Biology subjects

Campbell, M. T.

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

13 recordsLinked to original sources

bioRxiv2025

Tissue Factor Expression in Penile Squamous Cell Carcinoma: A Marker of HPV-Independent Disease

In a series of 33-patients, we evaluated tissue factor (TF) expression in penile squamous cell carcinoma (PSCC). A tissue microarray (TMA) was constructed with 3 cores per patient tumor (99 total cores). Anti-TF antibody staining was performed by immunohistochemistry and H-scores for membrane and cytoplasm staining were assessed (range 0-300). Percentage of cores and patient tumors staining positive for TF ([≥]10% of tumor cells with at least 1+ intensity in cytoplasm and/or membrane) and H-scores were described and compared with HPV and p16 status. Association of TF expression with tumor grade, presence of metastatic disease, lymphovascular invasion (LVI), perineural invasion (PNI), aberrant p53 expression, recurrence free survival (RFS), and cancer specific survival (CSS) were assessed. Nectin-4 and TROP2 staining and their association with clinical/pathological data was determined in a similar manner. TF staining was more prominent in HPV-negative tumors in both the membrane (H-score 69.6 vs 18.8; p=0.003) and cytoplasm (H-score 59.2 vs. 17.7, p=0.007). Cytoplasmic (H-score 61.7 vs 11.7, p=<0.001) and membrane TF staining (H-score 71.7 vs 15.0, p=<0.001) favored p16 negative tumors. The p53 status was more likely to be aberrant in the higher TF staining samples (cytoplasm H-score 61.7 vs 18.3, p=0.012; membrane H-score 67.5 vs 20.3, p=0.006). We observed an association with TROP2 staining and positive p16 status (membrane H-score 120.3 vs. 85, p=0.052; cytoplasmic H-score 135 vs. 107.5, p=0.041). We observed an association of TROP2 staining with positive LVI (membrane H-score 136.7 vs. 66.7, p=0.014; cytoplasmic H-score 110 vs. 93.3, p=0.04). We found no association between TF, TROP2, or nectin-4 staining with CSS or RFS; however, we suspect that this is due to our small sample size. Our results indicate that TF expression could be positive biomarker for HPV-independent, p53-aberrant PSCC, while TROP2 could be associated with HPV-associated PSCC.

pathology↗

bioRxiv2021

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important for plant breeders to improve nutrition and agronomic performance, yet integrating selection for metabolomic traits is limited by phenotyping expense and limited genetic characterization, especially of uncommon metabolites. As such, developing biologically-based and generalizable genomic selection methods for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for more than 600 metabolites measured by GC-MS and LC-MS in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite GWAS (mGWAS) and selected loci to use in multi-kernel models that encompassed metabolome-wide mGWAS results, or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel, consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We tested if similar metabolites had consistent model ranks and found that, while different metrics of similarity had different results, using annotation-free methods to group metabolites led to consistent within-group model rankings. Overall, testing biological rationales for developing kernels for genomic prediction across populations, contributes to developing frameworks for plant breeding for metabolite traits.

genomics↗

bioRxiv2021

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G+T, G+M and G+T+M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

genetics↗

bioRxiv2020

Translating insights from the seed metabolome into improved prediction for healthful compounds in oat

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Despite these characteristics, little is known regarding the genetic controllers of variation for these compounds in oat seed. We sought to characterize natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leverage this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for eight of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional ‘omics’ data to a set of biologically-meaningful variables and translate inferences on these data into improved breeding decisions.View Full Text

genomics↗

bioRxiv2019

Examining the relationships between phenotypic plasticity and local environments with genomic structural equation models

Environmental association analyses (EAA) seek to identify genetic variants associated with local adaptation by regressing local environmental conditions at collection sites on genome-wide polymorphisms. The rationale is that environmental conditions impose selective pressure on trait(s), and these traits are regulated in part by variation at a genomic level. Here, we present an alternative multivariate genomic approach that can be utilized when both phenotypic and environmental data are available for the population. This framework utilizes Bayesian networks (BN) to elucidate interdependancies between local environmental conditions and empirical phenotypes, and jointly estimates the direct and indirect genetic covariances between empirical phenotypes and environmental conditions using a mixed-effects structural equation model (SEM). Direct genomic covariance between empirical phenotypes and environmental conditions may provide insight into whether QTL that affect adaptation to an environmental gradient also affects the observed phenotype. To demonstrate the utility of this approach, we leveraged two existing datasets consisting of 55 climate variables for 1,130 Arabidopsis accessions and empirical phenotypes for fitness and phenology collected on 515 accessions in two common garden locations in Europe. BN showed that plasticity for fitness and phenology was highly dependant on local environmental conditions. Moreover, genomic SEM revealed relatively high positive genomic correlation between plasticity in fitness and environmental variables that describe the favorability of the local environment for plant growth, indicating the presence of common QTL or independent QTL that are tightly linked. We believe the frameworks presented in this manuscript can provide new insights into the genetic basis of local adaptation.

genomics↗

bioRxiv2019

Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

Random regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Moderate to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.87). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.

