A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation
A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation
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DOI：
10.1101/gr.172901
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发表时间：
2001-03-01
期刊：
影响因子：
7
通讯作者：
Sing, CF
中科院分区：
文献类型：
--
作者：
Nelson, MR;Kardia, SLR;Sing, CF
基因组研究的最新进展加速了定位候选基因及其可变位点的过程，并简化了基因型测量的任务。利用成百上千个可变基因座的信息来研究基因组变异和表型变异之间的关系的统计和计算策略的发展没有跟上步伐，特别是对于不遵循简单孟德尔遗传模式的数量性状。我们在这里提出的组合分区方法（CPM），检查多个基因，每个基因含有多个可变位点，以确定分区的多位点基因型，预测个体间变异的数量性状水平。我们说明了这种方法与应用程序收集的188名男性，年龄20-60岁，确定不考虑健康状况，从罗切斯特，明尼苏达州的血浆甘油三酯水平。基因型信息包括6个冠心病候选易感基因区域18个双等位基因位点的测量值：APOA 1-C3-A4、APOB、APOE、LDLR、LPL和PON 1。为了说明CPM，我们将两个基因座基因型的所有可能分区评估为2至9个分区（类似于10（6）评估）。我们发现，许多位点的组合都参与了预测甘油三酯变异性的基因型分区集，最具预测性的集显示非加性。这些结果表明，传统的方法，建立多位点模型，依赖于统计上显着的边际，单位点效应，可能无法确定最好的预测性状变异的基因座组合。CPM提供了一种探索高维基因型状态空间的策略，以便预测群体中的数量性状变异，而不需要预先指定的遗传模型的分析条件。
Recent advances in genome research have accelerated the process of locating candidate genes and the variable sites within them and have simplified the task of genotype measurement. The development of statistical and computational strategies to utilize information on hundreds - soon thousands - of variable loci to investigate the relationships between genome variation and phenotypic variation has not kept pace, particularly for quantitative traits that do not follow simple Mendelian patterns of inheritance. We present here the combinatorial partitioning method (CPM) that examines multiple genes, each containing multiple variable loci, to identify partitions of multilocus genotypes that predict interindividual variation in quantitative trait levels. We illustrate this method with an application to plasma triglyceride levels collected on 188 males, ages 20-60 yr, ascertained without regard to health status, from Rochester, Minnesota. Genotype information included measurements at 18 diallelic loci in six coronary heart disease-candidate susceptibility gene regions: APOA1-C3-A4, APOB, APOE, LDLR, LPL, and PON1. To illustrate the CPM, we evaluated all possible partitions of two-locus genotypes into two to nine partitions (similar to 10(6) evaluations). We found that many combinations of loci are involved in sets of genotypic partitions that predict triglyceride variability and that the most predictive sets show nonadditivity. These results suggest that traditional methods of building multilocus models that rely on statistically significant marginal, single-locus effects, may fail to identify combinations of loci that best predict trait variability. The CPM offers a strategy for exploring the high-dimensional genotype state space so as to predict the quantitative trait variation in the population at large that does not require the conditioning of the analysis on a prespecified genetic model.