Complex diseases, complex genes: keeping pathways on the right track.
Complex diseases, complex genes: keeping pathways on the right track.
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DOI：
10.1097/ede.0b013e3181a93b98
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发表时间：
2009-07
期刊：
影响因子：
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通讯作者：
Raychaudhuri S
中科院分区：
文献类型：
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作者：
Kraft P;Raychaudhuri S
在他的“豆袋遗传学的辩护”中，JBS Haldane回应了那些挑战边缘（即，一次一个位点）研究群体遗传学的批评者。这种“豆袋遗传学”被指责过于简单化，忽视了不同（和不断变化的）环境背景下多个基因座的贡献。Halfman承认了这一原则（“豆袋遗传学不能解释基因的生理相互作用以及基因型和环境的相互作用”），但他继续认为，尽管边缘方法被简化了，但它已经在实践中证明了自己，并带来了重要的见解。[1]来自全基因组关联研究（GWAS）的大量数据的到来，引发了遗传流行病学领域类似争论的热度。一方面，公认的简单方法，平均遗传和环境背景，并在GWAS中测试每个标记与所研究的性状的关联，已经证明是相当成功的，尽管早期的担忧，忽视潜在的复杂性，这种天真的方法会失败。另一方面，迄今为止发现的基因座并不能解释大多数研究性状的遗传力。6“途径分析”通过同时考虑多个基因座并将其与已知的功能注释相关联来认识复杂性。原则上，通径分析可能导致简单边际分析所遗漏的新发现。7-11此外，成功鉴定相关途径可以阐明疾病的发病机制;事实上，对于某些表型，通过GWAS鉴定的多个基因座与共同途径有关。12–15
In his “Defense of Beanbag Genetics,” JBS Haldane1 responded to critics who challenged a marginal (ie, one-locus-at-a-time) approach to studying population genetics. Such “beanbag genetics” had been accused of being too simplistic and ignoring the contributions of multiple loci in different (and ever-changing) environmental contexts. Haldane conceded this point of principle (“beanbag genetics do not explain the physiologic interaction of genes and the interaction of genotype and environment”) but went on to argue that despite its simplifications, the marginal approach had proven itself in practice and led to important insights. 1The arrival of massive amounts of data from genome-wide association studies (GWAS) has turned up the heat on a similar debate in the field of genetic epidemiology. On the one hand, the admittedly simple approach of averaging over genetic and environmental backgrounds and testing each marker in a GWAS for association with the studied trait marginally has proven quite successful, 2, 3 despite early concerns that by ignoring the underlying complexity this naıve approach would fail. 4, 5 On the other hand, the loci discovered to date do not come close to explaining the observed heritability for most studied traits. 6 “Pathway analyses” acknowledge complexity by considering multiple loci simultaneously and relating them to known functional annotations. In principle, pathway analyses could lead to new discoveries missed by the simple marginal analyses. 7–11 Moreover, successful identification of associated pathways can clarify disease pathogenesis; indeed, for some phenotypes, multiple loci identified through GWAS have been linked to common pathways. 12–15