Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction

Abstract

This study investigated the effect on the reliability of genomic prediction when a small number of significant variants from single marker analysis based on whole genome sequence data were added to the regular 54k single nucleotide polymorphism (SNP) array data. The extra markers were selected with the aim of augmenting the custom low-density Illumina BovineLD SNP chip (San Diego, CA) used in the Nordic countries. The single-marker analysis was done breed-wise on all 16 index traits included in the breeding goals for Nordic Holstein, Danish Jersey, and Nordic Red cattle plus the total merit index itself. Depending on the trait's economic weight, 15, 10, or 5 quantitative trait loci (QTL) were selected per trait per breed and 3 to 5 markers were selected to tag each QTL. After removing duplicate markers (same marker selected for more than one trait or breed) and filtering for high pairwise linkage disequilibrium and assaying performance on the array, a total of 1,623 QTL markers were selected for inclusion on the custom chip. Genomic prediction analyses were performed for Nordic and French Holstein and Nordic Red animals using either a genomic BLUP or a Bayesian variable selection model. When using the genomic BLUP model including the QTL markers in the analysis, reliability was increased by up to 4 percentage points for production traits in Nordic Holstein animals, up to 3 percentage points for Nordic Reds, and up to 5 percentage points for French Holstein. Smaller gains of up to 1 percentage point was observed for mastitis, but only a 0.5 percentage point increase was seen for fertility. When using a Bayesian model accuracies were generally higher with only 54k data compared with the genomic BLUP approach, but increases in reliability were relatively smaller when QTL markers were included. Results from this study indicate that the reliability of genomic prediction can be increased by including markers significant in genome-wide association studies on whole genome sequence data alongside the 54k SNP set.

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

“…However, as has been shown by BrØndum et al (2015), a change in accuracy may be limited to a 1% to 5% increase. MacLeod et al (2014) showed that a benefit from the use of sequence data mainly can be achieved in populations with a large effective population size and/or a comparatively low level of LD while in populations like the Holstein population, an increase of accuracy for genomic breeding values may only be very small.…”

“…Usually, imputation serves to assign the genotype in case single SNP alleles have not been called for during the technical genotyping process and thus are missing. Additionally, the imputation from genotypes at low-density chips to genotypes at high-density chips Erbe et al, 2012) or recently from high-density chips to whole genome information (BrØndum et al, 2015) has become of interest. Based on the existence of high LD between close markers, a genotyped marker allows for inference of the genotype at a nearby ungenotyped locus.…”

Review: How to improve genomic predictions in small dairy cattle populations

“…Consequently, the associations may increase further to reach their full potential to improve genomic predictions. The validity of this approach has been demonstrated within breed by Brøndum et al (2015). Here, 1623 QTL markers were selected based on GWAS results on WGS data within Nordic Holstein, Jersey and Red with the aim of including them on a custom low density chip.…”

Genome-wide association study for longevity with whole-genome sequencing in 3 cattle breeds

“…Identification of genomic regions associated with longevity may help in identifying genes and causal mutations that influence this trait. This information could be included in the genomic selection model for better prediction of its breeding values (Brøndum et al, 2015;Wientjes et al, 2015).…”