Biology subjects

Viana, J. M. S.

Publications and source records attributed to Viana, J. M. S..

10 recordsLinked to original sources

bioRxiv2025

The impact of epistasis on the genomic prediction of non-assessed and non-overlapping single crosses in multi-environment trials

Because no previous simulation-based study assessed the influence of epistasis on the genomic prediction of untested single crosses (SCs), the objective was to assess the impact of epistasis on the genomic prediction of untested SCs in multi-environment trials (METs), assuming seven types of digenic epistasis. We genotyped two groups of 70 doubled-haploid lines and phenotyped all 4900 SCs in five environments. The average density for SNPs was 0.03 cM. Regarding the distribution of the SCs over environments, we adopted 10 and 30% of tested SCs and 80% of non-overlapping SCs. To assess the efficacy of the genomic prediction we computed coincidence index (CI) and prediction accuracy. The percentage of the genotypic variance due to epistasis ranged from 18 to 48%. The CI ranged from 0.24 to 0.38, under the lower training set, and between 0.30 to 0.50, assuming the higher training set. Fixing complementary epistasis and increasing the ratio epistatic variance/genotypic variance from 18 to 39% led to a decrease in the coincidence index in the range from 6.5 to 22.0%. Accuracy showed a positive correlation with CI. Assuming epistasis but fitting the additive-dominance model led to a decrease in the CI. Epistasis can significantly affect the genomic prediction of SCs in METs, depending on type, proportion of the genotypic variance due to epistasis. The prediction accuracy efficiently expresses the efficacy of genomic prediction of untested SCs. The genomic prediction of untested single crosses in each environment and across environments is very effective, even under a low training set size.

genetics↗

bioRxiv2023

A data-driven approach for enhancing forest productivity by accounting for indirect genetic effects

Maintaining the past decades current genetic gains for tree species is a challenging task for foresters and tree breeders due to biotic and abiotic factors. Planting a mixture of genotypes or clonal composites can be an alternative to increase the phytosanitary security and yield of forest plantations. These clonal composites are more complex than monocultures due to inter-genotypic competition and indirect genetic effects that can affect the total heritable variation. This study aims to understand how indirect genetic effects can impact the response to selection and how the stand composition can be used to explore these effects and enhance forest yield. We used a clonally trial of Eucalyptus urophylla x Eucalyptus grandis hybrids in a randomized complete block design with 24 replications, containing a single tree per plot evaluated for mean annual increment at 3 and 6 years. We focus on partitioning the genetic variation of trees into direct and indirect genetic effects based on competition intensity factors. We identified clones as aggressive, homeostatic, and sensitive based on the magnitude of indirect genetic effects. By accounting for indirect genetic effects, for mean annual increment, the total heritability decreased 39 and 44% for 3 and 6 years, respectively. We proposed a workflow that uses the direct and indirect genetic effect to predict the mean value of clonal composite combinations and to select the one with highest yield. Our methodology accounted for spatial variability and interplot competition that can contribute to the total heritable variance and response to selection in forest trials. Based on the models evaluated, the clones are easily classified according to their deviation from the indirect genetic effects mean. Also, we extract useful information to predict different clonal composites compositions, their expected average performance, and define the best recommended combination to be planted in large scale.

genetics↗

bioRxiv2023

Characterization of popcorn temperate and tropical populations and GWAS for zeins and starch contents

