Biology subjects

Walia, H.

Publications and source records attributed to Walia, H..

At least 19 recordsLinked to original sources

bioRxiv2026

OsRAD23a negatively regulates salt tolerance and phosphorus uptake in rice

Salinity stress affects rice productivity due to reduced growth and sodium ion toxicity. Previously, we identified a splice variant of RADIATION SENSITIVE23a (RAD23a) as the potential basis for variation in salt-tolerance in rice germplasm. RAD23 is a known moonlighting protein associated with protein degradation. To validate the role of RAD23a in salt stress response, we characterized gene edited mutant lines that targeted the UBL and UBA2 domains of this protein. Mutation in either domain promoted shoot growth under saline and control conditions. The mutants also differed from wildtype plants in Na and K accumulation in roots and shoots under salt stress. Transcriptome analysis of mutants versus wildtype showed differential transcript abundance of multiple inorganic phosphate (Pi) starvation related genes, including OsSPX2 and OsPHO2. As a result, mutants accumulate higher Pi compared to wildtype plants. The two allelic groups for RAD23a locus also differ in root and shoot phosphorus (P) content. Further, we show that RAD23a interacts with OsSPX2, a negative post-translational regulator of OsPHR2, the master regulator of Pi starvation response. Mutants have higher shoot growth and Pi levels under low Pi conditions, linking enhanced growth of mutants to increased Pi uptake. The UBA2 domain specific mutants have higher single grain weight and per plant grain weight than wildtype. In summary, we show that the RAD23a regulates differential growth, salt response and Pi uptake in rice in a domain-specific manner supporting the moonlighting roles of RAD23a in salt tolerance and phosphorus-dependent shoot growth.

plant biology↗

bioRxiv2024

Transcriptome Enhanced Rice Grain Metabolic Model Identifies Histidine Level as a Marker for Grain Chalkiness

Rising temperatures due to global warming can negatively impact rice grain quality and yield. This study investigates the effects of increased warmer night temperatures (WNT), a consequence of global warming, on the quality of rice kernel, particularly grain chalkiness. By integrating computational and experimental approaches, we used a rice grain metabolic network to discover the metabolic factors of chalkiness. For this, we reconstructed the rice grain genome-scale metabolic model (GSM), iOSA3474-G and incorporated transcriptomics data from three different times of the day (dawn, dawn 7h, and dusk) for both control and WNT conditions with iOSA3474-G. Three distinct growth phases: anoxia, normoxia, and hyperoxia, were identified in rice kernels from the GSMs, highlighting the grain-filling pattern under varying oxygen levels. We predicted histidine as a marker of normoxia, during which kernel chalkiness occurs. Moreover, we proposed tyrosine as a marker for the hyperoxic growth phase. We also proposed a potential link between monodehydroascorbate reductase, an enzyme with evolutionary significance dating back to the carboniferous era, in regulating the hyperoxic growth phase. Metabolic bottleneck analysis identified nucleoside diphosphate kinase as a central regulator of metabolic flux under different conditions. These findings provide targeted insights into the complex metabolic network governing rice grain chalkiness under WNT conditions. Integration of GSM and transcriptomics data, enhanced our understanding of the intricate relationship between environmental factors, metabolic processes, and grain quality and also offer markers that can be useful to develop rice with improved resilience.

systems biology↗

bioRxiv2024

Genomic prediction of metabolic content in rice grain in response to warmer night conditions

It has been argued that metabolic content can be used as a selection marker to accelerate crop improvement because metabolic profiles in crops are often under genetic control. Evaluating the role of genetics in metabolic variation is a long-standing challenge. Rice, one of the worlds most important staple crops, is known to be sensitive to recent increases in nighttime temperatures. Quantification of metabolic levels can help measure rice responses to high night temperature (HNT) stress. However, the extent of metabolic variation that can be explained by regression on whole-genome molecular markers remains to be evaluated. In the current study, we generated metabolic profiles for mature grains from a subset of rice diversity panel accessions grown under optimal and HNT conditions. Metabolite accumulation was low to moderately heritable, and genomic prediction accuracies of metabolite accumulation were within the expected upper limit set by their genomic heritability estimates. Genomic heritability estimates were slightly higher in the control group than in the HNT group. Genomic correlation estimates for the same metabolite accumulation between the control and HNT conditions indicated the presence of genotype-by-environment interactions. Reproducing kernel Hilbert spaces regression and image-based deep learning improved prediction accuracy, suggesting that some metabolite levels are under non-additive genetic control. Joint analysis of multiple metabolite accumulation simultaneously was effective in improving prediction accuracy by exploiting correlations among metabolites. The current study serves as an important first step in evaluating the cumulative effect of markers in influencing metabolic variation under control and HNT conditions. Core ideasO_LIRice is sensitive to increases in nighttime and daytime temperatures C_LIO_LIMetabolite accumulation from rice grains was low to moderately heritable C_LIO_LINon-additive genomic prediction models improved prediction accuracy for some metabolites C_LIO_LIResults shed new light on the utility of genomic predictions for metabolite accumulation from rice grains C_LI

