Most Genotyping-by-Sequencing (GBS) strategies suffer from high rates of missing data and high error rates, particularly at heterozygous sites. Tunable genotyping-by-sequencing (tGBS®), a novel genome reduction method, consists of the ligation of single-strand oligos to restriction enzyme fragments. DNA barcodes are added during PCR amplification; additional (selective) nucleotides included at the 3'-end of the PCR primers result in more genome reduction as compared to conventional GBS methods. By adjusting the number of selective bases different numbers of genomic sites can be targeted for sequencing. Because this genome reduction strategy concentrates sequencing reads on fewer sites, SNP calls are based on more reads than conventional GBS, resulting in higher SNP calling accuracy (>97-99%) even for heterozygous sites and less missing data per marker. tGBS genotyping is expected to be particularly useful for genomic selection, which requires the ability to genotype populations of individuals that are heterozygous at many loci.
A one way reproductive barrier exists between most popcorn varieties and dent corn varieties grown in the United States. This barrier is predominantly controlled by the ga1 locus. Using data from a diverse population of popcorn accessions pollinated by a dent corn tester, we found that the non-reciprocal pollination barrier conferred by ga1 is more complex than previously described. Individual accessions ranged from 0% to 100% compatible with dent corn pollen. Using conventional genotyping-by-sequencing data from 371 popcorn accessions carrying Ga1-s, seven significant modifiers of dent pollen compatibility were identified on five chromosomes. One locus may either be a nonfunctional ga1 allele present within popcorn, or second necessary gene for the reproductive barrier in genetic linkage with ga1, while the other modifiers are clearly genetically unlinked. The existence of ga1 modifiers segregating in a popcorn genetic background may indicate selective pressure to allow gene flow between populations, which should be incorporated into future models of the impact of genetic incompatibility loci on gene flow in natural and agricultural plant populations.
The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track phenotypic change over time. Identifying genetic loci regulating differences in the pattern of phenotypic change remains challenging. In this study we used functional principal component analysis (FPCA) to achieve this aim. Time-series phenotype data was collected from a sorghum diversity panel using a number of technologies including RGB and hyperspectral imaging. Imaging lasted for thirty-seven days centered on reproductive transition. A new higher density SNP set was generated for the same population. Several genes known to controlling trait variation in sorghum have been cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotype. Partitioning was consistent with the known molecular function of the individual cloned genes. FPCA-based genome-wide association studies can enable robust time-series mapping analyses in a wide range of contexts. Time-series analysis can increase the accuracy and power of quantitative genetic analyses. 2/173/17 7/17
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple calibrated 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
The root-associated microbiome (rhizobiome) plays a non-negligible role in determining plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We show that the abundance of many root-associated microbes within a functional core microbial community of 150 abundant and consistently reproducible microbial groups is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in low N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association studies conducted using rhizobiome phenotypes identified n = 467 microbe-associated plant loci (MAPLs) in the maize genome linked to variation in the abundance of n = 115 microbial groups in the maize rhizosphere. In 62/115 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. This study provides insights into harnessing the full potential of root-associated microbial symbionts in maize production.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.