MaGelLAn

MaGelLAn performs quantitative and population genetic analysis of maternal inheritance by integrating mitogenome sequences with pedigree data to investigate mitochondrial genome polymorphism and its contribution to quantitative and adaptive trait variation.

Key Features:

• Modular Python implementation: Four Python scripts (modules) optimized for maternal lineage analysis and processing large pedigrees.
• Sampling strategy optimization: Aids design of effective sampling strategies for molecular analyses.
• Pedigree consistency checks: Identifies and corrects inconsistencies within pedigree data.
• Maternal lineage identification and mitogenome assignment: Identifies maternal lineages and assigns corresponding mitogenome sequences to individuals as input for quantitative genetic (association) analyses.
• Mitochondrial population genetics metrics: Computes effective population sizes of mitochondrial genomes and the probability of mitochondrial identity, relevant for conservation management of small populations.

Scientific Applications:

• Quantitative and population genetic analysis of maternal inheritance: Investigation of maternal transmission patterns and mitogenome-driven variation in traits.
• Identification of genetic contributors to trait variation: Support for identifying genes or nucleotides responsible for quantitative and adaptive trait variation linked to the mitogenome.
• Phenotype–mitogenome association studies: Provision of mitogenome assignments to enable standard quantitative genetic (association) analyses of phenotypic traits.
• Conservation genetics: Estimation of mitochondrial effective population size and probability of mitochondrial identity for management of small populations.

Methodology:

Implemented as four Python scripts that perform pedigree consistency checks, maternal lineage identification and mitogenome assignment, sampling strategy optimization, and calculations of mitochondrial effective population size and probability of mitochondrial identity.