Department of Biomedical Informatics, University of Utah, Salt Lake City, USA.
BMC Bioinformatics. 2010 Dec 9;11:592. doi: 10.1186/1471-2105-11-592.
Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols.
Our new phasing algorithm substantially outperformed the standard expectation-maximization algorithm that is ignorant of pedigree structure, and hence is preferable for resources that include pedigree structure. Through simulation we show that our Monte Carlo procedure maintains the correct type 1 error rates for all resource types. Power comparisons suggest that transmission-disequilibrium statistics are superior for performing association in resources of only nuclear families. For mixed structure resources, however, the newly implemented pseudocontrol approach appears to be the best choice. Results also indicated the value of large high-risk pedigrees for association analysis, which, in the simulations considered, were comparable in power to case-control resources of the same sample size.
We propose hapMC as a valuable new tool to perform haplotype association analyses, particularly for resources of mixed structure. The availability of meta-association and haplotype-mining modules in our suite of Monte Carlo haplotype procedures adds further value to the approach.
全基因组关联研究已经产生了许多可能包含疾病基因的基因组区域。对这些特定区域进行深入研究是合乎逻辑的下一步，包括进行区域单体型研究，以确定潜在关键变异所位于的风险单体型。由于病例中遗传疾病富集，因此为疾病确定的家系可用于遗传分析。在此，我们提出了一种基于蒙特卡罗的单体型关联分析方法。我们的方法 hapMC 允许对全长和亚单体型进行分析，包括缺失数据的推断，可应用于核家庭资源、一般家系、病例对照数据或其混合物。可以执行传统的关联统计和传递/不平衡统计。该方法包括一个相位算法，可用于大型家系，并可选使用伪对照。
我们的新相位算法大大优于标准的期望最大化算法，该算法忽略了家系结构，因此更适合包含家系结构的资源。通过模拟，我们表明我们的蒙特卡罗过程可保持所有资源类型的正确 1 型错误率。功率比较表明，传递不平衡统计对于仅核家庭资源的关联分析更为有效。然而，对于混合结构资源，新实现的伪对照方法似乎是最佳选择。结果还表明了大的高风险家系在关联分析中的价值，在考虑的模拟中，这些家系的功效与相同样本量的病例对照资源相当。
我们提出 hapMC 作为一种进行单体型关联分析的有价值的新工具，特别是对于混合结构的资源。我们的蒙特卡罗单体型程序套件中提供了元关联和单体型挖掘模块，这为该方法增加了更多的价值。
