Adjusting Family Relatedness in Data-driven Burden Test of Rare Variants

Abstract

Family data represents a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM) and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control of type I errors. We compare WSMM with a non-data-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.

A rare variant association test in family‐based designs and non‐normal quantitative traits

“…However, Tables and indicate that VC‐C2 and VC‐C3 are more powerful than ASKAT‐Normalized in all settings when , and the gains in power can be quite substantial. These results are in accordance with the findings of Chen et al and Zhang et al .…”

Permutation Testing in the Presence of Polygenic Variation

“…Once independent, any sets of dependent or independent genotypes, including permutations of the original ones, can be used to recover the correct null distribution of the test statistic. This approach has been used in mouse cross data to obtain proper genomewide significance levels [Cheng et al, 2010; Cheng and Palmer, 2013], and more recently in humans [Zhang et al, 2014]. It seems likely that any test statistic that removes the correlation in the phenotype data and does not depend on Mendelian segregation under the null hypothesis, would allow genotype permutations to be valid, though I have not investigated this further.…”

On Efficient and Accurate Calculation of Significance P‐Values for Sequence Kernel Association Testing of Variant Set

“…The sequence kernel association test (SKAT) is based on the variance component score test and works well under various combinations of protective and deleterious variants (Wu et al., ; Neale et al., ; Wu et al., ; Lee et al., ). SKAT has been extended to various outcomes and study designs (see, for example, Barnett et al., ; Chen et al., ; Oualkacha et al., ; Schaid et al., ; Chen et al., ; Zhang et al., ). A more flexible approach is SKAT‐O, which adaptively combines the burden and the SKAT statistics (Lee et al., ).…”