Falk C T
New York Blood Center, New York, NY 10021, USA.
Am J Hum Genet. 1997 Nov;61(5):1169-78. doi: 10.1086/301591.
Linkage studies of complex genetic traits raise questions about the effects of genetic heterogeneity and assortative mating on linkage analysis. To further understand these problems, I have simulated and analyzed family data for a complex genetic disease in which disease phenotype is determined by two unlinked disease loci. Two models were studied, a two-locus threshold model and a two-locus heterogeneity model. Information was generated for a marker locus linked to one of the disease-defining loci. Random-mating and assortative-mating samples were generated. Linkage analysis was then carried out by use of standard methods, under the assumptions of a single-locus disease trait and a random-mating population. Results were compared with those from analysis of a single-locus homogeneous trait in samples with the same levels of assortative mating as those considered for the two-locus traits. The results show that (1) introduction of assortative mating does not, in itself, markedly affect the estimate of the recombination fraction; (2) the power of the analysis, reflected in the LOD scores, is somewhat lower with assortative rather than random mating. Loss of power is greater with increasing levels of assortative mating; and (3) for a heterogeneous genetic disease, regardless of mating type, heterogeneity analysis permits more accurate estimate of the recombination fraction but may be of limited use in distinguishing which families belong to each homogeneous subset. These simulations also confirmed earlier observations that linkage to a disease "locus" can be detected even if the disease is incorrectly defined as a single-locus (homogeneous) trait, although the estimated recombination fraction will be significantly greater than the true recombination fraction between the linked disease-defining locus and the marker locus.
复杂遗传性状的连锁研究引发了关于遗传异质性和选型交配在连锁分析中的影响的问题。为了进一步理解这些问题,我对一种复杂遗传疾病的家系数据进行了模拟和分析,其中疾病表型由两个不连锁的疾病位点决定。研究了两种模型,一种是双位点阈值模型,另一种是双位点异质性模型。生成了与其中一个疾病定义位点连锁的标记位点的信息。生成了随机交配和选型交配样本。然后在单基因座疾病性状和随机交配群体的假设下,使用标准方法进行连锁分析。将结果与在与双基因座性状相同选型交配水平的样本中对单基因座同质性状分析的结果进行比较。结果表明:(1)选型交配的引入本身并不会显著影响重组率的估计;(2)分析的效能,以对数优势分数(LOD分数)体现,在选型交配时比随机交配时略低。随着选型交配水平的增加,效能损失更大;(3)对于异质性遗传疾病,无论交配类型如何,异质性分析允许更准确地估计重组率,但在区分哪些家系属于每个同质亚组方面可能用处有限。这些模拟也证实了早期的观察结果,即即使疾病被错误地定义为单基因座(同质)性状,与疾病“位点”的连锁也可以被检测到,尽管估计的重组率将显著大于连锁的疾病定义位点与标记位点之间的真实重组率。