%0 Journal Article %J Nat Genet %D 2021 %T A cross-population atlas of genetic associations for 220 human phenotypes. %A Sakaue, Saori %A Kanai, Masahiro %A Tanigawa, Yosuke %A Karjalainen, Juha %A Kurki, Mitja %A Koshiba, Seizo %A Narita, Akira %A Konuma, Takahiro %A Yamamoto, Kenichi %A Akiyama, Masato %A Ishigaki, Kazuyoshi %A Suzuki, Akari %A Suzuki, Ken %A Obara, Wataru %A Yamaji, Ken %A Takahashi, Kazuhisa %A Asai, Satoshi %A Takahashi, Yasuo %A Suzuki, Takao %A Shinozaki, Nobuaki %A Yamaguchi, Hiroki %A Minami, Shiro %A Murayama, Shigeo %A Yoshimori, Kozo %A Nagayama, Satoshi %A Obata, Daisuke %A Higashiyama, Masahiko %A Masumoto, Akihide %A Koretsune, Yukihiro %A Ito, Kaoru %A Terao, Chikashi %A Yamauchi, Toshimasa %A Komuro, Issei %A Kadowaki, Takashi %A Tamiya, Gen %A Yamamoto, Masayuki %A Nakamura, Yusuke %A Kubo, Michiaki %A Murakami, Yoshinori %A Yamamoto, Kazuhiko %A Kamatani, Yoichiro %A Palotie, Aarno %A Rivas, Manuel A %A Daly, Mark J %A Matsuda, Koichi %A Okada, Yukinori %K ABO Blood-Group System %K Biological Specimen Banks %K Genetic Association Studies %K Genetic Loci %K Genetic Pleiotropy %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Major Histocompatibility Complex %K Meta-Analysis as Topic %K Mutation %K Phenotype %X

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

%B Nat Genet %V 53 %P 1415-1424 %8 2021 10 %G eng %N 10 %1 https://www.ncbi.nlm.nih.gov/pubmed/34594039?dopt=Abstract %R 10.1038/s41588-021-00931-x %0 Journal Article %J Am J Hum Genet %D 2021 %T Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals. %K Case-Control Studies %K Cohort Studies %K Epilepsy %K Exome %K Genetic Markers %K Genetic Predisposition to Disease %K Genetic Testing %K Genetic Variation %K Humans %K Phenotype %K Whole Exome Sequencing %X

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.

%B Am J Hum Genet %V 108 %P 965-982 %8 2021 06 03 %G eng %N 6 %1 https://www.ncbi.nlm.nih.gov/pubmed/33932343?dopt=Abstract %R 10.1016/j.ajhg.2021.04.009 %0 Journal Article %J Am J Hum Genet %D 2020 %T Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts. %A Raffield, Laura M %A Iyengar, Apoorva K %A Wang, Biqi %A Gaynor, Sheila M %A Spracklen, Cassandra N %A Zhong, Xue %A Kowalski, Madeline H %A Salimi, Shabnam %A Polfus, Linda M %A Benjamin, Emelia J %A Bis, Joshua C %A Bowler, Russell %A Cade, Brian E %A Choi, Won Jung %A Comellas, Alejandro P %A Correa, Adolfo %A Cruz, Pedro %A Doddapaneni, Harsha %A Durda, Peter %A Gogarten, Stephanie M %A Jain, Deepti %A Kim, Ryan W %A Kral, Brian G %A Lange, Leslie A %A Larson, Martin G %A Laurie, Cecelia %A Lee, Jiwon %A Lee, Seonwook %A Lewis, Joshua P %A Metcalf, Ginger A %A Mitchell, Braxton D %A Momin, Zeineen %A Muzny, Donna M %A Pankratz, Nathan %A Park, Cheol Joo %A Rich, Stephen S %A Rotter, Jerome I %A Ryan, Kathleen %A Seo, Daekwan %A Tracy, Russell P %A Viaud-Martinez, Karine A %A Yanek, Lisa R %A Zhao, Lue Ping %A Lin, Xihong %A Li, Bingshan %A Li, Yun %A Dupuis, Josée %A Reiner, Alexander P %A Mohlke, Karen L %A Auer, Paul L %K African Continental Ancestry Group %K Asian Continental Ancestry Group %K C-Reactive Protein %K Cohort Studies %K European Continental Ancestry Group %K Gene Frequency %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Linkage Disequilibrium %K Polymorphism, Single Nucleotide %K Whole Genome Sequencing %X

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× 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.

