Using residual regressions to quantify and map signal leakage in genomic prediction.

ABSTRACT:

Background

Most genomic prediction applications in animal breeding use genotypes with tens of thousands of single nucleotide polymorphisms (SNPs). However, modern sequencing technologies and imputation algorithms can generate ultra-high-density genotypes (including millions of SNPs) at an affordable cost. Empirical studies have not produced clear evidence that using ultra-high-density genotypes can significantly improve prediction accuracy. However, (whole-genome) prediction accuracy is not very informative about the ability of a model to capture the genetic signals from specific genomic regions. To address this problem, we propose a simple methodology that detects chromosome regions for which a specific model (e.g., single-step genomic best linear unbiased prediction (ssGBLUP)) ma

SUBMITTER: Valente BD 

PROVIDER: S-EPMC10405418 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature