Phenomic Data Research Articles

Features Selection in Statistical Classification of High Dimensional Image Derived Maize (<i>Zea Mays</i> L.) Phenomic Data

Phenotyping has advanced with the application of high throughput phenotyping techniques such automated imaging. This has led to derivation of large quantities of high dimensional phenotypic data that could not have been achieved using manual phenotyping in a single run. Hence, the need for parallel development of statistical techniques that can appropriately handle such large and/or high dimensional data set. Moreover, there is need to come up with a statistical criteria for selecting the best image derived phenotypic features that can be used as best predictors in modelling plant growth. Information on such criteria is limited. The objective of this study is to apply feature importance, feature selection with Shapley values and LASSO regression techniques to find the subset of features with the highest predictive power for subsequent use in modelling maize plant growth using high-dimensional image derived phenotypic data. The study compared the statistical power of these features extraction methods by fitting an XGBoost model using the best features from each selection method. The image derived phenomic data was obtained from Leibniz Institute of Plant Genetics and Crop Plant Research, -Gatersleben, Germany. Data analysis was performed using R-statistical software. The data was subjected to data imputation using k Nearest Neighbours technique. Features extraction was performed using feature importance, Shapley values and LASSO regression. The Shapley values extracted 25 phenotypic features, feature importance extracted 31 features and LASSO regression extracted 12 features. Of the three techniques, the feature importance criterion emerged the best feature selection technique, followed by Shapley values and LASSO regression, respectively. The study demonstrated the potential of using feature importance as a selection technique in reduction of input variables in of high dimensional growth data set.

Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein.

Simple SummaryGenomic selection models aim at predicting the performance of individuals with the use of genomic markers. In animal breeding, prediction models are seldomly tested for their ability to predict new individuals’ performance under different environmental conditions, despite the changes in management and diet that the industry undergoes. In this study, we propose a method to use milk infrared spectra as descriptors of environmental variation among herds. These descriptors can be incorporated in genomic prediction models similarly to how genomic markers are included. The inclusion of environmental descriptors is shown to improve the predictive ability for new genotypes under new environmental conditions.The purpose of this study was to provide a procedure for the inclusion of milk spectral information into genomic prediction models. Spectral data were considered a set of covariates, in addition to genomic covariates. Milk yield and somatic cell score were used as traits to investigate. A cross-validation was employed, making a distinction for predicting new individuals’ performance under known environments, known individuals’ performance under new environments, and new individuals’ performance under new environments. We found an advantage of including spectral data as environmental covariates when the genomic predictions had to be extrapolated to new environments. This was valid for both observed and, even more, unobserved families (genotypes). Overall, prediction accuracy was larger for milk yield than somatic cell score. Fourier-transformed infrared spectral data can be used as a source of information for the calculation of the ‘environmental coordinates’ of a given farm in a given time, extrapolating predictions to new environments. This procedure could serve as an example of integration of genomic and phenomic data. This could help using spectral data for traits that present poor predictability at the phenotypic level, such as disease incidence and behavior traits. The strength of the model is the ability to couple genomic with high-throughput phenomic information.

Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry

SummaryDrugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens that rely on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope-tracer direct-infusion mass spectrometry that informs an algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening, integrating isotope-enriched metabolomics data and computational data mining, on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.

Systems genetics in the rat HXB/BXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.

Neurogenesis in the adult hippocampus contributes to learning and memory in the healthy brain but is dysregulated in metabolic and neurodegenerative diseases. The molecular relationships between neural stem cell activity, adult neurogenesis, and global metabolism are largely unknown. Here we applied unbiased systems genetics methods to quantify genetic covariation among adult neurogenesis and metabolic phenotypes in peripheral tissues of a genetically diverse family of rat strains, derived from a cross between the spontaneously hypertensive (SHR/OlaIpcv) strain and Brown Norway (BN-Lx/Cub). The HXB/BXH family is a very well established model to dissect genetic variants that modulate metabolic and cardiovascular diseases and we have accumulated deep phenome and transcriptome data in a FAIR-compliant resource for systematic and integrative analyses. Here we measured rates of precursor cell proliferation, survival of new neurons, and gene expression in the hippocampus of the entire HXB/BXH family, including both parents. These data were combined with published metabolic phenotypes to detect a neurometabolic quantitative trait locus (QTL) for serum glucose and neuronal survival on Chromosome 16: 62.1-66.3 Mb. We subsequently fine-mapped the key phenotype to a locus that includes the Telo2-interacting protein 2 gene (Tti2)-a chaperone that modulates the activity and stability of PIKK kinases. To verify the hypothesis that differences in neurogenesis and glucose levels are caused by a polymorphism in Tti2, we generated a targeted frameshift mutation on the SHR/OlaIpcv background. Heterozygous SHR-Tti2+/- mutants had lower rates of hippocampal neurogenesis and hallmarks of dysglycemia compared to wild-type littermates. Our findings highlight Tti2 as a causal genetic link between glucose metabolism and structural brain plasticity. In humans, more than 800 genomic variants are linked to TTI2 expression, seven of which have associations to protein and blood stem cell factor concentrations, blood pressure and frontotemporal dementia.

