Search

Three methods of Single Nucleotide Polymorphism (SNP) detection: SNaPshot, Pyrosequencing and Biplex Invader, with two different chemistries were investigated to compare, (1) accuracy, (2) ease of use, (3) throughput capability, and (4) cost. We genotyped 192 human DNA samples across 24 SNPs (minor allele frequencies above 30%), of which seven SNPs were genotyped with all three methods. We show that the Biplex Invader genotyping method was found to be the most accurate and easiest to use with lowest cost, although Pyrosequencing provided similar results at a low cost. With little optimization, the accuracy of the SNaPshot method was also comparable to these two methods with a higher cost, if only singleplex reactions are used.

The Applied Biosystems PRISMTM fluorescence-based genotyping system as well as the Invitrogen TA Cloning® vector system are influenced by the tendency of Taq DNA polymerase to add an adenine nucleotide to the 3′ end of PCR products after extension. Incomplete addition of adenine to a majority of PCR product strands creates problems in allele-calling during genotyping and potentially diminishes the cloning efficiency of such products. Experiments reported here show that certain terminal nucleotides can either inhibit or enhance adenine addition by Taq and that PCR primer design can be used to modulate this activity. The methods we propose can substantially improve allele-calling for problematic microsatellite markers when using GENOTYPERTM software.

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.

The role of genetics in male sexual orientation was investigated by pedigree and linkage analyses on 114 families of homosexual men. Increased rates of same-sex orientation were found in the maternal uncles and male cousins of these subjects, but not in their fathers or paternal relatives, suggesting the possibility of sex-linked transmission in a portion of the population. DNA linkage analysis of a selected group of 40 families in which there were two gay brothers and no indication of nonmaternal transmission revealed a correlation between homosexual orientation and the inheritance of polymorphic markers on the X chromosome in approximately 64 percent of the sib-pairs tested. The linkage to markers on Xq28, the subtelomeric region of the long arm of the sex chromosome, had a multipoint lod score of 4.0 (P = 10(-5), indicating a statistical confidence level of more than 99 percent that at least one subtype of male sexual orientation is genetically influenced.

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.

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 /= 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.

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.

A novel HLA-DQB1 allele, DQB1*060403, has been discovered in a type 1 diabetic patient and family members.

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.