GmAP1d regulates flowering time under long-day photoperiods in soybean

Flowering time is important for adaptation of soybean(Glycine max)to different environments.Here,we conducted a genome-wide association study of flowering time using a panel of 1490 cultivated soybean accessions.We identified three strong signals at the qFT02-2 locus(Chr02:12037319–12238569),which were associated with flowering time in three environments:Gongzhuling,Mengcheng,and Nanchang.By analyzing linkage disequilibrium,gene expression patterns,gene annotation,and the diversity of variants,we identified an AP1 homolog as the candidate gene for the qFT02-2 locus,which we named GmAP1d.Only one nonsynonymous polymorphism existed among 1490 soybean accessions at position Chr02:12087053.Accessions carrying the Chr02:12087053-T allele flowered significantly earlier than those carrying the Chr02:12087053-A allele.Thus,we developed a cleaved amplified polymorphic sequence(CAPS)marker for the SNP at Chr02:12087053,which is suitable for marker-assisted breeding of flowering time.Knockout of GmAP1d in the‘Williams 82’background by gene editing promoted flowering under long-day conditions,confirming that GmAP1d is the causal gene for qFT02-2.An analysis of the region surrounding GmAP1d revealed that GmAP1d was artificially selected during the genetic improvement of soybean.Through stepwise selection,the proportion of modern cultivars carrying the Chr02:12087053-T allele has increased,and this allele has become nearly fixed(95%)in northern China.These findings provide a theoretical basis for better understanding the molecular regulatory mechanism of flowering time in soybean and a target gene that can be used for breeding modern soybean cultivars adapted to different latitudes.

TWAS facilitates gene-scale trait genetic dissection through gene expression,structural variations,and alternative splicing in soybean

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.

Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points.Yet,most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data.Here,we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean(Glycine max(L.)Merr.)varieties to identify previously uncharacterized loci.Specifically,we focused on the dissection of canopy coverage(CC)variation from this rich data set.We also inferred the speed of canopy closure,an additional dimension of CC,from the time-series data,as it may represent an important trait for weed control.Genome-wide association studies(GWASs)identified 35 loci exhibiting dynamic associations with CC across developmental stages.The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci(QTLs)detected in previous studies of adult plants and the identification of novel QTLs influencing CC.These novel QTLs were disproportionately likely to act earlier in development,which may explain why they were missed in previous single-time-point studies.Moreover,this time-series data set contributed to the high accuracy of the GWASs,which we evaluated by permutation tests,as evidenced by the repeated identification of loci across multiple time points.Two novel loci showed evidence of adaptive selection during domestication,with different genotypes/haplotypes favored in different geographic regions.In summary,the time-series data,with soybean CC as an example,improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.