1,720,978 research outputs found
learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data
No full textWe introduce the R-package learnMET, developed as a flexible framework to enable a collection of analyses on multi-environment trial breeding data with machine learning-based models. learnMET allows the combination of genomic information with environmental data such as climate and/or soil characteristics. Notably, the package offers the possibility of incorporating weather data from field weather stations, or to retrieve global meteorological datasets from a NASA database. Daily weather data can be aggregated over specific periods of time based on naive (for instance, nonoverlapping 10-day windows) or phenological approaches. Different machine learning methods for genomic prediction are implemented, including gradient-boosted decision trees, random forests, stacked ensemble models, and multilayer perceptrons. These prediction models can be evaluated via a collection of cross-validation schemes that mimic typical scenarios encountered by plant breeders working with multi-environment trial experimental data in a user-friendly way. The package is published under an MIT license and accessible on GitHub
An R Framework for the Partitioning of Linkage Disequilibrium between and Within Populations
Full text linkPatterns of linkage disequilibrium (LD) across the genome result from a myriad of contributing factors including selection and genetic drift. Natural selection can increase LD near individually selected loci, or it can influence LD between epistatically selected groups of loci. Statistics have previously been derived which compare levels of linkage disequilibrium in subpopulations relative to the total population. These statistics may be leveraged to identify loci that may be under selection or epistatic selection. This is a powerful approach, but to date no framework exists to support its use on a genome-wide scale. We present ohtadstats, an R package designed to facilitate the implementation of Ohta’s D statistics in a variety of use cases. Statistics calculated by this package can be used to determine whether a locus is under selection or not, and can provide insight into the nature of the selection that is taking place (hard sweep or epistatic selection). This package is available on the Comprehensive R Archive Network (CRAN). Funding statement: This research was supported by funding from the USDA Agricultural Research Service. PFP is funded by the University of Missouri Life Sciences Fellowship and a training grant from the National Institute of Health (T32GM008396)
Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs
No full textGenetic diversity of pea (Pisum sativum L.) genotypes differing in leaf type using SNP markers
No full textAbstract A collection of 46 pea ( Pisum sativum L.) accessions, mostly from Europe, were analysed for genetic diversity using the GenoPea 13.2 K SNP Array chip. Of these accessions were 24 nomal-leaved and 22 semi-leafless. Principal components analysis (PCA) separated the peas into two groups characterized by the two different leaf types, although some genotypes were exceptions and appeared in the opposite group. Cluster analysis confirmed the two groups. A dendrogram showed larger genetic distances between genotypes in the normal-leafed group compared to semi-leafless genotypes. Both PCA and cluster analysis show that the two leave types are genetically divergent. So normal-leaved peas are an interesting genetic resource, even if the breeding goal is to develop semi-leafless varieties.DAAD http://dx.doi.org/10.13039/501100001655Georg-August-Universität Göttingen http://dx.doi.org/10.13039/50110000338
Genomic Prediction Informed by Biological Processes Expands Our Understanding of the Genetic Architecture Underlying Free Amino Acid Traits in Dry Arabidopsis Seeds
Full text linkPlant growth, development, and nutritional quality depends upon amino acid homeostasis, especially in seeds. However, our understanding of the underlying genetics influencing amino acid content and composition remains limited, with only a few candidate genes and quantitative trait loci identified to date. Improved knowledge of the genetics and biological processes that determine amino acid levels will enable researchers to use this information for plant breeding and biological discovery. Toward this goal, we used genomic prediction to identify biological processes that are associated with, and therefore potentially influence, free amino acid (FAA) composition in seeds of the model plant Arabidopsis thaliana. Markers were split into categories based on metabolic pathway annotations and fit using a genomic partitioning model to evaluate the influence of each pathway on heritability explained, model fit, and predictive ability. Selected pathways included processes known to influence FAA composition, albeit to an unknown degree, and spanned four categories: amino acid, core, specialized, and protein metabolism. Using this approach, we identified