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Bayesian Mapping of Quantitative Trait Loci for Multiple Complex Traits with the Use of Variance Components

机译:使用方差分量的多个复杂性状的数量性状位点的贝叶斯映射

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摘要

Complex traits important for humans are often correlated phenotypically and genetically. Joint mapping of quantitative-trait loci (QTLs) for multiple correlated traits plays an important role in unraveling the genetic architecture of complex traits. Compared with single-trait analysis, joint mapping addresses more questions and has advantages for power of QTL detection and precision of parameter estimation. Some statistical methods have been developed to map QTLs underlying multiple traits, most of which are based on maximum-likelihood methods. We develop here a multivariate version of the Bayes methodology for joint mapping of QTLs, using the Markov chain–Monte Carlo (MCMC) algorithm. We adopt a variance-components method to model complex traits in outbred populations (e.g., humans). The method is robust, can deal with an arbitrary number of alleles with arbitrary patterns of gene actions (such as additive and dominant), and allows for multiple phenotype data of various types in the joint analysis (e.g., multiple continuous traits and mixtures of continuous traits and discrete traits). Under a Bayesian framework, parameters—including the number of QTLs—are estimated on the basis of their marginal posterior samples, which are generated through two samplers, the Gibbs sampler and the reversible-jump MCMC. In addition, we calculate the Bayes factor related to each identified QTL, to test coincident linkage versus pleiotropy. The performance of our method is evaluated in simulations with full-sib families. The results show that our proposed Bayesian joint-mapping method performs well for mapping multiple QTLs in situations of either bivariate continuous traits or mixed data types. Compared with the analysis for each trait separately, Bayesian joint mapping improves statistical power, provides stronger evidence of QTL detection, and increases precision in estimation of parameter and QTL position. We also applied the proposed method to a set of real data and detected a coincident linkage responsible for determining bone mineral density and areal bone size of wrist in humans.
机译:对人类重要的复杂性状通常在表型和遗传上相关。多个相关性状的数量性状基因座(QTL)的联合作图在揭示复杂性状的遗传结构中起着重要作用。与单特征分析相比,联合映射解决了更多问题,并且在QTL检测的能力和参数估计的精度方面具有优势。已经开发出一些统计方法来绘制多重性状的QTL,大多数基于最大似然法。我们在这里使用Markov链-蒙特卡罗(MCMC)算法开发用于QTL联合映射的Bayes方法的多元版本。我们采用方差成分法来模拟远亲群体(例如人类)的复杂性状。该方法是鲁棒的,可以处理具有任意模式的基因作用的任意等位基因(例如加性和显性),并且可以在联合分析中获得各种类型的多种表型数据(例如,多个连续性状和连续性的混合物)性状和离散性状)。在贝叶斯框架下,参数(包括QTL的数量)是根据它们的边缘后验样本估算的,这些样本是通过两个采样器(吉布斯采样器和可逆跳MCMC)生成的。此外,我们计算与每个已识别QTL相关的贝叶斯因子,以测试重合性与多效性的关系。我们的方法的性能在全同胞族的仿真中进行了评估。结果表明,在双变量连续性状或混合数据类型的情况下,我们提出的贝叶斯联合映射方法在映射多个QTL方面表现良好。与分别对每个特征进行分析相比,贝叶斯联合映射提高了统计能力,提供了更强的QTL检测证据,并提高了参数和QTL位置估计的准确性。我们还将提出的方法应用于一组真实数据,并检测到一个可确定人的手腕骨矿物质密度和面积的大小的重合链。

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