两种常用数量性状连锁分析方法的原理和进展

在复杂性状疾病的家系连锁研究中，Haseman-Elston回归分析和方差组成模型是常用的两种数量性状连锁分析方法。前者主要针对同胞对的性状值差或和的平方进行回归分析；后者引用方差组成模型，将数量性状分解为遗传方差和环境方差，可估计二者对表型的影响。两种方法可应用于同胞对、核心家系或扩展家系，定位数量性状基因座。本文对这两种模型的原理、算法及其进展进行了综述，并给出了常用的统计软件包。 Abstract：In this article, we discussed two model-free methods for detecting genetic linkage for quantitative traits, Haseman-Elston regression approach and variance components approach. The former is a regression approach for detecting linkage based on the squared difference or squared sums in quantitative trait values of sib-pairs and their estimated marker IBD scores. The latter can jointly model covariate effects along with variance components, including genetic component and non-genetic sources of variability. We have outlined the model assumption, the algorithm and the extensions for the both methods.

Two Approaches of Quantitative-Trait Linkage Analysis

In this article, we discussed two model-free methods for detecting genetic linkage for quantitative traits, Haseman-Elston regression approach and variance components approach. The former is a regression approach for detecting linkage based on the squared difference or squared sums in quantitative trait values of sib-pairs and their estimated marker IBD scores. The latter can jointly model covariate effects along with variance components, including genetic component and non-genetic sources of variability. We have outlined the model assumption, the algorithm and the extensions for the both methods.