It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRS_Metabo); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PL_Metabo); validation of two comprehensive polygenic risk scores: GRS_Gola based on Metabochip data, and GRS_Khera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDI_rel) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRS_Metabo and PL_Metabo, GRS_Gola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDI_rel: 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRS_Khera yielded slightly worse prediction results than GRS_Gola.

中文翻译：

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在 MONICA/KORA 研究的两个大型队列中比较遗传风险预测模型以提高对冠心病的预测

目前尚不清楚以单核苷酸多态性 (SNP) 形式提供的遗传信息如何最有效地整合到冠心病 (CHD) 的风险预测模型中，以增加临床风险模型之外的重要预测价值。在本研究中，一个基于人群的病例队列被用作训练集（451 个事件病例，1488 个非病例）和一个独立队列作为测试集（160 个事件病例，2749 个非病例）。比较了以下量化遗传信息的策略： 加权遗传风险评分，包括文献中与 CHD 相关的 Metabochip SNP (GRS _Metabo )；使用优先套索（PL _Metabo); 两个综合多基因风险评分的验证：基于 Metabochip 数据的GRS _Gola和基于交叉验证的全基因组基因分型数据的GRS _Khera（仅在测试集中可用）。我们使用 Cox 回归来评估与冠心病的关联。C指数、无类别净重分类指数 (cfNRI) 和相对综合歧视改进 (IDI _rel ) 用于量化遗传信息的预测性能，超出弗雷明汉风险评分变量。与 GRS _Metabo和 PL _{Metabo 相比}，GRS _Gola显着提高了预测（delta C-index [95% 置信区间]: 0.0087 [0.0044, 0.0130]; IDI _rel : 0.0509 [0.0131, 0.0894]; cfNRI 仅在以下情况下有所改善：0.1761 [0.0253, 0.3219])。GRS _{Khera 的}预测结果比 GRS _Gola略差。