Search NASA⌕ Search

SEARCH · Search NASA

Results for “Variation (Genetics)”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
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 OSTI2024

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, N 2 O 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. mays), 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 N 2 O 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 N 2 O 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.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize

Abstract Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over 200,000 variants, genetic, epigenetic, or both (termed binding quantitative trait loci (bQTL)), linked tocis-element occupancy. Three lines of evidence support the functional significance of bQTL: (1) coincidence with causative loci that regulate traits, includingvgt1,ZmTRE1and the MITE transposon nearZmNAC111under drought; (2) bQTL allelic bias is shared between inbred parents and matches chromatin immunoprecipitation sequencing results; and (3) partitioning genetic variation across genomic regions demonstrates that bQTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functionalcis-variation accessible at scale for genetic studies and targeted engineering of complex traits.

Genetics & Heredity↗
DOE OSTI2026

Genetic variations and their interaction with thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice

Thirdhand smoke (THS) is linked to adverse health effects, but the effect of genetic variations on behavioral outcomes is poorly understood. To investigate this, we assessed anxiety- and memory-related behaviors in 820 mice from 21 strains of the genetically diverse Collaborative Cross (CC) mouse that were exposed to THS from 4 through 10 weeks of age. Anxiety was evaluated with a light/dark box assay with a previously established risk score system. Females were generally more sensitive: THS reduced anxiety risk in strains CC013, CC019, and CC051, but increased risk in CC036 and CC061, while males showed no significant effects. Memory was tested using passive avoidance: impairments were observed in both sexes in CC016 and CC019, with sex-dependent effects in CC002 and CC051. A genome-wide association study identified 2,347 SNPs associated with anxiety and 1,568 SNPs with memory, with 32 and 85 SNPs, respectively, interacting with THS exposure. Enrichment analyses revealed distinct biological processes underlying susceptibility, including axonogenesis, synapse organization, cognition, and learning and memory. KEGG pathway analysis identified distinct genetic pathways, including GTPase binding and GTPase regulatory activity, that act as critical molecular switches in the brain that regulate synaptic plasticity, dendritic spine structure, and neuronal signaling, directly influencing anxiety-like behaviors and memory formation. These findings show that THS exposure affects neurobehavioral outcomes in a sex- and genotype-dependent manner, highlighting critical gene-environment interactions and providing a foundation for mechanistic insights into THS neurotoxicity

Anxiety↗
DOE OSTI2024

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid–base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
DOE OSTI2026

Unraveling plant phenotype to genotype associations with daily hyperspectral traits in Populus trichocarpa

Hyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales. In this study, we combined tower-based continuous hyperspectral sensing with genome-wide association studies to analyze 1423 wavebands (400-900 nm) and derivative vegetation indices across 505 genotypes and the genetic architecture of hyperspectral phenotypes over time in Populus trichocarpa Torr. & Gray grown under field conditions. Wavelengths related to chlorophyll and carotenoid absorption spectra exhibited the strongest genetic variation resulting in 98 significant SNP associations. Notably, we found substantial overlap in genetic association between the blue and red spectral regions, indicative of carotenoids and chlorophyll, respectively, and identified more than 10 candidate genes associated with chloroplast function, underpinning photosynthetic activity. Furthermore, fluctuations in associations for vegetative indices, such as the chlorophyll:carotenoid index (CCI), across the growing season reveal a temporally dynamic genetic architecture of physiological traits associated with fall senescence of this temperate tree species. Finally, we also observed correlations (spearman rho = 0.3, p < 1x10 −8 ) between individual wavebands or vegetative indices and growth rate, assessed as the relative change of tree height over the growing season. The growth rate prediction was substantially improved by a regularization multivariate model (spearman rho>0.5, p < 1x10 −16 ), reinforcing the value of hyperspectral measurements for predicting traits linked to tree productivity. These findings highlight the potential of high-throughput, rapid, hyperspectral genome wide association studies GWAS to uncover physiologically meaningful genetic variation and offer promising insights for future acceleration for plant breeding.

