Terwilliger J D, Zöllner S, Laan M, Pääbo S
Department of Psychiatry and Columbia Genome Center, Columbia University, New York, NY 10032, USA.
Hum Hered. 1998 May-Jun;48(3):138-54. doi: 10.1159/000022794.
Linkage disequilibrium has been a powerful tool in identifying rare disease alleles in human populations. To date, most research has been directed to isolated populations which have undergone a bottleneck followed by rapid exponential expansion. While this strategy works well for rare diseases in which all disease alleles in the population today are clonal copies of some common ancestral allele, for common disease genes with substantial allelic heterogeneity, this approach is not predicted to work. In this paper, we describe the dynamics of linkage disequilibrium in populations which have not undergone a demographic expansion. In these populations, it is shown that genetic drift creates disequilibrium over time, while in expanded populations, the disequilibrium decays with time. We propose that common disease alleles might be more efficiently identified by drift mapping - linkage disequilibrium mapping in small, old populations of constant size where the disequilibrium is the result of genetic drift, not founder effect. Theoretical models, empirical data, and simulated population models are presented as evidence for the utility of this approach.
连锁不平衡一直是在人类群体中识别罕见疾病等位基因的有力工具。迄今为止,大多数研究都针对经历过瓶颈效应随后快速指数增长的孤立群体。虽然这种策略对于当今群体中所有疾病等位基因都是某个共同祖先等位基因的克隆拷贝的罕见疾病效果良好,但对于具有大量等位基因异质性的常见疾病基因,预计这种方法行不通。在本文中,我们描述了未经历人口扩张的群体中连锁不平衡的动态变化。在这些群体中,研究表明随着时间推移遗传漂变会产生不平衡,而在扩张的群体中,不平衡会随时间衰减。我们提出,通过在规模恒定的小型古老群体中进行漂变作图——连锁不平衡作图,可能更有效地识别常见疾病等位基因,在这些群体中,不平衡是遗传漂变而非奠基者效应的结果。文中给出了理论模型、实证数据和模拟群体模型,作为这种方法实用性的证据。