%B Am J Hum Genet %V 106 %P 112-120 %8 2020 01 02 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/31883642?dopt=Abstract %R 10.1016/j.ajhg.2019.12.002 %0 Journal Article %J Nat Genet %D 2020 %T Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. %A Li, Xihao %A Li, Zilin %A Zhou, Hufeng %A Gaynor, Sheila M %A Liu, Yaowu %A Chen, Han %A Sun, Ryan %A Dey, Rounak %A Arnett, Donna K %A Aslibekyan, Stella %A Ballantyne, Christie M %A Bielak, Lawrence F %A Blangero, John %A Boerwinkle, Eric %A Bowden, Donald W %A Broome, Jai G %A Conomos, Matthew P %A Correa, Adolfo %A Cupples, L Adrienne %A Curran, Joanne E %A Freedman, Barry I %A Guo, Xiuqing %A Hindy, George %A Irvin, Marguerite R %A Kardia, Sharon L R %A Kathiresan, Sekar %A Khan, Alyna T %A Kooperberg, Charles L %A Laurie, Cathy C %A Liu, X Shirley %A Mahaney, Michael C %A Manichaikul, Ani W %A Martin, Lisa W %A Mathias, Rasika A %A McGarvey, Stephen T %A Mitchell, Braxton D %A Montasser, May E %A Moore, Jill E %A Morrison, Alanna C %A O'Connell, Jeffrey R %A Palmer, Nicholette D %A Pampana, Akhil %A Peralta, Juan M %A Peyser, Patricia A %A Psaty, Bruce M %A Redline, Susan %A Rice, Kenneth M %A Rich, Stephen S %A Smith, Jennifer A %A Tiwari, Hemant K %A Tsai, Michael Y %A Vasan, Ramachandran S %A Wang, Fei Fei %A Weeks, Daniel E %A Weng, Zhiping %A Wilson, James G %A Yanek, Lisa R %A Neale, Benjamin M %A Sunyaev, Shamil R %A Abecasis, Gonçalo R %A Rotter, Jerome I %A Willer, Cristen J %A Peloso, Gina M %A Natarajan, Pradeep %A Lin, Xihong %K Cholesterol, LDL %K Computer Simulation %K Genetic Predisposition to Disease %K Genetic Variation %K Genome %K Genome-Wide Association Study %K Humans %K Models, Genetic %K Molecular Sequence Annotation %K Phenotype %K Whole Genome Sequencing %X

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 'annotation principal components', 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.

%B Nat Genet %V 52 %P 969-983 %8 2020 09 %G eng %N 9 %1 https://www.ncbi.nlm.nih.gov/pubmed/32839606?dopt=Abstract %R 10.1038/s41588-020-0676-4 %0 Journal Article %J Hum Genet %D 2020 %T Identifying causal variants and genes using functional genomics in specialized cell types and contexts. %A Liu, Boxiang %A Montgomery, Stephen B %K Cell Lineage %K Genes %K Genetic Predisposition to Disease %K Genome, Human %K Genome-Wide Association Study %K Genomics %K Humans %K Polymorphism, Single Nucleotide %K Quantitative Trait Loci %X

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

%B Hum Genet %V 139 %P 95-102 %8 2020 Jan %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/31317254?dopt=Abstract %R 10.1007/s00439-019-02044-2 %0 Journal Article %J Nat Genet %D 2020 %T Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. %A Amariuta, Tiffany %A Ishigaki, Kazuyoshi %A Sugishita, Hiroki %A Ohta, Tazro %A Koido, Masaru %A Dey, Kushal K %A Matsuda, Koichi %A Murakami, Yoshinori %A Price, Alkes L %A Kawakami, Eiryo %A Terao, Chikashi %A Raychaudhuri, Soumya %K Asian Continental Ancestry Group %K Base Sequence %K Computational Biology %K Enhancer Elements, Genetic %K European Continental Ancestry Group %K Gene Expression Regulation %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Models, Genetic %K Molecular Sequence Annotation %K Multifactorial Inheritance %K Polymorphism, Single Nucleotide %X

