StraplineThe National Center for Biotechnology Information has created the dbGaP public repository for individual-level phenotype, exposure, genotype, and sequence data, and the associations between them. dbGaP assigns stable, unique identifiers to studies and subsets of information from those studies, including documents, individual phenotypic variables, tables of trait data, sets of genotype data, computed phenotype-genotype associations and groups of study subjects who have given similar consents for use of their data. IntroductionThe technical advances and declining costs for high-throughput genotyping afford investigators fresh opportunities to do increasingly complex analyses of genetic associations with phenotypic and disease characteristics. The leading candidates for such genome wide association studies (GWAS) are existing large-scale cohort and clinical studies that collected rich sets of phenotype data. To support investigator access to data from these initiatives at the National Institutes of Health (NIH) and elsewhere, the National Center for Biotechnology Information (NCBI) has created a database of Genotypes and Phenotypes (dbGaP) with stable identifiers that make it possible for published studies to discuss or cite the primary data in a specific and uniform way. dbGaP provides unprecedented access to the large-scale genetic and phenotypic datasets required for GWAS designs, including public access to study documents linked to summary data on specific phenotype variables, statistical overviews of the genetic information, position of published associations on the genome, and authorized access to individual-level data.The purposes of this description of dbGaP are three-fold: (1) to describe dbGaP's functionality for users and submitters; (2) to describe dbGaP's design and operational processes for database methodologists to emulate or improve upon; and (3) to reassure the lay and scientific public that individual-level phenotype and genotype data are securely and responsibly managed. dbGaP accommodates studies of varying design. It contains four basic types of data: (1) Study documentation, including study descriptions, protocol documents, and data collection instruments, such as questionnaires; (2) Phenotypic data for each variable assessed, at both an individual level and in summary form; (3) Genetic data, including study subjects' individual genotypes, pedigree information, fine mapping results, and resequencing traces; and (4) Statistical results, including association and linkage analyses, when available.Address editorial correspondence to: Stephen Sherry, PhD, National Center for Biotechnology Information, 8600 Rockville Pike, MSC 3804, Bethesda, MD 20894-3804, phone: 301-435-7799, fax: 301-480-5789, e-mail: sherry@ncbi.nlm To protect the confidentiality of study subjects, dbGaP accepts only de-identified data and requires investigators to go through an authorization process in order to access individual-level phenotype and genotype datasets. Summary phenotype and genotype data, as well as stu...
Yenisei Siberia lies within the administrative borders of the Krasnoyarsk Territory, and the republics of Khakassia and Tyva; this helps to link forest inventory standards to specific forest areas. Significant variation in environmental conditions across the Yenisei Siberia is caused by the zonal affiliation of its different parts; they fall into one of the following zones: the near-tundra, taiga, forest-steppe, northern and southern mountain ranges. The variety of environmental conditions has resulted in a variety of forest stands that not only supply timber, but also provide environmental services. Despite the almost century-long history of the existence of standards for the valuation of forest resources in Yenisei Siberia, they cannot be considered as complete and perfect.
The study of the structure was based on a series of distribution by the steps of the thickness. Measurements were carried out in forest shelter belts of the southern part of Central Siberia. Plots were laid on the territory of the Republic of Khakassia (Ust- Abakan, Bay, Shirinsky, Shushensky districts), Krasnoyarsk territory (Minusinsk district) and the Republic of Tuva (Kyzylskiy district). Approximation of distribution rows by diameter has been implemented by two functions: normal distribution and Weibull distribution. With respect to protective forest belts for most forest stands, structure by diameter is more adequately approximated by normal distribution, indicating the symmetry rows. But at this, function of Weibull is characterized by high correlation of its ratios with parameters of plantations, in particular with average diameter and normalized variability. Based on the study of the structure of the plantings in diameter, the optimal agro-technical indicators for the cultivation of plantations presented a variety of wood species, were revealed. It is recommended to divide rows into two groups, before forecasting the series. The first category reflects the influence of the internal (growth) processes: growth, mortality, movement of trees on the steps. These rows can be predicted taking into account the average diameter of tree stand and theoretical density (square of nutrition of individual tree). The second group of rows was formed under the influence of external factors (supplement, renewal, felling, and forest fires).These rows are forecasted using the average diameter and standard deviation of trees. For the prediction of the structure of the stands of Siberian larch, silver birch, Siberian elm and black poplar, model based on the Weibull function, were obtained.
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