RESUMEN
In our previous research, we identified a QTL with an interval of 3.4 Mb for growth on chicken chromosome (GGA) 4 in an advanced intercross population of an initial cross between the New Hampshire inbred line (NHI) and the White Leghorn inbred line (WL77). In the current study, an association analysis was performed in a population of purebred white layers (WLA) with White Leghorn origin. Genotypic data of 130 SNPs within the previously identified 3.4-Mb region were obtained using a 60K SNP chip. In total, 24 significant SNPs (LOD ≥ 4.44) on GGA4 were detected for daily weigh gain from 8 to 14 weeks and two SNPs (LOD ≥ 4.80) for body weight at 14 weeks. The QTL interval was reduced by 1.9 Mb to an interval of 1.5 Mb (74.6-76.1 Mb) that harbors 15 genes. Furthermore, to identify additional loci for chicken growth, a genome-wide association study (GWAS) was carried out in a WLA population. The GWAS identified an additional QTL on GGA6 for body weight at six weeks (19.8-21.2 Mb). Our findings showed that by using a WLA population we were able to further reduce the QTL confidence interval previously detected using a NHI × WL77 advanced intercross population.
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Pollos/crecimiento & desarrollo , Pollos/genética , Sitios de Carácter Cuantitativo , Animales , Pollos/clasificación , Cromosomas , Estudio de Asociación del Genoma Completo , Polimorfismo de Nucleótido SimpleRESUMEN
A whole-genome scan for identifying selection acting on pairs of linked loci is proposed and implemented. The scan is based on , one of Ohta's 1982 measures of between-population linkage disequilibrium (LD). An approximate empirical null distribution for the statistic is suggested. Although the partitioning of LD into between-population components was originally used to investigate epistatic selection, we demonstrate that values of may also be influenced by single-locus selective sweeps with linkage but no epistasis. The proposed scan is implemented in a diverse panel of chickens including 72 distinct breeds genotyped at 538 298 single-nucleotide polymorphisms. In all, 1723 locus pairs are identified as putatively corresponding to a selective sweep or epistatic selection. These pairs of loci generally cluster to form overlapping or neighboring signals of selection. Known variants that were expected to have been under selection in the panel are identified, as well as an assortment of novel regions that have putatively been under selection in chickens. Notably, a promising pair of genes located 8 MB apart on chromosome 9 are identified based on as demonstrating strong evidence of dispersive epistatic selection between populations.
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Pollos/genética , Epistasis Genética , Genética de Población , Desequilibrio de Ligamiento , Selección Genética , Animales , Ligamiento Genético , Genotipo , Polimorfismo de Nucleótido SimpleRESUMEN
The aim of this study was to investigate the maternal genealogical pattern of chicken breeds sampled in Europe. Sequence polymorphisms of 1256 chickens of the hypervariable region (D-loop) of mitochondrial DNA (mtDNA) were used. Median-joining networks were constructed to establish evolutionary relationships among mtDNA haplotypes of chickens, which included a wide range of breeds with different origin and history. Chicken breeds which have had their roots in Europe for more than 3000 years were categorized by their founding regions, encompassing Mediterranean type, East European type and Northwest European type. Breeds which were introduced to Europe from Asia since the mid-19th century were classified as Asian type, and breeds based on crossbreeding between Asian breeds and European breeds were classified as Intermediate type. The last group, Game birds, included fighting birds from Asia. The classification of mtDNA haplotypes was based on Liu et al.'s (2006) nomenclature. Haplogroup E was the predominant clade among the European chicken breeds. The results showed, on average, the highest number of haplotypes, highest haplotype diversity, and highest nucleotide diversity for Asian type breeds, followed by Intermediate type chickens. East European and Northwest European breeds had lower haplotype and nucleotide diversity compared to Mediterranean, Intermediate, Game and Asian type breeds. Results of our study support earlier findings that chicken breeds sampled in Europe have their roots in the Indian subcontinent and East Asia. This is consistent with historical and archaeological evidence of chicken migration routes to Europe.
