HapDipKinship

HapDipKinship implements the KIMGENS algorithm to estimate kinship coefficients in populations with mixed haploid and diploid genetic systems for accurate relatedness inference in population genomic studies.

Key Features:

• KIMGENS (core algorithm): KIMGENS (Kinship Inference for Mixed GENetic Systems) provides a kinship estimator specifically tailored for mixed haploid and diploid genetic systems.
• Robustness to population structure: The estimator produces kinship estimates that are robust to population structure, enabling analysis of structured and admixed populations.
• Versatility across ploidies: HapDipKinship estimates kinship among haploid-haploid, haploid-diploid, and diploid-diploid pairs by using diploid relatives as heterozygosity references and extending the KING-robust estimator to mixed ploidy contexts.
• Accuracy in diverse populations: KIMGENS has demonstrated improved accuracy compared to existing methods across simulated panmictic, structured, and admixed populations.
• Empirical validation: The method has been validated on a honeybee dataset, demonstrating applicability to organisms with haplodiploid systems.

Scientific Applications:

• Population genomics: Estimating relatedness and testing assumptions in population genetic analyses of mixed ploidy populations.
• Evolutionary biology: Inferring kin structure and relatedness in species with mixed haploid and diploid life stages.
• Conservation genetics: Assessing kinship for management and genetic monitoring of populations that include haploid and diploid individuals.
• Social insect research: Resolving familial relationships and colony structure in haplodiploid species such as honeybees.

Methodology:

KIMGENS uses diploid relatives as heterozygosity references and adapts the KING-robust estimator to compute kinship coefficients in mixed haploid–diploid systems, producing estimates robust to population structure.