genetics↗

bioRxiv2019

Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice Oryza sativa

Elucidating genotype-by-environment interactions (GxE) and partitioning its contribution to the phenotypic variation remains a long standing challenge for plant scientists. Recent quantitative genetic frameworks have improved predictions of Gx E responses. However, these models do not explicitly consider the processes that give rise to GxE. To overcome this limitation, we propose a novel framework to elucidate the genetic basis of dynamic shoot growth trajectories under contrasting water regimes using genome-wide markers to model genotype-specific shoot growth trajectories as a function of soil water availability. A rice diversity panel was phenotyped daily over a period of twenty-one days during the early vegetative stage using an automated, high-throughput image-based, phenotyping platform that enabled us to estimate daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the soil water content and/or time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants tend to exhibit an earlier repression in growth compared to smaller, slow growing plants, indicating a potential trade off between early vigor and tolerance to prolonged water deficits. We integrated the growth model within a hierarchical Bayesian framework and used marker information to estimate model parameters and the associated loci through genome-wide association analysis. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. Among them an aquaporin, OsPIP1;1 was identified as a candidate for time of inflection under drought and showed significantly lower expression in accessions exhibiting later TOI in drought. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.

genomics↗

bioRxiv2019

Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines

Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, the high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions.

genetics↗

bioRxiv2019

Harnessing phenotypic networks and structural equation models to improve genome-wide association analysis

Plant breeders and breeders alike seek to develop cultivars with maximal agronomic value. The merit of breeding material is often assessed using many, often genetically correlated traits. As intervention on one trait will affect the value of another, breeding decisions should consider the relationships between traits. With the proliferation of multi-trait genome-wide association studies (MTM-GWAS), we can infer putative genetic signals at the multivariate scale. However, a standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated. We extended the scope of MTM-GWAS by incorporating phenotypic network structures into GWAS using structural equation models (SEM-GWAS). In this network GWAS model, one or more phenotypes appear in the equations for other phenotypes as explanatory variables. A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects into direct and indirect effects. In this paper, we illustrate the utility of SEM-GWAS using biomass, root biomass, water use, and water use efficiency in rice. We found that water use efficiency is directly impacted by biomass and water use and indirectly by biomass and root biomass. In addition, SEM-GWAS partitioned significant SNP effects influencing water use efficiency into direct and indirect effects as a function of biomass, root biomass, and water use efficiency, providing further biological insights. These results suggest that the use of SEM may enhance our understanding of complex relationships between GWAS traits.

genetics↗

bioRxiv2019

The genetic basis of cis-regulatory divergence between the subspecies of cultivated rice (Oryza sativa)

Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1). The transcriptomes of the two subspecies of rice are highly divergent. The expression and genetic diversity was significantly lower within Japonica relative to Indica, which is consistent with the known population bottleneck during Japonica domestication. Moreover, 1,860 and 1,325 genes showed differences in heritability in the broad and narrow sense respectively, between the subspecies, which was driven largely by environmental and genetic effects rather than differences in phenotypic variability. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation. We next explored the potential causes of this cis-regulatory divergence by assessing local genetic diversity for cis-eQTL. Local genetic diversity around subspecies-specific cis-eQTL was significantly lower than genome-wide averages in subspecies lacking the eQTL, suggesting that selective pressures may have shaped regulatory variation in each subspecies. This study provides the first comprehensive characterization of transcriptional and cis-regulatory variation in cultivated rice, and could be an important resource for future studies.

genomics↗

bioRxiv2018

Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits

Understanding the genetic basis of dynamic plant phenotypes has largely been limited due to lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science community with an effective means to non-destructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g. genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits, and provide a robust framework for modeling traits trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice using 33,674 SNPs. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over a conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent, as well as time-specific, transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements. O_LSTCore Ideas:C_LSTO_LIRandom regression models are an appealing framework for GWAS of longitudinal traits C_LIO_LIThis approach provides improvements over a conventional single time point analyses for GWAS C_LIO_LIWe identify QTL with transient and persistent effects on shoot growth in rice C_LI

genetics↗

bioRxiv2018

Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes

Drawing biological inferences from large data generated to dissect the genetic basis of complex traits remains a challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can accelerate the genetic improvement of plants and animals. A Bayesian network depicts a probabilistic directed acyclic graph representing conditional dependencies among variables. This study aims to characterize various phenotypes in rice (Oryza sativa) via confirmatory factor analysis and Bayesian network. Confirmatory factor analysis under the Bayesian treatment hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology (e.g., ion content), yield, and morphological salt response. This was followed by studying the genetics of each latent variable. Bayesian network structures involving the genomic component of six latent variables were established by fitting four different algorithms. Negative genomic correlations were obtained between salt response and yield, salt response and grain morphology, salt response and physiology, and morphology and yield, whereas a positive correlation was obtained between yield and grain morphology. There were four common directed edges across the different Bayesian networks. Physiological components influenced the flowering time and grain morphology, and morphology and 4 grain morphology influenced yield. This work suggests that the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the high-dimensional interrelated complex traits systems.

genetics↗

bioRxiv2018

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping

The accessibility of high-throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, have provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study is to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice (Oryza sativa). An estimate of shoot biomass, projected shoot area (PSA), was recored over a period of 20 days for a panel of 357 diverse rice accessions using an image-based greenhouse phenotyping platform. A RR that included a fixed second-order Legendre polynomial, a random second-order Legendre polynomial for the additive genetic effect, a first-order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point (TP) approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data. In all cases, PSA could be predicted with high accuracy (r: 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice, and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach.

genomics↗