Showing papers by "Michael Boehnke published in 2002"

Genome-wide linkage analysis of families with obsessive-compulsive disorder ascertained through pediatric probands.

Abstract: The goal of this study was to identify chromosomal regions likely to contain susceptibility alleles for early-onset obsessive-compulsive disorder (OCD). A genome scan was done in 56 individuals from seven families ascertained through pediatric OCD probands; 27 of the 56 subjects had a lifetime diagnosis of definite OCD. Denser mapping of regions on chromosomes 2, 9, and 16 was subsequently done with those subjects and ten additional subjects from the largest family in the study. Direct interviews were completed with 65 of the 66 genotyped individuals. Relatives were interviewed blind to proband status. Of the 65 interviewed individuals, 32 had a lifetime diagnosis of definite OCD. Three of the seven probands had a history of Tourette disorder. Two of the 25 relatives with OCD had a tic history, whereas none of the 33 relatives without OCD had tics. The genome scan consisted of 349 microsatellite markers with an average between-marker distance of 11.3 centiMorgan (cM). Fine mapping was done with 24 additional markers at an average spacing of 1.6 cM. Parametric and nonparametric linkage analyses were conducted using GENEHUNTER(+). The maximum multipoint LOD score with a dominant model was 2.25 on 9p. However, with fine mapping and additional subjects, that LOD score decreased to 1.97. The maximum multipoint nonparametric LOD* score was 1.73 on 19q. The maximum multipoint LOD score with a recessive model was 1.40 on 6p. The results provide suggestive evidence for linkage on 9p and identify regions requiring further study with much larger samples.

Probability of detection of genotyping errors and mutations as inheritance inconsistencies in nuclear-family data.

Abstract: Gene-mapping studies routinely rely on checking for Mendelian transmission of marker alleles in a pedigree, as a means of screening for genotyping errors and mutations, with the implicit assumption that, if a pedigree is consistent with Mendel’s laws of inheritance, then there are no genotyping errors. However, the occurrence of inheritance inconsistencies alone is an inadequate measure of the number of genotyping errors, since the rate of occurrence depends on the number and relationships of genotyped pedigree members, the type of errors, and the distribution of marker-allele frequencies. In this article, we calculate the expected probability of detection of a genotyping error or mutation as an inheritance inconsistency in nuclear-family data, as a function of both the number of genotyped parents and offspring and the marker-allele frequency distribution. Through computer simulation, we explore the sensitivity of our analytic calculations to the underlying error model. Under a random-allele–error model, we find that detection rates are 51%–77% for multiallelic markers and 13%–75% for biallelic markers; detection rates are generally lower when the error occurs in a parent than in an offspring, unless a large number of offspring are genotyped. Errors are especially difficult to detect for biallelic markers with equally frequent alleles, even when both parents are genotyped; in this case, the maximum detection rate is 34% for four-person nuclear families. Error detection in families in which parents are not genotyped is limited, even with multiallelic markers. Given these results, we recommend that additional error checking (e.g., on the basis of multipoint analysis) be performed, beyond routine checking for Mendelian consistency. Furthermore, our results permit assessment of the plausibility of an observed number of inheritance inconsistencies for a family, allowing the detection of likely pedigree—rather than genotyping—errors in the early stages of a genome scan. Such early assessments are valuable in either the targeting of families for resampling or discontinued genotyping.

High-throughput screening for evidence of association by using mass spectrometry genotyping on DNA pools

Abstract: To facilitate positional cloning of complex trait susceptibility loci, we are investigating methods to reduce the effort required to identify trait-associated alleles. We examined primer extension analysis by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to screen single-nucleotide polymorphisms (SNPs) for association by using DNA pools. We tested whether this method can accurately estimate allele frequency differences between pools while maintaining the high-throughput nature of assay design, sample handling, and scoring. We follow up interesting allele frequency differences in pools by genotyping individuals. We tested DNA pools of 182, 228, and 499 individuals using 16 SNPs with minor allele frequencies 0.026–0.486 and allele frequency differences 0.001–0.108 that we had genotyped previously on individuals and 381 SNPs that we had not. Precision, as measured by the average standard deviation among 16 semidependent replicates, was 0.021 ± 0.011 for the 16 SNPs and 0.018 ± 0.008 for the 291/381 SNPs used in further analysis. For the 16 SNPs, the average absolute error in predicting allele frequency differences between pools was 0.009; the largest errors were 0.031, 0.028, and 0.027. We determined that compensating for unequal peak heights in heterozygotes improved precision of allele frequency estimates but had only a very minor effect on accuracy of allele frequency differences between pools. Based on these data and assuming pools of 500 individuals, we conclude that at significance level 0.05 we would have 95% (82%) power to detect population allele frequency differences of 0.07 for control allele frequencies of 0.10 (0.50).

