Multivariate Analysis of Anthropometric Traits Using Summary Statistics of Genome-Wide Association Studies from GIANT Consortium

Abstract

Meta-analysis of single trait for multiple cohorts has been used for increasing statistical power in genome-wide association studies (GWASs). Although hundreds of variants have been identified by GWAS, these variants only explain a small fraction of phenotypic variation. Cross-phenotype association analysis (CPASSOC) can further improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this study, we performed CPASSOC analysis on the summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium using a novel method recently developed by our group. Sex-specific meta-analysis data for height, body mass index (BMI), and waist-to-hip ratio adjusted for BMI (WHRadjBMI) from discovery phase of the GIANT consortium study were combined using CPASSOC for each trait as well as 3 traits together. The conventional meta-analysis results from the discovery phase data of GIANT consortium studies were used to compare with that from CPASSOC analysis. The CPASSOC analysis was able to identify 17 loci associated with anthropometric traits that were missed by conventional meta-analysis. Among these loci, 16 have been reported in literature by including additional samples and 1 is novel. We also demonstrated that CPASSOC is able to detect pleiotropic effects when analyzing multiple traits.

A systematic SNP selection approach to identify mechanisms underlying disease aetiology: linking height to post-menopausal breast and colorectal cancer risk

“…In this report, however, we focused on the instrumental value of the clusters in terms of future gene-environment interaction analyses or mediation analyses aimed at elucidating disease aetiology, rather than on trying to pinpoint pleiotropic SNPs or genes. Nevertheless, it is good to realise that several other methods exist that are aimed at identifying potential pleiotropic effects474849. These methods may, in part, confirm the results at hand, when applied to the same topic.…”

Adjustment for covariates using summary statistics of genome‐wide association studies

“…(Appendix 1), in which r̂ y , c can be estimated from the SNP summary statistics of the trait and the covariate obtained from a set of independent SNPs across the genome after excluding the variants associated with the trait and the covariate (Park, Li, Song, He, & Zhu, 2016), or from the LD score regressions (Bulik-Sullivan et al, 2015). We can define the test statistic for the SNP-association of the trait as…”

Cross-Phenotype Association Analysis Using Summary Statistics from GWAS

“…In this section, we will illustrate the procedure for conducting CPASSOC analysis with the sex-specific summary statistics of the three traits: height, BMI and WHRadjBMI (38), which were downloaded from the GIANT consortium website (https://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files). …”

“…Six additional loci with suggestive association evidence (P<5.0×10 −7 ) were also observed, including CACNA1D and WNT3 . CPASSOC was also applied to anthropometric traits, such as height, BMI, and waist-to-hip ratio adjusted for BMI (WHRadjBMI), where the summary statistics were obtained from the GIANT consortium (38). Those applications strongly suggest that analyzing multiple phenotypes can improve statistical power and that such an analysis can be executed with the summary statistics from GWAS.…”