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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Efficient population representation with more genetic markers increases performance of a steelhead (Oncorhynchus mykiss) genetic stock identification baseline

Abstract Genetic stock identification (GSI) is an important fisheries management tool to identify the origin of fish harvested in mixed stock fisheries. Periodic updates of genetic baselines can improve performance via the addition of unsampled or under‐sampled populations and the inclusion of more informative markers. We used a combination of baselines to evaluate how population representation, marker number, and marker type affected the performance and accuracy of genetic stock assignments (self‐assignment, bias, and holdout group tests) for steelhead ( Oncorhynchus mykiss ) in the Snake River basin. First, we compared the performance of an existing genetic baseline with a newly developed one which had a reduced number of individuals from more populations using the same set of markers. Self‐assignment rates were significantly higher ( p < 0.001; +5.4%) for the older, larger baseline, bias did not differ significantly between the two, but there was a significant improvement in performance for the new baseline in holdout results ( p < 0.001; mean increase of 25.0%). Second, we compared the performance of the new baseline with increased numbers of genetic markers (~2x increase of single‐nucleotide polymorphisms; SNPs) for the same set of baseline individuals. In this comparison, results produced significantly higher rates of self‐assignment ( p < 0.001; +9.7%) but neither bias nor leave‐one‐out were significantly affected. Third, we compared 334 SNPs versus opportunistically discovered microhaplotypes from the same amplicons for the new baseline, and showed the latter produced significantly higher rates of self‐assignment ( p < 0.01; +2.6%), similar bias, but slightly lower holdout performance (−0.1%). Combined, we show the performance of genetic baselines can be improved via representative and efficient sampling, that increased marker number consistently improved performance over the original baseline, and that opportunistic discovery of microhaplotypes can lead to small improvements in GSI performance.

Hargrove, John S.↗
DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A common resequencing‐based genetic marker data set for global maize diversity

SUMMARY Maize ( Zea mays ssp. mays ) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole‐genome resequencing strategies, identifying millions of segregating single‐nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high‐confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering‐time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development

With new motivation to increase the proportion of energy demands met by zero-carbon sources, there is a greater focus on efforts to assess and mitigate the impacts of renewable energy development on sensitive ecosystems and wildlife, of which birds are of particular interest. One challenge for researchers, due in part to a lack of appropriate tools, has been estimating the effects from such development on individual breeding populations of migratory birds. To help address this, we utilize a newly developed, high-resolution genetic tagging method to rapidly identify the breeding population of origin of carcasses recovered from renewable energy facilities and combine them with maps of genetic variation across geographic space (called ‘genoscapes’) for five species of migratory birds known to be exposed to energy development, to assess the extent of population-level effects on migratory birds. We demonstrate that most avian remains collected were from the largest populations of a given species. In contrast, those remains from smaller, declining populations made up a smaller percentage of the total number of birds assayed. Results suggest that application of this genetic tagging method can successfully define population-level exposure to renewable energy development and may be a powerful tool to inform future siting and mitigation activities associated with renewable energy programs.

14 SOLAR ENERGY↗
DOE OSTI2020

Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome

Abstract Introduction Microbial residents of the human oral cavity have long been a major focus of microbiology due to their influence on host health and intriguing patterns of site specificity amidst the lack of dispersal limitation. However, the determinants of niche partitioning in this habitat are yet to be fully understood, especially among taxa that belong to recently discovered branches of microbial life. Results Here, we assemble metagenomes from tongue and dental plaque samples from multiple individuals and reconstruct 790 non-redundant genomes, 43 of which resolve to TM7, a member of the Candidate Phyla Radiation, forming six monophyletic clades that distinctly associate with either plaque or tongue. Both pangenomic and phylogenomic analyses group tongue-specific clades with other host-associated TM7 genomes. In contrast, plaque-specific TM7 group with environmental TM7 genomes. Besides offering deeper insights into the ecology, evolution, and mobilome of cryptic members of the oral microbiome, our study reveals an intriguing resemblance between dental plaque and non-host environments indicated by the TM7 evolution, suggesting that plaque may have served as a stepping stone for environmental microbes to adapt to host environments for some clades of microbes. Additionally, we report that prophages are widespread among oral-associated TM7, while absent from environmental TM7, suggesting that prophages may have played a role in adaptation of TM7 to the host environment. Conclusions Our data illuminate niche partitioning of enigmatic members of the oral cavity, including TM7, SR1, and GN02, and provide genomes for poorly characterized yet prevalent members of this biome, such as uncultivated Flavobacteriaceae.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Heterologous expression of phosphite dehydrogenase in the chloroplast or nucleus enables phosphite utilization and genetic selection in Picochlorum spp.

