5 results on '"Grundstad J"'
Search Results
2. A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data
- Author
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Kim, Y.J. (Young Jin), Lee, J. (Juyoung), Kim, B.-J. (Bong-Jo), Park, T. (Taesung), Abecasis, G.R. (Gonçalo), De Almeida, M.A.A. (Marcio), Altshuler, D. (David), Asimit, J.L. (Jennifer L.), Atzmon, G. (Gil), Barber, M. (Mathew), Barzilai, A. (Ari), Beer, N.L. (Nicola L.), Bell, G.I. (Graeme I.), Below, J. (Jennifer), Blackwell, T. (Tom), Blangero, J. (John), Boehnke, M. (Michael), Bowden, D.W. (Donald W.), Burtt, N.P. (Noël), Chambers, J.C. (John), Chen, H. (Han), Chen, P. (Ping), Chines, P.S. (Peter), Choi, S. (Sungkyoung), Churchhouse, C. (Claire), Cingolani, P. (Pablo), Cornes, B.K. (Belinda), Cox, N.J. (Nancy), Day-Williams, A.G. (Aaron), Duggirala, A. (Aparna), Dupuis, J. (Josée), Dyer, T. (Thomas), Feng, S. (Shuang), Fernandez-Tajes, J. (Juan), Ferreira, T. (Teresa), Fingerlin, T.E. (Tasha E.), Flannick, J. (Jason), Florez, J.C. (Jose), Fontanillas, P. (Pierre), Frayling, T.M. (Timothy), Fuchsberger, C. (Christian), Gamazon, E. (Eric), Gaulton, K. (Kyle), Ghosh, S. (Saurabh), Glaser, B. (Benjamin), Gloyn, A.L. (Anna), Grossman, R.L. (Robert L.), Grundstad, J. (Jason), Hanis, C. (Craig), Heath, A. (Allison), Highland, H. (Heather), Horikoshi, M. (Momoko), Huh, I.-S. (Ik-Soo), Huyghe, J.R. (Jeroen R.), Ikram, M.K. (Kamran), Jablonski, K.A. (Kathleen), Jun, Y. (Yang), Kato, N. (Norihiro), Kim, J. (Jayoun), King, C.R. (C. Ryan), Kooner, J.S. (Jaspal S.), Kwon, M.-S. (Min-Seok), Im, H.K. (Hae Kyung), Laakso, M. (Markku), Lam, K.K.-Y. (Kevin Koi-Yau), Lee, J. (Jaehoon), Lee, S. (Selyeong), Lee, S. (Sungyoung), Lehman, D.M. (Donna M.), Li, H. (Heng), Lindgren, C.M. (Cecilia), Liu, X. (Xuanyao), Livne, O.E. (Oren E.), Locke, A.E. (Adam E.), Mahajan, A. (Anubha), Maller, J.B. (Julian B.), Manning, A.K. (Alisa K.), Maxwell, T.J. (Taylor J.), Mazoure, A. (Alexander), McCarthy, M.I. (Mark), Meigs, J.B. (James B.), Min, B. (Byungju), Mohlke, K.L. (Karen), Morris, A.P. (Andrew), Musani, S. (Solomon), Nagai, Y. (Yoshihiko), Ng, M.C.Y. (Maggie C.Y.), Nicolae, D. (Dan), Oh, S. (Sohee), Palmer, N.D. (Nicholette), Pollin, T.I. (Toni I.), Prokopenko, I. (Inga), Reich, D. (David), Rivas, M.A. (Manuel), Scott, L.J. (Laura), Seielstad, M. (Mark), Cho, Y.S. (Yoon Shin), Sim, X. (Xueling), Sladek, R. (Rob), Smith, P. (Philip), Tachmazidou, I. (Ioanna), Tai, E.S. (Shyong), Teo, Y.Y. (Yik Ying), Teslovich, T.M. (Tanya M.), Torres, J. (Jason), Trubetskoy, V. (Vasily), Willems, S.M. (Sara), Williams, A.L. (Amy L.), Wilson, J.G. (James), Wiltshire, S. (Steven), Won, S. (Sungho), Wood, A.R. (Andrew), Xu, W. (Wang), Yoon, J. (Joon), Zawistowski, M. (Matthew), Zeggini, E. (Eleftheria), Zhang, W. (Weihua), Zöllner, S. (Sebastian), Kim, Y.J. (Young Jin), Lee, J. (Juyoung), Kim, B.-J. (Bong-Jo), Park, T. (Taesung), Abecasis, G.R. (Gonçalo), De Almeida, M.A.A. (Marcio), Altshuler, D. (David), Asimit, J.L. (Jennifer L.), Atzmon, G. (Gil), Barber, M. (Mathew), Barzilai, A. (Ari), Beer, N.L. (Nicola L.), Bell, G.I. (Graeme I.), Below, J. (Jennifer), Blackwell, T. (Tom), Blangero, J. (John), Boehnke, M. (Michael), Bowden, D.W. (Donald W.), Burtt, N.P. (Noël), Chambers, J.C. (John), Chen, H. (Han), Chen, P. (Ping), Chines, P.S. (Peter), Choi, S. (Sungkyoung), Churchhouse, C. (Claire), Cingolani, P. (Pablo), Cornes, B.K. (Belinda), Cox, N.J. (Nancy), Day-Williams, A.G. (Aaron), Duggirala, A. (Aparna), Dupuis, J. (Josée), Dyer, T. (Thomas), Feng, S. (Shuang), Fernandez-Tajes, J. (Juan), Ferreira, T. (Teresa), Fingerlin, T.E. (Tasha E.), Flannick, J. (Jason), Florez, J.C. (Jose), Fontanillas, P. (Pierre), Frayling, T.M. (Timothy), Fuchsberger, C. (Christian), Gamazon, E. (Eric), Gaulton, K. (Kyle), Ghosh, S. (Saurabh), Glaser, B. (Benjamin), Gloyn, A.L. (Anna), Grossman, R.L. (Robert L.), Grundstad, J. (Jason), Hanis, C. (Craig), Heath, A. (Allison), Highland, H. (Heather), Horikoshi, M. (Momoko), Huh, I.