3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture

Abstract

Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r(2) = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.

Fig. 1.

(A–C) Ground truth validations of 2D and 3D trait estimations. Images of a digital model (A), a physical resin model (B), and a reconstructed physical model (C). (D) Comparisons of 2D (column 3) and 3D (column 5) trait values from the imaged and reconstructed physical model with hand measurements (column 2) made on the physical model and with estimates of the in silico digital model used to print the physical model (column 4). Note that the 2D convex hull and solidity values are based on 3D projections from rotational image series and thus are under- and overestimated, respectively. (E–G) Horizontal 2D slices of digital (E) or reconstructed (G) models, color-coded by 50-voxel intervals shown in F, illustrate slight irregularities in 3D model reconstruction. (Scale bar in B: 10 mm.)

Fig. 2.

RSA of rice RILs and parental lines grown in nutrient-enriched gellan gum. Images are from day 16. (A–D) Bala (A) and Azucena (C) raw 2D rotational series images and respective 3D reconstructions (B and D). Movies S1 and S2 convey 3D views. (E) Mean minimum–maximum normalized values of RSA traits in parental and recombinant inbred lines on day 16 are shown. Error bars indicate 95% confidence intervals. (Scale bars in A and C: 10 mm.)

Fig. 3.

PCA on root trait correlations in the RIL and parental lines. Gray dots represent the genetic means of each RIL family; “A” (Azucena) and “B” (Bala) are the parental family means. Crosses indicate the loadings for each trait along the first two components, which comprise 63% of the total genetic variation for all 25 traits. Full PCA statistics are reported in Fig. S1.

Fig. 4.

Trait dynamics during 4 d of growth. (A) Percent changes for all 25 traits were calculated by subtracting average day 12 values from day 16 values and dividing by day 12. (B and C) Differences among RILs, Bala, and Azucena in the rates of change for ratios solidity 3D (C) and width:depth 2D and their constituent traits are shown in greater detail. Error bars represent 95% confidence intervals of the mean. All growth rates are reported in Table S2.

Fig. 5.

Univariate QTLs controlling RSA in a rice Bala × Azucena F6 mapping population. Linkage groups (chromosomes) generated by the Haldane function with QTL hits are shown with centimorgan positions on the left and the marker name on the right. The width of each box represents 1-LOD range, and whiskers are 2-LOD for each QTL. White boxes are day 12 QTL, gray boxes are day 14 QTL, and black boxes are day 16 QTL. Heat maps were generated based on the overlap of 2-LOD ranges with intensities scaled to the entire genome. Full univariate QTL results are reported in Dataset S2.

Fig. 6.

Genetic landscape of RSA QTL. Blue (Bala) or red (Azucena) color indicates the parent contributing the positive allele. Univariate QTLs on multiple days for the same trait are coded by hue. Columns indicated by darker gray lines separate traits by measurement type. Root trait hotspots identified by Khowaja et al. (29) that colocalize with those identified in this study are shown in gray. Multivariate QTLs are shown in black with significance rank (full results are given in Dataset S3), and the corresponding DFA-derived composite QTLs are shown near them in green, with percentage indicating phenotypic effect size (full results are given in Fig. 8). The composite QTL on chromosome 6 at MRG6488 corresponds to multivariate rank #2 at a18438, and the composite QTL on chromosome 7 at L09 corresponds to multivariate rank #1 at C39. For clarity, only markers associated with QTLs are shown. Clusters are grouped by linearity and proximity on the genetic map.

Fig. 7.

Genetic tradeoff for root biomass allocation between thorough and extensive soil exploration at QTL cluster #11. Thoroughness is measured in terms of the solidity (y-axis), and extensiveness is measured in terms of convex hull volume (x-axis). Logarithm-transformed genetic means of each RIL family with either the Azucena (green circles) or Bala allele (magenta circles) at marker C39 (chromosome 7). Gray circles indicate missing marker data. Allele means for Azucena (“A”) or Bala (“B”’) illustrate a genetic tradeoff between these phenotypes. Aggregate maximum allele values at all five multivariate QTL markers for solidity (filled square) or convex hull (open square) are shown.

Fig. 8.

Multivariate phenotypes and composite QTL analysis. (A) DFA was used to extract the relative contributions of each univariate trait to each multivariate QTL. (B) These data were used to map projected composite phenotypes as univariate QTLs. ch, chromosome; m, marker number on that chromosome.