Naidoo Nasheen, Chia Kee Seng
Centre for Molecular Epidemiology, Department of Epidemiology and Public Health, National University of Singapore, Singapore.
J Prev Med Public Health. 2009 Nov;42(6):356-9. doi: 10.3961/jpmph.2009.42.6.356.
In the more than 100 genome wide association studies (GWAS) conducted in the past 5 years, more than 250 genetic loci contributing to more than 40 common diseases and traits have been identified. Whilst many genes have been linked to a trait, both their individual and combined effects are small and unable to explain earlier estimates of heritability. Given the rapid changes in disease incidence that cannot be accounted for by changes in diagnostic practises, there is need to have well characterized exposure information in addition to genomic data for the study of gene-environment interactions. The case-control and cohort study designs are most suited for studying associations between risk factors and occurrence of an outcome. However, the case control study design is subject to several biases and hence the preferred choice of the prospective cohort study design in investigating gene-environment interactions. A major limitation of utilising the prospective cohort study design is the long duration of follow-up of participants to accumulate adequate outcome data. The GWAS paradigm is a timely reminder for traditional epidemiologists who often perform one- or few-at-a-time hypothesis-testing studies with the main hallmarks of GWAS being the agnostic approach and the massive dataset derived through large-scale international collaborations.
在过去5年进行的100多项全基因组关联研究(GWAS)中,已确定了250多个导致40多种常见疾病和性状的基因位点。虽然许多基因已与某一性状相关联,但其个体效应和综合效应都很小,无法解释早期的遗传率估计值。鉴于疾病发病率的快速变化无法用诊断方法的改变来解释,除了基因组数据外,还需要有特征明确的暴露信息来研究基因-环境相互作用。病例对照研究和队列研究设计最适合于研究风险因素与结局发生之间的关联。然而,病例对照研究设计存在若干偏倚,因此在调查基因-环境相互作用时,前瞻性队列研究设计是首选。使用前瞻性队列研究设计的一个主要限制是需要对参与者进行长时间随访,以积累足够的结局数据。GWAS范式适时地提醒了传统流行病学家,他们经常进行一次或几次假设检验研究,而GWAS的主要特点是不可知论方法和通过大规模国际合作获得的海量数据集。