单效应汇总回归模型在多组学数据共定位分析中的应用
The application of the sum of single effects regression model for colocalization analysis in multi-omics data
黄婷 1刘晋成 1李会琳 1吴怡雯 1郁尔 1季锴 1唐少文 2赵杨 3戴俊程 2易洪刚4
作者信息
- 1. 南京医科大学公共卫生学院生物统计学系,南京 211166
- 2. 南京医科大学公共卫生学院流行病学系,南京 211166
- 3. 南京医科大学公共卫生学院生物统计学系,南京 211166;南京医科大学公共卫生学院流行病学系,南京 211166
- 4. 南京医科大学生物医学大数据重点实验室,肿瘤个体化医学协同创新中心,南京 211166
- 折叠
摘要
目的 探讨单效应汇总(sum of single effects,SuSiE)回归模型在多组学数据共定位分析中的应用.方法 以多组学模拟数据为例,介绍单效应汇总回归模型的基本原理和R软件分析.结果 SuSiE回归模型通过利用单核苷酸多态性(single nucleotide polymorphism,SNPs)位点之间因连锁不平衡(linkage disequilibrium,LD)产生的相关性,允许在有多个因果变异的情况下,正确识别两个组学数据与表型相关的共定位点.结论 相对于传统方法,SuSiE回归模型拓展了单一因果变异假设这一适用条件,且计算效率较高,从而有助于利用多组学数据检测多个潜在与疾病相关联位点.
Abstract
Objective To explore the application of the sum of single effects(SuSiE)regression model for colocalization analysis with multi-omics data.Methods Taking the simulated data as an example,we introduced the basic principle of SuSiE regression model and the statistical analysis procedures using R software.Results The results showed that the SuSiE regression model could identify the shared casual variants as associated with traits through taking account the linkage disequilibrium(LD)between single nucleotide polymorphisms(SNPs).Despite the presence of multiple causal variants,the colocalization results were still stable.Conclusions Compared with those traditional approaches for colocalization,SuSiE regression model expands the applicability of the single causal variant hypothesis and it has higher computational efficiency,thus helping to detect multiple potential shared casual variants using multi-omics data.
关键词
多组学/共定位/近似贝叶斯因子/因果变异Key words
Multi-omics/Colocalization/Approximate Bayes factor/Casual variants引用本文复制引用
基金项目
国家自然科学基金(81941020)
大学生创新创业训练计划项目(202210312151)
出版年
2024
NETL
NSTL
万方数据