Haplotype association analyses in resources of mixed structure using Monte Carlo testing.
Haplotype association analyses in resources of mixed structure using Monte Carlo testing.
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DOI：
10.1186/1471-2105-11-592
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发表时间：
2010-12-09
影响因子：
3
通讯作者：
Camp NJ
中科院分区：
文献类型：
--
作者：
Abo R;Wong J;Thomas A;Camp NJ
全基因组关联研究已经产生了大量可能含有疾病基因的基因组区域。对这些特定区域的彻底询问是合乎逻辑的下一步，包括区域单倍型研究，以确定潜在关键变异所在的风险单倍型。由于遗传病的病例丰富，被确定为疾病的家系可能对遗传分析具有强大的作用。在这里，我们提出了一种基于蒙特卡罗的单倍型关联分析方法。我们的方法，hapMC，允许分析全长和子单倍型，包括核心家族资源、一般家系资源、病例对照数据或其混合物中缺失数据的归属。既可以进行传统关联统计，也可以进行传输/不平衡统计。该方法包括可在大型家系中使用的分段算法和可选的伪控件的使用。我们的新阶段化算法大大优于不考虑谱系结构的标准期望最大化算法，因此更适合于包含谱系结构的资源。通过仿真，我们证明了我们的蒙特卡罗方法对于所有资源类型都保持了正确的类型1错误率。功率比较表明，传输不平衡统计在仅对核心家庭的资源进行关联方面更好。然而，对于混合结构资源，新实施的伪控制方法似乎是最佳选择。结果还表明了大型高危家系在关联分析中的价值，在所考虑的模拟中，其能力可与相同样本量的病例对照资源相媲美。我们建议hapMC作为一种有价值的新工具来进行单倍型关联分析，特别是对于混合结构的资源。元关联和单倍型挖掘模块在我们的蒙特卡罗单倍型程序套件中的可用性进一步增加了该方法的价值。
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.
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