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Genome-wide association test of multiple continuous traits using imputed SNPs.

TitleGenome-wide association test of multiple continuous traits using imputed SNPs.
Publication TypeJournal Article
Year of Publication2017
AuthorsWu B
Secondary AuthorsPankow JS
JournalStat Interface
Volume10
Issue3
Pagination379-386
Date Published2017
ISSN1938-7989
Abstract

More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlated phenotypes that reflect common physiological processes. By jointly analyzing these correlated traits, we can gain more power by aggregating multiple weak effects and shed light on the mechanisms underlying complex human diseases. The majority of existing multi-trait association test methods are based on jointly modeling the multivariate traits conditional on the genotype as covariate, and can readily accommodate the imputed SNPs by using their imputed dosage as a covariate. An alternative class of multi-trait association tests is based on the inverted regression, which models the distribution of genotypes conditional on the covariate and multivariate traits, and has been shown to have competitive performance. To our knowledge, all existing inverted regression approaches have implicitly used the "best-guess" genotypes, which is not efficient and known to lead to dramatic power loss, and there have not been any proposed methods of incorporating imputation uncertainty into inverted regressions. In this work, we propose a general and efficient framework that can account for the imputation uncertainty to further improve the association test power of inverted regression models for imputed SNPs. We demonstrate through extensive numerical studies that the proposed method has competitive performance. We further illustrate its usefulness by application to association test of diabetes-related glycemic traits in the Atherosclerosis Risk in Communities (ARIC) Study.

DOI10.4310/SII.2017.v10.n3.a2
Alternate JournalStat Interface
PubMed ID28217245
PubMed Central IDPMC5310616
Grant ListHHSN268201100012C / HL / NHLBI NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
UL1 RR025005 / RR / NCRR NIH HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
R01 CA134848 / CA / NCI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
R01 HL059367 / HL / NHLBI NIH HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
R01 HL086694 / HL / NHLBI NIH HHS / United States
U01 HG004402 / HG / NHGRI NIH HHS / United States
HHSN268201100006C / HL / NHLBI NIH HHS / United States
HHSN268201100005I / HL / NHLBI NIH HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
R01 GM083345 / GM / NIGMS NIH HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States
HHSN268201100007I / HL / NHLBI NIH HHS / United States
R01 HL087641 / HL / NHLBI NIH HHS / United States