IU Indianapolis ScholarWorks :: Browsing by Author "Moore, Jason H."

Browsing by Author "Moore, Jason H."

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Computational genetics analysis of grey matter density in Alzheimer's disease
(Springer Nature, 2014-08-22) Zieselman, Amanda L.; Fisher, Jonathan M.; Hu, Ting; Andrews, Peter C.; Greene, Casey S.; Shen, Li; Saykin, Andrew J.; Moore, Jason H.; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
Background: Alzheimer's disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer's disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships. Results: We found a statistically significant synergistic interaction among two SNPs located in the intergenic region of an olfactory gene cluster. This model did not replicate in an independent dataset. However, genes in this region have high-confidence biological relationships and are consistent with previous findings implicating sensory processes in Alzheimer's disease. Conclusions: Previous genetic studies of Alzheimer's disease have revealed only a small portion of the overall variability due to DNA sequence differences. Some of this missing heritability is likely due to complex gene-gene and gene-environment interactions. We have introduced here a novel bioinformatics analysis pipeline that embraces the complexity of the genetic architecture of Alzheimer's disease while at the same time harnessing the power of functional genomics. These findings represent novel hypotheses about the genetic basis of this complex disease and provide open-access methods that others can use in their own studies.
Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers
(Springer, 2014) Shen, Li; Thompson, Paul M.; Potkin, Steven G.; Bertram, Lars; Farrer, Lindsay A.; Foroud, Tatiana M.; Green, Robert C.; Hu, Xiaolan; Huentelman, Matthew J.; Kim, Sungeun; Kauwe, John S. K.; Li, Qingqin; Liu, Enchi; Macciardi, Fabio; Moore, Jason H.; Munsie, Leanne; Nho, Kwangsik; Ramanan, Vijay K.; Risacher, Shannon L.; Stone, David J.; Swaminathan, Shanker; Toga, Arthur W.; Weiner, Michael W.; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative; Medical and Molecular Genetics, School of Medicine
The Genetics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer's disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.
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 Medicine
Identifying 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.
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 Science
Neuroimaging 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.
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 Computing
As 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.
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 Medicine
Background: 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.
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 Medicine
PURPOSE: 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.
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 Medicine
Brain 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.
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; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
The 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.
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 Medicine
Alzheimer'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.

Browsing by Author "Moore, Jason H."

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