Wagner Arbex

SNPs selection using support vector regression and genetic algorithms in GWAS

IntroductionThis paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence.ResultsThe suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS.ConclusionsThe method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.

PL-Science: A Scientific Software Product Line☆

Abstract A way to improve reusability and maintainability of a family of software products is through the use of Software Product Line (SPL) approach. Software families, also named SPLs, are a set of software intensive systems sharing a common set of features which are managed to satisfy specific needs of a particular market segment or mission and that are developed from a common set of core assets in a prescribed way. This paper presents the PL-Science approach that considers the context of SPL and aims to assist scientists to define a scientific experiment, specifying a workflow that encompasses scientific applications of a given experiment. Using SPL concepts, scientists can reuse models that specify the scientific product line, and carefully can make decisions according to their needs. In the context of this paper, Scientific Software Product Lines (SSPL) differs from the Software Product Lines (SPL) due to the fact that SSPL uses an abstract scientific workflow model. This workflow is defined according to a scientific domain and, using this abstract workflow model, the products (scientific applications/algorithms) will be instantiated. This paper also focuses on the use of ontologies to facilitate the process of applying Software Product Line (SPL) to scientific domains. Through the use of ontology as a domain model, we can provide additional information as well as add more semantics in the context of Scientific Software Product Lines (SSPL).

Whole genome sequencing of Guzera´ cattle reveals genetic variants in candidate genes for production, disease resistance, and heat tolerance

In bovines, artificial selection has produced a large number of breeds which differ in production, environmental adaptation, and health characteristics. To investigate the genetic basis of these phenotypical differences, several bovine breeds have been sequenced. Millions of new SNVs were described at every new breed sequenced, suggesting that every breed should be sequenced. Guzerat or Guzerá is an indicine breed resistant to drought and parasites that has been the base for some important breeds such as Brahman. Here, we describe the sequence of the Guzerá genome and the in silico functional analyses of intragenic breed-specific variations. Mate-paired libraries were generated using the ABI SOLiD system. Sequences were mapped to the Bos taurus reference genome (UMD 3.1) and 87% of the reference genome was covered at a 26X. Among the variants identified, 2,676,067 SNVs and 463,158 INDELs were homozygous, not found in any database searched, and may represent true differences between Guzerá and B. taurus. Functional analyses investigated with the NGS-SNP package focused on 1069 new, non-synonymous SNVs, splice-site variants (including acceptor and donor sites, and the conserved regions at both intron borders, referred to here as splice regions) and coding INDELs (NS/SS/I). These NS/SS/I map to 935 genes belonging to cell communication, environmental adaptation, signal transduction, sensory, and immune systems pathways. These pathways have been involved in phenotypes related to health, adaptation to the environment and behavior, and particularly, disease resistance and heat tolerance. Indeed, 105 of these genes are known QTLs for milk, meat and carcass, production, reproduction, and health traits. Therefore, in addition to describing new genetic variants, our approach provided groundwork for unraveling key candidate genes and mutations.

Genetic Parameters for Milk Yield and Lactation Persistency Using Random Regression Models in Girolando Cattle

A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre’s polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre’s polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from −0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from −0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits.

Modelos aleatórios na estimação da localização de QTLs em famílias de meios-irmãos

An interval mapping procedure based on the random model approach was applied to investigate its robustness and properties for QTL mapping in populations with prevailing half-sib structures. Under random model, QTL location and variance components were estimated using maximum likelihood procedures. The estimation of parameters was based on sib-pair approach. The proportions of genes identical-by-descent (IBD) at the two QTLs were estimated from the IBD at two flanking marker loci. Estimates for QTLs parameters (locations and variance components) and power of detection, in a trait with h2 = 0.25, were obtained using simulated data, varying the number of families, number of half-sibs by families, proportion of QTL variance and QTLs positions. These QTLs were positioned at the same interval, at adjacent intervals and at no adjacent intervals. The most important factors influencing the estimates of QTLs parameters and power were the proportion of variance due to QTLs the number and size of the half-sibs families. The most biased variance components estimations were obtained when sample size was ten. Given a sufficient number of families and high proportions of genetic variance due to QTLs, the random model approach can detect a QTLs with high power and provides accurate estimates of the QTLs position if two QTLs are not present in the same interval. For fine QTL mapping and proper estimation of QTLs variance, more sophisticated methods are required.