Organelle specific fluorescent phenomics and transcriptomic profiling to evaluate cellular response to tris(1,3 dichloro 2 propyl)phosphate

Tris(1,3-dichloro-2-propyl)phosphate (TDCPP) has been suspected to cause toxicity invertebrates, but its phenotypic effects and the underlying regulatory mechanism have not been fully revealed. Generally, cellular responses tightly control and affect various phenotypes. The scope of the whole organism or cellular toxicological phenotyping, however, has been limited, and quantitative analysis methods using phenotype data have not been fully established. Here, we demonstrated that fluorescence imaging of sub-organelle-based phenomic analysis together with transcriptomic profiling can enable a comprehensive understanding of correlations between molecular and phenomic events. To reveal the cellular response to TDCPP exposure, we obtained three sub-organelle images as fluorescent phenotypes. Transcriptomic perturbation data were measured from the RNA-seq experiment, and both profiling results were analyzed together. Interestingly, organelle phenomic data showed a unique fluorescent intensity increase in the endoplasmic reticulum (ER), and pathway analysis using transcriptomic data also revealed that ER was significantly enriched in gene ontology terms. Following the series of analyses, RNA-seq data also revealed potential carcinogenic effects of TDCPP. Our multi-dimensional profiling approach for organophosphate chemicals can uniquely correlate phenotypic changes with transcriptomic perturbations.

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion.

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

Development, Preparation, and Curation of High-Throughput Phenotypic Data for Genome-Wide Association Studies: A Sample Pipeline in R.

Genome-wide association studies (GWAS) have benefited from the advances of sequencing methods for the generation of high-density genomic data. By bridging genotype to phenotype, several genes have been associated with traits of agricultural interest. Despite this, there is still a gap between genotyping and phenotyping due to the large difference in throughput between the two disciplines. Although cutting-edge phenomics technologies are available to the community, their costs are still prohibitive at the small lab level. Semiautomated methods of investigation provide a valid alternative to generate large-scale phenotyping data able to deeply investigate the characteristics of different plant organs. Beyond automation, phenomics data management is another major constraint to consider; while bioinformatics pipelines are well-trained for releasing high-quality genomic data, fewer efforts have been done for phenotyping information. This chapter provides a guide for generating large-scale data related to the size and shape of fruits, leaves, seeds, and roots and for downstream analysis for curation and preparation of clean datasets, through removal of outliers and performing primary statistical analysis. Different steps to be carried out in the R environment will be shown for gathering the appropriate input information to use in GWAS avoiding any possible bias.

Phenomics data processing: extracting dose–response curve parameters from high-resolution temperature courses and repeated field-based wheat height measurements

Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modelling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang–Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose–responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose–response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.

An integrative genomic and phenomic analysis to investigate the nature of plant species in Escallonia (Escalloniaceae)

