Genetic mapping identifies a rice naringenin O-glucosyltransferase that influences insect resistance.

Naringenin, the biochemical precursor for predominant flavonoids in grasses, provides protection against UV damage, pathogen infection and insect feeding. To identify previously unknown loci influencing naringenin accumulation in rice (Oryza sativa), recombinant inbred lines derived from the Nipponbare and IR64 cultivars were used to map a quantitative trait locus (QTL) for naringenin abundance to a region of 50 genes on rice chromosome 7. Examination of candidate genes in the QTL confidence interval identified four predicted uridine diphosphate-dependent glucosyltransferases (Os07g31960, Os07g32010, Os07g32020 and Os07g32060). In vitro assays demonstrated that one of these genes, Os07g32020 (UGT707A3), encodes a glucosyltransferase that converts naringenin and uridine diphosphate-glucose to naringenin-7-O-ß-d-glucoside. The function of Os07g32020 was verified with CRISPR/Cas9 mutant lines, which accumulated more naringenin and less naringenin-7-O-ß-d-glucoside and apigenin-7-O-ß-d-glucoside than wild-type Nipponbare. Expression of Os12g13800, which encodes a naringenin 7-O-methyltransferase that produces sakuranetin, was elevated in the mutant lines after treatment with methyl jasmonate and insect pests, Spodoptera litura (cotton leafworm), Oxya hyla intricata (rice grasshopper) and Nilaparvata lugens (brown planthopper), leading to a higher accumulation of sakuranetin. Feeding damage from O. hyla intricata and N. lugens was reduced on the Os07g32020 mutant lines relative to Nipponbare. Modification of the Os07g32020 gene could be used to increase the production of naringenin and sakuranetin rice flavonoids in a more targeted manner. These findings may open up new opportunities for selective breeding of this important rice metabolic trait.

Association mapping by aerial drone reveals 213 genetic associations for Sorghum bicolor biomass traits under drought.

BACKGROUND: Sorghum bicolor is the fifth most commonly grown cereal worldwide and is remarkable for its drought and abiotic stress tolerance. For these reasons and the large size of biomass varieties, it has been proposed as a bioenergy crop. However, little is known about the genes underlying sorghum's abiotic stress tolerance and biomass yield. RESULTS: To uncover the genetic basis of drought tolerance in sorghum at a genome-wide level, we undertook a high-density phenomics genome wide association study (GWAS) in which 648 diverse sorghum lines were phenotyped at two locations in California once per week by drone over the course of a growing season. Biomass, height, and leaf area were measured by drone for individual field plots, subjected to two drought treatments and a well-watered control. The resulting dataset of ~ 171,000 phenotypic data-points was analyzed along with 183,989 genotype by sequence markers to reveal 213 high-quality, replicated, and conserved GWAS associations. CONCLUSIONS: The genomic intervals defined by the associations include many strong candidate genes, including those encoding heat shock proteins, antifreeze proteins, and other domains recognized as important to plant stress responses. The markers identified by our study can be used for marker assisted selection for drought tolerance and biomass. In addition, our results are a significant step toward identifying specific sorghum genes controlling drought tolerance and biomass yield.

The Tyrosine Aminomutase TAM1 Is Required for ß-Tyrosine Biosynthesis in Rice.

Non-protein amino acids, often isomers of the standard 20 protein amino acids, have defense-related functions in many plant species. A targeted search for jasmonate-induced metabolites in cultivated rice (Oryza sativa) identified (R)-ß-tyrosine, an isomer of the common amino acid (S)-α-tyrosine in the seeds, leaves, roots, and root exudates of the Nipponbare cultivar. Assays with 119 diverse cultivars showed a distinct presence/absence polymorphism, with ß-tyrosine being most prevalent in temperate japonica cultivars. Genetic mapping identified a candidate gene on chromosome 12, which was confirmed to encode a tyrosine aminomutase (TAM1) by transient expression in Nicotiana benthamiana and in vitro enzyme assays. A point mutation in TAM1 eliminated ß-tyrosine production in Nipponbare. Rice cultivars that do not produce ß-tyrosine have a chromosome 12 deletion that encompasses TAM1. Although ß-tyrosine accumulation was induced by the plant defense signaling molecule jasmonic acid, bioassays with hemipteran and lepidopteran herbivores showed no negative effects at physiologically relevant ß-tyrosine concentrations. In contrast, root growth of Arabidopsis thaliana and other tested dicot plants was inhibited by concentrations as low as 1 µM. As ß-tyrosine is exuded into hydroponic medium at higher concentrations, it may contribute to the allelopathic potential of rice.

