Research Article

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2016 Dec 19
Computational biology; Gene Regulatory Networks; Humans; Models, Genetic; Models, Statistical; Translational Medical Research

A salient problem in translational genomics is the use of gene regulatory networks to determine therapeutic intervention strategies. Theoretically, in a complete network, the optimal policy performs better than the suboptimal policy. However, this theory may not hold if we intervene in a system based on a control policy derived from imprecise inferred networks, especially in the small-sample ... more

X.Z. Zan; W.B. Liu; M.X. Hu; L.Z. Shen
2017 Mar 22
Crosses, Genetic; Edible Grain; Genotyping Techniques; Likelihood Functions; Models, Genetic; Plant breeding; Probability; Quantitative trait loci; Regression Analysis; Reproducibility of Results; Selection, Genetic; Zea mays

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise ... more

T. Olivoto; M. Nardino; I.R. Carvalho; D.N. Follmann; M. Ferrari; V.J. Szareski; A.J. de Pelegrin; V.Q. de Souza
2017 Mar 22
Animals; Bayes Theorem; Breeding; Genetic markers; Genomics; Genotype; Models, Genetic; Polymorphism, Single Nucleotide; Predictive Value of Tests; Quantitative trait loci; Regression Analysis; Selection, Genetic

Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic ... more

M. Nascimento; F.F.E. Silva; M.D.V. de Resende; C.D. Cruz; A.C.C. Nascimento; J.M.S. Viana; C.F. Azevedo; L.M.A. Barroso
2017 Mar 16
Eucalyptus; Genotype; Lignin; Models, Genetic; Phenotype; Plant breeding; Quantitative trait loci; Random Allocation; Selection, Genetic; Wood

Path analysis has been used for establishing selection criteria in genetic breeding programs for several crops. However, it has not been used in eucalyptus breeding programs yet. In the present study, we aimed to identify the wood technology traits that could be used as the criteria for direct and indirect selection of eucalyptus genotypes with high energy density of wood. Twenty-four ... more

A.M. Couto; P.E. Teodoro; P.F. Trugilho
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Arthritis, Rheumatoid; Gene expression profiling; Humans; Models, Genetic; Monte Carlo method; Signal transduction; Transcriptome

We attempted to identify significant pathway cross-talk in rheumatoid arthritis (RA) by the Monte Carlo cross-validation (MCCV) method. We therefore obtained and preprocessed the gene expression profile of RA. MCCV involves identifying differentially expressed genes (DEGs), identifying differential pathways (DPs), calculating the discriminating score (DS) of the pathway cross-talk, and random ... more

W. Song; Y.M. Zhang; T. Ma; J. Wang; K.Z. Wang
2017 Apr 13
Models, Genetic; Plant breeding; Quantitative Trait, Heritable; Sample Size; Zea mays

The objective of this study was to assess the sample size required for estimating the direct effects of explanatory variables on the grain yield of corn in the combinations formed by three types of hybrids x two harvests x nine scenarios of explanatory variables x two types of path analyses x four levels of accuracy. A total of 6340 plants were evaluated (361, 373, and 416 plants from single ... more

M. Toebe; C. Filho; L. Storck; A.D. Lúcio
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Brazil; Crops, Agricultural; Manihot; Models, Genetic; Quantitative Trait, Heritable

The aim of this study was to select morphoagronomic descriptors to characterize cassava accessions representative of Eastern Brazilian Amazonia. It was characterized 262 accessions using 21 qualitative descriptors. The multiple-correspondence analysis (MCA) technique was applied using the criteria: contribution of the descriptor in the last factorial axis of analysis in successive cycles (SMCA ... more

R.S. Silva; E.F. Moura; J.T. Farias-Neto; C.A.S. Ledo; J.E. Sampaio
2017 May 10
Epistasis, Genetic; Eucalyptus; Genotype; Models, Genetic; Quantitative Trait, Heritable

Understanding non-additive effects in the expression of quantitative traits is very important in genotype selection, especially in species where the commercial products are clones or hybrids. The use of molecular markers has allowed the study of non-additive genetic effects on a genomic level, in addition to a better understanding of its importance in quantitative traits. Thus, the purpose of ... more

I.C. Vieira; J.P.R.Dos Santos; L.P.M. Pires; B.M. Lima; F.M.A. Gonçalves; M. Balestre
2017 May 31
Genetic variation; Genotype; Models, Genetic; Plant breeding; Quantitative Trait, Heritable; Soybeans

The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for ... more

M.M. Oliveira; L.B. Sousa; M.C. Reis; E.G.Silva Junior; D.B.O. Cardoso; O.T. Hamawaki; A.P.O. Nogueira
2017 Jun 29
Hypocotyl; Models, Genetic; Neural Networks, Computer; Phenotype; Plant breeding; Quantitative Trait, Heritable; Soybeans

Classification using a scale of visual notes is a strategy used to select erect bean plants in order to improve bean plant architectures. Use of morphological traits associated with the phenotypic expression of bean architecture in classification procedures may enhance selection. The objective of this study was to evaluate the potential of artificial neural networks (ANNs) as auxiliary tools ... more

V.Q. Carneiro; G.N. Silva; C.D. Cruz; P.C.S. Carneiro; M. Nascimento; J.E.S. Carneiro

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