Todor, A. | bioRxiv Science

bioRxiv2021

A Kernel Method for Dissecting Genetic Signals in Tests of High-Dimensional Phenotypes

Genomewide association studies increasingly employ multivariate tests of multiple correlated phenotypes to exploit likely pleiotropy to improve power. Typical multivariate methods produce a global p-value of association between a variant (or set of variants) and multiple phenotypes. When the global test is significant, subsequent interest then focuses on dissecting the signal and, in particular, delineating the set of phenotypes where the genetic variant(s) have a direct effect from the remaining phenotypes where the genetic variant(s) possess either indirect or no effect. While existing techniques like mediation models can be utilized for this purpose, they generally cannot handle high-dimensional phenotypic and genotypic data. To assist in filling this important gap, we propose a modification of a kernel distance-covariance framework for gene mapping of multiple variants with multiple phenotypes to test instead whether the association between the variants and a group of phenotypes is driven through a direct association with just a subset of the phenotypes. We use simulated data to show that our new method controls for type I error and is powerful to detect a variety of models demonstrating different patterns of direct and indirect effects. We further illustrate our method using GWAS data from the Grady Trauma Project and show that an existing signal between genetic variants in the ZHX2 gene and 21 items within the Beck Depression Inventory appears to be due to a direct effect of these variants on only 3 of these items. Our approach scales to genomewide analysis, and is applicable to high-dimensional correlated phenotypes.

genomics↗

bioRxiv2019

Leveraging Family History in Case-Control Analyses of Rare Variation

Standard methods for case-control association studies of rare variation often treat disease outcome as a dichotomous phenotype. However, both theoretical and experimental studies have demonstrated that subjects with a family history of disease can be enriched for risk variation relative to subjects without such history. Assuming family history information is available, this observation motivates the idea of replacing the standard dichotomous outcome variable used in case-control studies with a more informative ordinal outcome variable that distinguishes controls (0), sporadic cases (1), and cases with a family history (2), with the expectation that we should observe increasing number of risk variants with increasing category of the ordinal variable. To leverage this expectation, we propose a novel rare-variant association test that incorporates family history information based on our previous GAMuT framework (Broadaway et al., 2016) for rare-variant association testing of multivariate phenotypes. We use simulated data to show that, when family history information is available, our new method outperforms standard rare-variant association methods like burden and SKAT tests that ignore family history. We further illustrate our method using a rare-variant study of cleft lip and palate.

genetics↗