genetics↗

bioRxiv2022

Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice

The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better understanding of the impact of higher night temperature on single grain at whole genome level is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature conditions of genotypes, and metabolites and single nucleotide polymorphisms to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and high night temperature conditions with high accuracy using random forest or extreme gradient boosting. The best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and high night temperature conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to single nucleotide polymorphisms, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of high night temperature responses and regression modeling of grain size-related phenotypes in rice.

genetics↗

bioRxiv2022

A non-destructive approach for measuring rice panicle-level photosynthetic responses using 3D-image reconstruction

Our understanding of the physiological response of rice inflorescence (panicle) to environmental stresses is limited by the challenge of accurately determining panicle photosynthetic parameters and their impact on grain yield. This is primarily due to lack of a suitable gas exchange methodology for panicles, as well as non-destructive methods to accurately determine panicle surface area. To address these challenges, we have developed a custom panicle gas exchange cylinder compatible with the LiCor 6800 Infra-red Gas Analyzer. Accurate surface area measurements were determined with a 3D panicle imaging platform to normalize the panicle-level photosynthetic measurements. We observed differential responses in both panicle and flag leaf for two temperate Japonica rice genotypes (accessions, TEJ-1 and TEJ-2) exposed to heat stress during early grain filling. There was a notable divergence in relative photosynthetic contribution of flag leaf and panicles for the genotype tolerant to heat stress (TEJ-2) compared to the less tolerant accession. The novelty of this approach is that it is non-destructive and more accurately determines panicle area and photosynthetic parameters, enabling researchers to monitor temporal changes in panicle physiology during the reproductive development. The method is useful for panicle-level measurements under diverse environmental stresses, and for evaluating genotypic variation for panicle physiology and architecture in other cereals with compact inflorescences.

physiology↗

bioRxiv2021

Wild Wheat Introgression Promotes Temporal Water fluxes Dynamics under Terminal Drought Stress through Plant-Atmospheric Interrelations

Drought intensity as experienced by plants depends upon soil moisture status and atmospheric variables such as temperature, radiation, and air vapour pressure deficit (VPD). Although the role of shoot architecture with these edaphic and atmospheric factors is well-characterized, the extent to which shoot and root dynamic interactions as a continuum are controlled by genotypic variation is less known. Here, we targeted these interactions using a wild emmer introgression line (IL20) with a distinct drought-induced shift in the shoot-to-root ratio and its drought-sensitive recurrent parent Svevo. Using a gravimetric platform, we show that IL20 maintained higher root water influx and gas exchange under drought stress, which supported a greater growth. Interestingly, the advantage of IL20 in root water influx and transpiration was expressed earlier during the daily diurnal cycle under lower VPD and therefore supported higher transpiration efficiency. Application of structural equation model indicates that under drought, VPD and radiation are antagonistic to transpiration rate, whereas the root water influx operates as feedback for the higher atmospheric responsiveness of leaves. Collectively, our results suggest that a drought-induced shift in root-to-shoot ratio can improve plant water uptake potential in a short preferable time window determined by both water and atmospheric parameters.

plant biology↗

bioRxiv2020

Deciphering the genetic basis of wheat seminal root anatomy uncovers ancestral axial conductance alleles

Root axial conductance which describes the ability of water to pass throw the xylem, contributes to the rate of water uptake from the soil throughout the whole plant lifecycle. In a rainfed wheat agro-system, grain-filling is typically occurring during declining water availability (i.e. terminal drought). Therefore, preserving soil water moisture during grain filling could serve as a key adaptive trait. We hypothesized that lower wheat root axial conductance can promote higher yields under terminal drought. A segregating population derived from a cross between durum wheat and its direct progenitor wild emmer wheat was used to underpin the genetic basis of seminal root architectural and functional traits. We detected 75 QTL associated with seminal roots morphological, anatomical, and physiological traits, with several hotspots harboring co-localized QTL. We further validated the axial conductance and central metaxylem QTL using wild introgression lines. Field-based characterization of genotypes with contrasting axial conductance suggested the contribution of low axial conductance as a mechanism for water conservation during grain filling and consequent increase in grain size and yield. Our findings underscore the potential of introducing wild alleles to reshape the wheat root system architecture for greater adaptability under changing climate.

