Academic journal article Genetics

Hitchhiking Both Ways: Effect of Two Interfering Selective Sweeps on Linked Neutral Variation

Academic journal article Genetics

Hitchhiking Both Ways: Effect of Two Interfering Selective Sweeps on Linked Neutral Variation

Article excerpt

ABSTRACT

The neutral polymorphism pattern in the vicinity of a selective sweep can be altered by both stochastic and deterministic factors. Here, we focus on the impact of another selective sweep in the region of influence of a first one. We study the signature left on neutral polymorphism by positive selection at two closely linked loci, when both beneficial mutations reach fixation. We show that, depending on the timing of selective sweeps and on their selection coefficients, the two hitchhiking effects can interfere with each other, leading to less reduction in heterozygosity than a single selective sweep of the same magnitude and more importantly to an excess of intermediate-frequency variants relative to neutrality under some parameter values. This pattern can be sustained and potentially alter the detection of positive selection, including by provoking spurious detection of balancing selection. In situations where positive selection is suspected a priori at several closely linked loci, the polymorphism pattern in the region may also be informative about their selective histories.

(ProQuest: ... denotes formulae omitted.)

THE search for molecular signatures of positive selection has been amatter of intense research and applications in the recent years, motivated by the hope to better understand the genetic bases of adaptation and the recent history of populations (Bamshad and Wooding 2003; Nielsen et al. 2007). The footprints of positive selection on neutral polymorphism are the consequenceof the hitchhikingeffect (MaynardSmithand Haigh1974), and current methods to detect the men compass two main approaches. The first one is genome scans of neutral variation and is a top-down process. It consists of gathering polymorphism data widely distributed throughout the genome and summarizing them with a particular measure, be it the nucleotide diversity, the frequency spectrum of mutations (Nielsen et al. 2005), or the length and frequency of haplotypes [for ongoing selective sweeps (Sabeti et al. 2002; Voightet al. 2006)]. The loci exhibiting extreme values in the distribution of the measure are then considered as putative targets of positive selection (but see Teshima et al. 2006 for caveats of this method). The second approach, the candidate-gene approach, is a bottom-up process in which one wishes to test someevolutionary scenarios, for instance, for a gene (orQTL)ofknownfunction (see, for instance, Edelist et al. 2006). It consists of analyzing neutral polymorphism at a finer scale (of the order of the megabase or lower), to test if positive selection occurred, and to infer some parameters of the selective sweep such as the target and strength of selection. This fine-scale analysis can also be carried out in regions identified after a genome scan (a good example of this kind is Pool et al. 2006; for a more comprehensive review see Thornton et al. 2007). Here, we focus on this finer-scale analysis of polymorphism.

The most popular method for the fine-scale analysis of selective sweeps uses the information at several markers distributed in the small region of interest, to perform a composite likelihood-ratio test on the frequency spectrum (Kim and Stephan 2002), to jointly estimate the parameters of the selective sweep and the relative likelihood of selection vs. neutrality. This can be followed by a goodness-of-fit test to confirm the robustness of the estimated parameters against several demographic scenarios ( Jensen et al. 2005). Though efficient, this method can be affected by ascertainment biases (Thornton and Jensen 2007). Moreover, some factors-e.g., differences in recombination or mutation rates between the two sides of a selective sweep-can modify the fine-scale polymorphism pattern around the selective sweep in a systematic way (i.e., nonstochastically). Here, we focus on one particular modifying factor, namely the presence of another locus under positive selection in the region of influence of a selective sweep. …

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