{{DISPLAYTITLE:Ka/Ks ratio}} In genetics, the Ka/Ks ratio, also known as ω or dN/dS ratio, is used to estimate the balance between neutral mutations, purifying selection and beneficial mutations acting on a set of homologous protein-coding genes. It is calculated as the ratio of the number of nonsynonymous substitutions per non-synonymous site (Ka), in a given period of time, to the number of synonymous substitutions per synonymous site (Ks), in the same period. The latter are assumed to be neutral, so that the ratio indicates the net balance between deleterious and beneficial mutations. Values of Ka/Ks significantly above 1 are unlikely to occur without at least some of the mutations being advantageous. If beneficial mutations are assumed to make little contribution, then Ka/Ks estimates the degree of evolutionary constraint. Context Selection acts on variation in phenotypes, which are often the result of mutations in protein-coding genes. The genetic code is written in DNA sequences as codons, groups of three nucleotides. Each codon represents a single amino acid in a protein chain. However, there are more codons (64) than amino acids found in proteins (20), so many codons are effectively synonyms. For example, the DNA codons TTT and TTC both code for the amino acid Phenylalanine, so a change from the third T to C makes no difference to the resulting protein. On the other hand, the codon GAG codes for Glutamic acid while the codon GTG codes for Valine, so a change from the middle A to T does change the resulting protein, for better or (more likely) worse, so the change is not a synonym. These changes are illustrated in the tables below. The Ka/Ks ratio measures the relative rates of synonymous and nonsynonymous substitutions at a particular site. Methods Methods for estimating Ka and Ks use a sequence alignment of two or more nucleotide sequences of homologous genes that code for proteins (rather than being genetic switches, controlling development or the rate of activity of other genes). Methods can be classified into three groups: approximate methods, maximum-likelihood methods, and counting methods. However, unless the sequences to be compared are distantly related (in which case maximum-likelihood methods prevail), the class of method used makes a minimal impact on the results obtained; more important are the assumptions implicit in the chosen method. Approximate methods Approximate methods involve three basic steps: (1) counting the number of synonymous and nonsynonymous sites in the