Browsing by Subject "Imaging genetics"
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Item Association of the top 20 Alzheimer's disease risk genes with [18F]flortaucipir PET(Alzheimer’s Association, 2022-05-11) Stage, Eddie; Risacher, Shannon L.; Lane, Kathleen A.; Gao, Sujuan; Nho, Kwangsik; Saykin, Andrew J.; Apostolova, Liana G.; Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of MedicineIntroduction: We previously reported genetic associations of the top Alzheimer's disease (AD) risk alleles with amyloid deposition and neurodegeneration. Here, we report the association of these variants with [18F]flortaucipir standardized uptake value ratio (SUVR). Methods: We analyzed the [18F]flortaucipir scans of 352 cognitively normal (CN), 160 mild cognitive impairment (MCI), and 54 dementia (DEM) participants from Alzheimer's Disease Neuroimaging Initiative (ADNI)2 and 3. We ran step-wise regression with log-transformed [18F]flortaucipir meta-region of interest SUVR as the outcome measure and genetic variants, age, sex, and apolipoprotein E (APOE) ε4 as predictors. The results were visualized using parametric mapping at familywise error cluster-level-corrected P < .05. Results: APOE ε4 showed significant (P < .05) associations with tau deposition across all disease stages. Other significantly associated genes include variants in ABCA7 in CN, CR1 in MCI, BIN1 and CASS4 in MCI and dementia participants. Discussion: We found significant associations to tau deposition for ABCA7, BIN1, CASS4, and CR1, in addition to APOE ε4. These four variants have been previously associated with tau metabolism through model systems.Item Characterization of gene expression patterns in mild cognitive impairment using a transcriptomics approach and neuroimaging endophenotypes(Wiley, 2022) Bharthur Sanjay, Apoorva; Patania, Alice; Yan, Xiaoran; Svaldi, Diana; Duran, Tugce; Shah, Niraj; Nemes, Sara; Chen, Eric; Apostolova, Liana G.; Neurology, School of MedicineIntroduction: Identification of novel therapeutics and risk assessment in early stages of Alzheimer's disease (AD) is a crucial aspect of addressing this complex disease. We characterized gene-expression patterns at the mild cognitive impairment (MCI) stage to identify critical mRNA measures and gene clusters associated with AD pathogenesis. Methods: We used a transcriptomics approach, integrating magnetic resonance imaging (MRI) and peripheral blood-based gene expression data using persistent homology (PH) followed by kernel-based clustering. Results: We identified three clusters of genes significantly associated with diagnosis of amnestic MCI. The biological processes associated with each cluster were mitochondrial function, NF-kB signaling, and apoptosis. Cluster-level associations with cortical thickness displayed canonical AD-like patterns. Driver genes from clusters were also validated in an external dataset for prediction of amyloidosis and clinical diagnosis. Discussion: We found a disease-relevant transcriptomic signature sensitive to prodromal AD and identified a subset of potential therapeutic targets associated with AD pathogenesis.Item The effect of the top 20 Alzheimer disease risk genes on gray-matter density and FDG PET brain metabolism(Elsevier, 2016-12-19) Stage, Eddie; Duran, Tugce; Risacher, Shannon L.; Goukasian, Naira; Do, Triet M.; West, John D.; Wilhalme, Holly; Nho, Kwangsik; Phillips, Meredith; Elashoff, David; Saykin, Andrew J.; Apostolova, Liana G.; Department of Neurology, IU School of MedicineINTRODUCTION: We analyzed the effects of the top 20 Alzheimer disease (AD) risk genes on gray-matter density (GMD) and metabolism. METHODS: We ran stepwise linear regression analysis using posterior cingulate hypometabolism and medial temporal GMD as outcomes and all risk variants as predictors while controlling for age, gender, and APOE ε4 genotype. We explored the results in 3D using Statistical Parametric Mapping 8. RESULTS: Significant predictors of brain GMD were SLC24A4/RIN3 in the pooled and mild cognitive impairment (MCI); ZCWPW1 in the MCI; and ABCA7, EPHA1, and INPP5D in the AD groups. Significant predictors of hypometabolism were EPHA1 in the pooled, and SLC24A4/RIN3, NME8, and CD2AP in the normal control group. DISCUSSION: Multiple