Browsing by Author "He, Bing"

Now showing 1 - 7 of 7
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    Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation
    (Research Square, 2024-02-08) Pugalenthi, Pradeep Varathan; He, Bing; Xie, Linhui; Nho, Kwangsik; Saykin, Andrew J.; Yan, Jingwen; Radiology and Imaging Sciences, School of Medicine
    Alzheimer’s disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a significant set of SNPs associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.
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    Deep trans-omic network fusion reveals altered synaptic network in Alzheimer’s Disease
    (CSH, 2023-02-21) Xie, Linhui; Raj, Yash; Varathan, Pradeep; He, Bing; Nho, Kwangsik; Risacher, Shannon L.; Salama, Paul; Saykin, Andrew J.; Yan, Jingwen; Electrical and Computer Engineering, School of Engineering and Technology
    Multi-omic data spanning from genotype, gene expression to protein expression have been increasingly explored to interpret findings from genome wide association studies of Alzheimer’s disease (AD) and to gain more insight of the disease mechanism. However, each -omics data type is usually examined individually and the functional interactions between genetic variations, genes and proteins are only used after discovery to interpret the findings, but not beforehand. In this case, multi-omic findings are likely not functionally related and therefore give rise to challenges in interpretation. To address this problem, we propose a new interpretable deep neural network model MoFNet to jointly model the prior knowledge of functional interactions and multi-omic data set. It aims to identify a subnetwork of functional interactions predictive of AD evidenced by multi-omic measures. Particularly, prior functional interaction network was embedded into the architecture of MoFNet in a way that it resembles the information flow from DNA to gene and protein. The proposed model MoFNet significantly outperformed all other state-of-art classifiers when evaluated using multi-omic data from the ROS/MAP cohort. Instead of individual markers, MoFNet yielded multi-omic sub-networks related to innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. Around 50% of these findings were replicated in another independent cohort. Our identified gene/proteins are highly related to synaptic vesicle function. Altered regulation or expression of these genes/proteins could cause disruption in neuron-neuron or neuron-glia cross talk and further lead to neuronal and synapse loss in AD. Further investigation of these identified genes/proteins could possibly help decipher the mechanisms underlying synaptic dysfunction in AD, and ultimately inform therapeutic strategies to modify AD progression in the early stage.
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    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 Technology
    Introduction: 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.
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    Gene co-expression changes underlying the functional connectomic alterations in Alzheimer's disease
    (BMC, 2022-04-23) He, Bing; Gorijala, Priyanka; Xie, Linhui; Cao, Sha; Yan, Jingwen; BioHealth Informatics, School of Informatics and Computing
    Background: There is growing evidence indicating that a number of functional connectivity networks are disrupted at each stage of the full clinical Alzheimer's disease spectrum. Such differences are also detectable in cognitive normal (CN) carrying mutations of AD risk genes, suggesting a substantial relationship between genetics and AD-altered functional brain networks. However, direct genetic effect on functional connectivity networks has not been measured. Methods: Leveraging existing AD functional connectivity studies collected in NeuroSynth, we performed a meta-analysis to identify two sets of brain regions: ones with altered functional connectivity in resting state network and ones without. Then with the brain-wide gene expression data in the Allen Human Brain Atlas, we applied a new biclustering method to identify a set of genes with differential co-expression patterns between these two set of brain regions. Results: Differential co-expression analysis using biclustering method led to a subset of 38 genes which showed distinctive co-expression patterns between AD-related and non AD-related brain regions in default mode network. More specifically, we observed 4 sub-clusters with noticeable co-expression difference, where the difference in correlations is above 0.5 on average. Conclusions: This work applies a new biclustering method to search for a subset of genes with altered co-expression patterns in AD-related default mode network regions. Compared with traditional differential expression analysis, differential co-expression analysis yielded many more significant hits with extra insights into the wiring mechanism between genes. Particularly, the differential co-expression pattern was observed between two sets of genes, suggesting potential upstream genetic regulators in AD development.
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    Global Brain Functional Network Connectivity in Infants With Prenatal Opioid Exposure
    (Frontiers Media, 2022-03-14) Radhakrishnan, Rupa; Vishnubhotla, Ramana V.; Zhao, Yi; Yan, Jingwen; He, Bing; Steinhardt, Nicole; Haas, David M.; Sokol, Gregory M.; Sadhasivam, Senthilkumar; Radiology and Imaging Sciences, School of Medicine
    Background: Infants with prenatal opioid and substance exposure are at higher risk of poor neurobehavioral outcomes in later childhood. Early brain imaging in infancy has the potential to identify early brain developmental alterations that may help predict behavioral outcomes in these children. In this study, using resting-state functional MRI in early infancy, we aim to identify differences in global brain network connectivity in infants with prenatal opioid and substance exposure compared to healthy control infants. Methods and materials: In this prospective study, we recruited 23 infants with prenatal opioid exposure and 29 healthy opioid naïve infants. All subjects underwent brain resting-state functional MRI before 3 months postmenstrual age. Covariate Assisted Principal (CAP) regression was performed to identify brain networks within which functional connectivity was associated with opioid exposure after adjusting for sex and gestational age. Associations of these significant networks with maternal comorbidities were also evaluated. Additionally, graph network metrics were assessed in these CAP networks. Results: There were four CAP network components that were significantly different between the opioid exposed and healthy control infants. Two of these four networks were associated with maternal psychological factors. Intra-network graph metrics, namely average flow coefficient, clustering coefficient and transitivity were also significantly different in opioid exposed infants compared to healthy controls. Conclusion: Prenatal opioid exposure is associated with alterations in global brain functional networks compared to non-opioid exposed infants, with intra-network alterations in graph network modeling. These network alterations were also associated with maternal comorbidity, especially mental health. Large-scale longitudinal studies can help in understanding the clinical implications of these early brain functional network alterations in infants with prenatal opioid exposure.
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    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, Jingwen
    Alzheimer’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.
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    Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease
    (Oxford University Press, 2021) Xie, Linhui; He, Bing; Varathan, Pradeep; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Salama, Paul; Yan, Jingwen; BioHealth Informatics, School of Informatics and Computing
    A large number of genetic variations have been identified to be associated with Alzheimer's disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of 'vision', a brain function partly controlled by frontal cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.