The 12th International Conference on Agricultural and Biological Sciences (ABS 2026)

Abstract: The rapid advancement of omics technologies and AI presents new challenges for genome-wide association studies, including large population sizes, diverse marker types and quantities, and various dependent variable types. Climate change is another challenge. However, studies identifying genes, gene-by-environment interactions (GEI) and gene-by-gene interactions (GGI), as well as breeding by design, remain limited. To identify large-scale genes, GEIs, GGIs and key genes for polygenic or complex traits in big datasets, five algorithms and two computer science technologies were integrated into a compressed variance component mixed model and a 3VmrMLM algorithm framework, combining 'genome-wide scanning + machine learning' to develop an innovative Fast3VmrMLM algorithm. In a reanalysis of 18K rice lines in a single environment, Fast3VmrMLM detected 211 known functional genes for 14 traits, which far exceeds the 100 genes identified by FarmCPU in Science. In a joint analysis across three environments, Fast3VmrMLM detected 103 known functional genes and 26 functional GEIs for six yield-related traits. In an epistasis analysis of 100K markers per environment across 18K rice lines, Fast3VmrMLM identified 133 known functional genes and 41 gene pairs with experimental interaction evidence for six yield-related traits. A gene interaction network was constructed using the known and candidate genes, GEIs and GGIs from the above analyses, identifying 23 key Hub genes related to rice yield traits. The analysis of superior haplotypes for early heading genes identified 38 known major-effect genes that could advance heading by 1–15 days for breeding purposes, as well as 14 GEIs that could advance heading by 1–19 days in Hangzhou. Ten early-heading breeding lines suitable for all three environments and ten region-adapted breeding lines for Hangzhou were identified. In twelve-environment maize dataset, six GEIs interacting with five meteorological factors and two MEJA-detected GEIs helped to explain flowering time plasticity. Thirteen known genes, eight known GEIs and seven plasticity genes advanced flowering by 1.10 to 6.61days, whereas nine known genes, one known GEIs and three plasticity genes increased yield by 0.51 to 3.56 MG·ha–1, identifying fifteen high breeding potential hybrids and 29 genes. Fast3VmrMLM took 12.96 hours and 4.88 GB of memory to jointly analyze phenotypes across 40 environments in 1,000 varieties, each with one million markers, on a small server with 60 CPUs and 1 TB of memory. Additionally, genetic analyses of maize NCII breeding populations, soybean structural variation data, cotton multi-omics data, bin haplotype data and Monte Carlo simulation datasets further validated Fast3VmrMLM. Fast3VmrMLM effectively overcomes the 'blind spots' of traditional approaches when it comes to detecting dominant, small-effect, small allelic substitution effect and rare loci, and expands GEI detection to gene-by-meteorological factor interactions. The size of association mapping populations has increased significantly, from thousands to millions, overcoming the 'computational barrier' of big crop data and the 'bottleneck' challenge of high-end chips. This study presents a method and software platform for large-scale GWAS that is highly effective, fast, broadly applicable, compact and low-power.

Biography: Yuan-Ming Zhang is a professor of statistical genomics at Huazhong Agricultural University (HZAU) in China. His major is quantitative genetics. He obtained his Bachelor's degree from Southwest Agricultural University in 1986, followed by his Master’s degree and PhD from Nanjing Agricultural University (NAU) in 1992 and 2001 respectively. He worked on the Rongchang campus of Southwest University from 1986 to 1999 before moving to NAU, and then to HZAU in 2014. He was a postdoctoral fellow at the University of California, Riverside, from 2003 to 2005, and became an associate professor in 1995, progressing to full professor in 2002. In 2022, he was selected as one of the world's top 2% of scientists. He is an editorial board member of Heredity, BMC Genomics, Front Plant Sci (Guest), and Acta Agronomica Sinica. He is also a council member of the Chinese Society of Agri-Biotechnology, and a full member of Sigma Xi, the Scientific Research Honor Society, and the Genetics Society UK. His team has developed several software packages for identifying genes associated with complex traits. These include mrMLM, IIIVmrMLM and Fast3VmrMLM for GWAS, GCIM for QTL mapping, dQTG-seq for bulked segregant analysis, and SEA for mixed inheritance analysis. They can be downloaded for free from https://github.com/YuanmingZhang65 and https://cran.r-project.org/web/packages/. He has published over 120 articles in journals such as Mol Plant, Plant Cell, Brief Bioinform and Heredity.

