A simulation study concerning the effect of varying the residual phenotypic correlation on the power of bivariate quantitative trait loci linkage analysis
A simulation study concerning the effect of varying the residual phenotypic correlation on the power of bivariate quantitative trait loci linkage analysis
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DOI：
10.1023/b:bege.0000013727.15845.f8
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发表时间：
2004-03-01
影响因子：
2.6
通讯作者：
Duffy, DL
中科院分区：
文献类型：
--
作者：
Evans, DM;Duffy, DL
双变量方差分量 (VC) 连锁分析的功效受到变量之间表型相关性的大小和来源的影响。特别是，一些作者提出，当数量性状基因座（QTL）和残余变异源诱导相反方向的表型共变时，检测连锁的功效最大，而当独特的环境变异源诱导与QTL相反方向的共变时，检测连锁的功效最大。本研究的目的是进一步研究改变变量之间的残差相关性对双变量方差分量连锁分析中检测连锁的能力的影响。对多效性影响两个变量的双等位基因 QTL 进行了数据模拟。在多种情况下计算检测连锁的功效，其中由共享变异源和独特变异源产生的表型协方差的比例是不同的。这些模拟是针对 QTL 对两个变量影响相同的情况以及 QTL 对每个变量的贡献不相等的情况进行的。我们的结果证实，在双变量连锁测试中检测QTL的能力取决于变量之间剩余相关性的大小和来源，当QTL和独特的环境变异源诱导相反方向的表型共变时，该能力最大。我们还发现，当 QTL 对两个变量的影响不同时，随着独特环境变异源之间的相关性从 0.6 增加到 0.9，检测连锁的能力显着增加。在各种遗传模型下，包括当等位基因频率和 QTL 显性不相等时，都获得了类似的结果。我们建议，提高检测 QTL 能力的一个有前途的策略可能是从变量中收集数据，这些变量要么有良好的观察证据（例如，来自双胞胎数据的多元结构方程模型），要么有合理的理论论据，即 QTL 和环境因素诱导相反方向的共变。
The power of bivariate variance components (VC) linkage analysis is affected by the size and source of the phenotypic correlation between variables. In particular, several authors have suggested that the power to detect linkage is greatest when the quantitative trait locus (QTL) and residual sources of variation induce phenotypic covariation in opposite directions, and that this increase in power is greatest when unique environmental sources of variation induce covariation in the direction opposite to the QTL. The purpose of the present study was to investigate further the effect of varying the residual correlation between variables on the power to detect linkage in a bivariate variance components linkage analysis. Data were simulated for a biallelic QTL that pleiotropically influenced two variables. The power to detect linkage was calculated under a variety of situations in which the proportion of phenotypic covariance resulting from shared sources of variation and from unique sources of variation was varied. These simulations were performed for the case in which the QTL affected the two variables equally and also for the case in which the QTL made unequal contributions to each variable. Our results confirm that the power to detect QTLs in a bivariate test for linkage depends upon the size and source of the residual correlation between variables, being greatest when the QTL and unique environmental sources of variation induce phenotypic covariation in opposite directions. We also found that when the QTL affected the two variables unequally, the power to detect linkage increased markedly as the correlation between unique environmental sources of variation increased from 0.6 to 0.9. Similar results were obtained under a variety of genetic models, including when there were unequal allele frequencies and dominance at the QTL. We suggest that a promising strategy to increase the power to detect QTLs might be to collect data from variables where there is either good observational evidence (e.g., from multivariate structural equation modeling of twin data) or a sound theoretical argument that the QTL and environmental factors induce covariation in opposite directions.