Abstract
Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype–phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summary p value are a robust approach to different genetic architectures when little a priori knowledge is available about the underlying genetic disease model. To date, however, little consideration has been given to combining gene-based tests of association for the analysis of pedigree data. We propose a flexible framework for combining any number of family-based rare-variant tests of association into a single summary statistic and for assessing the significance of that statistic. We show that this approach maintains type I error and improves the robustness, to different genetic architectures, of the statistical power of family- and gene-based rare-variant tests through application to simulated phenotype data from Genetic Analysis Workshop 19.
Highlights
Over the past decade, the rapid decrease in costs for DNA sequence data have made it possible to consider the association of rare single nucleotide variants (SNVs) and complex disease phenotypes
Our approach, which extends the approach by Derkach et al [5] to the case of family-based data, combines p values across a variety of family-based gene-level rarevariant tests of association into a single summary statistic
We considered the continuous response variable mean arterial pressure (MAP), calculated as (2/3)*(diastolic blood pressure) + (1/3)*(systolic blood pressure)
Summary
The rapid decrease in costs for DNA sequence data have made it possible to consider the association of rare single nucleotide variants (SNVs) and complex disease phenotypes. Many gene-based tests of association (simultaneously testing all variants within a gene) have recently been proposed, with the intent to improve statistical power over single marker association tests. Despite improvements over single-marker approaches, gene-based tests may still have limited utility in detecting causal rare variants because of an overall lack of power. This lack of power is exacerbated by the fact that optimizing the limited power of gene-based tests of. Burden tests are most powerful when the variants within a SNV set are mostly causal and have the same causal direction, whereas variance component tests perform optimally in circumstances where the causal direction varies (ie, in the presence of both risk-reducing and riskincreasing SNVs) [1], even for family data [2]
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