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Dive into the research topics where Keta Joshipura is active.

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Featured researches published by Keta Joshipura.


Neuron | 2007

GAB2 Alleles Modify Alzheimer's Risk in APOE ε4 Carriers

Eric M. Reiman; Jennifer A. Webster; Amanda J. Myers; John Hardy; Travis Dunckley; Victoria Zismann; Keta Joshipura; John V. Pearson; Diane Hu-Lince; Matthew J. Huentelman; David Craig; Keith D. Coon; Winnie S. Liang; RiLee H. Herbert; Thomas G. Beach; Kristen Rohrer; Alice S. Zhao; Doris Leung; Leslie Bryden; Lauren Marlowe; Mona Kaleem; Diego Mastroeni; Andrew Grover; Christopher B. Heward; Rivka Ravid; Joseph Rogers; Mike Hutton; Stacey Melquist; R. C. Petersen; Gene E. Alexander

The apolipoprotein E (APOE) epsilon4 allele is the best established genetic risk factor for late-onset Alzheimers disease (LOAD). We conducted genome-wide surveys of 502,627 single-nucleotide polymorphisms (SNPs) to characterize and confirm other LOAD susceptibility genes. In epsilon4 carriers from neuropathologically verified discovery, neuropathologically verified replication, and clinically characterized replication cohorts of 1411 cases and controls, LOAD was associated with six SNPs from the GRB-associated binding protein 2 (GAB2) gene and a common haplotype encompassing the entire GAB2 gene. SNP rs2373115 (p = 9 x 10(-11)) was associated with an odds ratio of 4.06 (confidence interval 2.81-14.69), which interacts with APOE epsilon4 to further modify risk. GAB2 was overexpressed in pathologically vulnerable neurons; the Gab2 protein was detected in neurons, tangle-bearing neurons, and dystrophic neuritis; and interference with GAB2 gene expression increased tau phosphorylation. Our findings suggest that GAB2 modifies LOAD risk in APOE epsilon4 carriers and influences Alzheimers neuropathology.


Nature Genetics | 2007

A survey of genetic human cortical gene expression

Amanda J. Myers; J. Raphael Gibbs; Jennifer A. Webster; Kristen Rohrer; Alice Zhao; Lauren Marlowe; Mona Kaleem; Doris Leung; Leslie Bryden; Priti Nath; Victoria Zismann; Keta Joshipura; Matthew J. Huentelman; Diane Hu-Lince; Keith D. Coon; David Craig; John V. Pearson; Peter Holmans; Christopher B. Heward; Eric M. Reiman; Dietrich A. Stephan; John Hardy

It is widely assumed that genetic differences in gene expression underpin much of the difference among individuals and many of the quantitative traits of interest to geneticists. Despite this, there has been little work on genetic variability in human gene expression and almost none in the human brain, because tools for assessing this genetic variability have not been available. Now, with whole-genome SNP genotyping arrays and whole-transcriptome expression arrays, such experiments have become feasible. We have carried out whole-genome genotyping and expression analysis on a series of 193 neuropathologically normal human brain samples using the Affymetrix GeneChip Human Mapping 500K Array Set and Illumina HumanRefseq-8 Expression BeadChip platforms. Here we present data showing that 58% of the transcriptome is cortically expressed in at least 5% of our samples and that of these cortically expressed transcripts, 21% have expression profiles that correlate with their genotype. These genetic-expression effects should be useful in determining the underlying biology of associations with common diseases of the human brain and in guiding the analysis of the genomic regions involved in the control of normal gene expression.


American Journal of Human Genetics | 2009

Genetic Control of Human Brain Transcript Expression in Alzheimer Disease

Jennifer A. Webster; J. Raphael Gibbs; Jennifer Clarke; Monika Ray; Weixiong Zhang; Peter Holmans; Kristen Rohrer; Alice Zhao; Lauren Marlowe; Mona Kaleem; Donald S. McCorquodale; Cindy Cuello; Doris Leung; Leslie Bryden; Priti Nath; Victoria Zismann; Keta Joshipura; Matthew J. Huentelman; Diane Hu-Lince; Keith D. Coon; David Craig; John V. Pearson; Christopher B. Heward; Eric M. Reiman; Dietrich A. Stephan; John Hardy; Amanda J. Myers

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.


