Linkage disequilibrium between microsatellite markers extends beyond 1 cM on chromosome 20 in Finns.

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.

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.

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.

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.

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.

Methionine synthase: high-resolution mapping of the human gene and evaluation as a candidate locus for neural tube defects.

Periconceptual folate supplementation has been found to prevent the occurrence of many neural tube defects (NTDs). Consequently, genetic variation in folate metabolism genes is expected to contribute to the risk for neural tube defects. Methionine synthase catalyzes the vitamin B(12)-dependent conversion of homocysteine and 5-methyltetrahydrofolate to methionine and tetrahydrofolate. The observation that homocysteine and vitamin B(12) levels are independent predictors of NTD risk suggested that methionine synthase could be a candidate gene for NTDs. To assess the role of the MS gene in NTDs, we performed high-resolution physical mapping of the MS locus, isolated highly polymorphic markers linked to the MS gene, and tested for an association between specific MS alleles and NTDs. We mapped the MS gene to a position between 909 and 913 cR(10000) on chromosome 1 by radiation hybrid mapping. Polymorphic markers D1S1567 and D1S1568 map to locations no more than 900 and 194 kb from the MS gene, respectively. The segregation of these polymorphic markers was measured in 85 Irish NTD families. No allele of either marker showed a significant association with NTDs using the transmission disequilibrium test. A lack of association was also observed for the D1919G missense mutation within the gene. Our results suggest that inherited variation in the MS gene does not contribute to NTD risk in this population.

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

We are conducting a genome scan at an average resolution of 10 centimorgans (cM) for type 2 diabetes susceptibility genes in 716 affected sib pairs from 477 Finnish families. To date, our best evidence for linkage is on chromosome 20 with potentially separable peaks located on both the long and short arms. The unweighted multipoint maximum logarithm of odds score (MLS) was 3.08 on 20p (location, chi = 19.5 cM) under an additive model, whereas the weighted MLS was 2.06 on 20q (chi = 57 cM, recurrence risk,lambda(s) = 1. 25, P = 0.009). Weighted logarithm of odds scores of 2.00 (chi = 69.5 cM, P = 0.010) and 1.92 (chi = 18.5 cM, P = 0.013) were also observed. Ordered subset analyses based on sibships with extreme mean values of diabetes-related quantitative traits yielded sets of families who contributed disproportionately to the peaks. Two-hour glucose levels in offspring of diabetic individuals gave a MLS of 2. 12 (P = 0.0018) at 9.5 cM. Evidence from this and other studies suggests at least two diabetes-susceptibility genes on chromosome 20. We have also screened the gene for maturity-onset diabetes of the young 1, hepatic nuclear factor 4-a (HNF-4alpha) in 64 affected sibships with evidence for high chromosomal sharing at its location on chromosome 20q. We found no evidence that sequence changes in this gene accounted for the linkage results we observed.

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.

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.