ShanghaiTech University Knowledge Management System
A Bayesian spatial model for imaging genetics | |
2022-06 | |
发表期刊 | BIOMETRICS (IF:1.4[JCR-2023],2.2[5-Year]) |
ISSN | 0006-341X |
EISSN | 1541-0420 |
DOI | 10.1111/biom.13460 |
摘要 | We develop a Bayesian bivariate spatial model for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. Our model is motivated by an imaging genetics study of the Alzheimer's Disease Neuroimaging Initiative (ADNI), where the objective is to examine the association between images of volumetric and cortical thickness values summarizing the structure of the brain as measured by magnetic resonance imaging (MRI) and a set of 486 single nucleotide polymorphism (SNPs) from 33 Alzheimer's disease (AD) candidate genes obtained from 632 subjects. A bivariate spatial process model is developed to accommodate the correlation structures typically seen in structural brain imaging data. First, we allow for spatial correlation on a graph structure in the imaging phenotypes obtained from a neighborhood matrix for measures on the same hemisphere of the brain. Second, we allow for correlation in the same measures obtained from different hemispheres (left/right) of the brain. We develop a mean-field variational Bayes algorithm and a Gibbs sampling algorithm to fit the model. We also incorporate Bayesian false discovery rate (FDR) procedures to select SNPs. We implement the methodology in a new release of the R package bgsmtr. We show that the new spatial model demonstrates superior performance over a standard model in our application. Data used in the preparation of this article were obtained from the ADNI database (). |
关键词 | Bayesian model Gbbs sampling imaging genetics spatial model variational Bayes |
URL | 查看原文 |
收录类别 | SCIE ; EI |
语种 | 英语 |
WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics |
WOS类目 | Biology ; Mathematical & Computational Biology ; Statistics & Probability |
WOS记录号 | WOS:000641085200001 |
出版者 | WILEY |
原始文献类型 | Article; Early Access |
引用统计 | 正在获取...
|
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/126513 |
专题 | 数学科学研究所_PI研究组(P)_葛淑菲组 |
通讯作者 | Nathoo, Farouk S. |
作者单位 | 1.Univ Victoria, Dept Math & Stat, Victoria, BC, Canada; 2.ShanghaiTech Univ, Inst Math Sci, Shanghai, Peoples R China; 3.Simon Fraser Univ, Stat & Actuarial Sci, Burnaby, BC, Canada |
推荐引用方式 GB/T 7714 | Song, Yin,Ge, Shufei,Cao, Jiguo,et al. A Bayesian spatial model for imaging genetics[J]. BIOMETRICS,2022. |
APA | Song, Yin,Ge, Shufei,Cao, Jiguo,Wang, Liangliang,&Nathoo, Farouk S..(2022).A Bayesian spatial model for imaging genetics.BIOMETRICS. |
MLA | Song, Yin,et al."A Bayesian spatial model for imaging genetics".BIOMETRICS (2022). |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 |
个性服务 |
查看访问统计 |
谷歌学术 |
谷歌学术中相似的文章 |
[Song, Yin]的文章 |
[Ge, Shufei]的文章 |
[Cao, Jiguo]的文章 |
百度学术 |
百度学术中相似的文章 |
[Song, Yin]的文章 |
[Ge, Shufei]的文章 |
[Cao, Jiguo]的文章 |
必应学术 |
必应学术中相似的文章 |
[Song, Yin]的文章 |
[Ge, Shufei]的文章 |
[Cao, Jiguo]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
修改评论
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。