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Featured researches published by Victoria L. Magnuson.


American Journal of Human Genetics | 2000

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. I. An autosomal genome scan for genes that predispose to type 2 diabetes

Soumitra Ghosh; Richard M. Watanabe; Timo T. Valle; Elizabeth R. Hauser; Victoria L. Magnuson; Carl D. Langefeld; Delphine S. Ally; Karen L. Mohlke; Kaisa Silander; Kimmo Kohtamäki; Peter S. Chines; James E. Balow; Gunther Birznieks; Jennie Chang; William Eldridge; Michael R. Erdos; Zarir E. Karanjawala; Julie I. Knapp; Kristina Kudelko; Colin Martin; Anabelle Morales-Mena; Anjene Musick; Tiffany Musick; Carrie Pfahl; Rachel Porter; Joseph B. Rayman; David Rha; Leonid Segal; Shane Shapiro; Ben Shurtleff

We performed a genome scan at an average resolution of 8 cM in 719 Finnish sib pairs with type 2 diabetes. Our strongest results are for chromosome 20, where we observe a weighted maximum LOD score (MLS) of 2.15 at map position 69.5 cM from pter and secondary weighted LOD-score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. Our next largest MLS is for chromosome 11 (MLS = 1.75 at 84.0 cM), followed by chromosomes 2 (MLS = 0.87 at 5.5 cM), 10 (MLS = 0.77 at 75.0 cM), and 6 (MLS = 0.61 at 112.5 cM), all under an additive model. When we condition on chromosome 2 at 8.5 cM, the MLS for chromosome 20 increases to 5.50 at 69.0 cM (P=.0014). An ordered-subsets analysis based on families with high or low diabetes-related quantitative traits yielded results that support the possible existence of disease-predisposing genes on chromosomes 6 and 10. Genomewide linkage-disequilibrium analysis using microsatellite marker data revealed strong evidence of association for D22S423 (P=.00007). Further analyses are being carried out to confirm and to refine the location of these putative diabetes-predisposing genes.


American Journal of Human Genetics | 2000

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. II. An autosomal genome scan for diabetes-related quantitative-trait loci

Richard M. Watanabe; Soumitra Ghosh; Carl D. Langefeld; Timo T. Valle; Elizabeth R. Hauser; Victoria L. Magnuson; Karen L. Mohlke; Kaisa Silander; Delphine S. Ally; Peter S. Chines; Jillian Blaschak-Harvan; Julie A. Douglas; William L. Duren; Michael P. Epstein; Tasha E. Fingerlin; Hong Shi Kaleta; Ethan M. Lange; Chun Li; Richard C. McEachin; Heather M. Stringham; Edward H. Trager; Peggy P. White; James E. Balow; Gunther Birznieks; Jennie Chang; William Eldridge; Michael R. Erdos; Zarir E. Karanjawala; Julie I. Knapp; Kristina Kudelko

Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.


Diabetes Care | 1998

Mapping Genes for NIDDM: Design of the Finland—United States Investigation of NIDDM Genetics (FUSION) Study

Timo T. Valle; Jaakko Tuomilehto; Richard N. Bergman; Soumitra Ghosh; Elizabeth R. Hauser; Johan G. Eriksson; Stella J. Nylund; Kimmo Kohtamäki; Liisa Toivanen; Gabriele Vidgren; Eva Tuomilehto-Wolf; Christian Ehnholm; Jillian Blaschak; Carl D. Langefeld; Richard M. Watanabe; Victoria L. Magnuson; Delphine S. Ally; William Hagopian; Edna H. Ross; Thomas A. Buchanan; Francis S. Collins; Michael Boehnke