What we mean by species and whether they have any biological reality has been debated since the early days of evolutionary biology. Some biologists even suggest that plant species are created by taxonomists as a subjective, artificial division of nature. However, the nature of plant species has been rarely tested critically with data while ignoring taxonomy. We integrate phenomic and genomic data collected across hundreds of individuals at a continental scale to investigate this question in Escallonia (Escalloniaceae), a group of plants which includes 40 taxonomic species (the species proposed by taxonomists). We first show that taxonomic species may be questionable as they match poorly to patterns of phenotypic and genetic variation displayed by individuals collected in nature. We then use explicit statistical methods for species delimitation designed for phenotypic and genomic data, and show that plant species do exist in Escallonia as an objective, discrete property of nature independent of taxonomy. We show that such species correspond poorly to current taxonomic species (< 20%) and that phenomic and genomic data seldom delimit congruent entities (< 20%). These discrepancies suggest that evolutionary forces additional to gene flow can maintain the cohesion of species. We propose that phenomic and genomic data analyzed on an equal footing build a broader perspective on the nature of plant species by helping delineate different ‘types of species’. Our results caution studies which take the accuracy of taxonomic species for granted and challenge the notion of plant species without empirical evidence. Note: A version of the complete manuscript in Spanish is available in the Supplemental Materials.

Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points

• General plot-level model for repeated high-throughput field phenotyping measurements. • Extraction of three main intermediate trait categories for dynamic modelling. • Seamless processing approach that integrates temporal and spatial modelling. • Phenomics data processing cheatsheet. Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput field phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variation in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless step-wise procedure that allows for carry on of estimated means and variances from stage to stage. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at defined time points or periods, and (3) dose-response curves. In a first stage, these intermediate traits are extracted from low-level traits’ time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as final height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the first two above-mentioned categories. Results indicate that, for the first stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if estimated means and variances are carried forward from one processing stage to the next. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at defined time points from HTFP data.

Integrating Genomic and Phenomic Approaches to Support Plant Genetic Resources Conservation and Use.

Plant genebanks provide genetic resources for breeding and research programs worldwide. These programs benefit from having access to high-quality, standardized phenotypic and genotypic data. Technological advances have made it possible to collect phenomic and genomic data for genebank collections, which, with the appropriate analytical tools, can directly inform breeding programs. We discuss the importance of considering genebank accession homogeneity and heterogeneity in data collection and documentation. Citing specific examples, we describe how well-documented genomic and phenomic data have met or could meet the needs of plant genetic resource managers and users. We explore future opportunities that may emerge from improved documentation and data integration among plant genetic resource information systems.

Tripal, a community update after 10years of supporting open source, standards-based genetic, genomic and breeding databases.

Online, open access databases for biological knowledge serve as central repositories for research communities to store, find and analyze integrated, multi-disciplinary datasets. With increasing volumes, complexity and the need to integrate genomic, transcriptomic, metabolomic, proteomic, phenomic and environmental data, community databases face tremendous challenges in ongoing maintenance, expansion and upgrades. A common infrastructure framework using community standards shared by many databases can reduce development burden, provide interoperability, ensure use of common standards and support long-term sustainability. Tripal is a mature, open source platform built to meet this need. With ongoing improvement since its first release in 2009, Tripal provides full functionality for searching, browsing, loading and curating numerous types of data and is a primary technology powering at least 31 publicly available databases spanning plants, animals and human data, primarily storing genomics, genetics and breeding data. Tripal software development is managed by a shared, inclusive governance structure including both project management and advisory teams. Here, we report on the most important and innovative aspects of Tripal after 11 years development, including integration of diverse types of biological data, successful collaborative projects across member databases, and support for implementing FAIR principles.

Abstract 2620: Ignoring left truncation in overall survival within real-world genomic-phenomic data leads to inflated survival estimates

