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Adaptive evolution in the human genome
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Adaptive evolution in the human genome : ウィキペディア英語版
Adaptive evolution in the human genome

Adaptive evolution results from the propagation of advantageous mutations through positive selection. This is the modern synthesis of the process which Darwin and Wallace originally identified as the mechanism of evolution. However, in the last half century there has been considerable debate as to whether evolutionary changes at the molecular level are largely driven by natural selection or random genetic drift. Unsurprisingly, the forces which drive evolutionary changes in our own species’ lineage have been of particular interest. Quantifying adaptive evolution in the human genome gives insights into our own evolutionary history and helps to resolve this neutralist-selectionist debate. Identifying specific regions of the human genome that show evidence of adaptive evolution helps us find functionally significant genes, including genes important for human health, such as those associated with diseases.
==Methods==
The methods used to identify adaptive evolution are generally devised to test the null hypothesis of neutral evolution, which, if rejected, provides evidence of adaptive evolution. These tests can be broadly divided into two categories.
Firstly, there are methods that use a comparative approach to search for evidence of function altering mutations. The dN/dS rates-ratio test estimates ω, the rates at which nonsynonymous ('dN') and synonymous ('dS') nucleotide substitutions occur ('synonymous' nucleotide substitutions do not lead to a change in the coding amino acid, while 'nonsynonymous' ones do). In this model, neutral evolution is considered the null hypothesis, in which dN and dS approximately balance so that ω ≈ 1. The two alternative hypotheses are a ''relative absence'' of nonsynonymous substitutions (dN < dS; ω < 1), suggesting the effect on fitness ('fitness effect', or 'selection pressure') of such mutations is negative (purifying selection has operated over time; or a ''relative excess'' of nonsynonymous substitutions (dN > dS; ω > 1), indicating positive effect on fitness, i.e. diversifying selection (Yang and Bielawski 2000).
The McDonald-Kreitman (MK) test quantifies the amount of adaptive evolution occurring by estimating the proportion of nonsynonymous substitutions which are adaptive, referred to as α (McDonald and Kreitman 1991, Eyre-Walker 2006). α is calculated as: α = 1-(dsps/dnps), where dn and ds are as above, and pn and ps are the number of nonsynonymous (fitness effect assumed neutral or deleterious) and synonymous (fitness effect assumed neutral) polymorphisms respectively (Eyre-Walker 2006).
Note, both these tests are presented here in basic forms, and these tests are normally modified considerably to account for other factors, such as the effect of slightly deleterious mutations.
The other methods for detecting adaptive evolution use genome wide approaches, often to look for evidence of selective sweeps. Evidence of complete selective sweeps is shown by a decrease in genetic diversity, and can be inferred from comparing the patterns of the Site Frequency Spectrum (SFS, i.e. the allele frequency distribution) obtained with the SFS expected under a neutral model (Willamson et al. 2007). Partial selective sweeps provide evidence of the most recent adaptive evolution, and the methods identify adaptive evolution by searching for regions with a high proportion of derived alleles (Sabeti et al. 2006).
Examining patterns of Linkage Disequilibrium (LD) can locate signatures of adaptive evolution (Hawks et al. 2007, Voight et al. 2006). LD tests work on the basic principle that, assuming equal recombination rates, LD will rise with increasing natural selection. These genomic methods can also be applied to search for adaptive evolution in non-coding DNA, where putatively neutral sites are hard to identify (Ponting and Lunter 2006).
Another recent method used to detect selection in non-coding sequences examines insertions and deletions (indels), rather than point mutations (Lunter et al. 2006), although the method has only been applied to examine patterns of negative selection.

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