ADuLT:一个高效和强大的时间到事件GWAS
Emil M Pedersen1,2, Esben Agerbo3,4,5, Oleguer Plana-Ripoll3,6
1National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark. emp@ncrr.au.dk.
Nature communications
|September 9, 2023
概括
年龄依赖责任门 (ADuLT) 模型提供了强大的全基因组关联研究 (GWAS) 能力,即使有确定偏差. 这种新模型在遗传研究中优于传统方法,如对基因研究中的时间到事件表型的考克斯回归.
科学领域:
- 遗传学 遗传学 是一个
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 全基因组关联研究 (GWAS) 通常使用比例危险模型分析时间到事件数据.
- 这些模型的性能,特别是考克斯回归,在确定偏差和变化的遗传模型下并不清楚.
- 在处理复杂的时间到事件表型和偏差采样时,案例控制GWAS可能缺乏力量.
研究的目的:
- 引入和评估年龄依赖的责任门 (ADuLT) 模型,作为基于考克斯回归的GWAS (SPACox) 的替代方案,用于时间到事件的表型.
- 在不同的遗传模型和确定场景下比较ADuLT,SPACox和标准病例控制GWAS的功率和稳定性.
- 评估这些方法在现实世界队列中的表现,对精神疾病进行强有力的病例确定.
主要方法:
- 模拟使用两个生成模型和不同程度的病例确定进行了模拟.
- 以年龄为依赖的责任门 (ADuLT) 模型被提出作为一种新的方法.
- 性能与基于考克斯回归的GWAS (SPACox) 和标准病例对照GWAS进行了比较.
- 分析了iPSYCH队列数据,包括四种精神疾病 (ADHD,自闭症,抑郁症,精神分裂症).
主要成果:
- 考克斯回归GWAS (SPACox) 在强例确定下显示显著降低功率 (5倍过量抽样).
- 在所有模拟场景中,ADuLT模型被证明是可靠的.
- 在iPSYCH队列中,ADuLT确定了20个独立的全基因组显著关联,超过了对照病例GWAS (17) 和SPACox (8).
结论:
- ADuLT模型为GWAS提供了一种强大且可靠的确定方法,用于时间到事件的表型.
- 传统的考克斯回归GWAS方法在存在重大病例确定时具有不足的性能.
- 将发病年龄信息与强大的GWAS方法 (如ADuLT) 整合起来,对于增加公共健康结果分析的权力至关重要.
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