Biomedical subjects

Sanjay Shete

Publications and source records attributed to Sanjay Shete.

39 records · Page 3Linked to original sources

PubMed2002

Improving the power of sib pair quantitative trait loci detection by phenotype winsorization.

OBJECTIVES: In sib pair studies, quantitative trait loci (QTL) identification may be adversely affected by non-normality in the phenotypic distribution, particularly when subjects falling in the tails of the distribution bias the trait mean or variance. We evaluated the robustness and power of reducing the influence of subjects with extreme phenotypic values by Winsorizing non-normal distributions in three versions of Haseman-Elston regression-based methods of QTL linkage analysis. METHODS: Data were simulated for normal and non-normal distributions. Phenotypic values that correspond to cutoff points at the omega and 1 - omega percentiles of the distribution were identified, and phenotypic values falling outside the boundaries of the omega and 1 - omega cutoff points were replaced by the omega and 1 - omega values, respectively. One million replications were performed for the three tests of linkage for Winsorized and non-Winsorized data. RESULTS: Winsorization reduced conservatism in the tails of the empirical type I error rate for the vast majority of the tests of linkage, increased the power of QTL detection in non-normal data and created a slight negative bias in symmetrical phenotypic distributions. CONCLUSIONS: Winsorizing can improve the power of QTL detection with certain non-normal distributions but can also introduce bias into the estimate of the QTL effect.

Genetic Predisposition to Disease↗

PubMed2002

Haplotypes of two variants in p16 (CDKN2/MTS-1/INK4a) exon 3 and risk of squamous cell carcinoma of the head and neck: a case-control study.

The frequent loss or promoter methylation of the tumor suppressor gene p16 in head and neck cancer suggests an etiologic role of p16 in this disease. Two adjacent polymorphisms of p16 exon 3, C540G and C580T, were identified recently. C540G is associated with low expression of p53, and both polymorphisms are associated with tumor aggressiveness, suggesting a possible functional relevance. We hypothesized that these two polymorphisms, particularly their haplotypes, are associated with the risk of developing squamous cell carcinoma of the head and neck (SCCHN). To test this hypothesis, we conducted a hospital-based case-control study of 208 patients with SCCHN and 224 cancer-free control subjects to evaluate the association between p16 genotypes/haplotypes and the risk of SCCHN, using a PCR-single strand conformation polymorphism-based genotyping assay. However, our results suggested that no significant differences exist in the distribution of p16 C540G and C580T genotypes between cases and controls. For the C540G polymorphism, the CC, CG, and GG genotype frequencies were 76.9%, 22.1%, and 1.0%, respectively, in the cases, compared with 76.8%, 21.9%, and 1.3%, respectively, for the controls. For the C580T polymorphism, the CC, CT, and TT genotype frequencies were 83.6%, 15.9%, and 0.5%, respectively, in the cases, compared with 82.6%, 16.5%, and 0.9%, respectively, for the controls. The frequencies of three predominant 540C/580C, 540G/580C, and 540C/580T haplotype alleles were distributed similarly in the cases (79.6%, 12.0%, and 8.4%) and in the controls (78.6%, 12.3%, and 9.1%). None of these differences were statistically significant. We conclude that these polymorphic p16 genotypes or haplotypes may not play a major role in the etiology of SCCHN, if any. However, our limited sample size and power call for larger studies for additional verification of our findings.

Aged↗

PubMedDate not supplied

Uniformly minimum variance unbiased estimation of gene diversity.

Gene diversity is an important measure of genetic variability in inbred populations. The survival of species in changing environments depends on, among other factors, the genetic variability of the population. In this communication, I have derived the uniformly minimum variance unbiased estimator of gene diversity. The proposed estimator of gene diversity does not assume that the inbreeding coefficient is known. I have also provided the approximate variance of this estimator according to Fisher's method. In addition, I have developed a numerical resampling-based method for obtaining variances and confidence intervals based on the maximum likelihood estimator and the uniformly minimum variance unbiased estimator. Efficiency in estimation of the gene diversity based on these two estimators is discussed. In accordance with the simulation results, I found that the uniformly minimum variance estimator developed in this report is more accurate for estimation of gene diversity than the maximum likelihood estimator.

Analysis of Variance↗