MLHKA

MLHKA performs maximum likelihood ratio tests of natural selection using multilocus polymorphism and divergence data to identify loci deviating from Hudson-Kreitman-Aguadé (HKA) expectations.

Key Features:

• Multilocus framework: Utilizes polymorphism within species and divergence between species across multiple loci to increase power for detecting selection.
• Maximum likelihood ratio test: Implements an ML ratio statistic that extends HKA principles to evaluate selection hypotheses.
• Explicit locus-level testing: Enables testing for selection at individual loci within the multilocus analysis.
• Coalescent simulation validation: Uses coalescent simulations to assess the behavior of the likelihood-ratio statistic and to demonstrate conservativeness.
• Robustness to recombination violations: The ML statistic is reported to be conservative even when assumptions such as no recombination are violated.

Scientific Applications:

• Detection of balanced polymorphisms: Identifies loci showing evidence of balanced polymorphism, such as a candidate locus linked to the centromere in Arabidopsis lyrata.
• Directional selection analysis: Detects genes under directional selection, exemplified by analyses of genes in the starch pathway in maize.

Methodology:

Integrates multilocus polymorphism and divergence data, applies a maximum likelihood ratio statistic based on HKA extensions, and evaluates statistical behavior using coalescent simulations.