Neurobiology of Aging | 2010

Evidence for an association between KIBRA and late-onset Alzheimer's disease

Jason J. Corneveaux; Winnie S. Liang; Eric M. Reiman; Jennifer A. Webster; Amanda J. Myers; Victoria Zismann; Keta Joshipura; John V. Pearson; Diane Hu-Lince; David Craig; Keith D. Coon; Travis Dunckley; Daniel Bandy; Wendy Lee; Kewei Chen; Thomas G. Beach; Diego Mastroeni; Andrew Grover; Rivka Ravid; Sigrid Botne Sando; Jan O. Aasly; Reinhard Heun; Frank Jessen; Heike Kölsch; Joseph G. Rogers; Mike Hutton; Stacey Melquist; R. C. Petersen; Gene E. Alexander; Richard J. Caselli

We recently reported evidence for an association between the individual variation in normal human episodic memory and a common variant of the KIBRA gene, KIBRA rs17070145 (T-allele). Since memory impairment is a cardinal clinical feature of Alzheimers disease (AD), we investigated the possibility of an association between the KIBRA gene and AD using data from neuronal gene expression, brain imaging studies, and genetic association tests. KIBRA was significantly over-expressed and three of its four known binding partners under-expressed in AD-affected hippocampal, posterior cingulate and temporal cortex regions (P<0.010, corrected) in a study of laser-capture microdissected neurons. Using positron emission tomography in a cohort of cognitively normal, late-middle-aged persons genotyped for KIBRA rs17070145, KIBRA T non-carriers exhibited lower glucose metabolism than did carriers in posterior cingulate and precuneus brain regions (P<0.001, uncorrected). Lastly, non-carriers of the KIBRA rs17070145 T-allele had increased risk of late-onset AD in an association study of 702 neuropathologically verified expired subjects (P=0.034; OR=1.29) and in a combined analysis of 1026 additional living and expired subjects (P=0.039; OR=1.26). Our findings suggest that KIBRA is associated with both individual variation in normal episodic memory and predisposition to AD.


Neurodegenerative Diseases | 2008

Sorl1 as an Alzheimer’s Disease Predisposition Gene?

Jennifer A. Webster; Amanda J. Myers; John V. Pearson; David Craig; Diane Hu-Lince; Keith D. Coon; Victoria Zismann; Thomas G. Beach; Doris Leung; Leslie Bryden; Rebecca F. Halperin; Lauren Marlowe; Mona Kaleem; Matthew J. Huentelman; Keta Joshipura; Douglas G. Walker; Christopher B. Heward; Rivka Ravid; Joseph Rogers; Andreas Papassotiropoulos; J. Hardy; Eric M. Reiman; Dietrich A. Stephan

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressively disabling impairments in memory, cognition, and non-cognitive behavioural symptoms. Sporadic AD is multifactorial and genetically complex. While several monogenic mutations cause early-onset AD and gene alleles have been suggested as AD susceptibility factors, the only extensively validated susceptibility gene for late-onset AD is the apolipoprotein E (APOE) Ε4 allele. Alleles of the APOE gene do not account for all of the genetic load calculated to be responsible for AD predisposition. Recently, polymorphisms across the neuronal sortilin-related receptor (SORL1) gene were shown to be significantly associated with AD in several cohorts. Here we present the results of our large case-control whole-genome scan at over 500,000 polymorphisms which presents weak evidence for association and potentially narrows the association interval.


