Pan Qinxin, Hu Ting, Moore Jason H
Computational Genetics Laboratory, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.
Methods Mol Biol. 2013;1019:465-77. doi: 10.1007/978-1-62703-447-0_22.
Genome-wide association studies (GWASs) and other high-throughput initiatives have led to an information explosion in human genetics and genetic epidemiology. Conversion of this wealth of new information about genomic variation to knowledge about public health and human biology will depend critically on the complexity of the genotype to phenotype mapping relationship. We review here computational approaches to genetic analysis that embrace, rather than ignore, the complexity of human health. We focus on multifactor dimensionality reduction (MDR) as an approach for modeling one of these complexities: epistasis or gene-gene interaction.
全基因组关联研究（GWAS）及其他高通量研究项目已引发了人类遗传学和遗传流行病学领域的信息爆炸。将这些关于基因组变异的丰富新信息转化为有关公共卫生和人类生物学的知识，将严重依赖于基因型与表型映射关系的复杂性。我们在此回顾那些接受而非忽视人类健康复杂性的遗传分析计算方法。我们重点关注多因素降维法（MDR），它是一种用于对其中一种复杂性——上位性或基因-基因相互作用进行建模的方法。
