Kernel-based whole-genome prediction of complex traits: A review

Morota Gota, Daniel Gianola

Research output: Contribution to journalReview article

57 Scopus citations

Abstract

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

Original languageEnglish (US)
Article number363
JournalFrontiers in Genetics
Volume5
Issue numberOCT
DOIs
StatePublished - Jan 1 2014

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Keywords

  • Kernel methods
  • SNP
  • Semi-parametric regression
  • Spatial distance
  • Whole-genome prediction

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)

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