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Meta-Analysis
. 2015 Jan 8;96(1):21-36.
doi: 10.1016/j.ajhg.2014.11.011. Epub 2014 Dec 11.

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Xiaofeng Zhu  1 , Tao Feng  2 , Bamidele O Tayo  3 , Jingjing Liang  4 , J Hunter Young  5 , Nora Franceschini  6 , Jennifer A Smith  7 , Lisa R Yanek  5 , Yan V Sun  8 , Todd L Edwards  9 , Wei Chen  10 , Mike Nalls  11 , Ervin Fox  12 , Michele Sale  13 , Erwin Bottinger  14 , Charles Rotimi  15 ; COGENT BP Consortium; Yongmei Liu  16 , Barbara McKnight  17 , Kiang Liu  18 , Donna K Arnett  19 , Aravinda Chakravati  20 , Richard S Cooper  3 , Susan Redline  21
Collaborators, Affiliations
Meta-Analysis

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Xiaofeng Zhu et al. Am J Hum Genet. .

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.

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Figures

Figure 1
Figure 1
SHet Distribution Distribution of the test statistic SHet under three scenarios: trait correlation is 0 (A and B), trait correlation is 0.25 (C and D), and trait correlation is 0.5 (E and F). We generated 5 cohorts, each with sample size 3,000, with no overlapping samples between cohorts. Left panel is the histogram of SHet based on 100,000 replications and the red curve represents the theoretical distribution gamma(α,β), where α,β are the shape and scale parameters that were estimated by matching the first two moments. Right panel is a QQ plot of SHet.
Figure 2
Figure 2
SHet Distribution when Cohorts Have Overlapping Subjects Distribution of the test statistic SHet under three scenarios as in Figure 1. We generated 5 cohorts, each with sample size 3,000; 500 subjects were overlapping between cohorts. Left and right panels are as in Figure 1.
Figure 3
Figure 3
Power Comparison of SHom and SHet when One Cohort Has Genetic Contribution SBP and DBP were simulated independently. HTN was simulated according to SBP and DBP and simulated medication status. Five cohorts were simulated, but only one of the five cohorts has a genetic contribution. Left: No overlapping samples among the five cohorts. Right: 500 samples were the same in each cohort and a genetic variant contributes phenotypic variation for the same samples. (A and B) A genetic variant affects only SBP. (C and D) A genetic variant affects both SBP and DBP but with opposite effect directions. (E and F) A genetic variant affects both SBP and DBP with the same effect direction.
Figure 4
Figure 4
Power Comparison of SHom and SHet when Five Cohorts Have Genetic Contribution Five cohorts were simulated and the genetic variant has contribution in all five cohorts. Details as in Figure 3.
Figure 5
Figure 5
Power Comparison of SHom and SHet with Correlation 0.5 when One Cohort Has Genetic Contribution SBP and DBP were simulated with correlation 0.5. Five cohorts were simulated but only one of the five cohorts has a genetic contribution. Details as in Figure 3.
Figure 6
Figure 6
Power Comparison of SHom and SHet with Correlation 0.5 when Five Cohorts Have Genetic Contribution SBP and DBP were simulated with correlation 0.5. Five cohorts were simulated and the genetic variant has a contribution in all five cohorts. Details as in Figure 3.
Figure 7
Figure 7
QQ Plots and Manhattan Plots after Combining SBP, DBP, and HTN via SHom and SHet for the COGENT BP GWAS Data Shown are QQ plots (A), Manhattan plot of SHet (B), and Manhattan plot of SHom (C).
Figure 8
Figure 8
Regional Association Plots Regional association plots of the four SNPs reaching genome-wide significance (p < 5 × 10−8) by SHet for the COGENT BP GWAS data. The most significant SNP at each locus is shown in purple. The fine-scale recombination rate is shown as a blue vertical line. Gene positions are shown at the bottom.
See this image and copyright information in PMC

References

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