959 resultados para Allele frequency data


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Myeloproliferative neoplasms (MPNs) are rare diseases that include classic entities; polycythaemia vera, essential thrombocythaemia and primary myelofibrosis. In this short report, minor allele frequencies of common MPN mutations are compared between the Irish blood donor population and other populations of European descent using data from the Haplotype Map project. The Affymetrix array 6.0 platform was utilised identifying nine single nucleotide polymorphisms (SNPs) and six proxy SNPs. The variability of allele frequencies for MPN mutations could account for the different incidence rates seen between populations of European ancestry, giving a better understanding of the genetic predisposition to MPNs. 

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ALFRED (the ALelle FREquency Database) is designed to store and disseminate frequencies of alleles at human polymorphic sites for multiple populations, primarily for the population genetics and molecular anthropology communities. Currently ALFRED has information on over 180 polymorphic sites for more than 70 populations. Since our initial release of the database we have focussed on increasing the quantity and quality of data, making reciprocal links between ALFRED and other related databases, and providing useful tools to make the data more comprehensible to the end user. ALFRED is accessible from the Kidd Lab home page (http://info.med.yale.edu/genetics/kkidd/) or from ALFRED directly (http://alfred.med.yale.edu/alfred/index.asp).

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The extended recruitment season for short-lived species such as prawns biases the estimation of growth parameters from length-frequency data when conventional methods are used. We propose a simple method for overcoming this bias given a time series of length-frequency data. The difficulties arising from extended recruitment are eliminated by predicting the growth of the succeeding samples and the length increments of the recruits in previous samples. This method requires that some maximum size at recruitment can be specified. The advantages of this multiple length-frequency method are: it is simple to use; it requires only three parameters; no specific distributions need to be assumed; and the actual seasonal recruitment pattern does not have to be specified. We illustrate the new method with length-frequency data on the tiger prawn Penaeus esculentus from the north-western Gulf of Carpentaria, Australia.

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We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock and derive the underlying length distribution of the population and the catch when there is individual variability in the von Bertalanffy growth parameter L-infinity. The model is flexible enough to accommodate 1) any recruitment pattern as a function of both time and length, 2) length-specific selectivity, and 3) varying fishing effort over time. The maximum likelihood method gives consistent estimates, provided the underlying distribution for individual variation in growth is correctly specified. Simulation results indicate that our method is reasonably robust to violations in the assumptions. The method is applied to tiger prawn data (Penaeus semisulcatus) to obtain estimates of natural and fishing mortality.

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We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock when there is individual variability in the von Bertalanffy growth parameter L-infinity and investigate the possible bias in the estimates when the individual variability is ignored. Three methods are examined: (i) the regression method based on the Beverton and Holt's (1956, Rapp. P.V. Reun. Cons. Int. Explor. Mer, 140: 67-83) equation; (ii) the moment method of Powell (1979, Rapp. PV. Reun. Int. Explor. Mer, 175: 167-169); and (iii) a generalization of Powell's method that estimates the individual variability to be incorporated into the estimation. It is found that the biases in the estimates from the existing methods are, in general, substantial, even when individual variability in growth is small and recruitment is uniform, and the generalized method performs better in terms of bias but is subject to a larger variation. There is a need to develop robust and flexible methods to deal with individual variability in the analysis of length-frequency data.

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ENGLISH: The spawning of Pacific northern bluefin tuna, Thunnus thynnus, takes place only in the western Pacific Ocean (WPO), but substantial numbers of the juveniles migrate to the eastern Pacific Ocean (EPO), where they remain for several months, or longer, and the.n return to the WPO. Lengthfrequency and tagging data show that many bluefin arrive in the EPO as 1-and 2-year olds, and remain there for one or two fishing seasons before returning to the WPO. The proportion of the fish which make the west-to-east migration varies among years. The numbers of 1-, 2-, 3-, 4, and >4 –year olds in the catches of the EPO are estimated for most years of the 1952-1991 period. SPANISH: EI desove del atun aleta azul del norte del Pacifico, Thunnus thynnus, ocurre solamente en el Océano Pacifico occidental (WPO), pero números substanciales de los juveniles migran al Océano Pacifico oriental (OPO), donde permanecen unos meses, 0 mas, antes de regresar al WPO. Datos de marcado y frecuencia de talla indican que muchos aletas azules llegan al OPO a 1 o 2 anos de edad, y permanecen alIi una 0 dos temporadas de pesca antes de regresar al WPO. La proporcion de los peces que migra del oeste al este varia entre anos. Se estima el numero de peces de 1, 2, 3, 4, Y>4 anos de edad en las capturas del OPO para la mayoria de los anos del periodo de 1952-1991. (PDF contains 40 pages.)

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For most fisheries applications, the shape of a length-frequency distribution is much more important than its mean length or variance. This makes it difficult to evaluate at which point a sample size is adequate. By estimating the coefficient of variation of the counts in each length class and taking a weighted mean of these, a measure of precision was obtained that takes the precision in all length classes into account. The precision estimates were closely associated with the ratio of the sample size to the number of size classes in each sample. As a rule-of-thumb, a minimum sample size of 10 times the number of length classes in the sample is suggested because the precision deteriorates rapidly for smaller sample sizes. In absence of such a rule-of-thumb, samplers have previously under-estimated the required sample size for samples with large fish, while over-sampling small fish of the same species.

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A method is presented through which the total mortality undergone by several fish stocks of the same species can be compared when growth parameters are poorly known or unknown. Whereas the estimate of Z obtained via the length-converted catch curve is highly sensitive to the input parameters K and L sub( infinity ), the ratio of Z estimates obtained for different stocks with the same combination of parameters is almost independent of these inputs, at least when the fit of the linear regression is good. The method is tested on simulated data and an application is presented using real data from the Lesser Antilles. It provides the possibility of qualitatively comparing several stocks in situations of scarce biological knowledge.

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We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock and derive the underlying length distribution of the population and the catch when there is individual variability in the von Bertalanffy growth parameter L∞. The model is flexible enough to accommodate 1) any recruitment pattern as a function of both time and length, 2) length-specific selectivity, and 3) varying fishing effort over time. The maximum likelihood method gives consistent estimates, provided the underlying distribution for individual variation in growth is correctly specified. Simulation results indicate that our method is reasonably robust to violations in the assumptions. The method is applied to tiger prawn data (Penaeus semisulcatus) to obtain estimates of natural and fishing mortality.

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We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.