04476nas a2200937 4500008004100000022001400041245014100055210006900196260001300265300001300278490000600291520177500297653002202072653001502094653002102109653002302130653002102153653003002174653001102204653001902215653002202234653002502256653001802281653003402299653001302333653001102346653002702357653000902384653001502393653001402408653003602422653003302458653004702491653001302538100002102551700001802572700002202590700001802612700001702630700002002647700002002667700002002687700002402707700001802731700001702749700002102766700002502787700001602812700002502828700002302853700001902876700002102895700001602916700002802932700002402960700002802984700002003012700002203032700001503054700002003069700002303089700002103112700002203133700001903155700002203174700002403196700002203220700002803242700002303270700001903293700002003312700002403332700002403356700001703380700002703397700001703424700002003441700002103461700002003482856003603502 2011 eng d a1553-740400aEnhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.0 aEnhanced statistical tests for GWAS in admixed populations asses c2011 Apr ae10013710 v73 a
While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations.
10aAfrican Americans10aAlgorithms10aBreast Neoplasms10aChromosome Mapping10aCoronary Disease10aDiabetes Mellitus, Type 210aFemale10aGene Frequency10aGenetic Variation10aGenetics, Population10aGenome, Human10aGenome-Wide Association Study10aGenotype10aHumans10aLinkage Disequilibrium10aMale10aOdds Ratio10aPhenotype10aPolymorphism, Single Nucleotide10aPrincipal Component Analysis10aReceptor, Fibroblast Growth Factor, Type 210aSoftware1 aPasaniuc, Bogdan1 aZaitlen, Noah1 aLettre, Guillaume1 aChen, Gary, K1 aTandon, Arti1 aKao, Linda, W H1 aRuczinski, Ingo1 aFornage, Myriam1 aSiscovick, David, S1 aZhu, Xiaofeng1 aLarkin, Emma1 aLange, Leslie, A1 aCupples, Adrienne, L1 aYang, Qiong1 aAkylbekova, Ermeg, L1 aMusani, Solomon, K1 aDivers, Jasmin1 aMychaleckyj, Joe1 aLi, Mingyao1 aPapanicolaou, George, J1 aMillikan, Robert, C1 aAmbrosone, Christine, B1 aJohn, Esther, M1 aBernstein, Leslie1 aZheng, Wei1 aHu, Jennifer, J1 aZiegler, Regina, G1 aNyante, Sarah, J1 aBandera, Elisa, V1 aIngles, Sue, A1 aPress, Michael, F1 aChanock, Stephen, J1 aDeming, Sandra, L1 aRodriguez-Gil, Jorge, L1 aPalmer, Cameron, D1 aBuxbaum, Sarah1 aEkunwe, Lynette1 aHirschhorn, Joel, N1 aHenderson, Brian, E1 aMyers, Simon1 aHaiman, Christopher, A1 aReich, David1 aPatterson, Nick1 aWilson, James, G1 aPrice, Alkes, L uhttps://chs-nhlbi.org/node/128804450nas a2201069 4500008004100000022001400041245015100055210006900206260001300275300001200288490000700300520139200307100002101699700001901720700002301739700002301762700001601785700002001801700001901821700002101840700002501861700002301886700002101909700001801930700002201948700001701970700002701987700002202014700001702036700002102053700002002074700002002094700001502114700001402129700001702143700001602160700001702176700001702193700002102210700001702231700001302248700002002261700002102281700001902302700002302321700001402344700002102358700001902379700002302398700001802421700001802439700003202457700002702489700001702516700002202533700001602555700001902571700002302590700002102613700001902634700002102653700002202674700001802696700002002714700002202734700001702756700002002773700001702793700001902810700002102829700001902850700002002869700001902889700002402908700002302932700001802955700002202973700002102995700002103016700002503037700001903062700002303081700001803104700002403122700001803146700002203164700002203186700002003208700001803228710009803246856003603344 2019 eng d a1939-327X00aMultiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control.0 aMultiethnic GenomeWide Association Study of Diabetic Retinopathy c2019 Feb a441-4560 v683 aTo identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts ( = 3,246) and seven African American cohorts ( = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a value <1 × 10 were investigated in replication cohorts that included 18,545 European, 16,453 Asian, and 2,710 Hispanic subjects. After correction for multiple testing, the C allele of rs142293996 in an intron of nuclear VCP-like () was associated with DR in European discovery cohorts ( = 2.1 × 10), but did not reach genome-wide significance after meta-analysis with replication cohorts. We applied the Disease Association Protein-Protein Link Evaluator (DAPPLE) to our discovery results to test for evidence of risk being spread across underlying molecular pathways. One protein-protein interaction network built from genes in regions associated with proliferative DR was found to have significant connectivity ( = 0.0009) and corroborated with gene set enrichment analyses. These findings suggest that genetic variation in as well as variation within a protein-protein interaction network that includes genes implicated in inflammation, may influence risk for DR.
1 aPollack, Samuela1 aIgo, Robert, P1 aJensen, Richard, A1 aChristiansen, Mark1 aLi, Xiaohui1 aCheng, Ching-Yu1 aC Y Ng, Maggie1 aSmith, Albert, V1 aRossin, Elizabeth, J1 aSegrè, Ayellet, V1 aDavoudi, Samaneh1 aTan, Gavin, S1 aChen, Yii-Der Ida1 aKuo, Jane, Z1 aDimitrov, Latchezar, M1 aStanwyck, Lynn, K1 aMeng, Weihua1 aHosseini, Mohsen1 aImamura, Minako1 aNousome, Darryl1 aKim, Jihye1 aHai, Yang1 aJia, Yucheng1 aAhn, Jeeyun1 aLeong, Aaron1 aShah, Kaanan1 aPark, Kyu, Hyung1 aGuo, Xiuqing1 aIpp, Eli1 aTaylor, Kent, D1 aAdler, Sharon, G1 aSedor, John, R1 aFreedman, Barry, I1 aLee, I-Te1 aSheu, Wayne, H-H1 aKubo, Michiaki1 aTakahashi, Atsushi1 aHadjadj, Samy1 aMarre, Michel1 aTrégouët, David-Alexandre1 aMcKean-Cowdin, Roberta1 aVarma, Rohit1 aMcCarthy, Mark, I1 aGroop, Leif1 aAhlqvist, Emma1 aLyssenko, Valeriya1 aAgardh, Elisabet1 aMorris, Andrew1 aDoney, Alex, S F1 aColhoun, Helen, M1 aToppila, Iiro1 aSandholm, Niina1 aGroop, Per-Henrik1 aMaeda, Shiro1 aHanis, Craig, L1 aPenman, Alan1 aChen, Ching, J1 aHancock, Heather1 aMitchell, Paul1 aCraig, Jamie, E1 aChew, Emily, Y1 aPaterson, Andrew, D1 aGrassi, Michael, A1 aPalmer, Colin1 aBowden, Donald, W1 aYaspan, Brian, L1 aSiscovick, David1 aCotch, Mary, Frances1 aWang, Jie, Jin1 aBurdon, Kathryn, P1 aWong, Tien, Y1 aKlein, Barbara, E K1 aKlein, Ronald1 aRotter, Jerome, I1 aIyengar, Sudha, K1 aPrice, Alkes, L1 aSobrin, Lucia1 aFamily Investigation of Nephropathy and Diabetes-Eye Research Group, DCCT/EDIC Research Group uhttps://chs-nhlbi.org/node/7990