Genome-wide association studies from spoken phenotypic descriptions: a proof of concept from maize field studies.

We present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits. Utilizing the Wisconsin Diversity panel, we collected spoken descriptions of Zea mays ssp. mays traits, converting these qualitative observations into quantitative data amenable to GWAS analysis. First, we determined that visually striking phenotypes could be detected from unstructured spoken phenotypic descriptions. Next, we developed two methods to process the same descriptions to derive the trait plant height, a well-characterized phenotypic feature in maize: (1) a semantic similarity metric that assigns a score based on the resemblance of each observation to the concept of 'tallness' and (2) a manual scoring system that categorizes and assigns values to phrases related to plant height. Our analysis successfully corroborated known genomic associations and uncovered novel candidate genes potentially linked to plant height. Some of these genes are associated with gene ontology terms that suggest a plausible involvement in determining plant stature. This proof-of-concept demonstrates the viability of spoken phenotypic descriptions in GWAS and introduces a scalable framework for incorporating unstructured language data into genetic association studies. This methodology has the potential not only to enrich the phenotypic data used in GWAS and to enhance the discovery of genetic elements linked to complex traits but also to expand the repertoire of phenotype data collection methods available for use in the field environment.

Wisconsin diversity panel phenotypes: spoken descriptions of plants and supporting data.

OBJECTIVES: Phenotyping plants in a field environment can involve a variety of methods including the use of automated instruments and labor-intensive manual measurement and scoring. Researchers also collect language-based phenotypic descriptions and use controlled vocabularies and structures such as ontologies to enable computation on descriptive phenotype data, including methods to determine phenotypic similarities. In this study, spoken descriptions of plants were collected and observers were instructed to use their own vocabulary to describe plant features that were present and visible. Further, these plants were measured and scored manually as part of a larger study to investigate whether spoken plant descriptions can be used to recover known biological phenomena. DATA DESCRIPTION: Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants' (whose personal data are protected for ethical reasons) and volunteers' observation transcripts, volunteers' audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected phenotypes (e.g., plant height, ear and tassel features, etc.; measurements and scores). Data were collected during the summer of 2021 at Iowa State University's Agricultural Engineering and Agronomy Research Farms.

Gene function annotations for the maize NAM founder lines.

OBJECTIVES: We annotated the latest published sequences of the 26 Zea mays Nested Association Mapping (NAM) founder lines using GOMAP, the Gene Ontology Meta Annotator for Plants. The maize NAM panel enables researchers to understand and identify the genetic basis of complex traits. Annotations of predicted functions for genes can help researchers investigate gene-phenotype associations, prioritize candidate genes for phenotypes of interest, and formulate testable hypotheses about gene function/phenotype associations. The creation and release of high-confidence, high-coverage gene function annotation sets for the NAM founder lines is critical to accelerate the generation of knowledge in maize genetics research. GOMAP is a high-throughput computational pipeline that annotates gene functions genome-wide in plant genomes using Gene Ontology functional class terms. Here we report and share GOMAP-generated functional annotations for the NAM founder lines. DATA DESCRIPTION: Datasets include the protein sequences used as input, GOMAP-generated annotation files, scripts used to update obsolete terms, and GAF-formatted tab-delimited text files of gene function annotations along with README files that describe formatting, content, and how files relate to each other.

Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants.

BACKGROUND: Genome-wide gene function annotations are useful for hypothesis generation and for prioritizing candidate genes potentially responsible for phenotypes of interest. We functionally annotated the genes of 18 crop plant genomes across 14 species using the GOMAP pipeline. RESULTS: By comparison to existing GO annotation datasets, GOMAP-generated datasets cover more genes, contain more GO terms, and are similar in quality (based on precision and recall metrics using existing gold standards as the basis for comparison). From there, we sought to determine whether the datasets across multiple species could be used together to carry out comparative functional genomics analyses in plants. To test the idea and as a proof of concept, we created dendrograms of functional relatedness based on terms assigned for all 18 genomes. These dendrograms were compared to well-established species-level evolutionary phylogenies to determine whether trees derived were in agreement with known evolutionary relationships, which they largely are. Where discrepancies were observed, we determined branch support based on jackknifing then removed individual annotation sets by genome to identify the annotation sets causing unexpected relationships. CONCLUSIONS: GOMAP-derived functional annotations used together across multiple species generally retain sufficient biological signal to recover known phylogenetic relationships based on genome-wide functional similarities, indicating that comparative functional genomics across species based on GO data holds promise for generating novel hypotheses about comparative gene function and traits.

Genomic Features of Vibrio parahaemolyticus from Lebanon and Comparison to Globally Diverse Strains by Whole-Genome Sequencing.

Gastroenteritis is a disease that can be caused by virulent strains of Vibrio parahaemolyticus in humans upon the consumption of contaminated seafood. In summer 2017, a sudden increase in the number of patients suffering from gastroenteritis due to a V. parahaemolyticus infection was observed at the Middle East Institute of Health University Hospital in Lebanon. The aim of this study was to analyze the isolates recovered from stool specimens, and to compare them using different phenotypic assays, genomic profiling techniques, and whole-genome sequencing, to achieve a better understanding of the current V. parahaemolyticus strains available in Lebanon. Virulence potential was analyzed based on the detection of the hemolysins: thermostable direct hemolysin (tdh), thermostable direct hemolysin-related hemolysin (trh), and thermolabile hemolysin (tlh). Resistance was determined by testing antibiotic susceptibility and performing PCR assays for ß-lactamases and quinolone resistance determinants. Genetic relatedness was verified by multilocus sequence typing, pulsed-field gel electrophoresis, and whole genome-based single nucleotide polymorphism analysis. All of the isolates had the tdh+, trh-, group-specific PCR+ genotype, which is a characteristic of the O3:K6 pandemic clone. The isolates were resistant to ampicillin (100%), ceftazidime (86%), ticarcillin (14%), and amikacin (14%), belonged to the sequence type ST3, and had very similar phylogenetic fingerprints. The isolates undertaken in this study exhibited almost identical resistance, virulence, and phylogenetic patterns, confirming an outbreak linked to the spread of the pandemic O3:K6 serotype in the country.