Blangero John, Goring Harald H H, Kent Jack W, Williams Jeff T, Peterson Charles P, Almasy Laura, Dyer Thomas D
Department of Genetics, Southwest Foundation for Biomedical Research, 620 NW Loop 410, San Antonio, TX 78245-0549, USA.
Hum Biol. 2005 Oct;77(5):541-59. doi: 10.1353/hub.2006.0003.
Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis.
尽管人们已经对用于数量性状基因座（QTL）初始定位和精细定位的统计遗传方法给予了大量关注，但迄今为止，在利用序列数据统计识别最可能的功能多态性问题上，几乎没有开展方法学研究。在本文中，我们提供了一个通用的统计遗传框架，称为贝叶斯数量性状核苷酸（BQTN）分析，用于评估遗传变异的可能功能状态。该方法需要首先列举一组重测序个体中的所有遗传变异。然后在大量个体（可能是家系个体）中对这些多态性进行分型，并使用贝叶斯模型选择和平均法将标记变异与数量表型变异关联起来。对于每个序列变异，都能获得一个效应后验概率，可用于确定后续分子功能实验的优先级。使用GAW12模拟数据给出了这种数量核苷酸分析的一个示例。结果表明，BQTN方法可能有助于在一个基因（或一组基因）内选择最可能的功能变异。我们还提供了有关如何使用我们的计算机程序SOLAR进行关联分析和BQTN分析的说明。
