74 resultados para Circular Statistics
Resumo:
Statistics of causes of death remain an important source of epidemiological data for the evaluation of various medical and health problems. The improvement of analytical techniques and, above all, the transformation of demographic and morbid structures of populations have prompted researchers in the field to give more importance to the quality of death certificates. After describing the data collection system presently used in Switzerland, the paper discusses various indirect estimations of the quality of Swiss data and reviews the corresponding international literature.
Resumo:
Selective pressures related to gene function and chromosomal architecture are acting on genome sequences and can be revealed, for instance, by appropriate genometric methods. Cumulative nucleotide skew analyses, i.e., GC, TA, and ORF orientation skews, predict the location of the origin of DNA replication for 88 out of 100 completely sequenced bacterial chromosomes. These methods appear fully reliable for proteobacteria, Gram-positives, and spirochetes as well as for euryarchaeotes. Based on this genome architecture information, coorientation analyses reveal that in prokaryotes, ribosomal RNA (rRNA) genes encoding the small and large ribosomal subunits are all transcribed in the same direction as DNA replication; that is, they are located along the leading strand. This result offers a simple and reliable method for circumscribing the region containing the origin of the DNA replication and reveals a strong selective pressure acting on the orientation of rRNA genes similar to the weaker one acting on the orientation of ORFs. Rate of coorientation of transfer RNA (tRNA) genes with DNA replication appears to be taxon-specific. Analyzing nucleotide biases such as GC and TA skews of genes and plotting one against the other reveals a taxonomic clusterization of species. All ribosomal RNA genes are enriched in Gs and depleted in Cs, the only so far known exception being the rRNA genes of deuterostomian mitochondria. However, this exception can be explained by the fact that in the chromosome of the human mitochondrion, the model of the deuterostomian organelle genome, DNA replication, and rRNA transcription proceed in opposite directions. A general rule is deduced from prokaryotic and mitochondrial genomes: ribosomal RNA genes that are transcribed in the same direction as the DNA replication are enriched in Gs, and those transcribed in the opposite direction are depleted in Gs.
Resumo:
Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.
Resumo:
Background: Previous magnetic resonance imaging (MRI) studies in young patients with bipolar disorder indicated the presence of grey matter concentration changes as well as microstructural alterations in white matter in various neocortical areas and the corpus callosum. Whether these structural changes are also present in elderly patients with bipolar disorder with long-lasting clinical evolution remains unclear. Methods: We performed a prospective MRI study of consecutive elderly, euthymic patients with bipolar disorder and healthy, elderly controls. We conducted a voxel-based morphometry (VBM) analysis and a tract-based spatial statistics (TBSS) analysis to assess fractional anisotropy and longitudinal, radial and mean diffusivity derived by diffusion tensor imaging (DTI). Results: We included 19 patients with bipolar disorder and 47 controls in our study. Fractional anisotropy was the most sensitive DTI marker and decreased significantly in the ventral part of the corpus callosum in patients with bipolar disorder. Longitudinal, radial and mean diffusivity showed no significant between-group differences. Grey matter concentration was reduced in patients with bipolar disorder in the right anterior insula, head of the caudate nucleus, nucleus accumbens, ventral putamen and frontal orbital cortex. Conversely, there was no grey matter concentration or fractional anisotropy increase in any brain region in patients with bipolar disorder compared with controls. Limitations: The major limitation of our study is the small number of patients with bipolar disorder. Conclusion: Our data document the concomitant presence of grey matter concentration decreases in the anterior limbic areas and the reduced fibre tract coherence in the corpus callosum of elderly patients with long-lasting bipolar disorder.
Resumo:
The package HIERFSTAT for the statistical software R, created by the R Development Core Team, allows the estimate of hierarchical F-statistics from a hierarchy with any numbers of levels. In addition, it allows testing the statistical significance of population differentiation for these different levels, using a generalized likelihood-ratio test. The package HIERFSTAT is available at http://www.unil.ch/popgen/softwares/hierfstat.htm.