DOE OSTI · 2422186
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Li, Gen, Qi, Yiping. 2023-08-01. CRISPR Empowers Tree Bioengineering for a Sustainable Future. https://doi.org/10.1089/crispr.2023.29161.gli
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Abstract Wild emmer wheat (Triticum turgidumsubsp.dicoccoides) is the tetraploid progenitor of hexaploid bread wheat (Triticum aestivumL.) and is known to be a valuable source of genetic variation for wheat improvement. However, direct evaluation of wild emmer diversity for agronomic potential has limited value unless performed in the backgrounds of adapted cultivars. Here, we present a genetic characterization of a population of 1601 backcross recombinant inbred lines, with an average genome composition of 75% bread wheat and 25% wild emmer. Low‐coverage whole‐genome sequencing allowed introgressions and aneuploidies to be identified at a relatively low cost per sample. We identified a relatively large proportion of small introgressions (median length 38 Mb), and we found introgressions to be distributed across all chromosomes. Approximately 44% of genotyped progeny carried at least one aneuploidy, with monosomies being by far the most common. This population, which we have denoted as the Great Plains Wild Emmer/Hard Winter Wheat introgression population (GPWEW‐IP), is, to our knowledge, the first introgression population developed through the direct hybridization of wild emmer wheat and US‐adapted hard winter wheat. We believe that this population represents a valuable resource for wheat breeders and will accelerate the discovery and integration of useful variation from wild emmer wheat.
Abstract Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high‐throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field‐grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild‐type alleles at two known RSA genes,DEEPER ROOTING 1(DRO1) andRootless1(Rt1). We show the utility of increasing the dimensionality of traditional two‐dimensional (2D) techniques, referred to as the “2D multi‐view” method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome‐wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three‐dimensional (3D) root models generated from X‐ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome‐wide and single‐gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field‐grown maize.
Abstract Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over 200,000 variants, genetic, epigenetic, or both (termed binding quantitative trait loci (bQTL)), linked tocis-element occupancy. Three lines of evidence support the functional significance of bQTL: (1) coincidence with causative loci that regulate traits, includingvgt1,ZmTRE1and the MITE transposon nearZmNAC111under drought; (2) bQTL allelic bias is shared between inbred parents and matches chromatin immunoprecipitation sequencing results; and (3) partitioning genetic variation across genomic regions demonstrates that bQTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functionalcis-variation accessible at scale for genetic studies and targeted engineering of complex traits.