Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations
Siddhartha KarSara LindströmRayjean J. HungKate LawrensonMarjanka K. SchmidtTracy A. O’MaraDylan M. GlubbJonathan P. TyrerJoellen M. SchildkrautJenny Chang‐ClaudeAhmad AlsulimaniFernando Moreno AntónAlicia Beeghly‐FadielLine BjørgeClara BodelónHiltrud BrauchStefanie BurghausDaniele CampaMichael E. CarneyChu ChenZhihua ChenMary B. DalyAndreas du BoisArif B. EkiciAilith EwingPeter A. FaschingJames M. FlanaganJan GawełkoGraham G. GilesRobert J. HamiltonHolly R. HarrisFlorian HeitzMichelle HildebrandtPeter HillemannsRuea‐Yea HuangLiher ImazArvīds IrmejsAnna JakubowskaAllan JensenEsther M. JohnPäivi KannistoBeth Y. KarlanЭ. К. ХуснутдиноваLambertus A. KiemeneySusanne K. KjærRüdiger KlapdorPetra KleiblováMartin KöbelBożena KonopkaCamilla KrakstadDavor LesselArtitaya LophatananonTaymaa MayAgnieszka D. MieszkowskaÁlvaro N.A. MonteiroKirsten MoysichKenneth MuirSune F. NielsenKunle OdunsiHåkan OlssonTjoung-Won Park-SimonJennifer B. PermuthPaolo PeterlongoAgnieszka PodgorskiRoss L. PrenticePaolo RadiceHarvey A. RischIngo B. RunnebaumIwona K. RzepeckaRodney J. ScottVeronica Wendy SetiawanNadeem SiddiquiWeiva SiehBeata ŚpiewankiewiczLukasz M. SzafronCheryl L. ThompsonLinda TitusClare TurnbullNawaid UsmaniAnne M. van AltenaAna Vega‐GliemmoIgnace VergoteRobert A. VierkantJoseph VijaiStacey J. WinhamRobert WinqvistHerbert YuDiether LambrechtsDeborah J. ThompsonEllen L. GoodeWei ZhengIan TomlinsonAndrew BerchuckSusan J. RamusStephen J. ChanockDouglas F. EastonGeorgia Chenevix‐TrenchSimon A. GaytherAmanda B. SpurdleRosalind A. EelesPeter KraftPaul D.P. Pharoah
6
Citation
80
Reference
10
Related Paper
Citation Trend
Abstract:
ABSTRACT We report a meta-analysis of breast, prostate, ovarian, and endometrial cancer genome-wide association data (effective sample size: 237,483 cases/317,006 controls). This identified 465 independent lead variants ( P <5×10 −8 ) across 192 genomic regions. Four lead variants were >1Mb from previously identified risk loci for the four cancers and an additional 23 lead variant-cancer associations were novel for one of the cancers. Bayesian models supported pleiotropic effects involving at least two cancers at 222/465 lead variants in 118/192 regions. Gene-level association analysis identified 13 shared susceptibility genes ( P <2.6×10 −6 ) in 13 regions not previously implicated in any of the four cancers and not uncovered by our variant-level meta-analysis. Several lead variants had opposite effects across cancers, including a cluster of such variants in the TP53 pathway. Fifty-four lead variants were associated with blood cell traits and suggested genetic overlaps with clonal hematopoiesis. Our study highlights the remarkable pervasiveness of pleiotropy across hormone-related cancers, further illuminating their shared genetic and mechanistic origins at variant- and gene-level resolution.Keywords:
Genome-wide Association Study
Genetic Association
Pleiotropy
Abstract Physiological systems are subject to interindividual variation encoded by genetics. Genome‐wide association studies (GWAS) operate by surveying thousands of genetic variants from a substantial number of individuals and assessing their association to a trait of interest, be it a physiological variable, a molecular phenotype (e.g. gene expression), or even a disease or condition. Through a myriad of methods, GWAS downstream analyses then explore the functional consequences of each variant and attempt to ascertain a causal relationship to the phenotype of interest, as well as to delve into its links to other traits. This type of investigation allows mechanistic insights into physiological functions, pathological disturbances and shared biological processes between traits (i.e. pleiotropy). An exciting example is the discovery of a new thyroid hormone transporter (SLC17A4) and hormone metabolising enzyme (AADAT) from a GWAS on free thyroxine levels. Therefore, GWAS have substantially contributed with insights into physiology and have been shown to be useful in unveiling the genetic control underlying complex traits and pathological conditions; they will continue to do so with global collaborations and advances in genotyping technology. Finally, the increasing number of trans‐ancestry GWAS and initiatives to include ancestry diversity in genomics will boost the power for discoveries, making them also applicable to non‐European populations. image
Genome-wide Association Study
Pleiotropy
Genetic Association
Mendelian Randomization
Trait
Cite
Citations (0)
Genome-wide Association Study
Genetic Association