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

%B Nat Genet %V 52 %P 1346-1354 %8 2020 12 %G eng %N 12 %1 https://www.ncbi.nlm.nih.gov/pubmed/33257898?dopt=Abstract %R 10.1038/s41588-020-00740-8 %0 Journal Article %J PLoS Genet %D 2020 %T A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population. %A Tcheandjieu, Catherine %A Aguirre, Matthew %A Gustafsson, Stefan %A Saha, Priyanka %A Potiny, Praneetha %A Haendel, Melissa %A Ingelsson, Erik %A Rivas, Manuel A %A Priest, James R %K Alagille Syndrome %K Alleles %K Biological Variation, Population %K DiGeorge Syndrome %K European Continental Ancestry Group %K Female %K Gene Frequency %K Genetic Association Studies %K Genetic Predisposition to Disease %K Genetic Testing %K Genetic Variation %K Genome-Wide Association Study %K Humans %K Male %K Marfan Syndrome %K Noonan Syndrome %K Phenotype %K Polymorphism, Single Nucleotide %K United Kingdom %X

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.

%B PLoS Genet %V 16 %P e1008802 %8 2020 11 %G eng %N 11 %1 https://www.ncbi.nlm.nih.gov/pubmed/33226994?dopt=Abstract %R 10.1371/journal.pgen.1008802 %0 Journal Article %J PLoS Genet %D 2020 %T Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma. %A Tanigawa, Yosuke %A Wainberg, Michael %A Karjalainen, Juha %A Kiiskinen, Tuomo %A Venkataraman, Guhan %A Lemmelä, Susanna %A Turunen, Joni A %A Graham, Robert R %A Havulinna, Aki S %A Perola, Markus %A Palotie, Aarno %A Daly, Mark J %A Rivas, Manuel A %K Adult %K Aged %K Aged, 80 and over %K Angiopoietin-like Proteins %K Biological Specimen Banks %K Case-Control Studies %K Cohort Studies %K Female %K Finland %K Gene Frequency %K Genetic Predisposition to Disease %K Genetics, Population %K Genome-Wide Association Study %K Glaucoma %K Humans %K Intraocular Pressure %K Loss of Function Mutation %K Male %K Middle Aged %K Mutation, Missense %K Polymorphism, Single Nucleotide %K United Kingdom %X

Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (β = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.

%B PLoS Genet %V 16 %P e1008682 %8 2020 05 %G eng %N 5 %1 https://www.ncbi.nlm.nih.gov/pubmed/32369491?dopt=Abstract %R 10.1371/journal.pgen.1008682 %0 Journal Article %J Cancer Res %D 2020 %T A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk. %A Liu, Duo %A Zhou, Dan %A Sun, Yanfa %A Zhu, Jingjing %A Ghoneim, Dalia %A Wu, Chong %A Yao, Qizhi %A Gamazon, Eric R %A Cox, Nancy J %A Wu, Lang %K Age Factors %K Case-Control Studies %K European Continental Ancestry Group %K Female %K Gene Expression Regulation, Neoplastic %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Male %K Models, Genetic %K Pancreatic Neoplasms %K Polymorphism, Single Nucleotide %X

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.

