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

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.

Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation

“…3 b). This may be the reason why previous studies found that genomic prediction accuracy and heritability estimates decreased as marker density increased [ 18 , 19 ]. Similarly, the relative weights of SNPs in high LD regions of the high-density panel are larger than those of the medium-density panel.…”

“…It is generally assumed that the greater is the genetic variance (or heritability) explained by the model, the higher is the genomic prediction accuracy [ 20 ]. However, in the literature, there are exceptions to this relationship and for example, Ren et al [ 18 ] reported estimates of heritability that increased while the genomic prediction accuracies remained unchanged or even decreased. By performing a joint analysis of the performance of each model in terms of genome prediction and heritability estimation, we found that more the heritability estimates were unbiased, the higher was the genomic prediction accuracy (Figs.…”

“…Previous studies have shown that the genomic prediction accuracy of medium-density panels (20 to 50K) with SNPs evenly distributed throughout the genome is higher than that of high-density SNP panels [ 18 ]. In the simulation study, we constructed a medium-density panel (50K) using evenly distributed SNPs and compared the medium- and high-density panels in terms of genomic prediction and heritability estimation.…”

“…Compared with SNP chip data (i.e., medium-density SNP data), high-density SNP data provide more information, but how to use this information effectively remains a challenge. Recent studies have found that, compared with medium-density SNP data, the use of high-density SNP data has no advantage or even results in a decline in genomic prediction [ 7 , 18 , 19 ]. The GRM constructed in the classical genomic prediction model can accurately capture the relationship between individuals using medium-density markers.…”

“…However, studies have shown that the variation of the estimates of heritability is not consistent with the variation of genomic prediction accuracy, i.e. the genomic prediction accuracy does not necessarily increase or it even decreases as the estimates of heritability increase [ 18 ]. This makes the results of many methods difficult to understand [ 20 – 22 ].…”