McDonald-Kreitman Test
Also known as: MK test, Positive selection test
The McDonald-Kreitman (MK) test is a statistical method for detecting adaptive evolution by comparing ratios of synonymous and nonsynonymous substitutions within and between species. Developed by James McDonald and Martin Kreitman in 1991, this test exploits the key insight that neutral mutations accumulate at similar rates within and between species, while adaptive (nonsynonymous) substitutions should be enriched between species if they have been fixed by positive selection. The MK test has become a standard tool in molecular evolutionary biology for identifying genes under natural selection.
Key highlights
- Simple to perform and interpret, requiring basic sequence alignment
- Does not assume a specific selection model or fitness landscape
- Compares fixed differences and polymorphisms, accounting for mutation rates
- Can detect positive selection even when effect sizes are modest
- Provides an estimate of the proportion of adaptive substitutions
Intuition
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How it works
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When to use it
Use the MK test when you have sequence data from two species and polymorphism data from at least one species, and wish to test whether a gene has experienced positive selection. It is particularly effective for protein-coding genes in organisms with available population data. The MK test works best when mutation rates are similar between species and when the species are not too distantly diverged. Avoid the test when synonymous substitution rates are elevated due to positive selection on codon usage or other non-neutral forces.
Strengths & limitations
- Simple to perform and interpret, requiring basic sequence alignment
- Does not assume a specific selection model or fitness landscape
- Compares fixed differences and polymorphisms, accounting for mutation rates
- Can detect positive selection even when effect sizes are modest
- Provides an estimate of the proportion of adaptive substitutions
- Assumes similar mutation rates between species and at synonymous sites, which may be violated
- Limited power when synonymous substitution rates are low (few independent events)
- Cannot pinpoint the target of selection within a gene
- Sensitive to demographic history and population structure, which affect polymorphism ratios
- Codon usage bias and other non-selective forces can inflate nonsynonymous substitution rates
Common pitfalls
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Applications
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Frequently asked
What does it mean if Dn/Ds > Pn/Ps?
This pattern suggests that nonsynonymous substitutions are overrepresented in fixed differences compared to polymorphisms. This is consistent with positive selection having fixed advantageous nonsynonymous mutations. Conversely, if Dn/Ds < Pn/Ps, it suggests purifying selection removing nonsynonymous changes.
Why are synonymous sites assumed to be neutral?
Synonymous changes do not alter amino acid sequences, so they were traditionally assumed to be invisible to selection. However, synonymous sites can affect mRNA stability, translation efficiency, and codon usage, meaning they may experience weak selection. This assumption should be checked.
Can the MK test detect weak positive selection?
Yes, if sample sizes are large. The test compares ratios, so even modest enrichment of nonsynonymous substitutions can be significant with many sites. However, power decreases as effect sizes diminish, and very weak selection may be undetectable.
How are synonymous and nonsynonymous sites counted when codons overlap or have incomplete information?
Standard approaches count potential synonymous and nonsynonymous sites based on codon positions. For example, third codon positions are often silent (synonymous), while first and second positions are typically nonsynonymous. Detailed counting accounts for wobble base pairing and degenerate codon patterns.
Sources
- 1.McDonald, J. H., & Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in Drosophila. Nature, 351(6328), 652–654.
- 2.Smith, N. G., & Eyre-Walker, A. (2002). Estimating the proportion of sites subject to positive selection across a large dataset. Genetics, 160(3), 1079–1086.
- 3.Charlesworth, B. (2010). The rate of adaptive evolution. Trends in Ecology & Evolution, 11(1), 22–26.
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Cite this page
ScholarGate. (2026, June 3). McDonald-Kreitman Test. ScholarGate. https://scholargate.app/genetics/mcdonald-kreitman-test