MI-MAAP

MI-MAAP infers and prioritizes Ancestry Informative Markers (AIMs) from allele frequency data to enable ancestry inference and marker selection in multi-ancestry admixed populations.

Key Features:

• Ancestry Inference: Calculates genome-wide ancestry proportions and traces ancestral origin along chromosomes in admixed individuals.
• Lancaster Estimator of Independence (LEI): Uses an allele frequency-based feature selection algorithm to identify AIMs with allele frequency differences or selection pressures among ancestries without requiring individual-level genotype data.
• Integration of Genomics Resources: Leverages allele frequency data from the 1000 Genomes Project and the Human Genome Diversity Project for marker prioritization.
• Genotype-Based Methods: Incorporates Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF) to improve AIM selection accuracy.

Scientific Applications:

• Ancestry Inference: Provides ancestry information to support studies of population history and migration patterns.
• Admixture Mapping: Supports identification of genomic regions associated with traits or diseases that vary across ancestries.
• Forensic Applications: Prioritizes AIMs for ancestry determination in forensic investigations.
• Detection of Recent Selection: Identifies genomic regions under recent selection pressure to inform evolutionary analyses.

Methodology:

Applies the Lancaster Estimator of Independence (LEI) for allele frequency-based feature selection, combines LEI with PCA, SVM, and RF for marker prioritization, and integrates allele frequency datasets from the 1000 Genomes Project and the Human Genome Diversity Project.