Abstract: Growing recognition of the importance of soils has elevated “soil health” as a central concept in agricultural productivity and environmental sustainability. However, defining soil health in both conceptual and practical terms remains challenging. Unlike air and water, where standardized metrics exist, soils involve complex interactions among physical, chemical, and biological properties, making the development of reliable soil tests difficult. To guide land management, indicators must be dynamic by responding over short time scales (1–3 years), rather than static characteristics that change over decades (e.g., soil organic matter) or geologic time. While physical properties are informative, they are labor-intensive and spatially variable. Furthermore, the commercially available soil health tests are inconsistent in detecting management and do not correlate well with crop yield. There is a need for a soil health test that is temporally sensitive, robust, scalable, and interpretable across soil types. Biological indicators, particularly soil enzyme activities, show strong potential. Enzyme assays reflect microbial function, respond to management within 2–3 years, exhibit seasonal stability, and are compatible with high-throughput analysis. Some can be normalized to clay or carbon content, making measurement independent of soil type; and be measured on air-dried samples, facilitating adoption by commercial labs. Soils from across the U.S. Midwest under diverse management—including tillage, cropping intensity, and organic amendments—were evaluated using enzyme activities (arylsulfatase, N-acetylglucosaminidase, β-glucosidase), microbial markers (FAME), and selected chemical properties. These indicators were integrated into a soil health score that was sensitive to management and correlated with crop yields. This research underscores the importance of dynamic, biologically relevant indicators for advancing soil health assessment as a practical tool to guide land management and policy.

Biography: Richard Dick is an Ohio Eminent Scholar and Professor of Soil Microbial Ecology at Ohio State University who leads a highly influential research program focused on soil microbial communities and their role in regulating biogeochemical processes and ecosystem services. He has authored over 164 peer-reviewed journal articles, contributed invited book chapters, and edited two books. His work integrates soil microbiome structure and function with applied soil health outcomes, linking biological indicators to nutrient cycling, soil resilience, and sustainable management. A defining contribution of his program is pioneering research on soil enzymology that established enzyme activities as practical, sensitive soil health indicators, now widely used. He has extensive international experience, including work in Bangladesh and over 25 years leading research in West Africa where he directs a multidisciplinary team studying shrub–crop interplanting systems, demonstrating how these systems enhance water, nutrient dynamics and crop yields, and restore degraded soils. He is a Gordon Research Conference Lecturer, Fulbright Scholar, Fellow of the Agronomy Society of America, and Fellow and past President of the Soil Science Society of America. His work is widely cited (over 12,000 citations; h-index >50) and recognized globally, including designation among the world’s top 2% of scientists.

Abstract: Parkinson's disease (PD) is a chronic, progressive neurological disorder characterized by the progressive loss of dopaminergic neurons in specific areas of the brain. The clinical presentation is characterized mainly by tremors, rigidity, bradykinesia, postural instability, and non-motor symptoms, with the etiology remaining unknown to date. Numerous studies indicate that the primary cause of PD is the misfolding and aggregation of α-synuclein (α-syn). Marine polysaccharides exhibit various biological activities such as anti-tumor, antioxidant, immunomodulatory, wound healing promotion, and regulation of blood glucose and lipid levels, indicating extensive potential for medical applications. Therefore, developing efficient functional factors from marine polysaccharides to prevent and treat PD is of significant research importance. Firstly, a simple and efficient method was developed for the heterologous expression and purification of Aβ and α-syn using an E. coli expression system. Subsequently, an uncomplicated, widely applicable, and high-throughput screening system for inhibiting α-syn aggregation was established in both in vivo and in vitro settings. Polysaccharides and oligosaccharides from Ulva were identified using the above system as the effective inhibitors against α-syn fibrogenesis. Moreover, an efficient enzymatic preparation method was also established. Finally, the in vitro experiments demonstrated the effective inhibitory capacity against α-syn fibrogenesis, reduction of cytotoxicity induced by α-syn aggregates, and protection of neurons from α-syn-induced functional impairment. Furthermore, the molecular dynamics simulations were also utilized to thoroughly investigate the molecular mechanism underlying the disassembly of the formed αS fibrils.