variants showed associations with GMD and brain metabolism. For most genes, the effects were limited to specific stages of the cognitive continuum, indicating that the genetic influences on brain metabolism and GMD in AD are complex and stage dependent.Item Fused multi-modal similarity network as prior in guiding brain imaging genetic association(Frontiers Media, 2023-05-05) He, Bing; Xie, Linhui; Varathan, Pradeep; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Yan, Jingwen; Alzheimer’s Disease Neuroimaging Initiative; Engineering Technology, School of Engineering and TechnologyIntroduction: Brain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that the incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable. Methods: In this study, we explore a new data-driven prior knowledge that captures the subject-level similarity by fusing multi-modal similarity networks. It was incorporated into the sparse canonical correlation analysis (SCCA) model, which is aimed to identify a small set of brain imaging and genetic markers that explain the similarity matrix supported by both modalities. It was applied to amyloid and tau imaging data of the ADNI cohort, respectively. Results: Fused similarity matrix across imaging and genetic data was found to improve the association performance better or similarly well as diagnosis information, and therefore would be a potential substitute prior when the diagnosis information is not available (i.e., studies focused on healthy controls). Discussion: Our result confirmed the value of all types of prior knowledge in improving association identification. In addition, the fused network representing the subject relationship supported by multi-modal data showed consistently the best or equally best performance compared to the diagnosis network and the co-expression network.Item Genome-wide transcriptome analysis identifies novel dysregulated genes implicated in Alzheimer's pathology(Wiley, 2020-08-05) Nho, Kwangsik; Nudelman, Kelly; Allen, Mariet; Hodges, Angela; Kim, Sungeun; Risacher, Shannon L.; Apostolova, Liana G.; Lin, Kuang; Lunnon, Katie; Wang, Xue; Burgess, Jeremy D.; Ertekin-Taner, Nilüfer; Petersen, Ronald C.; Wang, Lisu; Qi, Zhenhao; He, Aiqing; Neuhaus, Isaac; Patel, Vishal; Foroud, Tatiana; Faber, Kelley M.; Lovestone, Simon; Simmons, Andrew; Weiner, Michael W.; Saykin, Andrew J.; Radiology and Imaging Sciences, School of MedicineINTRODUCTION: Abnormal gene expression patterns may contribute to the onset and progression of late-onset Alzheimer’s disease (LOAD). METHODS: We performed transcriptome-wide meta-analysis (N=1,440) of blood-based microarray gene expression profiles as well as neuroimaging and CSF endophenotype analysis. RESULTS: We identified and replicated five genes (CREB5, CD46, TMBIM6, IRAK3, and RPAIN) as significantly dysregulated in LOAD. The most significantly altered gene, CREB5, was also associated with brain atrophy and increased amyloid-β accumulation, especially in the entorhinal cortex region. cis-eQTL mapping analysis of CREB5 detected five significant associations (p<5x10−8), where rs56388170 (most significant) was also significantly associated with global cortical amyloid-β (Aβ) deposition measured by [18F]Florbetapir PET and CSF Aβ1-42. DISCUSSION: RNA from peripheral blood indicated a differential gene expression pattern in LOAD. Genes identified have been implicated in biological processes relevant to AD. CREB, in particular, plays a key role in nervous system development, cell survival, plasticity and learning and memory.Item Genomic loci influence patterns of structural covariance in the human brain(National Academy of Science, 2023) Wen, Junhao; Nasrallah, Ilya M.; Abdulkadir, Ahmed; Satterthwaite, Theodore D.; Yang, Zhijian; Erus, Guray; Robert-Fitzgerald, Timothy; Singh, Ashish; Sotiras, Aristeidis; Boquet-Pujadas, Aleix; Mamourian, Elizabeth; Doshi, Jimit; Cui, Yuhan; Srinivasan, Dhivya; Skampardoni, Ioanna; Chen, Jiong; Hwang, Gyujoon; Bergman, Mark; Bao, Jingxuan; Veturi, Yogasudha; Zhou, Zhen; Yang, Shu; Dazzan, Paola; Kahn, Rene S.; Schnack, Hugo G.; Zanetti, Marcus V.; Meisenzahl, Eva; Busatto, Geraldo F.; Crespo-Facorro, Benedicto; Pantelis, Christos; Wood, Stephen J.; Zhuo, Chuanjun; Shinohara, Russell