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Cruzamiento , Pollos/genética , Genética de Población , Polimorfismo Genético , Animales , ADN Mitocondrial/genética , Europa (Continente) , Evolución Molecular , Femenino , Haplotipos , Datos de Secuencia Molecular , Análisis de Secuencia de ADNRESUMEN
Genetic diversity and population structure of 113 chicken populations from Africa, Asia and Europe were studied using 29 microsatellite markers. Among these, three populations of wild chickens and nine commercial purebreds were used as reference populations for comparison. Compared to commercial lines and chickens sampled from the European region, high mean numbers of alleles and a high degree of heterozygosity were found in Asian and African chickens as well as in Red Junglefowl. Population differentiation (FST ) was higher among European breeds and commercial lines than among African, Asian and Red Junglefowl populations. Neighbour-Net genetic clustering and structure analysis revealed two main groups of Asian and north-west European breeds, whereas African populations overlap with other breeds from Eastern Europe and the Mediterranean region. Broilers and brown egg layers were situated between the Asian and north-west European clusters. structure analysis confirmed a lower degree of population stratification in African and Asian chickens than in European breeds. High genetic differentiation and low genetic contributions to global diversity have been observed for single European breeds. Populations with low genetic variability have also shown a low genetic contribution to a core set of diversity in attaining maximum genetic variation present from the total populations. This may indicate that conservation measures in Europe should pay special attention to preserving as many single chicken breeds as possible to maintain maximum genetic diversity given that higher genetic variations come from differentiation between breeds.
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Pollos/genética , Variación Genética , Población , África , Alelos , Animales , Asia , Cruzamiento , Pollos/clasificación , Análisis por Conglomerados , Europa (Continente) , Heterocigoto , Repeticiones de MicrosatéliteRESUMEN
Exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static splits, and shows potential pitfalls of ignoring variability across splits. Using a bootstrap or resampling method, we compare the uncertainty in the solution stemming from the data splitting with neural-network specific uncertainties (parameter initialization, choice of number of hidden units, etc.). We present two results on data from the New York Stock Exchange. First, the variation due to different resamplings is significantly larger than the variation due to different network conditions. This result implies that it is important to not over-interpret a model (or an ensemble of models) estimated on one specific split of the data. Second, on each split, the neural-network solution with early stopping is very close to a linear model; no significant nonlinearities are extracted.
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In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used: Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).
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Inteligencia Artificial , Modelos Económicos , Redes Neurales de la Computación , Modelos LinealesRESUMEN
While many trading strategies are based on price prediction, traders in financial markets are typically interested in optimizing risk-adjusted performance such as the Sharpe Ratio, rather than the price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can indeed be achieved with this nonlinear approach.
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Inteligencia Artificial , Modelos Económicos , Redes Neurales de la Computación , Dinámicas no Lineales , Algoritmos , Interpretación Estadística de DatosRESUMEN
This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).
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Inteligencia Artificial , Modelos Económicos , Redes Neurales de la Computación , AlgoritmosRESUMEN
In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes) and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e. on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space, only learning to model their region. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) a computer-generated series, obtained by randomly switching between two nonlinear processes; (2) a time series from the Santa Fe Time Series Competition (the light intensity of a laser in chaotic state); and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several time scales. The main results are: (1) the gating network correctly discovers the different regimes of the process; (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the sub-processes); and (3) there is less overfitting compared to single networks (homogeneous multilayer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data.
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Redes Neurales de la Computación , Dinámicas no Lineales , Computadores , Modelos TeóricosRESUMEN
Most traditional prediction techniques deliver a single point, usually the mean of a probability distribution. For multimodal processes, instead of predicting the mean, it is important to predict the full distribution. This article presents a new connectionist method to predict the conditional probability distribution in response to an input. The main idea is to transform the problem from a regression problem to a classification problem. The conditional probability distribution network can perform both direct predictions and iterated predictions, the latter task being specific for time series problems. We compare this new method to fuzzy logic and discuss important differences, and also demonstrate the architecture on two time series. The first is the benchmark laser series used in the Santa Fe competition, a deterministic chaotic system. The second is a time series from a Markov process which exhibits structure on two time scales. The network produces multimodal predictions for this series. We compare the predictions of the network with a nearest-neighbor predictor and find that the conditional probability network is more than twice as likely a model.
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Redes Neurales de la Computación , Teoría de la Probabilidad , Análisis de Regresión , Procesos EstocásticosRESUMEN
The calculation of second derivatives is required by recent training and analysis techniques of connectionist networks, such as the elimination of superfluous weights, and the estimation of confidence intervals both for weights and network outputs. We review and develop exact and approximate algorithms for calculating second derivatives. For networks with |w| weights, simply writing the full matrix of second derivatives requires O(|w|(2)) operations. For networks of radial basis units or sigmoid units, exact calculation of the necessary intermediate terms requires of the order of 2h+2 backward/forward-propagation passes where h is the number of hidden units in the network. We also review and compare three approximations (ignoring some components of the second derivative, numerical differentiation, and scoring). The algorithms apply to arbitrary activation functions, networks, and error functions.