%B Cancer Res %V 80 %P 4346-4354 %8 2020 10 15 %G eng %N 20 %1 https://www.ncbi.nlm.nih.gov/pubmed/32907841?dopt=Abstract %R 10.1158/0008-5472.CAN-20-1353 %0 Journal Article %J Cell Rep %D 2020 %T Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex. %A Werling, Donna M %A Pochareddy, Sirisha %A Choi, Jinmyung %A An, Joon-Yong %A Sheppard, Brooke %A Peng, Minshi %A Li, Zhen %A Dastmalchi, Claudia %A Santpere, Gabriel %A Sousa, André M M %A Tebbenkamp, Andrew T N %A Kaur, Navjot %A Gulden, Forrest O %A Breen, Michael S %A Liang, Lindsay %A Gilson, Michael C %A Zhao, Xuefang %A Dong, Shan %A Klei, Lambertus %A Cicek, A Ercument %A Buxbaum, Joseph D %A Adle-Biassette, Homa %A Thomas, Jean-Leon %A Aldinger, Kimberly A %A O'Day, Diana R %A Glass, Ian A %A Zaitlen, Noah A %A Talkowski, Michael E %A Roeder, Kathryn %A State, Matthew W %A Devlin, Bernie %A Sanders, Stephan J %A Sestan, Nenad %K Base Sequence %K Brain %K Computational Biology %K Databases, Genetic %K Genetic Predisposition to Disease %K Genetic Variation %K Genome-Wide Association Study %K Genomics %K Humans %K Phenotype %K Polymorphism, Single Nucleotide %K Prefrontal Cortex %K Quantitative Trait Loci %K Sequence Analysis, RNA %K Transcriptome %K Whole Exome Sequencing %K Whole Genome Sequencing %X

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.

%B Cell Rep %V 31 %P 107489 %8 2020 04 07 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/32268104?dopt=Abstract %R 10.1016/j.celrep.2020.03.053 %0 Journal Article %J Nat Neurosci %D 2019 %T A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. %A Wang, Quan %A Chen, Rui %A Cheng, Feixiong %A Wei, Qiang %A Ji, Ying %A Yang, Hai %A Zhong, Xue %A Tao, Ran %A Wen, Zhexing %A Sutcliffe, James S %A Liu, Chunyu %A Cook, Edwin H %A Cox, Nancy J %A Li, Bingshan %K Animals %K Bayes Theorem %K Disease Models, Animal %K Gene Regulatory Networks %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Genomics %K Humans %K Mice %K Risk Factors %K Schizophrenia %X

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.

%B Nat Neurosci %V 22 %P 691-699 %8 2019 05 %G eng %N 5 %1 https://www.ncbi.nlm.nih.gov/pubmed/30988527?dopt=Abstract %R 10.1038/s41593-019-0382-7 %0 Journal Article %J Curr Protoc Hum Genet %D 2019 %T Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits. %A Weissenkampen, J Dylan %A Jiang, Yu %A Eckert, Scott %A Jiang, Bibo %A Li, Bingshan %A Liu, Dajiang J %K Algorithms %K Genetic Predisposition to Disease %K Genome, Human %K Genome-Wide Association Study %K Genotype %K High-Throughput Nucleotide Sequencing %K Humans %K Multifactorial Inheritance %K Phenotype %K Polymorphism, Single Nucleotide %K Whole Exome Sequencing %K Whole Genome Sequencing %X

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

%B Curr Protoc Hum Genet %V 101 %P e83 %8 2019 04 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/30849219?dopt=Abstract %R 10.1002/cphg.83 %0 Journal Article %J Nat Genet %D 2019 %T Opportunities and challenges for transcriptome-wide association studies. %A Wainberg, Michael %A Sinnott-Armstrong, Nasa %A Mancuso, Nicholas %A Barbeira, Alvaro N %A Knowles, David A %A Golan, David %A Ermel, Raili %A Ruusalepp, Arno %A Quertermous, Thomas %A Hao, Ke %A Björkegren, Johan L M %A Im, Hae Kyung %A Pasaniuc, Bogdan %A Rivas, Manuel A %A Kundaje, Anshul %K Crohn Disease %K Genetic Predisposition to Disease %K Genetic Variation %K Genome-Wide Association Study %K Humans %K Lipoproteins, LDL %K Quantitative Trait Loci %K Schizophrenia %K Transcriptome %X

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'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.

%B Nat Genet %V 51 %P 592-599 %8 2019 04 %G eng %N 4 %1 https://www.ncbi.nlm.nih.gov/pubmed/30926968?dopt=Abstract %R 10.1038/s41588-019-0385-z %0 Journal Article %J Nat Genet %D 2018 %T Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. %A Verbanck, Marie %A Chen, Chia-Yen %A Neale, Benjamin %A Do, Ron %K Disease %K Genetic Pleiotropy %K Genetic Predisposition to Disease %K Genetic Variation %K Humans %X

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 'no horizontal pleiotropy' 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.

