Author: Juan Burgueño

Data on a genome-wide association study of type 2 diabetes in a Maya population

Juan Burgueño (2020)

Maya communities have been shown to exhibit type 2 diabetes (T2D) with high prevalence compared with Mexican mestizo populations. Furthermore, some variants associated with the risk for T2D have been described. In this study, we describe the results of a pilot genome wide association study (GWAS) using 817,823 single nucleotide polymorphisms (SNPs) to identify candidate variants for replication in future studies. Herein, we present the GWAS study data, which were divided into three parts: first, 1289 ancestry informative markers (AIMs) were selected for Latino populations containing European, African, and Native American SNPs obtained from the literature; second, a GWAS hypothesis free to select candidate genes associated with T2D was performed, which identified 24 candidate genes; and third, 39 SNPs previously associated with T2D or related traits were replicated. This article is associated with the original article published in “Gene” under the title “Pilot genome-wide association study identifying novel risk loci for type 2 diabetes in a Maya population”.

Article

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INDIGENOUS PEOPLES GENETIC MARKERS ANCESTRY DIABETES

An informational view of accession rarity and allele specificity in germplasm banks for management and conservation

Juan Burgueño Carolina Sansaloni (2018)

Germplasm banks are growing in their importance, number of accessions and amount of characterization data, with a large emphasis on molecular genetic markers. In this work, we offer an integrated view of accessions and marker data in an information theory framework. The basis of this development is the mutual information between accessions and allele frequencies for molecular marker loci, which can be decomposed in allele specificities, as well as in rarity and divergence of accessions. In this way, formulas are provided to calculate the specificity of the different marker alleles with reference to their distribution across accessions, accession rarity, defined as the weighted average of the specificity of its alleles, and divergence, defined by the Kullback-Leibler formula. Albeit being different measures, it is demonstrated that average rarity and divergence are equal for any collection. These parameters can contribute to the knowledge of the structure of a germplasm collection and to make decisions about the preservation of rare variants. The concepts herein developed served as the basis for a strategy for core subset selection called HCore, implemented in a publicly available R script. As a proof of concept, the mathematical view and tools developed in this research were applied to a large collection of Mexican wheat accessions, widely characterized by SNP markers. The most specific alleles were found to be private of a single accession, and the distribution of this parameter had its highest frequencies at low levels of specificity. Accession rarity and divergence had largely symmetrical distributions, and had a positive, albeit non-strictly linear relationship. Comparison of the HCore approach for core subset selection, with three state-of-the-art methods, showed it to be superior for average divergence and rarity, mean genetic distance and diversity. The proposed approach can be used for knowledge extraction and decision making in germplasm collections of diploid, inbred or outbred species.

Article

Alleles Germplasm banks Germplasm conservation Conservation Genetics Shannon Index Information Theory AGRICULTURAL SCIENCES AND BIOTECHNOLOGY WHEAT CLIMATE CHANGE ALLELES GERMPLASM BANKS PLANT BREEDING PLANT GENETICS GERMPLASM CONSERVATION CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Hermetic storage technologies reduce maize pest damage in smallholder farming systems in Mexico

Sylvanus Odjo Juan Burgueño Nele Verhulst (2020)

In Mexico, smallholder farmers use a variety of technologies to store their maize grain for several months, which may result in high losses in quantity and quality of grain. This work compared the effectiveness of different storage technologies for minimizing losses in smallholder conditions in 109 different locations from 21 to 2816 m above sea level (asl) across different agroecological zones of Mexico, under “controlled” (i.e. managed by researchers), and “non-controlled” conditions (i.e. on-farm managed by extension agents). Depending on the common practice at each site, conventional storage technologies (polypropylene bag with and/or without insecticide) were compared to alternative storage technologies (selected from hermetic metal silos, hermetic bags, recycled plastic containers, silage plastic bags, and inert dusts-micronized and standard lime) during one to 12 months. Data on grain damages were collected at the beginning and end of the storage period. Climatic variables and initial grain infestation with pests influenced the ability of a technology to minimize losses, particularly under tropical conditions. After six months of storage, percentages of insect-damaged grain with polypropylene bags, the most common farmers’ practice, were 39.4% and 4.1%, respectively, in lowlands (<500 m asl) and highlands (>2000 m asl). With hermetic metal silos, percentages of insect-damaged grain after six months of storage were 3.8% on average in the highlands and similar in lowlands, with 2.9%. Hermetic technologies, which prevent the introduction of oxygen, were effective in reducing losses under farmers’ conditions across agroecological areas, regardless of storage time. Recycled hermetic containers had similar results and were a viable low-cost alternative to more expensive options like hermetic metal silos. With adequate technical support for their appropriate use, hermetic technologies have the potential to reduce grain losses during storage and strengthen food security in Mexico and Latin American countries with similar conditions.

