Biology subjects

Beissinger, T. M.

Publications and source records attributed to Beissinger, T. M..

8 recordsLinked to original sources

bioRxiv2022

Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the models sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield - those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

plant biology↗

bioRxiv2022

Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

Low-density genotyping followed by imputation reduces genotyping costs while still providing high-density marker information. An increased marker density has the potential to improve the outcome of all applications that are based on genomic data. This study investigates techniques for 1k to 20k genomic marker imputation for plant breeding programs with sugar beet as an example crop, where these are realistic marker numbers for modern breeding applications. The generally accepted gold standard for imputation, Beagle 5.1, was compared to the recently developed software AlphaPlantImpute2 which is designed specifically for plant breeding. For Beagle 5.1 and AlphaPlantImpute2, the imputation strategy as well as the imputation parameters were optimized in this study. We found that the imputation accuracy of Beagle could be tremendously improved (0.22 to 0.67) by tuning parameters, mainly by lowering the values for the parameter for the effective population size and increasing the number of iterations performed. Separating the phasing and imputation steps also improved accuracies when optimized parameters were used (0.67 to 0.82). We also found that the imputation accuracy of Beagle decreased when more low-density lines were included for imputation. AlphaPlantImpute2 produced very high accuracies without optimization (0.89) and was generally less responsive to optimization. Overall, AlphaPlantImpute2 performed relatively better for imputation while Beagle was better for phasing. Combining both tools yielded the highest accuracies. SummaryGenotype marker information allows the prediction of an individuals breeding value without the need to observe its actual phenotype which can accelerate the breeding progress. The more markers are genotyped, the better the genomic prediction may be. However, analyzing many markers is costly, particularly in commercial breeding programs where thousands of new individuals are genotyped. A solution to obtain information for all markers, while spending comparatively little on genotyping, is to genotype only a small fraction of markers in most individuals. Together with high-density information on other individuals, the low-density individuals can be imputed to high-density. High-density individuals are typically parents or highly influential individuals. In this study, we compare the widely used software Beagle with the recently developed software AlphaPlantImpute2 on plant breeding data. To allow a fair comparison, we first optimized existing methods and developed new approaches. This was done to avoid comparing results of a less ideal version of one software to optimized settings of another software. After optimization, the software were evaluated in different scenarios with regards to genotyping errors, population types and number of markers based on simulated data. Simulated data were based on real marker data from a sugar beet population as input to mimic the population history of a commercial breeding population. AlphaPlantImpute2 performs well with default parameters, while much optimization with regards to parameters and strategy was needed to boost accuracies of Beagle. A pipeline is presented which uses Beagle for phasing and AlphaPlantImpute2 for imputation. This pipeline yielded the highest accuracies and shortest run time. Core IdeasBeagle is sensitive to parameter tuning Best imputation accuracies could be achieved by using a combination of Beagle and AlphaPlantImpute2 The population structure influence imputation accuracy

bioinformatics↗

bioRxiv2021

learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data

We introduce the R-package learnMET, developed as a flexible framework to enable a collection of analyses on multi-environment trial (MET) 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 can retrieve global meteorological datasets from a NASA database. Daily weather data can be aggregated over specific periods of time based on naive (for instance, non-overlapping 10-day windows) or phenological approaches. Different machine learning methods for genomic prediction are implemented, including gradient boosted trees, random forests, stacked ensemble models, and multi-layer perceptrons. These prediction models can be evaluated via a collection of cross-validation schemes that mimic typical scenarios encountered by plant breeders working with MET experimental data in a user-friendly way. The package is fully open source and accessible on GitHub.

genetics↗

bioRxiv2021

The Evolutionary History of Wild, Domesticated, and Feral Brassica oleracea (Brassicaceae)