%B Nat Genet %V 50 %P 693-698 %8 2018 05 %G eng %N 5 %1 https://www.ncbi.nlm.nih.gov/pubmed/29686387?dopt=Abstract %R 10.1038/s41588-018-0099-7 %0 Journal Article %J Nat Genet %D 2018 %T Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes. %A Westra, Harm-Jan %A Martínez-Bonet, Marta %A Onengut-Gumuscu, Suna %A Lee, Annette %A Luo, Yang %A Teslovich, Nikola %A Worthington, Jane %A Martin, Javier %A Huizinga, Tom %A Klareskog, Lars %A Rantapaa-Dahlqvist, Solbritt %A Chen, Wei-Min %A Quinlan, Aaron %A Todd, John A %A Eyre, Steve %A Nigrovic, Peter A %A Gregersen, Peter K %A Rich, Stephen S %A Raychaudhuri, Soumya %K Alleles %K Arthritis, Rheumatoid %K Case-Control Studies %K CD28 Antigens %K Chromosome Mapping %K CTLA-4 Antigen %K Diabetes Mellitus, Type 1 %K Gene Frequency %K Genetic Loci %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Jurkat Cells %K Mutation %K Polymorphism, Single Nucleotide %K Quantitative Trait Loci %K RNA, Long Noncoding %K Tumor Necrosis Factor alpha-Induced Protein 3 %X

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

%B Nat Genet %V 50 %P 1366-1374 %8 2018 10 %G eng %N 10 %1 https://www.ncbi.nlm.nih.gov/pubmed/30224649?dopt=Abstract %R 10.1038/s41588-018-0216-7 %0 Journal Article %J Science %D 2018 %T Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. %A Bastarache, Lisa %A Hughey, Jacob J %A Hebbring, Scott %A Marlo, Joy %A Zhao, Wanke %A Ho, Wanting T %A Van Driest, Sara L %A McGregor, Tracy L %A Mosley, Jonathan D %A Wells, Quinn S %A Temple, Michael %A Ramirez, Andrea H %A Carroll, Robert %A Osterman, Travis %A Edwards, Todd %A Ruderfer, Douglas %A Velez Edwards, Digna R %A Hamid, Rizwan %A Cogan, Joy %A Glazer, Andrew %A Wei, Wei-Qi %A Feng, QiPing %A Brilliant, Murray %A Zhao, Zhizhuang J %A Cox, Nancy J %A Roden, Dan M %A Denny, Joshua C %K Databases, Genetic %K DNA Mutational Analysis %K Electronic Health Records %K Exome %K Genetic Association Studies %K Genetic Diseases, Inborn %K Genetic Predisposition to Disease %K Genetic Variation %K Humans %K Phenotype %K Risk Factors %X

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.

%B Science %V 359 %P 1233-1239 %8 2018 03 16 %G eng %N 6381 %1 https://www.ncbi.nlm.nih.gov/pubmed/29590070?dopt=Abstract %R 10.1126/science.aal4043 %0 Journal Article %J J Invest Dermatol %D 2018 %T Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk. %A Sordillo, Joanne E %A Kraft, Peter %A Wu, Ann Chen %A Asgari, Maryam M %K Carcinoma, Squamous Cell %K Female %K Genetic Loci %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Linear Models %K Logistic Models %K Male %K Models, Biological %K Polymorphism, Single Nucleotide %K Risk Assessment %K Risk Factors %K Sex Factors %K Skin Neoplasms %X

Genetic factors play an important role in cutaneous squamous cell carcinoma risk. Genome-wide association studies have identified 21 single nucleotide polymorphisms associated with cutaneous squamous cell carcinoma risk. Yet no studies have attempted to quantify the contribution of heritability to cutaneous squamous cell carcinoma risk by calculating the population attributable risk using a combination of all discovered genetic variants. Using an additive multi-locus linear logistic model, we determined the cumulative association of these 21 genetic regions to cutaneous squamous cell carcinoma population attributable risk. We computed a multi-locus population attributable risk of 62%, suggesting that if the effects of all the risk alleles were removed from a population, the cutaneous squamous cell carcinoma risk would drop by 62%. Using stratified analysis, we also examined the impact of sex on polygenic risk score, and found that men have an increased relative risk throughout the spectrum of the polygenic risk score. Quantifying the impact of genetic predisposition on the proportion of cancer cases can guide future research decisions and public health policy planning.