OBJECTIVE To map and identify susceptibility genes for NIDDM and for the intermediate quantitative traits associated with NIDDM. RESEARCH DESIGN AND METHODS We describe the methodology and sample of the Finland-United States Investigation of NIDDM Genetics (FUSION) study. The whole genome search approach is being applied in studies of several different ethnic groups to locate susceptibility genes for NIDDM. Detailed description of the study materials and designs of such studies are important, particularly when comparing the findings in these studies and when combining different data sets. RESULTS Using a careful selection strategy, we have ascertained 495 families with confirmed NIDDM in at least two siblings and no history of IDDM among the first-degree relatives. These families were chosen from more than 22,000 NIDDM patients, representative of patients with NIDDM in the Finnish population. In a subset of families, a spouse and offspring were sampled, and they participated in a frequently sampled intravenous glucose tolerance test (FSIGT) analyzed with the Minimal Model. An FSIGT was completed successfully for at least two nondiabetic offspring in 156 families with a confirmed nondiabetic spouse and no history of IDDM in first-degree relatives. CONCLUSIONS Our work demonstrates the feasibility of collecting a large number of affected sib-pair families with NIDDM to provide data that will enable a whole genome search approach, including linkage analysis.


Diabetologia | 1999

The W64R variant of the β3-adrenergic receptor is not associated with Type II diabetes or obesity in a large Finnish sample

Soumita Ghosh; Carl D. Langefeld; Delphine S. Ally; Richard M. Watanabe; Elizabeth R. Hauser; Victoria L. Magnuson; S. J. Nylund; Timo T. Valle; Johan G. Eriksson; Richard N. Bergman; J. Tuomilehto; Francis S. Collins; Michael Boehnke

Summary Recent studies have suggested an association between Type II (non-insulin-dependent) diabetes mellitus-related phenotypes and a cytosine-to-thymidine substitution that results in the replacement of tryptophan by arginine at codon 64 (Trp64Arg or W64R) of the β3-adrenergic receptor gene. Here, we present the results of possibly the largest association study to date on the variant in a sample of 526 families with a total of 1725 subjects, 1053 of whom had Type II diabetes. Preliminary calculations suggested that we had excellent power to detect the moderate associations which were reported in previous studies. No associations were found between the W64R variant and the following phenotypes in our sample: Type II diabetes, age at diagnosis for Type II diabetes, measures of obesity, fasting glucose, fasting insulin, minimal model variables, and systolic and diastolic blood pressures. In the analysis of plasma lipids, we detected an association between the variant and HDL ratios (HDL cholesterol/total cholesterol) (p = 0.013), which remained significant even after adjusting for sex, affection status and age. Since W64R homozygotes (n = 11) had the highest HDL ratios, however, heterozygotes had the lowest and the wild-type subjects had intermediate values, we conclude that the W64R variant is unlikely to reduce HDL ratios in a dose-dependent, pathogenic manner. [Diabetologia (1999) 42: 238–244]


Journal of Clinical Investigation | 1998

A large sample of finnish diabetic sib-pairs reveals no evidence for a non-insulin-dependent diabetes mellitus susceptibility locus at 2qter.

Soumitra Ghosh; Elizabeth R. Hauser; Victoria L. Magnuson; Timo T. Valle; Delphine S. Ally; Zarir E. Karanjawala; Joseph B. Rayman; Julie I. Knapp; Anjene Musick; Joyce Tannenbaum; Catherine Te; William Eldridge; Shane Shapiro; Tiffany Musick; Colin Martin; Alistair So; Alyson Witt; Julian Blaschak Harvan; Richard M. Watanabe; William Hagopian; Johan G. Eriksson; Stella J. Nylund; Kimmo Kohtamäki; Eva Tuomilehto-Wolf; Liisa Toivanen; Gabriele Vidgren; Christian Ehnholm; Richard N. Bergman; Jaakko Tuomilehto; Francis S. Collins

In the first reported positive result from a genome scan for non-insulin-dependent diabetes mellitus (NIDDM), Hanis et al. found significant evidence of linkage for NIDDM on chromosome 2q37 and named the putative disease locus NIDDM1 (Hanis et al. 1996. Nat. Genet. 13:161-166). Their total sample was comprised of 440 Mexican-American affected sib-pairs from 246 sibships. The strongest evidence for linkage was at marker D2S125 and best estimates of lambdas (risk to siblings of probands/population prevalence) using this marker were 1.37 under an additive model and 1.36 under a multiplicative model. We examined this chromosomal region using linkage analysis in a Finnish sample comprised of 709 affected sib-pairs from 472 sibships. We excluded this region in our sample (multipoint logarithm of odds score </= -2) for lambdas >/= 1.37. We discuss possible reasons why linkage to 2q37 was not found and conclude that this region is unlikely to be playing a major role in NIDDM susceptibility in the Finnish Caucasian population.


