Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits.
Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits.
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DOI：
10.1186/s12864-016-2443-6
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发表时间：
2016-02-27
期刊：
影响因子：
4.4
通讯作者：
Goddard ME
中科院分区：
文献类型：
--
作者：
MacLeod IM;Bowman PJ;Vander Jagt CJ;Haile-Mariam M;Kemper KE;Chamberlain AJ;Schrooten C;Hayes BJ;Goddard ME
密集的SNP基因型通常与复杂的性状表型相结合，以绘制因果变异，研究遗传结构，并为具有基因型但没有表型的个体提供基因组预测。在贝叶斯混合模型（BayesR）中联合拟合所有基因型的单一分析方法已被证明可以同时竞争性地解决所有3个目的。然而，BayesR和其他类似的方法忽略了先前的生物学知识，并假设所有基因型都同样可能影响性状。虽然这种假设对于SNP阵列基因型是合理的，但如果基因型是应该包括因果变异的全基因组序列变异，则不太合理。我们介绍了一种新的方法（BayesRC）的基础上BayesR，通过定义类的变异可能是丰富的因果突变的分析，将先前的生物信息。这些信息可以来自一系列来源，包括变异注释、候选基因列表和已知的致病变异。然后，根据数据丰富的证据，将这一信息客观地纳入分析。我们证明了增加的权力贝叶斯RC相比贝叶斯R使用真实的奶牛基因型与模拟表型。基因型是在编码区中结合密集SNP标记的全基因组序列变体。BayesRC提高了检测因果变异的能力，并提高了基因组预测的准确性。基因组预测的相对改善在与参考群体不密切相关的验证群体中最为明显。我们还将贝叶斯RC应用于奶牛真实的产奶表型，使用来自基因表达分析的独立生物先验。虽然目前关于哪些基因和变异影响产奶量的生物学知识仍然非常不完整，但我们的研究结果表明，新的BayesRC方法在检测候选因果变异和牛奶性状的基因组预测方面与BayesR方法相当或更强大。BayesRC提供了一种新的和灵活的方法，同时提高QTL发现和基因组预测的准确性，利用先验生物学知识。随着生物学知识的积累，贝叶斯RC等方法将变得越来越有用，这些知识涉及一系列性状和物种的基因组功能区域。本文的在线版本（doi：10.1186/s12864-016-2443-6）包含补充材料，可供授权用户使用。
Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants. We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits. BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species. The online version of this article (doi:10.1186/s12864-016-2443-6) contains supplementary material, which is available to authorized users.