![]() ![]() GWAS is carried out by either genotyping a small fraction of known variants to “tag” other highly correlated variants or with whole genome sequencing (WGS) data. Genome-wide association studies (GWAS) also apply LD to identify genotype-phenotype associations for a range of disease phenotypes ( Welter et al., 2013 Chanock et al., 2014). Population geneticists calculate LD to assess population structure and population history ( Mueller, 2004) and LD analysis can be employed to detect natural selection and estimate allelic age ( Slatkin, 2008). Measures of LD are important for biomedical research and are useful in a wide range of applications. Linkage disequilibrium (LD) is a population-based parameter that describes the degree to which an allele of one genetic variant is inherited or correlated with an allele of a nearby genetic variant within a given population ( Bush and Moore, 2012). ![]() LDlinkR is a free and publicly available R package that can be installed from the Comprehensive R Archive Network (CRAN) or downloaded from. LDlinkR accelerates genomic research by providing efficient and user-friendly functions to programmatically interrogate and download pairwise LD estimates from expansive lists of genetic variants. As an expansion to this resource, we have developed an R package, LDlinkR, designed to rapidly calculate statistics for large lists of variants and LD attributes that eliminates the time needed to perform repetitive requests from the web-based LDlink tool. LDlink is an interactive suite of web-based tools developed to query germline variants in 1000 Genomes Project population groups of interest and generate interactive tables and plots of LD estimates. Interactive and powerful tools are needed to calculate population-specific LD estimates for integrative genomics research. Genomic research involving human genetics and evolutionary biology relies heavily on linkage disequilibrium (LD) to investigate population-specific genetic structure, functionally map regions of disease susceptibility and uncover evolutionary history. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States. ![]()
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