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

文献类型： 外文期刊

作者： Ren, Duanyang 1 ; Teng, Jinyan 1 ; Diao, Shuqi 1 ; Lin, Qing 1 ; Li, Jiaqi 1 ; Zhang, Zhe 1 ;

作者机构： 1.South China Agr Univ, Coll Anim Sci, Guangdong Lab Lingnan Modern Agr, Guangdong Prov Key Lab Agroanim Genom & Mol Breed, Guangzhou 510642, Peoples R China

关键词： genomic prediction; marker density; genetic distance; physical distance; Holstein dairy cattle; high-density SNP data

期刊名称：ANIMALS （ 影响因子：2.752； 五年影响因子：2.942 ）

ISSN： 2076-2615

年卷期： 2021 年 11 卷 7 期

页码：

收录情况： SCI

摘要： 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) over bar) and variance (sigma(2)(d)) of the physical distance between SNPs and the mean (r (2) over bar) and variance (sigma(2)(r2)) 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 sigma(2)(d), r((2) over bar) and sigma(2)(r2) are different. Therefore, we only investigated the effects of sigma(2)(d), r((2) over bar) and sigma(2)(r2) on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with sigma(2)(d), but not with r((2) over bar) and sigma(2)(r2). Compared with GenD and RanD, the sigma(2)(d) of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large sigma(2)(d), which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8 similar to 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.

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