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

## TL;DR

Using SNPs with high linkage disequilibrium improves genetic variance explanation and prediction reliability in Holstein cattle traits.

## Contribution

This study demonstrates that SNPs with high linkage disequilibrium enhance genomic prediction reliability and genetic variance explanation in cattle.

## Key findings

- Using SNPs with high linkage disequilibrium increased genomic prediction reliability by 0.05 to 0.11 on average.
- SNPs with high linkage disequilibrium doubled the explained genetic variance across all traits.
- Haplotype-based SNPs showed larger individual marker effect estimates compared to general SNPs.

## Abstract

This study aimed to evaluate and compare the proportion of explained genetic variance and the reliability of genomic breeding value predictions for six traits in Holstein cattle: milk yield, fat yield, protein yield, fat percentage, protein percentage, and somatic cell score. Three types of genomic information were tested. The first approach included 88,911 single nucleotide polymorphisms from 8290 animals. The second approach used haplotypes defined by strong linkage disequilibrium (r2 ≥ 0.80), encoded as pseudo-SNPs, with a total of 35,552 pseudo-SNPs from 8331 animals. The third approach analyzed only the SNPs forming haplotypes, resulting in 33,010 SNPs from 8192 animals. All analyses were performed using the single-step genome-wide association study method implemented in BLUPF90. The findings revealed that using single nucleotide polymorphisms with high linkage disequilibrium improved the reliability of genomic breeding value predictions compared with the use of single nucleotide polymorphisms in general, with average increases ranging from 0.05 to 0.11. Furthermore, analysis using single nucleotide polymorphisms with high linkage disequilibrium doubled the explained genetic variance across all traits, likely due to larger estimates of individual marker effects. Overall, the study highlights the advantages of haplotype-based information for improving prediction reliability and explaining genetic variance in Holstein cattle.

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.