在大规模的全基因组协会研究中学习可重复性和可复制性的教训
1Department of Biostatistics and Department of Statistics, Harvard University.
概括
加强科学研究需要强大的可重复性和可复制性,特别是在大规模的全基因组关联研究中. 这涉及透明的数据处理,标准化分析和协作框架,以确保可靠的科学发现.
科学领域:
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 可复制性和可复制性是科学研究的基本原则.
- 大规模的全基因组关联研究 (GWAS) 为确保可靠的结果带来了独特的挑战.
研究的目的:
- 强调在GWAS中增强数据可复制性,分析可复制性和结果可复制性的关键重要性.
- 为改善GWAS发现的可靠性提供可行的建议.
主要方法:
- 对大规模GWAS当前实践的审查和反思.
- 制定研究设计,数据管理和分析协议的建议.
主要成果:
- 确定需要改进的关键领域:研究设计 (批量效应,选择偏差),不同的发现/复制阶段,以及适当的样本大小.
- 重点是系统的,透明的数据管道和标准化的,特定领域的分析协议.
- 关于协作框架,开放访问工具和数据/资源共享基础设施的建议.
结论:
- 实施拟议的战略将大大提高GWAS的可重复性和可复制性.
- 通过激励和研究人员,资助者和期刊之间的合作伙伴关系来培养可重复性研究的文化至关重要.
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