-S. (Ik-Soo), Huyghe, J.R. (Jeroen R.), Ikram, M.K. (Kamran), Jablonski, K.A. (Kathleen), Jun, Y. (Yang), Kato, N. (Norihiro), Kim, J. (Jayoun), King, C.R. (C. Ryan), Kooner, J.S. (Jaspal S.), Kwon, M.-S. (Min-Seok), Im, H.K. (Hae Kyung), Laakso, M. (Markku), Lam, K.K.-Y. (Kevin Koi-Yau), Lee, J. (Jaehoon), Lee, S. (Selyeong), Lee, S. (Sungyoung), Lehman, D.M. (Donna M.), Li, H. (Heng), Lindgren, C.M. (Cecilia), Liu, X. (Xuanyao), Livne, O.E. (Oren E.), Locke, A.E. (Adam E.), Mahajan, A. (Anubha), Maller, J.B. (Julian B.), Manning, A.K. (Alisa K.), Maxwell, T.J. (Taylor J.), Mazoure, A. (Alexander), McCarthy, M.I. (Mark), Meigs, J.B. (James B.), Min, B. (Byungju), Mohlke, K.L. (Karen), Morris, A.P. (Andrew), Musani, S. (Solomon), Nagai, Y. (Yoshihiko), Ng, M.C.Y. (Maggie C.Y.), Nicolae, D. (Dan), Oh, S. (Sohee), Palmer, N.D. (Nicholette), Pollin, T.I. (Toni I.), Prokopenko, I. (Inga), Reich, D. (David), Rivas, M.A. (Manuel), Scott, L.J. (Laura), Seielstad, M. (Mark), Cho, Y.S. (Yoon Shin), Sim, X. (Xueling), Sladek, R. (Rob), Smith, P. (Philip), Tachmazidou, I. (Ioanna), Tai, E.S. (Shyong), Teo, Y.Y. (Yik Ying), Teslovich, T.M. (Tanya M.), Torres, J. (Jason), Trubetskoy, V. (Vasily), Willems, S.M. (Sara), Williams, A.L. (Amy L.), Wilson, J.G. (James), Wiltshire, S. (Steven), Won, S. (Sungho), Wood, A.R. (Andrew), Xu, W. (Wang), Yoon, J. (Joon), Zawistowski, M. (Matthew), Zeggini, E. (Eleftheria), Zhang, W. (Weihua), and Zöllner, S. (Sebastian)
- Abstract
Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific referenc
- Published
- 2015
- Full Text
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3. Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.
- Author
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Jun G, Manning A, Almeida M, Zawistowski M, Wood AR, Teslovich TM, Fuchsberger C, Feng S, Cingolani P, Gaulton KJ, Dyer T, Blackwell TW, Chen H, Chines PS, Choi S, Churchhouse C, Fontanillas P, King R, Lee S, Lincoln SE, Trubetskoy V, DePristo M, Fingerlin T, Grossman R, Grundstad J, Heath A, Kim J, Kim YJ, Laramie J, Lee J, Li H, Liu X, Livne O, Locke AE, Maller J, Mazur A, Morris AP, Pollin TI, Ragona D, Reich D, Rivas MA, Scott LJ, Sim X, Tearle RG, Teo YY, Williams AL, Zöllner S, Curran JE, Peralta J, Akolkar B, Bell GI, Burtt NP, Cox NJ, Florez JC, Hanis CL, McKeon C, Mohlke KL, Seielstad M, Wilson JG, Atzmon G, Below JE, Dupuis J, Nicolae DL, Lehman D, Park T, Won S, Sladek R, Altshuler D, McCarthy MI, Duggirala R, Boehnke M, Frayling TM, Abecasis GR, and Blangero J
- Subjects
- Diabetes Mellitus, Type 2 ethnology, Diabetes Mellitus, Type 2 pathology, Family Health, Female, Gene Frequency, Genetic Predisposition to Disease ethnology, Genome-Wide Association Study methods, Genotype, Humans, Male, Pedigree, Phenotype, Quantitative Trait Loci genetics, Whole Genome Sequencing methods, Diabetes Mellitus, Type 2 genetics, Genetic Predisposition to Disease genetics, Genetic Variation, Mexican Americans genetics
- Abstract
A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis -expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants., Competing Interests: Conflict of interest statement: S.E.L., J. Laramie, and R.G.T. were employees of Complete Genomics during this study. T.M.T. is an employee of Regeneron Pharmaceuticals. D.A. is an employee of Vertex Pharmaceuticals.
