改进机器学习预测ADHD使用基因组多基因风险得分和基因相关的表型的风险得分
Eric J Barnett1, Yanli Zhang-James1, Stephen V Faraone1,2
1Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, New York, USA.
基因组多基因风险评分 (gsPRSs) 提高了注意力缺陷/多动症 (ADHD) 预测的准确性. 结合相关疾病的遗传风险可以增强预测模型,这表明更好的遗传风险会计可以更准确地预测ADHD.
科学领域:
- 遗传学 遗传学 是一个
- 精神病学是一个精神病学.
- 计算生物学 计算生物学
背景情况:
- 多基因风险评分 (PRS) 估计了诸如注意力缺陷/多动障碍 (ADHD) 等疾病的遗传风险.
- 对于ADHD的PRS的预测准确性尚未得到广泛的研究.
- 常见的遗传变异有助于ADHD风险.
研究的目的:
- 用基因组多基因风险评分 (gsPRSs) 调查ADHD风险预测的准确性.
- 确定是否将gsPRS和来自相关表型的遗传风险结合起来可以改善ADHD预测模型.
主要方法:
- 对GWAS数据的基因组分析确定了与ADHD相关的基因组.
- 基因组多基因风险评分 (gsPRSs) 为ADHD和遗传相关的表型生成.
- 机器学习模型 (随机森林) 结合了标准的PRS和优化的gsPRS来预测ADHD.
主要成果:
- 使用PRS和20个优化gsPRS的随机森林模型实现了0.72的AUC (95%CI:0.70-0.74).
- 这一AUC代表了与仅使用标准PRSs的模型相比的显著改进.
- 结合基因组级风险和相关疾病的遗传风险,提高了ADHD预测的准确性.
结论:
- 在基因组水平上总结遗传风险可以改善ADHD的预测.
- 考虑到遗传背景和相关疾病风险,可以提高预测模型的性能.
- 预计更大的研究规模将进一步提高预测准确度.
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