Abstract: Cis-regulatory element (CRE) editing via CRISPR-Cas9 is a powerful precision breeding strategy for fine-tuning gene expression and optimizing complex agronomic traits, avoiding the extreme adverse phenotypes caused by traditional gene coding-region knockout in crop improvement. Rice plant height and tiller number are pivotal architectural traits that determine lodging resistance and grain yield, while synergistically improving plant architecture, yield and grain quality remains a major challenge in high-quality rice breeding. This study presents two efficient CRE editing strategies to precisely modulate key regulatory genes for the comprehensive improvement of rice agronomic performance. Firstly, targeting the gibberellin biosynthesis key gene SD1 in the aromatic rice variety Kam sweet rice, we performed targeted editing on the SD1 promoter cis-regulatory element. A specific adenine insertion enhanced the binding affinity of the transcription factor TCP19, thereby strengthening the endogenous TCP19–SD1 repression module and downregulating SD1 expression. The edited lines exhibited reduced gibberellin accumulation, shortened internode cell length and significantly decreased plant height, which effectively improved lodging resistance, while completely retaining original grain yield and nitrogen utilization efficiency without compromising grain quality. Secondly, focusing on the tillering regulatory gene D14 in Kam sweet rice, haplotype analysis of 533 rice accessions verified that the low-expression D14 haplotype is tightly associated with higher tiller number and grain yield. A targeted “CT” deletion at the -1011 bp promoter region was generated via CRISPR-Cas9 editing, which weakened the binding and transcriptional activation effects of OsGL6, OsBLR1 and OsP10 on D14, thus reducing D14 expression. The D14 promoter-edited lines displayed significantly enhanced tillering capacity and grain yield, accompanied by improved grain quality with increased gel consistency and reduced amylose content. Transcriptomic analysis confirmed that both CRE editing strategies remodeled the regulatory networks of phytohormone (gibberellin, auxin and strigolactone) signaling, and further integrated nitrogen metabolism pathways to precisely regulate rice growth and development. Collectively, this study demonstrates that precise promoter CRE editing can fine-tune endogenous gene expression without disrupting gene function, achieving the coordinated improvement of rice plant height, tiller number, yield and grain quality. Our findings highlight the great potential of CRE editing as a superior alternative to conventional gene knockout, providing a reliable and versatile precision breeding strategy for the molecular design and improvement of complex agronomic traits in modern rice and other cereal crops.

Abstract: Antioxidant enzymes represent essential biological macromolecules efficiently removing or regulating dangerous and toxic reactive oxygen species that occur during aerobic metabolism in almost all cells. If ROS are not regulated or removed their accumulation can lead to oxidative stress with damaging consequences for the organisms. On the other hand, these efficient oxidoreductases represent a very attractive group of enzymes for focused research with many promising applications in the agriculture and various branches of biology. Our research is focused on systematic screening and discovery of natural antioxidant enzymes originating mainly in filamentous fungi and green algae. For this reason, we perform systematic genomic and transcriptomic screening in primeval forests of Carpathian region in middle Europe. Catalases, peroxidases, peroxygenases and superoxide dismutases are mostly large and rather fragile proteins that need to be protected for their efficient delivery to their biological target sites. Therefore, it is important to find strategies for their stable immobilization and/or encapsulation. We monitor the enzymatic activity and operational stability of selected enzymatic antioxidants after their immobilization in various nanoparticles and compare them with native enzymes. Our results shall suggest the best variants for application in various soils to promote productivity of diverse plants by protecting them from oxidative stress and allow environmental sustainability of applied nanobiotechnologies.