Understanding the evolutionary history of crops, including identifying wild relatives, helps to provide insight for designing new approaches in crop breeding efforts. Cultivated Brassica oleracea has intrigued researchers for centuries due to its wide diversity in forms, which include cabbage, broccoli, cauliflower, kale, kohlrabi, and Brussels sprouts. Yet, the evolutionary history of this species remains understudied. With such different vegetables produced from a single species, B. oleracea is a model organism for understanding the power of artificial selection. Persistent challenges in the study of B. oleracea include conflicting hypotheses regarding domestication and the identity of the closest living wild relative. Using a diversity panel of 224 accessions, which represents 14 different B. oleracea crop types and nine potential wild progenitor species, we integrate phylogenetic and population genetic techniques with ecological niche modeling, archaeological, and literary evidence to examine relationships among cultivars and wild relatives to clarify the origin of this horticulturally important species. Our analyses point to the Aegean endemic B. cretica as the closest living relative of cultivated B. oleracea, supporting an origin of cultivation in the Eastern Mediterranean region. Additionally, we identify several feral lineages, suggesting that cultivated plants of this species are able to revert to a wild-like state with relative ease. By expanding our understanding of the evolutionary history in B. oleracea, these results contribute to a growing body of knowledge on crop domestication that will facilitate continued breeding efforts including adaptation to changing environmental conditions.

evolutionary biology↗

bioRxiv2020

Genetic Architecture of Maize Rind Strength Revealed by the Analysis of Divergently Selected Populations

Stalk lodging, breakage of the stalk at or below the ear, causes substantial yield losses in maize. The strength of the stalk rind, commonly measured as rind penetrometer resistance (RPR), is an important contributor to stalk lodging resistance. To enhance RPR genetic architecture, we conducted selection mapping on populations developed by 15 cycles of divergent selection for high (C15-H) and low (C15-L) RPR. We also performed time-course transcriptome and metabolic analyses on developing stalks of high (Hrpr1) and low (Lrpr1) RPR inbred lines derived from the C15-H and C15-L populations, respectively. Divergent selection significantly altered allele frequencies at 3,656 and 3,412 single nucleotide polymorphisms (SNP) in the C15-H and C15-L populations, respectively. While the majority of the SNPs under selection were unique, 110 SNPs were common in both populations indicating the fixation of alleles with alternative effects. Remarkably, preferential selection on the genomic regions associated with lignin and polysaccharide biosynthesis genes was observed in C15-H and C15-L populations, respectively. This observation was supported by higher lignification and lower extractability of cell wall-bound sugars in Hrpr1 compared to Lrpr1. Tricin, a monolignol important for incorporation of lignin in grass cell walls, emerged as a key determinant of the different cell wall properties of Hrpr1 and Lrpr1. Integration of selection mapping with transcriptomics and previous genetic studies on RPR identified 40 novel candidate genes including ZmMYB31, ZmNAC25, ZmMADS1, two PAL paralogues, two lichenases, ZmEXPA2, ZmIAA41, and Caleosin. Enhanced mechanistic and genetic understanding of RPR provides a foundation for improved stalk lodging resistance.

genetics↗

bioRxiv2018

Subset-based genomic prediction provides insights into the genetic architecture of free amino acid levels in dry Arabidopsis thaliana seeds

Plant 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. Towards 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 the 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.

genetics↗

bioRxiv2017

A simple test identifies selection on complex traits in breeding and experimentally-evolved populations

Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome wide association studies and selection mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique utilizes additive effects estimates from all available markers, and relates these estimates to allele frequency change over time. Using this information, we generate a composite statistic, denoted[G] , which can be used to test for significant evidence of selection on a trait. Our test requires pre- and post-selection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that[G] is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.

evolutionary biology↗

bioRxiv2017

The interplay of demography and selection during maize domestication and expansion

The history of maize has been characterized by major demographic events including changes in population size associated with domestication and range expansion as well as gene flow with wild relatives. The interplay between demographic history and selection has shaped diversity across maize populations and genomes. Here, we investigate these processes based on high-depth resequencing data from 31 maize landraces spanning the pre-Columbian distribution of maize as well as four wild progenitor individuals (Zea mays ssp. parviglumis) from the Balsas River Valley in Mexico. Genome-wide demographic analyses reveal that maize domestication and spread resulted in pronounced declines in effective population size due to both a protracted bottleneck and serial founder effects, while, concurrently, parviglumis experienced population growth. The cost of maize domestication and spread was an increase in deleterious alleles in the domesticate relative to its wild progenitor. This cost is particularly pronounced in Andean maize, which appears to have experienced a more dramatic founder event when compared to other maize populations. Introgression from the wild teosinte Zea mays ssp. mexicana into maize in the highlands of Mexico and Guatemala is found found to decrease the prevalence of deleterious alleles, likely due to the higher long-term effective population size of wild maize. These findings underscore the strong interaction between historical demography and the efficiency of selection species- and genome-wide and suggest domesticated species with well-characterized histories may be particularly useful for understanding this interplay.

evolutionary biology↗