Because measuring expansion volume (EV) is simple and inexpensive, popcorn breeders have developed high-quality single crosses ignoring the contents of zeins, starch, lipids, and cellular wall components in selection. However, some methods of quantification of these quality-related traits can be applied to popcorn breeding, increasing the selection efficacy for quality. The objectives of this study were assess methods of quantification of zeins and starch that can be used in popcorn breeding, characterize a temperate and a tropical populations for zeins and starch contents, and identify candidate genes for these quality-related traits. We genotyped and phenotyped 286 plants. For quantification of total zeins and zein subunits we choose the lab-on-a-chip microfluidic electrophoresis. For quantification of starch and amylose/amylopectin we choose the Megazymes Amylose/Amylopectin kit assay. The temperate population has superior EV (+36%), a higher level (+32%) of the 19 kDa zein subunit, and lower levels of the 21, 22, and 27 kDa subunits (-1543, -40 and -47%, respectively). Although there are statistical differences between the two populations regarding starch, amylose, and amylose/amylopectin ratio, the differences are not significant (-2 to 8%). Six candidate genes were identified for the 19 and 22 kDa zeins, one for the 21 kDa zein, one for total zeins, two for starch, and four for amylose, with emphasis on three genes from the Z1C subfamily, coding for the 19 and 22 kDa alpha-zeins, located on chromosome 4. The evaluated quantification methods can be used in popcorn breeding programs, with the potential to revolutionize the breeding for quality. Key messageThe protein chip and the Amylose/Amylopectin kit assay for zein and starch quantification, respectively, can be effectively used in popcorn breeding, with the potential to revolutionize the breeding for quality.

genetics↗

bioRxiv2023

Popcorn haploids identified by Navajo phenotype and ploidy level

For popcorn, obtaining and identifying haploids are still challenging steps. We aimed to induce and screen haploids in popcorn using the Navajo phenotype, seedling vigor and ploidy level. We used the Krasnodar Haploid Inducer (KHI) in crosses with 20 popcorn source germplasms and five maize controls. The field trial design was completely randomized, with three replications. We assessed the efficacy of induction and of identification of haploids based on haploidy induction rate (HIR) and false positive and negative rates (FPR and FNR). Additionally, we also measured the penetrance of the Navajo marker gene (R1-nj). All putative haploids classified by R1-nj were germinated together with a diploid sample and evaluated for false positives and negatives based on vigor. Seedlings from 14 females were submitted to flow cytometry to determine the ploidy level. The HIR and penetrance were analyzed by fitting a generalized linear model with a logit link function. The HIR of the KHI, adjusted by cytometry, ranged from 0.0 to 1.2%, with a mean of 0.34%. The average FPR from screening based on the Navajo phenotype were 26.2% and 76.4%, by the vigor and ploidy, respectively. The FNR was zero. The penetrance of R1-nj ranged from 30.8 to 98.6%. The average number of seeds per ear in temperate germplasm (76) was lower than that obtained in tropical germplasm (98). There is induction of haploids in germplasm of tropical and temperate origin. We recommend the selection of haploids associating the Navajo phenotype with a direct method of confirming the ploidy level, such as flow cytometry. We also show that haploid screening based on Navajo phenotype and seedling vigor reduces misclassification. The origin and genetic background of the source germplasm influence the R1-nj penetrance. Because the known inducers are maize, developing doubled-haploid technology for popcorn hybrid breeding requires overcoming the unilateral cross-incompatibility.

genetics↗

bioRxiv2021

The impact of epistasis in the heterosis and combining ability analyses

The current theoretical knowledge concerning the influence of epistasis on heterosis is based on simplified multiplicative model. The objective of this study was to assess the impact of epistasis in the heterosis and combining ability analyses, assuming additive model, hundreds of genes, linkage disequilibrium (LD), dominance, and seven types of digenic epistasis. We developed the quantitative genetics theory for supporting the simulation of the individual genotypic values in nine populations, the selfed populations, the 36 interpopulation crosses, 180 doubled haploids (DHs) and their 16,110 crosses, assuming 400 genes in 10 chromosomes of 200 cM. Epistasis only affects population heterosis if there is LD. Only additive x additive and dominance x dominance epistasis can affect the components of the heterosis and combining ability analyses of populations. Both analyses can lead to completely wrong inferences regarding the identification of the superior populations, the populations with greater differences of gene frequencies, and the populations with maximum variability, when the number of interacting genes and the magnitude of the epistatic effects are high. There was a decrease in the average heterosis by increasing the number of epistatic genes and the magnitude of their epistatic effects. The same results are generally true for the combining ability analysis of DHs. Surprisingly, the combining ability analyses of subsets of 20 DHs showed no significant average impact of epistasis on the identification of the most divergent ones, even assuming a high number of epistatic genes and great magnitude of their effects. However, a significant negative effect can occur. Statements and DeclarationsThe author has no relevant financial or non-financial interests to disclose. The author has no competing interests to declare that are relevant to the content of this article. The author certifies that he has no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The author has no financial or proprietary interests in any material discussed in this article.