Abstract Studies linking genomic and phenomic data are subject to selection biases, including delayed entry or immortal time bias. Delayed entry can be problematic for time-to-event analyses, but utilization of appropriate statistical methods to account for delayed entry are underutilized. Delayed entry commonly occurs when genomic sequencing results are obtained after the start time for survival estimation. To evaluate the impact of left truncation on overall survival (OS) estimates, we explored outcomes in patients with de novo stage IV non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) from the AACR GENIE Biopharma Collaborative, who had genomic sequencing within a specified timeframe. We analyzed OS from diagnosis and from start of the most common first-line regimen, carboplatin/pemetrexed for NSCLC (N = 212 patients) and FOLFOX for CRC (N = 369 patients). We compared median OS using standard Kaplan-Meier methods to median OS using left truncation methods to account for delayed entry. All NSCLC and CRC patients underwent genomic sequencing after their diagnosis date. Among NSCLC patients on carboplatin/pemetrexed, 41% and among CRC patients on FOLFOX, 14% had sequencing determined after starting first-line regimen. The survfit function in R package survival was used, and the absolute differences and percent differences in median OS estimates were calculated. Failure to account for delayed entry leads to an overestimation of OS, regardless of cohort and start date. Adjusting survival outcomes using left truncation methods reduces the influence of some aspects of selection bias and results in better estimates of time to event outcomes. Analyses from these cohorts can provide meaningful insights about survival outcomes outside the clinical trial setting and may support trial design and reliable selection of control arms. As such, it is imperative that analytic methods to account for the inflated survival estimates are incorporated. EstimateCRC Stage IV (N = 658)NSCLC Stage IV (N = 722)Unadjusted Median (IQR) Overall Survival from Diagnosis (Years)3.2 (2.9, 3.4)2.3 (2.0, 2.5)Median (IQR) Overall Survival from Diagnosis in Years, Adjusting for Delayed Entry2.1 (1.9, 2.4)1.3 (1.1, 1.6)Difference in Medians (Years)1.11.0% Difference in Medians34%44%EstimateCRC Stage IV (N = 369)NSCLC Stage IV (N = 212)Unadjusted Median (IQR) Overall Survival from Most Common First-Line Regimen (Years)2.9 (2.6, 3.4)1.3 (1.0, 1.6)Median (IQR) Overall Survival from Most Common First-Line Regimen in Years, Adjusting for Delayed Entry2.1 (1.8, 2.5)0.9 (0.7, 1.2)Difference in Medians (Years)0.80.4% Difference in Medians28%31% Citation Format: Samantha Brown, Jessica A. Lavery, Eva M. Lepisto, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Ritika Kundra, Brooke Mastrogiacomo, Phillipe Bedard, Jeremy L. Warner, Gregory J. Riely, Deborah Schrag, Katherine S. Panageas, The AACR Project GENIE Consortium. Ignoring left truncation in overall survival within real-world genomic-phenomic data leads to inflated survival estimates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2620.

Abstract 2619: Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data

Abstract Obtaining information regarding cancer recurrence from a retrospective, EHR-based dataset poses several challenges primarily due to the lack of structured data. Patients are at risk for cancer recurrence beginning at a time point at which they are characterized as having no evidence of disease. The absence of cancer may be indicated on a radiology report or a medical oncologist assessment, requiring manual review and interpretation of potentially ambiguous free text. Further, the recurrence event itself can be defined based on several distinct data sources including pathology, imaging, clinician assessments, or tumor markers. The likelihood of ascertaining recurrence is dependent on the frequency and type of surveillance performed and varies based on tumor type and based on clinicians' thresholds for pursuing workup of borderline or suspicious findings; if follow up assessments are infrequent, there are fewer opportunities to detect recurrence. Given these challenges, there is currently no standardized approach to evaluating cancer recurrence in EHR data, impeding analyses of rare molecular tumor subtypes in multi-institutional linked clinico-genomic databases. For this analysis, we leveraged the AACR Project GENIE Biopharma Collaborative data based on the PRISSMM curation model to develop an algorithm for identifying recurrence among patients diagnosed with stage I-III non-small cell lung cancer or with stage I-III colorectal cancer. This algorithm involves using curated pathology report data to identify a definitive surgery as the time at which patients have completed curative intent treatment. Subsequent imaging reports, pathology reports, medical oncologist assessments and tumor marker data are then evaluated in order to characterize the timing of specific recurrence events. We will present the real-world recurrence algorithm, its underlying rationale and discuss applications of recurrence endpoints. Beyond enabling estimates of recurrence-free survival, identifying cancer recurrence will allow for estimation of progression-free survival among stage I-III patients in addition to estimation of PFS among de novo stage IV patients. Estimating PFS in a large cohort of patients with linked phenomic and genomic data has historically been a limitation of these types of datasets. Overcoming this limitation will allow for precision medicine advances in oncology by facilitating data pooling across institutions and enabling examination of rare molecular subtypes in relation to clinically meaningful endpoints. Citation Format: Jessica A. Lavery, Samantha Brown, Eva Lepisto, Michele L. Lenoue-Newton, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Brooke Mastrogiacomo, Ritika Kundra, Jeremy Warner, Philippe Bedard, Gregory J. Riely, Katherine S. Panageas, Deborah Schrag, AACR Project GENIE Consortium. Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2619.

