High-Throughput Seed Phenotyping and GWAS Uncover Key Genetic Variants Influencing Seed Quality in Leymus chinensis

High-Throughput Seed Phenotyping and GWAS Uncover Key Genetic Variants Influencing Seed Quality in Leymus chinensis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article High-Throughput Seed Phenotyping and GWAS Uncover Key Genetic Variants Influencing Seed Quality in Leymus chinensis Shuangyan Chen, Lei Tian, Chuifan Kong, Shuyi Hu, Xiaoyun Han, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7733701/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Leymus chinensis (Trin.) Tzvel. (sheepgrass) is an important forage species, yet the relationships between seed phenotypic traits, agronomic performance, and their underlying genetic mechanisms remain unclear. In this study, we utilized the AIseed high-throughput phenotyping platform to systematically analyze 54 image-based traits (i-traits)—encompassing morphology, color, and texture—in 262 dehusked seeds of sheepgrass. Coupled with 50K single nucleotide polymorphism (SNP) chip genotyping data, we performed a genome-wide association study (GWAS) to elucidate genetic correlations among seed phenotypic traits. Elastic net regression was employed to identify informative phenotypic predictors, revealing significant associations between seed size, seed coat texture, and color with hundred-seed weight (HGW), hundred-seed weight without glumes (HGWwg), and germination rate (GR). Additionally, a germplasm screening approach based on principal component analysis (PCA) achieved a 71% accuracy rate in predicting high-germination germplasm and identified 10 germplasm lines with superior comprehensive performance. GWAS identified several SNPs significantly associated with seed color and morphology, mainly on chromosomes Lc2Xm and Lc6Xm. KEGG analysis highlighted the roles of phenylpropanoid and flavonoid biosynthesis pathways, with candidate genes such as PAL, PER18, PER50, BGLU16, BACOVA_02659, and ANR implicated. This study offers effective phenotypic screening strategies and valuable genetic resources for the molecular breeding of sheepgrass. Biological sciences/Plant sciences/Plant breeding Biological sciences/Genetics/Functional genomics Biological sciences/Plant sciences/Plant genetics Leymus chinensis High-hroughput phenotyping GWAS Seed traits Molecular breeding Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7733701","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":554684681,"identity":"33e1550b-8a5b-475e-a953-740ba23b918e","order_by":0,"name":"Shuangyan 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