Work

A review of kernel methods for genetic association studies

Year: 2019

Type: review

Abstract: Evaluating the association of multiple genetic variants with a trait of interest by use of kernel-based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.

Source: Genetic Epidemiology

DOI: 10.1002/gepi.22180

Authors: Nicholas B. Larson, Jun Chen, Daniel J. Schaid

Institution: Mayo Clinic

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Mayo Clinic, Center for Individualized Medicine, Mayo Clinic, National Institute of General Medical Sciences

Award: Quantitative Methods for Genetic Epidemiology