MAXIMUM-LIKELIHOOD ESTIMATES OF SELECTION COEFFICIENTS FROM DNA SEQUENCE DATA.

Area of Science:

• Evolutionary biology
• Molecular evolution
• Bioinformatics

Background:

• Phylogenetic relationships reflect evolutionary history and the impact of selection on DNA sequences.
• Selection can reduce deleterious nucleotide frequencies, offering a method to test for its presence.
• Comparing theoretical nucleotide frequencies with observed values can infer selection, provided sequences are not too closely related and have undergone sufficient mutation.

Purpose of the Study:

• To develop an algorithm for maximum-likelihood estimation of selection coefficients using phylogenetic information.
• To introduce a k-allele model for measuring relative mutation rates and relatedness.
• To evaluate the likelihood of selection acting on sequence data.

Main Methods:

• Developed a novel algorithm for maximum-likelihood estimation of selection coefficients.
• Utilized a k-allele model incorporating phylogenetic data to assess mutation rates and relatedness.
• Applied the method to NS2 genes of influenza viruses and MHC genes of mice.

Main Results:

• Maximum-likelihood estimates indicated very high mutation rates for influenza viruses.
• Statistically significant selection was found to maintain specific coding sequences in influenza and MHC genes.
• Selection at the antigen recognition site of MHC genes was reversed, promoting genetic variation.

Conclusions:

• The developed method effectively estimates selection coefficients from sequence data.
• Influenza and MHC genes exhibit strong purifying selection, while MHC antigen sites show diversifying selection.
• Phylogenetic analysis provides crucial insights into the evolutionary forces shaping genetic sequences.