Genomic prediction and QTL analysis for grain Zn content and yield in Aus-derived rice populations

Abstract

Zinc (Zn) biofortification of rice can address Zn malnutrition in Asia. Identification and introgression of QTLs for grain Zn content and yield (YLD) can improve the efficiency of rice Zn biofortification. In four rice populations we detected 56 QTLs for seven traits by inclusive composite interval mapping (ICIM), and 16 QTLs for two traits (YLD and Zn) by association mapping. The phenotypic variance (PV) varied from 4.5% (qPN4.1) to 31.7% (qPH1.1). qDF1.1, qDF7.2, qDF8.1, qPH1.1, qPH7.1, qPL1.2, qPL9.1, qZn5.1, qZn5.2, qZn6.1 and qZn7.1 were identified in both dry and wet seasons; qZn5.1, qZn5.2, qZn5.3, qZn6.2, qZn7.1 and qYLD1.2 were detected by both ICIM and association mapping. qZn7.1 had the highest PV (17.8%) and additive effect (2.5 ppm). Epistasis and QTL co-locations were also observed for different traits. The multi-trait genomic prediction values were 0.24 and 0.16 for YLD and Zn respectively. qZn6.2 was co-located with a gene (OsHMA2) involved in Zn transport. These results are useful for Zn biofortificatiton of rice.