A multi-tissue metabolome atlas of primate pregnancy.

Pregnancy induces dramatic metabolic changes in females; yet, the intricacies of this metabolic reprogramming remain poorly understood, especially in primates. Using cynomolgus monkeys, we constructed a comprehensive multi-tissue metabolome atlas, analyzing 273 samples from 23 maternal tissues during pregnancy. We discovered a decline in metabolic coupling between tissues as pregnancy progressed. Core metabolic pathways that were rewired during primate pregnancy included steroidogenesis, fatty acid metabolism, and arachidonic acid metabolism. Our atlas revealed 91 pregnancy-adaptive metabolites changing consistently across 23 tissues, whose roles we verified in human cell models and patient samples. Corticosterone and palmitoyl-carnitine regulated placental maturation and maternal tissue progenitors, respectively, with implications for maternal preeclampsia, diabetes, cardiac hypertrophy, and muscle and liver regeneration. Moreover, we found that corticosterone deficiency induced preeclampsia-like inflammation, indicating the atlas's potential clinical value. Overall, our multi-tissue metabolome atlas serves as a framework for elucidating the role of metabolic regulation in female health during pregnancy.

Large-scale snake genome analyses provide insights into vertebrate development.

Snakes are a remarkable squamate lineage with unique morphological adaptations, especially those related to the evolution of vertebrate skeletons, organs, and sensory systems. To clarify the genetic underpinnings of snake phenotypes, we assembled and analyzed 14 de novo genomes from 12 snake families. We also investigated the genetic basis of the morphological characteristics of snakes using functional experiments. We identified genes, regulatory elements, and structural variations that have potentially contributed to the evolution of limb loss, an elongated body plan, asymmetrical lungs, sensory systems, and digestive adaptations in snakes. We identified some of the genes and regulatory elements that might have shaped the evolution of vision, the skeletal system and diet in blind snakes, and thermoreception in infrared-sensitive snakes. Our study provides insights into the evolution and development of snakes and vertebrates.

The association of cigarette smoking with DNA methylation and gene expression in human tissue samples.

Cigarette smoking adversely affects many aspects of human health, and epigenetic responses to smoking may reflect mechanisms that mediate or defend against these effects. Prior studies of smoking and DNA methylation (DNAm), typically measured in leukocytes, have identified numerous smoking-associated regions (e.g., AHRR). To identify smoking-associated DNAm features in typically inaccessible tissues, we generated array-based DNAm data for 916 tissue samples from the GTEx (Genotype-Tissue Expression) project representing 9 tissue types (lung, colon, ovary, prostate, blood, breast, testis, kidney, and muscle). We identified 6,350 smoking-associated CpGs in lung tissue (n = 212) and 2,735 in colon tissue (n = 210), most not reported previously. For all 7 other tissue types (sample sizes 38-153), no clear associations were observed (false discovery rate 0.05), but some tissues showed enrichment for smoking-associated CpGs reported previously. For 1,646 loci (in lung) and 22 (in colon), smoking was associated with both DNAm and local gene expression. For loci detected in both lung and colon (e.g., AHRR, CYP1B1, CYP1A1), top CpGs often differed between tissues, but similar clusters of hyper- or hypomethylated CpGs were observed, with hypomethylation at regulatory elements corresponding to increased expression. For lung tissue, 17 hallmark gene sets were enriched for smoking-associated CpGs, including xenobiotic- and cancer-related gene sets. At least four smoking-associated regions in lung were impacted by lung methylation quantitative trait loci (QTLs) that co-localize with genome-wide association study (GWAS) signals for lung function (FEV1/FVC), suggesting epigenetic alterations can mediate the effects of smoking on lung health. Our multi-tissue approach has identified smoking-associated regions in disease-relevant tissues, including effects that are shared across tissue types.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer.

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Multitissue Circadian Proteome Atlas of WT and Per1-/-/Per2-/- Mice.

The molecular basis of circadian rhythm, driven by core clock genes such as Per1/2, has been investigated on the transcriptome level, but not comprehensively on the proteome level. Here we quantified over 11,000 proteins expressed in eight types of tissues over 46 h with an interval of 2 h, using WT and Per1/Per2 double knockout mouse models. The multitissue circadian proteome landscape of WT mice shows tissue-specific patterns and reflects circadian anticipatory phenomena, which are less obvious on the transcript level. In most peripheral tissues of double knockout mice, reduced protein cyclers are identified when compared with those in WT mice. In addition, PER1/2 contributes to controlling the anticipation of the circadian rhythm, modulating tissue-specific cyclers as well as key pathways including nucleotide excision repair. Severe intertissue temporal dissonance of circadian proteome has been observed in the absence of Per1 and Per2. The γ-aminobutyric acid might modulate some of these temporally correlated cyclers in WT mice. Our study deepens our understanding of rhythmic proteins across multiple tissues and provides valuable insights into chronochemotherapy. The data are accessible at https://prot-rhythm.prottalks.com/.

