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Summary: As a frequently consumed beverage worldwide, tea is rich in naturally important bioactive metabolites. Combining genetic, metabolomic and biochemical methodologies, here, we present a comprehensive study to dissect the chemical diversity in tea plant. A total of 2837 metabolites were identified at high‐resolution with 1098 of them being structurally annotated and 63 of them were structurally identified. Metabolite‐based genome‐wide association mapping identified 6199 and 7823 metabolic quantitative trait loci (mQTL) for 971 and 1254 compounds in young leaves (YL) and the third leaves (TL), respectively. The major mQTL (i.e., P < 1.05 × 10−5, and phenotypic variation explained (PVE) > 25%) were further interrogated. Through extensive annotation of the tea metabolome as well as network‐based analysis, this study broadens the understanding of tea metabolism and lays a solid foundation for revealing the natural variations in the chemical composition of the tea plant. Interestingly, we found that galloylations, rather than hydroxylations or glycosylations, were the largest class of conversions within the tea metabolome. The prevalence of galloylations in tea is unusual, as hydroxylations and glycosylations are typically the most prominent conversions of plant specialized metabolism. The biosynthetic pathway of flavonoids, which are one of the most featured metabolites in tea plant, was further refined with the identified metabolites. And we demonstrated the further mining and interpretation of our GWAS results by verifying two identified mQTL (including functional candidate genes CsUGTa, CsUGTb, and CsCCoAOMT) and completing the flavonoid biosynthetic pathway of the tea plant. [ABSTRACT FROM AUTHOR]

SUMMARY: Deep sequencing is a term that has become embedded in the plant genomic literature in recent years and with good reason. A torrent of (largely) high‐quality genomic and transcriptomic data has been collected and most of this has been publicly released. Indeed, almost 1000 plant genomes have been reported (www.plabipd.de) and the 2000 Plant Transcriptomes Project has long been completed. The EarthBioGenome project will dwarf even these milestones. That said, massive progress in understanding plant physiology, evolution, and crop domestication has been made by sequencing broadly (across a species) as well as deeply (within a single individual). We will outline the current state of the art in genome and transcriptome sequencing before we briefly review the most visible of these broad approaches, namely genome‐wide association and transcriptome‐wide association studies, as well as the compilation of pangenomes. This will include both (i) the most commonly used methods reliant on single nucleotide polymorphisms and short InDels and (ii) more recent examples which consider structural variants. We will subsequently present case studies exemplifying how their application has brought insight into either plant physiology or evolution and crop domestication. Finally, we will provide conclusions and an outlook as to the perspective for the extension of such approaches to different species, tissues, and biological processes. Significance Statement: This article reviews how sequencing technology has boosted pangenomics, promoted analysis of more complex genetic variations and single‐cell omics, and driven recent advances in studying plant physiology and evolution. [ABSTRACT FROM AUTHOR]

Background: Maize (Zea mays L. ssp. mays) was domesticated from teosinte (Zea mays ssp. parviglumis) about 9000 years ago in southwestern Mexico and adapted to a range of environments worldwide. Researchers have depicted the maize domestication and adaptation processes over the past two decades, but efforts have been limited either in sample size or genetic diversity. To better understand these processes, we conducted a genome-wide survey of 982 maize inbred lines and 190 teosinte accessions using over 40,000 single-nucleotide polymorphism markers. Results: Population structure, principal component analysis, and phylogenetic trees all confirmed the evolutionary relationship between maize and teosinte, and determined the evolutionary lineage of all species within teosinte. Shared haplotype analysis showed similar levels of ancestral alleles from Zea mays ssp. parviglumis and Zea mays ssp. mexicana in maize. Scans for selection signatures identified 394 domestication sweeps by comparing wild and cultivated maize and 360 adaptation sweeps by comparing tropical and temperate maize. Permutation tests revealed that the public association signals for flowering time were highly enriched in the domestication and adaptation sweeps. Genome-wide association study identified 125 loci significantly associated with flowering-time traits, ten of which identified candidate genes that have undergone selection during maize adaptation. Conclusions: In this study, we characterized the history of maize domestication and adaptation at the population genomic level and identified hundreds of domestication and adaptation sweeps. This study extends the molecular mechanism of maize domestication and adaptation, and provides resources for basic research and genetic improvement in maize. [ABSTRACT FROM AUTHOR]

