相关实验视频
PGS-Depot:通过基于总结统计的方法构建的多基因分数的全面资源
Chen Cao1, Shuting Zhang1, Jianhua Wang2
1Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
Nucleic acids research
|November 12, 2023
概括
PGS-Depot提供了使用GWAS总结统计数据的多基因分数 (PGS) 的全面资源. 它标准化了多种方法和种群的PGS计算和比较,简化了遗传预测研究.
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:
- 多基因分数 (PGS) 对于从遗传上预测复杂的特征至关重要.
- 现有的资源缺乏基于公布的总结统计数据计算的全面的PGS.
- 由于管道的复杂性,实施各种PGS方法具有挑战性.
研究的目的:
- 介绍PGS-Depot,这是公开可用的与疾病相关的GWAS总结统计数据的新型资源.
- 在各种方法和种群中标准化PGS的计算和比较.
- 通过提供可访问的工具和数据,促进遗传预测研究.
主要方法:
- 策划了5585个高质量的GWAS总结统计数据 (1933个定量数据,3652个二进制数据) 针对1564个特征.
- 使用标准化管道实施了11种基于总结统计的PGS方法.
- 启用了亲属和交叉祖先预测性能比较和定制PGS生成.
主要成果:
- PGS-Depot提供了与疾病相关的GWAS总结统计数据的全面集合.
- 一个标准化的管道促进了11个PGS方法的应用和比较.
- 用户可以下载分数,效果大小和总结统计数据,并根据各种标准进行搜索.
结论:
- PGS-Depot解决了对多基因分数的集中,标准化资源的需求.
- 该平台简化了PGS方法的实施和比较.
- 它为研究人员提供了可访问的数据和用于遗传预测研究的工具.
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