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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| Veröffentlicht in: | American journal of human genetics Jg. 101; H. 4; S. 539 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Benner, Christian Havulinna, Aki S Ripatti, Samuli Järvelin, Marjo-Riitta Salomaa, Veikko Pirinen, Matti |
| Author_xml | – sequence: 1 givenname: Christian surname: Benner fullname: Benner, Christian email: christian.benner@helsinki.fi organization: Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; Department of Public Health, University of Helsinki, 00014 Helsinki, Finland. Electronic address: christian.benner@helsinki.fi – sequence: 2 givenname: Aki S surname: Havulinna fullname: Havulinna, Aki S organization: Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; National Institute for Health and Welfare, 00271 Helsinki, Finland – sequence: 3 givenname: Marjo-Riitta surname: Järvelin fullname: Järvelin, Marjo-Riitta organization: Center for Life-Course Health Research and Northern Finland Cohort Center, Biocenter Oulu, University of Oulu, 90014 Oulu, Finland; Faculty of Medicine, University of Oulu, 90014 Oulu, Finland; Unit of Primary Care, Oulu University Hospital, 90220 Oulu, Finland; Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, W2 1PG, UK – sequence: 4 givenname: Veikko surname: Salomaa fullname: Salomaa, Veikko organization: National Institute for Health and Welfare, 00271 Helsinki, Finland – sequence: 5 givenname: Samuli surname: Ripatti fullname: Ripatti, Samuli organization: Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; Department of Public Health, University of Helsinki, 00014 Helsinki, Finland; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, Cambridge, UK – sequence: 6 givenname: Matti surname: Pirinen fullname: Pirinen, Matti email: matti.pirinen@helsinki.fi organization: Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; Department of Public Health, University of Helsinki, 00014 Helsinki, Finland; Helsinki Institute for Information Technology and Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland. Electronic address: matti.pirinen@helsinki.fi |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28942963$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
| Title | Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies |
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