Modelling temporal genetic and spatio‐temporal residual effects for high‐throughput phenotyping data*

SummaryHigh‐throughput phenomics data are being collected in both the laboratory and the field. The data are often collected at many time points and there may be spatial variation in the laboratory or field that impacts on the growth of the plants, and that may influence the traits of interest. Modelling the genetic effects is of primary interest in such studies, but these effects might be biased if non‐genetic effects present in the experiment are ignored. With data that are collected both in time and space, there may be a need to jointly model these multi‐dimensional non‐genetic effects. Thus both modelling of genetic effects over time and non‐genetic effects over time and space in a one‐stage analysis is considered. An experiment that involves field phenomics data with four dimensions, two in space and two in time, provides the vehicle to examine the models. Factor analytic (FA) models are often used for genetic effects for different environments to provide reliable estimates of genetic variances and correlations. As the time dimension defines the environments, FA models are examined for the phenomics data. Reduced rank tensor smoothing splines are presented as a possible approach for modelling the spatio‐temporal effects, although an additional term is included for heterogeneity over the two time dimensions. This approach is feasible, although very time‐consuming. The process of model selection for the genetic effects is presented including tests, information criteria and diagnostics. Comparisons of more simplistic models are made with the reduced rank tensor spline. This also shows the interplay between the genetic and residual models in model selection.

The Role of Electronic Health Records in Advancing Genomic Medicine.

Recent advances in genomic technology and widespread adoption of electronic health records (EHRs) have accelerated the development of genomic medicine, bringing promising research findings from genome science into clinical practice. Genomic and phenomic data, accrued across large populations through biobanks linked to EHRs, have enabled the study of genetic variation at a phenome-wide scale. Through new quantitative techniques, pleiotropy can be explored with phenome-wide association studies, the occurrence of common complex diseases can be predicted using the cumulative influence of many genetic variants (polygenic risk scores), and undiagnosed Mendelian syndromes can be identified using EHR-based phenotypic signatures (phenotype risk scores). In this review, we trace the role of EHRs from the development of genome-wide analytic techniques to translational efforts to test these new interventions to the clinic. Throughout, we describe the challenges that remain when combining EHRs with genetics to improve clinical care.

A comprehensive Memorial Sloan Kettering Cancer Center real-world data model: Core clinical data elements.

e18755 Background: The 2016 21st Century Cures Act supports the use of Real-World Data (RWD) for regulatory decision/approval. Due to technological advances, a vast amount of health-related data are now available, but most are not standardized nor readily useable for research. Also, currently available standardized RWD models are not applicable across cancer types or oncology specialties (surgery, medical oncology, radiation oncology, pathology, radiology, etc.). To address these deficiencies Memorial Sloan Kettering Cancer Center (MSKCC) built a comprehensive, pan-cancer, pan-specialty RWD model. Methods: The Core Clinical Data Element (CCDE) data model incorporates aspects of existing academic and biopharma data models, including PRISSMM framework, ASCO’s mCODE, and NAACCR tumor registry model. The data model encompasses 11 domains that are critical to the understanding of the patient’s cancer journey, including: demographic, comorbidities, diagnosis, pathology, imaging, genomics, cancer surgeries, radiation oncology treatments, medical oncology treatments, cancer status/progression, and additional health information. To align with current standards, we are using ICD-10, ICDO3, CTACE V5.0, HL7, SNOMED and LOINC code sets. Further, this adaptable model allows for 5-10 disease specific elements to accommodate for disease heterogenicity and capture the differences among cancer types. Results: The CCDE database includes 1,126 of total data elements. MSKCC has 52,704 patients with MSK-IMPACT (Next-Generation sequencing platform with 505 genes panel) testing of which, we have identified 1,132 bladder cancer patients with at-least one year of cancer care follow-up for the initial curation cohort. Patients were identified as having an OncoTree bladder tumor type code that is assigned by a pathologist who attests the diagnosis by reviewing results from clinical tests on tumor specimens. To the date, 641 patients including 46,415 curated forms have been curated (Table). Conclusions: The comprehensive MSKCC’s CCDE data model standardizes the common and critical pan-cancer and pan-specialty elements for RWD. The dataset resulting from this curation efforts will provide robust structured and unified genomic and phenomic data across tumor types for future research enabling greater collaboration across various cancer types as well as oncology specialties.[Table: see text]

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.