%B J Invest Dermatol %V 138 %P 1507-1510 %8 2018 07 %G eng %N 7 %1 https://www.ncbi.nlm.nih.gov/pubmed/29452120?dopt=Abstract %R 10.1016/j.jid.2018.01.031 %0 Journal Article %J Am J Epidemiol %D 2017 %T Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. %A McAllister, Kimberly %A Mechanic, Leah E %A Amos, Christopher %A Aschard, Hugues %A Blair, Ian A %A Chatterjee, Nilanjan %A Conti, David %A Gauderman, W James %A Hsu, Li %A Hutter, Carolyn M %A Jankowska, Marta M %A Kerr, Jacqueline %A Kraft, Peter %A Montgomery, Stephen B %A Mukherjee, Bhramar %A Papanicolaou, George J %A Patel, Chirag J %A Ritchie, Marylyn D %A Ritz, Beate R %A Thomas, Duncan C %A Wei, Peng %A Witte, John S %K Disease %K Gene-Environment Interaction %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K High-Throughput Nucleotide Sequencing %K Humans %K Software %X

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

%B Am J Epidemiol %V 186 %P 753-761 %8 2017 Oct 01 %G eng %N 7 %1 https://www.ncbi.nlm.nih.gov/pubmed/28978193?dopt=Abstract %R 10.1093/aje/kwx227 %0 Journal Article %J Nat Genet %D 2017 %T Estimating the selective effects of heterozygous protein-truncating variants from human exome data. %A Cassa, Christopher A %A Weghorn, Donate %A Balick, Daniel J %A Jordan, Daniel M %A Nusinow, David %A Samocha, Kaitlin E %A O'Donnell-Luria, Anne %A MacArthur, Daniel G %A Daly, Mark J %A Beier, David R %A Sunyaev, Shamil R %K Algorithms %K Animals %K Bayes Theorem %K Exome %K Gene Frequency %K Genetic Predisposition to Disease %K Genetic Variation %K Genome-Wide Association Study %K Genotype %K Heterozygote %K Humans %K Mice, Knockout %K Models, Genetic %K Mutation %K Selection, Genetic %K Sequence Analysis, DNA %X

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'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.

%B Nat Genet %V 49 %P 806-810 %8 2017 May %G eng %N 5 %1 https://www.ncbi.nlm.nih.gov/pubmed/28369035?dopt=Abstract %R 10.1038/ng.3831 %0 Journal Article %J Genet Epidemiol %D 2017 %T A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data. %A He, Liang %A Zhbannikov, Ilya %A Arbeev, Konstantin G %A Yashin, Anatoliy I %A Kulminski, Alexander M %K Biomarkers %K Body Mass Index %K Computational Biology %K Diabetes Mellitus, Type 2 %K Female %K Gene-Environment Interaction %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Male %K Models, Genetic %K Models, Statistical %K Polymorphism, Single Nucleotide %K Risk %K Stochastic Processes %X

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 × 10 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 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.

%B Genet Epidemiol %V 41 %P 620-635 %8 2017 Nov %G eng %N 7 %1 https://www.ncbi.nlm.nih.gov/pubmed/28636232?dopt=Abstract %R 10.1002/gepi.22058 %0 Journal Article %J Am J Hum Genet %D 2017 %T Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. %A Martin, Alicia R %A Gignoux, Christopher R %A Walters, Raymond K %A Wojcik, Genevieve L %A Neale, Benjamin M %A Gravel, Simon %A Daly, Mark J %A Bustamante, Carlos D %A Kenny, Eimear E %K Americas %K Genetic Predisposition to Disease %K Genetics, Medical %K Genetics, Population %K Haplotypes %K Human Genome Project %K Humans %K Multifactorial Inheritance %K Racial Groups %X

