bioRxivPlant BiologyPreprint
Integrative genomic and transcriptomic analyses uncover regulatory landscape of symbiotic nitrogen fixation in soybean natural population
Yaling LiiD, Wanjie FengiD, Xilong FengiD, Xuantong LiuiD, Shanmeng HaoiD, Lijie LianiD, Luyao GaoiD, Ying ShaoiD, Hao CheniD, Chen ZhaoiD, Jing YuaniD, Li QiniD, Xiao‐Ming LiiD, Xia LiiD, Xutong WangiD
Abstract
Symbiotic nitrogen fixation (SNF) is a key trait in legume productivity, yet the genetic and regulatory basis underlying its natural variation remains poorly understood. Here, we integrated genome, transcriptome, and chromatin accessibility data from a soybean diversity panel comprising 380 accessions, including 108 wild and 272 cultivated lines. Genome-wide association studies (GWAS) detected multiple loci for SNF traits but with limited resolution due to polygenic architecture and environmental influences. Independent component analysis (ICA) identified 136 co-expression modules; ten ICs were strongly correlated with SNF phenotypes and enriched in circadian clock components (e.g., GmLHY1a/b), lipid metabolism, or defense signaling pathways. Transcriptome-wide association studies (TWAS) linked 1,453, 806, and 178 genes to NFP, NW, and NFE traits, respectively. Among TWAS hits, 185 transcription factors were identified, with 39.0% overlapping selective sweeps, suggesting regulatory evolution under domestication. To further dissect expression regulation, we performed eQTL mapping and detected 4,654 significant eQTLs, including 1,241 local (cis), 2,505 distal (trans), and 908 mixed. By integrating ATAC-seq data from sorted nodule nuclei, we found that eQTLs, particularly local eQTLs, are significantly enriched within open chromatin regions, indicating their regulatory potential. Notably, we validated the circadian clock gene GmLHY1b as a negative regulator of nodulation using CRISPR mutagenesis and CUT&Tag. Our integrative study provides comprehensive genomic and transcriptomic resources from a diverse soybean population, offering novel insights into SNF regulatory networks and a valuable foundation for future SNF research and soybean improvement.
- organism: plant (other)
- assay: whole-genome sequencing
- assay: bulk RNA-seq
- assay: ATAC-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: chromatin & epigenetic regulation
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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