plant biology↗

bioRxiv2020

PhenoImage: an open-source GUI for plant image analysis

High-throughput genotyping coupled with molecular breeding approaches has dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into the development of large-scale downstream processing of the imaging datasets derived from high-throughput phenotyping (HTP) platforms. However, most available tools require some programing skills. We developed PhenoImage - an open-source GUI based cross-platform solution for HTP image processing with the aim to make image analysis accessible to users with either little or no programming skills. The open-source nature provides the possibility to extend its usability to meet user-specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high-performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived RGB and fluorescence shoot images from two plant species: sorghum and wheat differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage. HighlightPhenoImage is an open-source application designed for analyzing images derived from high-throughput phenotyping.

plant biology↗

bioRxiv2020

SeedExtractor: an open-source GUI for seed image analysis

ABSTRACTAccurate measurement of seed size parameters is essential for both: breeding efforts□aimed at□enhancing yields and basic research□focused on discovering genetic components that regulate seed size. To address this need, we have developed an open-source graphical user interface (GUI) software, SeedExtractor that□determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color with capability to process a large number of images in a time-efficient manner. In this context, our application takes ∼2 seconds for analyzing an image, i.e. significantly less compared to the other tools. As this software is open-source, it can be modified by users□to serve more specific needs. The adaptability of SeedExtractor was demonstrated by analyzing scanned seeds from multiple crops. We further validated the utility of this application by analyzing mature-rice seeds from 231 accessions in Rice Diversity Panel 1. The derived seed-size traits, such as seed length, width, were subjected to genome-wide association analysis. We identified well-known loci for regulating seed length (GS3) and width (qSW5/GW5) in rice, which demonstrated the accuracy of this application to extract seed phenotypes and accelerate trait discovery. In summary, we present a publicly available application that can be used to determine key yield-related traits in crops.HIGHLIGHT SeedExtractor is an open-source application designed to accurately measure seed size and seed color in a time-efficient manner for a wide variety of plant species.Competing Interest StatementThe authors have declared no competing interest.View Full Text

plant biology↗

bioRxiv2020

Transgressive segregation for salt tolerance in rice due to physiological coupling and uncoupling and genetic network rewiring

Transgressive segregation is common in plant breeding populations, where a small minority of recombinants are outliers relative to parental phenotypes. While this phenomenon has been attributed to complementation and epistatic effects, the physiological, biochemical, and molecular bases have not been fully illuminated. By systems-level scrutiny of the IR29 x Pokkali recombinant inbred population of rice, we addressed the hypothesis that novel salt tolerance phenotypes are created by positive or negative coupling or uncoupling effects and novel regulatory networks. Hyperspectral profiling distinguished the transgressive individuals in terms of stress penalty to growth. Non-parental network signatures that led to either optimal or non-optimal integration of developmental with stress-related mechanisms were evident at the macro-physiological, biochemical, metabolic, and transcriptomic levels. The large positive net gain in super-tolerant progeny was due to ideal complementation of beneficial traits, while shedding antagonistic traits. Super-sensitivity was explained by the stacking of multiple antagonistic traits and loss of major beneficial traits. The mechanisms elucidated in this study are consistent with the Omnigenic Theory, emphasizing the synergy or lack thereof between core and peripheral components. This study supports a breeding paradigm based on genomic modeling to create the novel adaptive phenotypes for the crops of the 21st century.

genetics↗

bioRxiv2020

Wild emmer introgressions alter root-to-shoot growth dynamics under water stress

Water deficit is a major limiting factor for wheat (Triticum sp.) development and productivity. One approach to increase water stress adaptation in wheat is incorporating novel alleles from the drought-adapted wheat progenitor, wild emmer (T. turgidum ssp. dicoccoides). We explored this idea in the context of vegetative growth by examining the phenotypic consequence of a series of wild emmer (acc. Zavitan) introgressions into elite durum wheat (cv. Svevo) under water-limited conditions. Using image-based phenotyping we cataloged divergent (from Svevo) growth responses to water stress ranging from high plasticity to high stability among the introgression lines. We identified an introgression line (IL20) that exhibits a highly plastic response to water stress by shifting its root-to-shoot biomass ratio for detailed characterization. By combining genotypic information with root transcriptome analysis, we propose several candidate genes (including a root-specific kinase) that can confer the shoot-to-root carbon resource allocation in IL20 under water stress. Discovery of high plasticity trait in IL20 in response to water stress highlights the potential of wild introgressions for enhancing stress adaptation via mechanisms that may be absent or rare in elite breeding material.

plant biology↗

bioRxiv2019

PI-Plat: A high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits

BackgroundRecent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. ResultsThe major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. ConclusionsFor harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.

