Christelle and Taylor, Alan and Mahida, Sonal and Park, Meredith and Kelly, McKenna and Shain, Catherine and Rockowitz, Shira and Rosen Sheidley, Beth and Poduri, Annapurna} } @article {128, title = {A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 11}, pages = {e1008802}, abstract = {

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

}, keywords = {Alagille Syndrome, Alleles, Biological Variation, Population, DiGeorge Syndrome, European Continental Ancestry Group, Female, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Genome-Wide Association Study, Humans, Male, Marfan Syndrome, Noonan Syndrome, Phenotype, Polymorphism, Single Nucleotide, United Kingdom}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008802}, author = {Tcheandjieu, Catherine and Aguirre, Matthew and Gustafsson, Stefan and Saha, Priyanka and Potiny, Praneetha and Haendel, Melissa and Ingelsson, Erik and Rivas, Manuel A and Priest, James R} } @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 {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 {63, title = {Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.}, journal = {Nat Med}, volume = {25}, year = {2019}, month = {2019 06}, pages = {911-919}, abstract = {

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

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

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

}, keywords = {Crohn Disease, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Lipoproteins, LDL, Quantitative Trait Loci, Schizophrenia, Transcriptome}, issn = {1546-1718}, doi = {10.1038/s41588-019-0385-z}, author = {Wainberg, Michael and Sinnott-Armstrong, Nasa and Mancuso, Nicholas and Barbeira, Alvaro N and Knowles, David A and Golan, David and Ermel, Raili and Ruusalepp, Arno and Quertermous, Thomas and Hao, Ke and Bj{\"o}rkegren, Johan L M and Im, Hae Kyung and Pasaniuc, Bogdan and Rivas, Manuel A and Kundaje, Anshul} } @article {36, title = {Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 May}, pages = {693-698}, abstract = {

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the {\textquoteright}no horizontal pleiotropy{\textquoteright} assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50\% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48\% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from -131\% to 201\%; (iii) induced false-positive causal relationships in up to 10\% of relationships; and (iv) could be corrected in some but not all instances.

}, keywords = {Disease, Genetic Pleiotropy, Genetic Predisposition to Disease, Genetic Variation, Humans}, issn = {1546-1718}, doi = {10.1038/s41588-018-0099-7}, author = {Verbanck, Marie and Chen, Chia-Yen and Neale, Benjamin and Do, Ron} } @article {40, title = {Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 Nov}, pages = {1600-1607}, abstract = {

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5\%<= minor allele frequency <5\%) and common (minor allele frequency >=5\%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 {\textpm} 1\% of low-frequency variant heritability ([Formula: see text]) versus 2.1 {\textpm} 0.2\% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 {\textpm} 12\% of [Formula: see text] versus 12 {\textpm} 2\% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5\%).

}, keywords = {Alleles, Biological Specimen Banks, Gene Frequency, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Molecular Sequence Annotation, Open Reading Frames, Polymorphism, Single Nucleotide, Selection, Genetic, United Kingdom, White People}, issn = {1546-1718}, doi = {10.1038/s41588-018-0231-8}, author = {Gazal, Steven and Loh, Po-Ru and Finucane, Hilary K and Ganna, Andrea and Schoech, Armin and Sunyaev, Shamil and Price, Alkes L} } @article {61, title = {Genetic diversity in populations across Latin America: implications for population and medical genetic studies.}, journal = {Curr Opin Genet Dev}, volume = {53}, year = {2018}, month = {2018 12}, pages = {98-104}, abstract = {

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

}, keywords = {African Continental Ancestry Group, European Continental Ancestry Group, Genetic Variation, Genetics, Medical, Genetics, Population, Genome, Human, Genomics, Humans, Latin America}, issn = {1879-0380}, doi = {10.1016/j.gde.2018.07.006}, author = {Belbin, Gillian M and Nieves-Col{\'o}n, Maria A and Kenny, Eimear E and Moreno-Estrada, Andres and Gignoux, Christopher R} } @article {29, title = {Phenotype risk scores identify patients with unrecognized Mendelian disease patterns.}, journal = {Science}, volume = {359}, year = {2018}, month = {2018 Mar 16}, pages = {1233-1239}, abstract = {

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

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

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

}, keywords = {Algorithms, Benchmarking, Cell Line, Tumor, Databases, Genetic, Diploidy, Female, Genetic Variation, Genome, Human, Homozygote, Humans, Hydatidiform Mole, Pregnancy, Synthetic Biology, Uterine Neoplasms, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-018-0054-7}, author = {Li, Heng and Bloom, Jonathan M and Farjoun, Yossi and Fleharty, Mark and Gauthier, Laura and Neale, Benjamin and MacArthur, Daniel} } @article {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 {11, title = {Estimating the selective effects of heterozygous protein-truncating variants from human exome data.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 May}, pages = {806-810}, abstract = {

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

}, keywords = {Algorithms, Animals, Bayes Theorem, Exome, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotype, Heterozygote, Humans, Mice, Knockout, Models, Genetic, Mutation, Selection, Genetic, Sequence Analysis, DNA}, issn = {1546-1718}, doi = {10.1038/ng.3831}, author = {Cassa, Christopher A and Weghorn, Donate and Balick, Daniel J and Jordan, Daniel M and Nusinow, David and Samocha, Kaitlin E and O{\textquoteright}Donnell-Luria, Anne and MacArthur, Daniel G and Daly, Mark J and Beier, David R and Sunyaev, Shamil R} } @article {18, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

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

}, keywords = {Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci}, issn = {1476-4687}, doi = {10.1038/nature24277}, author = {Battle, Alexis and Brown, Christopher D and Engelhardt, Barbara E and Montgomery, Stephen B} } @article {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 {24, title = {Lessons Learned From Past Gene-Environment Interaction Successes.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {778-786}, abstract = {

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

}, keywords = {Biomedical Research, Disease, Environmental Exposure, Gene-Environment Interaction, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Models, Biological}, issn = {1476-6256}, doi = {10.1093/aje/kwx230}, author = {Ritz, Beate R and Chatterjee, Nilanjan and Garcia-Closas, Montserrat and Gauderman, W James and Pierce, Brandon L and Kraft, Peter and Tanner, Caroline M and Mechanic, Leah E and McAllister, Kimberly} } @article {38, title = {Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Oct}, pages = {1421-1427}, abstract = {

Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (P = 2.38 {\texttimes} 10); the youngest 20\% of common SNPs explain 3.9 times more heritability than the oldest 20\%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.

}, keywords = {Alleles, Chi-Square Distribution, Datasets as Topic, Genetic Fitness, Genetic Variation, Humans, Linkage Disequilibrium, Models, Genetic, Molecular Sequence Annotation, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Selection, Genetic}, issn = {1546-1718}, doi = {10.1038/ng.3954}, author = {Gazal, Steven and Finucane, Hilary K and Furlotte, Nicholas A and Loh, Po-Ru and Palamara, Pier Francesco and Liu, Xuanyao and Schoech, Armin and Bulik-Sullivan, Brendan and Neale, Benjamin M and Gusev, Alexander and Price, Alkes L} } @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} }