The Impact of Genetic Architecture on Genome-Wide Evaluation Methods
The Impact of Genetic Architecture on Genome-Wide Evaluation Methods
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DOI：
10.1534/genetics.110.116855
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发表时间：
2010-07-01
期刊：
影响因子：
3.3
通讯作者：
Woolliams, John A.
中科院分区：
文献类型：
--
作者：
Daetwyler, Hans D.;Pong-Wong, Ricardo;Woolliams, John A.
随着高通量单核苷酸多态性数据的迅速增加，人们对应用全基因组评估方法来识别个体的遗传优点产生了极大的兴趣。全基因组评估结合了统计方法和基因组数据来预测复杂性状的遗传价值。目前在确定哪种全基因组评估方法最合适方面存在相当大的不确定性。我们假设全基因组方法处理数量性状和基因组的遗传结构不同。采用随机模拟的方法，对基因组线性方法（GBLUP）和基因组非线性贝叶斯变量选择方法（BayesB）在三种有效种群规模和大量数量性状位点（N-QTL）上进行了比较。无论NQTL如何，对于给定的遗传力和样本量，GBLUP具有恒定的准确性。当N-QTL较低时，BayesB的准确率高于GBLUP，但随着N-QTL的增加，这种优势逐渐减弱，当N-QTL变大时，GBLUP的准确率略高于BayesB。此外，还扩展了确定性方程，以预测两种方法的准确性，并估计独立染色体片段（Me）和N-QTL的数量。Me和N-QTL的预测精度和估计值与模拟数据的结果基本一致。我们得出的结论是，GBLUP和BayesB对给定数量记录和遗传力的相对准确性高度依赖于Me，这是目标基因组的特性，以及性状的结构（N-QTL）。
The rapid increase in high-throughput single-nucleotide polymorphism data has led to a great interest in applying genome-wide evaluation methods to identify an individual's genetic merit. Genome-wide evaluation combines statistical methods with genomic data to predict genetic values for complex traits. Considerable uncertainty currently exists in determining which genome-wide evaluation method is the most appropriate. We hypothesize that genome-wide methods deal differently with the genetic architecture of quantitative traits and genomes. A genomic linear method (GBLUP), and a genomic nonlinear Bayesian variable selection method (BayesB) are compared using stochastic simulation across three effective population sizes and a wide range of numbers of quantitative trait loci (N-QTL). GBLUP had a constant accuracy, for a given heritability and sample size, regardless of NQTL. BayesB had a higher accuracy than GBLUP when N-QTL was low, but this advantage diminished as N-QTL increased and when N-QTL became large, GBLUP slightly outperformed BayesB. In addition, deterministic equations are extended to predict the accuracy of both methods and to estimate the number of independent chromosome segments (Me) and N-QTL. The predictions of accuracy and estimates of Me and N-QTL were generally in good agreement with results from simulated data. We conclude that the relative accuracy of GBLUP and BayesB for a given number of records and heritability are highly dependent on Me, which is a property of the target genome, as well as the architecture of the trait (N-QTL).