Department of Biomedical Informatics, University of Utah School of Medicine, UT, USA.
Bioinformatics. 2011 Jan 1;27(1):134-6. doi: 10.1093/bioinformatics/btq616. Epub 2010 Nov 13.
It has been argued that the missing heritability in common diseases may be in part due to rare variants and gene-gene effects. Haplotype analyses provide more power for rare variants and joint analyses across genes can address multi-gene effects. Currently, methods are lacking to perform joint multi-locus association analyses across more than one gene/region. Here, we present a haplotype-mining gene-gene analysis method, which considers multi-locus data for two genes/regions simultaneously. This approach extends our single region haplotype-mining algorithm, hapConstructor, to two genes/regions. It allows construction of multi-locus SNP sets at both genes and tests joint gene-gene effects and interactions between single variants or haplotype combinations. A Monte Carlo framework is used to provide statistical significance assessment of the joint and interaction statistics, thus the method can also be used with related individuals. This tool provides a flexible data-mining approach to identifying gene-gene effects that otherwise is currently unavailable.
http://bioinformatics.med.utah.edu/Genie/hapConstructor.html.
有人认为，常见疾病的遗传缺失部分可能是由于罕见变异和基因-基因相互作用所致。单体型分析为罕见变异提供了更大的效力，而跨基因的联合分析则可以解决多基因效应的问题。目前，缺乏在一个以上基因/区域中进行联合多基因座关联分析的方法。在这里，我们提出了一种单体型挖掘基因-基因分析方法，该方法同时考虑了两个基因/区域的多基因座数据。这种方法扩展了我们的单区域单体型挖掘算法 hapConstructor，使其适用于两个基因/区域。它允许在两个基因上构建多基因座 SNP 集，并检验单变体或单体型组合之间的联合基因-基因效应和相互作用。使用蒙特卡罗框架提供联合和相互作用统计数据的统计显著性评估，因此该方法也可用于相关个体。该工具提供了一种灵活的数据挖掘方法，用于识别基因-基因效应，而目前尚无其他方法可用。
http://bioinformatics.med.utah.edu/Genie/hapConstructor.html。
