Gene expression profile of the skin in the 'hairpoor' (HrHp) mice by microarray analysis

<p>Abstract</p> <p>Background</p> <p>The transcriptional cofactor, Hairless (HR), acts as one of the key regulators of hair follicle cycling; the loss of function mutations is the cause of the expression of the hairless phenotype in humans and mice. Recently, we reported a new <it>Hr </it>mutant mouse called 'Hairpoor' (<it>Hr^Hp</it>). These mutants harbor a gain of the function mutation, T403A, in the <it>Hr </it>gene. This confers the overexpression of HR and <it>Hr^Hp</it>is an animal model of Marie Unna hereditary hypotrichosis in humans. In the present study, the expression profile of <it>Hr^Hp/Hr^Hp</it>skin was investigated using microarray analysis to identify genes whose expression was affected by the overexpression of HR.</p> <p>Results</p> <p>From 45,282 mouse probes, differential expressions in 43 (>2-fold), 306 (>1.5-fold), and 1861 genes (>1.2-fold) in skin from <it>Hr^Hp/Hr^Hp</it>mice were discovered and compared with skin from wild-type mice. Among the 1861 genes with a > 1.2-fold increase in expression, further analysis showed that the expression of eight genes known to have a close relationship with hair follicle development, ascertained by conducting real-time PCR on skin RNA produced during hair follicle morphogenesis (P0-P14), indicated that four genes, <it>Wif1</it>, <it>Casp14</it>, <it>Krt71</it>, and <it>Sfrp1</it>, showed a consistent expression pattern with respect to HR overexpression in vivo.</p> <p>Conclusion</p> <p><it>Wif1 </it>and <it>Casp14 </it>were found to be upregulated, whereas <it>Krt71 </it>and <it>Sfrp1 </it>were downregulated in cells overexpressing HR in transient transfection experiments on keratinocytes, suggesting that HR may transcriptionally regulate these genes. Further studies are required to understand the mechanism of this regulation by the HR cofactor.</p

Identifying Copy Number Variants under Selection in Geographically Structured Populations Based on -statistics

Large-scale copy number variants (CNVs) in the human provide the raw material for delineating population differences, as natural selection may have affected at least some of the CNVs thus far discovered. Although the examination of relatively large numbers of specific ethnic groups has recently started in regard to inter-ethnic group differences in CNVs, identifying and understanding particular instances of natural selection have not been performed. The traditional FST measure, obtained from differences in allele frequencies between populations, has been used to identify CNVs loci subject to geographically varying selection. Here, we review advances and the application of multinomial-Dirichlet likelihood methods of inference for identifying genome regions that have been subject to natural selection with the FST estimates. The contents of presentation are not new; however, this review clarifies how the application of the methods to CNV data, which remains largely unexplored, is possible. A hierarchical Bayesian method, which is implemented via Markov Chain Monte Carlo, estimates locus-specific FST and can identify outlying CNVs loci with large values of FST. By applying this Bayesian method to the publicly available CNV data, we identified the CNV loci that show signals of natural selection, which may elucidate the genetic basis of human disease and diversity

Statistical Inference Methods for Detecting Altered Gene Associations

The higher incidence of liver disease in the Asian population raises a great concern to clinicians

Multireader, multicase receiver operating characteristic analysis: an empirical comparison of five methods

Several statistical methods have been developed for analyzing multireader, multicase (MRMC) receiver operating characteristic (ROC) studies. The objective of this article is to increase awareness of these methods and determine if their results are concordant for published datasets. Data from three previously published studies were reanalyzed using five MRMC methods. For each method the 95% confidence intervals (CIs) for the mean of the readers' ROC areas for each diagnostic test, the P value for the comparison of the diagnostic tests' mean accuracies, and the 95% CIs for the mean difference in ROC areas of the diagnostic tests were reported. Important differences in P values and CIs were seen when using parametric versus nonparametric estimates of accuracy, and there were the expected differences for random-reader versus fixed-reader models. Controlling for these differences, the Dorfman-Berbaum-Metz (DBM), Obuchowski-Rockette, Beiden-Wagner-Campbell, and Song's multivariate Wilcoxon-Mann-Whitney (WMW) methods gave almost identical results for the fixed-reader model. For the random-reader model, the DBM, Obuchowski-Rockette, and Beiden-Wagner-Campbell methods yielded approximately the same inferences, but the CIs for the Beiden-Wagner-Campbell method tend to be broader. Ishwaran's hierarchical ROC sometimes yielded significance not found with other methods. Song's modification of DBM's jack-knifing algorithm sometimes led to different conclusions than the original DBM algorithm. In choosing and applying MRMC methods, it is important to recognize: (1) the distinction between random-reader and fixed-reader models, the uncertainties accounted for by each, and thus the level of generalizeability expected from each; (2) assumptions made by the various MRMC methods; and (3) limitations of a five- or six-reader study when the reader variability is great

Multireader, Multicase Receiver Operating Characteristic Analysis: An Empirical Comparison of Five Methods 1

Rationale and Objectives. Several statistical methods have been developed for analyzing multireader, multicase (MRMC) receiver operating characteristic (ROC) studies. The objective of this article is to increase awareness of these methods and determine if their results are concordant for published datasets. Materials and Methods. Data from three previously published studies were reanalyzed using five MRMC methods. For each method the 95 % confidence intervals (CIs) for the mean of the readers ’ ROC areas for each diagnostic test, the P value for the comparison of the diagnostic tests ’ mean accuracies, and the 95 % CIs for the mean difference in ROC areas of the diagnostic tests were reported. Results. Important differences in P values and CIs were seen when using parametric versus nonparametric estimates of accuracy, and there were the expected differences for random-reader versus fixed-reader models. Controlling for these differences, the Dorfman-Berbaum-Metz (DBM), Obuchowski-Rockette, Beiden-Wagner-Campbell, and Song’s multivariate Wilcoxon-Mann-Whitney (WMW) methods gave almost identical results for the fixed-reader model. For the randomreader model, the DBM, Obuchowski-Rockette, and Beiden-Wagner-Campbell methods yielded approximately the same inferences, but the CIs for the Beiden-Wagner-Campbell method tend to be broader. Ishwaran’s hierarchical ROC sometimes yielded significance not found with other methods. Song’s modification of DBM’s jack-knifing algorithm sometimes led to different conclusions than the original DBM algorithm