Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies

During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these approaches require information about the linkage disequilibrium (LD) between variants, there has not been...

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Vydané v:American journal of human genetics Ročník 101; číslo 4; s. 539
Hlavní autori: Benner, Christian, Havulinna, Aki S, Järvelin, Marjo-Riitta, Salomaa, Veikko, Ripatti, Samuli, Pirinen, Matti
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 05.10.2017
Predmet:
Adult
Aged
Biostatistics - methods
Chromosome Mapping - methods
Cohort Studies
Female
Finland
Genome, Human
Genome-Wide Association Study - methods
Genotype
Humans
Linkage Disequilibrium
Male
Middle Aged
Polymorphism, Single Nucleotide
Quantitative Trait Loci
Software
ISSN:1537-6605, 1537-6605
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Shrnutí:During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1537-6605
1537-6605
DOI:10.1016/j.ajhg.2017.08.012