Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method
Abstract Background Predicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn’s disease (CD) and ulcerati...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-08-01
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| Series: | BMC Medical Genetics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12881-017-0451-2 |
| _version_ | 1818968020026916864 |
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| author | Guo-Bo Chen Sang Hong Lee Grant W. Montgomery Naomi R. Wray Peter M. Visscher Richard B. Gearry Ian C. Lawrance Jane M. Andrews Peter Bampton Gillian Mahy Sally Bell Alissa Walsh Susan Connor Miles Sparrow Lisa M. Bowdler Lisa A. Simms Krupa Krishnaprasad the International IBD Genetics Consortium Graham L. Radford-Smith Gerhard Moser |
| author_facet | Guo-Bo Chen Sang Hong Lee Grant W. Montgomery Naomi R. Wray Peter M. Visscher Richard B. Gearry Ian C. Lawrance Jane M. Andrews Peter Bampton Gillian Mahy Sally Bell Alissa Walsh Susan Connor Miles Sparrow Lisa M. Bowdler Lisa A. Simms Krupa Krishnaprasad the International IBD Genetics Consortium Graham L. Radford-Smith Gerhard Moser |
| author_sort | Guo-Bo Chen |
| collection | DOAJ |
| description | Abstract Background Predicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn’s disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach. Methods We performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation. Results On average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis. Conclusions Our analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis. |
| first_indexed | 2024-12-20T13:58:03Z |
| format | Article |
| id | doaj.art-7e80f872b9fa4900b531fd08ac729c24 |
| institution | Directory Open Access Journal |
| issn | 1471-2350 |
| language | English |
| last_indexed | 2024-12-20T13:58:03Z |
| publishDate | 2017-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genetics |
| spelling | doaj.art-7e80f872b9fa4900b531fd08ac729c242022-12-21T19:38:24ZengBMCBMC Medical Genetics1471-23502017-08-0118111110.1186/s12881-017-0451-2Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score methodGuo-Bo Chen0Sang Hong Lee1Grant W. Montgomery2Naomi R. Wray3Peter M. Visscher4Richard B. Gearry5Ian C. Lawrance6Jane M. Andrews7Peter Bampton8Gillian Mahy9Sally Bell10Alissa Walsh11Susan Connor12Miles Sparrow13Lisa M. Bowdler14Lisa A. Simms15Krupa Krishnaprasad16the International IBD Genetics ConsortiumGraham L. Radford-Smith17Gerhard Moser18Queensland Brain Institute, The University of QueenslandQueensland Brain Institute, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandQueensland Brain Institute, The University of QueenslandQueensland Brain Institute, The University of QueenslandDepartment of Medicine, University of OtagoHarry Perkins Institute of Medical Research, School of Medicine and Pharmacology, University of Western AustraliaInflammatory Bowel Disease Service, Department of Gastroenterology and Hepatology, Royal Adelaide Hospital, School of Medicine, University of AdelaideDepartment of Gastroenterology and Hepatology, Flinders Medical CentreDepartment of Gastroenterology, Townsville HospitalDepartment of Gastroenterology, St Vincent’s HospitalDepartment of Gastroenterology and Hepatology, St Vincent’s HospitalDepartment of Gastroenterology and Hepatology, Liverpool HospitalDepartment of Gastroenterology, Alfred HealthInstitute for Molecular Bioscience, The University of QueenslandInflammatory Bowel Disease Research Group, Immunology Division, QIMR Berghofer Medical Research InstituteInflammatory Bowel Disease Research Group, Immunology Division, QIMR Berghofer Medical Research InstituteSchool of Medicine, The University of QueenslandQueensland Brain Institute, The University of QueenslandAbstract Background Predicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn’s disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach. Methods We performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation. Results On average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis. Conclusions Our analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis.http://link.springer.com/article/10.1186/s12881-017-0451-2Inflammatory bowel diseaseCrohn’s diseaseUlcerative colitisCase-control studyRisk scoreSNP array |
| spellingShingle | Guo-Bo Chen Sang Hong Lee Grant W. Montgomery Naomi R. Wray Peter M. Visscher Richard B. Gearry Ian C. Lawrance Jane M. Andrews Peter Bampton Gillian Mahy Sally Bell Alissa Walsh Susan Connor Miles Sparrow Lisa M. Bowdler Lisa A. Simms Krupa Krishnaprasad the International IBD Genetics Consortium Graham L. Radford-Smith Gerhard Moser Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method BMC Medical Genetics Inflammatory bowel disease Crohn’s disease Ulcerative colitis Case-control study Risk score SNP array |
| title | Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method |
| title_full | Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method |
| title_fullStr | Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method |
| title_full_unstemmed | Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method |
| title_short | Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method |
| title_sort | performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method |
| topic | Inflammatory bowel disease Crohn’s disease Ulcerative colitis Case-control study Risk score SNP array |
| url | http://link.springer.com/article/10.1186/s12881-017-0451-2 |
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