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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for Genetic Variation in Zea mays Influences Microbial Nitrification and DeNitrification in Conventional Agroecosystems

Nitrogenous fertilizers provide a short-lived benefit to crops in agroecosystems, but stimulate nitrification and denitrification, processes that result in nitrate pollution, N2O production, and reduced soil fertility. Recent advances in plant microbiome science suggest that genetic variation in plants can modulate the composition and activity of rhizosphere N-cycling microorganisms. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the rhizosphere nitrifier and denitrifier microbiome under “real-world” conventional agricultural conditions. To capture an extensive amount of genetic diversity within maize we grew and sampled the rhizosphere microbiome of a diversity panel of germplasm that included ex-PVP inbreds ( Z. mays ssp. mays ), ex-PVP hybrids ( Z. mays ssp. may s), and teosinte ( Z. mays ssp. mexicana and Z. mays ssp. parviglumis ). From these samples, we characterized the microbiome, a suite of microbial genes involved in nitrification and denitrification and carried out N-cycling potential assays. Here we are showing that populations/genotypes of a single species can vary in their ecological interaction with denitrifers and nitrifers. Some hybrid and teosinte genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbred genotypes stimulated/did not inhibit these N-cycling activities. These potential differences translated to functional differences in N2O fluxes, with teosinte plots producing less GHG than maize plots. Taken together, these results suggest that Zea genetic variation can lead to changes in N-cycling processes that result in N leaching and N2O production, and thereby are selectable targets for crop improvement. Understanding the underlying genetic variation contributing to belowground microbiome N-cycling into our conventional agricultural system could be useful for sustainability.

Nitrogen↗
DOE OSTI2026

Data for "Genetics of flooding tolerance in an F2 Miscanthus sacchariflorus ssp. lutarioriparius × M. sinensis population"

This dataset contains all data and supplementary materials from "Genetics of flooding tolerance in an F2 Miscanthus sacchariflorus ssp. lutarioriparius × M. sinensis population". 1. The dataset S1 table contains the raw phenotypic data collected during the experiment. 2. The dataset S2 table contains the LSmean values for the 24 traits studied. 3. The dataset S3 table contains the TASSEL GBSv2 map, marker information, and genotype data used for mapping. 4. The dataset S4 table contains information on candidate genes found in each of the QTL intervals. 5. The dataset S5 table contains the GO annotations and KEGG enrichment analyses for those candidate genes. 6. The dataset S6 table contains information on the sequences used to classify AP2 ERF transcription factors. 7. The dataset S7 table contains information on AP2 ERF orthologs between Miscanthus and rice based on synteny. 8. Supplementary file 1 contains the ANOVA results using the raw phenotypic data collected from protocol "A". 9. Supplementary file 2 contains the ANOVA results using the raw phenotypic data collected from protocol "B". 10. Supplementary file 3 contains notes on the comparison of SNP calling methods. 11. Supplementary file 4 is a script for analyzing candidate genes found in QTL intervals.

Miscanthus, flood, partial submergence, complete s↗
DOE OSTI2026

Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

AI/ML↗
DOE OSTI2026

Ecological connectivity and habitat loss shape patterns of genetic diversity in a threatened salamander

Context The maintenance of genetic diversity is essential for preserving adaptive potential in populations, yet it is increasingly threatened by landscape alteration. The field of landscape genetics offers a framework for assessing how patch-level landscape conditions, modeled at multiple scales, influence genetic diversity. Objectives We sought to assess how local environmental features and connectivity influence genetic diversity across 74 four-toed salamander (Hemidactylium scutatum) breeding wetlands in the southeastern United States. Methods Using next-generation sequencing data and hierarchical Bayesian models, we examined genome-wide heterozygosity in relation to local landscape features and ecological connectivity. We also assessed the scale of effect of landscape features and tested for temporal lag effects. Results Genetic diversity was lower in wetlands with higher levels of historic deforestation and lower connectivity. An interaction between deforestation and connectivity indicated that deforestation had stronger negative effects in isolated wetlands but weaker effects in well-connected wetlands. Accounting for scale of effect and temporal lags was critical for detecting these relationships. Conclusions Our analyses highlight the importance of assessing the spatial scale (scale of effect) and temporal lag of landscape features to detect key drivers of genetic diversity. In line with population genetic theory, our results indicate that the genetic consequences of habitat loss do not affect populations uniformly and are most severe in isolated populations where gene flow cannot buffer against loss of diversity. Altogether, we highlight the importance of considering the interaction of habitat loss and connectivity in conservation genetic management.

Hemidactylium scutatum↗
NASA NTRS2000

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.

