Browsing by Author "Swaminathan, Shanker"

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    Analysis of Copy Number Variation in Alzheimer’s Disease in a Cohort of Clinically Characterized and Neuropathologically Verified Individuals
    (Public Library of Science, 2012) Swaminathan, Shanker; Huentelman, Matthew J.; Corneveaux, Jason J.; Myers, Amanda J.; Faber, Kelley M.; Foroud, Tatiana; Mayeux, Richard; Shen, Li; Kim, Sungeun; Turk, Mari; Hardy, John; Reiman, Eric M.; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative (ADNI); NIA-LOAD/NCRAD Family Study Group; Radiology and Imaging Sciences, School of Medicine
    Copy number variations (CNVs) are genomic regions that have added (duplications) or deleted (deletions) genetic material. They may overlap genes affecting their function and have been shown to be associated with disease. We previously investigated the role of CNVs in late-onset Alzheimer's disease (AD) and mild cognitive impairment using Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Institute of Aging-Late Onset AD/National Cell Repository for AD (NIA-LOAD/NCRAD) Family Study participants, and identified a number of genes overlapped by CNV calls. To confirm the findings and identify other potential candidate regions, we analyzed array data from a unique cohort of 1617 Caucasian participants (1022 AD cases and 595 controls) who were clinically characterized and whose diagnosis was neuropathologically verified. All DNA samples were extracted from brain tissue. CNV calls were generated and subjected to quality control (QC). 728 cases and 438 controls who passed all QC measures were included in case/control association analyses including candidate gene and genome-wide approaches. Rates of deletions and duplications did not significantly differ between cases and controls. Case-control association identified a number of previously reported regions (CHRFAM7A, RELN and DOPEY2) as well as a new gene (HLA-DRA). Meta-analysis of CHRFAM7A indicated a significant association of the gene with AD and/or MCI risk (P = 0.006, odds ratio = 3.986 (95% confidence interval 1.490-10.667)). A novel APP gene duplication was observed in one case sample. Further investigation of the identified genes in independent and larger samples is warranted.
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    Analysis of Copy Number Variation in Alzheimer’s Disease: the NIA-LOAD/NCRAD Family Study
    (Bentham Science, 2012) Swaminathan, Shanker; Shen, Li; Kim, Sungeun; Inlow, Mark; West, John D.; Faber, Kelley M.; Foroud, Tatiana; Mayeux, Richard; Saykin, Andrew J.; Alzheimer's Disease Neuroimaging Initiative (ADNI); NIA-LOAD/NCRAD Family Study Group; Radiology and Imaging Sciences, School of Medicine
    Copy number variants (CNVs) are DNA regions that have gains (duplications) or losses (deletions) of genetic material. CNVs may encompass a single gene or multiple genes and can affect their function. They are hypothesized to play an important role in certain diseases. We previously examined the role of CNVs in late-onset Alzheimer's disease (AD) and mild cognitive impairment (MCI) using participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and identified gene regions overlapped by CNVs only in cases (AD and/or MCI) but not in controls. Using a similar approach as ADNI, we investigated the role of CNVs using 794 AD and 196 neurologically evaluated control non-Hispanic Caucasian NIA-LOAD/NCRAD Family Study participants with DNA derived from blood/brain tissue. The controls had no family history of AD and were unrelated to AD participants. CNV calls were generated and analyzed after detailed quality review. 711 AD cases and 171 controls who passed all quality thresholds were included in case/control association analyses, focusing on candidate gene and genome-wide approaches. We identified genes overlapped by CNV calls only in AD cases but not controls. A trend for lower CNV call rate was observed for deletions as well as duplications in cases compared to controls. Gene-based association analyses confirmed previous findings in the ADNI study (ATXN1, HLA-DPB1, RELN, DOPEY2, GSTT1, CHRFAM7A, ERBB4, NRXN1) and identified a new gene (IMMP2L) that may play a role in AD susceptibility. Replication in independent samples as well as further analyses of these gene regions is warranted.
