A Weighted Genomic Relationship Matrix Based on Fixation Index (FST) Prioritized SNPs for Genomic Selection

Abstract

A dramatic increase in the density of marker panels has been expected to increase the accuracy of genomic selection (GS), unfortunately, little to no improvement has been observed. By including all variants in the association model, the dimensionality of the problem should be dramatically increased, and it could undoubtedly reduce the statistical power. Using all Single nucleotide polymorphisms (SNPs) to compute the genomic relationship matrix (G) does not necessarily increase accuracy as the additive relationships can be accurately estimated using a much smaller number of markers. Due to these limitations, variant prioritization has become a necessity to improve accuracy. The fixation index (F_ST) as a measure of population differentiation has been used to identify genome segments and variants under selection pressure. Using prioritized variants has increased the accuracy of GS. Additionally, F_ST can be used to weight the relative contribution of prioritized SNPs in computing G. In this study, relative weights based on F_ST scores were developed and incorporated into the calculation of G and their impact on the estimation of variance components and accuracy was assessed. The results showed that prioritizing SNPs based on their F_ST scores resulted in an increase in the genetic similarity between training and validation animals and improved the accuracy of GS by more than 5%.

Keywords: accuracy; genomic selection; high density; sequence data.

Figure 1

Accuracy of genomic prediction for different weighting scenarios for the contribution of the 20K prioritized SNPs and the remaining 380K markers (x,y). Horizontal lines indicate the accuracy using only the top 20K SNPs with (red) or without (green) weights SNPs.