genetics↗

bioRxiv2021

Deciphering the major metabolic pathways associated with aluminum tolerance in popcorn roots using label-free quantitative proteomics

Aluminum toxicity is one of the most important abiotic stresses that affect crop production worldwide. The soluble form (Al3+) inhibits root growth by altering water and nutrients uptake, which also reduces plant growth and development. Under a long term Al3+ exposure, plants can activate several tolerance mechanisms, and to date, there are no reports of large-scale proteomic data of maize in response to this ion. To investigate the post-transcriptional regulation in response to Al toxicity, we performed a label-free quantitative proteomics for comparative analysis of two Al-contrasting popcorn inbred lines and an Al-tolerant commercial hybrid during 72 h under Al-stress. A total of 489 differentially accumulated proteins (DAPs) were identified in the Al-sensitive inbred line, 491 in the Al-tolerant inbred line, and 277 in the commercial hybrid. Among them, 120 DAPs were co-expressed in both Al tolerant genotypes. Bioinformatics analysis indicated that starch and sucrose metabolism, glycolysis/gluconeogenesis, and carbohydrate metabolism were significant biochemical processes regulated in response to Al toxicity. The up accumulation of sucrose synthase and the increase of sucrose content and starch degradation suggest that these components may enhance popcorn tolerance to Al stress. The up-accumulation of citrate synthase suggests a key role of this enzyme in the detoxification process in the Al-tolerant inbred line. The integration of transcriptomic and proteomic data indicated that the Al tolerance response presents a complex regulatory network into the transcription and translation dynamics of popcorn roots development.

plant biology↗

bioRxiv2021

Significance of linkage disequilibrium and epistasis on the genetic variances and covariance between relatives in non-inbred and inbred populations

Because no feasible theoretical model can depict the complexity of phenotype development from a genotype, the joint significance of linkage disequilibrium (LD), epistasis, and inbreeding on the genetic variances remains unclear. The objective of this investigation was to assess the impact of LD and epistasis on the genetic variances and covariances between relatives in non-inbred and inbred populations using simulated data. We provided the theoretical background and simulated grain yield assuming 400 genes in 10 chromosomes of 200 and 50 cM. We generated five populations with low to high LD levels, assuming 10 generations of random cross and selfing. The analysis of the parametric LD in the populations shows that the LD level depends mainly on the gene density. The significance of the LD level is impressive on the magnitude of the genotypic and additive variances, which is the most important component of the genotypic variance, regardless of the LD level and the degree of inbreeding. Regardless of the type of epistasis, the ratio epistatic variance/genotypic variance is proportional to the percentage of the epistatic genes. For the epistatic variances, except for duplicate epistasis and dominant and recessive epistasis, with 100% of epistatic genes, their magnitudes are much lower than the magnitude of the additive variance. The additive x additive variance is the most important epistatic variance. Our results explain why LD for genes and relationship information are key factors affecting the genomic prediction accuracy of complex traits and the efficacy of association studies.