09 BIOMASS FUELS↗
DOE OSTI2025

Footprints of Worldwide Adaptation in Structured Populations of Drosophila melanogaster Through the Expanded DEST 2.0 Genomic Resource

Abstract Large-scale genomic resources can place genetic variation into an ecologically informed context. To advance our understanding of the population genetics of the fruit fly Drosophila melanogaster, we present an expanded release of the community-generated population genomics resource Drosophila Evolution over Space and Time (DEST 2.0; https://dest.bio/). This release includes 530 high-quality pooled libraries from flies collected across six continents over more than a decade (2009 to 2021), most at multiple time points per year; 211 of these libraries are sequenced and shared here for the first time. We used this enhanced resource to elucidate several aspects of the species' demographic history and identify novel signs of adaptation across spatial and temporal dimensions. For example, we showed that the spatial genetic structure of populations is stable over time, but that drift due to seasonal contractions of population size causes populations to diverge over time. We identified signals of adaptation that vary between continents in genomic regions associated with xenobiotic resistance, consistent with independent adaptation to common pesticides. Moreover, by analyzing samples collected during spring and fall across Europe, we provide new evidence for seasonal adaptation related to loci associated with pathogen response. Furthermore, we have also released an updated version of the DEST genome browser. This is a useful tool for studying spatiotemporal patterns of genetic variation in this classic model system.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Testing for the Genomic Footprint of Conflict Between Life Stages in an Angiosperm and Moss Species

Abstract The maintenance of genetic variation by balancing selection is of considerable interest to evolutionary biologists. An important but understudied potential driver of balancing selection is antagonistic pleiotropy between diploid and haploid stages of the plant life cycle. Despite sharing a common genome, sporophytes (2n) and gametophytes (n) may undergo differential or even opposing selection. Theoretical work suggests antagonistic pleiotropy between life stages can generate balancing selection and maintain genetic variation. Despite the potential for far-reaching consequences of gametophytic selection, empirical tests of its pleiotropic effects (neutral, synergistic, or antagonistic) on sporophytes are generally lacking. Here, we examined the population genomic signals of selection across life stages in the angiosperm Rumex hastatulus and the moss Ceratodon purpureus. We compared gene expression between life stages and sexes, combined with neutral diversity statistics and the analysis of the distribution of fitness effects. In contrast to what would be predicted under balancing selection due to antagonistic pleiotropy, we found that unbiased genes between life stages were under stronger purifying selection, likely explained by a predominance of synergistic pleiotropy between life stages and strong purifying selection on broadly expressed genes. In addition, we found that 30% of candidate genes under balancing selection in R. hastatulus were located within inversion polymorphisms. Our findings provide novel insights into the genome-wide characteristics and consequences of plant gametophytic selection.

Evolutionary Biology↗
DOE OSTI2025

Engineering the green algae Chlamydomonas incerta for recombinant protein production

Chlamydomonas incerta , a genetically close relative of the model green alga Chlamydomonas reinhardtii , shows significant potential as a host for recombinant protein expression. Because of the close genetic relationship between C. incerta and C. reinhardtii , this species offers an additional reference point for advancing our understanding of photosynthetic organisms, and also provides a potential new candidate for biotechnological applications. This study investigates C. incerta ’s capacity to express three recombinant proteins: the fluorescent protein mCherry, the hemicellulose-degrading enzyme xylanase, and the plastic-degrading enzyme PHL7. We have also examined the capacity to target protein expression to various cellular compartments in this alga, including the cytosol, secretory pathway, cytoplasmic membrane, and cell wall. When compared directly with C. reinhardtii , C. incerta exhibited a distinct but notable capacity for recombinant protein production. Cellular transformation with a vector encoding mCherry revealed that C. incerta produced approximately 3.5 times higher fluorescence levels and a 3.7-fold increase in immunoblot intensity compared to C. reinhardtii . For xylanase expression and secretion, both C. incerta and C. reinhardtii showed similar secretion capacities and enzymatic activities, with comparable xylan degradation rates, highlighting the industrial applicability of xylanase expression in microalgae. Finally, C. incerta showed comparable PHL7 activity levels to C. reinhardtii , as demonstrated by the in vitro degradation of a polyester polyurethane suspension, Impranil® DLN. Finally, we also explored the potential of cellular fusion for the generation of genetic hybrids between C. incerta and C. reinhardtii as a means to enhance phenotypic diversity and augment genetic variation. We were able to generate genetic fusion that could exchange both the recombinant protein genes, as well as associated selectable marker genes into recombinant offspring. These findings emphasize C. incerta ’s potential as a robust platform for recombinant protein production, and as a powerful tool for gaining a better understanding of microalgal biology.

