Mendeley Data Showcase

This dataset contains all data and scripts used to run the analysis for the manuscript: Association mapping for components of reaction norms to environmental covariates in public tropical maize panel under water stress. ABSTRACT: Using reaction norm components instead of traditional phenotypic data in genetic association studies (GWAS) may allow the identification of genomic regions that are more influenced by environmental variables in terms of tolerance and responsiveness to water stress. To test this hypothesis, we used a public genetic diversity panel of tropical maize inbred lines, evaluated in eight environments, four in well-watered (WW) and four in water stress (WS) conditions. Most SNPs explained at least 40% of the genetic variability, and some reached 67%. The identified genes and genomic regions revealed physiological responses and direct or indirect molecular mechanisms related to water deficit tolerance and responsiveness. This information will enable more assertive selections and subsidize breeding programs aimed at obtaining cultivars for water deficit conditions while reducing the costs of the evaluation processes of reaction standards. More details about the files can be found in the "Read me.txt" file.

Genomic prediction, based on molecular markers, enables speed breeding schemes and increases the response to selection. Even though there are several genotyping platforms for obtaining single nucleotide polymorphism (SNP) markers, lacking comparative information on how these platforms affect hybrid prediction or the inclusion of non-additive effects. Moreover, SNP discovery techniques are commonly based on a unique reference genome, which can introduce an ascertainment bias when tested germplasms are distant from reference. We employed a tropical maize single-crosses panel and genomic data from two genotyping platforms: array and genotyping-by-sequencing, both based on the B73 genome (temperate). Also, we used a pipeline to build a mock reference genome for SNP discovery aiming to capture unique SNP markers within the tropical maize population, independent from an external reference genome. Our results indicate that mock reference genomes deliver reliable estimates for genetic diversity and population structure assessment. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering additive effects or simple traits. However, mock genomes were slightly worse to predict complex traits and estimate dominance effects, but still with similar GBS performance using B73 as the reference genome. Nevertheless, the SNP-array methods achieved the best predictive ability and reliability to estimate variance components. Finally, the mock genomes can be a worthy alternative to perform genetic diversity and genomic selection studies, especially for those species where the reference genome is not available.

This repository contains R scripts used for the phenotypic, genomic, and genomic selection analyses of a tropical maize population evaluated for **double haploid (DH) rate induction**, including both **putative (HIRp)** and **real (HIRr)** induction rates. More details can be found in the READ ME file.

This research aimed to understand the critical role of adopting advanced breeding tools and optimizing breeding strategies to ensure the sustainability and success of public breeding programs in meeting future food security challenges. In this context, we estimated the genetic gains achieved over 110 years in the rice breeding program of Louisiana State University (LSU) and evaluated through stochastic simulations the impacts of modern selection tools such as genomic selection (GS) and high-throughput phenotyping (HTP).

This dataset comprises all R codes, phenotypic and molecular data necessary to replicate this study. Iin short this study is: Genome-wide Association Studies (GWAS) identify genome variations related to specific phenotypes, typically analyzed by Single Nucleotide Polymorphism (SNP) markers. Genotyping platforms such as those involving genomic hybridization microarray (SNP-Chip or SNP-Array) or sequencing-based genotyping techniques (GBS) are effective in genotyping various samples with hundreds of thousands of SNPs. However, these approaches can introduce bias in tropical maize germplasm analyses, as the temperate line B73 is commonly used as the reference genome. Therefore, an alternative to overcome this limitation is using a simulated genome called “Mock,” adapted to the population and created with bioinformatics tools. A few recent studies have shown that SNP-Array, GBS, and Mock yield similar results concerning population structure, definition of heterotic groups, tester selection, and genomic hybrid prediction. However, no studies have been identified thus far regarding the results generated by these different genotyping approaches for GWAS. Therefore, this study aims to test the equivalence among the three genotyping scenarios in identifying significant effect genes in GWAS. To achieve this, maize was used as the model species, where SNP-Array genotyped 360 inbred lines from a public panel via the Affymetrix platform and GBS. The GBS data were used to perform SNP calling using the temperate inbred line B73 as the reference genome (GBS-B73) and a simulated genome “Mock” obtained in-silico (GBS-Mock). The study encompassed four above-ground traits with plants grown under two levels of water supply: well-watered (WW) and water-stressed (WS). In total, 46, 34, and 31 SNP were identified in the SNP-Array, GBS-B73, and GBS-Mock scenarios, respectively, across the two water levels. Overall, the identified candidate genes varied along the various scenarios but had the same functionality. Regarding SNP-Array and GBS-B73, genes with functional similarity were identified even without coincidence in the physical position of the SNPs. These genes and regions are involved in various processes and responses with applications in plant breeding. In terms of accuracy, the combination of genotyping scenarios compared to those isolated is feasible and recommended, as it increased all traits under both water supply conditions. In this sense, it is worth highlighting the combination of GBS-B73 and GBS-Mock scenarios, not only due to the increase in the resolution of GWAS results but also due to the reduction of costs associated with genotyping as well as the possibility of conducting genomic breeding methods.