2006 IEEE/NLM Life Science Systems and Applications Workshop | 2006

SNiPer 500K: A Novel SNP Genotype Calling Algorithm

Jianping Hua; David Craig; Marcel Brun; Jennifer A. Webster; Victoria Zismann; Keta Joshipura; Matthew J. Huentelman; Edward R. Dougherty; Dietrich A. Stephan

The fast development in high-throughput single nucleotide polymorphism (SNP) genotyping technology provides unprecedented power for genome-wide SNP association studies. However, inaccurate genotyping can lead to false association between markers and phenotype. We introduce a new SNP genotyping scheme, SNiPer 500K, for Affymetrix 500K GeneChip. SNiPer 500K designs a parametric classifier for each SNP, by using expectation-maximization algorithm to estimate the parameters. A quality index provided by SNiPer 500K shows that it can reliably genotype about 65% of all SNPs, with superior call rate, better Hardy-Weinberg equilibrium and reproducibility. SNiPer 500K provides a good balance between coverage and accuracy for high-throughput SNP genotyping and would prove to be an invaluable tool for genome-wide association study


The New England Journal of Medicine | 2007

Whole-Genome Analysis of Sporadic Amyotrophic Lateral Sclerosis

Travis Dunckley; Matthew J. Huentelman; David Craig; John V. Pearson; Szabolcs Szelinger; Keta Joshipura; Rebecca F. Halperin; Chelsea Stamper; Kendall Jensen; David Letizia; Sharon E. Hesterlee; Alan Pestronk; Todd Levine; Tulio E. Bertorini; Michael C. Graves; Tahseen Mozaffar; Carlayne E. Jackson; Peter E. Bosch; April L. McVey; Arthur Dick; Richard J. Barohn; Catherine Lomen-Hoerth; Jeffrey Rosenfeld; Daniel T. O'Connor; Kuixing Zhang; Richard Crook; Henrik Ryberg; Mike Hutton; Jonathan S. Katz; Ericka Simpson


Bioinformatics | 2007

SNiPer-HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays

Jianping Hua; David Craig; Marcel Brun; Jennifer A. Webster; Victoria Zismann; Waibhav Tembe; Keta Joshipura; Matthew J. Huentelman; Edward R. Dougherty; Dietrich A. Stephan


International journal of molecular epidemiology and genetics | 2010

Whole genome association analysis shows that ACE is a risk factor for Alzheimer's disease and fails to replicate most candidates from Meta-analysis

Jennifer A. Webster; Eric M. Reiman; Victoria Zismann; Keta Joshipura; John V. Pearson; Diane Hu-Lince; Matthew J. Huentelman; David Craig; Keith D. Coon; Thomas G. Beach; Kristen Rohrer; Alice S. Zhao; Doris Leung; Leslie Bryden; Lauren Marlowe; Mona Kaleem; Diego Mastroeni; Andrew Grover; Joseph G. Rogers; Reinhard Heun; Frank Jessen; Heike Kölsch; Christopher B. Heward; Rivka Ravid; Mike Hutton; Stacey Melquist; R. C. Petersen; Richard J. Caselli; Andreas Papassotiropoulos; Dietrich A. Stephan


Neurodegenerative Diseases | 2008

Sorl1 as an Alzheimers Disease Predisposition Gene

Jennifer A. Webster; Amanda J. Myers; John V. Pearson; David Craig; Diane Hu-Lince; Keith D. Coon; Victoria Zismann; Thomas G. Beach; Doris Leung; Leslie Bryden; Rebecca F. Halperin; Lauren Marlowe; Mona M. Kaleem; Matthew J. Huentelman; Keta Joshipura; Douglas G. Walker; Christopher B. Heward; Rivka Ravid; Joseph Rogers; Andreas Papassotiropoulos; J. Hardy; Eric M. Reiman; Dietrich A. Stephan

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David Craig

Translational Genomics Research Institute

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Jennifer A. Webster

Translational Genomics Research Institute

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Matthew J. Huentelman

Translational Genomics Research Institute

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Victoria Zismann

Translational Genomics Research Institute

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Diane Hu-Lince

Translational Genomics Research Institute

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Dietrich A. Stephan

Translational Genomics Research Institute

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Eric M. Reiman

Missouri State University

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Keith D. Coon

Translational Genomics Research Institute

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John V. Pearson

QIMR Berghofer Medical Research Institute

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