cell membranes↗
DOE OSTI2025

Changes in leaf economic trait relationships across a precipitation gradient are related to differential gene expression in a C 4 perennial grass

Summary The leaf economics spectrum (LES) describes a suite of functional traits that consistently covary at large spatial and taxonomic scales. Despite its importance at these larger scales, few studies have examined the major drivers of intraspecific variation in the LES – phenotypic plasticity and standing genetic variation. Using experimental precipitation manipulations, we examined whether covariation among leaf economics traits and selection on leaf economics traits and trait combinations change as diverse genotypes of the widespread perennial grass Panicum virgatum are exposed to differences in precipitation. We also used RNA‐Seq to examine whether groups of co‐expressed genes that align with leaf economics traits function in processes hypothesized to underlie the LES. Water availability impacted leaf economics trait covariation in important ways – covariation between leaf economics traits and selection on covariation between traits (i.e. correlational selection) tended to be strongest when water availability was high. Additionally, many genes associated with leaf economics traits functioned in processes that may explain how the LES originates, such as chloroplasts, cell walls, and nitrogen metabolism. Water availability is likely an important modulator of selection and evolution of the LES in P. virgatum that can be better understood by examining gene expression.

Heckman, Robert W. [Department of Integrative Biol↗
DOE OSTI2025

Climate adaptation in Populus trichocarpa : key adaptive loci identified for stomata and leaf traits

We investigated adaptive genetic variation in Populus trichocarpa, a potential biofuel feedstock crop, to better understand how physiological traits may influence tolerance to water limitation. Our study focused on leaf and stomatal traits, given their roles in plant–water relations and adaptation. Using a diversity panel of over 1300 genotypes, we measured 14 leaf and stomatal traits under control (well-watered) and drought (water-limited) conditions. We conducted genome-wide association studies (GWAS), climate association analyses, and transcriptome (RNA-seq) profiling to identify genetic loci associated with phenotypic variation and adaptation. Stomatal traits, including size and density, were correlated with the climate of origin, with genotypes from more arid regions tending to have smaller but denser stomata. GWAS identified multiple loci associated with trait variation, including a major-effect region on chromosome 10 linked to stomatal size and abaxial contact angle. This locus overlapped with a tandem array of 3-ketoacyl-CoA synthase (KCS) genes and showed strong allele–climate and gene expression associations. Our findings reveal genetic and phenotypic variation consistent with local adaptation and suggest that future climates may favor alleles associated with smaller stomata, particularly under increasing aridity. This work provides insights into climate adaptation and breeding strategies for resilience in perennial crops.

Populus trichocarpa↗
DOE OSTI2024

Genome-scale analysis of interactions between genetic perturbations and natural variation

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 8046 CRISPRi perturbations targeting 1721 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual’s response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comprehensive SNP Data for 1,323 GWAS Population in Populus trichocarpa and Combined Annotation Files for P. trichocarpa v3.0 and v3.1

The VCF dataset includes genetic variations found in 1,323 Populus trichocarpa genotypes, providing valuable information for scientists studying plant genetics. Researchers have generated this dataset using whole-genome DNA short-read sequencing on the Illumina Genome Analyzer, HiSeq 2000, and HiSeq 2500 platforms. This sequencing effort ensured a minimum expected sequencing depth of 15×. The dataset comprises more than 9.7 million single nucleotide polymorphisms (SNPs) and indel variants. The combined annotation files are derived from P. trichocarpa v3.0 and v3.1. We merged these files to create a comprehensive annotation file used for GWAS analysis. In total, 38,830 genes overlapped between the two versions. For overlapping genes, we defined the start as the smaller and the end as the larger among the two versions to increase the likelihood of locating candidate genetic loci. Additionally, we included 2,505 unique genes from v3.0 and 4,120 unique genes from v3.1, resulting in a total of 45,455 genes in the updated annotation file.

