Covariance Association Test (CVAT) Identifies Genetic Markers Associated with Schizophrenia in Functionally Associated Biological Processes

Abstract

Schizophrenia is a psychiatric disorder with large personal and social costs, and understanding the genetic etiology is important. Such knowledge can be obtained by testing the association between a disease phenotype and individual genetic markers; however, such single-marker methods have limited power to detect genetic markers with small effects. Instead, aggregating genetic markers based on biological information might increase the power to identify sets of genetic markers of etiological significance. Several set test methods have been proposed: Here we propose a new set test derived from genomic best linear unbiased prediction (GBLUP), the covariance association test (CVAT). We compared the performance of CVAT to other commonly used set tests. The comparison was conducted using a simulated study population having the same genetic parameters as for schizophrenia. We found that CVAT was among the top performers. When extending CVAT to utilize a mixture of SNP effects, we found an increase in power to detect the causal sets. Applying the methods to a Danish schizophrenia case-control data set, we found genomic evidence for association of schizophrenia with vitamin A metabolism and immunological responses, which previously have been implicated with schizophrenia based on experimental and observational studies.

Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle

“…This approach is more likely to match the genetic architecture underlying complex phenotypes, whereby genetic variation is governed by many loci with small effects. Our previous studies4445 using simulations have shown that the performance of this procedure is better or similar to other approaches ( e.g ., count or score-based) in most scenarios, and the number of false positives could be effectively controlled when the following criteria are met: 1) the average number of markers in each gene is approximately the same among the genomic features, and 2) the average linkage disequilibrium (LD) between markers in different genes is approximately the same4445.…”

“…A commonly used approach is count-based; that is, to compare the proportion of associations over a certain pre-defined significance threshold in the genomic feature to the proportion of such associations in the remaining genome414243. One major limitation of this type approach is the dichotomization of associations into significant and non-significant groups based on a pre-specified significance cut-off, which ignores information about the strength of association4445. Our post-GWAS approach assessed the enrichment of association signals in a genomic feature by comparing the sum of squared single marker test statistics ( i.e ., t 2 ) within the region to an empirically derived distribution under a competitive null hypothesis.…”

“…The approach applied to test the association between a phenotype and a genomic feature has been described previously4445. Briefly, the observed test statistic ( e.g ., t 2 ) was first ranked based on the chromosome position of the markers, and a test statistic was then randomly chosen from this vector.…”

Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior

“…The genomic analyses included a genome-wide association (GWA) using single marker regression to assess the contribution of individual polymorphic markers on aggressive behavior in the two environments, and for the difference in aggressiveness between socialized and socially isolated individuals (GSEI). The small sample size of the DGRP, expected small effects of individual genetic markers, and the large number of tests to be performed with sequence data results in limited statistical power to detect true associated genetic variants (Hirschhorn and Daly 2005;McCarthy et al 2008;Wang et al 2010aWang et al , 2011Fridley and Biernacka 2011;Rohde et al 2016a). Methods that combine the signals from multiple genetic markers, including set-test approaches (SKAT (Wu et al 2011) and CVAT (Rohde et al 2016a)) and genomic prediction models (GBLUP (Meuwissen et al 2001)), may better capture the signal from numerous genetic markers with small effect sizes.…”

“…The small sample size of the DGRP, expected small effects of individual genetic markers, and the large number of tests to be performed with sequence data results in limited statistical power to detect true associated genetic variants (Hirschhorn and Daly 2005;McCarthy et al 2008;Wang et al 2010aWang et al , 2011Fridley and Biernacka 2011;Rohde et al 2016a). Methods that combine the signals from multiple genetic markers, including set-test approaches (SKAT (Wu et al 2011) and CVAT (Rohde et al 2016a)) and genomic prediction models (GBLUP (Meuwissen et al 2001)), may better capture the signal from numerous genetic markers with small effect sizes. Extending GBLUP by fitting multiple genetic components has been shown to increase predictive ability (Speed and Balding 2014;Tucker et al 2015).…”