Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.

准确的结直肠癌 (CRC) 风险预测模型对于识别患 CRC 的低风险和高风险个体至关重要，因为随后可以为他们提供有针对性的筛查和干预措施，以解决他们患疾病的风险（如果他们属于高危人群） ) 并避免不必要的筛查和干预（如果他们属于低风险人群）。由于可能有数以千计的遗传变异导致 CRC 风险，因此研究这些遗传变异是否可以联合用于 CRC 风险预测在临床上很重要。在本文中，我们推导并比较了从全基因组关联研究 (GWAS) 中生成预测性多基因风险评分 (PRS) 的不同方法，包括 55,105 名受 CRC 影响的病例受试者和 65,079 名欧洲血统的对照受试者。我们以三种方式构建 PRS，使用 (1) 140 个先前识别和验证的 CRC 基因座；(2) 基于连锁不平衡 (LD) 聚集的 SNP 选择，然后是机器学习方法；(3) LDpred，一种用于全基因组风险预测的贝叶斯方法。我们在一个由 101,987 名个体和 1,699 名受 CRC 影响的病例受试者组成的独立队列中测试了 PRS。根据接受者操作特征曲线 (AUC) 下的年龄和性别调整面积计算的区分准确度对于 LDpred 衍生的 PRS (AUC = 0.654) 是最高的，包括近 1.2 M 遗传变异（因果遗传变异的比例CRC 假设为 0.003），而从 GWAS 识别的 140 个已知变体的 PRS 具有最低的 AUC（AUC = 0.629）。基于 LDpred 衍生的 PRS，我们能够确定 30% 没有家族史的个体与具有 CRC 家族史的个体具有相似的 CRC 风险，而基于已知 GWAS 变体的 PRS 仅确定前 10% 具有相似的相对风险。这些人中约有 90% 没有家族史，根据当前的筛查指南，这些人被认为是平均风险，但可能会受益于早期筛查。开发的 PRS 为风险分层的 CRC 筛查和其他有针对性的干预措施提供了一种方法。