LDlinkR

"LDlinkR" is an R package developed as an extension of the LDlink suite (see links), a web-based toolset for assessing linkage disequilibrium (LD) across human population groups. LDlinkR enables researchers to perform large-scale LD calculations rapidly and efficiently within the R programming environment, addressing the need for interactive and powerful tools to calculate population-specific LD estimates crucial for genomic research in human genetics and evolutionary biology.

Key Features and Functionalities:

- Accelerated Genomic Research: By providing efficient functions to programmatically query and download pairwise LD estimates from extensive lists of genetic variants, LDlinkR significantly speeds up genomic research that requires detailed LD analysis.

- Integration with 1000 Genomes Project Data: LDlinkR calculations are based on Phase 3 (Version 5) data from the 1000 Genomes Project, covering 2,504 individuals across 26 ancestral population groups. This comprehensive data support enables detailed population-specific LD analysis.

- Advanced Computational Efficiency: Leveraging cloud computing resources, LDlinkR eliminates the need for local storage of large variant call format (VCF) files and datasets, freeing up local computing resources and streamlining the LD analysis process.

- Seamless R Environment Integration: Designed for users familiar with R, LDlinkR allows for the rapid calculation of LD statistics for expansive lists of genetic variants, easily integrating results into local analytical pipelines for further statistical analysis.

- Flexible Population and Variant Input: Users can specify any combination of super or sub-population groups as input, along with reference single nucleotide polymorphism (SNP) numbers or genomic coordinates, facilitating tailored LD analysis across diverse ancestral populations.

- Comprehensive Module Suite: The package includes seven main modules (LDhap, LDmatrix, LDpair, LDpop, LDproxy, SNPchip, and SNPclip) to support a wide range of LD-based calculations, enhancing the flexibility and applicability of LD analyses.