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Improved methods for multi-trait fine mapping of pleiotropic risk loci
Author(s) -
Gleb Kichaev,
Megan Roytman,
Ruth Johnson,
Eleazar Eskin,
Sara Lindström,
Peter Kraft,
Bogdan Paşaniuc
Publication year - 2016
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btw615
Subject(s) - genome wide association study , linkage disequilibrium , computer science , annotation , genetic association , inference , computational biology , trait , quantitative trait locus , single nucleotide polymorphism , association mapping , data mining , biology , genetics , artificial intelligence , gene , genotype , programming language
Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.

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