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      LDlinkR: An R Package for Rapidly Calculating Linkage Disequilibrium Statistics in Diverse Populations

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          Abstract

          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. Interactive and powerful tools are needed to calculate population-specific LD estimates for integrative genomics research. 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. 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. 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. LDlinkR is a free and publicly available R package that can be installed from the Comprehensive R Archive Network (CRAN) or downloaded from https://github.com/CBIIT/LDlinkR.

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          Linkage disequilibrium--understanding the evolutionary past and mapping the medical future.

          Linkage disequilibrium--the nonrandom association of alleles at different loci--is a sensitive indicator of the population genetic forces that structure a genome. Because of the explosive growth of methods for assessing genetic variation at a fine scale, evolutionary biologists and human geneticists are increasingly exploiting linkage disequilibrium in order to understand past evolutionary and demographic events, to map genes that are associated with quantitative characters and inherited diseases, and to understand the joint evolution of linked sets of genes. This article introduces linkage disequilibrium, reviews the population genetic processes that affect it and describes some of its uses. At present, linkage disequilibrium is used much more extensively in the study of humans than in non-humans, but that is changing as technological advances make extensive genomic studies feasible in other species.
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            Chapter 11: Genome-Wide Association Studies

            Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.
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              Linkage disequilibrium for different scales and applications.

              J. Mueller (2004)
              Assessing the patterns of linkage disequilibrium (LD) has become an important issue in both evolutionary biology and medical genetics since the rapid accumulation of densely spaced DNA sequence variation data in several organisms. LD deals with the correlation of genetic variation at two or more loci or sites in the genome within a given population. There are a variety of LD measures which range from traditional pairwise LD measures such as D' or r2 to entropy-based multi-locus measures or haplotype-specific approaches. Understanding the evolutionary forces (in particular recombination) that generate the observed variation of LD patterns across genomic regions is addressed by model-based LD analysis. Marker type and its allelic composition also influence the observed LD pattern, microsatellites having a greater power to detect LD in population isolates than SNPs. This review aims to explain basic LD measures and their application properties.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                28 February 2020
                2020
                : 11
                : 157
                Affiliations
                Division of Cancer Epidemiology and Genetics, National Cancer Institute , Rockville, MD, United States
                Author notes

                Edited by: Robert Friedman, University of South Carolina, United States

                Reviewed by: Patricio Ricardo Munoz, University of Florida, United States; Marcio Resende, University of Florida, United States

                *Correspondence: Timothy A. Myers, myersta@ 123456mail.nih.gov

                This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2020.00157
                7059597
                32180801
                7d23491c-2ed3-4be9-a066-a3166f4dabc3
                Copyright © 2020 Myers, Chanock and Machiela.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 25 November 2019
                : 11 February 2020
                Page count
                Figures: 2, Tables: 1, Equations: 14, References: 13, Pages: 5, Words: 0
                Funding
                Funded by: Division of Cancer Epidemiology and Genetics, National Cancer Institute 10.13039/100011541
                Categories
                Genetics
                Technology and Code

                Genetics
                linkage disequilibrium,population genetics,bioinformatics,r,ldlink
                Genetics
                linkage disequilibrium, population genetics, bioinformatics, r, ldlink

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