Long, Jason D.↗
DOE OSTI2026

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

Abstract The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitness and genotype-by-environment interactions hinder our understanding of the genetic basis of fitness. Here, we established fitness prediction models for 35 environments using machine learning and existing fitness data and different genetic variant types for a Saccharomyces cerevisiae population. Models revealed that the predictive ability of genetic variants varied across environments, with copy number variants explaining the majority of fitness variation in most cases. Model interpretation showed that different variant types identified distinct gene sets associated with predictive variants. These gene sets were significantly enriched in experimentally validated genes affecting fitness in only a subset of environments, indicating that many genes influencing fitness remain unexplored. Notably, non-experimentally validated genes were more important than validated ones for fitness predictions. Gene contributions to predictions were both isolate- and environment-dependent, pointing to gene-by-gene and gene-by-environment interactions. Furthermore, models uncovered experimentally validated and novel candidate genetic interactions for a well-characterized stress, the fungicide benomyl. These findings highlight the feasibility of identifying the genetic basis of fitness by using different genetic variant types and offer novel targets for future functional analysis.

DNA copy number variations↗
NASA NTRS1997

An analysis of the metabolic theory of the origin of the genetic code

A computer program was used to test Wong's coevolution theory of the genetic code. The codon correlations between the codons of biosynthetically related amino acids in the universal genetic code and in randomly generated genetic codes were compared. It was determined that many codon correlations are also present within random genetic codes and that among the random codes there are always several which have many more correlations than that found in the universal code. Although the number of correlations depends on the choice of biosynthetically related amino acids, the probability of choosing a random genetic code with the same or greater number of codon correlations as the universal genetic code was found to vary from 0.1% to 34% (with respect to a fairly complete listing of related amino acids). Thus, Wong's theory that the genetic code arose by coevolution with the biosynthetic pathways of amino acids, based on codon correlations between biosynthetically related amino acids, is statistical in nature.

NASA Discipline Exobiology↗
DOE OSTI2025

Genetic tools for engineering Zymomonas mobilis , Cereibacter sphaeroides and Novosphingobium aromaticivorans to improve production of bioenergy compounds

Limited genetic tools for non-model bacteria are one of the limiting factors for genetic studies. This review compiles genetic tools used for three non-model alpha-proteobacteria, such as Zymomonas mobilis, Cereibacter (Rhodobacter) sphaeroides, and Novosphingobium aromaticivorans, which hold significant potential to produce industrially essential bioenergy compounds due to their distinctive metabolic pathways and resilience in extreme environments. Each of these strains has a unique genetic profile that enables them to efficiently carry out key reactions relevant to producing bioenergy compounds, such as converting sugars into bioenergy compounds and breaking down lignotoxins. Genetic tools can further optimize these strains for enhanced bioenergy compound production. This review explores the metabolic advantages of these organisms. It highlights the available array of genetic toolkits that can be shared among them to unlock their full potential for sustainable biofuel production.

Biofuel↗
DOE OSTI2026

Methods for safely sharing dual-use genetic data

Background: Some genetic data has dual-use potential. Sharing pathogen data has shown tremendous value. For example therapeutic development and lineage tracking during the COVID pandemic. This data sharing is complicated by the fact that these data have the potential to be used for harm. The genome sequence of a pathogen can be used to enable malicious genetic engineering approaches or to recreate the pathogen from synthetic DNA. Standard data security methods can be applied to genetic data, but when data is shared between institutions, ensuring appropriate security can be difficult. Sensitive data that is shared internationally among a wide array of institutions can be especially difficult to control. Methods for securely storing and sharing genetic data with potential for dual-use are needed to mitigate this potential harm.Results: Here we propose new methods that allow genetic data to be shared in a data format that prevents a nefarious actor from accessing sensitive aspects of the data. Our methods obfuscate raw sequence data by pooling reads from different samples. This approach can ensure that data is secure while stored and during electronic transfer. We demonstrate that by pooling raw sequence data from multiple samples of the same organism, the ability to fully reconstruct any individual sample is prevented. In the pooled data, most genomic information remains, but reads or mutations cannot be directly attributed to any individual sample. To further restrict access to information, regions of a genome can be removed from the reads.Conclusion: Our methods obscure genomic information within raw sequence reads. This method can allow genetic data to be stored and shared while preventing a nefarious actor from being able to perfectly reconstruct an organism. Broad-scale sequence information remains, while fine scale details about specific samples are difficult or impossible to reconstruct. Our software is available at https://github.com/Geneinfosec-Inc/ReadMixer.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

An investigation of messy genetic algorithms

Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

Goldberg, David E.↗
NASA NTRS1991

Problem solving with genetic algorithms and Splicer

Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.

Bayer, Steven E.↗
NASA NTRS1992

Optimization of laminated stacking sequence for buckling load maximization by genetic algorithm

The use of a genetic algorithm to optimize the stacking sequence of a composite laminate for buckling load maximization is studied. Various genetic parameters including the population size, the probability of mutation, and the probability of crossover are optimized by numerical experiments. A new genetic operator - permutation - is proposed and shown to be effective in reducing the cost of the genetic search. Results are obtained for a graphite-epoxy plate, first when only the buckling load is considered, and then when constraints on ply contiguity and strain failure are added. The influence on the genetic search of the penalty parameter enforcing the contiguity constraint is studied. The advantage of the genetic algorithm in producing several near-optimal designs is discussed.