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    APOE and BCHE as modulators of cerebral amyloid deposition: a florbetapir PET genome-wide association study
    (Springer Nature, 2014) Ramanan, Vijay K.; Risacher, Shannon L.; Nho, Kwangsik; Kim, Sungeun; Swaminathan, Shanker; Shen, Li; Foroud, Tatiana M.; Hakonarson, Hakon; Huentelman, Matthew J.; Aisen, Paul S.; Petersen, Ronald C.; Green, Robert C.; Jack, Clifford R.; Koeppe, Robert A.; Jagust, William J.; Weiner, Michael W.; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Deposition of amyloid-β (Aβ) in the cerebral cortex is thought to be a pivotal event in Alzheimer's disease (AD) pathogenesis with a significant genetic contribution. Molecular imaging can provide an early noninvasive phenotype, but small samples have prohibited genome-wide association studies (GWAS) of cortical Aβ load until now. We employed florbetapir ((18)F) positron emission tomography (PET) imaging to assess brain Aβ levels in vivo for 555 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). More than six million common genetic variants were tested for association to quantitative global cortical Aβ load controlling for age, gender and diagnosis. Independent genome-wide significant associations were identified on chromosome 19 within APOE (apolipoprotein E) (rs429358, P=5.5 × 10(-14)) and on chromosome 3 upstream of BCHE (butyrylcholinesterase) (rs509208, P=2.7 × 10(-8)) in a region previously associated with serum BCHE activity. Together, these loci explained 15% of the variance in cortical Aβ levels in this sample (APOE 10.7%, BCHE 4.3%). Suggestive associations were identified within ITGA6, near EFNA5, EDIL3, ITGA1, PIK3R1, NFIB and ARID1B, and between NUAK1 and C12orf75. These results confirm the association of APOE with Aβ deposition and represent the largest known effect of BCHE on an AD-related phenotype. BCHE has been found in senile plaques and this new association of genetic variation at the BCHE locus with Aβ burden in humans may have implications for potential disease-modifying effects of BCHE-modulating agents in the AD spectrum.
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    Association of plasma and cortical beta-amyloid is modulated by APOE ε4 status.
    (Elsevier, 2014-01) Swaminathan, Shanker; Risacher, Shannon L.; Yoder, Karmen K.; West, John D.; Shen, Li; Kim, Sungeun; Inlow, Mark; Foroud, Tatiana; Jagust, William J.; Koeppe, Robert A.; Mathis, Chester A.; Shaw, Leslie M.; Trojanowski, John Q.; Soares, Holly; Aisen, Paul S.; Petersen, Ronald C.; Weiner, Michael W.; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, IU School of Medicine
    Background: APOE ε4’s role as a modulator of the relationship between soluble plasma beta-amyloid (Aβ) and fibrillar brain Aβ measured by Pittsburgh Compound-B positron emission tomography ([11C]PiB PET) has not been assessed. Methods: Ninety-six Alzheimer’s Disease Neuroimaging Initiative participants with [11C]PiB scans and plasma Aβ1-40 and Aβ1-42 measurements at time of scan were included. Regional and voxel-wise analyses of [11C]PiB data were used to determine the influence of APOE ε4 on association of plasma Aβ1-40, Aβ1-42, and Aβ1-40/Aβ1-42 with [11C]PiB uptake. Results: In APOE ε4− but not ε4+ participants, positive relationships between plasma Aβ1-40/Aβ1-42 and [11C]PiB uptake were observed. Modeling the interaction of APOE and plasma Aβ1-40/Aβ1-42 improved the explained variance in [11C]PiB binding compared to using APOE and plasma Aβ1-40/Aβ1-42 as separate terms. Conclusions: The results suggest that plasma Aβ is a potential Alzheimer’s disease biomarker and highlight the importance of genetic variation in interpretation of plasma Aβ levels.