09 BIOMASS FUELS↗
DOE OSTI2025

Development and characterization of a wild emmer wheat backcross introgression population for hard winter wheat improvement

Abstract Wild emmer wheat (Triticum turgidumsubsp.dicoccoides) is the tetraploid progenitor of hexaploid bread wheat (Triticum aestivumL.) and is known to be a valuable source of genetic variation for wheat improvement. However, direct evaluation of wild emmer diversity for agronomic potential has limited value unless performed in the backgrounds of adapted cultivars. Here, we present a genetic characterization of a population of 1601 backcross recombinant inbred lines, with an average genome composition of 75% bread wheat and 25% wild emmer. Low‐coverage whole‐genome sequencing allowed introgressions and aneuploidies to be identified at a relatively low cost per sample. We identified a relatively large proportion of small introgressions (median length 38 Mb), and we found introgressions to be distributed across all chromosomes. Approximately 44% of genotyped progeny carried at least one aneuploidy, with monosomies being by far the most common. This population, which we have denoted as the Great Plains Wild Emmer/Hard Winter Wheat introgression population (GPWEW‐IP), is, to our knowledge, the first introgression population developed through the direct hybridization of wild emmer wheat and US‐adapted hard winter wheat. We believe that this population represents a valuable resource for wheat breeders and will accelerate the discovery and integration of useful variation from wild emmer wheat.

Genetics & Heredity↗
DOE OSTI2026

Global population structures and demographic history of Suillus luteus, a pine co‐introduced ectomycorrhizal fungus associated with exotic forestry and invasion

Human colonization since the 19th century has resulted in the global spread of pines beyond their original northern boreal distribution. Although the introduction history of pines is documented through historical records, little is known about the introduction history of their ectomycorrhizal (ECM) fungi, which are critical symbionts for the survival and invasion of pines. Using Suillus luteus as an example, whole genomes of 208 individuals collected across native and introduced ranges were sequenced to reveal the introduction history of pine co-introduced ECM fungi. Population genomics analyses showed that all introductions originated from Europe. With the exception of North America, introduced populations were genetically differentiated from the European population, with varying magnitudes of population expansion in different introduced regions. Genetic variation within the native European population followed isolation by distance, but not in the introduced range, highlighting the disparity in the spatial-genetic patterns of native vs exotic habitats. The spread of S. luteus is mediated by human activities accompanying pine introductions, with its demographic history linked to forestry practices. The spatial, temporal, and demographic patterns observed in S. luteus offer insight into the population genetics of a widely introduced ECM fungus and are likely applicable to other pine co-introduced ECM fungi.

Ke, Yi‐Hong↗
DOE OSTI2025

Phenome‐to‐genome insights for evaluating root system architecture in field studies of maize

Abstract Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high‐throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field‐grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild‐type alleles at two known RSA genes,DEEPER ROOTING 1(DRO1) andRootless1(Rt1). We show the utility of increasing the dimensionality of traditional two‐dimensional (2D) techniques, referred to as the “2D multi‐view” method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome‐wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three‐dimensional (3D) root models generated from X‐ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome‐wide and single‐gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field‐grown maize.

Genetics & Heredity↗
DOE OSTI2025

Integrating Intermediate Traits in Phylogenetic Genotype-to-Phenotype Studies

A major goal of research in evolution and genetics is linking genotype to phenotype. This work could be direct, such as determining the genetic basis of a phenotype by leveraging genetic variation or divergence in a developmental, physiological, or behavioral trait. The work could also involve studying the evolutionary phenomena (e.g., reproductive isolation, adaptation, sexual dimorphism, behavior) that reveal an indirect link between genotype and a trait of interest. When the phenotype diverges across evolutionarily distinct lineages, this genotype-to-phenotype problem can be addressed using phylogenetic genotype-to-phenotype (PhyloG2P) mapping, which uses genetic signatures and convergent phenotypes on a phylogeny to infer the genetic bases of traits. The PhyloG2P approach has proven powerful in revealing key genetic changes associated with diverse traits, including the mammalian transition to marine environments and transitions between major mechanisms of photosynthesis. However, there are several intermediate traits layered in between genotype and the phenotype of interest, including but not limited to transcriptional profiles, chromatin states, protein abundances, structures, modifications, metabolites, and physiological parameters. Each intermediate trait is interesting and informative in its own right, but synthesis across data types has great promise for providing a deep, integrated, and predictive understanding of how genotypes drive phenotypic differences and convergence. We argue that an expanded PhyloG2P framework (the PhyloG2P matrix) that explicitly considers intermediate traits, and imputes those that are prohibitive to obtain, will allow a better mechanistic understanding of any trait of interest. Furthermore, this approach provides a proxy for functional validation and mechanistic understanding in organisms where laboratory manipulation is impractical.

59 BASIC BIOLOGICAL SCIENCES↗