- Published
- 2018
- Full Text
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4. Next-generation sequencing of disseminated tumor cells.
- Author
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Møller EK, Kumar P, Voet T, Peterson A, Van Loo P, Mathiesen RR, Fjelldal R, Grundstad J, Borgen E, Baumbusch LO, Naume B, Børresen-Dale AL, White KP, Nord S, and Kristensen VN
- Abstract
Disseminated tumor cells (DTCs) detected in the bone marrow have been shown as an independent prognostic factor for women with breast cancer. However, the mechanisms behind the tumor cell dissemination are still unclear and more detailed knowledge is needed to fully understand why some cells remain dormant and others metastasize. Sequencing of single cells has opened for the possibility to dissect the genetic content of subclones of a primary tumor, as well as DTCs. Previous studies of genetic changes in DTCs have employed single-cell array comparative genomic hybridization which provides information about larger aberrations. To date, next-generation sequencing provides the possibility to discover new, smaller, and copy neutral genetic changes. In this study, we performed whole-genome amplification and subsequently next-generation sequencing to analyze DTCs from two breast cancer patients. We compared copy-number profiles of the DTCs and the corresponding primary tumor generated from sequencing and SNP-comparative genomic hybridization (CGH) data, respectively. While one tumor revealed mostly whole-arm gains and losses, the other had more complex alterations, as well as subclonal amplification and deletions. Whole-arm gains or losses in the primary tumor were in general also observed in the corresponding DTC. Both primary tumors showed amplification of chromosome 1q and deletion of parts of chromosome 16q, which was recaptured in the corresponding DTCs. Interestingly, clear differences were also observed, indicating that the DTC underwent further evolution at the copy-number level. This study provides a proof-of-principle for sequencing of DTCs and correlation with primary copy-number profiles. The analyses allow insight into tumor cell dissemination and show ongoing copy-number evolution in DTCs compared to the primary tumors.
- Published
- 2013
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5. Protein expression in a transformed trabecular meshwork cell line: proteome analysis.
- Author
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Steely HT, Dillow GW, Bian L, Grundstad J, Braun TA, Casavant TL, McCartney MD, and Clark AF
- Subjects
- Cell Line, Transformed, Chromatography, Liquid, Electrophoresis, Gel, Two-Dimensional, Eye Proteins chemistry, Humans, Mass Spectrometry, Molecular Weight, Trabecular Meshwork cytology, Eye Proteins metabolism, Proteome, Proteomics, Trabecular Meshwork metabolism
- Abstract
Purpose: Characterization of the human trabecular meshwork (TM) proteome is hindered by the small mass of intact tissue and the slow growth of cultured cell strains. We have previously characterized a transformed TM cell strain (GTM3) that demonstrates many of the same protein expression and cell signaling systems of nontransformed cell strains. The aim of this study was to initiate a proteomic survey of GTM3 cells as the initial step toward characterization of the complete human TM proteome., Methods: GTM3 cells were cultured to confluence, harvested and solubilized in urea/Nonidet. The protein extract (600 mug) was focused in immobilized isoelectric focusing (IEF) strips, separated by 10% SDS PAGE, and visualized with colloidal Coomassie Blue. Spots of interest were excised, destained, and the contained proteins subjected to in-gel reduction, derivatization, and tryptic digestion. Tryptic peptides were extracted and analyzed by electrospray LC/MS/MS. Protein identification was made using the TurboSequest search algorithm and a recent version of the nonredundant human protein database downloaded from the National Center for Biotechnology Information (NCBI)., Results: Eighty-seven (87) primary proteins and 93 variants of these proteins were identified. A website was created (TM proteome) that combines data such as graphic spot location within the gel, peptide sequence, apparent and calculated pI, apparent and calculated mass, percentage of coverage, and protein informatic website links., Conclusions: Proteomic analysis of a transformed human TM cell line has been initiated combining preparative two-dimensional PAGE separation, LC/MS/MS analysis of major proteins, and bioinformatic cataloging of the data. Further investigation of data from the transformed cell strain will be used in a comparative fashion for spot identification of analytical proteomic gels of human TM tissue and cultured normal cells. These initial data will form the base from which the characterization of protein expression in the normal and glaucomatous TM can be accomplished.
- Published
- 2006
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