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    Characteristics of Bipolar I patients grouped by externalizing disorders
    (Elsevier, 2015-06-01) Swaminathan, Shanker; Koller, Daniel L.; Foroud, Tatiana; Edenberg, Howard J.; Xuei, Xiaoling; Niculescu, Alexander B.; Bipolar Genome Study (BiGS) Consortium; Nurnberger, John I.; Department of Psychiatry, IU School of Medicine
    BACKGROUND: Bipolar disorder co-occurs with a number of disorders with externalizing features. The aim of this study is to determine whether Bipolar I (BPI) subjects with comorbid externalizing disorders and a subgroup with externalizing symptoms prior to age 15 have different clinical features than those without externalizing disorders and whether these could be attributed to specific genetic variations. METHODS: A large cohort (N=2505) of Bipolar I subjects was analyzed. Course of illness parameters were compared between an Externalizing Group, an Early-Onset Subgroup and a Non-Externalizing Group in the Discovery sample (N=1268). Findings were validated using an independent set of 1237 BPI subjects (Validation sample). Genetic analyses were carried out. RESULTS: Subjects in the Externalizing Group (and Early-Onset Subgroup) tended to have a more severe clinical course, even in areas specifically related to mood disorder such as cycling frequency and rapid mood switching. Regression analysis showed that the differences are not completely explainable by substance use. Genetic analyses identified nominally associated SNPs; calcium channel genes were not enriched in the gene variants identified. LIMITATIONS: Validation in independent samples is needed to confirm the genetic findings in the present study. CONCLUSIONS: Our findings support the presence of an externalizing disorder subphenotype within BPI with greater severity of mood disorder and possible specific genetic features.
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    Effect of complement CR1 on brain amyloid burden during aging and its modification by APOE genotype
    (Elsevier, 2013) Thambisetty, Madhav; An, Yang; Nalls, Michael; Sojkova, Jitka; Swaminathan, Shanker; Zhou, Yun; Singleton, Andrew B.; Wong, Dean F.; Ferrucci, Luigi; Saykin, Andrew J.; Resnick, Susan M.; Baltimore Longitudinal Study of Aging; Alzheimer's Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Background: The rs3818361 single nucleotide polymorphism in complement component (3b/4b) receptor-1 (CR1) is associated with increased risk of Alzheimer's disease (AD). Although this novel variant is associated with a small effect size and is unlikely to be useful as a predictor of AD risk, it might provide insights into AD pathogenesis. We examined the association between rs3818361 and brain amyloid deposition in nondemented older individuals. Methods: We used (11)C-Pittsburgh Compound-B positron emission tomography to quantify brain amyloid burden in 57 nondemented older individuals (mean age 78.5 years) in the neuroimaging substudy of the Baltimore Longitudinal Study of Aging. In a replication study, we analyzed (11)C-Pittsburgh Compound-B positron emission tomography data from 22 cognitively normal older individuals (mean age 77.1 years) in the Alzheimer's Disease Neuroimaging Initiative dataset. Results: Risk allele carriers of rs3818361 have lower brain amyloid burden relative to noncarriers. There is a strikingly greater variability in brain amyloid deposition in the noncarrier group relative to risk carriers, an effect explained partly by APOE genotype. In noncarriers of the CR1 risk allele, APOE ε4 individuals showed significantly higher brain amyloid burden relative to APOE ε4 noncarriers. We also independently replicate our observation of lower brain amyloid burden in risk allele carriers of rs3818361 in the Alzheimer's Disease Neuroimaging Initiative sample. Conclusions: Our findings suggest complex mechanisms underlying the interaction of CR1, APOE, and brain amyloid pathways in AD. Our results are relevant to treatments targeting brain Aβ in nondemented individuals at risk for AD and suggest that clinical outcomes of such treatments might be influenced by complex gene-gene interactions.
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    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.
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    Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models
    (Office of the Vice Chancellor for Research, 2012-04-13) Wan, Jing; Kim, Sungeun; Inlow, Mark; Nho, Kwangsik; Swaminathan, Shanker; Risacher, Shannon L.; Fang, Shiaofen; Weiner, Michael W.; Beg, M. Faisal; Wang, Lei; Saykin, Andrew J.; Shen, Li; ADNI
    Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.
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    Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net
    (Springer-Verlag, 2011-09) Shen, Li; Kim, Sungeun; Qi, Yuan; Inlow, Mark; Swaminathan, Shanker; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, IU School of Medicine
    Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.
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    Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net
    (Office of the Vice Chancellor for Research, 2012-04-13) Shen, Li; Kim, Sungeun; Qi, Yuan; Inlow, Mark; Swaminathan, Shanker; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Saykin, Andrew J.; ADNI
    Abstract Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.