A Bayesian spatial model for imaging genetics
2022-06
发表期刊BIOMETRICS (IF:1.4[JCR-2023],2.2[5-Year])
ISSN0006-341X
EISSN1541-0420
DOI10.1111/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
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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
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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).
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