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A Building Block Model for Quantitative Genetics

机译:定量遗传学的构建模块模型

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摘要

We introduce a quantitative genetic model for multiple alleles which permits the parameterization of the degree, D, of dominance of favorable or unfavorable alleles. We assume gene effects to be random from some distribution and independent of the D's. We then fit the usual least-squares population genetic model of additive and dominance effects in an infinite equilibrium population to determine the five genetic components--additive variance σ(a)(2), dominance variance σ(d)(2), variance of homozygous dominance effects d(2), covariance of additive and homozygous dominance effects d(1), and the square of the inbreeding depression h--required to treat finite populations and large populations that have been through a bottleneck or in which there is inbreeding. The effects of dominance can be summarized as functions of the average, D, and the variance, σ(D)(2). An important distinction arises between symmetrical and nonsymmetrical distributions of gene effects. With symmetrical distributions d(1) = -d(2)/2 which is always negative, and the contribution of dominance to σ(a)(2) is equal to d(2)/2. With nonsymmetrical distributions there is an additional contribution H to σ(a)(2) and -H/2 to d(1), the sign of H being determined by D and the skew of the distribution. Some numerical evaluations are presented for the normal and exponential distributions of gene effects, illustrating the effects of the number of alleles and of the variation in allelic frequencies. Random additive by additive (a*a) epistatic effects contribute to σ(a)(2) and to the a*a variance, σ &, the relative contributions depending on the number of alleles and the variation in allelic frequencies. There are plausible situations where the contribution to σ(a)(2) can be larger than that to σ &. When the number of alleles is large and there is little variation in allelic frequencies most of the variance is σ &. The effects of the genetic components on the additive variance within finite populations are discussed briefly.
机译:我们介绍了多个等位基因的定量遗传模型,该模型允许对有利或不利等位基因的优势度D进行参数化。我们假设基因效应从某些分布是随机的,并且与D无关。然后,我们对无限均衡总体中加性和支配效应的通常最小二乘总体遗传模型进行拟合,以确定五个遗传成分-加性方差σ(a)(2),支配方差σ(d)(2),方差纯优势效应d(2),加性和纯合优势效应d(1)的协方差以及近亲衰退h的平方-需要治疗通过瓶颈或存在瓶颈的有限种群和大种群近交。优势的影响可以概括为平均值D和方差σ(D)(2)的函数。基因效应的对称和非对称分布之间存在重要区别。对称分布d(1)= -d(2)/ 2始终为负,并且对σ(a)(2)的支配作用等于d(2)/ 2。对于非对称分布,对σ(a)(2)有附加的贡献H,对d(1)有-H / 2的附加贡献,H的符号由D和分布的偏斜确定。提出了一些关于基因效应的正态分布和指数分布的数值评估,说明了等位基因数量和等位基因频率变化的影响。由加性(a * a)上位效应引起的随机加性有助于σ(a)(2)和a * a方差σ&,其相对贡献取决于等位基因的数量和等位基因频率的变化。在某些情况下,对σ(a)(2)的贡献可能大于对σ&的贡献。当等位基因数量大且等位基因频率几乎没有变化时,大多数方差为σ&。简要讨论了遗传成分对有限种群内加性方差的影响。

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