Microalgae present a path to ameliorate problems associated with climate change via capture and reduction of CO2 to sustainable fuels and chemicals. Picochlorum is a genus of algae recently recognized for potential application in these regards due to its high productivity, thermotolerance, and halotolerance. Foundational genetic tools have recently been established in this genus. However, at present, genetic markers are limited, hindering genetic throughput and trait stacking approaches. To expand the suite of genetic tools and markers available for this genus, we sought to heterologously express the phosphite dehydrogenase (ptxD) gene from Pseudomonas stutzeri WM88 in both the nucleus and chloroplast of Picochlorum renovo and Picochlorum celeri. Additionally, the resultant strains allow for utilization of phosphite as a sole phosphorous source and as a nuclear and plastidial selection marker for genetic engineering. Growth analysis indicated comparable growth and composition when transgenic algae were grown in media containing phosphite as a sole phosphorus source, as compared to the conventionally used phosphate. Combined, these results expand the genetic toolbox available to the Picochlorum genus and present a potential crop protection and biocontainment strategy.

09 BIOMASS FUELS↗
DOE OSTI2022

Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum

We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.

Zhang, Zeyu↗
DOE OSTI2022

Association mapping across a multitude of traits collected in diverse environments in maize

Abstract Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structure-informed clustering for population stratification in association studies

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Efficient species identification for Pacific salmon genetic monitoring programs

Abstract Genetic monitoring of Pacific salmon in the Columbia River basin provides crucial information to fisheries managers that is otherwise challenging to obtain using traditional methods. Monitoring programs such as genetic stock identification (GSI) and parentage‐based tagging (PBT) involve genotyping tens of thousands of individuals annually. Although rare, these large sample collections inevitably include misidentified species, which exhibit low genotyping success on species‐specific Genotyping‐in‐Thousands by sequencing (GT‐seq) panels. For laboratories involved in large‐scale genotyping efforts, diagnosing non‐target species and reassigning them to the appropriate monitoring program can be costly and time‐consuming. To address this problem, we identified 19 primer pairs that exhibit consistent cross‐species amplification among salmonids and contain 51 species informative variants. These genetic markers reliably discriminate among 11 salmonid species and two subspecies of Cutthroat Trout and have been included in species‐specific GT‐seq panels for Chinook Salmon, Coho Salmon, Sockeye Salmon, and Rainbow Trout commonly used for Pacific salmon genetic monitoring. The majority of species‐informative amplicons (16) were newly identified from the four existing GT‐seq panels, thus demonstrating a low‐cost approach to species identification when using targeted sequencing methods. A species‐calling script was developed that is tailored for routine GT‐seq genotyping pipelines and automates the identification of non‐target species. Following extensive testing with empirical and simulated data, we demonstrated that the genetic markers and accompanying script accurately identified species and are robust to missing genotypic data and low‐frequency, shared polymorphisms among species. Finally, we used these tools to identify Coho Salmon incidentally caught in the Columbia River Chinook Salmon sport fishery and used PBT to determine their hatchery of origin. These molecular and computing resources provide a valuable tool for Pacific salmon conservation in the Columbia River basin and demonstrate a cost‐effective approach to species identification for genetic monitoring programs.

Robinson, Zachary L.↗
DOE OSTI2022

Do Genetic Drift and Gene Flow Affect the Geographic Distribution of Female Plants in Gynodioecious Lobelia siphilitica?

Variation in population sex ratio is particularly pronounced in gynodioecious angiosperms. Extremely high female frequencies in gynodioecious populations cannot be readily explained by selective forces alone. To assess the contributions of drift and gene flow to extreme sex-ratio variation, we documented sex ratio and population size in 92 populations of Lobelia siphilitica across its range and genotyped plants using plastid and nuclear genetic markers. Similarity in spatial patterns of genetic and demographic variables may suggest that drift and/or gene flow have contributed to population sex-ratio variation in L. siphilitica. We found strong spatial structuring of extremely high female frequencies: populations with >50% female plants are restricted to the south–central portion of the range. However, we did not detect any spatial structuring in population size nor metrics of genetic diversity, suggesting that extreme variation in female frequency is not strongly affected by drift or gene flow. Extreme sex-ratio variation is frequently observed in gynodioecious plants, but its causes are difficult to identify. Further investigation into mechanisms that create or maintain the spatial structure of sex ratios in gynodioecious species will provide much needed insight.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species

Abstract Genotyping‐by‐sequencing (GBS) is a widely used strategy for obtaining large numbers of genetic markers in model and non‐model organisms. In crop plants, GBS‐derived marker datasets are frequently used to perform quantitative trait locus (QTL) mapping. In some plant species, however, high heterozygosity and complex genome structure mean that researchers must use care in handling GBS data to conduct QTL mapping most effectively. Such outbred crops include most of the perennial grass and tree species used for bioenergy. To identify strategies for increasing accuracy and precision of QTL mapping using GBS data in outbred crops, we conducted an empirical study of SNP‐calling and genetic map‐building pipeline parameters in a Miscanthus sinensis population, and a complementary simulation study to estimate the relationship between genome‐wide error rate, read depth, and marker number. The bioenergy grass Miscanthus is an obligate outcrossing species with a recent (diploidized) whole‐genome duplication. For the study of empirical M. sinensis data, we compared two SNP‐calling methods (one non‐reference‐based and one reference‐based), a series of depth filters (12×, 20×, 30×, and 40×) and two map‐construction methods (i.e., marker ordering: linkage‐only and order‐corrected based on a reference genome). We found that correcting the order of markers on a linkage map by using a high‐quality reference genome improved QTL precision (shorter confidence intervals). For typical GBS datasets of between 1000 and 5000 markers to build a genetic map for biparental populations, a depth filter set at 30× to 40× applied to outbred populations provided a genome‐wide genotype‐calling error rate of less than 1%, improved accuracy of QTL point estimates and minimized type I errors for identifying QTL. Based on these results, we recommend using a reference genome to correct the marker order of genetic maps and a robust genotype depth filter to improve QTL mapping for outbred crops.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1988

