MML

MML reconstructs genome-wide haplotype structure from pedigree and population high-density SNP data to support gene-disease association and population genetics analyses.

Key Features:

• Integration of Data Sources: Integrates pedigree data and broader population samples, including high-density SNP datasets, for joint haplotype inference.
• Recombination Event Detection: Detects recombination events from SNP data to delineate haplotype boundaries and inform linkage disequilibrium analysis.
• Maximum Likelihood Optimization: Applies maximum likelihood methods to estimate haplotype frequencies and optimize haplotype diversity within populations.
• Zero-Recombinant Haplotype Reconstruction: Reconstructs haplotypes under zero-recombination assumptions based on prior zero-recombinant algorithms.

Scientific Applications:

• Gene-disease association studies: Provides haplotype structure information that supports mapping and interpretation of disease-associated loci in human populations.
• Population genetics: Characterizes genome-wide haplotypic variation to study genetic variation and linkage disequilibrium across human populations.
• Personalized medicine: Informs interpretation of individual-level haplotypic variation relevant to personalized medicine analyses.

Methodology:

Jointly applies Mendelian inheritance constraints and local population structure analysis, uses maximum likelihood estimation, detects recombination events, reconstructs zero-recombinant haplotypes, and has been validated on real and simulated datasets.