On Robust Association Testing for Quantitative Traits and Rare Variants

Abstract

With the advance of sequencing technologies, it has become a routine practice to test for association between a quantitative trait and a set of rare variants (RVs). While a number of RV association tests have been proposed, there is a dearth of studies on the robustness of RV association testing for nonnormal distributed traits, e.g., due to skewness, which is ubiquitous in cohort studies. By extensive simulations, we demonstrate that commonly used RV tests, including sequence kernel association test (SKAT) and optimal unified SKAT (SKAT-O), are not robust to heavy-tailed or right-skewed trait distributions with inflated type I error rates; in contrast, the adaptive sum of powered score (aSPU) test is much more robust. Here we further propose a robust version of the aSPU test, called aSPUr. We conduct extensive simulations to evaluate the power of the tests, finding that for a larger number of RVs, aSPU is often more powerful than SKAT and SKAT-O, owing to its high data-adaptivity. We also compare different tests by conducting association analysis of triglyceride levels using the NHLBI ESP whole-exome sequencing data. The QQ plots for SKAT and SKAT-O were severely inflated (λ = 1.89 and 1.78, respectively), while those for aSPU and aSPUr behaved normally. Due to its relatively high robustness to outliers and high power of the aSPU test, we recommend its use complementary to SKAT and SKAT-O. If there is evidence of inflated type I error rate from the aSPU test, we would recommend the use of the more robust, but less powerful, aSPUr test.

Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics

“…Our findings reinforce the necessity of incorporating multi-ethnic study populations in genomics in order to accurately characterize RBC trait loci and encourage equitable application of the results to translational work [39]. The complexity of association signals at loci previously characterized in European-and East Asianancestry populations also demonstrates improved power to perform conditional analysis using a combined-phenotype model [47].…”

FunSPU: A versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data

“…Using the conservative Bonferroni procedure, we set the family-wise error rate at 0.05 with a significance level = 0.05/319306 = 1.56e-07, which equals 6.81 on the -log 10 scale. To achieve this genome-wide significance level, we used a step-up permutation strategy [22, 28]. We first performed B = 10,000 permutations for all sliding windows and gradually increased B ; if those sliding windows with estimated p -values <10/B, we increased B to 10 times the current value and re-estimated the p -values for these sliding windows.…”

An adaptive test for meta‐analysis of rare variant association studies

“…To improve the statistical power of detecting RVs, lots of recent efforts have been put into developing powerful statistical tests (Chen, Lin, & Wang, 2017;Derkach, Lawless, & Sun, 2013;Sha, Wang, & Zhang, 2013;Wang, 2016;Wei et al, 2016), leveraging information from multiple traits (Zhang, Sha, Liu, & Wang, 2019), and incorporating gene-by-environment interaction (Su, Di, & Hsu, 2017;Yang, Chen, Tang, Li, & Wei, 2019;Zhao, Marceau, Zhang, & Tzeng, 2015). However, there is still limited findings.…”

“…We included nonsynonymous and splice-site variants of MAF ≤ 1% within each gene and excluded genes with cumulative MACs < 5, resulting in a total of 16,806 genes and the Bonferroni-adjusted genome-wide significance level at 3.0E−6. The specific QC procedure was described previously (Crosby et al, 2014;Wei et al, 2016). For the aSPU-meta test, we calculated the p values by using a stepwise procedure.…”