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Open Access Publications from the University of California

Department of Plant Sciences

UC Davis

This series is automatically populated with publications deposited by UC Davis Department of Plant Sciences researchers in accordance with the University of California’s open access policies. For more information see Open Access Policy Deposits and the UC Publication Management System.

Cover page of High-resolution mapping of Yr78, an adult plant resistance gene to wheat stripe rust.

High-resolution mapping of Yr78, an adult plant resistance gene to wheat stripe rust.


Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is responsible for significant yield losses worldwide, which can be minimized by the deployment of Pst resistance genes. Yr78 is an adult plant partial-resistance gene that has remained effective against the post-2000 virulent Pst races. In this study, we generated a high-resolution map of Yr78 based on 6,124 segregating chromosomes. We mapped Yr78 within a 0.05-cM interval on the short arm of chromosome 6B, which corresponds to an 11.16 Mb region between TraesCS6B02G116200 and TraesCS6B02G118000 in the 'Chinese Spring' Ref Seq. v1.1 genome. This interval is likely larger because it includes the unassembled NOR-B2 region, which may have contributed to the low recombination rate detected in this region. The Yr78 candidate region includes 15 genes that were prioritized for future functional studies based on their annotated function and polymorphisms between susceptible and resistant genotypes. Using exome capture data, we identified five major haplotypes in the candidate gene region, with the H1 haplotype associated with Yr78. The H1 haplotype was not detected in tetraploid wheat (Triticum turgidum L.) but was found in ∼30% of the common wheat cultivars (Triticum aestivum L.), suggesting that the associated resistance to stripe rust may have favored the selection of this haplotype. We developed two diagnostic molecular markers for the H1 haplotype that will facilitate the deployment of Yr78 in wheat breeding programs.

Cover page of MiR172-APETALA2-like genes integrate vernalization and plant age to control flowering time in wheat.

MiR172-APETALA2-like genes integrate vernalization and plant age to control flowering time in wheat.


Plants possess regulatory mechanisms that allow them to flower under conditions that maximize reproductive success. Selection of natural variants affecting those mechanisms has been critical in agriculture to modulate the flowering response of crops to specific environments and to increase yield. In the temperate cereals, wheat and barley, the photoperiod and vernalization pathways explain most of the natural variation in flowering time. However, other pathways also participate in fine-tuning the flowering response. In this work, we integrate the conserved microRNA miR172 and its targets APETALA2-like (AP2L) genes into the temperate grass flowering network involving VERNALIZATION 1 (VRN1), VRN2 and FLOWERING LOCUS T 1 (FT1 = VRN3) genes. Using mutants, transgenics and different growing conditions, we show that miR172 promotes flowering in wheat, while its target genes AP2L1 (TaTOE1) and AP2L5 (Q) act as flowering repressors. Moreover, we reveal that the miR172-AP2L pathway regulates FT1 expression in the leaves, and that this regulation is independent of VRN2 and VRN1. In addition, we show that the miR172-AP2L module and flowering are both controlled by plant age through miR156 in spring cultivars. However, in winter cultivars, flowering and the regulation of AP2L1 expression are decoupled from miR156 downregulation with age, and induction of VRN1 by vernalization is required to repress AP2L1 in the leaves and promote flowering. Interestingly, the levels of miR172 and both AP2L genes modulate the flowering response to different vernalization treatments in winter cultivars. In summary, our results show that conserved and grass specific gene networks interact to modulate the flowering response, and that natural or induced mutations in AP2L genes are useful tools for fine-tuning wheat flowering time in a changing environment.

The impact of non-structural carbohydrates (NSC) concentration on yield in Prunus dulcis, Pistacia vera, and Juglans regia.


Successful yield in orchards is the culmination of a series of events that start with plants entering dormancy with adequate energy reserves (non-structural carbohydrates; NSC). These NSC are responsible for the maintenance of activities during dormancy and extending onto the period of activeness. Using multi-year yield information and monthly NSC content in twigs, we show that high levels of carbohydrate in Prunus dulcis, Pistachio vera, and Juglans regia during the winter months are indeed associated with high yield, while high levels of the NSC in late summer often correlate with low yield. An evaluation of monthly NSC level importance on yield revealed that for P. dulcis high levels in February were a good predictor of yield and that low levels throughout summer were associated with high yield. In P. vera, high levels of NSC in December were best predictors of yield. J. regia exhibited peculiar patterns; while high pre-budbreak reserves were associated with high yields they only played a minor role in explaining crop, the most important months for predicting yields were June and July. Results suggest that NSC levels can serve as good predictors of orchard yield potential and should be monitored to inform orchard management.

Analysis of genotype-by-environment interactions in a maize mapping population.


Genotype-by-environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype-by-environment interactions in a maize multiparent advanced generation intercross population grown across 5 environments. We found that genotype-by-environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. To understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype-by-environment variance. We also performed a genome-wide association study to identify markers associated with genotype-by-environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype-by-environment interactions in this population.

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci.


The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.

Cover page of Mutant combinations of lycopene ɛ-cyclase and β-carotene hydroxylase 2 homoeologs increased β-carotene accumulation in endosperm of tetraploid wheat (Triticum turgidum L.) grains.

Mutant combinations of lycopene ɛ-cyclase and β-carotene hydroxylase 2 homoeologs increased β-carotene accumulation in endosperm of tetraploid wheat (Triticum turgidum L.) grains.


