iHDSel

iHDSel implements the JHAC statistic to detect genomic patterns of selection by applying Jeffreys divergence, also known as the population stability index, to the distribution of allelic classes of haplotypes in two samples.

Key Features:

• Known null distribution: The JHAC statistic has a known distribution, avoiding the need for simulations and resampling to assign significance.
• Allelic class analysis: Uses allelic classes of haplotypes along with the informational interpretation of the Price equation to construct the test statistic.
• Optimal performance on simulated data: Demonstrates optimal performance in detecting divergent selection in simulation studies.
• Real data validation: Detects sites playing key roles in fitness and immune escape capability in severe acute respiratory syndrome coronavirus 2 genome data.

Scientific Applications:

• Selective sweep detection: Identifies genomic patterns compatible with selective sweeps in population genomic data.
• Viral genome analysis: Detects functionally important sites in viral genomes such as SARS-CoV-2 that contribute to fitness and immune escape.
• Divergent selection identification: Pinpoints sites with divergent selection based on haplotype allelic class distributions between samples.

Methodology:

The JHAC statistic applies Jeffreys divergence to the distribution of allelic classes of haplotypes in two samples, using the informational interpretation of the Price equation.