Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks

Abstract

Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 × Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction (GP) methods. We found GPAs were improved using multitrait (BN) compared to single trait GP in traits with low to moderate heritability. Two groups of individuals were selected based on GPs and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on GPs did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multitrait GP model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

Keywords: Bayesian network; GenPred; Genomic Prediction; QTL mapping; Shared Data Resource; genomic selection; machine learning; root architecture; root structure; selection index.

Figure 1

An image of 13-day-old rice seedlings grown in agar plates in a Conviron growth chamber, (A) image of parent “PI312777” showing different components of seedling root architecture (RSA), (B) the image of parent “PI312777” showing analyzed region in green color loop and bar chart of different color pixels extracted from RSA components based on 0.15 mm diameter class intervals shown on top, (C) an image of parent “Katy” seedling showing analyzed region in green color loop and bar chart of different color pixels extracted from RSA components based on 0.15 mm diameter class intervals shown on top of image. To improve visual clarity in photographs, images shown above were obtained after seedlings had been removed from the agar and rescanned in water.

Figure 2

Genetic map of rice chromosome 3, 4–7, 9–11 on the PI312777 × Katy RIL population along with the previously identified QTLs and major genes identified from SNP Seek database (https://snp-seek.irri.org/), Oryzabase (https://shigen.nig.ac.jp/rice/oryzabase/) and the rice annotation project (RAP) database (https://rapdb.dna.affrc.go.jp/). The identified QTL are positioned in floating vertical color bar graph whereas the known priori major gene, QTL and QTL detected by BN is shown as loci with the lead SNP reported in previous studies. The abbreviation in parenthesis represent effect on traits in this and prior studies. The length of chromosome is shown in mega base (Mb) scale.

Figure 3

Bar chart displaying GPA of cross validated Bayes B (BB), BRR, BN for seven important above-ground and five RSA traits. Error bars represent SEM.

Figure 4

Violin and box plot of the distribution of means in 36 divergent progeny seedlings for RSA traits selected based on GSRI using 68 RILs TS and 1578 polymorphic SNPs in PI312777 × Katy RIL population.

Figure 5

Subnetwork of learned BN with P = 0.001 in 31 traits (18 above ground agronomic and 13 RSA traits). Arrows shows unidirectional relationship among variables and the value above arrows shows direct and indirect allelic effect of, (A) QTL qKTPI4 affecting early growth rates (GREV), leaf length (LFLV), flowering (HDT), tiller angle (TAR), and lateral root traits (LRSA and LRD), (B) QTL qKTPI8 affecting early growth rates (GREV and GRLFEV), flowering (HDT), leaf chlorophyll content (CHLF), leaf width (LFWDV), lateral root (LRSA), and coarse roots (CRL), and (C) showing direct effect of above-ground traits early growth rates (GREV), tiller number (TNR), and plant height (PHT) and lateral roots (LRAL) leading to grain yield in PI312777 × Katy RIL population. Nodes with solid edges represent above-ground traits and with dashed edges are root traits. Note: The BN was learned using 18 above-ground agronomic, 13 root system architecture traits (RSA), 981 nonredundant SNPs using 10 runs of 10-fold CV with P = 0.001.

Figure 6

Proposed genomic selection strategy to incorporate GSRI in breeding of crops. Genomic breeding values (GEBVs) calculated from genotype and phenotype information from multiple traits of the parent TS facilitate calculation of GSRI which can be used for both parent selection and selection of segregating progenies through index selection.