Graham J, Thompson E A
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Am J Hum Genet. 1998 Nov;63(5):1517-30. doi: 10.1086/302102.
Genetic linkage studies based on pedigree data have limited resolution, because of the relatively small number of segregations. Disequilibrium mapping, which uses population associations to infer the location of a disease mutation, provides one possible strategy for narrowing the candidate region. The coalescent process provides a model for the ancestry of a sample of disease alleles, and recombination events between disease locus and marker may be placed on this ancestral phylogeny. These events define the recombinant classes, the sets of sampled disease copies descending from the meiosis at which a given recombination occurred. We show how Monte Carlo generation of the recombinant classes leads to a linkage likelihood for fine-scale mapping from disease haplotypes. We compare single-marker disequilibrium mapping with interval-disequilibrium mapping and discuss how the approach may be extended to multipoint-disequilibrium mapping. The method and its properties are illustrated with an example of simulated data, constructed to be typical of fine-scale mapping of a rare disease in the Japanese population. The method can take into account known features of population history, such as changing patterns of population growth.
基于家系数据的基因连锁研究分辨率有限，这是因为分离的数量相对较少。不平衡图谱绘制利用群体关联来推断疾病突变的位置，为缩小候选区域提供了一种可能的策略。合并过程为疾病等位基因样本的祖先提供了一个模型，疾病位点与标记之间的重组事件可以置于这个祖先系统发育树上。这些事件定义了重组类别，即从发生给定重组的减数分裂中衍生出来的抽样疾病拷贝集。我们展示了如何通过蒙特卡罗方法生成重组类别，从而从疾病单倍型中得出用于精细定位的连锁似然性。我们将单标记不平衡图谱绘制与区间不平衡图谱绘制进行了比较，并讨论了该方法如何扩展到多点不平衡图谱绘制。该方法及其特性通过一个模拟数据示例进行了说明，该模拟数据构建为日本人群中罕见疾病精细定位的典型数据。该方法可以考虑群体历史的已知特征，如群体增长模式的变化。
