Statistical methods for detecting natural selection from genomic data

Abstract

In the study of molecular and phenotypic evolution, understanding the relative importance of random genetic drift and positive selection as the mechanisms for driving divergences between populations and maintaining polymorphisms within populations has been a central issue. A variety of statistical methods has been developed for detecting natural selection operating at the amino acid and nucleotide sequence levels. These methods may be largely classified into those aimed at detecting recurrent and/or recent/ongoing natural selection by utilizing the divergence and/or polymorphism data. Using these methods, pervasive positive selection has been identified for protein-coding and non-coding sequences in the genomic analysis of some organisms. However, many of these methods have been criticized by using computer simulation and real data analysis to produce excessive false-positives and to be sensitive to various disturbing factors. Importantly, some of these methods have been invalidated experimentally. These facts indicate that many of the statistical methods for detecting natural selection are unreliable. In addition, the signals that have been believed as the evidence for fixations of advantageous mutations due to positive selection may also be interpreted as the evidence for fixations of deleterious mutations due to random genetic drift. The genomic diversity data are rapidly accumulating in various organisms, and detection of natural selection may play a critical role for clarifying the relative role of random genetic drift and positive selection in molecular and phenotypic evolution. It is therefore important to develop reliable statistical methods that are unbiased as well as robust against various disturbing factors, for inferring natural selection.

Positive selection for gains of N-linked glycosylation sites in hemagglutinin during evolution of H3N2 human influenza A virus

“…These observations indicate that the single-site analysis is suitable for detecting recurrent natural selection (Suzuki, 2010), whereas the single-substitution analysis is suitable for detecting episodic natural selection.…”

Human adaptation to the hypoxia of high altitude: the Tibetan paradigm from the pregenomic to the postgenomic era

“…They identified chromosomal regions of positive selective sweeps by calculating two separate genomic statistics: the cross-population extended haplotype homozygosity statistic (XP-EHH), and the integrated haplotype score (iHS) for 200-kb non-overlapping genomic regions. The basis of these and other techniques is described in recent reviews (15, 82). They then scanned the positive regions for 247 a priori selected candidate genes (based on their involvement in oxygen homeostasis) and found 10 genes that were located in or near these regions.…”

A Genome Scan for Selection Signatures in Pigs

“…Most studies of selection signatures have only implemented a single method, but different methods emphasize different information in the data and are sensitive to different categories of selection signatures [13,14]. Hence, only applying a single method to detect selection signatures might result in some unknown bias.…”

“…Thus, selection signatures could be detected through the decay of linkage disequilibrium and the variation of allele frequency. These methods for detecting selection signatures can be grouped into three categories according to the information used: population differentiation, site-frequency spectrum and linkage disequilibrium [13,14]. Corresponding to these groups, the F ST , the Tajima’s D test, the Cross Population Extend Haplotype Homozygosity Test (XPEHH) and the long range haplotype (LRH) are the representative methods widely used in identifying selection signatures.…”