Variation in three single nucleotide polymorphisms in the calpain-10 gene not associated with type 2 diabetes in a large Finnish cohort.

Abstract: Variations in the calpain-10 gene have recently been reported to be associated with type 2 diabetes in a Mexican-American population. We typed three single nucleotide polymorphisms (SNPs) in the calpain-10 gene (SNPs 43, 56, and 63) to test for association between variation at these loci and type 2 diabetes and diabetes-related traits in 1,603 Finnish subjects: two samples of 526 (Finland-U.S. Investigation of NIDDM Genetics [FUSION] 1) and 255 (FUSION 2) index case subjects with type 2 diabetes, 185 and 414 unaffected spouses and offspring of FUSION 1 index case subjects or their affected siblings, and 223 elderly normal glucose-tolerant control subjects. We found no significant differences in allele, genotype, haplotype, or haplogenotype frequencies between index case subjects with diabetes and the elderly and spouse control populations (all P > 0.087). Although variation in these three SNPs was associated with variation in some type 2 diabetes-related traits within each of the case and control groups, no consistent pattern of the implicated variant or combination of variants was discerned. We conclude that variation in these three SNPs in the calpain-10 gene is unlikely to confer susceptibility to type 2 diabetes in this Finnish cohort.

Ascertainment-adjusted parameter estimates revisited.

Abstract: Ascertainment-adjusted parameter estimates from a genetic analysis are typically assumed to reflect the parameter values in the original population from which the ascertained data were collected. Burton et al. (2000) recently showed that, given unmodeled parameter heterogeneity, the standard ascertainment adjustment leads to biased parameter estimates of the population-based values. This finding has important implications in complex genetic studies, because of the potential existence of unmodeled genetic parameter heterogeneity. The authors further stated the important point that, given unmodeled heterogeneity, the ascertainment-adjusted parameter estimates reflect the true parameter values in the ascertained subpopulation. They illustrated these statements with two examples. By revisiting these examples, we demonstrate that if the ascertainment scheme and the nature of the data can be correctly modeled, then an ascertainment-adjusted analysis returns population-based parameter estimates. We further demonstrate that if the ascertainment scheme and data cannot be modeled properly, then the resulting ascertainment-adjusted analysis produces parameter estimates that generally do not reflect the true values in either the original population or the ascertained subpopulation.

Linkage of chromosome 13q32 to schizophrenia in a large veterans affairs cooperative study sample

Abstract: Several prior reports have suggested that chromosomal region 13q32 may harbor a schizophrenia susceptibility gene. In an attempt to replicate this finding, we assessed linkage between chromosome 13 markers and schizophrenia in 166 families, each with two or more affected members. The families, assembled from multiple centers by the Department of Veterans Affairs Cooperative Studies Program, included 392 sampled affected subjects and 216 affected sib pairs. By DSM-III-R criteria, 360 subjects (91.8%) had a diagnosis of schizophrenia and 32 (8.2%) were classified as schizoaffective disorder, depressed. The families had mixed ethnic backgrounds. The majority were northern European-American families (n = 62, 37%), but a substantial proportion were African-American kindreds (n = 60, 36%). Chromosome 13 markers, spaced at intervals of approximately 10 cM over the entire chromosome and 2–5 cM for the 13q32 region were genotyped and the data analyzed using semi-parametric affected only linkage analysis. For the combined sample (with race broadly defined and schizophrenia narrowly defined) the maximum LOD score was 1.43 (Z-score of 2.57; P = 0.01) at 79.0 cM between markers D13S1241 (76.3 cM) and D13S159 (79.5 cM). Both ethnic groups showed a peak in this region. The peak is within 3 cM of the peak reported by Brzustowicz et al. [1999: Am J Hum Genet 65:1096–1103]. © 2002 Wiley-Liss, Inc.