Article

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE POSTHARVEST LOSSES STORAGE SMALLHOLDERS

Modelación de la interacción genotipo x ambiente en rendimiento de hibridos de maiz blanco en ambientes múltiples

Amalio Santacruz-Varela Jose Crossa Juan Burgueño (2015)

Plant breeding programs aimed at obtaining genotypes with high grain yield and stable in a wide range of environmental conditions face environmental factors that mask potential genotypes. The genotype x environment interaction (g × e) might cause the suitability of predicted genotypes to a particular environment to be inaccurate. This study modelled the g × e interaction using different statistical models in a group of hybrids of maize (zea mays l.) evaluated in tropical environments. Twenty-nine white-endosperm hybrids were evaluated in 15 environments of tropical america, with an alpha-lattice design. Grain yield was first analyzed with a combined analysis of variance. Subsequently, the additive main effect and multiplicative interaction (ammi) and the site regression (sreg) with analytic factors (fa) model were applied to study and model g × e and to define environments that best discriminate genotypes and allow the grouping of environments and genotypes. The ammi method pointed out a locality from guatemala, one from méxico and one from nicaragua as the ones with highest g × e; generated four mega-environments; and defined the most stable and good-yielding hybrid. The sreg fa method proved a good predictor since it allowed the identification of four subgroups and grouped environments of different countries with similar features.

Article

Zea Mays G×A Ammi SREG Fa CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

A DNA Microarray-based assay to detect dual infection with two dengue virus serotypes

Juan Burgueño Julio G Mendoza-Alvarez (2014)

Here; we have described and tested a microarray based-method for the screening of dengue virus (denv) serotypes. This dna microarray assay is specific and sensitive and can detect dual infections with two dengue virus serotypes and single-serotype infections. Other methodologies may underestimate samples containing more than one serotype. This technology can be used to discriminate between the four denv serotypes. Single-stranded dna targets were covalently attached to glass slides and hybridised with specific labelled probes. Denv isolates and dengue samples were used to evaluate microarray performance. Our results demonstrate that the probes hybridized specifically to denv serotypes; with no detection of unspecific signals. This finding provides evidence that specific probes can effectively identify single and double infections in denv samples

Article

Dengue Virus Humans Aedes Microarrays CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Identification of in vivo induced maternal haploids in maize using seedling traits

Vijay Chaikam Juan Burgueño Prasanna Boddupalli (2017)

In vivo haploid induction in high frequency followed by efficient identification of haploids are important components of deriving completely homozygous doubled haploid (DH) lines in maize. Several genetic marker systems were proposed and/or used for identification of in vivo maternal haploids in maize, such as R1-nj (Navajo), high oil, red root and transgenic markers. In this study, we propose a new method of haploid/diploid identification based on natural differences in seedling traits of haploids and diploids, which can be used in any induction cross independently of the genetic marker systems. Using confirmed haploids and diploids from five different populations, the study established that haploid and diploid seedlings exhibit significant differences for seedling traits, particularly radicle length (RL), coleoptile length (CL), and number of lateral seminal roots (NLSR). In six populations that exhibited complete inhibition of the commonly used R1-nj (Navajo) marker, we could effectively differentiate haploids from diploids by visual inspection of the seedling traits. In the haploid seed fraction identified based on R1-nj marker in ten populations, false positives were reduced several-fold by early identification of haploids at seedling stage using the seedling traits. We propose that seedling traits may be integrated at the haploid identification stage, especially in populations that are not amenable to use of genetic markers, and for improving the efficiency of DH line production by reducing the false positives.

Article

Maize Haploidy Seedlings CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Sparse designs for genomic selection using multi-environment data

Yoseph Beyene Juan Burgueño Jose Crossa (2020)

This research study the genomic-enabled prediction accuracy of the composition of the following sparse testing allocation design: (1) all non-overlapping (0 overlapping) lines in environments, (2) all overlapping (0 non-overlapping) lines tested in all the environments, and (3) combinations of the two previous cases where certain numbers of non-overlapping (NO)/overlapping (O) lines were distributed in the environments. We also studied cases where the size of the testing population was decreased. The study used two large maize data sets (T1 and T2). Four different genomic-enabled prediction models were studied, two models (M1 and M2) that do not include the genomic × environment interaction (GE), whereas models M3 and M4 incorporate two forms of modeling GE. The results show that genome-based models including GE (M3 and M4) captured more genetic variability with the GE component than the other models for both data sets. Also, models M3 and M4 provide higher prediction accuracy than models M1 and M2 for the different allocation designs comprising different combinations of NO/O lines in environments. Results indicate that substantial savings of testing resources can be achieved by optimizing the allocation design using genome-based models including genomic × environment interaction.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Sashaydiall: A SAS program for hayman’s diallel analysis

Dan Makumbi Gregorio Alvarado Jose Crossa Juan Burgueño (2018)

Different methods of diallel crossing are commonly used in plant breeding. The diallel cross analysis method proposed by Hayman is particularly useful because it provides information, among others, on additive and dominance effects of genes, average degree of dominance, proportion of dominance, direction of dominance, distribution of genes, maternal and reciprocal effects, number of groups of genes that control a trait and exhibit dominance, ratio of dominant to recessive alleles in all the parents, and broad-sense and narrow-sense heritability. In this paper, we fully describe a SAS-based software SASHAYDIALL for performing a complete diallel cross analysis based on Hayman’s model with or without reciprocals. We demonstrate the use of SASHAYDIALL with two data sets; one is a published diallel cross data set with reciprocals in cabbage (Brassica oleracea L.), and the second is a data set from a multilocation diallel cross trial in maize (Zea mays L.) without reciprocals. With SASHAYDIALL, diallel experiments conducted in single sites can be analyzed to estimate various genetic parameters, and this analysis is extended over locations or environments to assess genetic effect × environment interaction. SASHAYDIALL is user-friendly software that provides detailed genetic information from diallel crosses involving any number of parents and locations.

Article

Diallel analysis Genotypes Plant breeding Diallel Crossing SAS Statistical Software DIALLEL ANALYSIS PLANT BREEDING CABBAGES MAIZE STATISTICAL METHODS CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years

DIEGO JARQUIN Sergio Pérez-Elizalde Juan Burgueño Jose Crossa (2016)

In agriculture and plant breeding, multienvironment trials over multiple years are conducted to evaluate and predict genotypic performance under different environmental conditions and to analyze, study, and interpret genotype´ environment interaction (g x e). In this study, we propose a hierarchical bayesian formulation of a linear–bilinear model, where the conditional conjugate prior for the bilinear (multiplicative) g x e term is the matrix von mises–fisher (mvmf) distribution (with environments and sites defined as synonymous). A hierarchical normal structure is assumed for linear effects of sites, and priors for precision parameters are assumed to follow gamma distributions. Bivariate highest posterior density (hpd) regions for the posterior multiplicative components of the interaction are shown within the usual biplots. Simulated and real maize (zea mays l.) breeding multisite data sets were analyzed. Results showed that the proposed model facilitates identifying groups of genotypes and sites that cause g ´ e across years and within years, since the hierarchical bayesian structure allows using plant breeding data from different years by borrowing information among them. This model offers the researcher valuable information about g x e patterns not only for each 1-yr period of the breeding trials but also for the general process that originates the response across these periods.

Article

AGRICULTURE ZEA MAYS BREEDING CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA