Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and MRC Human Genetics Unit, Edinburgh EH4 2XU, UK.
Gut. 2013 Jun;62(6):871-81. doi: 10.1136/gutjnl-2011-300537. Epub 2012 Apr 5.
Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. A study was conducted in a large multi-population study to assess the feasibility of CRC risk prediction using common genetic variant data combined with other risk factors. A risk prediction model was built and applied to the Scottish population using available data.
Nine populations of European descent were studied to develop and validate CRC 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 CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39,266) and in combination with gender, age and FH (n=11,324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks.
The 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)). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve 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.
Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised 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.
结直肠癌（CRC）具有很大的遗传成分。已证实常见遗传变异可导致 CRC 风险增加。本研究在一项大型多人群研究中进行，旨在评估使用常见遗传变异数据结合其他危险因素进行 CRC 风险预测的可行性。构建了风险预测模型，并使用可用数据对苏格兰人群进行了应用。
对 9 个人群进行研究，以开发和验证 CRC 风险预测模型。使用二元逻辑回归评估年龄、性别、家族史（FH）和 10 个易感性位点基因型的综合效应，这些位点单独对 CRC 风险的影响较小。风险模型是根据病例对照数据构建的，这些数据仅包含基因型（n=39266）和结合性别、年龄和 FH（n=11324）。通过 10 倍内部交叉验证和 4187 个独立样本的外部验证评估模型的判别性能。通过对年龄和性别特异性人群风险进行基因型和 FH 建模，估计 10 年的绝对风险。
病例组的中位风险等位基因数多于对照组（10 对 9，p<2.2×10(-16)），在外部验证集中得到证实（瑞典 p=1.2×10(-6)，芬兰 p=2×10(-5)）。风险每增加一个等位基因的平均百分比为 9%（OR 1.09；95%CI 1.05 至 1.13）。整个风险谱的判别性能均较差（仅基因型的曲线下面积为 0.57；基因型/年龄/性别/FH 的曲线下面积为 0.59）。然而，使用苏格兰人群数据对基因型数据、FH、年龄和性别进行建模表明，识别出预测 10 年绝对风险>5%的亚组是可行的。
基因型数据提供了补充年龄、性别和 FH 等危险因素的额外信息，但个体化遗传风险预测目前不可行。尽管如此，建模练习表明具有公共卫生潜力，因为有可能将人群分层为 CRC 风险类别，从而为有针对性的预防和监测提供信息。
