Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method.
Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method.
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基于基因分型平台和基因组风险评分方法的炎症性肠病的风险预测性能。
DOI：
10.1186/s12881-017-0451-2
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发表时间：
2017-08-29
影响因子：
--
通讯作者：
Moser G
中科院分区：
文献类型：
--
作者：
Chen GB;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;International IBD Genetics Consortium;Radford-Smith GL;Moser G
人们越来越多地提出通过基因型预测疾病风险用于各种诊断和预后目的。全基因组关联研究 (GWAS) 已识别出大量克罗恩病 (CD) 和溃疡性结肠炎 (UC) 这两种炎症性肠病 (IBD) 亚型的全基因组显着易感位点。最近的研究表明，在预测模型中仅包含与疾病显着相关的基因座的预测能力较低，并且可以使用多基因方法大幅提高预测能力。我们使用大型病例对照队列对风险预测模型进行了全面分析，该队列在定制设计的免疫芯片上对 909,763 个 GWAS SNP 或 123,437 个 SNP 进行了基因分型，并使用四种预测方法（多基因评分、最佳线性基因组预测、弹性网络正则化和贝叶斯混合模型）。我们使用曲线下面积 (AUC) 来评估交叉验证中具有不同样本量和 SNP 数量的发现群体的预测性能。平均而言，贝叶斯混合方法具有最佳的预测性能。通过交叉验证，我们发现 GWAS 和免疫芯片之间的预测性能几乎没有差异，尽管 GWAS 阵列提供了 10 倍大的有效基因组覆盖范围。使用免疫芯片的预测性能很大程度上归功于初始 GWAS 在标记选择方面的强大功能以及能够实现更大样本量的低成本。基于免疫芯片的基因组风险评分的预测能力在外部数据中得到复制，CD的AUC为0.75，UC的AUC为0.70。风险评分较高的 CD 患者表现出通常与更严重的病程相关的临床特征，包括回肠位置和诊断时年龄较早。我们的分析表明，IBD 基因组风险预测的力量主要归功于具有相当大效应大小的强相关 SNP。仅由高密度 GWAS 阵列标记的其他 SNP 以及免疫芯片上高密度区域中过度代表的低或稀有变异对预测准确性贡献甚微。尽管目前不可能对个体的 IBD 风险进行定量评估，但我们显示了基因组风险评分的足够能力，可以在诊断时对个体之间的 IBD 风险进行分层。本文的在线版本 (doi:10.1186/s12881-017-0451-2) 包含补充材料，可供授权用户使用。
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. 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. 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. 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. The online version of this article (doi:10.1186/s12881-017-0451-2) contains supplementary material, which is available to authorized users.
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影响因子：
30.8
作者：
通讯作者：
--
克罗恩病和溃疡性结肠炎表型的遗传决定因素：遗传关联研究。
DOI：
10.1016/s0140-6736(15)00465-1
发表时间：
2016-01-09
期刊：
Lancet (London, England)
影响因子：
--
作者：
Cleynen I;Boucher G;Jostins L;Schumm LP;Zeissig S;Ahmad T;Andersen V;Andrews JM;Annese V;Brand S;Brant SR;Cho JH;Daly MJ;Dubinsky M;Duerr RH;Ferguson LR;Franke A;Gearry RB;Goyette P;Hakonarson H;Halfvarson J;Hov JR;Huang H;Kennedy NA;Kupcinskas L;Lawrance IC;Lee JC;Satsangi J;Schreiber S;Théâtre E;van der Meulen-de Jong AE;Weersma RK;Wilson DC;International Inflammatory Bowel Disease Genetics Consortium;Parkes M;Vermeire S;Rioux JD;Mansfield J;Silverberg MS;Radford-Smith G;McGovern DP;Barrett JC;Lees CW
通讯作者：
Lees CW
影响因子：
9.8
作者：
Maier R;Moser G;Chen GB;Ripke S;Cross-Disorder Working Group of the Psychiatric Genomics Consortium;Coryell W;Potash JB;Scheftner WA;Shi J;Weissman MM;Hultman CM;Landén M;Levinson DF;Kendler KS;Smoller JW;Wray NR;Lee SH
通讯作者：
Lee SH
影响因子：
4.5
作者：
Moser G;Lee SH;Hayes BJ;Goddard ME;Wray NR;Visscher PM
通讯作者：
Visscher PM
影响因子：
30.8
作者：
Loh, Po-Ru;Tucker, George;Bulik-Sullivan, Brendan K.;Vilhjalmsson, Bjarni J.;Finucane, Hilary K.;Salem, Rany M.;Chasman, Daniel I.;Ridker, Paul M.;Neale, Benjamin M.;Berger, Bonnie;Patterson, Nick;Price, Alkes L.
通讯作者：
Price, Alkes L.