Person: Gusev, Alexander
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Gusev
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Alexander
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Gusev, Alexander
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Publication Quantifying Genetic Effects on Disease Mediated by Assayed Gene Expression Levels(Springer Science and Business Media LLC, 2020-05-18) Yao, Doug; O'Connor, Luke; Price, Alkes; Gusev, AlexanderDisease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs), but it remains unclear whether this overlap is driven by gene expression levels βmediatingβ genetic effects on disease. Here, we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis genetic component of gene expression levels. We applied MESC to GWAS summary statistics for 42 traits (average Nβ=β323,000) and cis-eQTL summary statistics for 48 tissues from the Genotype-Tissue Expression (GTEx) consortium. Averaging across traits, only 11βΒ±β2% of heritability was mediated by assayed gene expression levels. Expression-mediated heritability was enriched in genes with evidence of selective constraint and genes with disease-appropriate annotations. Our results demonstrate that assayed bulk tissue eQTLs, although disease relevant, cannot explain the majority of disease heritability.Publication Leveraging local ancestry to detect gene-gene interactions in genome-wide data(BioMed Central, 2015) Aschard, Hugues; Gusev, Alexander; Brown, Robert; Pasaniuc, BogdanBackground: Although genome-wide association studies have successfully identified thousands of variants associated to complex traits, these variants only explain a small amount of the entire heritability of the trait. Gene-gene interactions have been proposed as a source to explain a significant percentage of the missing heritability. However, detecting gene-gene interactions has proven to be very difficult due to computational and statistical challenges. The vast number of possible interactions that can be tested induces very stringent multiple hypotheses corrections that limit the power of detection. These issues have been mostly highlighted for the identification of pairwise effects and are even more challenging when addressing higher order interaction effects. In this work we explore the use of local ancestry in recently admixed individuals to find signals of gene-gene interaction on human traits and diseases. Results: We introduce statistical methods that leverage the correlation between local ancestry and the hidden unknown causal variants to find distant gene-gene interactions. We show that the power of this test increases with the number of causal variants per locus and the degree of differentiation of these variants between the ancestral populations. Overall, our simulations confirm that local ancestry can be used to detect gene-gene interactions, solving the computational bottleneck. When compared to a single nucleotide polymorphism (SNP)-based interaction screening of the same sample size, the power of our test was lower on all settings we considered. However, accounting for the dramatic increase in sample size that can be achieve when genotyping only a set of ancestry informative markers instead of the whole genome, we observe substantial gain in power in several scenarios. Conclusion: Local ancestry-based interaction tests offer a new path to the detection of gene-gene interaction effects. It would be particularly useful in scenarios where multiple differentiated variants at the interacting loci act in a synergistic manner. