SNPs with High Linkage Disequilibrium Increase the Explained Genetic Variance and the Reliability of Genomic Predictions

The objective of this study was to compare the proportion of explained genetic variance (EXGV) and the reliability of genomic breeding values (GBVs) predictions for milk yield (MY), fat yield (FY), protein yield (PY) fat percentage (FP), protein percentage (PP), and somatic cell score (SCS) in Holstein cattle. Three types of genomic information were evaluated. (a) SNP-ALL: this analysis included 88,911 single nucleotide polymorphisms (SNP) from 8290 animals. (b) HAP-PSEUDOSNP: haplotypes, defined based on high linkage disequilibrium (LD, r² ≥ 0.80) between SNPs, which were encoded as pseudo-SNPs, with a total of 35,552 pseudo-SNPs and 8331 animals included. (c) SNP-HAP: analysis using only individual SNPs included in the haplotypes (without recoding); for this analysis, 33,010 SNPs and 8192 individuals were retained. All analyses were conducted using the single-step genome-wide association study method implemented in the BLUPF90 software package. The results showed that the inclusion of SNPs with high LD (SNP-HAP) increases the reliability of GBVs' predictions compared to the SNP-ALL analysis; average reliability increased between 0.05 and 0.11. Moreover, the SNP-HAP analysis resulted in a twofold increase in the EXGV for all traits, likely due to increased estimates of individual marker effects compared to the SNP-ALL analysis.