A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

中文翻译：

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性状预测的多多基因评分方法。

多基因评分汇总了全基因组关联研究 (GWAS) 中发现的数千个性状相关 DNA 变异的影响，其主要目标是估计个体特定的遗传倾向并预测结果。这通常是使用单个多基因评分来实现的，但在这里，我们使用多多基因评分（MPS）方法，通过利用多个发现 GWAS 的联合力量来提高预测能力，而不需要对预测因子之间的关系进行假设。我们使用 81 个认知、医学和人体测量特征的 GWAS 的汇总统计数据来预测独立目标样本中的三个核心发展结果：教育成就、体重指数 (BMI) 和一般认知能力。我们使用正则化回归和重复交叉验证，从英国 6710 名不相关青少年的代表性样本中选择 81 个多基因分数并估计其贡献。在独立测试集中，MPS 方法预测教育成绩的方差为 10.9%，一般认知能力的方差为 4.8%，BMI 的方差为 5.4%，比最佳单分预测的方差多出 1.1%、1.1% 和 1.6%。正如其他相关 GWA 分析的报道一样，它们可以合并到 MPS 模型中以最大化表型预测。MPS 方法应该在样本量适中的研究中有用，以调查发育、多变量和基因-环境相互作用问题，并最终在临床环境中使用个性化干预来预测和预防问题。