Abstract
Multi-marker association tests can be more powerful than single-locus analyses because they aggregate the variant information within a gene/region. However, combining the association signals of multiple markers within a gene/region may cause noise due to the inclusion of neutral variants, which usually compromises the power of a test. To reduce noise, the “adaptive combination of P-values” (ADA) method removes variants with larger P-values. However, when both rare and common variants are considered, it is not optimal to truncate variants according to their P-values. An alternative summary measure, the Bayes factor (BF), is defined as the ratio of the probability of the data under the alternative hypothesis to that under the null hypothesis. The BF quantifies the “relative” evidence supporting the alternative hypothesis. Here, we propose an “adaptive combination of Bayes factors” (ADABF) method that can be directly applied to variants with a wide spectrum of minor allele frequencies. The simulations show that ADABF is more powerful than single-nucleotide polymorphism (SNP)-set kernel association tests and burden tests. We also analyzed 1,109 case-parent trios from the Schizophrenia Trio Genomic Research in Taiwan. Three genes on chromosome 19p13.2 were found to be associated with schizophrenia at the suggestive significance level of 5 × 10−5.
Highlights
Multi-marker association tests can be more powerful than single-locus analyses because these tests combine variant information within a gene/region
We propose the adaptive combination of Bayes factors” (ADABF) method, which is based on the concept of VT, but we assume that a certain unknown Bayes factor (BF) threshold exists, and variants with BFs larger than this threshold are more likely to be disease-associated
By performing extensive simulations with case-parent trios and unrelated case-control data, we find that our ADABF test is valid because the type I error rates match the nominal significance levels
Summary
Multi-marker association tests can be more powerful than single-locus analyses because these tests combine variant information within a gene/region. Combining the association signals of multiple markers within a gene/region may cause noise due to the inclusion of neutral variants, which usually compromises the power of a multi-marker association test. The ADA method was originally proposed for rare-variant association testing[2]. In genome-wide association studies (GWAS), the power to detect disease-associated single-nucleotide polymorphisms (SNPs) varies with MAFs. In this work, we show that truncating variants according to P-values is not optimal, when both rare and common variants are considered (see the subsection “Ranking by Bayes factor vs P-value”). In RC-ADA8, rare variants and common variants are weighted according to Beta(MAF;1,25) and Beta(MAF;0.5,0.5)[4], respectively, where MAF is the MAF of the considered SNP.
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