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

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, Graham L Radford-Smith, Gerhard Moser, International IBD Genetics Consortium & 3 others Angelo Andriulli, Orazio Palmieri, Anna Latiano

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)94
JournalBMC Medical Genetics
Volume18
Issue number1
DOIs
Publication statusPublished - Aug 29 2017

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Inflammatory Bowel Diseases
Genome-Wide Association Study
Single Nucleotide Polymorphism
Crohn Disease
Ulcerative Colitis
Sample Size
Area Under Curve
Genome
Bayes Theorem
Genotype
Costs and Cost Analysis
Population

Keywords

  • Bayes Theorem
  • Case-Control Studies
  • Cohort Studies
  • Colitis, Ulcerative
  • Crohn Disease
  • Genetic Predisposition to Disease
  • Genotype
  • Humans
  • Models, Genetic
  • Polymorphism, Single Nucleotide
  • Predictive Value of Tests
  • Risk Assessment
  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

Cite this

Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method. / Chen, Guo-Bo; Lee, Sang Hong; Montgomery, Grant W; Wray, Naomi R; Visscher, Peter M; Gearry, Richard B; Lawrance, Ian C; Andrews, Jane M; Bampton, Peter; Mahy, Gillian; Bell, Sally; Walsh, Alissa; Connor, Susan; Sparrow, Miles; Bowdler, Lisa M; Simms, Lisa A; Krishnaprasad, Krupa; Radford-Smith, Graham L; Moser, Gerhard; International IBD Genetics Consortium ; Andriulli, Angelo; Palmieri, Orazio; Latiano, Anna.

In: BMC Medical Genetics, Vol. 18, No. 1, 29.08.2017, p. 94.

Research output: Contribution to journalArticle

Chen, G-B, Lee, SH, Montgomery, GW, Wray, NR, Visscher, PM, Gearry, RB, Lawrance, IC, Andrews, JM, Bampton, P, Mahy, G, Bell, S, Walsh, A, Connor, S, Sparrow, M, Bowdler, LM, Simms, LA, Krishnaprasad, K, Radford-Smith, GL, Moser, G, International IBD Genetics Consortium, Andriulli, A, Palmieri, O & Latiano, A 2017, 'Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method', BMC Medical Genetics, vol. 18, no. 1, pp. 94. https://doi.org/10.1186/s12881-017-0451-2
Chen, Guo-Bo ; Lee, Sang Hong ; Montgomery, Grant W ; Wray, Naomi R ; Visscher, Peter M ; Gearry, Richard B ; Lawrance, Ian C ; Andrews, Jane M ; Bampton, Peter ; Mahy, Gillian ; Bell, Sally ; Walsh, Alissa ; Connor, Susan ; Sparrow, Miles ; Bowdler, Lisa M ; Simms, Lisa A ; Krishnaprasad, Krupa ; Radford-Smith, Graham L ; Moser, Gerhard ; International IBD Genetics Consortium ; Andriulli, Angelo ; Palmieri, Orazio ; Latiano, Anna. / Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method. In: BMC Medical Genetics. 2017 ; Vol. 18, No. 1. pp. 94.
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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

AU - Andriulli, Angelo

AU - Palmieri, Orazio

AU - Latiano, Anna

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 - Bayes Theorem

KW - Case-Control Studies

KW - Cohort Studies

KW - Colitis, Ulcerative

KW - Crohn Disease

KW - Genetic Predisposition to Disease

KW - Genotype

KW - Humans

KW - Models, Genetic

KW - Polymorphism, Single Nucleotide

KW - Predictive Value of Tests

KW - Risk Assessment

KW - Journal Article

KW - Research Support, N.I.H., Extramural

KW - Research Support, Non-U.S. Gov't

U2 - 10.1186/s12881-017-0451-2

DO - 10.1186/s12881-017-0451-2

M3 - Article

VL - 18

SP - 94

JO - BMC Medical Genetics

JF - BMC Medical Genetics

SN - 1471-2350

IS - 1

ER -