A Distributed Infrastructure to Support Scientific Experiments

Abstract[Context] Significant changes are occurring in the scientific scenario. In particular, there is increasing collaboration among researchers, which has led to the increasing use of processing techniques and the need to share results and observations. Researchers usually need to compose experiments using available services to fulfill their requirements. However, finding and/or specifying scientific applications is not a trivial task and many scientists lack the computational knowledge necessary to compose their experiments. [Objective] Therefore, the objective of this work is to develop an infrastructure in which researchers can work with heterogeneous information, accessing scientific communities according to their interests. As a result, they can create new experiments with the help of compositions of semantic web services. [Methods] Considering the infrastructure model, a prototype was specified and evaluated in a real-world context. [Results] This evaluation found evidence for the feasibility of the use of this infrastructure in scientific scenarios. Therefore, this paper proposes a distributed platform where scientists can collaborate in order to specify an experiment and share its results, considering semantic information. This is in agreement with the ideas of “collaboratories”.

Sistema Fuzzy Aplicado À Bioinformática Na Tomada De Decisão Para Identificação De SNPs

Research involving the discovery of single nucleotide polymorphisms (SNPs) requires bioinformatics tools to be applied to different cases, with the ability to analyze “reads” from different sources, levels of coverage and to establish reliable measures. These tools work with different methodologies on different attributes, however, it is expected similar results, even when dealing with a same data set, but it’s not unusual to provide different results, which leads to uncertainty in decision making, when the results are discordant. This papes shows a fuzzy inference system that implements a fuzzy inference model decision support applied to bioinformatics, specifically, in the identification of SNPs, based on results from two other SNPs discovery tools

On the robustness of SNPs filtering using computational intelligence

This work uses a filter based on neural networks to verify the mismatches in two Arabidopsis thaliana germplasm. Aiming to demonstrate the robustness and adaptability of the filter it will be applied in a reuse model context. The neural network filter previously defined and performed using the genome of an animal of the species Bos Taurus is used maintaining the main parameterization pre-defined to identify the SNPs on the mismatches detected in the reassembled germplasm. The experiments with the adapted filter in the new genome indicate that the quality and level of SNPs detection are preserved despite of the lack of a training process for this specific data.

Decision support in attribute selection with machine learning approach

This paper proposes a method to simultaneously select the most relevant single nucleotide polymorphisms (SNPs) markers - the attributes - for the characterization of any measurable phenotype described by a continuous variable using support vector regression (SVR) with Pearson VII Universal Kernel (PUK). The proposed study is multiattribute towards considering several markers simultaneously to explain the phenotype and is based jointly on a statistical tools, machine learning and computational intelligence.

Mapeamento de QTL em famílias de irmãos completos por meio de modelos aleatórios

A study was carried out by simulation to evaluate the efficiency and robustness of the random model approach for estimation of the QTL location and variance components in an outbred population with full-sib family structure. Two QTL were positioned in the chromosome in the same interval, in adjacent and at no adjacent intervals. The population was created with different sizes and numbers of families and variances due to QTL in a trait with h2 = 0.25. The estimations of QTL parameters (locations and variance components) were based on the sib-pair approach. The proportions of genes identical-by-descent (IBD) at the two QTL were estimated from the IBD at two flanking markers. The most important factors afeccting the estimates of QTL parameters and power of detection were the proportion of variance due to QTL, and the number and size of the full-sibs families. Given a sufficient number of families and high proportions of genetic variance due to QTL, the random model approach can detect a QTL with high power and provides accurate estimates of the QTL position if there are no two QTL in the same interval.