Grains of tetraploid wheat (Triticum turgidum L.) mainly accumulate the non-provitamin A carotenoid lutein-with low natural variation in provitamin A β-carotene in wheat accessions necessitating alternative strategies for provitamin A biofortification. Lycopene ɛ-cyclase (LCYe) and β-carotene hydroxylase (HYD) function in diverting carbons from β-carotene to lutein biosynthesis and catalyzing the turnover of β-carotene to xanthophylls, respectively. However, the contribution of LCYe and HYD gene homoeologs to carotenoid metabolism and how they can be manipulated to increase β-carotene in tetraploid wheat endosperm (flour) is currently unclear. We isolated loss-of-function Targeting Induced Local Lesions in Genomes (TILLING) mutants of LCYe and HYD2 homoeologs and generated higher order mutant combinations of lcye-A, lcye-B, hyd-A2, and hyd-B2. Hyd-A2 hyd-B2, lcye-A hyd-A2 hyd-B2, lcye-B hyd-A2 hyd-B2, and lcye-A lcye-B hyd-A2 hyd-B2 achieved significantly increased β-carotene in endosperm, with lcye-A hyd-A2 hyd-B2 exhibiting comparable photosynthetic performance and light response to control plants. Comparative analysis of carotenoid profiles suggests that eliminating HYD2 homoeologs is sufficient to prevent β-carotene conversion to xanthophylls in the endosperm without compromising xanthophyll production in leaves, and that β-carotene and its derived xanthophylls are likely subject to differential catalysis mechanisms in vegetative tissues and grains. Carotenoid and gene expression analyses also suggest that the very low LCYe-B expression in endosperm is adequate for lutein production in the absence of LCYe-A. These results demonstrate the success of provitamin A biofortification using TILLING mutants while also providing a roadmap for guiding a gene editing-based approach in hexaploid wheat.

Cover page of Identification and characterization of Sr22b, a new allele of the wheat stem rust resistance gene Sr22 effective against the Ug99 race group.

Identification and characterization of Sr22b, a new allele of the wheat stem rust resistance gene Sr22 effective against the Ug99 race group.


Wheat stem (or black) rust, caused by Puccinia graminis f. sp. tritici (Pgt), has been historically among the most devastating global fungal diseases of wheat. The recent occurrence and spread of new virulent races such as Ug99 have prompted global efforts to identify and isolate more effective stem rust resistance (Sr) genes. Here, we report the map-based cloning of the Ug99-effective SrTm5 gene from diploid wheat Triticum monococcum accession PI 306540 that encodes a typical coiled-coil nucleotide-binding leucine-rich repeat protein. This gene, designated as Sr22b, is a new allele of Sr22 with a rare insertion of a large (13.8-kb) retrotransposon into its second intron. Biolistic transformation of an ~112-kb circular bacterial artificial chromosome plasmid carrying Sr22b into the susceptible wheat variety Fielder was sufficient to confer resistance to stem rust. In a survey of 168 wheat genotypes, Sr22b was present only in cultivated T. monococcum subsp. monococcum accessions but absent in all tested tetraploid and hexaploid wheat lines. We developed a diagnostic molecular marker for Sr22b and successfully introgressed a T. monococcum chromosome segment containing this gene into hexaploid wheat to accelerate its deployment and pyramiding with other Sr genes in wheat breeding programmes. Sr22b can be a valuable component of gene pyramids or transgenic cassettes combining different resistance genes to control this devastating disease.

Genomic variants affecting homoeologous gene expression dosage contribute to agronomic trait variation in allopolyploid wheat.


Allopolyploidy greatly expands the range of possible regulatory interactions among functionally redundant homoeologous genes. However, connection between the emerging regulatory complexity and expression and phenotypic diversity in polyploid crops remains elusive. Here, we use diverse wheat accessions to map expression quantitative trait loci (eQTL) and evaluate their effects on the population-scale variation in homoeolog expression dosage. The relative contribution of cis- and trans-eQTL to homoeolog expression variation is strongly affected by both selection and demographic events. Though trans-acting effects play major role in expression regulation, the expression dosage of homoeologs is largely influenced by cis-acting variants, which appear to be subjected to selection. The frequency and expression of homoeologous gene alleles showing strong expression dosage bias are predictive of variation in yield-related traits, and have likely been impacted by breeding for increased productivity. Our study highlights the importance of genomic variants affecting homoeolog expression dosage in shaping agronomic phenotypes and points at their potential utility for improving yield in polyploid crops.

Cover page of Development of the Wheat Practical Haplotype Graph database as a resource for genotyping data storage and genotype imputation.

Development of the Wheat Practical Haplotype Graph database as a resource for genotyping data storage and genotype imputation.


To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

Mutation bias reflects natural selection in Arabidopsis thaliana.


Since the first half of the twentieth century, evolutionary theory has been dominated by the idea that mutations occur randomly with respect to their consequences1. Here we test this assumption with large surveys of de novo mutations in the plant Arabidopsis thaliana. In contrast to expectations, we find that mutations occur less often in functionally constrained regions of the genome-mutation frequency is reduced by half inside gene bodies and by two-thirds in essential genes. With independent genomic mutation datasets, including from the largest Arabidopsis mutation accumulation experiment conducted to date, we demonstrate that epigenomic and physical features explain over 90% of variance in the genome-wide pattern of mutation bias surrounding genes. Observed mutation frequencies around genes in turn accurately predict patterns of genetic polymorphisms in natural Arabidopsis accessions (r = 0.96). That mutation bias is the primary force behind patterns of sequence evolution around genes in natural accessions is supported by analyses of allele frequencies. Finally, we find that genes subject to stronger purifying selection have a lower mutation rate. We conclude that epigenome-associated mutation bias2 reduces the occurrence of deleterious mutations in Arabidopsis, challenging the prevailing paradigm that mutation is a directionless force in evolution.