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通过测试的一群而不是单个的变异与疾病的关联捕获变异的集体行为,文章提出了单倍型关联分析.这种分析通常涉及未知相位多位置基因型在病例和对照组中稀疏频率.它从推断单倍型基因型,共同分类和边际筛查疾病相关的基因单倍型开始.不幸的是,解未知相位的不确定性可能对单倍型共同分类(因此对预测风险单倍型的精度)产生强烈影响.在这里,为了解决这一问题,文章提出一个替代办法:在阶段一中,选择风险基因型而不是共同分类推断单倍型.在阶段二中,从前一阶段选择风险基因型推断出风险单体型.采用仿真研究和实际数据分析评定提议的程序性能.相比现有的多个Z-检验程序,通过使用建议的程序可增加全基因组关联研究的功效.
By examining the collective behavior of mutations captured by a population rather than a single mutation-disease association, the paper presents haplotype association analysis, which usually involves sparse frequencies of unknown phase multi-locus genotypes in both cases and controls. It is inferred that haplotypes, co-taxonomic and haplotyping of genes associated with marginal screening of diseases begin Unfortunately, the uncertainty of unknown phases may be commonly categorized by haplotype (hence the accuracy of the predicted risk haplotype Here, in order to address this issue, the article proposes an alternative: to select risk genotypes rather than co-categorize haplotypes in stage 1. In stage two, select the risk genotype from the previous stage The risk haplotype is inferred and the proposed program performance is assessed using simulation studies and actual data analysis The efficacy of genome-wide association studies can be increased by using the proposed procedure compared to existing multiple Z-test procedures.