T.; Gur, Ruben C.; Gur, Raquel E.; Koutsouleris, Nikolaos; Wolf, Daniel H.; Saykin, Andrew J.; Ritchie, Marylyn D.; Shen, Li; Thompson, Paul M.; Colliot, Olivier; Wittfeld, Katharina; Grabe, Hans J.; Tosun, Duygu; Bilgel, Murat; An, Yang; Marcus, Daniel S.; LaMontagne, Pamela; Heckbert, Susan R.; Austin, Thomas R.; Launer, Lenore J.; Espeland, Mark; Masters, Colin L.; Maruff, Paul; Fripp, Jurgen; Johnson, Sterling C.; Morris, John C.; Albert, Marilyn S.; Bryan, R. Nick; Resnick, Susan M.; Fan, Yong; Habes, Mohamad; Wolk, David; Shou, Haochang; Davatzikos, Christos; Radiology and Imaging Sciences, School of MedicineNormal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.Item Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease(Wiley, 2024) He, Bing; Wu, Ruiming; Sangani, Neel; Pugalenthi, Pradeep Varathan; Patania, Alice; Risacher, Shannon L.; Nho, Kwangsik; Apostolova, Liana G.; Shen, Li; Saykin, Andrew J.; Yan, Jingwen; Alzheimer’s Disease Neuroimaging Initiative; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringIntroduction: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. Methods: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). Results: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. Discussion: Our risk score holds great potential to improve the precision of early risk assessment. Highlights: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.Item Integrating Imaging and Genetics Data for Improved Understanding and Detection of Alzheimer's Disease(2024-08) He, Bing; Janga, Sarath Chandra; Saykin, Andrew J.; Yu, Meichen; Yan, JingwenAlzheimer’s disease (AD) is a progressive and irreversible brain disorder characterized by a slow and intricate progression, in which the initial pathological changes occur long before noticeable symptoms. AD is highly heritable and genetic factors play an essential role in AD development. Large scale genome-wide association studies have identified numerous SNPs related to AD. However, our understanding of the connections between genetics findings and altered brain phenotype is still limited. Brain imaging genetics, an emerging approach, aims to investigate the relationship between genetic variations and brain structure or function. It has great potential to provide insights into the underlying biological mechanisms and to enable the early detection of AD. Our study aimed to develop and apply novel computational approaches for more robust discovery of imaging genetics associations and for improved detection of AD in early stage. Specifically, we focused on addressing the heterogeneity problem inherent in integrating imaging and genetics data. In aim 1, we applied a novel biclustering method to associate genetic variations with functional brain connectivity altered in AD patients. In aim 2, we proposed novel strategy to integrate imaging and genetic data to serve as a new type of prior knowledge and investigated their role in guiding imaging genetics association. Finally, in aim 3, we proposed a multi-factorial pseudotime approach to integrate heterogeneous genotype and amyloid imaging data and examined its potential for staging and early detection of AD. Collectively, results from these objectives aimed to enhance our understanding and detection of AD, providing valuable information to inform therapeutic strategies to slow or halt disease progression.Item Integration of bioinformatics and imaging informatics for identifying rare PSEN1 variants in Alzheimer's disease(BioMed Central, 2016-08-12) Nho, Kwangsik; Horgusluoglu, Emrin; Kim, Sungeun; Risacher, Shannon L.; Kim, Dokyoon; Foroud, Tatiana; Aisen, Paul S.; Peterson, Ronald C.; Jack Jr., Clifford R.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Green, Robert C.; Toga, Arthur W.; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, IU School of MedicineBACKGROUND: Pathogenic mutations in PSEN1 are known to cause familial early-onset Alzheimer's disease (EOAD) but common variants in PSEN1 have not been found to strongly influence late-onset AD (LOAD). The association of rare variants in PSEN1 with LOAD-related endophenotypes has received little attention. In this study, we performed a rare variant association analysis of PSEN1 with quantitative biomarkers of LOAD using whole genome sequencing (WGS) by integrating bioinformatics and imaging informatics. METHODS: A WGS data set (N = 815) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort was used in this analysis. 