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0283-z) contains supplementary material, which is available to authorized users.Publication Extended haplotype association study in Crohnβs disease identifies a novel, Ashkenazi Jewish-specific missense mutation in the NF-ΞΊB pathway gene, HEATR3(2013) Zhang, Wei; Hui, Ken Y.; Gusev, Alexander; Warner, Neil; Evelyn Ng, Sok Meng; Ferguson, John; Choi, Murim; Burberry, Aaron; Abraham, Clara; Mayer, Lloyd; Desnick, Robert J.; Cardinale, Christopher J.; Hakonarson, Hakon; Waterman, Matti; Chowers, Yehuda; Karban, Amir; Brant, Steven R.; Silverberg, Mark S.; Gregersen, Peter K.; Katz, Seymour; Lifton, Richard P.; Zhao, Hongyu; NuΓ±ez, Gabriel; Peβer, Itsik; Peter, Inga; Cho, Judy H.The Ashkenazi Jewish population has a several-fold higher prevalence of Crohnβs disease compared to non-Jewish European ancestry populations and has a unique genetic history. Haplotype association is critical to Crohnβs disease etiology in this population, most notably at NOD2, in which three causal, uncommon, and conditionally independent NOD2 variants reside on a shared background haplotype. We present an analysis of extended haplotypes which showed significantly greater association to Crohnβs disease in the Ashkenazi Jewish population compared to a non-Jewish population (145 haplotypes and no haplotypes with P-value < 10β3, respectively). Two haplotype regions, one each on chromosomes 16 and 21, conferred increased disease risk within established Crohnβs disease loci. We performed exome sequencing of 55 Ashkenazi Jewish individuals and follow-up genotyping focused on variants in these two regions. We observed Ashkenazi Jewish-specific nominal association at R755C in TRPM2 on chromosome 21. Within the chromosome 16 region, R642S of HEATR3 and rs9922362 of BRD7 showed genome-wide significance. Expression studies of HEATR3 demonstrated a positive role in NOD2-mediated NF-ΞΊB signaling. The BRD7 signal showed conditional dependence with only the downstream rare Crohnβs disease-causal variants in NOD2, but not with the background haplotype; this elaborates NOD2 as a key illustration of synthetic association.Publication Quantifying Missing Heritability at Known GWAS Loci(Public Library of Science, 2013) Gusev, Alexander; Bhatia, Gaurav; Zaitlen, Noah; VilhjΓ‘lmsson, Bjarni J; Diogo, Dorothee; Stahl, Eli A.; Gregersen, Peter K.; Worthington, Jane; Klareskog, Lars; Raychaudhuri, Soumya; Plenge, Robert M.; Pasaniuc, Bogdan; Price, AlkesRecent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.Publication Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities(BioMed Central, 2016) LindstrΓΆm, Sara; Ablorh, Akweley; Chapman, Brad; Gusev, Alexander; Chen, Gary; Turman, Constance; Eliassen, A; Price, Alkes; Henderson, Brian E.; Le Marchand, Loic; Hofmann, Oliver; Haiman, Christopher A.; Kraft, PhillipBackground: Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants. Methods: Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry. Results: After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) <0.005. For SNVs with MAF >0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing. Conclusions: This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0772-7) contains supplementary material, which is available to authorized users.Publication Leveraging population admixture to explain missing heritability of complex traits(2014) Zaitlen, Noah; Pasaniuc, Bogdan; Sankararaman, Sriram; Bhatia, Gaurav; Zhang, Jianqi; Gusev, Alexander; Young, Taylor; Tandon, Arti; Pollack, Samuela; VilhjΓ‘lmsson, Bjarni J; Assimes, Themistocles L.; Berndt, Sonja I.; Blot, William J.; Chanock, Stephen; Franceschini, Nora; Goodman, Phyllis G.; He, Jing; Hennis, Anselm JM; Hsing, Ann; Ingles, Sue A.; Isaacs, William; Kittles, Rick