Genomic Association between SNP Markers and QTLs for Protein and Oil Content in Grain Weight in Soybean (Glycine max)
Damião Nascimento
Biotechnology Applied to Agriculture, UNIPAR, Umuarama, Brazil.
Leandra Regina Texeira Polo
R&D Department, Coodetec, Cascavel, Brazil.
Fabiane Lazzari
R&D Department, Coodetec, Cascavel, Brazil.
Glacy Jaqueline da Silva
Biotechnology Applied to Agriculture, UNIPAR, Umuarama, Brazil.
Ivan Schuster *
R&D Department, Longping High-Tech, Cravinhos, Brazil.
*Author to whom correspondence should be addressed.
Abstract
Aims: This work aimed to identify SNP marker associated with QTLs controlling grain weight and protein and oil content in soybean grains, using genome wide association study (GWAS).
Study Design: Experimental design for field study was Complete Randomized Block.
Place and Duration of Study: Departments of Breeding and Biotechnology at Coodetec in Cascavel, Palotina, Rio Verde and Sorriso, Brazil, between July 2013 and July 2016,
Methodology: A set of 168 soybean varieties were evaluated in five environments and genotyped with a panel of 6,000 SNP markers. Protein and Oil content was obtained through NIR (Near Infrared Reflectance). GWAS was made by mixed linear model and multiple regression analysis.
Results: Six QTLs in five chromosomes explained from 10.4% to 26.6% of protein variation in three environments. Eleven QTLs in seven chromosomes explained from 14.4% to 39% of oil variation in five environments. Six QTLs in five chromosome explained 32.5% and 37.1% of grain weight variation in two environments.
Conclusion: For the same trait, different QTLs was identified in different environments. It means that to use marker assisted selection for this traits in soybean breeding, the markers need to be validated or identified in the breeding population in each environment, before being used for selection.
Keywords: Genome wide association study, GWAS, mixed linear model, multiple regression, stepwise