Le Riche, Rodolphe↗
NASA NTRS2017

Temporal Dependence of Chromosomal Aberration on Radiation Quality and Cellular Genetic Background

Radiation induced cancer risks are driven by genetic instability. It is not well understood how different radiation sources induce genetic instability in cells with different genetic background. Here we report our studies on genetic instability, particularly chromosome instability using fluorescence in situ hybridization (FISH), in human primary lymphocytes, normal human fibroblasts, and transformed human mammary epithelial cells in a temporal manner after exposure to high energy protons and Fe ions. The chromosome spread was prepared 48 hours, 1 week, 2 week, and 1 month after radiation exposure. Chromosome aberrations were analyzed with whole chromosome specific probes (chr. 3 and chr. 6). After exposure to protons and Fe ions of similar cumulative energy (??), Fe ions induced more chromosomal aberrations at early time point (48 hours) in all three types of cells. Over time (after 1 month), more chromosome aberrations were observed in cells exposed to Fe ions than in the same type of cells exposed to protons. While the mammary epithelial cells have higher intrinsic genetic instability and higher rate of initial chromosome aberrations than the fibroblasts, the fibroblasts retained more chromosomal aberration after long term cell culture (1 month) in comparison to their initial frequency of chromosome aberration. In lymphocytes, the chromosome aberration frequency at 1 month after exposure to Fe ions was close to unexposed background, and the chromosome aberration frequency at 1 month after exposure to proton was much higher. In addition to human cells, mouse bone marrow cells isolated from strains CBA/CaH and C57BL/6 were irradiated with proton or Fe ions and were analyzed for chromosome aberration at different time points. Cells from CBA mice showed similar frequency of chromosome aberration at early and late time points, while cells from C57 mice showed very different chromosome aberration rate at early and late time points. Our results suggest that relative biological effectiveness (RBE) of radiation are different for different radiation sources, for different cell types, and for the same cell type with different genetic background at different times after radiation exposure. Caution must be taken in using RBE value to estimate biological effects from radiation exposure.

Lu, Tao↗
DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Correlational selection and genetic architecture shape the evolution of the leaf economics spectrum in a perennial grass

The generality of the worldwide leaf economics spectrum (LES) has made it a pillar of trait-based ecological research. Yet, few studies have examined the processes shaping the evolution of the LES within species, in part, because most species occupy only a small portion of the LES. Here, to address this gap, we took advantage of the distinct leaf economics strategies present in different ecotypes of the phenotypically diverse perennial grass Panicum virgatum (switchgrass) to generate a genetic mapping population, which we planted in common gardens at three sites spanning 12 degrees of latitude in the central United States. With this genetic mapping population, we evaluated two potentially interacting causes of LES evolution: 1) genetic architecture, where multiple traits are influenced by either the same gene (pleiotropy) or by genes in close physical proximity (genetic linkage), and 2) correlational selection, where selection acts on traits in combination rather than in isolation. We found that shared genetic architecture influenced covariation between photosynthetic rate (A MASS ) and leaf nitrogen (N MASS ) and between A MASS and leaf mass per area (LMA). We also found that correlational selection favored the trait combinations predicted by the LES (e.g., high LMA with low N MASS or low LMA with high N MASS ) and disfavored other, mismatched trait combinations at two of the three sites. Together, these results demonstrate how the evolution of an integrated LES within species can arise from multiple evolutionary causes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

From quantitative trait loci towards mechanisms: Linkage Integration Hypothesis Testing (LIgHT) sheds light on the mechanisms of genetically modulated stress tolerance

The goal of this work is to assess the mechanistic bases of natural genetic variations in plant responses of photosynthesis to stress. To achieve this goal, we devised the Linkage Integration Hypothesis Testing (LIgHT) approach, comparing chromosomal locations of quantitative trait loci (QTLs) for multiple phenotypes to distinguish between hypothetical mechanisms. As a use case, we explored genetic variations in photosynthesis-related processes under chilling stress in recombinant inbred lines of cowpea ( Vigna unguiculata L. Walp.). We focused on photosynthesis-related parameters measurable in high throughput and indicative of proposed chilling responses, including the states of PSI and PSII, photoprotective non-photochemical quenching, PSII photodamage, and nyctinastic leaf movements (NLMs). The patterns of QTL linkages indicated that chilling stress tolerance is genetically controlled by avoiding PSII photodamage rather than PSI damage or NLMs. This model was validated in a separate experiment measuring the rates of PSII photodamage and repair. Additional linkages suggest that chilling-induced damage to PSII is controlled by the thylakoid proton motive force and redox state of PSII. This regulation appears to be modulated by thylakoid fatty acid composition, previously associated with the same genetic loci and now supported by broader mechanistic evidence. We propose that the LIgHT approach can be broadly applied to test mechanisms underlying genetic variations.

MultispeQ↗