Nonselective enrichment for yeast adenine mutants by flow cytometry

The expression of certain adenine biosynthetic mutations in the yeast Saccharomyces cerevisiae results in a red colony color. This phenomenon has historically provided an ideal genetic marker for the study of mutation, recombination, and aneuploidy in lower eukaryotes by classical genetic analysis. In this paper, it is reported that cells carrying ade1 and/or ade2 mutations exhibit primary fluorescence. Based on this observation, the nonselective enrichment of yeast cultures for viable adenine mutants by using the fluorescence-activated cell sorter has been achieved. The advantages of this approach over conventional genetic analysis of mutation, recombination, and mitotic chromosomal stability include speed and accuracy in acquiring data for large numbers of clones. By using appropriate strains, the cell sorter has been used for the isolation of both forward mutations and chromosomal loss events in S. cerevisiae. The resolving power of this system and its noninvasiveness can easily be extended to more complex organisms, including mammalian cells, in which analogous metabolic mutants are available.

Non-NASA Center↗
NASA NTRS1993

Diploid yeast cells yield homozygous spontaneous mutations

A leucine-requiring hybrid of Saccharomyces cerevisiae, homoallelic at the LEU1 locus (leu1-12/leu1-12) and heterozygous for three chromosome-VII genetic markers distal to the LEU1 locus, was employed to inquire: (1) whether spontaneous gene mutation and mitotic segregation of heterozygous markers occur in positive nonrandom association and (2) whether homozygous LEU1/LEU1 mutant diploids are generated. The results demonstrate that gene mutation of leu1-12 to LEU1 and mitotic segregation of heterozygous chromosome-VII markers occur in strong positive nonrandom association, suggesting that the stimulatory DNA lesion is both mutagenic and recombinogenic. In addition, genetic analysis of diploid Leu+ revertants revealed that approximately 3% of mutations of leu1-12 to LEU1 result in LEU1/LEU1 homozygotes. Red-white sectored Leu+ colonies exhibit genotypes that implicate post-replicational chromatid breakage and exchange near the site of leu1-12 reversion, chromosome loss, and subsequent restitution of diploidy, in the sequence of events leading to mutational homozygosis. By analogy, diploid cell populations can yield variants homozygous for novel recessive gene mutations at biologically significant rates. Mutational homozygosis may be relevant to both carcinogenesis and the evolution of asexual diploid organisms.

Non-NASA Center↗
NASA NTRS1973

Effects of space environment on T-7 bacteriophage and spores of Bacillus subtilis 168

Two strains of Bacillus subtilis were exposed to components of the ultraviolet spectrum in space. Both strains possess multiple genetic markers, and one of the strains is defective in the ability to repair ultraviolet damage. The T-7 bacteriophage of Escherichia coli was also exposed to selected wavelengths and energy levels of ultraviolet light in space. Preliminary findings do not reveal anomalies in survival rates. Data are not yet available on detailed genetic analyses.

Spizizen, J.↗
DOE OSTI2020

Genetic identification of 91 poplar cultivars based on SSR markers

Accurate genetic identification and relationship analysis of poplar cultivars is necessary to establish commercial poplar plantations and select suitable breeding strategies. In this study, 91 poplar cultivars belonging to four sections (Aigeiros, Tacamahaca, Populus and Turanga) and inter/intra-sectional hybrids were genotyped using 18 polymorphic simple sequence repeat (SSR) markers. In total, 222 alleles were amplified with an average of 12.3 alleles per marker. The mean polymorphic information content and power of discrimination were 0.706 and 0.813, respectively. Five SSR markers (ORPM_103, ORPM_247, GCPM_1048, GCPM_1255 and LG_X_19) constituted a core fingerprint and were sufficient to identify all the tested cultivars. With some notable exceptions, cultivars of the same species generally clustered together in cluster (UPGMA) and ordination (PCO) analyses. Flow cytometry indicated that 11 poplar cultivars were triploid. Among these, seven had three alleles at some loci, suggesting that SSR markers could indicate the ploidy level to some extent. This study provides useful genetic information for the identification and protection of poplar cultivars in China and offers a guideline for the selection of poplar crossing parents based on ploidy level and genetic relationships.

59 BASIC BIOLOGICAL SCIENCES↗