Christiansen F B, Otto S P, Bergman A, Feldman M W
Department of Ecology and Genetics, University of Aarhus, Denmark.
Theor Popul Biol. 1998 Jun;53(3):199-215. doi: 10.1006/tpbi.1997.1358.
R.A. Fisher and H.J. Muller argued in the 1930s that a major evolutionary advantage of recombination is that it allows favorable mutations to be combined within an individual even when they first appear in different individuals. This effect is evaluated in a two-locus, two-allele model by calculating the average waiting time until a new genotypic combination first appears in a haploid population. Three approximations are developed and compared with Monte Carlo simulations of the Wright-Fisher process of random genetic drift in a finite population. First, a crude method, based on the deterministic accumulation of single mutants, produces a waiting time of 1/square root of N mu(2) with no recombination and [formula: see text] with recombination between the two loci, where mu is the mutation rate, N is the haploid population size, and R is the recombination rate. Second, the waiting time is calculated as the expected value of a heterogeneous geometric distribution obtained from a branching process approximation. This gives accurate estimates for small values of N mu large. The estimates for small values of N mu are considerably lower than the simulated values. Finally, diffusion analysis of the Wright-Fisher process provides accurate estimates for N mu small, and the time scales of the diffusion process show a difference between R = 0 and for R >> 0 of the same order of magnitude as seen in the deterministic analysis. In the absence of recombination, accurate approximations to the waiting time are obtained by using the branching process for high N mu and the diffusion approximation for low N mu. For low N mu the waiting time is well approximated by 1/the square root of 8N2 mu(3). With R >> 0, the following dependence on N mu is observed: For N mu > 1 the waiting time is virtually independent of recombination and is well described by the branching process approximation. For N mu approximately equal to 1 the waiting time is well described by a simplified diffusion approximation that assumes symmetry in the frequencies of single mutants. For N mu << 1 the waiting time is well described by the diffusion approximation allowing asymmetry in the frequencies of single mutants. Recombination lowers the waiting time until a new genotypic combination first appears, but the effect is small compared to that of the mutation rate and population size. For large N mu, recombination has a negligible effect, and its effect is strongest for small N mu, in which case the waiting time approaches a fixed fraction of the waiting time for R = 0. Free recombination lowers the waiting time to about 45% of the waiting time for absolute linkage for small N mu. Selection has little effect on the importance of recombination in general.
R.A.费希尔和H.J.米勒在20世纪30年代提出,重组的一个主要进化优势在于,即使有利突变最初出现在不同个体中,它也能使它们在一个个体内组合起来。通过计算新基因型组合首次出现在单倍体群体中的平均等待时间,在一个双位点、双等位基因模型中评估了这种效应。开发了三种近似方法,并与有限群体中随机遗传漂变的赖特 - 费希尔过程的蒙特卡罗模拟进行了比较。首先,一种基于单个突变体确定性积累的粗略方法,在没有重组的情况下产生的等待时间为1/√(Nμ²),在两个位点之间有重组时为[公式:见原文],其中μ是突变率,N是单倍体群体大小,R是重组率。其次,等待时间被计算为从分支过程近似得到的非均匀几何分布的期望值。对于较大的Nμ值,这给出了准确的估计。对于较小的Nμ值,估计值明显低于模拟值。最后,赖特 - 费希尔过程的扩散分析对于较小的Nμ值提供了准确的估计,并且扩散过程的时间尺度显示出R = 0和R >> 0之间的差异与确定性分析中看到的相同数量级。在没有重组的情况下,对于高Nμ值使用分支过程,对于低Nμ值使用扩散近似,可以得到等待时间的准确近似。对于低Nμ值,等待时间可以很好地近似为1/√(8N²μ³)。当R >> 0时,观察到对Nμ的以下依赖性:对于Nμ > 1,等待时间实际上与重组无关,并且可以通过分支过程近似很好地描述。对于Nμ ≈ 1,等待时间可以通过假设单个突变体频率对称的简化扩散近似很好地描述。对于Nμ << 1,等待时间可以通过允许单个突变体频率不对称的扩散近似很好地描述。重组降低了新基因型组合首次出现的等待时间,但与突变率和群体大小相比,这种效应较小。对于较大的Nμ值,重组的影响可以忽略不计,并且其效应在较小的Nμ值时最强,在这种情况下,等待时间接近R = 0时等待时间的固定比例。对于较小的Nμ值,自由重组将等待时间降低到绝对连锁时等待时间的约45%。一般来说,选择对重组的重要性影响很小。