Localization of a Susceptibility Gene for Type 2 Diabetes to Chromosome 5q34–q35.2

Reynisdóttir, Inga; Þorleifsson, Guðmar; Benediktsson, Rafn; Sigurðsson, Gunnar; Emilsson, Valur; Einarsdottir, A. S.; Hjörleifsdóttir, Eyrún Edda; Orlygsdottir, Gudbjorg; Bjornsdottir, Gudrun; Saemundsdottir, Jona; Halldórsson, Skarphéðinn; Hrafnkelsdóttir, Soffia M.; Sigurjonsdottir, Steinunn Bjorg; Steinsdottir, Svana; Martin, Mitchell; Kochan, Jarema; Rhees, Brian; Grant, Struan F.A.; Frigge, Michael L.; Kong, Augustine; Gudnason, Vilmundur; Stefánsson, Kāri; Gulcher, Jeffrey R.

doi:10.1086/377139

Cited by 176 publications

(102 citation statements)

References 65 publications

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“…They were ϳ1-25 cM wide, with linkage evidence at significance levels up to P Ͻ 0.001. These results illustrate the advantage of refined scans, such as ours or the ones routinely used now by teams such as the investigators at Decode Genetics (13)(14)(15), to detect most putative disease regions.…”

Section: Discussionmentioning

confidence: 53%

“…Use of a denser marker map may increase power and better smooth the logarithm of odds (LOD) score curve, enabling more accurate assessment of the linkage evidence. The personnel at Decode Genetics (Reykjavik, Iceland) routinely perform genome scans with 1,000 markers, i.e., a mean marker spacing of 4 cM (13)(14)(15). Computer simulations allow more accurate assessment of the significance of the results.…”

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confidence: 99%

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Dense genome‐wide linkage analysis of rheumatoid arthritis, including covariates

Fortéa¹,

Bükülmez²,

Petit-Teixeira³

et al. 2004

Arthritis & Rheumatism

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Objective. Rheumatoid arthritis (RA) is a heterogeneous disease that exhibits a complex genetic component. Previous RA genome scans confirmed the involvement of the HLA region and generated data on suggestive signals at non-HLA regions, albeit with few overlaps in findings between studies. The present study was undertaken to detect potential RA gene regions and to estimate the number of true RA gene regions, taking into account the heterogeneity of RA, through performance of a dense genome scan.Methods. In a study of 88 French Caucasian families (105 RA sibpairs), 1,088 microsatellite markers were genotyped (3.3-cM genome scan), and a multipoint model-free linkage analysis was performed. The statistical assessment of the results relied on 10,000 computer simulations. A covariate-based multipoint model-free linkage analysis was performed on the locations of regions with suggestive evidence for linkage.Results. Involvement of the HLA region was strongly confirmed (P ‫؍‬ 6 ؋ 10 ؊5 ), and 19 non-HLA regions showed suggestive evidence for linkage (P < 0.05); 9 of these overlapped with regions suggested in other published RA genome scans. A routine 12-cM genome scan with the same families would have detected only 7 of the 19 regions, including only 4 of the 9 overlapping regions. From the 10,000 computer simulations, we estimated that 8 ؎ 4 regions (mean ؎ SD) were true-positives. RA covariate-based analysis provided additional linkage evidence for 3 regions, with age at disease onset, erosions, and HLA-DRB1 shared epitope as covariates.Conclusion. The results of this study provide evidence of 19 non-HLA RA gene regions, with an estimate of 8 ؎ 4 as true-positives, and provide additional evidence for 3 regions from covariate-based analysis.

