Genomic selection for salinity tolerance in japonica rice.

Improving plant performance in salinity-prone conditions is a significant challenge in breeding programs. Genomic selection is currently integrated into many plant breeding programs as a tool for increasing selection intensity and precision for complex traits and for reducing breeding cycle length. A rice reference panel (RP) of 241 Oryza sativa L. japonica accessions genotyped with 20,255 SNPs grown in control and mild salinity stress conditions was evaluated at the vegetative stage for eight morphological traits and ion mass fractions (Na and K). Weak to strong genotype-by-condition interactions were found for the traits considered. Cross-validation showed that the predictive ability of genomic prediction methods ranged from 0.25 to 0.64 for multi-environment models with morphological traits and from 0.05 to 0.40 for indices of stress response and ion mass fractions. The performances of a breeding population (BP) comprising 393 japonica accessions were predicted with models trained on the RP. For validation of the predictive performances of the models, a subset of 41 accessions was selected from the BP and phenotyped under the same experimental conditions as the RP. The predictive abilities estimated on this subset ranged from 0.00 to 0.66 for the multi-environment models, depending on the traits, and were strongly correlated with the predictive abilities on cross-validation in the RP in salt condition (r = 0.69). We show here that genomic selection is efficient for predicting the salt stress tolerance of breeding lines. Genomic selection could improve the efficiency of rice breeding strategies for salinity-prone environments.

Genome Scan of Rice Landrace Populations Collected Across Time Revealed Climate Changes' Selective Footprints in the Genes Network Regulating Flowering Time.

Analyses of the genetic bases of plant adaptation to climate changes, using genome-scan approaches, are often conducted on natural populations, under hypothesis of out-crossing reproductive regime. We report here on a study based on diachronic sampling (1980 and 2011) of the autogamous crop species, Oryza sativa and Oryza glaberrima, in the tropical forest and the Sudanian savannah of West Africa. First, using historical meteorological data we confirmed changes in temperatures (+ 1 °C on average) and rainfall regime (less predictable and reduced amount) in the target areas. Second, phenotyping the populations for phenology, we observed significantly earlier heading time in the 2010 samples. Third, implementing two genome-scan methods (one of which specially developed for selfing species) on genotyping by sequencing genotypic data of the two populations, we detected 31 independent selection footprints. Gene ontology analysis detected significant enrichment of these selection footprints in genes involved in reproductive processes. Some of them bore known heading time QTLs and genes, including OsGI, Hd1 and OsphyB. This rapid adaptive evolution, originated from subtle changes in the standing variation in genetic network regulating heading time, did not translate into predominance of multilocus genotypes, as it is often the case in selfing plants, and into notable selective sweeps. The high adaptive potential observed results from the multiline genetic structure of the rice landraces, and the rather large and imbricated genetic diversity of the rice meta-population at the farm, the village and the region levels, that hosted the adaptive variants in multiple genetic backgrounds before the advent of the environmental selective pressure. Our results illustrate the evolution of in situ diversity through processes of human and natural selection, and provide a model for rice breeding and cultivars deployment strategies aiming resilience to climate changes. It also calls for further development of population genetic models for adaptation of plant populations to environmental changes. To our best knowledge, this is the first study dealing with climate-changes' selective footprint in crops.

Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction.

Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

Evolutionary Processes Involved in the Emergence and Expansion of an Atypical O. sativa Group in Madagascar.

Understanding crops genetic diversity and the evolutionary processes that accompanied their worldwide spread is useful for designing effective breeding strategies. Madagascar Island was one of the last major Old World areas where human settlement brought the introduction of Oryza sativa. Early studies in the island had reported the presence of a rice group specific to Madagascar. Using 24 K SNP, we compared diversity patterns at the whole genome and at haplotype (30 SNP-long segments along the genome) levels, between 620 Malagasy and 1929 Asian rice accessions. The haplotype level analysis aimed at identifying local genotypic variations, relative to the whole genome level, using a group assignment method that relies on kernel density estimation in a Principal Component Analysis feature space. Migration bottleneck had resulted in 10-25% reduction of diversity among the Malagasy representatives of indica and japonica populations. Compared to their Asian counterpart, they showed slightly lower indica and japonica introgressions, suggesting the two populations had undergone less recombination when migration to the island occurred. The origins of the Malagasy indica and japonica groups were delineated to indica subpopulation from the Indian subcontinent and to tropical japonica from the Malay Archipelago, respectively. The Malagasy-specific group (Gm) had a rather high gene diversity and an original haplotype pattern: much lower share of indica haplotypes, and much higher share of Aus and japonica haplotypes than indica. Its emergence and expansion are most probably due to inter-group recombination facilitated by sympatry between indica-Aus admixes and "Bulu" type landraces of japonica in the central high plateaux of Madagascar, and to human selection for adaptation to the lowland rice cultivation. Pattern of rice genetic diversity was also tightly associated with the history of human settlement in the island. Emergence of the Gm group is associated with the latest arrivals of Austronesians, who founded the Merina kingdom in the high plateaux and developed lowland rice cultivation. As an intermediary form between Aus, indica and japonica, the three pillars of O. sativa domestication, Gm represents a very valuable genetic resource in breeding for adaptation to cold tolerance in tropical highlands. We proposed the name Rojo for this new rice group.

