Code to recreate the analysis in this manuscript is available at CyVerse Data Commons from ( Yanarella et al . 2023b ) and can be accessed from: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_Maize_WiDiv_Association_Studies_Dataset_September_2023 . The deidentified spoken data described in this manuscript is exempted by Iowa State University’s Institutional Review Board (IRB ID: 21-179-00). Phenotypic data was obtained from ( Yanarella et al . 2023a ) and is available from: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_D
Open resource ↗CyVerse Data Commons · Carolyn_Lawrence_Dill_Maize_WiDiv_Association_Studies_Dataset_September_2023 · lines:526-547Unverified paper record
Genome-wide association studies from spoken phenotypic descriptions: a proof of concept from maize field studies.
G3 (Bethesda, Md.) · 1 Sept 2024 · 10.1093/g3journal/jkae161
Abstract
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.
Plant phenotyping relevance
非構造化音声による植物表現型記述を定量化し、草丈を推定する手法を開発・実証した研究であり、表現型取得・抽出法が中心である。
abstractWe present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits.
abstractwe developed two methods to process the same descriptions to derive the trait plant height
abstractThis 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.
Code and data availability
The paper's Data Availability statement provides public CyVerse and figshare deposits containing the authors' analysis code, the spoken-phenotype/phenotypic dataset, and the genotypic input data used for the GWAS analyses.
commons_repo/curated/Carolyn_Lawrence_Dill_Maize_WiDiv_Association_Studies_Dataset_September_2023 . The deidentified spoken data described in this manuscript is exempted by Iowa State University’s Institutional Review Board (IRB ID: 21-179-00). Phenotypic data was obtained from ( Yanarella et al . 2023a ) and is available from: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_Maize_WiDiv_Summer_2021_Dataset_June_2023 . Genotypic data was obtained from ( Mural et al . 2022b , 2022a ) and can be accessed from: https://figshare.com/articles/dataset/Maize_WiDiv_SAM_1051Genotype_vcf_gz_genotype_file/19175888/1 . Gene Ontology data was obtained f
Open resource ↗Carolyn_Lawrence_Dill_Maize_WiDiv_Summer_2021_Dataset_June_2023 · lines:526-547179-00). Phenotypic data was obtained from ( Yanarella et al . 2023a ) and is available from: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_Maize_WiDiv_Summer_2021_Dataset_June_2023 . Genotypic data was obtained from ( Mural et al . 2022b , 2022a ) and can be accessed from: https://figshare.com/articles/dataset/Maize_WiDiv_SAM_1051Genotype_vcf_gz_genotype_file/19175888/1 . Gene Ontology data was obtained from ( Wimalanathan and Lawrence-Dill 2017 ) and is available from https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence-Dill_maize-GAMER_maize.B73_RefGen_v4_Zm00001d.2_Oct_2017.r1 . Supplemental
Open resource ↗figshare · Maize_WiDiv_SAM_1051Genotype_vcf_gz_genotype_file/19175888 · lines:526-547This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.