Estimating polygenic effects using markers of the entire genome.
Estimating polygenic effects using markers of the entire genome.
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作者：
Shizhong Xu
分子标记已被用来绘制数量性状基因座。然而，它们很少用于评估整个基因组染色体片段的影响。原始的区间作图方法及其各种修改版本在评估整个基因组的遗传效应方面可能用途有限，因为它们需要评估多个模型和模型选择。在这里，我们提出了一种贝叶斯回归方法来同时估计与整个基因组标记相关的遗传效应。使用贝叶斯方法，我们能够处理效应数量甚至大于观察数量的情况。成功的关键是我们允许每个标记效应有自己的方差参数，而该参数又具有自己的先验分布，以便可以根据数据估计方差。在这种分层模型下，我们能够处理大量标记，并且大多数标记的影响可以忽略不计。结果，可以评估标记效果的分布。利用北美大麦基因组作图计划在双单倍体大麦中的数据，我们发现基因效应的分布紧密遵循L形Gamma分布，这与从区间作图估计基因效应时的钟形Gamma分布相反。此外，我们表明贝叶斯方法可以作为替代甚至更好的 QTL 定位方法，因为它可以产生更清晰的 QTL 信号。从 F(2) 和回交 (BC) 家族的模拟数据集也发现了类似的结果。
Molecular markers have been used to map quantitative trait loci. However, they are rarely used to evaluate effects of chromosome segments of the entire genome. The original interval-mapping approach and various modified versions of it may have limited use in evaluating the genetic effects of the entire genome because they require evaluation of multiple models and model selection. Here we present a Bayesian regression method to simultaneously estimate genetic effects associated with markers of the entire genome. With the Bayesian method, we were able to handle situations in which the number of effects is even larger than the number of observations. The key to the success is that we allow each marker effect to have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, we were able to handle a large number of markers and most of the markers may have negligible effects. As a result, it is possible to evaluate the distribution of the marker effects. Using data from the North American Barley Genome Mapping Project in double-haploid barley, we found that the distribution of gene effects follows closely an L-shaped Gamma distribution, which is in contrast to the bell-shaped Gamma distribution when the gene effects were estimated from interval mapping. In addition, we show that the Bayesian method serves as an alternative or even better QTL mapping method because it produces clearer signals for QTL. Similar results were found from simulated data sets of F(2) and backcross (BC) families.