associations for pathways containing known variants for FAA traits, in addition to finding new trait-pathway associations. Markers related to amino acid metabolism, which are directly involved in FAA regulation, improved predictive ability for branched chain amino acids and histidine. The use of genomic partitioning also revealed patterns across biochemical families, in which serine-derived FAAs were associated with protein related annotations and aromatic FAAs were associated with specialized metabolic pathways. Taken together, these findings provide evidence that genomic partitioning is a viable strategy to uncover the relative contributions of biological processes to FAA traits in seeds, offering a promising framework to guide hypothesis testing and narrow the search space for candidate genes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks
No full textThe development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method ( elastic net ) and to two nonlinear gradient boosting methods based on decision tree algorithms ( XGBoost, LightGBM ). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method ( elastic net ) and to two nonlinear gradient boosting methods based on decision tree algorithms ( XGBoost, LightGBM ). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.Open-Access-Publikationsfonds 202
Genetic Architecture of Maize Rind Strength Revealed by the Analysis of Divergently Selected Populations
No full textAbstract The strength of the stalk rind, measured as rind penetrometer resistance (RPR), is an important contributor to stalk lodging resistance. To enhance the genetic architecture of RPR, we combined selection mapping on populations developed by 15 cycles of divergent selection for high and low RPR with time-course transcriptomic and metabolic analyses of the stalks. Divergent selection significantly altered allele frequencies of 3,656 and 3,412 single- nucleotide polymorphisms (SNPs) in the high and low RPR populations, respectively. Surprisingly, only 110 (1.56%) SNPs under selection were common in both populations, while the majority (98.4%) were unique to each population. This result indicated that high and low RPR phenotypes are produced by biologically distinct mechanisms. Remarkably, regions harboring lignin and polysaccharide genes were preferentially selected in high and low RPR populations, respectively. The preferential selection was manifested as higher lignification and increased saccharification of the high and low RPR stalks, respectively. The evolution of distinct gene classes according to the direction of selection was unexpected in the context of parallel evolution and demonstrated that selection for a trait, albeit in different directions, does not necessarily act on the same genes. Tricin, a grass-specific monolignol that initiates the incorporation of lignin in the cell walls, emerged as a key determinant of RPR. Integration of selection mapping and transcriptomic analyses with published genetic studies of RPR identified several candidate genes including ZmMYB31, ZmNAC25, ZmMADS1, ZmEXPA2, ZmIAA41 and hk5. These findings provide a foundation for an enhanced understanding of RPR and the improvement of stalk lodging resistance.Abstract The strength of the stalk rind, measured as rind penetrometer resistance (RPR), is an important contributor to stalk lodging resistance. To enhance the genetic architecture of RPR, we combined selection mapping on populations developed by 15 cycles of divergent selection for high and low RPR with time-course transcriptomic and metabolic analyses of the stalks. Divergent selection significantly altered allele frequencies of 3,656 and 3,412 single- nucleotide polymorphisms (SNPs) in the high and low RPR populations, respectively. Surprisingly, only 110 (1.56%) SNPs under selection were common in both populations, while the majority (98.4%) were unique to each population. This result indicated that high and low RPR phenotypes are produced by biologically distinct mechanisms. Remarkably, regions harboring lignin and polysaccharide genes were preferentially selected in high and low RPR populations, respectively. The preferential selection was manifested as higher lignification and increased saccharification of the high and low RPR stalks, respectively. The evolution of distinct gene classes according to the direction of selection was unexpected in the context of parallel evolution and demonstrated that selection for a trait, albeit in different directions, does not necessarily act on the same genes. Tricin, a grass-specific monolignol that initiates the incorporation of lignin in the cell walls, emerged as a key determinant of RPR. Integration of selection mapping and transcriptomic analyses with published genetic studies of RPR identified several candidate genes including ZmMYB31, ZmNAC25, ZmMADS1, ZmEXPA2, ZmIAA41 and hk5. These findings provide a foundation for an enhanced understanding of RPR and the improvement of stalk lodging resistance
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