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

%B Am J Hum Genet %V 100 %P 635-649 %8 2017 Apr 06 %G eng %N 4 %1 https://www.ncbi.nlm.nih.gov/pubmed/28366442?dopt=Abstract %R 10.1016/j.ajhg.2017.03.004 %0 Journal Article %J Am J Epidemiol %D 2017 %T Lessons Learned From Past Gene-Environment Interaction Successes. %A Ritz, Beate R %A Chatterjee, Nilanjan %A Garcia-Closas, Montserrat %A Gauderman, W James %A Pierce, Brandon L %A Kraft, Peter %A Tanner, Caroline M %A Mechanic, Leah E %A McAllister, Kimberly %K Biomedical Research %K Disease %K Environmental Exposure %K Gene-Environment Interaction %K Genetic Predisposition to Disease %K Genetic Variation %K Genome-Wide Association Study %K Humans %K Models, Biological %X

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×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×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×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×E in several examples. Future studies may benefit from these lessons learned.

%B Am J Epidemiol %V 186 %P 778-786 %8 2017 Oct 01 %G eng %N 7 %1 https://www.ncbi.nlm.nih.gov/pubmed/28978190?dopt=Abstract %R 10.1093/aje/kwx230 %0 Journal Article %J Nat Genet %D 2017 %T Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. %A Chun, Sung %A Casparino, Alexandra %A Patsopoulos, Nikolaos A %A Croteau-Chonka, Damien C %A Raby, Benjamin A %A De Jager, Philip L %A Sunyaev, Shamil R %A Cotsapas, Chris %K Autoimmune Diseases %K Gene Expression %K Gene Regulatory Networks %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Immunity %K Polymorphism, Single Nucleotide %K Quantitative Trait Loci %X

Most autoimmune-disease-risk effects identified by genome-wide association studies (GWAS) localize to open chromatin with gene-regulatory activity. GWAS loci are also enriched in expression quantitative trait loci (eQTLs), thus suggesting that most risk variants alter gene expression. However, because causal variants are difficult to identify, and cis-eQTLs occur frequently, it remains challenging to identify specific instances of disease-relevant changes to gene regulation. Here, we used a novel joint likelihood framework with higher resolution than that of previous methods to identify loci where autoimmune-disease risk and an eQTL are driven by a single shared genetic effect. Using eQTLs from three major immune subpopulations, we found shared effects in only ∼25% of the loci examined. Thus, we show that a fraction of gene-regulatory changes suggest strong mechanistic hypotheses for disease risk, but we conclude that most risk mechanisms are not likely to involve changes in basal gene expression.

%B Nat Genet %V 49 %P 600-605 %8 2017 Apr %G eng %N 4 %1 https://www.ncbi.nlm.nih.gov/pubmed/28218759?dopt=Abstract %R 10.1038/ng.3795 %0 Journal Article %J Nat Genet %D 2017 %T Population- and individual-specific regulatory variation in Sardinia. %A Pala, Mauro %A Zappala, Zachary %A Marongiu, Mara %A Li, Xin %A Davis, Joe R %A Cusano, Roberto %A Crobu, Francesca %A Kukurba, Kimberly R %A Gloudemans, Michael J %A Reinier, Frederic %A Berutti, Riccardo %A Piras, Maria G %A Mulas, Antonella %A Zoledziewska, Magdalena %A Marongiu, Michele %A Sorokin, Elena P %A Hess, Gaelen T %A Smith, Kevin S %A Busonero, Fabio %A Maschio, Andrea %A Steri, Maristella %A Sidore, Carlo %A Sanna, Serena %A Fiorillo, Edoardo %A Bassik, Michael C %A Sawcer, Stephen J %A Battle, Alexis %A Novembre, John %A Jones, Chris %A Angius, Andrea %A Abecasis, Gonçalo R %A Schlessinger, David %A Cucca, Francesco %A Montgomery, Stephen B %K Alternative Splicing %K Chromosome Mapping %K Family Health %K Female %K Gene Expression Profiling %K Genetic Predisposition to Disease %K Genetic Variation %K Genetics, Population %K Genome-Wide Association Study %K Genotype %K Humans %K Italy %K Male %K Polymorphism, Single Nucleotide %K Quantitative Trait Loci %K Transcription Initiation Site %X

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.

%B Nat Genet %V 49 %P 700-707 %8 2017 May %G eng %N 5 %1 https://www.ncbi.nlm.nih.gov/pubmed/28394350?dopt=Abstract %R 10.1038/ng.3840