Multi-Scale Evaluation on Two Locations and Digital Fruit Imaging Highlight Morpho-Agronomic Performances and Antioxidant Properties in Chilli Pepper Hybrids

Chilli pepper is a vegetable crop widely consumed mostly as fresh food or dried as a spice. The nutritional contribution due to the presence of beneficial healthy-related compounds and the versatility of uses have increased its cultivation over the last decade. In Southern European countries chilli production uses established cultivars and/or landraces that are well adapted to specific environments but do not often meet the requirements of the industry, particularly for packaging and processing. In this study, 10 commercial hybrids were evaluated in two diverse environment sites for their productivity and the content of phytochemicals including, carotenoids, capsaicinoids, ascorbic acid and tocopherols. Fruits were assessed using automated tools for the analysis of size, shape and colour parameters. The pepper materials were promising in terms of productivity, whereas a lower level of capsaicinoids and ascorbic acid were detected. Genotype by environment analysis indicated minimal environmental influence on yield, fruit shape, and capsaicinoids. The integration of different sources of phenomics data demonstrates how breeding activities of hybrids have focused on yield and morphology rather than quality linked to phytochemicals content.

The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data.

Continued increases in genetic gain demonstrate the success of established public and private plant breeding programs. Nevertheless, in the last two decades, a growing body of modern technologies has been developed and now awaits efficient integration into traditional breeding pipelines. This integration offers attractive benefits, yet comes with the challenges of making modifications in established and operational systems, a recent example of which is rice breeding (Collard et al., 2019). Newly available technologies, genomics rapid cycling (Crossa et al., 2017), high throughput phenotyping (HTP, phenomics) (Montesinos-Lopez et al., 2017) and historical descriptions of environmental relatedness (enviromics) (Costa-Neto et al., 2020a,b; Resende et al., 2020; Rogers et al., 2021) are crucial to improving conventional breeding schemes and increasing genetic gain. Integrating these new technologies into routine breeding pipelines will support the delivery of cultivars with robust yields in the face of the expected unfavorable future environmental conditions caused by climate change and the consequently increased occurrence of biotic and abiotic stresses. Here, we briefly describe the use of these technologies and their implementation to provide cost-effective and time-saving approaches to plant breeding. We also give an overview of the interconnections between these techniques. Finally, we envision future perspectives to implement a more interconnected breeding approach that takes advantage of the so-called modern plant breeding triangle: integrating genomics, phenomics, and enviromics. Why Genomics for Improving Breeding? One of the most popular uses of genomics in breeding is the prediction of breeding values. Genomic selection (GS) reduces cycle time, increases the accuracy of estimated breeding values and improves selection accuracy. For instance, in maize, the effectiveness of GS has been proven for the case of bi-parental populations (Massman et al., 2013; Beyene et al., 2015; Vivek et al., 2017), as well as in multi-parental populations (Zhang et al., 2017). Its use has also been documented in species with long generation times such as trees (Grattapaglia et al., 2018) and dairy cattle breeding, where the reduction of the breeding cycle has increased the response to selection in comparison with the progeny testing system (Garcia-Ruiz et al., 2016). Genomic selection has been implemented in many crops, including wheat, chickpea, cassava and rice (Roorkiwal et al., 2016; Crossa et al., 2017; Wolfe et al., 2017; Huang et al., 2019), and the number of programs that are moving from “conventional” to GS is growing. Results in wheat show that genomic predictions used early in the breeding cycle led to a substantial increase in performance in later generations (Bonnett et al., 2021 this issue). Defining Foundational Core Parents for Genomic Selection-Assisted Breeding In genomic selection, the optimization of the training set composition is an important topic because training and testing sets should be genetically related in such a way that the genetic diversity present in the testing set could be covered and captured by the diversity in the training set. Breeding programs must start forming initial foundational core parents (training populations) that represent the genetic diversity found in the current progeny and conform to the testing population(s) to the greatest extent possible (Hickey et al., 2012). These foundational parents should be extensively phenotyped in different target populations of environments and genotyped with high-density marker systems. These training sets of foundation parents will be able to produce a model with a high accuracy for current highly selected progenies (Zhang et al., 2017).