Brøndum R F, Su G, Janss L, Sahana G, Guldbrandtsen B, Boichard D, Lund M S
Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Blichers Allé 20, Aarhus University, DK-8830 Tjele, Denmark.
Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Blichers Allé 20, Aarhus University, DK-8830 Tjele, Denmark.
J Dairy Sci. 2015 Jun;98(6):4107-16. doi: 10.3168/jds.2014-9005. Epub 2015 Apr 16.
This study investigated the effect on the reliability of genomic prediction when a small number of significant variants from single marker analysis based on whole genome sequence data were added to the regular 54k single nucleotide polymorphism (SNP) array data. The extra markers were selected with the aim of augmenting the custom low-density Illumina BovineLD SNP chip (San Diego, CA) used in the Nordic countries. The single-marker analysis was done breed-wise on all 16 index traits included in the breeding goals for Nordic Holstein, Danish Jersey, and Nordic Red cattle plus the total merit index itself. Depending on the trait's economic weight, 15, 10, or 5 quantitative trait loci (QTL) were selected per trait per breed and 3 to 5 markers were selected to tag each QTL. After removing duplicate markers (same marker selected for more than one trait or breed) and filtering for high pairwise linkage disequilibrium and assaying performance on the array, a total of 1,623 QTL markers were selected for inclusion on the custom chip. Genomic prediction analyses were performed for Nordic and French Holstein and Nordic Red animals using either a genomic BLUP or a Bayesian variable selection model. When using the genomic BLUP model including the QTL markers in the analysis, reliability was increased by up to 4 percentage points for production traits in Nordic Holstein animals, up to 3 percentage points for Nordic Reds, and up to 5 percentage points for French Holstein. Smaller gains of up to 1 percentage point was observed for mastitis, but only a 0.5 percentage point increase was seen for fertility. When using a Bayesian model accuracies were generally higher with only 54k data compared with the genomic BLUP approach, but increases in reliability were relatively smaller when QTL markers were included. Results from this study indicate that the reliability of genomic prediction can be increased by including markers significant in genome-wide association studies on whole genome sequence data alongside the 54k SNP set.
本研究调查了将基于全基因组序列数据的单标记分析中的少量显著变异添加到常规的54k单核苷酸多态性（SNP）芯片数据时，对基因组预测可靠性的影响。额外的标记是为了扩充北欧国家使用的定制低密度Illumina BovineLD SNP芯片（加利福尼亚州圣地亚哥）而选择的。单标记分析是按品种对北欧荷斯坦牛、丹麦泽西牛和北欧红牛育种目标中包含的所有16个指标性状以及总性能指数本身进行的。根据性状的经济权重，每个品种每个性状选择15个、10个或5个数量性状基因座（QTL），并选择3至5个标记来标记每个QTL。在去除重复标记（为多个性状或品种选择的相同标记）并针对阵列上的高成对连锁不平衡和检测性能进行筛选后，总共选择了1623个QTL标记纳入定制芯片。使用基因组最佳线性无偏预测（GBLUP）或贝叶斯变量选择模型对北欧和法国荷斯坦牛以及北欧红牛进行了基因组预测分析。当在分析中使用包含QTL标记的GBLUP模型时，北欧荷斯坦牛生产性状的可靠性提高了多达4个百分点，北欧红牛提高了多达3个百分点，法国荷斯坦牛提高了多达5个百分点。乳腺炎的增幅较小，最高为1个百分点，而繁殖力仅提高了0.5个百分点。使用贝叶斯模型时，与GBLUP方法相比，仅使用54k数据时准确性通常更高，但纳入QTL标记时可靠性的提高相对较小。本研究结果表明，将全基因组序列数据的全基因组关联研究中显著的标记与54k SNP集一起纳入，可以提高基因组预测的可靠性。
