Wiehe T H, Stephan W
Department of Zoology, University of Maryland, College Park 20742.
Mol Biol Evol. 1993 Jul;10(4):842-54. doi: 10.1093/oxfordjournals.molbev.a040046.
Begun and Aquadro have demonstrated that levels of nucleotide variation correlate with recombination rate among 20 gene regions from across the genome of Drosophila melanogaster. It has been suggested that this correlation results from genetic hitchhiking associated with the fixation of strongly selected mutants. The hitchhiking process can be described as a series of two-step events. The first step consists of a strongly selected substitution wiping out linked variation in a population; this is followed by a recovery period in which polymorphism can build up via neutral mutations and random genetic drift. Genetic hitchhiking has previously been modeled as a steady-state process driven by recurring selected substitutions. We show here that the characteristic parameter of this steady-state model is alpha v, the product of selection intensity (alpha = 2Ns) and the frequency of beneficial mutations v (where N is population size and s is the selective advantage of the favored allele). We also demonstrate that the steady-state model describes the hitchhiking process adequately, unless the recombination rate is very low. To estimate alpha v, we use the data of DNA sequence variation from 17 D. melanogaster loci from regions of intermediate to high recombination rates. We find that alpha v is likely to be > 1.3 x 10(-8). Additional data are needed to estimate this parameter more precisely. The estimation of alpha v is important, as this parameter determines the shape of the frequency distribution of strongly selected substitutions.
贝根和阿夸德罗已经证明，核苷酸变异水平与黑腹果蝇基因组中20个基因区域的重组率相关。有人认为，这种相关性是由与强选择突变体固定相关的遗传搭便车导致的。遗传搭便车过程可以描述为一系列两步事件。第一步包括一个强选择替代消除群体中的连锁变异；接下来是一个恢复期，在此期间多态性可以通过中性突变和随机遗传漂变积累起来。遗传搭便车以前被建模为一个由反复出现的选择替代驱动的稳态过程。我们在此表明，这个稳态模型的特征参数是αv，即选择强度（α = 2Ns）与有益突变频率v的乘积（其中N是种群大小，s是有利等位基因的选择优势）。我们还证明，除非重组率非常低，稳态模型能充分描述遗传搭便车过程。为了估计αv，我们使用了来自中等至高重组率区域的17个黑腹果蝇基因座的DNA序列变异数据。我们发现αv可能大于1.3×10⁻⁸。需要更多数据来更精确地估计这个参数。αv的估计很重要，因为这个参数决定了强选择替代频率分布的形状。
