Cooper Richard S, Tayo Bamidele, Zhu Xiaofeng
Department of Preventive Medicine and Epidemiology, Loyola University Chicago Stritch School of Medicine, 2160 S. First Ave., Maywood, IL 60153, USA.
Hum Mol Genet. 2008 Oct 15;17(R2):R151-5. doi: 10.1093/hmg/ddn263.
The current gene mapping for complex diseases is heavily weighted by studies of population samples from northern Europe. To capture the full range of genetic diversity and exploit the potential of genetic epidemiology to identify important variants, multiple additional populations will need to be examined. The conduct of genome-wide association studies will therefore confront many of the challenges identified in the first generation of candidate gene and linkage studies, with a substantial increase in complexity. Initial efforts to map causal effects will have to take account of varying patterns of linkage disequilibrium through careful attention to local haplotype structure. Refined statistical techniques that permit joint analyses of samples from multiple populations will also be required, as well as improved methods to account for on-going gene flow between populations with geographically distinct ancestral origins. This variation can either be an impediment, slowing the process of replication, or an opportunity, allowing finer dissection of the relevant variants. Clinical translation of these data will present major challenges. Large cosmopolitan populations, such as those found in large urban centers, are likely to exhibit both known and cryptic sub-structure across groups, as well as admixture within individuals. Great care will need to be devoted to generalizability of association findings to avoid their premature adoption as predictive tests in the face of this widespread heterogeneity.
目前复杂疾病的基因定位研究严重依赖于来自北欧人群样本的研究。为了全面捕捉遗传多样性并挖掘遗传流行病学识别重要变异的潜力,需要对更多不同人群进行研究。因此,开展全基因组关联研究将面临第一代候选基因和连锁研究中所发现的诸多挑战,且复杂性大幅增加。在绘制因果效应图谱的初期,必须通过仔细关注局部单倍型结构来考虑连锁不平衡的不同模式。还需要能够对来自多个群体的样本进行联合分析的精细统计技术,以及改进的方法来解释具有不同地理祖先起源的群体之间持续存在的基因流动。这种变异既可能是阻碍,减缓复制进程,也可能是机遇,有助于更精细地剖析相关变异。这些数据的临床转化将面临重大挑战。像大型城市中心的人群这样的大型世界性群体,很可能在群体间呈现出已知和隐藏的亚结构,个体内部也存在混合情况。面对这种广泛的异质性,要非常谨慎地考虑关联研究结果的可推广性,以免在面对这种情况时过早地将其用作预测性检测。