Summary: Lettuce (Lactuca sativa L.) is an important vegetable crop species worldwide. The primary metabolism of this species is essential for its growth, development and reproduction as well as providing a considerable direct source of energy and nutrition for humans. Here, through investigating 77 primary metabolites in 189 accessions including all major horticultural types and wild lettuce L. serriola we showed that the metabolites in L. serriola were different from those in cultivated lettuce. The findings were consistent with the demographic model of lettuce and supported a single domestication event for this species. Selection signals among these metabolic traits were detected. Specifically, galactinol, malate, quinate and threonate were significantly affected by the domestication process and cultivar differentiation of lettuce. Galactinol and raffinose might have been selected during stem lettuce cultivation as an adaption to the local environments in China. Furthermore, we identified 154 loci significantly associated with the level of 51 primary metabolites. Three genes (LG8749721, LG8763094 and LG5482522) responsible for the levels of galactinol, raffinose, quinate and chlorogenic acid were further dissected, which may have been the target of domestication and/or affected by local adaptation. Additionally, our findings strongly suggest that human selection resulted in reduced quinate and chlorogenic acid levels in cultivated lettuce. Our study thus provides beneficial genetic resources for lettuce quality improvement and sheds light on the domestication and evolution of this important leafy green. Significance Statement: We investigate natural variation of 77 primary metabolites among 189 diverse lettuce accessions and find large difference between wild lettuce L. serriola and L. sativa at the metabolic level, which is consistent with the demographic model of lettuce. 154 genome‐wide loci significantly associated with the level of 51 primary metabolites are identified and three genes (LsGol2, LsHCT and LsC'3H) which are potential target of domestication and responsible for metabolic variation are further dissected. [ABSTRACT FROM AUTHOR]

Macroautophagy/autophagy is a conserved mechanism responsible for the degradation of unnecessary or dysfunctional components and recycling of the nutrients they contain in order to promote cellular or organismal longevity. In plants photosynthesis is massively impaired under extended darkness stress and the transition to heterotrophic metabolism results in carbon and nitrogen starvation which induces metabolic and autophagic shifts to recycle nutrients for plant survival. The majority of research concerning dark-induced senescence focuses on single genes or pathways, and the global characterization of primary and lipid metabolites and autophagy remains limited. To address these aspects we recently developed a time-resolved genome-wide association-based approach to analyze these shifts following 0 d, 3 d and 6 d of darkness. Six patterns of metabolic shifts and 215 associations with enzymes, transcriptional regulators and autophagy genes (such as AT2G31260/ATG9, AT4G16520/ATG8F, AT5G45900/ATG7 and AT2G05630/ATG8D) were identified. Furthermore detailed characterization of candidate genes further demonstrated that the metabolic and autophagic shifts in response to dark-induced senescence is under tightly coordinated genetic regulation. [ABSTRACT FROM AUTHOR]

Association mapping is a powerful approach for dissecting the genetic architecture of complex quantitative traits using high-density SNP markers in maize. Here, we expanded our association panel size from 368 to 513 inbred lines with 0.5 million high quality SNPs using a two-step data-imputation method which combines identity by descent (IBD) based projection and k-nearest neighbor (KNN) algorithm. Genome-wide association studies (GWAS) were carried out for 17 agronomic traits with a panel of 513 inbred lines applying both mixed linear model (MLM) and a new method, the Anderson-Darling (A-D) test. Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold −log10 (P) >5.74 (α = 1). Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold −log10 (P) >7.05 (α = 0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test, a few of which were also detected in independent linkage analysis. This study indicates that combining IBD based projection and KNN algorithm is an efficient imputation method for inferring large missing genotype segments. In addition, we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power. The candidate SNPs and associated genes also provide a rich resource for maize genetics and breeding. [ABSTRACT FROM AUTHOR]