PopLDdecay

PopLDdecay computes linkage disequilibrium (LD) decay directly from variant call format (VCF) files to quantify non-random allele associations and characterize LD decay patterns in population genetics studies.


Key Features:

  • Direct VCF File Processing: Processes VCF files without requiring intermediate steps that generate large pair-wise LD measurement files.
  • Scalability and Speed: Handles large numbers of variants derived from sequencing data, including whole-genome single nucleotide polymorphisms (SNPs), to support rapid analysis.
  • Storage Efficiency: Avoids exporting pair-wise LD measurement results to conserve storage space.
  • Subgroup Analysis Support: Provides functionality for analyses within specific population subsets or genomic regions.
  • LD Decay Metric Computation: Computes LD decay metrics to quantify how linkage disequilibrium decreases with genomic distance.

Scientific Applications:

  • Evolutionary Inference: Infers historical recombination events and patterns affecting LD decay across genomes.
  • Genetic Architecture Characterization: Assesses LD decay to inform the distribution of linkage around causal variants and marker density requirements.
  • Association Mapping Support: Aids identification of genetic markers associated with traits by characterizing local LD structure.
  • Population Structure and Selection Analysis: Provides insights into population structure and potential selection pressures via LD decay patterns.

Methodology:

Uses algorithms to compute LD decay metrics directly from VCF files while avoiding generation or exportation of pair-wise LD measurement files.

Topics

Details

License:
MIT
Maturity:
Mature
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Linux, Mac
Programming Languages:
Perl
Added:
7/4/2019
Last Updated:
11/24/2024

Operations

Publications

Zhang C, Dong S, Xu J, He W, Yang T. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics. 2018;35(10):1786-1788. doi:10.1093/bioinformatics/bty875. PMID:30321304.

PMID: 30321304
Funding: - National Natural Science Foundation of China: 31701095, 81573241 - China Postdoctoral Science Foundation: 2016M602797, 2018T111038 - Young Sci-Tech New Star: 2018KJXX-010

Documentation

Links