Proceedings of the National Academy of Sciences of the United States of America | 1999

Type 2 diabetes: Evidence for linkage on chromosome 20 in 716 Finnish affected sib pairs

Soumitra Ghosh; Richard M. Watanabe; Elizabeth R. Hauser; Timo T. Valle; Victoria L. Magnuson; Michael R. Erdos; Carl D. Langefeld; James E. Balow; Delphine S. Ally; Kimmo Kohtamäki; Peter S. Chines; Gunther Birznieks; Hong-Shi Kaleta; Anjene Musick; Catherine Te; Joyce Tannenbaum; William Eldridge; Shane Shapiro; Colin Martin; Alyson Witt; Alistair So; Jennie Chang; Ben Shurtleff; Rachel Porter; Kristina Kudelko; Arun Unni; Leonid Segal; Jillian Blaschak-Harvan; Johan G. Eriksson; Tuula Tenkula


BioTechniques | 1996

Substrate nucleotide-determined non-templated addition of adenine by Taq DNA polymerase : Implications for PCR-based genotyping and cloning

Victoria L. Magnuson; Delphine S. Ally; Stella J. Nylund; Zarir E. Karanjawala; Joseph B. Rayman; Julie I. Knapp; Soumitra Ghosh; Francis S. Collins


Genome Research | 1995

Approach to genotyping errors caused by nontemplated nucleotide addition by Taq DNA polymerase.

Jeffrey R. Smith; John D. Carpten; Michael J. Brownstein; Shyamali Ghosh; Victoria L. Magnuson; D A Gilbert; Jeffrey M. Trent; Francis S. Collins


Genome Research | 1997

Methods for precise sizing, automated binning of alleles, and reduction of error rates in large-scale genotyping using fluorescently labeled dinucleotide markers. FUSION (Finland-U.S. Investigation of NIDDM Genetics) Study Group.

Soumitra Ghosh; Zarir E. Karanjawala; Elizabeth R. Hauser; Delphine S. Ally; Julie I. Knapp; Joseph B. Rayman; Anjene Musick; Joyce Tannenbaum; Catherine Te; Shane Shapiro; William Eldridge; Tiffany Musick; Colin Martin; Jeffrey R. Smith; John D. Carpten; Michael J. Brownstein; John Powell; Raymond Whiten; Peter S. Chines; Stella J. Nylund; Victoria L. Magnuson; Michael Boehnke; Francis S. Collins


Diabetes | 2004

A Large Set of Finnish Affected Sibling Pair Families With Type 2 Diabetes Suggests Susceptibility Loci on Chromosomes 6, 11, and 14

Kaisa Silander; Laura J. Scott; Timo T. Valle; Karen L. Mohlke; Heather M. Stringham; Kerry R. Wiles; William L. Duren; Kimberly F. Doheny; Elizabeth W. Pugh; Peter S. Chines; Peggy P. White; Tasha E. Fingerlin; Anne U. Jackson; Chun Li; Soumitra Ghosh; Victoria L. Magnuson; Kimberly Colby; Michael R. Erdos; Jason E. Hill; Pablo Hollstein; Kathleen M. Humphreys; Roshni A. Kasad; Jessica Lambert; Konstantinos N. Lazaridis; George Lin; Anabelle Morales-Mena; Kristin Patzkowski; Carrie Pfahl; Rachel Porter; David Rha

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Soumitra Ghosh

National Institutes of Health

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Delphine S. Ally

National Institutes of Health

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Timo T. Valle

National Institute for Health and Welfare

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Francis S. Collins

National Institutes of Health

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Peter S. Chines

National Institutes of Health

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Richard M. Watanabe

University of Southern California

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William Eldridge

National Institutes of Health

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Anjene Musick

National Institutes of Health

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