Vitezica Z G, Aguilar I, Misztal I, Legarra A
Université de Toulouse, TANDEM, INRA/INPT-ENSAT/ENVT, Castanet-Tolosan, France.
Genet Res (Camb). 2011 Oct;93(5):357-66. doi: 10.1017/S001667231100022X. Epub 2011 Jul 18.
Prediction of genetic merit or disease risk using genetic marker information is becoming a common practice for selection of livestock and plant species. For the successful application of genome-wide marker-assisted selection (GWMAS), genomic predictions should be accurate and unbiased. The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities. Prediction of genetic values was by best-linear unbiased prediction (BLUP) using data either from relatives summarized in pseudodata for genotyped individuals (multiple-step method) or using all available data jointly (single-step method). The single-step method combined genomic- and pedigree-based relationship matrices. Predictions by the multiple-step method were biased. Predictions by a single-step method were less biased and more accurate but under strong selection were less accurate. When genomic relationships were shifted by a constant, the single-step method was unbiased and the most accurate. The value of that constant, which adjusts for non-random selection of genotyped individuals, can be derived analytically.
利用遗传标记信息预测遗传价值或疾病风险正成为家畜和植物品种选育的常用方法。为了成功应用全基因组标记辅助选择（GWMAS），基因组预测应准确且无偏差。在两个模拟动物群体中，研究了在弱选择或强选择以及几种遗传力条件下选择对基因组预测偏差和准确性的影响。遗传值的预测采用最佳线性无偏预测（BLUP），数据来源为要么是根据基因型个体的伪数据汇总的亲属数据（多步法），要么是联合使用所有可用数据（单步法）。单步法结合了基于基因组和系谱的关系矩阵。多步法的预测存在偏差。单步法的预测偏差较小且更准确，但在强选择条件下准确性较低。当基因组关系通过一个常数进行偏移时，单步法无偏差且最为准确。该常数的值可通过分析得出，它用于调整基因型个体的非随机选择。