A.; Klein, Eric A.; Lange, Leslie A.; Nemesure, Barbara; Patterson, Nick; Reich, David; Rybicki, Benjamin A.; Stanford, Janet L.; Stevens, Victoria L; Strom, Sara S.; Whitsel, Eric A; Witte, John S.; Xu, Jianfeng; Haiman, Christopher; Wilson, James G.; Kooperberg, Charles; Stram, Daniel; Reiner, Alex P.; Tang, Hua; Price, AlkesDespite recent progress on estimating the heritability explained by genotyped SNPs (hg2), a large gap between hg2 and estimates of total narrow-sense heritability (h2) remains. Explanations for this gap include rare variants, or upward bias in family-based estimates of h2 due to shared environment or epistasis. We estimate h2 from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (hΞ³2). We show that hΞ³2 = 2FSTCΞΈ(1βΞΈ)h2, where FSTC measures frequency differences between populations at causal loci and ΞΈ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We examined 21,497 African Americans from three cohorts, analyzing 13 phenotypes. For height and BMI, we obtained h2 estimates of 0.55 Β± 0.09 and 0.23 Β± 0.06, respectively, which are larger than estimates of hg2 in these and other data, but smaller than family-based estimates of h2.Publication A Transcriptome-Wide Association Study of 229,000 Women Identifies New Candidate Susceptibility Genes for Breast Cancer(Springer Science and Business Media LLC, 2018-06-18) Wu, Lang; Shi, Wei; Long, Jirong; Guo, Xingyi; Michailidou, Kyriaki; Beesley, Jonathan; Bolla, Manjeet K.; Shu, Xiao-Ou; Lu, Yingchang; Cai, Qiuyin; Al-Ejeh, Fares; Rozali, Esdy; Wang, Qin; Dennis, Joe; Li, Bingshan; Zeng, Chenjie; Feng, Helian; Gusev, Alexander; Barfield, Richard T.; Andrulis, Irene L.; Anton-Culver, Hoda; Arndt, Volker; Aronson, Kristan J.; Auer, Paul L.; Barrdahl, Myrto; Baynes, Caroline; Beckmann, Matthias W.; Benitez, Javier; Bermisheva, Marina; Blomqvist, Carl; Bogdanova, Natalia V.; Bojesen, Stig E.; Brauch, Hiltrud; Brenner, Hermann; Brinton, Louise; Broberg, Per; Brucker, Sara Y.; Burwinkel, Barbara; CaldΓ©s, Trinidad; Canzian, Federico; Carter, Brian D.; Castelao, J. Esteban; Chang-Claude, Jenny; Chen, Xiaoqing; Cheng, Ting-Yuan David; Christiansen, Hans; Clarke, Christine L.; CollΓ©e, Margriet; Cornelissen, Sten; Couch, Fergus J.; Cox, David; Cox, Angela; Cross, Simon S.; Cunningham, Julie M.; Czene, Kamila; Daly, Mary B.; Devilee, Peter; Doheny, Kimberly F.; DΓΆrk, Thilo; dos-Santos-Silva, Isabel; Dumont, Martine; Dwek, Miriam; Eccles, Diana M.; Eilber, Ursula; Eliassen, A; Engel, Christoph; Eriksson, Mikael; Fachal, Laura; Fasching, Peter A.; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fritschi, Lin; Gabrielson, Marike; Gago-Dominguez, Manuela; Gapstur, Susan M.; GarcΓa-Closas, Montserrat; Gaudet, Mia M.; Ghoussaini, Maya; Giles, Graham G.; Goldberg, Mark S.; Goldgar, David E.; GonzΓ‘lez-Neira, Anna; GuΓ©nel, Pascal; Hahnen, Eric; Haiman, Christopher A.; HΓ₯kansson, Niclas; Hall, Per; Hallberg, Emily; Hamann, Ute; Harrington, Patricia; Hein, Alexander; Hicks, Belynda; Hillemanns, Peter; Hollestelle, Antoinette; Hoover, Robert N.; Hopper, John L.; Huang, Guanmengqian; Humphreys, Keith; Hunter, David; Jakubowska, Anna; Janni, Wolfgang; John, Esther M.; Johnson, Nichola; Jones, Kristine; Jones, Michael E.; Jung, Audrey; Kaaks, Rudolf; Kerin, Michael J.; Khusnutdinova, Elza; Kosma, Veli-Matti; Kristensen, Vessela N.; Lambrechts, Diether; Le Marchand, Loic; Li, Jingmei; LindstrΓΆm, Sara; Lissowska, Jolanta; Lo, Wing-Yee; Loibl, Sibylle; Lubinski, Jan; Luccarini, Craig; Lux, Michael P.; MacInnis, Robert J.; Maishman, Tom; Kostovska, Ivana Maleva; Mannermaa, Arto; Manson, JoAnn; Margolin, Sara; Mavroudis, Dimitrios; Meijers-Heijboer, Hanne; Meindl, Alfons; Menon, Usha; Meyer, Jeffery; Mulligan, Anna Marie; Neuhausen, Susan L.; Nevanlinna, Heli; Neven, Patrick; Nielsen, Sune F.; Nordestgaard, BΓΈrge G.; Olopade, Olufunmilayo I.; Olson, Janet E.; Olsson, HΓ₯kan; Peterlongo, Paolo; Peto, Julian; Plaseska-Karanfilska, Dijana; Prentice, Ross; Presneau, Nadege; PylkΓ€s, Katri; Rack, Brigitte; Radice, Paolo; Rahman, Nazneen; Rennert, Gad; Rennert, Hedy S.; Rhenius, Valerie; Romero, Atocha; Romm, Jane; Rudolph, Anja; Saloustros, Emmanouil; Sandler, Dale P.; Sawyer, Elinor J.; Schmidt, Marjanka K.; Schmutzler, Rita K.; Schneeweiss, Andreas; Scott, Rodney J.; Scott, Christopher G.; Seal, Sheila; Shah, Mitul; Shrubsole, Martha J.; Smeets, Ann; Southey, Melissa C.; Spinelli, John J.; Stone, Jennifer; Surowy, Harald; Swerdlow, Anthony J.; Tamimi, Rulla; Tapper, William; Taylor, Jack A.; Terry, Mary Beth; Tessier, Daniel C.; Thomas, Abigail; ThΓΆne, Kathrin; Tollenaar, Rob A. E. M.; Torres, Diana; Truong, ThΓ©rΓ¨se; Untch, Michael; Vachon, Celine; Van Den Berg, David; Vincent, Daniel; Waisfisz, Quinten; Weinberg, Clarice R.; Wendt, Camilla; Whittemore, Alice S.; Wildiers, Hans; Willett, Walter; Winqvist, Robert; Wolk, Alicja; Xia, Lucy; Yang, Xiaohong R.; Ziogas, Argyrios; Ziv, Elad; Dunning, Alison M.; Pharoah, Paul D. P.; Simard, Jacques; Milne, Roger L.; Edwards, Stacey L.; Kraft, Phillip; Easton, Douglas F.; Chenevix-Trench, Georgia; Zheng, WeiBreast cancer risk variants identified in genome-wide association studies explain only a small fraction of familial relative risk, and genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82Γ10β6, including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony forming efficiency. Our study provides new insights into breast cancer genetics and biology.Publication Partitioning heritability by functional annotation using genome-wide association summary statistics(2015) Finucane, Hilary; Bulik-Sullivan, Brendan; Gusev, Alexander; Trynka, Gosia; Reshef, Yakir; Loh, Po-Ru; Anttila, Verneri; Xu, Han; Zang, Chongzhi; Farh, Kyle; Ripke, Stephan; Day, Felix R.; Consortium, ReproGen; Purcell, Shaun M.; Stahl, Eli; Lindstrom, Sara; Perry, John R. B.; Okada, Yukinori; Raychaudhuri, Soumya; Daly, Mark; Patterson, Nick; Neale, Benjamin; Price, AlkesRecent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior.Publication Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis(2015) Loh, Po-Ru; Bhatia, Gaurav; Gusev, Alexander; Finucane, Hilary K; Bulik-Sullivan, Brendan K; Pollack, Samuela; de Candia, Teresa R; Lee, Sang Hong; Wray, Naomi R; Kendler, Kenneth S; OβDonovan, Michael C; Neale, Benjamin; Patterson, Nick; Price, AlkesHeritability analyses of GWAS cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here, we analyze the genetic architecture of schizophrenia in 49,806 samples from the PGC, and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which β₯71% of 1Mb genomic regions harbor β₯1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) among several pairs of GERA diseases; genetic correlations were on average 1.3x stronger than correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multi-component, multi-trait variance components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.Publication An Atlas of Genetic Correlations across Human Diseases and Traits(2015) Bulik-Sullivan, Brendan; Finucane, Hilary K; Anttila, Verneri; Gusev, Alexander; Day, Felix R.; Loh, Po-Ru; Duncan, Laramie; Perry, John R.B.; Patterson, Nick; Robinson, Elise; Daly, Mark; Price, Alkes; Neale, BenjaminIdentifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique β cross-trait LD Score regression β for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.