Identification of SNPs and genes related to grain yield in rice using Machine Learning

• Grant number: 20/11611-4
• Support Opportunities: Research Grants - eScience and Data Science Program - Regular Program Grants
• Start date: February 01, 2021
• End date: July 31, 2023
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Ricardo Cerri
• Grantee: Ricardo Cerri
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• City of the host institution: São Carlos
• Associated researchers: Claudio Brondani ; Marcelo Gonçalves Narciso ; Rosana Pereira Vianello

Abstract

The aim of this project is to provide a series of molecular markers and candidate genes, both associated with grain yield to assist in the development of new commercial rice strains and cultivars. For this, the project will use two databases from Embrapa's Nuclear Rice Collection, one phenotypic, generated from a series of nine field experiments where its 550 accessions were evaluated for productivity, and another genotypic, obtained by characterizing these accessions, where 445,589 SNPs were generated. Through the development of machine learning algorithms, productivity-associated SNPs will be identified, which, after validation, will enter the assisted selection routine of the rice breeding program. The team of this proposal (Embrapa and UFSCar) recently worked with the same strategy studying the amylose content (AC) character of the grain, and a SNP marker was found and validated which discriminates genotypes that have low TA from genotypes with high TA. This positive result motivated the team to elaborate the proposal for a highly relevant character of interest for the genetic improvement of rice from Brazil and the world. The combined efforts of the UFSCar and Embrapa research groups, which work in different but complementary areas, will synergistically achieve new and important results for the development of science and agriculture in the country. (AU)

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

FUNICHELI, BRENO O.; BRONDANI, CLAUDIO; VIANELLO, ROSANA P.; CERRI, RICARDO. Ensemble Methods for Selecting Single Nucleotide Polymorphisms Associated to Rice Phenotypes. 2024 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN 2024, v. N/A, p. 8-pg., 2024-01-01. (20/11611-4, 22/02981-8)

CERRI, RICARDO; FARIA, ELAINE R.; GAMA, JOAO; WANI, MA; KANTARDZIC, M; PALADE, V; NEAGU, D; YANG, L; CHAN, KY. An Algorithm Adaptation Method for Multi-Label Stream Classification using Self-Organizing Maps. 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, v. N/A, p. 6-pg., 2022-01-01. (18/19829-9, 20/11611-4)

RIBEIRO ALVES, ANDRE HALLWAS; BRAGA ILIDIO SILVA, PEDRO DE CARVALHO; CERRI, RICARDO. Semi-Supervised Hybrid Predictive Bi-Clustering Trees for Drug-Target Interaction Prediction. 38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023, v. N/A, p. 8-pg., 2023-01-01. (20/11611-4)