MAPDISTO

MAPDISTO estimates recombination fractions and constructs genetic linkage maps in experimental segregating populations (e.g., backcrosses, doubled haploids, single-seed descent), explicitly accounting for segregation distortion.

Key Features:

• Recombination fraction estimation under segregation distortion: Provides precise recombination fraction estimates in datasets exhibiting segregation distortion due to differential viability of gametes or zygotes.
• Linkage detection and grouping: Detects linkage and constructs linkage groups even when segregation distortion occurs.
• VCF import and conversion: Supports direct importation and conversion of Variant Call Format (VCF) files.
• Data imputation (LB-Impute): Implements LB-Impute to perform genotype imputation on VCF files.
• QTL detection (R/qtl integration): Integrates with R/qtl for Quantitative Trait Loci detection.
• Scalability for GBS datasets: Optimized to manage large genotyping-by-sequencing (GBS) datasets with computational efficiency in speed and memory usage.

Scientific Applications:

• Genetic linkage mapping in segregating populations: Construction of linkage maps for backcrosses, doubled haploids, and single-seed descent populations while accounting for segregation distortion.
• QTL mapping: Detection of Quantitative Trait Loci using integrated R/qtl methods on mapped genotypes.
• Large-scale GBS analyses: Processing and analysis of extensive genotyping-by-sequencing datasets where speed, memory, and segregation distortion impact map accuracy.
• Genotype imputation for VCF datasets: Imputation of missing genotypes in VCF-formatted data using LB-Impute prior to downstream mapping and QTL analysis.

Methodology:

Estimates recombination fractions under segregation distortion, detects linkage and constructs linkage groups, imports and converts VCF files, performs genotype imputation using LB-Impute on VCFs, integrates with R/qtl for QTL detection, and includes optimizations for speed and memory to handle GBS datasets.