In accordance with other methods to estimating brand new variables of positive selection, i have generated multiple literally unlikely presumptions. Since BGS seemingly have seemingly little affect the newest ? and you can p estimates (Dining table step one), an element of the question is the end result out of demographic items towards SSW quotes. Inclusion of these difficulty for the tricks for quoting choices variables was a challenging problem. Yet not, i note that the brand new wide spread to a top volume regarding an effective positive mutation inside an inhabitants spread-over a-two-dimensional ecosystem is much slow than in an excellent panmictic inhabitants, which implies that there’s much more window of opportunity for recombination to help you dilute the results off SSWs than simply having panmixia (47). This course of action perform therefore result in all of our ? estimates as reduced than the correct viewpoints, while the p estimates are larger.

Product and methods

Second, we have assumed “hard” sweeps, based on unique mutations, rather than “soft sweeps” based on recurrent mutations or mutations arising from standing variation (48). If soft sweeps are prevalent in Drosophila, as has recently been argued (49), then the same pattern of bias as from a subdivided population would arise (50, 51). (Note, however, that gene conversion of a favored mutation onto an ancestral haplotype could generate the appearance of a soft sweep.) The opposite would apply to incomplete sweeps (52), if their incidence in a gene is correlated with its K_A value. These were omitted from our models because they do not affect K_A. However, the lack of evidence for intermediate-frequency NS and synonymous variants in pooled site frequency spectra for the Rwandan population of D. melanogaster, as seen in figure 5 of ref. 33, suggests that incomplete sweeps are relatively infrequent in this population. If favorable mutations do not arise as single events, the estimates of the proportions of favorable mutations are likely to be overestimated as well.

These considerations mean that the estimates of the parameters of positive selection obtained in this and previous studies need to be treated with caution Frisco TX escort service, and will no doubt be revised with future improvements in inference procedures. It seems clear, however, that hitchhiking effects greatly reduce neutral or nearly neutral sequence diversity in genes in normally recombining regions of the Drosophila genome. There is increasing evidence that this is also true for many other organisms (1, 3). Such processes have important implications for attempts to estimate demographic parameters, which usually ignore these complications, as has been pointed out before (53 ? ? –56). This is especially important when selection at linked sites distorts gene genealogies and hence site frequency spectra, because these are the main basis for inferring demographic parameters. There is evidence from our unbinned data for mel-yak that K_A is weakly positively correlated with the proportion of singletons at synonymous sites (Spearman partial rank correlation, ? = 0.044, P = 0.002), consistent with increased distortions of the frequency spectra caused by hitchhiking in genes with large K_A, as was previously found by Andolfatto (15). The problem of relating the magnitude of these effects to the BGS and SSW models remains to be explored.

Primary Data Analyses.

We used polymorphism data for coding sequences of 7,099 autosomal genes, using 17 haploid genomes from the Gikongoro (Rwanda) population of Drosophila melanogaster provided by the Drosophila Population Genomics Project 2 (57), with Drosophila yakuba as an outgroup. The coding sequence data were filtered and analyzed as described in materials and methods in ref. 19. We excluded 225 genes located in the autosomal heterochromatic regions and on chromosome 4, where crossing over is absent (19, 58). We obtained diversity and divergence statistics for synonymous and NS sites, as well as for 5?- and 3?-UTRs for D. melanogaster genes with UTR annotations. For the analyses of UTRs, we followed the annotations of Flybase, version 5.33, masking any UTRs included in coding sequences and excluding UTRs with no available sequence in the outgroup, leaving a dataset of 5,992 genes with 3?- and/or 5?-UTRs. After applying a Kimura two-parameter correction (59), the mean level of divergence of UTR sequences between species, K_U, was 0.10, which is intermediate between the mean values for NS sites (K_A = 0.038) and synonymous sites (K_S = 0.262).