Browsing by Author "Swaminathan, Shanker"
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Item 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 MedicineDeposition 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.Item 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 MedicineBackground: 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.Item 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 MedicineBACKGROUND: 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.Item 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 MedicineThe 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.Item 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; ADNIGenetic 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.Item 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 MedicineMulti-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.Item 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.; ADNIAbstract 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.Item Influence of Genetic Variation on Plasma Protein Levels in Older Adults Using a Multi-Analyte Panel(Public Library of Science, 2013-07-23) Kim, Sungeun; Swaminathan, Shanker; Inlow, Mark; Risacher, Shannon L.; Nho, Kwangsik; Shen, Li; Foroud, Tatiana M.; Petersen, Ronald C.; Aisen, Paul S.; Soares, Holly; Toledo, Jon B.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; McDonald, Brenna C.; Farlow, Martin R.; Ghetti, Bernardino; Saykin, Andrew J.; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of MedicineProteins, widely studied as potential biomarkers, play important roles in numerous physiological functions and diseases. Genetic variation may modulate corresponding protein levels and point to the role of these variants in disease pathophysiology. Effects of individual single nucleotide polymorphisms (SNPs) within a gene were analyzed for corresponding plasma protein levels using genome-wide association study (GWAS) genotype data and proteomic panel data with 132 quality-controlled analytes from 521 Caucasian participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Linear regression analysis detected 112 significant (Bonferroni threshold p=2.44×10(-5)) associations between 27 analytes and 112 SNPs. 107 out of these 112 associations were tested in the Indiana Memory and Aging Study (IMAS) cohort for replication and 50 associations were replicated at uncorrected p<0.05 in the same direction of effect as those in the ADNI. We identified multiple novel associations including the association of rs7517126 with plasma complement factor H-related protein 1 (CFHR1) level at p<1.46×10(-60), accounting for 40 percent of total variation of the protein level. We serendipitously found the association of rs6677604 with the same protein at p<9.29×10(-112). Although these two SNPs were not in the strong linkage disequilibrium, 61 percent of total variation of CFHR1 was accounted for by rs6677604 without additional variation by rs7517126 when both SNPs were tested together. 78 other SNP-protein associations in the ADNI sample exceeded genome-wide significance (5×10(-8)). Our results confirmed previously identified gene-protein associations for interleukin-6 receptor, chemokine CC-4, angiotensin-converting enzyme, and angiotensinogen, although the direction of effect was reversed in some cases. This study is among the first analyses of gene-protein product relationships integrating multiplex-panel proteomics and targeted genes extracted from a GWAS array. With intensive searches taking place for proteomic biomarkers for many diseases, the role of genetic variation takes on new importance and should be considered in interpretation of proteomic results.Item PARP1 gene variation and microglial activity on [11C]PBR28 PET in older adults at risk for Alzheimer's disease(Springer, 2013) Kim, Sungeun; Nho, Kwangsik; Risacher, Shannon L.; Inlow, Mark; Swaminathan, Shanker; Yoder, Karmen K.; Shen, Li; West, John D.; McDonald, Brenna C.; Tallman, Eileen F.; Hutchins, Gary D.; Fletcher, James W.; Farlow, Martin R.; Ghetti, Bernardino; Saykin, Andrew J.; Radiology and Imaging Sciences, School of MedicineIncreasing evidence suggests that inflammation is one pathophysio-logical mechanism in Alzheimer's disease (AD). Recent studies have identified an association between the poly (ADP-ribose) polymerase 1 (PARP1) gene and AD. This gene encodes a protein that is involved in many biological functions, including DNA repair and chromatin remodeling, and is a mediator of inflammation. Therefore, we performed a targeted genetic association analysis to investigate the relationship between the PARP1 polymorphisms and brain micro-glial activity as indexed by [11C]PBR28 positron emission tomography (PET). Participants were 26 non-Hispanic Caucasians in the Indiana Memory and Aging Study (IMAS). PET data were intensity-normalized by injected dose/total body weight. Average PBR standardized uptake values (SUV) from 6 bilateral regions of interest (thalamus, frontal, parietal, temporal, and cingulate cortices, and whole brain gray matter) were used as endophenotypes. Single nucleotide polymorphisms (SNPs) with 20% minor allele frequency that were within +/− 20 kb of the PARP1 gene were included in the analyses. Gene-level association analyses were performed using a dominant genetic model with translocator protein (18-kDa) (TSPO) genotype, age at PET scan, and gender as covariates. Analyses were performed with and without APOE ε4 status as a covariate. Associations with PBR SUVs from thalamus and cingulate were significant at corrected p<0.014 and <0.065, respectively. Subsequent multi-marker analysis with cingulate PBR SUV showed that individuals with the “C” allele at rs6677172 and “A” allele at rs61835377 had higher PBR SUV than individuals without these alleles (corrected P<0.03), and individuals with the “G” allele at rs6677172 and “G” allele at rs61835377 displayed the opposite trend (corrected P<0.065). A previous study with the same cohort showed an inverse relationship between PBR SUV and brain atrophy at a follow-up visit, suggesting possible protective effect of microglial activity against cortical atrophy. Interestingly, all 6 AD and 2 of 3 LMCI participants in the current analysis had one or more copies of the “GG” allele combination, associated with lower cingulate PBR SUV, suggesting that this gene variant warrants further investigation.Item ROLE OF GENOMIC COPY NUMBER VARIATION IN ALZHEIMER'S DISEASE AND MILD COGNITIVE IMPAIRMENT(2013-02-14) Swaminathan, Shanker; Saykin, Andrew J.; Foroud, Tatiana; Shen, Li; Nurnberger, John I., 1946-Alzheimer's disease (AD) is the most common form of dementia defined by loss in memory and cognitive abilities severe enough to interfere significantly with daily life activities. Amnestic mild cognitive impairment (MCI) is a clinical condition in which an individual has memory deficits not normal for the individual's age, but not severe enough to interfere significantly with daily functioning. Every year, approximately 10-15% of individuals with MCI will progress to dementia. Currently, there is no treatment to slow or halt AD progression, but research studies are being conducted to identify causes that can lead to its earlier diagnosis and treatment. Genetic variation plays a key role in the development of AD, but not all genetic factors associated with the disease have been identified. Copy number variants (CNVs), a form of genetic variation, are DNA regions that have added genetic material (duplications) or loss of genetic material (deletions). The regions may overlap one or more genes possibly affecting their function. CNVs have been shown to play a role in certain diseases. At the start of this work, only one published study had examined CNVs in late-onset AD and none had examined MCI. In order to determine the possible involvement of CNVs in AD and MCI susceptibility, genome-wide CNV analyses were performed in participants from three cohorts: the ADNI cohort, the NIA-LOAD/NCRAD Family Study cohort, and a unique cohort of clinically characterized and neuropathologically verified individuals. Only participants with DNA samples extracted from blood/brain tissue were included in the analyses. CNV calls were generated using genome-wide array data available on these samples. After detailed quality review, case (AD and/or MCI)/control association analyses including candidate gene and genome-wide approaches were performed. Although no excess CNV burden was observed in cases compared to controls in the three cohorts, gene-based association analyses identified a number of genes including the AD candidate genes CHRFAM7A, RELN and DOPEY2. Thus, the present work highlights the possible role of CNVs in AD and MCI susceptibility warranting further investigation. Future work will include replication of the findings in independent samples and confirmation by molecular validation experiments.