Using Genetic Distance to Infer the Accuracy of Genomic Prediction

TL;DR

This paper investigates how genetic distance affects the accuracy of genomic predictions, revealing a roughly linear decay in prediction correlation as genetic divergence increases, with implications for breeding and medical applications.

Contribution

It introduces a clustering and resampling approach to quantify how predictive accuracy declines with genetic distance, aiding in optimal training population selection.

Findings

Prediction accuracy decays linearly with genetic distance measures.

Simulation and real data confirm the linear decay relationship.

Guidelines for recalibrating models based on genetic divergence.

Abstract

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either $\F$ or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.