Sequence based mapping identifies AWNS1, a candidate transcription repressor underlying awn suppression at the B1 locus in wheat

DeWitt, N.; Guedira, M.; Lauer, E.; Sarinelli, M.; Tyagi, P.; Hao, Q.; Fu, D.; Murphy, J. P.; Marshall, D.; Akhunova, A.; Jordan, K.; Akhunov, E.; Brown-Guedira, G.

doi:10.1101/519264

bioRxiv · 10.1101/519264

Sequence based mapping identifies AWNS1, a candidate transcription repressor underlying awn suppression at the B1 locus in wheat

DeWitt, N.·Guedira, M.·Lauer, E.·Sarinelli, M.·Tyagi, P.·Hao, Q.·Fu, D.·Murphy, J. P.·Marshall, D.·Akhunova, A.·Jordan, K.·Akhunov, E.·Brown-Guedira, G.

View original source ↗Export metadata ↓

Abstract

Awns are stiff, hair-like structures that grow from the spikelets of wheat (Triticum aestivum L.) and other grasses. In wild wheat relatives, awns play a role in seed dispersal. Awn suppression in domesticated wheat, where awns are shortened or even eliminated entirely, is variably adaptive with both awned and awnless types under widespread cultivation. Although the B1 locus on the long arm of chromosome 5A is a major determinant of awn suppression, no underlying gene or mechanism of action has been identified. Using association mapping, we identified a SNP marker located on the distal end of 5AL (5A28417) predictive of awn status in a panel of 640 U.S. winter wheat breeding lines, indicating that B1 was the determinant of awn suppression in this germplasm. Analysis of historical data available for the panel determined 5A28417 was also significantly associated with grain test weight. Evaluation of spike morphology and kernel traits was undertaken in a 341 RIL population developed from a cross between awned soft winter wheat cultivar LA95135 and awnless cultivar SS-MPV57. Awn suppression in the population co-segregated with 5A28417 and was co-located with QTL for number of spikelets per spike, kernel weight and kernel length. Fine-mapping located B1 to a region containing only two predicted genes, including a C2H2 zinc finger transcription factor 219 bp from 5A28417 that we named AWNS-A1. Deletions encompassing both genes were present in awned mutants of the awnless cultivar Brundage. Polymorphisms in the AWNS-A1 coding region were not observed in diverse wheat germplasm. However, sequencing of wheat lines representing different marker haplotypes in the surrounding region identified a deletion 3 kb downstream of AWNS-A1. A marker for this deletion was highly predictive of awn suppression in a collection of diverse wheat accessions, and indicates that AWNS-A1 is likely the major determinant of awn suppression in global wheat germplasm. AWNS-A1 is more highly expressed in developing spikes of awnless individuals, suggesting a mechanism for awn suppression.

Source connections

Published article ↗

Explore related subjects

genetics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

DeWitt, N., Guedira, M., Lauer, E., Sarinelli, M., Tyagi, P., Hao, Q., Fu, D., Murphy, J. P., Marshall, D., Akhunova, A., Jordan, K., Akhunov, E., Brown-Guedira, G.. 2019-01-18. Sequence based mapping identifies AWNS1, a candidate transcription repressor underlying awn suppression at the B1 locus in wheat. https://doi.org/10.1101/519264

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis

Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.

genetics↗

bioRxiv2026

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗

bioRxiv2026

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