Abstract
Genome wide association studies have been usually analyzed in a univariate manner. The commonly used univariate tests have one degree of freedom and assume an additive mode of inheritance. The experiment-wise significance of these univariate statistics is obtained by adjusting for multiple testing. Next generation sequencing studies, which assay 10-20 million variants, are beginning to come online. For these studies, the strategy of additive univariate testing and multiple testing adjustment is likely to result in a loss of power due to (1) the substantial multiple testing burden and (2) the possibility of a non-additive causal mode of inheritance. To reduce the power loss we propose: a new method (1) to summarize in a single statistic the strength of the association signals coming from all not-very-rare variants in a linkage disequilibrium block and (2) to incorporate, in any linkage disequilibrium block statistic, the strength of the association signals under multiple modes of inheritance. The proposed linkage disequilibrium block test consists of the sum of squares of nominally significant univariate statistics. We compare the performance of this method to the performance of existing linkage disequilibrium block/gene-based methods. Simulations show that (1) extending methods to combine testing for multiple modes of inheritance leads to substantial power gains, especially for a recessive mode of inheritance, and (2) the proposed method has a good overall performance. Based on simulation results, we provide practical advice on choosing suitable methods for applied analyses.
Highlights
Genome-wide association studies (GWASs) have been broadly used to test for association between genetic variants and various phenotypes. These studies have been quite successful in identifying numerous single nucleotide polymorphisms (SNPs) associated with a variety of human traits and diseases [1]
VminP might suffer some statistical power loss by using only the most significant statistic. To avoid such a loss of power we propose a new statistical test consisting of the sum of squared statistics exceeding a threshold, i.e.∑mj=1 Z2A, jI
For Experiment I, under the null hypothesis some tests (VSS, V-minimum p-value (minP), sum of square above a threshold (SS-T) and aSUM) using Monte-Carlo simulation/ permutation to assess the significance of the test statistic seem to have slightly inflated size of the test (Figure S1)
Summary
Genome-wide association studies (GWASs) have been broadly used to test for association between genetic variants and various phenotypes. The experiment-wise significance of the univariate statistics was assessed using a Bonferroni adjustment [5,6] or a permutation procedure [7,8,9] While this approach was reasonably successful for GWAS, the field is moving away from this paradigm towards whole genome sequencing. When compared to GWAS, variant panels for sequencing studies (1) are substantially denser and (2) have different patterns of linkage disequilibrium (LD). For these studies it is not clear if the most desirable approach is still a univariate testing for an additive mode of inheritance followed by the adjustment of the statistics for the large number of tests
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