A review of kernel methods for genetic association studies

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)122-136
Number of pages15
JournalGenetic epidemiology
Issue number2
StatePublished - Mar 2019


  • genetic association analysis
  • kernel statistic
  • mixed model
  • multivariate
  • pedigree data

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)


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