In the Age of Genomics, Statistical Tests of Selective Neutrality

Jennifer Blessy^*
*Correspondence: Jennifer Blessy, Department of Biomedical Sciences for Health, University of Milan, Milan, Italy, Email:

Keywords

Haplotype structure • Genomic data • Statistical tests

Introduction

The concept of natural selection was first proposed by Charles Darwin in 1859 in his seminal work, On the Origin of Species. However, it was not until the mid-20th century that researchers began to develop mathematical models to study the effects of natural selection on genetic variation. The early models of natural selection were based on assumptions of strong selection and rapid evolution. However, in the 1960s, Motoo Kimura proposed the neutral theory of molecular evolution, which challenged these assumptions. The neutral theory of molecular evolution proposes that most genetic variation is due to random genetic drift, rather than natural selection. According to this theory, most mutations are neutral or nearly neutral, meaning that they do not have a significant effect on the fitness of the organism. Therefore, changes in the frequency of these mutations are due to chance rather than selection. Kimura's theory was based on the observation that the rate of nucleotide substitutions in non-coding regions of DNA was much higher than the rate of amino acid substitutions in coding regions. This suggested that most mutations were not subject to selection because they did not affect the function of the protein. Kimura's theory was controversial when it was first proposed, but subsequent studies confirmed the prediction that non-coding regions of DNA were more variable than coding regions.

Literature Review

The neutral theory of molecular evolution provided a framework for testing whether changes in the frequency of genetic variants were due to selection or chance. Researchers developed a variety of statistical tests to detect selective neutrality, including tests of allele frequency, haplotype structure, and population differentiation. Tests of allele frequency compare the observed frequency of an allele in a population to the frequency expected under neutral evolution. One commonly used test is the Tajima's D test, which compares the difference between two estimates of genetic diversity. One estimate is based on the number of segregating sites, or the number of sites where there is variation among individuals in the population. The other estimate is based on the average number of pairwise differences between individuals in the population. Tajima's D test compares the two estimates of diversity and tests whether they are significantly different from each other [1,2].

Discussion

Another test of allele frequency is the McDonald-Kreitman test, which compares the ratio of non-synonymous to synonymous substitutions in coding regions of the genome to the ratio of non-synonymous to synonymous polymorphisms in non-coding regions. This test is based on the assumption that non-synonymous substitutions are more likely to be subject to selection than synonymous substitutions. Therefore, if a gene has a higher ratio of nonsynonymous to synonymous substitutions than expected, this suggests that the gene is under positive selection. Tests of haplotype structure examine the pattern of linkage disequilibrium (LD) in a population. LD refers to the nonrandom association between alleles; Statistical tests of selective neutrality are used to determine whether genetic variation within a population is the result of random genetic drift or natural selection. With the advent of genomics, new statistical methods have been developed to better understand the role of natural selection in shaping genetic variation [3-6].

Conclusion

These methods have enabled researchers to identify regions of the genome that are under strong positive or negative selection, and to investigate the evolutionary forces that have driven the emergence of new traits. Additionally, these methods have helped to elucidate the genetic basis of complex diseases and to identify potential targets for therapeutic intervention. However, these methods are not without their limitations and challenges, such as the difficulty in distinguishing between different modes of selection and the need for large sample sizes to detect subtle signals of selection. Overall, statistical tests of selective neutrality have become an important tool for understanding the evolutionary history of populations and the genetic basis of complex traits in the age of genomics.

Acknowledgement

None

Conflict of Interest

None

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