Biology subjects

Holleman, A. M.

Publications and source records attributed to Holleman, A. M..

2 recordsLinked to original sources

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↗

bioRxiv2018

Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies

Genetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom data from questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate questionnaire data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only associated with a subset of collapsed symptoms, the effect will be challenging to detect using the univariate outcome. We describe a more powerful strategy for genetic association testing in this situation that jointly analyzes the original multivariate symptom data collectively using a statistical framework that compares similarity in multivariate symptom-scale data from questionnaires to similarity in common genetic variants across a gene. We use simulated data to demonstrate this strategy provides substantially increased power over standard approaches that collapse questionnaire data into a single surrogate outcome. We also illustrate our approach using GWAS data from the Grady Trauma Project and identify genes associated with BDI not identified using standard univariate techniques. The approach is computationally efficient, scales to genome-wide studies, and is applicable to correlated symptom data of arbitrary dimension (thereby aligning with National Institute of Mental Healths Research Domain Criteria).

genetics↗