Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42,103 individuals.
Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42,103 individuals.
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DOI：
10.1136/gutjnl-2011-300537
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发表时间：
2013-06
期刊：
影响因子：
24.5
通讯作者：
Houlston RS
中科院分区：
文献类型：
--
作者：
Dunlop MG;Tenesa A;Farrington SM;Ballereau S;Brewster DH;Koessler T;Pharoah P;Schafmayer C;Hampe J;Völzke H;Chang-Claude J;Hoffmeister M;Brenner H;von Holst S;Picelli S;Lindblom A;Jenkins MA;Hopper JL;Casey G;Duggan D;Newcomb PA;Abulí A;Bessa X;Ruiz-Ponte C;Castellví-Bel S;Niittymäki I;Tuupanen S;Karhu A;Aaltonen L;Zanke B;Hudson T;Gallinger S;Barclay E;Martin L;Gorman M;Carvajal-Carmona L;Walther A;Kerr D;Lubbe S;Broderick P;Chandler I;Pittman A;Penegar S;Campbell H;Tomlinson I;Houlston RS
结直肠癌（CRC）具有相当大的遗传成分。常见的遗传变异已被证明有助于CRC风险。在一项大型多人群研究中，我们着手评估使用常见遗传变异数据并结合其他风险因素进行CRC风险预测的可行性。我们建立了一个风险预测模型，并利用现有数据将其应用于苏格兰人口。研究了9个欧洲血统的人群，以开发和验证结直肠癌风险预测模型。二元逻辑回归用于评估年龄，性别，家族史（FH）和基因型在10个易感基因座的综合效应，单独只适度影响结直肠癌的风险。风险模型由病例对照数据生成，仅包括基因型（n= 39，266），并结合性别，年龄和家族史（n= 11，324）。使用10倍内部交叉验证和外部使用4，187个独立样本评估模型的区分性能。10-年绝对风险是通过对基因型和FH与年龄和性别特异性人群风险进行建模来估计的。在外部验证集（瑞典p=1.2 ×10−6，芬兰p=2× 10−5）中证实，病例组的风险等位基因中位数高于对照组（10 vs 9，p <2.2 × 10−16）。每个等位基因的平均风险增加为9%（OR 1.09; 95% CI 1.05-1.13）。在整个风险谱中，区分性能较差（单独基因型的曲线下面积（AUC）为0.57;基因型/年龄/性别/FH的AUC为0.59）。然而，用苏格兰人口数据对基因型数据、FH、年龄和性别进行建模，显示了确定10年绝对风险>5%的亚组的实用性。我们表明，基因型数据提供了额外的信息，补充年龄，性别和FH的风险因素。然而，个体化遗传风险预测目前尚不可行。尽管如此，建模工作表明了公共卫生潜力，因为有可能将人口划分为CRC风险类别，从而为有针对性的预防和监测提供信息。
Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. In a large, multi-population study, we set out to assess the feasibility of CRC risk prediction using common genetic variant data, combined with other risk factors. We built a risk prediction model and applied it to the Scottish population using available data. Nine populations of European descent were studied to develop and validate colorectal cancer risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence colorectal cancer risk. Risk models were generated from case-control data incorporating genotypes alone (n=39,266), and in combination with gender, age and family history (n=11,324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4,187 independent samples. 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks. Median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10−16), confirmed in external validation sets (Sweden p=1.2×10−6, Finland p=2×10−5). Mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05–1.13). Discriminative performance was poor across the risk spectrum (area under curve (AUC) for genotypes alone - 0.57; AUC for genotype/age/gender/FH - 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk. We show that genotype data provides additional information that complements age, gender and FH as risk factors. However, individualized genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential, since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.
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