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单核苷酸多态性(SNP)标记的方差成分比较、方差分析以及子代对中亲值回归(ROMP)方法

Comparison of variance components, ANOVA and regression of offspring on midparent (ROMP) methods for SNP markers.

作者信息

Pugh E W, Papanicolaou G J, Justice C M, Roy-Gagnon M H, Sorant A J, Kingman A, Wilson A F

机构信息

Center for Inherited Disease Research, Johns Hopkins University, Baltimore, Maryland, USA.

出版信息

Genet Epidemiol. 2001;21 Suppl 1:S794-9. doi: 10.1002/gepi.2001.21.s1.s794.

Abstract

An extension of the traditional regression of offspring on midparent (ROMP) method was used to estimate the heritability of the trait, test for marker association, and estimate the heritability attributable to a marker locus. The fifty replicates of the Genetic Analysis Workshop (GAW) 12 simulated general population data were used to compare the ROMP method with the variance components method as implemented in SOLAR as a test for marker association, and to a standard analysis of variance (ANOVA) method. Large sample statistical properties of the ROMP and ANOVA methods were compared using 2,000 replicates resampled from the families of the original 50 replicates. Overall, the power to detect a completely associated single nucleotide polymorphism (SNP) marker was high, and the type I error rates were similar to nominal significance levels for all three methods. The standard deviations of the estimates of the heritability of the trait were large for both SOLAR and ROMP, but the estimates were, on average, close to those of the generating model for both methods. However, on average, SOLAR overestimated the heritability attributable to the associated SNP marker (by 256%) while ROMP underestimated it (by 26%).

摘要

使用传统的子代对中亲回归(ROMP)方法的扩展来估计性状的遗传力、检验标记关联并估计归因于标记位点的遗传力。遗传分析研讨会(GAW)12模拟的一般人群数据的50个重复样本用于将ROMP方法与SOLAR中实施的方差成分法作为标记关联检验进行比较,并与标准方差分析(ANOVA)方法进行比较。使用从原始50个重复样本的家系中重新采样的2000个重复样本比较ROMP和ANOVA方法的大样本统计特性。总体而言,检测完全关联的单核苷酸多态性(SNP)标记的能力很高,并且所有三种方法的I型错误率均与名义显著性水平相似。对于SOLAR和ROMP,性状遗传力估计值的标准差都很大,但平均而言,两种方法的估计值都接近生成模型的估计值。然而,平均而言,SOLAR高估了归因于相关SNP标记的遗传力(高估了256%),而ROMP低估了它(低估了26%)。

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