Ekstrøm Claus Thorn, Dalgaard Peter
Department of Mathematics and Physics, Royal Veterinary and Agricultural University, Frederiksberg, Denmark.
Hum Hered. 2003;55(1):16-26. doi: 10.1159/000071806.
Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.
用于数量性状连锁分析的方差成分模型是检测和定位影响目标性状基因的有力工具，但遗传异质性的存在会降低连锁研究的效能，甚至可能给出数量性状基因座位置的有偏估计。许多复杂疾病被认为受多个基因影响，因此在连锁分析的许多实际应用中可能存在遗传异质性。我们考虑用多元正态混合模型来模拟基因座异质性，即允许只有一部分抽样家系在特定基因座上分离影响性状的等位基因。然而，对于正态混合模型，最大似然估计的经典渐近分布理论并不成立，因此使用重抽样方法来评估连锁和/或异质性检验。结果表明，考虑遗传异质性会提高检测连锁的效能。当基因座的遗传效应较小时或当在该基因座上不分离影响性状等位基因的家系百分比很高时，这种提高更为显著。