Genome-Wide Association Study for Yield and Yield Related Traits under Reproductive Stage Drought in a Diverse indica-aus Rice Panel.

BACKGROUND: Reproductive-stage drought stress is a major impediment to rice production in rainfed areas. Conventional and marker-assisted breeding strategies for developing drought-tolerant rice varieties are being optimized by mining and exploiting adaptive traits, genetic diversity; identifying the alleles, and understanding their interactions with genetic backgrounds for their increased contribution to drought tolerance. Field experiments were conducted in this study to identify marker-trait associations (MTAs) involved in response to yield under reproductive-stage (RS) drought. A diverse set of 280 indica-aus accessions was phenotyped for ten agronomic traits including yield and yield-related traits under normal irrigated condition and under two managed reproductive-stage drought environments. The accessions were genotyped with 215,250 single nucleotide polymorphism markers. RESULTS: The study identified a total of 219 significant MTAs for 10 traits and candidate gene analysis within a 200 kb window centred from GWAS identified SNP peaks detected these MTAs within/ in close proximity to 38 genes, 4 earlier reported major grain yield QTLs and 6 novel QTLs for 7 traits out of the 10. The significant MTAs were mainly located on chromosomes 1, 2, 5, 6, 9, 11 and 12 and the percent phenotypic variance captured for these traits ranged from 5 to 88%. The significant positive correlation of grain yield with yield-related and other agronomic traits except for flowering time, observed under different environments point towards their contribution in improving rice yield under drought. Seven promising accessions were identified for use in future genomics-assisted breeding programs targeting grain yield improvement under drought. CONCLUSION: These results provide a promising insight into the complex genetic architecture of grain yield under reproductive-stage drought in different environments. Validation of major genomic regions reported in the study will enable their effectiveness to develop drought-tolerant varieties following marker-assisted selection as well as to identify genes and understanding the associated physiological mechanisms.

Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains.

The high concentration of arsenic (As) in rice grains, in a large proportion of the rice growing areas, is a critical issue. This study explores the feasibility of conventional (QTL-based) marker-assisted selection and genomic selection to improve the ability of rice to prevent As uptake and accumulation in the edible grains. A japonica diversity panel (RP) of 228 accessions phenotyped for As concentration in the flag leaf (FL-As) and in the dehulled grain (CG-As), and genotyped at 22,370 SNP loci, was used to map QTLs by association analysis (GWAS) and to train genomic prediction models. Similar phenotypic and genotypic data from 95 advanced breeding lines (VP) with japonica genetic backgrounds, was used to validate related QTLs mapped in the RP through GWAS and to evaluate the predictive ability of across populations (RP-VP) genomic estimate of breeding value (GEBV) for As exclusion. Several QTLs for FL-As and CG-As with a low-medium individual effect were detected in the RP, of which some colocalized with known QTLs and candidate genes. However, less than 10% of those QTLs could be validated in the VP without loosening colocalization parameters. Conversely, the average predictive ability of across populations GEBV was rather high, 0.43 for FL-As and 0.48 for CG-As, ensuring genetic gains per time unit close to phenotypic selection. The implications of the limited robustness of the GWAS results and the rather high predictive ability of genomic prediction are discussed for breeding rice for significantly low arsenic uptake and accumulation in the edible grains.

Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice.

Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments.

Genomic Prediction Accounting for Genotype by Environment Interaction Offers an Effective Framework for Breeding Simultaneously for Adaptation to an Abiotic Stress and Performance Under Normal Cropping Conditions in Rice.

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6-4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed.

Tolerance to mild salinity stress in japonica rice: A genome-wide association mapping study highlights calcium signaling and metabolism genes.

