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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.

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.

Thermostable DNA polymerases can catalyze nontemplated addition of a nucleotide to the 3' end of amplification products. This presents a potential source of error in genotyping studies employing Taq DNA polymerase to amplify microsatellite loci. Although the activity is marker specific, experimental variation is often seen in the degree of modification. Consequently, for a given microsatellite marker, an allele may be inconsistently identified as either the unmodified or modified amplification product. Full automation of high-throughput genotyping has been hampered by the need for manual editing of data because of this source of allele misidentification. In this study we estimate a 1% to 3% error rate attributable to nontemplated nucleotide addition in the ABI PRISM genotyping system. We present a PCR-based strategy to minimize this source of error.

Betacellulin, a member of the epidermal growth factor family, is expressed in fetal and adult pancreas. In vitro and in vivo studies suggest a role for betacellulin in islet neogenesis and regeneration. Therefore, a mutation in the betacellulin gene might lead to fewer beta cells. With reduced beta cell reserve, beta cells may not be able to compensate for an autoimmune attack, and in turn, susceptibility to type 1 diabetes mellitus (T1DM) would increase. Previous mutational analysis identified seven polymorphisms in the betacellulin gene [5′ UT (−233G>C, −226A>G), exon 1 (TGC19GGC, Cys7Gly), exon 2 (CTC130TTC, Leu44Phe), exon 4 (TTG370ATG, Leu124Met), intron 2 (-31T>C), and intron 4 (-4C>T)]. An association study of these variants with T1DM was first carried out in 100 Caucasian subjects with T1DM and 282 Caucasian subjects without diabetes recruited at the University of Maryland. The frequency of the intron 4 T-4 allele was significantly higher among nondiabetic controls than that among diabetic cases (0.29 vs 0.21, p=0.04). Allele frequencies for the other polymorphisms did not differ significantly between cases and controls. The intron 4 T-4 association was then replicated by transmission disequilibrium testing in a separate population of Caucasian parent/offspring with T1DM trios (n=168 trios, 113 informative) recruited at the Medical College of Wisconsin (p=0.024). An interaction of the intron 4 T-4 allele and human leukocyte antigen (HLA) was also detected with undertransmission of the T allele in those T1DM subjects with susceptible HLA types as compared to those T1DM subjects without susceptible HLA types (p=0.018). RNA studies of the intron T-4 variant showed similar RNA levels for intron 4 T-4 and intron 4 C-4 alleles. Additionally, there was no evidence for an effect of this variant on exon–intron splicing. We conclude that the intron 4 T-4 allele in the betacellulin gene is associated with lower risk of T1DM and may interact with HLA. Further studies will be necessary to establish the significance of this association.

Linkage disequilibrium (LD) is a proven tool for evaluating population structure and localizing genes for monogenic disorders. LD-based methods may also help localize genes for complex traits. We evaluated marker–marker LD using 43 microsatellite markers spanning chromosome 20 with an average density of 2.3 cM. We studied 837 individuals affected with type 2 diabetes and 386 mostly unaffected spouse controls. A test of homogeneity between the affected individuals and their spouses showed no difference, allowing the 1223 individuals to be analyzed together. Significant (P < 0.01) LD was observed using a likelihood ratio test in all (11/11) marker pairs within 1 cM, 78% (25/32) of pairs 1–3 cM apart, and 39% (7/18) of pairs 3–4 cM apart, but for only 12 of 842 pairs more than 4 cM apart. We used the human genome project working draft sequence to estimate kilobase (kb) intermarker distances, and observed highly significant LD (P < 10−10) for all six marker pairs up to 350 kb apart, although the correlation of LD with cM is slightly better than the correlation with megabases. These data suggest that microsatellites present at 1-cM density are sufficient to observe marker–marker LD in the Finnish population.

Large-scale genotyping is required to generate dense identity-by-descent maps to map genes for human complex disease. In some studies the number of genotypes needed can approach or even exceed 1 million. Generally, linkage and linkage disequilibrium analyses depend on clear allele identification and subsequent allele frequency estimation. Accurate grouping or categorization of each allele in the sample (allele calling or binning) is therefore an absolute requirement. Hence, a genotyping system that can reliably achieve this is necessary. In the case of affected sib-pair analysis without parents, the need for accurate allele calling is even more critical. We describe methods that permit precise sizing of alleles across multiple gels using the fluorescence-based, Applied Biosystems (ABI) genotyping technology and discuss ways to reduce genotyping error rates. Using database utilities, we show how to minimize intergel allele size variation, to combine data effectively from different models of ABI sequencing machines, and automatically bin alleles. The final data can then be converted into a format ready for analysis by statistical genetic packages such as MENDEL.