Astles P A, Moore A J, Preziosi R F
Faculty of Life Sciences, The University of Manchester, Manchester, UK.
J Evol Biol. 2006 Jan;19(1):114-22. doi: 10.1111/j.1420-9101.2005.00997.x.
Advanced techniques for quantitative genetic parameter estimation may not always be necessary to answer broad genetic questions. However, simpler methods are often biased, and the extent of this determines their usefulness. In this study we compare family mean correlations to least squares and restricted error maximum likelihood (REML) variance component approaches to estimating cross-environment genetic correlations. We analysed empirical data from studies where both types of estimates were made, and from studies in our own laboratories. We found that the agreement between estimates was better when full-sib rather than half-sib estimates of cross-environment genetic correlations were used and when mean family size increased. We also note biases in REML estimation that may be especially important when testing to see if correlations differ from 0 or 1. We conclude that correlations calculated from family means can be used to test for the presence of genetic correlations across environments, which is sufficient for some research questions. Variance component approaches should be used when parameter estimation is the objective, or if the goal is anything other than determining broad patterns.
对于回答广泛的遗传学问题而言，先进的数量遗传参数估计技术并非总是必要的。然而，更简单的方法往往存在偏差，而偏差的程度决定了它们的实用性。在本研究中，我们将家系均值相关性与最小二乘法以及限制误差最大似然法（REML）方差分量法进行比较，以估计跨环境遗传相关性。我们分析了来自已进行这两种类型估计的研究以及我们自己实验室研究的实证数据。我们发现，当使用全同胞而非半同胞的跨环境遗传相关性估计值且家系平均规模增加时，估计值之间的一致性更好。我们还注意到REML估计中的偏差，在检验相关性是否不同于0或1时，这些偏差可能尤为重要。我们得出结论，从家系均值计算出的相关性可用于检验跨环境遗传相关性的存在，这对于一些研究问题而言已足够。当目标是参数估计，或者目标不是确定广泛模式时，应使用方差分量法。