Salinity tolerance is an important quality for European rice grown in river deltas. We evaluated the salinity tolerance of a panel of 235 temperate japonica rice accessions genotyped with 30,000 SNP markers. The panel was exposed to mild salt stress (50 mM NaCl; conductivity of 6 dS m-1) at the seedling stage. Eight different root and shoot growth parameters were measured for both the control and stressed treatments. The Na+ and K+ mass fractions of the stressed plants were measured using atomic absorption spectroscopy. The salt treatment affected plant growth, particularly the shoot parameters. The panel showed a wide range of Na+/K+ ratio and the temperate accessions were distributed over an increasing axis, from the most resistant to the most susceptible checks. We conducted a genome-wide association study on indices of stress response and ion mass fractions in the leaves using a classical mixed model controlling structure and kinship. A total of 27 QTLs validated by sub-sampling were identified. For indices of stress responses, we also used another model that focused on marker × treatment interactions and detected 50 QTLs, three of which were also identified using the classical method. We compared the positions of the significant QTLs to those of approximately 300 genes that play a role in rice salt tolerance. The positions of several QTLs were close to those of genes involved in calcium signaling and metabolism, while other QTLs were close to those of kinases. These results reveal the salinity tolerance of accessions with a temperate japonica background. Although the detected QTLs must be confirmed by other approaches, the number of associations linked to candidate genes involved in calcium-mediated ion homeostasis highlights pathways to explore in priority to understand the salinity tolerance of temperate rice.

Genome-wide association mapping for root cone angle in rice.

BACKGROUND: Plant root systems play a major role in anchoring and in water and nutrient uptake from the soil. The root cone angle is an important parameter of the root system architecture because, combined with root depth, it helps to determine the volume of soil explored by the plant. Two genes, DRO1 and SOR1, and several QTLs for root cone angle have been discovered in the last 5 years. RESULTS: To find other QTLs linked to root cone angle, a genome-wide association mapping study was conducted on two panels of 162 indica and 169 japonica rice accessions genotyped with two sets of SNP markers (genotyping-by-sequencing set with approximately 16,000 markers and high-density-rice-array set with approximately 300,000 markers). The root cone angle of all accessions was measured using a screen protractor on images taken after 1 month of plant growth in the Rhizoscope phenotyping system. The distribution of the root cone angle in the indica panel was Gaussian, but several accessions of the japonica panel (all the bulus from Indonesia and three temperate japonicas from Nepal or India) appeared as outliers with a very wide root cone angle. The data were submitted to association mapping using a mixed model with control of structure and kinship. A total of 15 QTLs for the indica panel and 40 QTLs for the japonica panel were detected. Genes underlying these QTLs (+/-50 kb from the significant markers) were analyzed. We focused our analysis on auxin-related genes, kinases, and genes involved in root developmental processes and identified 8 particularly interesting genes. CONCLUSIONS: The present study identifies new sources of wide root cone angle in rice, proposes ways to bypass some drawbacks of association mapping to further understand the genetics of the trait and identifies candidate genes deserving further investigation.

Genome-wide association analysis for heat tolerance at flowering detected a large set of genes involved in adaptation to thermal and other stresses.

Fertilization sensitivity to heat in rice is a major issue within climate change scenarios in the tropics. A panel of 167 indica landraces and improved varieties was phenotyped for spikelet sterility (SPKST) under 38°C during anthesis and for several secondary traits potentially affecting panicle micro-climate and thus the fertilization process. The panel was genotyped with an average density of one marker per 29 kb using genotyping by sequencing. Genome-wide association analyses (GWAS) were conducted using three methods based on single marker regression, haplotype regression and simultaneous fitting of all markers, respectively. Fourteen loci significantly associated with SPKST under at least two GWAS methods were detected. A large number of associations was also detected for the secondary traits. Analysis of co-localization of SPKST associated loci with QTLs detected in progenies of bi-parental crosses reported in the literature allowed to narrow -down the position of eight of those QTLs, including the most documented one, qHTSF4.1. Gene families underlying loci associated with SPKST corresponded to functions ranging from sensing abiotic stresses and regulating plant response, such as wall-associated kinases and heat shock proteins, to cell division and gametophyte development. Analysis of diversity at the vicinity of loci associated with SPKST within the rice three thousand genomes, revealed widespread distribution of the favourable alleles across O. sativa genetic groups. However, few accessions assembled the favourable alleles at all loci. Effective donors included the heat tolerant variety N22 and some Indian and Taiwanese varieties. These results provide a basis for breeding for heat tolerance during anthesis and for functional validation of major loci governing this trait.

Association mapping of resistance to rice blast in upland field conditions.

