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Browsing by Author "Moore, Jason H."
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Item GN-SCCA: GraphNet based Sparse Canonical Correlation Analysis for Brain Imaging Genetics(2015) Du, Lei; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L.; Huang, Heng; Inlow, Mark; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of MedicineIdentifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings.Item GPU Accelerated Browser for Neuroimaging Genomics(Springer, 2018-10) Zigon, Bob; Li, Huang; Yao, Xiaohui; Fang, Shiaofen; Hasan, Mohammad Al; Yan, Jingwen; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Computer and Information Science, School of ScienceNeuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates. The ANOVA algorithm is 110 times faster than the 4-core OpenMP version, while the VEGAS algorithm is 375 times faster than its 4-core OpenMP counter part. This approach lays a solid foundation for researchers to address the challenges of mining large-scale imaging genomics datasets via interactive visual exploration.Item Hippocampal transcriptome-guided genetic analysis of correlated episodic memory phenotypes in Alzheimer's disease(2015) Yan, Jingwen; Kim, Sungeun; Nho, Kwangsik; Chen, Rui; Risacher, Shannon L.; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Department of BioHealth Informatics, School of Informatics and ComputingAs the most common type of dementia, Alzheimer's disease (AD) is a neurodegenerative disorder initially manifested by impaired memory performances. While the diagnosis information indicates a dichotomous status of a patient, memory scores have the potential to capture the continuous nature of the disease progression and may provide more insights into the underlying mechanism. In this work, we performed a targeted genetic study of memory scores on an AD cohort to identify the associations between a set of genes highly expressed in the hippocampal region and seven cognitive scores related to episodic memory. Both main effects and interaction effects of the targeted genetic markers on these correlated memory scores were examined. In addition to well-known AD genetic markers APOE and TOMM40, our analysis identified a new risk gene NAV2 through the gene-level main effect analysis. NAV2 was found to be significantly and consistently associated with all seven episodic memory scores. Genetic interaction analysis also yielded a few promising hits warranting further investigation, especially for the RAVLT list B Score.Item Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer's disease(BMC, 2022-08-01) Kim, Mansu; Wu, Ruiming; Yao, Xiaohui; Saykin, Andrew J.; Moore, Jason H.; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineBackground: Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80[Formula: see text]. The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. Results: We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer's Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. Conclusion: We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes.Item Longitudinal assessment of cognitive changes associated with adjuvant treatment for breast cancer: the impact of APOE and smoking(Wiley Blackwell (John Wiley & Sons), 2014-12) Ahles, Tim A.; Li, Yuelin; McDonald, Brenna C.; Schwartz, Gary N.; Kaufman, Peter A.; Tsongalis, Gregory J.; Moore, Jason H.; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, IU School of MedicinePURPOSE: This study examined the association of post-treatment changes in cognitive performance, apolipoprotein E (APOE), and smoking in breast cancer patients treated with adjuvant therapy. PARTICIPANTS AND METHODS: Breast cancer patients treated with chemotherapy (N = 55, age = 51.9 ± 7.1, education = 15.7 ± 2.6) were evaluated with a battery of neuropsychological tests prior to chemotherapy and at 1, 6, and 18 months post-chemotherapy. Matched groups of breast cancer patients not exposed to chemotherapy (N = 68, age = 56.8 ± 8.3, education = 14.8 ± 2.2) and healthy controls (N = 43, age = 53.0 ± 10.1, education = 15.2 ± 2.6) were evaluated at similar intervals. APOE epsilon 4 carrier status (APOE4+) and smoking history were also evaluated. RESULTS: The detrimental effect of APOE4+ genotype on post-treatment cognitive functioning was moderated by smoking history, that is, patients without a smoking history had significantly lower performance on measures of processing speed and working memory compared with those with a smoking history and healthy controls. Exploratory analyses revealed that APOE4+ patients without a smoking history who were exposed to chemotherapy showed a decline in performance in processing speed, compared with patients with a smoking history. A similar but less pronounced pattern was seen in the no chemotherapy group (primarily endocrine treatment). For working memory, the APOE4+ by smoking interaction was observed in the no chemotherapy group only. CONCLUSIONS: The association between APOE status, breast cancer treatment, and cognitive functioning was moderated by smoking history suggesting that both chemotherapy and endocrine therapy interact with APOE status and smoking to influence cognition. A putative mechanism is that smoking corrects a deficit in nicotinic receptor functioning and dopamine levels in APOE4+ individuals.Item Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns(MDPI, 2022-08-24) Wu, Ruiming; Bao, Jingxuan; Kim, Mansu; Saykin, Andrew J.; Moore, Jason H.; Shen, Li; Radiology and Imaging Sciences, School of MedicineBrain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer's disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP-SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.Item Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics(Elsevier, 2022-02) Kim, Mansu; Min, Eun Jeong; Liu, Kefei; Yan, Jingwen; Saykin, Andrew J.; Moore, Jason H.; Long, Qi; Shen, Li; BioHealth Informatics, School of Informatics and ComputingThe advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulation data, our proposed model has achieved the best performance in terms of canonical correlation coefficients, estimation accuracy, and feature selection accuracy. On the real imaging genetic data, our proposed model has revealed promising features of single-nucleotide polymorphisms and brain regions related to sleep. The identified features can be used to improve clinical score prediction using promising imaging genetic biomarkers. An interesting future direction is to apply our model to additional neurological or psychiatric cohorts such as patients with Alzheimer’s or Parkinson’s disease to demonstrate the generalizability of our method.Item Network-guided sparse learning for predicting cognitive outcomes from MRI measures(Springer, 2013) Yan, Jingwen; Huang, Heng; Risacher, Shannon L.; Kim, Sungeun; Inlow, Mark; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, School of MedicineAlzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful.Item A novel structure-aware sparse learning algorithm for brain imaging genetics(Springer, 2014) Du, Lei; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L.; Huang, Heng; Inlow, Mark; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of MedicineBrain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.Item Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer’s Disease(IEEE, 2022-12) Sha, Jiahang; Bao, Jingxuan; Liu, Kefei; Yang, Shu; Wen, Zixuan; Cui, Yuhan; Wen, Junhao; Davatzikos, Christos; Moore, Jason H.; Saykin, Andrew J.; Long, Qi; Shen, Li; Radiology and Imaging Sciences, School of MedicineInvestigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer’s disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.