The impact of population structure on genomic prediction in stratified populations
The impact of population structure on genomic prediction in stratified populations
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DOI：
10.1007/s00122-013-2255-x
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发表时间：
2014-03-01
影响因子：
5.4
通讯作者：
Gay, Gilles
中科院分区：
文献类型：
--
作者：
Guo, Zhigang;Tucker, Dominic M.;Gay, Gilles
利用高密度标记对水稻和玉米不同群体的基因组遗传力和预测进行了研究和定量分析。群体结构是影响分层群体基因组遗传力估计和基因组预测评估的重要因素。在这项研究中，我们的第一个目标是评估群体结构对水稻和玉米基因组遗传力估计的影响。结果表明，群体结构分别解释了水稻和玉米基因组遗传力的33%和7.5%，这取决于性状，其余遗传力由亚群体内变异解释。亚群内遗传力的估计值高于全基因组关联研究中确定的数量性状位点的估计值，表明遗传增益提高了65%。第二个目标是使用交叉验证实验评估群体结构对基因组预测的影响。当群体结构存在于训练集和验证集中时，校正群体结构会导致基因组预测的准确性显着降低。与此相反，当预测仅限于一个特定的亚群，人口结构的准确性和亚群内的遗传方差占主导地位的预测几乎没有影响。最后，研究了基因组遗传力对基因组预测的影响。在训练集和验证集中，基因组预测的准确性随着基因组遗传力的增加而增加，前者的影响略大。总之，我们的结果表明，群体结构对基因组预测的贡献因预测策略而异，并且还受到性状和群体的遗传结构的影响。在育种实践中，这些结论有助于更好地理解和利用不同的遗传资源进行基因组预测。
Key message Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize.Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5 % of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65 % improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.