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Section: Discussionmentioning

confidence: 53%

mentioning

confidence: 99%

Dense genome‐wide linkage analysis of rheumatoid arthritis, including covariates

Fortéa¹,

Bükülmez²,

Petit-Teixeira³

et al. 2004

Arthritis & Rheumatism

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“…In the past few years it became feasible to genotype cheaply large pedigrees with much greater numbers of microsatellites than were previously used for genome scans, and statistical programs that are now available permit efficient computation of linkage even in complex pedigrees 28,29 . These advances allowed a substantial increase in the scale of pedigree-based linkage studies 2,4,[30][31][32][33][34][35][36][37][38] . Inadequate technology and statistical methodology have similarly hindered implementation of alternatives to pedigreebased mapping.…”

Section: Appropriate Technology and Statistics For Each Approachmentioning

confidence: 99%

The use of pedigree, sib-pair and association studies of common diseases for genetic mapping and epidemiology

2004

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Efforts to identify gene variants associated with susceptibility to common diseases use three approaches: pedigree and affected sib-pair linkage studies and association studies of population samples. The different aims of these study designs reflect their derivation from biological versus epidemiological traditions. Similar principles regarding determination of the evidence levels required to consider the results statistically significant apply to both linkage and association studies, however. Such determination requires explicit attention to the prior probability of particular findings, as well as appropriate correction for multiple comparisons. For most common diseases, increasing the sample size in a study is a crucial step in achieving statistically significant genetic mapping results. Recent studies suggest that the technology and statistical methodology will soon be available to make wellpowered studies feasible using any of these approaches.

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“…Recently, Reynisdottir et al [2] found suggestive linkage of type 2 diabetes to chromosome 10q in the Icelandic population. Fine-mapping with 228 microsatellite markers in Icelandic individuals with type 2 diabetes and controls throughout a 10.5 Mb interval on 10q identified one microsatellite marker, DG10S478 in intron 3 of the TCF7L2 gene, as being strongly associated with type 2 diabetes (p ¼ 2:1 Â 10 À9 ) [3].…”

Section: Introductionmentioning

confidence: 98%

Association of variants of transcription factor 7-like 2 (TCF7L2) with susceptibility to type 2 diabetes in the Dutch Breda cohort

et al. 2006

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Aim/hypothesis A strong association between susceptibility to type 2 diabetes and common variants of transcription factor 7-like 2 (TCF7L2), encoding an enteroendocrine transcription factor involved in glucose homeostasis, has been reported in three different populations (Iceland, Denmark and USA) by Grant et al. We aimed to replicate these findings in a Dutch cohort. Methods We analysed the genotypes of two intronic single nucleotide polymorphisms (SNPs) in TCF7L2 gene in 502 unrelated type 2 diabetes patients and in a set of healthy controls (n=920). The two SNPs showed almost complete linkage disequilibrium (D′=0.91). Results We were able to replicate the previously reported association in our Breda cohort. The minor alleles of both variants were significantly over-represented in cases (odds ratio [OR] 1.29, 95% CI 1.09-1.52, p ¼ 3 Â 10 À3 for rs12255372; OR 1.41, 95% CI 1.19-1.66, p ¼ 4:4 Â 10 À5 for rs7903146). In addition, TCF7L2 haplotypes were analysed for association with the disease. The analysis of haplotypes did not reveal any strong association beyond that expected from analysing individual SNPs. The TT haplotype carrying the minor alleles was more frequent among cases (OR 1.38, p ¼ 2:7 Â 10 À3 ). Conclusions/interpretation Our data strongly confirm that variants of the TCF7L2 gene contribute to the risk of type 2 diabetes. The population-attributable risk from this factor in the Dutch type 2 diabetes population is 10%.

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Localization of a Susceptibility Gene for Type 2 Diabetes to Chromosome 5q34–q35.2

Cited by 176 publications

References 65 publications

Dense genome‐wide linkage analysis of rheumatoid arthritis, including covariates

Dense genome‐wide linkage analysis of rheumatoid arthritis, including covariates

The use of pedigree, sib-pair and association studies of common diseases for genetic mapping and epidemiology

Association of variants of transcription factor 7-like 2 (TCF7L2) with susceptibility to type 2 diabetes in the Dutch Breda cohort

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