A genome-wide association study(GWAS)identifies trait-associated loci,but identifying the causal genes can be a bottleneck,due in part to slow decay of linkage disequilibrium(LD).A transcriptome-wide association study...A genome-wide association study(GWAS)identifies trait-associated loci,but identifying the causal genes can be a bottleneck,due in part to slow decay of linkage disequilibrium(LD).A transcriptome-wide association study(TWAS)addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results.Here,we used self-pollinated soybean(Glycine max[L.]Merr.)as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay.We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29286 soybean genes.Different TWAS solutions were less affected by LD and were robust to the source of expression,identifing known genes related to traits from different tissues and developmental stages.The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing.By introducing a new exon proportion feature,we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing.As a result,the genes identified through our TWAS approach exhibited a diverse range of causal variations,including SNPs,insertions or deletions,gene fusion,copy number variations,and alternative splicing.Using this approach,we identified genes associated with flowering time,including both previously known genes and novel genes that had not previously been linked to this trait,providing insights complementary to those from GWAS.In summary,this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.展开更多
High-throughput plant phenotyping—the use of imaging and remote sensing to record plant growth dynamics—is becoming more widely used.The first step in this process is typically plant segmentation,which requires a we...High-throughput plant phenotyping—the use of imaging and remote sensing to record plant growth dynamics—is becoming more widely used.The first step in this process is typically plant segmentation,which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants.However,preparing such training data is both time and labor intensive.To solve this problem,we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems.This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages.The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed.We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes.We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques.This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.展开更多
基金supported by the National Key Research and Development Program of China(2021YFD1201600)the National Natural Science Foundation of China(32201759 and U22A20473)+3 种基金the China Scientific Innovation 2030 Project(2022ZD0401703)the Earmarked Fund for CARS(CARS-04-PS01)the Agricultural Science and Technology Innovation Program(ASTIPCAAS-ZDRW202109).
文摘A genome-wide association study(GWAS)identifies trait-associated loci,but identifying the causal genes can be a bottleneck,due in part to slow decay of linkage disequilibrium(LD).A transcriptome-wide association study(TWAS)addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results.Here,we used self-pollinated soybean(Glycine max[L.]Merr.)as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay.We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29286 soybean genes.Different TWAS solutions were less affected by LD and were robust to the source of expression,identifing known genes related to traits from different tissues and developmental stages.The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing.By introducing a new exon proportion feature,we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing.As a result,the genes identified through our TWAS approach exhibited a diverse range of causal variations,including SNPs,insertions or deletions,gene fusion,copy number variations,and alternative splicing.Using this approach,we identified genes associated with flowering time,including both previously known genes and novel genes that had not previously been linked to this trait,providing insights complementary to those from GWAS.In summary,this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.
基金supported in part by the United States Department of Agriculture–National Institute of Food and Agriculture Hatch project IOW03717the AI Institute for Resilient Agriculture(AIIRA)+2 种基金funded by the United States National Science Foundation and United States Department of Agriculture–National Institute of Food and Agriculture award#2021-67021-35329the Office of Science(BER),U.S.Department of Energy,grant no.DE-SC0020355the Iowa State University Plant Sciences Institute Scholars Program.
文摘High-throughput plant phenotyping—the use of imaging and remote sensing to record plant growth dynamics—is becoming more widely used.The first step in this process is typically plant segmentation,which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants.However,preparing such training data is both time and labor intensive.To solve this problem,we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems.This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages.The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed.We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes.We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques.This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.
