Artikel ; Online: Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases.
2019 Band 10, Heft 1, Seite(n) 3216
Abstract: Thousands of genetic variants are associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. Both lack of power, and joint tagging of two or more distinct causal variants by a single non-causal SNP, lead ...
Abstract | Thousands of genetic variants are associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. Both lack of power, and joint tagging of two or more distinct causal variants by a single non-causal SNP, lead to inaccuracies in fine-mapping, with stochastic search more robust than stepwise. We develop a computationally efficient multinomial fine-mapping (MFM) approach that borrows information between diseases in a Bayesian framework. We show that MFM has greater accuracy than single disease analysis when shared causal variants exist, and negligible loss of precision otherwise. MFM analysis of six immune-mediated diseases reveals causal variants undetected in individual disease analysis, including in IL2RA where we confirm functional effects of multiple causal variants using allele-specific expression in sorted CD4 |
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Mesh-Begriff(e) | Alleles ; Autoimmunity/genetics ; Bayes Theorem ; CD4-Positive T-Lymphocytes ; CTLA-4 Antigen/genetics ; Chromosome Mapping ; Gene Expression Regulation ; Genetic Association Studies/methods ; Genetic Predisposition to Disease/genetics ; Genome-Wide Association Study/methods ; Genotype ; Humans ; Interleukin-2 Receptor alpha Subunit/genetics ; Linkage Disequilibrium ; Models, Genetic ; Phenotype ; Polymorphism, Single Nucleotide |
Chemische Substanzen | CTLA-4 Antigen ; CTLA4 protein, human ; IL2RA protein, human ; Interleukin-2 Receptor alpha Subunit |
Sprache | Englisch |
Erscheinungsdatum | 2019-07-19 |
Erscheinungsland | England |
Dokumenttyp | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
ZDB-ID | 2553671-0 |
ISSN | 2041-1723 ; 2041-1723 |
ISSN (online) | 2041-1723 |
ISSN | 2041-1723 |
DOI | 10.1038/s41467-019-11271-0 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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