An adaptive test based on principal components for detecting multiple phenotype associations using GWAS summary data

1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65.https://doi.org/10.1038/nature09270

Barnett I, Mukherjee R, Lin X (2017) The generalized higher criticism for testing snp-set effects in genetic association studies. J Am Stat Assoc 112:64–76. https://doi.org/10.1080/01621459.2016.1192039

Article  CAS  Google Scholar 

Bulik-Sullivan B, Finucane HK, Anttila V et al (2015) An atlas of genetic correlations across human diseases and traits. Nat Genet 47:1236–1241. https://doi.org/10.1038/ng.3406

Article  CAS  Google Scholar 

Chesmore K, Bartlett J, Williams SM (2018) The ubiquity of pleiotropy in human disease. Hum Genet 137:39–44. https://doi.org/10.1007/s00439-017-1854-z

Article  CAS  Google Scholar 

Conneely KN, Boehnke M (2007) So many correlated tests, so little time! Rapid adjustment of p values for multiple correlated tests. Am J Hum Genet 81:1158–1168. https://doi.org/10.1086/522036

Article  CAS  Google Scholar 

Guo B, Wu B (2019) Integrate multiple traits to detect novel trait-gene association using gwas summary data with an adaptive test approach. Bioinformatics 35:2251–2257. https://doi.org/10.1093/bioinformatics/bty961

Article  CAS  Google Scholar 

Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211

Article  CAS  Google Scholar 

Kim YJ, Go MJ, Hu C et al (2011) Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat Genet 43(10):990–995. https://doi.org/10.1038/ng.939

Article  CAS  Google Scholar 

Liu Z, Lin X (2018) Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 74:165–175. https://doi.org/10.1111/biom.12735

Article  Google Scholar 

Liu Y, Chen S, Li Z, Morrison AC, Boerwinkle E, Lin X (2019) ACAT: A fast and powerful p value combination method for rare-variant analysis in sequencing studies. Am J Hum Genet 104:410–421. https://doi.org/10.1016/j.ajhg.2019.01.002

Article  CAS  Google Scholar 

Liu W, Guo Y, Liu Z (2021) An omnibus test for detecting multiple phenotype associations based on gwas summary level data. Front Genet 12:1–7. https://doi.org/10.3389/fgene.2021.644419

Article  Google Scholar 

McLaren W, Gil L, Hunt SE et al (2016) The ensembl variant effect predictor. Genome Biol 17(1):122–135. https://doi.org/10.1186/s13059-016-0974-4

Article  CAS  Google Scholar 

Pasaniuc B, Price AL (2017) Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet 18:117–127. https://doi.org/10.1038/nrg.2016.142

Article  CAS  Google Scholar 

Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW (2013) Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet 14:483–495. https://doi.org/10.1038/nrg3461

Article  CAS  Google Scholar 

Spracklen CN, Chen P, Kim YJ et al (2017) Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum Mol Genet 26(9):1770–1784. https://doi.org/10.1093/hmg/ddx062

Article  CAS  Google Scholar 

Stearns FW (2010) One hundred years of pleiotropy: a retrospective. Genetics 186:767–773. https://doi.org/10.1534/genetics.110.122549

Article  CAS  Google Scholar 

Stephens M (2013) A unified framework for association analysis with multiple related phenotypes. PLoS ONE 8:e65245. https://doi.org/10.1371/journal.pone.0065245

Article  CAS  Google Scholar 

Sun R, Lin X (2020) Genetic variant set-based tests using the generalized berk-jones statistic with application to a genome-wide association study of breast cancer. J Am Stat Assoc 115:1079–1091. https://doi.org/10.1080/01621459.2019.1660170

Article  CAS  Google Scholar 

Surakka I, Horikoshi M, Mägi R et al (2015) The impact of low-frequency and rare variants on lipid levels. Nat Genet 47(6):589–597. https://doi.org/10.1038/ng.3300

Article  CAS  Google Scholar 

Teslovich TM, Musunuru K, Smith AV et al (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466:707–713. https://doi.org/10.1038/nature09270

Article  CAS  Google Scholar 

Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of gwas discovery. Am J Hum Genet 90:7–24. https://doi.org/10.1016/j.ajhg.2011.11.029

Article  CAS  Google Scholar 

Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J (2017) 10 years of gwas discovery: biology, function, and translation. Am J Hum Genet 101:5–22. https://doi.org/10.1016/j.ajhg.2017.06.005

Article  CAS  Google Scholar 

Willer CJ, Schmidt EM, Sengupta S et al (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45(11):1274–1283. https://doi.org/10.1038/ng.2797

Article  CAS  Google Scholar 

Zhu X, Feng T, Tayo BO et al (2015) Meta-analysis of correlated traits via summary statistics from gwass with an application in hypertension. Am J Hum Genet 96:21–36. https://doi.org/10.1016/j.ajhg.2014.11.011

Article  CAS  Google Scholar 

Comments (0)

No login
gif