Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes

作者：Bader Arouisse, Tom P. J. M. Theeuwen, Fred A. van Eeuwijk, Willem Kruijer · 发表于：Frontiers in Genetics · 年份：2021 · DOI：10.3389/fgene.2021.667358 · 被引用次数：16 · 研究领域：Genetic and phenotypic traits in livestock、Genetic Mapping and Diversity in Plants and Animals、Genetics and Plant Breeding

In the past decades, genomic prediction has had a large impact on plant breeding. Given the current advances of high-throughput phenotyping and sequencing technologies, it is increasingly common to observe a large number of traits, in addition to the target trait of interest. This raises the important question whether these additional or "secondary" traits can be used to improve genomic prediction for the target trait. With only a small number of secondary traits, this is known to be the case, given sufficiently high heritabilities and genetic correlations. Here we focus on the more challenging situation with a large number of secondary traits, which is increasingly common since the arrival of high-throughput phenotyping. In this case, secondary traits are usually incorporated through additional relatedness matrices. This approach is however infeasible when secondary traits are not measured on the test set, and cannot distinguish between genetic and non-genetic correlations. An alternative direction is to extend the classical selection indices using penalized regression. So far, penalized selection indices have not been applied in a genomic prediction setting, and require plot-level data in order to reliably estimate genetic correlations. Here we aim to overcome these limitations, using two novel approaches. Our first approach relies on a dimension reduction of the secondary traits, using either penalized regression or random forests (LS-BLUP/RF-BLUP). We then compute the biv...