genetics↗

bioRxiv2019

Linkage disequilibrium and haplotype block patterns in popcorn populations

Linkage disequilibrium (LD) analysis provides information on evolutionary aspects of the populations and allows selecting populations and single nucleotide polymorphisms (SNPs) for association studies. Recently, haplotype blocks have been used to increase the power of quantitative trait loci detection in genome-wide association studies and the prediction accuracy with genomic selection. The objectives of this study were to compare the degree of LD, the LD decay, the LD decay extent, and the number and length of haplotype blocks in the populations and to elaborate the first LD map for maize, for elucidating if the maize chromosomes also had a pattern of interspaced regions of high and low rates of recombination. We used a biparental temperate population, a tropical synthetic, and a tropical breeding population, genotyped for approximately 75,000 SNPs. The level of LD expressed by the r2 values is surprisingly low (0.02, 0.04, and 0.04), but comparable to some non-isolated human populations. The general evidence is that the synthetic is the population with higher LD. It is not expected a significant advantage of haplotype-based association study and along generations genomic selection due to the reduced number of SNPs in the haplotype blocks (2 to 3). The results concerning LD decay (rapid decay after 5-10 kb) and LD decay extent (along up to 300 kb) are in the range observed with maize inbred line panels. Our most important result is that maize chromosomes had a pattern of regions of extensive LD interspaced with regions of low LD. However, our simple simulated LD map provides evidence that this pattern can reflect regions with differences of allele frequencies and LD level (expressed by D) and not regions with high and low rates of recombination.

genetics↗

bioRxiv2017

Efficiency Of Genomic Prediction Of Non-Assessed Single Crosses

An important application of genomic selection in plant breeding is the prediction of untested single crosses (SCs). Most investigations on the prediction efficiency were based on tested SCs, using cross-validation. The main objective was to assess the prediction efficiency by correlating the predicted and true genotypic values of untested SCs (accuracy) and measuring the efficacy of identification of the best 300 untested SCs (coincidence), using simulated data. We assumed 10,000 SNPs, 400 QTLs, two groups of 70 selected DH lines, and 4,900 SCs. The heritabilities for the assessed SCs were 30, 60 and 100%. The scenarios included three sampling processes of DH lines, two sampling processes of SCs for testing, two SNP densities, DH lines from distinct and same populations, DH lines from populations with lower LD, two genetic models, three statistical models, and three statistical approaches. We derived a model for genomic prediction based on SNP average effects of substitution and dominance deviations. The prediction accuracy is not affected by the linkage phase. The prediction of untested SCs is very efficient. The accuracies and coincidences ranged from approximately 0.8 and 0.5, respectively, under low heritability, to 0.9 and 0.7, assuming high heritability. Additionally, we highlighted the relevance of the overall LD and evidenced that efficient prediction of untested SCs can be achieved for crops that show no heterotic pattern, for reduced training set size (10%), for SNP density of 1 cM, and for distinct sampling processes of DH lines, based on random choice of the SCs for testing.

genetics↗

bioRxiv2017

Efficiency of genome-wide association study in random cross populations

Genome-wide association studies (GWAS) with plant species have employed inbred lines panels. Our objectives were to present additional quantitative genetics theory for GWAS, evaluate the relative efficiency of GWAS in non-inbred and inbred populations and in an inbred lines panel, and assess factors affecting GWAS. Fifty samples of 400 individuals from populations with linkage disequilibrium were simulated. Individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for traits controlled by 10 quantitative trait loci (QTLs) and 90 minor genes, assuming different degrees of dominance and heritabilities of 40 and 80%. The average SNP density was 0.1 centiMorgan and the QTL heritabilities ranged from 3.2 to 11.8%. To increase the QTL detection power, the additive-dominance model must be fitted for traits controlled by dominance effects but must not be fitted for traits showing no dominance. The power of detection was maximized increasing the sample size to 400 and the false discovery rate (FDR) to 5%. The average power of detection for the low, intermediate, and high heritability QTLs were 9.7, 32.7, and 87.7%, respectively. Under sample size of 400 the observed FDR was equal to or lower than the specified level of significance. The association mapping was highly precise. The analysis of the inbred random cross population provided essentially the same results from the non-inbred population. The inbred lines panel provided the best results concerning the low and intermediate heritability QTL detection power, FDR, and mapping precision. The FDR is mainly affected by population structure, compared to relationship information.

genetics↗