Genetic architecture
Identification
Candidate gene
Cite
Citations (42)
Genome-wide Association Study
Pleiotropy
Genetic Association
Epistasis
Trait
Cite
Citations (16)
Genome-wide Association Study
Pleiotropy
Genetic Association
Cite
Citations (138)
Abstract Genome-wide association studies (GWAS) can serve as strong evidence in correlating biological pathways with human diseases. Although ischemic stroke has been found to be associated with many biological pathways, the genetic mechanism of ischemic stroke is still unclear. Here, we performed GWAS for a major subtype of stroke—small-vessel occlusion (SVO)—to identify potential genetic factors contributing to ischemic stroke. GWAS were conducted on 342 individuals with SVO stroke and 1,731 controls from a Han Chinese population residing in Taiwan. The study was replicated in an independent Han Chinese population comprising an additional 188 SVO stroke cases and 1,265 controls. Three SNPs (rs2594966, rs2594973, rs4684776) clustered at 3p25.3 in ATG7 (encoding Autophagy Related 7), with P values between 2.52 × 10 −6 and 3.59 × 10 −6 , were identified. Imputation analysis also supported the association between ATG7 and SVO stroke. To our knowledge, this is the first GWAS to link stroke and autophagy. ATG7 , which has been implicated in autophagy, could provide novel insights into the genetic basis of ischemic stroke.
Genome-wide Association Study
Genetic Association
Stroke
Imputation (statistics)
Han Chinese
Cite
Citations (19)
Recent genome-wide association studies (GWAS) have identified a number of novel genetic associations with complex human diseases. In spite of these successes, results from GWAS generally explain only a small proportion of disease heritability, an observation termed the 'missing heritability problem'. Several sources for the missing heritability have been proposed, including the contribution of many common variants with small individual effect sizes, which cannot be reliably found using the standard GWAS approach. The goal of our study was to explore a complimentary approach, which combines GWAS results with functional data in order to identify novel genetic associations with small effect sizes. To do so, we conducted a GWAS for lymphocyte count, a physiologic quantitative trait associated with asthma, in 462 Hutterites. In parallel, we performed a genome-wide gene expression study in lymphoblastoid cell lines from 96 Hutterites. We found significant support for genetic associations using the GWAS data when we considered variants near the 193 genes whose expression levels across individuals were most correlated with lymphocyte counts. Interestingly, these variants are also enriched with signatures of an association with asthma susceptibility, an observation we were able to replicate. The associated loci include genes previously implicated in asthma susceptibility as well as novel candidate genes enriched for functions related to T cell receptor signaling and adenosine triphosphate synthesis. Our results, therefore, establish a new set of asthma susceptibility candidate genes. More generally, our observations support the notion that many loci of small effects influence variation in lymphocyte count and asthma susceptibility.
Genome-wide Association Study
Candidate gene
Genetic Association
Missing heritability problem
Cite
Citations (51)
Genome-wide Association Study
Genetic Association
SNP
Identification
SNP array
Cite
Citations (241)
Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.
Genome-wide Association Study
Mendelian Randomization
Genetic Association
Pathway Analysis
Association (psychology)
Cite
Citations (4)
The application of high-throughput genotyping in humans has yielded numerous insights into the genetic basis of human phenotypes and an unprecedented amount of genetic data. Genome-wide association studies (GWAS) have increased in number in recent years, but the variants that have been found have generally explained only a tiny proportion of the estimated genetic contribution to phenotypic variation. This article summarizes the progress made in the development of gene set analysis (GSA) and network analysis for GWAS was a way to identify the underlying molecular processes of human phenotypes. It also highlights some promising findings and indicates future directions that may greatly enhance the analysis and interpretation of GWAS.
Genome-wide Association Study
Genetic Association
Cite
Citations (24)
Genome-wide Association Study
Pleiotropy
Linkage Disequilibrium
Genetic Association
Genetic architecture
Trait
Cite
Citations (0)