Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits.

ABSTRACT:

Background

Genomic prediction encompasses the techniques used in agricultural technology to predict the genetic merit of individuals towards valuable phenotypic traits. It is related to Genome Interpretation in humans, which models the individual risk of developing disease traits. Genomic prediction is dominated by linear mixed models, such as the Genomic Best Linear Unbiased Prediction (GBLUP), which computes kinship matrices from SNP array data, while Genome Interpretation applications to clinical genetics rely mainly on Polygenic Risk Scores.

Results

In this article, we exploit the positive semidefinite characteristics of the kinship matrices that are conventionally used in GBLUP to propose a novel Genomic Multiple Kernel Learning method (GMKL), in which the multiple kins

SUBMITTER: Raimondi D 

PROVIDER: S-EPMC11969835 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature