Impact of Marker Pruning Strategies Based on Different Measurements of Marker Distance on Genomic Prediction in Dairy Cattle

Simple Summary

The usefulness of genomic prediction (GP) has been widely proofed by breeding analysis in livestock, plants and aquatic populations. It is well known that ‘marker density’ is a critical factor that affects the accuracy of GP, however, how to properly measure ‘marker density’ in GP is yet to be determined. With population-level whole-genome sequence data or high-density single nucleotide polymorphism (SNP) data available, this question seems to be answered more convincingly. In this study, we investigated and discussed the impact of four ‘marker density’ measures that reflect genetic or physical distances between SNPs on the accuracy of GP in a Germany Holstein dairy cattle population. Our results showed that the degree of variation of physical distance between adjacent SNPs had significant effects on the accuracy of GP, while the genetic distance between SNPs had no relationship with the accuracy of GP. Therefore, for studies based on high-density SNP data, the default strategy of pruning SNPs based on genetic distance is detrimental to heritability estimation and genomic prediction. The results extended the communities knowledge of ‘marker density’ and provided useful suggestions for the application and research on genome prediction.

Abstract

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of ‘marker density’ seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of ‘marker density’ based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean (d¯) and variance (σd2) of the physical distance between SNPs and the mean (r2¯) and variance (σr22) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d¯ of all panels is basically the same, but the σd2, r2¯ and σr22 are different. Therefore, we only investigated the effects of σd2, r2¯ and σr22 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σd2, but not with r2¯ and σr22. Compared with GenD and RanD, the σd2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large σd2, which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20–30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data.

Genome-wide association analysis and gene set enrichment analysis with SNP data identify genes associated with 305-day milk yield in Holstein dairy cows

Milk production traits, such as 305-day milk yield (305MY), have been under direct selection to improve production in dairy cows. Over the past 50 years, the average milk yield has nearly doubled, and over 56% of the increase is attributable to genetic improvement. As such, additional improvements in milk yield are still possible as new loci are identified. The objectives of this study were to detect SNPs and gene sets associated with 305MY in order to identify new candidate genes contributing to variation in milk production. A population of 781 primiparous Holstein cows from six central Washington dairies with records of 305MY and energy corrected milk were used to perform a genome-wide association analysis (GWAA) using the Illumina BovineHD BeadChip (777 962 SNPs) to identify QTL associated with 305MY (P < 1.0 × 10^-5 ). A gene set enrichment analysis with SNP data (GSEA-SNP) was performed to identify gene sets (normalized enrichment score > 3.0) and leading edge genes (LEGs) influencing 305MY. The GWAA identified three QTL comprising 34 SNPs and 30 positional candidate genes. In the GSEA-SNP, five gene sets with 58 unique and 24 shared LEGs contributed to 305MY. Identification of QTL and LEGs associated with 305MY can provide additional targets for genomic selection to continue to improve 305MY in dairy cattle.

Influenza D Virus in Animal Species in Guangdong Province, Southern China

Molecular tests revealed influenza D viruses of D/OK lineage widely circulating in farmed animal species in Guangdong Province, southern China. In particular, we found high levels of influenza D virus infection in goats and pigs. We also detected viral RNA in serum specimens and feces of animals with certain severe diseases.