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

被引:140
作者
Benner, Christian [1 ,2 ]
Havulinna, Aki S. [1 ,3 ]
Jarvelin, Marjo-Riitta [4 ,5 ,6 ,7 ,8 ]
Salomaa, Veikko [3 ]
Ripatti, Samuli [1 ,2 ,9 ]
Pirinen, Matti [1 ,2 ,10 ,11 ]
机构
[1] Univ Helsinki, Inst Mol Med Finland, FIN-00014 Helsinki, Finland
[2] Univ Helsinki, Dept Publ Hlth, FIN-00014 Helsinki, Finland
[3] Natl Inst Hlth & Welf, Helsinki 00271, Finland
[4] Univ Oulu, Bioctr Oulu, Ctr Life Course Hlth Res, Oulu 90014, Finland
[5] Univ Oulu, Bioctr Oulu, Northern Finland Cohort Ctr, Oulu 90014, Finland
[6] Univ Oulu, Fac Med, Oulu 90014, Finland
[7] Oulu Univ Hosp, Unit Primary Care, Oulu 90220, Finland
[8] Imperial Coll London, Fac Med, Sch Publ Hlth, Dept Epidemiol & Biostat, London W2 1PG, England
[9] Wellcome Trust Sanger Inst, Wellcome Genome Campus, Cambridge CB10 1SA, England
[10] Univ Helsinki, Helsinki Inst Informat Technol, FIN-00014 Helsinki, Finland
[11] Univ Helsinki, Dept Math & Stat, FIN-00014 Helsinki, Finland
基金
芬兰科学院;
关键词
GENETIC ARCHITECTURE; CAUSAL VARIANTS; BAYESIAN FRAMEWORK; DIRECT IMPUTATION; COMPLEX TRAITS; RISK; METAANALYSIS; INSIGHTS; LOCI; PLINK;
D O I
10.1016/j.ajhg.2017.08.012
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页码:539 / 551
页数:13
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