Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method

International IBD Genetics Consortium, Vibeke Andersen (Medlem af forfattergruppering)

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Resumé

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.

OriginalsprogEngelsk
Artikelnummer94
TidsskriftBMC Medical Genetics
Vol/bind18
Antal sider11
ISSN1471-2350
DOI
StatusUdgivet - 29. aug. 2017

Fingeraftryk

Inflammatory Bowel Diseases
Genome-Wide Association Study
Single Nucleotide Polymorphism
Crohn Disease
Ulcerative Colitis
Sample Size
Area Under Curve
Population

Citer dette

@article{396fdf58ebf94e16990efc353d94d946,
title = "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 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.",
keywords = "Case-control study, Complex trait, Crohn's disease, Inflammatory bowel disease, Risk score, SNP array, Ulcerative colitis",
author = "Chen, {Guo Bo} and Lee, {Sang Hong} and Montgomery, {Grant W.} and Wray, {Naomi R.} and Visscher, {Peter M.} and Gearry, {Richard B.} and Lawrance, {Ian C.} and Andrews, {Jane M.} and Peter Bampton and Gillian Mahy and Sally Bell and Alissa Walsh and Susan Connor and Miles Sparrow and Bowdler, {Lisa M.} and Simms, {Lisa A.} and Krupa Krishnaprasad and Radford-Smith, {Graham L.} and Gerhard Moser and {International IBD Genetics Consortium} and Vibeke Andersen",
year = "2017",
month = "8",
day = "29",
doi = "10.1186/s12881-017-0451-2",
language = "English",
volume = "18",
journal = "B M C Medical Genetics",
issn = "1471-2350",
publisher = "BioMed Central",

}

Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method. / International IBD Genetics Consortium ; Andersen, Vibeke (Medlem af forfattergruppering).

I: BMC Medical Genetics, Bind 18, 94, 29.08.2017.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method

AU - Chen, Guo Bo

AU - Lee, Sang Hong

AU - Montgomery, Grant W.

AU - Wray, Naomi R.

AU - Visscher, Peter M.

AU - Gearry, Richard B.

AU - Lawrance, Ian C.

AU - Andrews, Jane M.

AU - Bampton, Peter

AU - Mahy, Gillian

AU - Bell, Sally

AU - Walsh, Alissa

AU - Connor, Susan

AU - Sparrow, Miles

AU - Bowdler, Lisa M.

AU - Simms, Lisa A.

AU - Krishnaprasad, Krupa

AU - Radford-Smith, Graham L.

AU - Moser, Gerhard

AU - International IBD Genetics Consortium

A2 - Andersen, Vibeke

PY - 2017/8/29

Y1 - 2017/8/29

N2 - 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.

AB - 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.

KW - Case-control study

KW - Complex trait

KW - Crohn's disease

KW - Inflammatory bowel disease

KW - Risk score

KW - SNP array

KW - Ulcerative colitis

U2 - 10.1186/s12881-017-0451-2

DO - 10.1186/s12881-017-0451-2

M3 - Journal article

VL - 18

JO - B M C Medical Genetics

JF - B M C Medical Genetics

SN - 1471-2350

M1 - 94

ER -