plant biology↗

bioRxiv2019

Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

Random regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Moderate to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.87). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.

genetics↗

bioRxiv2019

Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice Oryza sativa

Elucidating genotype-by-environment interactions (GxE) and partitioning its contribution to the phenotypic variation remains a long standing challenge for plant scientists. Recent quantitative genetic frameworks have improved predictions of Gx E responses. However, these models do not explicitly consider the processes that give rise to GxE. To overcome this limitation, we propose a novel framework to elucidate the genetic basis of dynamic shoot growth trajectories under contrasting water regimes using genome-wide markers to model genotype-specific shoot growth trajectories as a function of soil water availability. A rice diversity panel was phenotyped daily over a period of twenty-one days during the early vegetative stage using an automated, high-throughput image-based, phenotyping platform that enabled us to estimate daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the soil water content and/or time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants tend to exhibit an earlier repression in growth compared to smaller, slow growing plants, indicating a potential trade off between early vigor and tolerance to prolonged water deficits. We integrated the growth model within a hierarchical Bayesian framework and used marker information to estimate model parameters and the associated loci through genome-wide association analysis. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. Among them an aquaporin, OsPIP1;1 was identified as a candidate for time of inflection under drought and showed significantly lower expression in accessions exhibiting later TOI in drought. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.

genomics↗

bioRxiv2019

Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

Genome-wide association mapping identifies quantitative trait loci (QTL) that influence the mean differences between the marker genotypes for a given trait. While most loci influence the mean value of a trait, certain loci, known as variance heterogeneity QTL (vQTL) determine the variability of the trait instead of the mean trait value (mQTL). In the present study, we performed a variance heterogeneity genome-wide association study (vGWAS) for grain cadmium (Cd) concentration in bread wheat. We used double generalized linear model and hierarchical generalized linear model to identify vQTL associated with grain Cd. We identified novel vQTL regions on chromosomes 2A and 2B that contribute to the Cd variation and loci that affect both mean and variance heterogeneity (mvQTL) on chromosome 5A. In addition, our results demonstrated the presence of epistatic interactions between vQTL and mvQTL, which could explain variance heterogeneity. Overall, we provide novel insights into the genetic architecture of grain Cd concentration and report the first application of vGWAS in wheat. Moreover, our findings indicated that epistasis is an important mechanism underlying natural variation for grain Cd concentration.

genetics↗

bioRxiv2019

Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines

Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, the high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions.

genetics↗

bioRxiv2019

Harnessing phenotypic networks and structural equation models to improve genome-wide association analysis

Plant breeders and breeders alike seek to develop cultivars with maximal agronomic value. The merit of breeding material is often assessed using many, often genetically correlated traits. As intervention on one trait will affect the value of another, breeding decisions should consider the relationships between traits. With the proliferation of multi-trait genome-wide association studies (MTM-GWAS), we can infer putative genetic signals at the multivariate scale. However, a standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated. We extended the scope of MTM-GWAS by incorporating phenotypic network structures into GWAS using structural equation models (SEM-GWAS). In this network GWAS model, one or more phenotypes appear in the equations for other phenotypes as explanatory variables. A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects into direct and indirect effects. In this paper, we illustrate the utility of SEM-GWAS using biomass, root biomass, water use, and water use efficiency in rice. We found that water use efficiency is directly impacted by biomass and water use and indirectly by biomass and root biomass. In addition, SEM-GWAS partitioned significant SNP effects influencing water use efficiency into direct and indirect effects as a function of biomass, root biomass, and water use efficiency, providing further biological insights. These results suggest that the use of SEM may enhance our understanding of complex relationships between GWAS traits.

genetics↗

bioRxiv2019

The genetic basis of cis-regulatory divergence between the subspecies of cultivated rice (Oryza sativa)

Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1). The transcriptomes of the two subspecies of rice are highly divergent. The expression and genetic diversity was significantly lower within Japonica relative to Indica, which is consistent with the known population bottleneck during Japonica domestication. Moreover, 1,860 and 1,325 genes showed differences in heritability in the broad and narrow sense respectively, between the subspecies, which was driven largely by environmental and genetic effects rather than differences in phenotypic variability. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation. We next explored the potential causes of this cis-regulatory divergence by assessing local genetic diversity for cis-eQTL. Local genetic diversity around subspecies-specific cis-eQTL was significantly lower than genome-wide averages in subspecies lacking the eQTL, suggesting that selective pressures may have shaped regulatory variation in each subspecies. This study provides the first comprehensive characterization of transcriptional and cis-regulatory variation in cultivated rice, and could be an important resource for future studies.

genomics↗