BACKGROUND: Rice blast is one of the most damaging disease of rice. The use of resistant cultivars is the only practical way to control the disease in developing countries where most farmers cannot afford fungicides. However resistance often breaks down. Genome wide association studies (GWAS) allow high resolution exploration of rice genetic diversity for quantitative and qualitative resistance alleles that can be combined in breeding programs to achieve durability. We undertook a GWAS of resistance to rice blast using a tropical japonica panel of 150 accessions genotyped with 10,937 markers and an indica panel of 190 accessions genotyped with 14,187 markers. RESULTS: The contrasted distribution of blast disease scores between the indica and tropical japonica groups observed in the field suggest a higher level of quantitative resistance in the japonica panel than in the indica panel. In the japonica panel, two different loci significantly associated with blast resistance were identified in two experimental sites. The first, detected by seven SNP markers located on chromosome 1, colocalized with a cluster of four NBS-LRR including the two cloned resistance genes Pi37 and Pish/Pi35. The second is located on chromosome 12 and is associated with partial resistance to blast. In the indica panel, we identified only one locus associated with blast resistance. The three markers significantly detected at this locus were located on chromosome 8 in the 240 kb region carrying Pi33, which encompasses a cluster of three nucleotide binding site-leucine-rich repeat (NBS-LRRs) and six LRR-kinases in the Nipponbare sequence. Within this region, there is an insertion in the IR64 sequence compared to the Nipponbare sequence which also contains resistance gene analogs. Pi33 may belong to this insertion. The analysis of haplotype diversity in the target region revealed two distinct haplotypes, both associated with Pi33 resistance. CONCLUSIONS: It was possible to identify three chromosomal regions associated with resistance in the field through GWAS in this study. Future research should concentrate on specific indica markers targeting the identified insertion in the Pi33 zone. Specific experimental designs should also be implemented to dissect quantitative resistance among tropical japonica varieties.

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.

One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel "trick" concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available.

Correction: Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.

[This corrects the article DOI: 10.1371/journal.pone.0136594.].

DHOEM: a statistical simulation software for simulating new markers in real SNP marker data.

BACKGROUND: Numerous simulation tools based on specific assumptions have been proposed to simulate populations. Here we present a simulation tool named DHOEM (densification of haplotypes by loess regression and maximum likelihood) which is free from population assumptions and simulates new markers in real SNP marker data. The main objective of DHOEM is to generate a new population, which incorporates real and simulated SNP by statistical learning from an initial population, which match the realized features of the latter. RESULTS: To demonstrate DHOEM's abilities, we used a sample of 704 haplotypes for 12 chromosomes with 8336 SNP from a synthetic population, used for breeding upland rice in Latin America. The distributions of allele frequencies, pairwise SNP LD coefficients and data structures, before and after marker densification of the associated marker data set, were shown to be in relatively good agreement at moderate degrees of marker densification. DHOEM is a user-friendly tool that allows the user to specify the level of marker density desired, with a user defined minor allele frequency (MAF) limit, which is produced in a reasonable computation time. CONCLUSIONS: DHOEM is a user-friendly and useful tool for simulation and methodological studies in quantitative genetics and breeding.

Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.

Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.

The roots of future rice harvests.

Rice production faces the challenge to be enhanced by 50% by year 2030 to meet the growth of the population in rice-eating countries. Whereas yield of cereal crops tend to reach plateaus and a yield is likely to be deeply affected by climate instability and resource scarcity in the coming decades, building rice cultivars harboring root systems that can maintain performance by capturing water and nutrient resources unevenly distributed is a major breeding target. Taking advantage of gathering a community of rice root biologists in a Global Rice Science Partnership workshop held in Montpellier, France, we present here the recent progresses accomplished in this area and focal points where an international network of laboratories should direct their efforts.

Genome-wide association mapping of root traits in a japonica rice panel.

Rice is a crop prone to drought stress in upland and rainfed lowland ecosystems. A deep root system is recognized as the best drought avoidance mechanism. Genome-wide association mapping offers higher resolution for locating quantitative trait loci (QTLs) than QTL mapping in biparental populations. We performed an association mapping study for root traits using a panel of 167 japonica accessions, mostly of tropical origin. The panel was genotyped at an average density of one marker per 22.5 kb using genotyping by sequencing technology. The linkage disequilibrium in the panel was high (r(2)>0.6, on average, for 20 kb mean distances between markers). The plants were grown in transparent 50 cm × 20 cm × 2 cm Plexiglas nailboard sandwiches filled with 1.5 mm glass beads through which a nutrient solution was circulated. Root system architecture and biomass traits were measured in 30-day-old plants. The panel showed a moderate to high diversity in the various traits, particularly for deep (below 30 cm depth) root mass and the number of deep roots. Association analyses were conducted using a mixed model involving both population structure and kinship to control for false positives. Nineteen associations were significant at P<1e-05, and 78 were significant at P<1e-04. The greatest numbers of significant associations were detected for deep root mass and the number of deep roots, whereas no significant associations were found for total root biomass or deep root proportion. Because several QTLs for different traits were co-localized, 51 unique loci were detected; several co-localized with meta-QTLs for root traits, but none co-localized with rice genes known to be involved in root growth. Several likely candidate genes were found in close proximity to these loci. Additional work is necessary to assess whether these markers are relevant in other backgrounds and whether the genes identified are robust candidates.