Effect of case and control definitions on genome‐wide association study (GWAS) findings
Genome‐wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank dat...
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Published in | Genetic epidemiology Vol. 47; no. 5; pp. 394 - 406 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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United States
Wiley Subscription Services, Inc
01.07.2023
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ISSN | 0741-0395 1098-2272 1098-2272 |
DOI | 10.1002/gepi.22523 |
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Abstract | Genome‐wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e., self‐reported questionnaires, hospital records, etc.) or different levels of granularity (i.e., specificity of inclusion criteria) to define cases and controls. The extent to which this variability in cohort definitions impacts the end‐results of a GWAS study is unclear. In this study, we systematically evaluated the effect of the data sources used for case and control definitions on GWAS findings. Using the UK Biobank, we selected three diseases—glaucoma, migraine, and iron‐deficiency anemia. For each disease, we designed 13 GWAS, each using different combinations of data sources to define cases and controls, and then calculated the pairwise genetic correlations between all GWAS for each disease. We found that the data sources used to define cases for a given disease can have a significant impact on GWAS end‐results, but the extent of this depends heavily on the disease in question. This suggests the need for greater scrutiny on how case cohorts are defined for GWAS. |
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AbstractList | Genome‐wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e., self‐reported questionnaires, hospital records, etc.) or different levels of granularity (i.e., specificity of inclusion criteria) to define cases and controls. The extent to which this variability in cohort definitions impacts the end‐results of a GWAS study is unclear. In this study, we systematically evaluated the effect of the data sources used for case and control definitions on GWAS findings. Using the UK Biobank, we selected three diseases—glaucoma, migraine, and iron‐deficiency anemia. For each disease, we designed 13 GWAS, each using different combinations of data sources to define cases and controls, and then calculated the pairwise genetic correlations between all GWAS for each disease. We found that the data sources used to define cases for a given disease can have a significant impact on GWAS end‐results, but the extent of this depends heavily on the disease in question. This suggests the need for greater scrutiny on how case cohorts are defined for GWAS. Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e., self-reported questionnaires, hospital records, etc.) or different levels of granularity (i.e., specificity of inclusion criteria) to define cases and controls. The extent to which this variability in cohort definitions impacts the end-results of a GWAS study is unclear. In this study, we systematically evaluated the effect of the data sources used for case and control definitions on GWAS findings. Using the UK Biobank, we selected three diseases-glaucoma, migraine, and iron-deficiency anemia. For each disease, we designed 13 GWAS, each using different combinations of data sources to define cases and controls, and then calculated the pairwise genetic correlations between all GWAS for each disease. We found that the data sources used to define cases for a given disease can have a significant impact on GWAS end-results, but the extent of this depends heavily on the disease in question. This suggests the need for greater scrutiny on how case cohorts are defined for GWAS.Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e., self-reported questionnaires, hospital records, etc.) or different levels of granularity (i.e., specificity of inclusion criteria) to define cases and controls. The extent to which this variability in cohort definitions impacts the end-results of a GWAS study is unclear. In this study, we systematically evaluated the effect of the data sources used for case and control definitions on GWAS findings. Using the UK Biobank, we selected three diseases-glaucoma, migraine, and iron-deficiency anemia. For each disease, we designed 13 GWAS, each using different combinations of data sources to define cases and controls, and then calculated the pairwise genetic correlations between all GWAS for each disease. We found that the data sources used to define cases for a given disease can have a significant impact on GWAS end-results, but the extent of this depends heavily on the disease in question. This suggests the need for greater scrutiny on how case cohorts are defined for GWAS. |
Author | Wang, May Dongmei Isgut, Monica Davitte, Jonathan Ehm, Margaret G. Song, Kijoung |
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Cites_doi | 10.1016/S2213-2600(15)00283-0 10.1038/s41588-018-0079-y 10.1038/527S14a 10.1038/s41588-020-0594-5 10.1038/ng.3406 10.1371/journal.pone.0029427 10.1126/science.1109557 10.1038/ng1047 10.1016/j.jpeds.2008.01.034 10.1371/journal.pone.0023473 10.1192/bjp.bp.108.063156 10.5808/GI.2012.10.4.220 10.1038/ng.2480 10.1016/j.jclinepi.2015.09.016 10.1038/s41467-018-03819-3 10.1038/s41398-017-0034-1 10.1016/S2213-2600(19)30055-4 10.1093/bioinformatics/btq559 10.1093/nar/gkw1133 10.1007/s10654-015-9998-4 10.1038/s41588-018-0327-1 10.1002/humu.22080 10.1038/s41586-018-0579-z 10.1513/AnnalsATS.201304-087AW 10.1038/s41576-019-0127-1 10.1016/j.ajhg.2019.02.022 |
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SubjectTerms | Biobanks Disease genetic correlation Genetic Predisposition to Disease Genome-Wide Association Study Genomes Glaucoma GWAS Headache Humans Iron deficiency Migraine Nutrient deficiency Polymorphism, Single Nucleotide selection bias Self Report study design UK Biobank |
Title | Effect of case and control definitions on genome‐wide association study (GWAS) findings |
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