757 non-Hispanic Caucasian participants underwent WGS from a blood sample and high resolution T1-weighted structural MRI at baseline. An automated MRI analysis technique (FreeSurfer) was used to measure cortical thickness and volume of neuroanatomical structures. We assessed imaging and cerebrospinal fluid (CSF) biomarkers as LOAD-related quantitative endophenotypes. Single variant analyses were performed using PLINK and gene-based analyses of rare variants were performed using the optimal Sequence Kernel Association Test (SKAT-O). RESULTS: A total of 839 rare variants (MAF < 1/√(2 N) = 0.0257) were found within a region of ±10 kb from PSEN1. Among them, six exonic (three non-synonymous) variants were observed. A single variant association analysis showed that the PSEN1 p. E318G variant increases the risk of LOAD only in participants carrying APOE ε4 allele where individuals carrying the minor allele of this PSEN1 risk variant have lower CSF Aβ1-42 and higher CSF tau. A gene-based analysis resulted in a significant association of rare but not common (MAF ≥ 0.0257) PSEN1 variants with bilateral entorhinal cortical thickness. CONCLUSIONS: This is the first study to show that PSEN1 rare variants collectively show a significant association with the brain atrophy in regions preferentially affected by LOAD, providing further support for a role of PSEN1 in LOAD. The PSEN1 p. E318G variant increases the risk of LOAD only in APOE ε4 carriers. Integrating bioinformatics with imaging informatics for identification of rare variants could help explain the missing heritability in LOAD.Item Mining brain imaging and genetics data via structured sparse learning(2015-04-29) Yan, Jingwen; Wu, Huanmei; Shen, Li; Fang, Shiaofen; Liu, XiaowenAlzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual loss of brain functions, usually preceded by memory impairments. It has been widely affecting aging Americans over 65 old and listed as 6th leading cause of death. More importantly, unlike other diseases, loss of brain function in AD progression usually leads to the significant decline in self-care abilities. And this will undoubtedly exert a lot of pressure on family members, friends, communities and the whole society due to the time-consuming daily care and high health care expenditures. In the past decade, while deaths attributed to the number one cause, heart disease, has decreased 16 percent, deaths attributed to AD has increased 68 percent. And all of these situations will continue to deteriorate as the population ages during the next several decades. To prevent such health care crisis, substantial efforts have been made to help cure, slow or stop the progression of the disease. The massive data generated through these efforts, like multimodal neuroimaging scans as well as next generation sequences, provides unprecedented opportunities for researchers to look into the deep side of the disease, with more confidence and precision. While plenty of efforts have been made to pull in those existing machine learning and statistical models, the correlated structure and high dimensionality of imaging and genetics data are generally ignored or avoided through targeted analysis. Therefore their performances on imaging genetics study are quite limited and still have plenty to be improved. The primary contribution of this work lies in the development of novel prior knowledge-guided regression and association models, and their applications in various neurobiological problems, such as identification of cognitive performance related imaging biomarkers and imaging genetics associations. In summary, this work has achieved the following research goals: (1) Explore the multimodal imaging biomarkers toward various cognitive functions using group-guided learning algorithms, (2) Development and application of novel network structure guided sparse regression model, (3) Development and application of novel network structure guided sparse multivariate association model, and (4) Promotion of the computation efficiency through parallelization strategies.