The multi-tissue gene expression and physiological responses of water deprived Peromyscus eremicus.

The harsh and dry conditions of desert environments have resulted in genomic adaptations, allowing for desert organisms to withstand prolonged drought, extreme temperatures, and limited food resources. Here, we present a comprehensive exploration of gene expression across five tissues (kidney, liver, lung, gastrointestinal tract, and hypothalamus) and 19 phenotypic measurements to explore the whole-organism physiological and genomic response to water deprivation in the desert-adapted cactus mouse (Peromyscus eremicus). The findings encompass the identification of differentially expressed genes and correlative analysis between phenotypes and gene expression patterns across multiple tissues. Specifically, we found robust activation of the vasopressin renin-angiotensin-aldosterone system (RAAS) pathways, whose primary function is to manage water and solute balance. Animals reduced food intake during water deprivation, and upregulation of PCK1 highlights the adaptive response to reduced oral intake via its actions aimed at maintained serum glucose levels. Even with such responses to maintain water balance, hemoconcentration still occurred, prompting a protective downregulation of genes responsible for the production of clotting factors while simultaneously enhancing angiogenesis which is thought to maintain tissue perfusion. In this study, we elucidate the complex mechanisms involved in water balance in the desert-adapted cactus mouse, P. eremicus. By prioritizing a comprehensive analysis of whole-organism physiology and multi-tissue gene expression in a simulated desert environment, we describe the complex response of regulatory processes.

Anterior expansion and posterior addition to the notochord mechanically coordinate zebrafish embryo axis elongation.

How force generated by the morphogenesis of one tissue impacts the morphogenesis of other tissues to achieve an elongated embryo axis is not well understood. The notochord runs along the length of the somitic compartment and is flanked on either side by somites. Vacuolating notochord cells undergo a constrained expansion, increasing notochord internal pressure and driving its elongation and stiffening. Therefore, the notochord is appropriately positioned to play a role in mechanically elongating the somitic compartment. We used multi-photon cell ablation to remove specific regions of the zebrafish notochord and quantify the impact on axis elongation. We show that anterior expansion generates a force that displaces notochord cells posteriorly relative to adjacent axial tissues, contributing to the elongation of segmented tissue during post-tailbud stages. Unexpanded cells derived from progenitors at the posterior end of the notochord provide resistance to anterior notochord cell expansion, allowing for stress generation along the anterior-posterior axis. Therefore, notochord cell expansion beginning in the anterior, and addition of cells to the posterior notochord, act as temporally coordinated morphogenetic events that shape the zebrafish embryo anterior-posterior axis.

Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images.

BACKGROUND: Colorectal cancer (CRC) is a malignant tumor within the digestive tract with both a high incidence rate and mortality. Early detection and intervention could improve patient clinical outcomes and survival. METHODS: This study computationally investigates a set of prognostic tissue and cell features from diagnostic tissue slides. With the combination of clinical prognostic variables, the pathological image features could predict the prognosis in CRC patients. Our CRC prognosis prediction pipeline sequentially consisted of three modules: (1) A MultiTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologists. (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape, and hidden features from a deep network. (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC. RESULTS: Experimental results suggest that each group of features has a particular relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77. CONCLUSION: This paper constructs a model that can predict the postoperative survival of patients by using image features and clinical information. Some features were found to be associated with the prognosis and survival of patients.

A Multi-tissue Transcriptome Analysis of Human Metabolites Guides Interpretability of Associations Based on Multi-SNP Models for Gene Expression.

