Ritchie Marylyn D
Department of Molecular Physiology, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232-0700, USA.
Ann Hum Genet. 2011 Jan;75(1):172-82. doi: 10.1111/j.1469-1809.2010.00630.x.
The search for the missing heritability in genome-wide association studies (GWAS) has become an important focus for the human genetics community. One suspected location of these genetic effects is in gene-gene interactions, or epistasis. The computational burden of exploring gene-gene interactions in the wealth of data generated in GWAS, along with small to moderate sample sizes, have led to epistasis being an afterthought, rather than a primary focus of GWAS analyses. In this review, I discuss some potential approaches to filter a GWAS dataset to a smaller, more manageable dataset where searching for epistasis is considerably more feasible. I describe a number of alternative approaches, but primarily focus on the use of prior biological knowledge from databases in the public domain to guide the search for epistasis. The manner in which prior knowledge is incorporated into a GWA study can be many and these data can be extracted from a variety of database sources. I discuss a number of these approaches and propose that a comprehensive approach will likely be most fruitful for searching for epistasis in large-scale genomic studies of the current state-of-the-art and into the future.
在全基因组关联研究(GWAS)中寻找缺失的遗传力已成为人类遗传学领域的一个重要焦点。这些遗传效应的一个可疑位置在于基因与基因的相互作用,即上位性。在GWAS产生的大量数据中探索基因与基因相互作用的计算负担,再加上样本量小到中等,导致上位性成为事后才考虑的因素,而非GWAS分析的主要重点。在这篇综述中,我讨论了一些潜在的方法,可将GWAS数据集筛选为一个更小、更易于管理的数据集,在其中寻找上位性要可行得多。我描述了许多替代方法,但主要关注利用公共领域数据库中的先验生物学知识来指导上位性的搜索。将先验知识纳入全基因组关联研究的方式有很多种,这些数据可以从各种数据库来源中提取。我讨论了其中一些方法,并提出一种综合方法可能在当前最先进的大规模基因组研究以及未来寻找上位性方面最有成效。