Meta-analysis of Correlated Traits via Summary Statistics from GWASs with an Application in Hypertension

Abstract

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.

Integrative approaches for large-scale transcriptome-wide association studies

“…On average, a correlation of 0.01, 0.01, and 0.43 was observed between YFS:METSIM, NTR:METSIM, and YFS:NTR, highlighting the same tissue of origin the last pair. We then used the three-entry vector of TWAS predictions, Z i , to compute the statistic omnibus i = Z i ′ C i −1 Z i which is approximately χ 2 (3-dof) distributed and provides an omnibus test for effect in any tissue while accounting for correlation 57,58 . Though the correlation observed in our data was almost entirely driven by the YFS:NTR blood datasets, we expect this to be an especially useful strategy for future studies with many correlated tissues.…”

Detecting Heterogeneity in Population Structure Across the Genome in Admixed Populations

“…For example, previous methods using this general approach have been developed for valid genetic association testing between a phenotype and genetic markers in correlated samples (Wei and Johnson 1985;Xu et al 2003;Yang et al 2010;Zhu et al 2015), and CAnD is an adaptation of this approach for detecting heterogeneity in ancestry across the genome while accounting for correlations among chromosomes within an admixed individual. …”

Gene- and pathway-based association tests for multiple traits with GWAS summary statistics

“…To compare our multiple SNP-multiple trait method with single SNP-multiple trait and multiple SNP-single trait methods, we applied a gene-based aSPUs test (Kwak and Pan, 2016, multiple SNP-single trait method) for each trait, then combined the results across the multiple traits with a significance threshold of 0.05/ 6 ¼ 0.0083 based on the Bonferroni correction; we also considered two single SNP-multiple trait association tests proposed by Zhu et al (2015) (S het ) and Kim et al (2015) (MTaSPUs). The two tests were applied to each SNP and then we used a significance threshold of 0.05/d based on the Bonferroni correction, where d is the number of SNPs in a gene.…”

“…We applied two other single SNP-multiple trait association tests proposed by Zhu et al (2015) (S het ) and Kim et al (2015) (MTaSPUs) to compare with MTaSPUsSet, a multiple SNP-multiple trait testing method.…”