There is particular interest in transcriptome-wide association studies (TWAS) gene-level tests based on multi-SNP predictive models of gene expression-for identifying causal genes at loci associated with complex traits. However, interpretation of TWAS associations may be complicated by divergent effects of model SNPs on phenotype and gene expression. We developed an iterative modeling scheme for obtaining multi-SNP models of gene expression and applied this framework to generate expression models for 43 human tissues from the Genotype-Tissue Expression (GTEx) Project. We characterized the performance of single- and multi-SNP models for identifying causal genes in GWAS data for 46 circulating metabolites. We show that: (A) multi-SNP models captured more variation in expression than did the top cis-eQTL (median 2-fold improvement); (B) predicted expression based on multi-SNP models was associated (false discovery rate < 0.01) with metabolite levels for 826 unique gene-metabolite pairs, but, after stepwise conditional analyses, 90% were dominated by a single eQTL SNP; (C) among the 35% of associations where a SNP in the expression model was a significant cis-eQTL and metabolomic-QTL (met-QTL), 92% demonstrated colocalization between these signals, but interpretation was often complicated by incomplete overlap of QTLs in multi-SNP models; and (D) using a "truth" set of causal genes at 61 met-QTLs, the sensitivity was high (67%), but the positive predictive value was low, as only 8% of TWAS associations (19% when restricted to colocalized associations at met-QTLs) involved true causal genes. These results guide the interpretation of TWAS and highlight the need for corroborative data to provide confident assignment of causality.

Comparison of sparse biclustering algorithms for gene expression datasets.

MOTIVATION: Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, genes may cluster differently in heterogeneous samples (e.g. different tissues or disease states), whilst traditional methods assume that clusters are consistent across samples. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics. RESULTS: We compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. All algorithms generally struggled on simulated datasets with a large number of genes or implanted biclusters. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and did not require any post-processing, but were considerably slower than other algorithm classes. We found that non-negative matrix factorisation algorithms performed poorly, but could be re-purposed for biclustering through a sparsity-inducing post-processing procedure we introduce; one such algorithm was one of the most highly ranked on real datasets. In a multi-tissue knockout mouse RNA-seq dataset, the algorithms rarely returned clusters containing samples from multiple different tissues, whilst such clusters were identified in a human dataset of more closely related cell types (sorted blood cell subsets). This highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis. AVAILABILITY: Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing. The full tables of results are available at https://doi.org/10.5281/zenodo.4581206.

A robust two-sample transcriptome-wide Mendelian randomization method integrating GWAS with multi-tissue eQTL summary statistics.

By treating genetic variants as instrumental variables (IVs), two-sample Mendelian randomization (MR) methods detect genetically regulated risk exposures for complex diseases using only summary statistics. When considering gene expression as exposure in transcriptome-wide MR (TWMR) analyses, the eQTLs (expression-quantitative-trait-loci) may have pleiotropic effects or be correlated with variants that have effects on disease not via expression, and the presence of those invalid IVs would lead to biased inference. Moreover, the number of eQTLs as IVs for a gene is generally limited, making the detection of invalid IVs challenging. We propose a method, "MR-MtRobin," for accurate TWMR inference in the presence of invalid IVs. By leveraging multi-tissue eQTL data in a mixed model, the proposed method makes identifiable the IV-specific random effects due to pleiotropy from estimation errors of eQTL summary statistics, and can provide accurate inference on the dependence (fixed effects) between eQTL and GWAS (genome-wide association study) effects in the presence of invalid IVs. Moreover, our method can improve power and precision in inference by selecting cross-tissue eQTLs as IVs that have improved consistency of effects across eQTL and GWAS data. We applied MR-MtRobin to detect genes associated with schizophrenia risk by integrating summary-level data from the Psychiatric Genomics Consortium and the Genotype-Tissue Expression project (V8).

Evolution and Impact of Subclonal Mutations in Papillary Thyroid Cancer.

Unlike many cancers, the pattern of tumor evolution in papillary thyroid cancer (PTC) and its potential role in relapse have not been elucidated. In this study, multi-region whole-exome sequencing (WES) was performed on early-stage PTC tumors (n = 257 tumor regions) from 79 individuals, including 17 who had developed relapse, to understand the temporal and spatial framework within which subclonal mutations catalyze tumor evolution and its potential clinical relevance. Paired primary-relapse tumor tissues were also available for a subset of individuals. The resulting catalog of variants was analyzed to explore evolutionary histories, define clonal and subclonal events, and assess the relationship between intra-tumor heterogeneity and relapse-free survival. The multi-region WES approach was key in correctly classifying subclonal mutations, 40% of which would have otherwise been erroneously considered clonal. We observed both linear and branching evolution patterns in our PTC cohort. A higher burden of subclonal mutations was significantly associated with increased risk of relapse. We conclude that relapse in PTC, while generally rare, does not follow a predictable evolutionary path and that subclonal mutation burden may serve as a prognostic factor. Larger studies utilizing multi-region sequencing in relapsed PTC case subjects with matching primary tissues are needed to confirm these observations.

A multi-tissue study of immune gene expression profiling highlights the key role of the nasal epithelium in COVID-19 severity.

