Adaptive Set-Based Methods for Association Testing

Abstract

With a typical sample size of a few thousand subjects, a single genomewide association study (GWAS) using traditional one-SNP-at-a-time methods can only detect genetic variants conferring a sizable effect on disease risk. Set-based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. While self-contained set-based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set-based approaches (those that rely on comparison of variants in the set of interest with variants not in the set), there is no consensus as to which self-contained methods are best. In particular, several self-contained set tests have been proposed to directly or indirectly ‘adapt’ to the a priori unknown proportion and distribution of effects of the truly associated SNPs in the set, which is a major determinant of their power. A popular adaptive set-based test is the adaptive rank truncated product (ARTP), which seeks the set of SNPs that yields the best-combined evidence of association. We compared the standard ARTP, several ARTP variations we introduced, and other adaptive methods in a comprehensive simulation study to evaluate their performance. We used permutations to assess significance for all the methods and thus provide a level playing field for comparison. We found the standard ARTP test to have the highest power across our simulations followed closely by the global model of random effects (GMRE) and a LASSO based test.

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics

“…FLAGS showed consistent high performance over a wide range of disease models. The robustness of FLAGS is consistent with a recent study that found the adaptive rank truncated product (ARTP) to perform best in a comparison of a number of set-based association tests (Su et al 2015). The ARTP adaptively seeks a subset of SNPs that yields the best evidence for genetic association.…”

TFisher Tests: Optimal and Adaptive Thresholding for Combining $p$-Values

“…Finally we compare the power of three omnibus tests: oTFisher with soft-thresholding, the adaptive TPM (ATPM, hard-thresholding), and the adaptive RTP (ARTP). ARTP was shown to have the highest power among a group of adaptive set-based methods for genetic association testing (Su et al, 2016;Yu et al, 2009). The Supplementary Figures S1 and S2 in the Supplementary Materials illustrate the power of the optimal TFisher and the three omnibus tests under the same settings as Figures 11 and 12, respectively.…”

“…The p-value combination methods have been widely used for genetic association studies, but most of them were based on hard-thresholding, including TPM and RTP methods (Biernacka et al, 2012;Dai et al, 2014;Dudbridge and Koeleman, 2003;Hoh et al, 2001;Li and Tseng, 2011;Su et al, 2016;Yu et al, 2009). In this section we apply and assess the soft-thresholding TFisher by analyzing a whole exome sequencing data of amyotrophic lateral sclerosis (ALS).…”

“…One example is in the meta-analysis of differential gene expression, where the positive outcomes could happen in one or some of the studies only (Song and Tseng, 2014). Another example is in detecting genetic associations for a group of genetic markers, where some of these markers are associated but some others are not (Hoh et al, 2001;Su et al, 2016). In fact, it has been shown that when true signals are in a very small proportion, e.g., at the level of n −α with α ∈ (3/4, 1), an optimal choice is to simply use the minimal p-value as the statistic (Tippert, 1931).…”

Speeding up Monte Carlo simulations for the adaptive sum of powered score test with importance sampling

“…Since there is no uniformly most powerful test, it is desirable to apply an adaptive test such that high power can be maintained against various alternatives (e.g. Chen et al 2010;Lee et al 2012;Zhang et al 2014;Su et al 2015;Huang et al 2016;Su et al 2017;Ma and Wei 2019;Yang et al 2019), most of which require Monte Carlo methods to calculate their p-values. Pan et al (2014) proposed such a test, called the adaptive sum of powered score (aSPU) test, based on combining a family of so-called sum of powered score (SPU) tests, which cover some existing tests, such as the Sum (or burden) test, the sum of squared score test (Pan 2009)…”