Datasets of "Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize" Phenotypic, Genotypic and Enviromic data from two different sources of tropical maize single-cross hybrids. Dataset 1 is from Helix Seeds/Biomatrix (HEL), 452 maize hybrids, 3 locations, RCBD with two blocks. Dataset 2 is from University of Sao Paulo (USP), 903 maize hybrids, 2 locations, 2 years, Augmented block design, 2 comercial varieties as checks Three traits were evaluated: grain yield (GY, in ton ha-1), plant height (PH, in cm), and ear height (EH, in cm). Optimized training sets (OTS) obtained with STPGA R package (Akdemir, 2017) for both datasets are available, as well as the R code used for prediction.

Water stress is the factor that most negatively impacts agricultural production. In this context, root system traits, such as length, surface area, volume, and mass, are paramount in water deficit studies, as they play a central role in plant growth, allocation, and acquisition of soil resources. However, the plant evaluation for them and under water stress is very difficult. Therefore, an alternative has been to obtain surrogate variables from image processing. Moreover, identifying genomic regions or genes associated with the expression of the root system under water deficit may allow breeding programs to outline more effective strategies for obtaining efficient genotypes. Hence, a public diversity panel composed of 360 inbred maize lines was evaluated via image-based root traits at phenological stage V6 (six expanded leaves) under well-water (WW) and water-stress (WS) conditions. Then, genetic association analyses (GWAS) were conducted for each image-based trait in WW and WS using the Fixed and Random Model Circulating Probability Unification (FarmCPU) method. A total of 23 markers were identified in association with all the traits in the two water supply conditions, 12 only in WW, four associated with traits in WW and WS, and seven exclusives to WS. All those genomic regions are associated with physiological mechanisms and molecular responses related to water deficit tolerance that can be explored in subsequent studies and by breeding programs to obtain more resilient genotypes for this condition. Furthermore, image-based features are a valuable tool to dissect root traits in WS conditions.' Here you can find all the data and scripts used to perform this study.

Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computational time-consumption and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles, but not for the long term. Moreover, to the best of our knowledge, no GS models consider the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are an important and unexplored issue in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models (additive, additive-dominant, general (GCA), and specific combining ability (SCA)). Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or SCA for reciprocal.

Dataset of related to the study: Breeding trials in water-stress conditions: identification of sensitive traits in early stages and characterization of a tropical maize public panel. Water stress is the factor that most negatively impacts agricultural production. In this context, root system traits, such as length, surface area, volume, and mass, are paramount in water deficit studies, as they play a central role in plant growth, allocation, and acquisition of soil resources. However, the plant evaluation for them and under water stress is very difficult. Therefore, an alternative has been to obtain surrogate variables from image processing. Moreover, identifying genomic regions or genes associated with the expression of the root system under water deficit may allow breeding programs to outline more effective strategies for obtaining efficient genotypes. Hence, a public diversity panel composed of 360 inbred maize lines was evaluated via image-based root traits at phenological stage V6 (six expanded leaves) under well-water (WW) and water-stress (WS) conditions. Then, genetic association analyses (GWAS) were conducted for each image-based trait in WW and WS using the Fixed and Random Model Circulating Probability Unification (FarmCPU) method. A total of 23 markers were identified in association with all the traits in the two water supply conditions, 12 only in WW, four associated with traits in WW and WS, and seven exclusives to WS. All those genomic regions are associated with physiological mechanisms and molecular responses related to water deficit tolerance that can be explored in subsequent studies and by breeding programs to obtain more resilient genotypes for this condition. Furthermore, image-based features are a valuable tool to dissect root traits in WS conditions.

This repository contains: - The proposed overlapping community detection algorithm NetSCAN - The proposed ontology NetO ontology - A container with neo4j databases for the NetSCAN tests reported in this paper.