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首页> 外文期刊>Genetic epidemiology. >Performance and Robustness of Penalized and Unpenalized Methods for Genetic Prediction of Complex Human Disease
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Performance and Robustness of Penalized and Unpenalized Methods for Genetic Prediction of Complex Human Disease

机译：复杂人类疾病遗传预测中受罚和无罚方法的性能和鲁棒性

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摘要

A central goal of medical genetics is to accurately predict complex disease from genotypes. Here, we present a comprehensive analysis of simulated and real data using lasso and elastic-net penalized support-vector machine models, a mixed-effects linear model, a polygenic score, and unpenalized logistic regression. In simulation, the sparse penalized models achieved lower false-positive rates and higher precision than the other methods for detecting causal SNPs. The common practice of prefiltering SNP lists for subsequent penalized modeling was examined and shown to substantially reduce the ability to recover the causal SNPs. Using genome-wide SNP profiles across eight complex diseases within cross-validation, lasso and elastic-net models achieved substantially better predictive ability in celiac disease, type 1 diabetes, and Crohn's disease, and had equivalent predictive ability in the rest, with the results in celiac disease strongly replicating between independent datasets. We investigated the effect of linkage disequilibrium on the predictive models, showing that the penalized methods leverage this information to their advantage, compared with methods that assume SNP independence. Our findings show that sparse penalized approaches are robust across different disease architectures, producing as good as or better phenotype predictions and variance explained. This has fundamental ramifications for the selection and future development of methods to genetically predict human disease.
机译：医学遗传学的中心目标是根据基因型准确预测复杂疾病。在这里，我们介绍了使用套索和弹性网惩罚支持向量机模型，混合效应线性模型，多基因得分和无罚逻辑回归对模拟和真实数据进行的全面分析。在仿真中，与其他用于检测因果SNP的方法相比，稀疏惩罚模型具有更低的假阳性率和更高的精度。预过滤SNP列表以进行后续惩罚模型的通用做法已得到检查，结果表明该方法显着降低了恢复因果SNP的能力。使用交叉验证中横跨八种复杂疾病的全基因组SNP图谱，套索和弹性网模型在乳糜泻，1型糖尿病和克罗恩病中获得了更好的预测能力，在其余方面具有同等的预测能力，结果在腹腔疾病中的强复制在独立数据集之间。我们调查了连锁不平衡对预测模型的影响，表明与假定SNP独立的方法相比，惩罚方法利用了这些信息的优势。我们的研究结果表明，稀疏的惩罚方法在不同的疾病体系结构中均很健壮，可产生与表型相同或更好的预测值和解释的方差。这对于基因预测人类疾病的方法的选择和未来发展具有根本的影响。

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