Biomedical subjects

Kevin C Cartier

Publications and source records attributed to Kevin C Cartier.

5 recordsLinked to original sources

PubMed2006

Speech sound disorder influenced by a locus in 15q14 region.

Despite a growing body of evidence indicating that speech sound disorder (SSD) has an underlying genetic etiology, researchers have not yet identified specific genes predisposing to this condition. The speech and language deficits associated with SSD are shared with several other disorders, including dyslexia, autism, Prader-Willi Syndrome (PWS), and Angelman's Syndrome (AS), raising the possibility of gene sharing. Furthermore, we previously demonstrated that dyslexia and SSD share genetic susceptibility loci. The present study assesses the hypothesis that SSD also shares susceptibility loci with autism and PWS. To test this hypothesis, we examined linkage between SSD phenotypes and microsatellite markers on the chromosome 15q14-21 region, which has been associated with autism, PWS/AS, and dyslexia. Using SSD as the phenotype, we replicated linkage to the 15q14 region (P=0.004). Further modeling revealed that this locus influenced oral-motor function, articulation and phonological memory, and that linkage at D15S118 was potentially influenced by a parent-of-origin effect (LOD score increase from 0.97 to 2.17, P=0.0633). These results suggest shared genetic determinants in this chromosomal region for SSD, autism, and PWS/AS.

Autistic Disorder↗

PubMed2005

An artificial neural network for estimating haplotype frequencies.

The problem of estimating haplotype frequencies from population data has been considered by numerous investigators, resulting in a wide variety of possible algorithmic and statistical solutions. We propose a relatively unique approach that employs an artificial neural network (ANN) to predict the most likely haplotype frequencies from a sample of population genotype data. Through an innovative ANN design for mapping genotype patterns to diplotypes, we have produced a prototype that demonstrates the feasibility of this approach, with provisional results that correlate well with estimates produced by the expectation maximization algorithm for haplotype frequency estimation. Given the computational demands of estimating haplotype frequencies for 20 or more single-nucleotide polymorphisms, the ANN approach is promising because its design fits well with parallel computing architectures.

Algorithms↗

PubMed2005

A regression based transmission/disequilibrium test for binary traits: the power of joint tests for linkage and association.

BACKGROUND: In this analysis we applied a regression based transmission disequilibrium test to the binary trait presence or absence of Kofendred Personality Disorder in the Genetic Analysis Workshop 14 (GAW14) simulated dataset and determined the power and type I error rate of the method at varying map densities and sample sizes. To conduct this transmission disequilibrium test, the logit transformation was applied to a binary outcome and regressed on an indicator variable for the transmitted allele from informative matings. All 100 replicates from chromosomes 1, 3, 5, and 9 for the Aipotu and the combined Aipotu, Karangar, and Danacaa populations were used at densities of 3, 1, and 0.3 cM. Power and type I error were determined by the number of replicates significant at the 0.05 level. RESULTS: The maximum power to detect linkage and association with the Aipotu population was 93% for chromosome 3 using a 0.3-cM map. For chromosomes 1, 5, and 9 the power was less than 10% at the 3-cM scan and less than 22% for the 0.3-cM map. With the larger sample size, power increased to 38% for chromosome 1, 100% for chromosome 3, 31% for chromosome 5, and 23% for chromosome 9. Type I error was approximately 7%. CONCLUSION: The power of this method is highly dependent on the amount of information in a region. This study suggests that single-point methods are not particularly effective in narrowing a fine-mapping region, particularly when using single-nucleotide polymorphism data and when linkage disequilibrium in the region is variable.

Chromosomes, Human↗

PubMed2005

Data mining.

Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of type I errors. The general motivations were to find the "story" in the data and to summarize information from a multitude of measures. Several methods illustrated strategies for better trait definition, using summarization of related traits. In the few studies that sought to identify genes for alcoholism, there was little agreement among the different strategies, likely reflecting the complexities of the disease. Nevertheless, Group 14 found that these methods offered strategies to gain a better understanding of the complex pathways by which disease develops.

Alcoholism↗

PubMed2003

Genome-wide linkage scan for genes affecting longitudinal trends in systolic blood pressure.

Only one genome scan to date has attempted to make use of the longitudinal data available in the Framingham Heart Study, and this attempt yielded evidence of linkage to a gene for mean systolic blood pressure. We show how the additional information available in these longitudinal data can be utilized to examine linkages for not only mean systolic blood pressure (SBP), but also for its trend with age and its variability. Prior to linkage analysis, individuals treated for hypertension were adjusted to account for right-censoring of SBP. Regressions on age were fitted to obtain orthogonal measures of slope, curvature, and residual variance of SBP that were then used as dependent variables in the model-free linkage program SIBPAL. We included mean age, gender, and cohort as covariates in the analysis. To improve power, sibling pairs were weighted for informativity using weights derived from both the marker and trait. The most significant results from our analyses were found on chromosomes 12, 15, and 17 for mean SBP, and chromosome 20 for both SBP slope and curvature.

Adult↗