Jingwen Yan1, Vinesh Raja V1, Zhi Huang2, Enrico Amico3, Kwangsik Nho4, Shiaofeng Fang5, Olaf Sporns6, Yu-Chien Wu4, Andrew Saykin4, Joaquin Goni3, Li Shen7. 1. Department of BioHealth Informatics, Indiana University Purdue University Indianapolis, 719 Indiana Ave, Indianapolis, USA. 2. Electrical and Computing Engineering, Indiana University Purdue University Indianapolis, 46202 Indianapolis, USA. 3. Industrial Engineering, Purdue University, 47096 West Lafayette, USA. 4. Radiology and Imaging Sciences, Indiana University School of Medicine, 46202 Indianapolis, USA. 5. Computer Science, Indiana University Purdue University Indianapolis, 46202 Indianapolis, USA. 6. Psychological and Brain Sciences, Indiana University, 47405 Bloomington, USA. 7. Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 19104 Philadelphia, USA.
Abstract
BACKGROUND: Alzheimer's disease is the most common form of brain dementia characterized by gradual loss of memory followed by further deterioration of other cognitive function. Large-scale genome-wide association studies have identified and validated more than 20 AD risk genes. However, how these genes are related to the brain-wide breakdown of structural connectivity in AD patients remains unknown. METHODS: We used the genotype and DTI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. After constructing the brain network for each subject, we extracted three types of link measures, including fiber anisotropy, fiber length and density. We then performed a targeted genetic association analysis of brain-wide connectivity measures using general linear regression models. Age at scan and gender were included in the regression model as covariates. For fair comparison of the genetic effect on different measures, fiber anisotropy, fiber length and density were all normalized with mean as 0 and standard deviation as one.We aim to discover the abnormal brain-wide network alterations under the control of 34 AD risk SNPs identified in previous large-scale genome-wide association studies. RESULTS: After enforcing the stringent Bonferroni correction, rs10498633 in SLC24A4 were found to significantly associated with anisotropy, total number and length of fibers, including some connecting brain hemispheres. With a lower level of significance at 5e-6, we observed significant genetic effect of SNPs in APOE, ABCA7, EPHA1 and CASS4 on various brain connectivity measures.
BACKGROUND: Alzheimer's disease is the most common form of brain dementia characterized by gradual loss of memory followed by further deterioration of other cognitive function. Large-scale genome-wide association studies have identified and validated more than 20 AD risk genes. However, how these genes are related to the brain-wide breakdown of structural connectivity in AD patients remains unknown. METHODS: We used the genotype and DTI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. After constructing the brain network for each subject, we extracted three types of link measures, including fiber anisotropy, fiber length and density. We then performed a targeted genetic association analysis of brain-wide connectivity measures using general linear regression models. Age at scan and gender were included in the regression model as covariates. For fair comparison of the genetic effect on different measures, fiber anisotropy, fiber length and density were all normalized with mean as 0 and standard deviation as one.We aim to discover the abnormal brain-wide network alterations under the control of 34 AD risk SNPs identified in previous large-scale genome-wide association studies. RESULTS: After enforcing the stringent Bonferroni correction, rs10498633 in SLC24A4 were found to significantly associated with anisotropy, total number and length of fibers, including some connecting brain hemispheres. With a lower level of significance at 5e-6, we observed significant genetic effect of SNPs in APOE, ABCA7, EPHA1 and CASS4 on various brain connectivity measures.
Entities:
Keywords:
Alzheimer’s disease; brain connectivity; imaging genetics association
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