Coronavirus Disease-19 (COVID-19) symptoms range from mild to severe illness; the cause for this differential response to infection remains unknown. Unravelling the immune mechanisms acting at different levels of the colonization process might be key to understand these differences. We carried out a multi-tissue (nasal, buccal and blood; n = 156) gene expression analysis of immune-related genes from patients affected by different COVID-19 severities, and healthy controls through the nCounter technology. Mild and asymptomatic cases showed a powerful innate antiviral response in nasal epithelium, characterized by activation of interferon (IFN) pathway and downstream cascades, successfully controlling the infection at local level. In contrast, weak macrophage/monocyte driven innate antiviral response and lack of IFN signalling activity were present in severe cases. Consequently, oral mucosa from severe patients showed signals of viral activity, cell arresting and viral dissemination to the lower respiratory tract, which ultimately could explain the exacerbated innate immune response and impaired adaptative immune responses observed at systemic level. Results from saliva transcriptome suggest that the buccal cavity might play a key role in SARS-CoV-2 infection and dissemination in patients with worse prognosis. Co-expression network analysis adds further support to these findings, by detecting modules specifically correlated with severity involved in the abovementioned biological routes; this analysis also provides new candidate genes that might be tested as biomarkers in future studies. We also found tissue specific severity-related signatures mainly represented by genes involved in the innate immune system and cytokine/chemokine signalling. Local immune response could be key to determine the course of the systemic response and thus COVID-19 severity. Our findings provide a framework to investigate severity host gene biomarkers and pathways that might be relevant to diagnosis, prognosis, and therapy.

Multi-tissue spherical deconvolution of tensor-valued diffusion MRI.

Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.

Multi-tissue to whole plant metabolic modelling.

Genome-scale metabolic models have been successfully applied to study the metabolism of multiple plant species in the past decade. While most existing genome-scale modelling studies have focussed on studying the metabolic behaviour of individual plant metabolic systems, there is an increasing focus on combining models of multiple tissues or organs to produce multi-tissue models that allow the investigation of metabolic interactions between tissues and organs. Multi-tissue metabolic models were constructed for multiple plants including Arabidopsis, barley, soybean and Setaria. These models were applied to study various aspects of plant physiology including the division of labour between organs, source and sink tissue relationship, growth of different tissues and organs and charge and proton balancing. In this review, we outline the process of constructing multi-tissue genome-scale metabolic models, discuss the strengths and challenges in using multi-tissue models, review the current status of plant multi-tissue and whole plant metabolic models and explore the approaches for integrating genome-scale metabolic models into multi-scale plant models.

Multi-scale quantification of tissue behavior during amniote embryo axis elongation.

Embryonic axis elongation is a complex multi-tissue morphogenetic process responsible for the formation of the posterior part of the amniote body. How movements and growth are coordinated between the different posterior tissues (e.g. neural tube, axial and paraxial mesoderm, lateral plate, ectoderm, endoderm) to drive axis morphogenesis remain largely unknown. Here, we use quail embryos to quantify cell behavior and tissue movements during elongation. We quantify the tissue-specific contribution to axis elongation using 3D volumetric techniques, then quantify tissue-specific parameters such as cell density and proliferation. To study cell behavior at a multi-tissue scale, we used high-resolution 4D imaging of transgenic quail embryos expressing fluorescent proteins. We developed specific tracking and image analysis techniques to analyze cell motion and compute tissue deformations in 4D. This analysis reveals extensive sliding between tissues during axis extension. Further quantification of tissue tectonics showed patterns of rotations, contractions and expansions, which are consistent with the multi-tissue behavior observed previously. Our approach defines a quantitative and multi-scale method to analyze the coordination between tissue behaviors during early vertebrate embryo morphogenetic events.

A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis.

BACKGROUND: The potential of multi-shell diffusion imaging to produce accurate brain connectivity metrics able to unravel key pathophysiological processes in multiple sclerosis (MS) has scarcely been investigated. OBJECTIVE: To test, in patients with a clinically isolated syndrome (CIS), whether multi-shell imaging-derived connectivity metrics can differentiate patients from controls, correlate with clinical measures, and perform better than metrics obtained with conventional single-shell protocols. METHODS: Nineteen patients within 3 months from the CIS and 12 healthy controls underwent anatomical and 53-direction multi-shell diffusion-weighted 3T images. Patients were cognitively assessed. Voxel-wise fibre orientation distribution functions were estimated and used to obtain network metrics. These were also calculated using a conventional single-shell diffusion protocol. Through linear regression, we obtained effect sizes and standardised regression coefficients. RESULTS: Patients had lower mean nodal strength (p = 0.003) and greater network modularity than controls (p = 0.045). Greater modularity was associated with worse cognitive performance in patients, even after accounting for lesion load (p = 0.002). Multi-shell-derived metrics outperformed single-shell-derived ones. CONCLUSION: Connectivity-based nodal strength and network modularity are abnormal in the CIS. Furthermore, the increased network modularity observed in patients, indicating microstructural damage, is clinically relevant. Connectivity analyses based on multi-shell imaging can detect potentially relevant network changes in early MS.

