Environmental Response and Genomic Regions Correlated with Rice Root Growth and Yield under Drought in the OryzaSNP Panel across Multiple Study Systems

Abstract

The rapid progress in rice genotyping must be matched by advances in phenotyping. A better understanding of genetic variation in rice for drought response, root traits, and practical methods for studying them are needed. In this study, the OryzaSNP set (20 diverse genotypes that have been genotyped for SNP markers) was phenotyped in a range of field and container studies to study the diversity of rice root growth and response to drought. Of the root traits measured across more than 20 root experiments, root dry weight showed the most stable genotypic performance across studies. The environment (E) component had the strongest effect on yield and root traits. We identified genomic regions correlated with root dry weight, percent deep roots, maximum root depth, and grain yield based on a correlation analysis with the phenotypes and aus, indica, or japonica introgression regions using the SNP data. Two genomic regions were identified as hot spots in which root traits and grain yield were co-located; on chromosome 1 (39.7-40.7 Mb) and on chromosome 8 (20.3-21.9 Mb). Across experiments, the soil type/ growth medium showed more correlations with plant growth than the container dimensions. Although the correlations among studies and genetic co-location of root traits from a range of study systems points to their potential utility to represent responses in field studies, the best correlations were observed when the two setups had some similar properties. Due to the co-location of the identified genomic regions (from introgression block analysis) with QTL for a number of previously reported root and drought traits, these regions are good candidates for detailed characterization to contribute to understanding rice improvement for response to drought. This study also highlights the utility of characterizing a small set of 20 genotypes for root growth, drought response, and related genomic regions.

Fig 1. The OryzaSNP germplasm set was phenotyped with a range of root-screening techniques.

A) rhizotron (Ab09CR: Univ Aberdeen), B) penetration of nonwoven fabric (Ab09CNWNW: Univ Aberdeen), C) monoliths from line source sprinkler (Na10 and Na11; Nagoya Univ), D) soil-filled cylinders (Ba10C; Barwale Foundation, and CS09C; Charles Sturt Univ), E) hydroponics (Ab09CH: Univ Aberdeen), F) monoliths in the field (IR08FL and IR09dFL; IRRI), G) lysimeters (IR08C; IRRI, IC09C; ICRISAT), and (H) in the field by excavation (TN10F, TN11F; Tamil Nadu Agric. Univ).

Fig 2. Comparison of genotypic response to drought severity across experiments.

A) Shoot mass reduction in the drought treatment compared to the well-watered control. Response of B) root dry weight, C) maximum root depth, and D) % deep roots to increasing drought severity, as indicated by shoot mass reduction. Each data point represents the average difference between DS and WW treatments for one genotype in one experiment. Data previously reported by Henry et al (2011) and Gowda et al (2012) were used to calculate some of the results shown in this figure.

Fig 3. Dendrograms of genotypic groupings.

A) grain yield, B) root dry weight, C) maximum root depth, and D) % deep roots. Data previously reported by Henry et al (2011), Gowda et al (2012), and Shrestha et al (2013) were used to calculate some of the results shown in this figure.

Fig 4. Dendrograms of environmental groupings.

A) grain yield, B) root dry weight, C) maximum root depth, and D) % deep roots. Data previously reported by Henry et al (2011), Gowda et al (2012), and Shrestha et al (2013) were used to calculate some of the results shown in this figure.

Fig 5. Biplots for A) grain yield, B) root dry weight, C) maximum root depth, and D) % deep roots.

Data previously reported by Henry et al (2011), Gowda et al (2012), and Shrestha et al (2013) were used to calculate some of the results shown in this figure.

Fig 6. A genomic region on chromosome 1 (39.7–40.7 Mb) was identified as a hot spot in which root dry weight, percent deep roots, and yield aligned.

Colors indicate each experiment from which the phenotypic data and introgression regions were correlated. Experiments from which introgression regions fell within the hot spot are identified in the legend.

Fig 7. A genomic region on chromosome 8 (20.3–21.9 Mb) was identified as a hot spot in which percent deep roots and yield aligned.

Colors indicate each experiment from which the phenotypic data and introgression regions were correlated. Experiments from which introgression regions fell within the hot spot are identified in the legend.