Whole-genome characterization in pedigreed non-human primates using genotyping-by-sequencing (GBS) and imputation.

BACKGROUND: Rhesus macaques are widely used in biomedical research, but the application of genomic information in this species to better understand human disease is still in its infancy. Whole-genome sequence (WGS) data in large pedigreed macaque colonies could provide substantial experimental power for genetic discovery, but the collection of WGS data in large cohorts remains a formidable expense. Here, we describe a cost-effective approach that selects the most informative macaques in a pedigree for 30X WGS, followed by low-cost genotyping-by-sequencing (GBS) at 30X on the remaining macaques in order to generate sparse genotype data at high accuracy. Dense variants from the selected macaques with WGS data are then imputed into macaques having only sparse GBS data, resulting in dense genome-wide genotypes throughout the pedigree. RESULTS: We developed GBS for the macaque genome using a digestion with PstI, followed by sequencing of size-selected fragments at 30X coverage. From GBS sequence data collected on all individuals in a 16-member pedigree, we characterized high-confidence genotypes at 22,455 single nucleotide variant (SNV) sites that were suitable for guiding imputation of dense sequence data from WGS. To characterize dense markers for imputation, we performed WGS at 30X coverage on nine of the 16 individuals, yielding 10,193,425 high-confidence SNVs. To validate the use of GBS data for facilitating imputation, we initially focused on chromosome 19 as a test case, using an optimized panel of 833 sparse, evenly-spaced markers from GBS and 5,010 dense markers from WGS. Using the method of "Genotype Imputation Given Inheritance" (GIGI), we evaluated the effects on imputation accuracy of 3 different strategies for selecting individuals for WGS, including 1) using "GIGI-Pick" to select the most informative individuals, 2) using the most recent generation, or 3) using founders only.  We also evaluated the effects on imputation accuracy of using a range of from 1 to 9 WGS individuals for imputation. We found that the GIGI-Pick algorithm for selection of WGS individuals outperformed common heuristic approaches, and that genotype numbers and accuracy improved very little when using >5 WGS individuals for imputation. Informed by our findings, we used 4 macaques with WGS data to impute variants at up to 7,655,491 sites spanning all 20 autosomes in the 12 remaining macaques, based on their GBS genotypes at only 17,158 loci. Using a strict confidence threshold, we imputed an average of 3,680,238 variants per individual at >99 % accuracy, or an average 4,458,883 variants per individual at a more relaxed threshold, yielding >97 % accuracy. CONCLUSIONS: We conclude that an optimal tradeoff between genotype accuracy, number of imputed genotypes, and overall cost exists at the ratio of one individual selected for WGS using the GIGI-Pick algorithm, per 3-5 relatives selected for GBS. This approach makes feasible the collection of accurate, dense genome-wide sequence data in large pedigreed macaque cohorts without the need for more expensive WGS data on all individuals.

When more is better: how data sharing would accelerate genomic selection of crop plants.

Genomic selection is proving an effective new strategy for increasing breeding efficiency in a wide variety of cereal species - the staple crops that feed the world. A preponderance of studies, reviewed here, has confirmed that the more correlated phenotypic and environmental data that are used to feed genomics-assisted breeding models, the better the prediction accuracies of the models and the more useful the breeding outcomes. We argue that based on these empirical results, new alliances to share data across genomic selection breeding programs are critical to the rapid development and deployment of new crop varieties.

Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa).

Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.

Partitioning the apical domain of the Arabidopsis embryo requires the BOBBER1 NudC domain protein.

The apical domain of the embryo is partitioned into distinct regions that will give rise to the cotyledons and the shoot apical meristem. In this article, we describe a novel screen to identify Arabidopsis thaliana embryo arrest mutants that are defective in this partitioning, and we describe the phenotype of one such mutant, bobber1. bobber1 mutants arrest at the globular stage of development, they express the meristem-specific SHOOTMERISTEMLESS gene throughout the top half of the embryo, and they fail to express the AINTEGUMENTA transcript normally found in cotyledons. Thus, BOBBER1 is required to limit the extent of the meristem domain and/or to promote the development of the cotyledon domains. Based on expression of early markers for apical development, bobber1 mutants differentiate protodermis and undergo normal early apical development. Consistent with a role for auxin in cotyledon development, BOBBER1 mutants fail to express localized maxima of the DR5:green fluorescent protein reporter. BOBBER1 encodes a protein with homology to the Aspergillus nidulans protein NUDC that has similarity to protein chaperones, indicating a possible role for BOBBER1 in synthesis or transport of proteins involved in patterning the Arabidopsis embryo.