Dynamics and mechanisms of posterior axis elongation in the vertebrate embryo.

During development, the vertebrate embryo undergoes significant morphological changes which lead to its future body form and functioning organs. One of these noticeable changes is the extension of the body shape along the antero-posterior (A-P) axis. This A-P extension, while taking place in multiple embryonic tissues of the vertebrate body, involves the same basic cellular behaviors: cell proliferation, cell migration (of new progenitors from a posterior stem zone), and cell rearrangements. However, the nature and the relative contribution of these different cellular behaviors to A-P extension appear to vary depending upon the tissue in which they take place and on the stage of embryonic development. By focusing on what is known in the neural and mesodermal tissues of the bird embryo, I review the influences of cellular behaviors in posterior tissue extension. In this context, I discuss how changes in distinct cell behaviors can be coordinated at the tissue level (and between tissues) to synergize, build, and elongate the posterior part of the embryonic body. This multi-tissue framework does not only concern axis elongation, as it could also be generalized to morphogenesis of any developing organs.

A comparative analysis of the transcriptome profiles of liver and muscle tissue in pigs divergent for feed efficiency.

BACKGROUND: The improvement of feed efficiency is a key economic goal within the pig production industry. The objective of this study was to examine transcriptomic differences in both the liver and muscle of pigs divergent for feed efficiency, thus improving our understanding of the molecular mechanisms influencing feed efficiency and enabling the identification of candidate biomarkers. Residual feed intake (RFI) was calculated for two populations of pigs from two different farms of origin/genotype. The 6 most efficient (LRFI) and 6 least efficient (HRFI) animals from each population were selected for further analysis of Longissimus Dorsi muscle (n = 22) and liver (n = 23). Transcriptomic data were generated from liver and muscle collected post-slaughter. RESULTS: The transcriptomic data segregated based on the RFI value of the pig rather than genotype/farm of origin. A total of 6463 genes were identified as being differentially expressed (DE) in muscle, while 964 genes were identified as being DE in liver. Genes that were commonly DE between muscle and liver (n = 526) were used for the multi-tissue analysis. These 526 genes were associated with protein targeting to membrane, extracellular matrix organisation and immune function. In the muscle-only analysis, genes associated with RNA processing, protein synthesis and energy metabolism were down regulated in the LRFI animals while in the liver-only analysis, genes associated with cell signalling and lipid homeostasis were up regulated in the LRFI animals. CONCLUSIONS: Differences in the transcriptome segregated on pig RFI value rather than the genotype/farm of origin. Multi-tissue analysis identified that genes associated with GO terms protein targeting to membrane, extracellular matrix organisation and a range of terms relating to immune function were over represented in the differentially expressed genes of both liver and muscle.

Transcriptomic and presence/absence variation in the barley genome assessed from multi-tissue mRNA sequencing and their power to predict phenotypic traits.

BACKGROUND: Barley is the world's fourth most cultivated cereal and is an important crop model for genetic studies. One layer of genomic information that remains poorly explored in barley is presence/absence variation (PAV), which has been suggested to contribute to phenotypic variation of agronomic importance in various crops. RESULTS: An mRNA sequencing approach was used to study genomic PAV and transcriptomic variation in 23 spring barley inbreds. 1502 new genes identified here were physically absent from the Morex reference sequence, and 11,523 previously unannotated genes were not expressed in Morex. The procedure applied to detect expression PAV revealed that more than 50% of all genes of our data set are not expressed in all inbreds. Interestingly, expression PAV were not in strong linkage disequilibrium with neighboring sequence variants (SV), and therefore provided an additional layer of genetic information. Optimal combinations of expression PAV, SV, and gene abundance data could enhance the prediction accuracy of predicting three different agronomic traits. CONCLUSIONS: Our results highlight the advantage of mRNA sequencing for genomic prediction over other technologies, as it allows extracting multiple layers of genomic data from a single sequencing experiment. Finally, we propose low coverage mRNA sequencing based characterization of breeding material harvested as seedlings in petri dishes as a powerful and cost efficient approach to replace current single nucleotide polymorphism (SNP) based characterizations.