Simultaneously mapping loci related to two plant architecture traits by phenotypic recombination BSA/BSR in peanut (Arachis hypogaea L.)

Abstract

BSA-seq has been widely used for identifying the genomic regions affecting a certain trait. In this study, we developed a modi ed BSA/BSR-seq method, which we named Phenotypic Recombination BSA/BSR (PR-BSA/BSR), to simultaneously identify candidate genomic regions associated with two traits in a segregating population. Lateral branch angle (LBA) and ower-branch pattern (FBP) are two important traits associated with the peanut plant architecture because they affect the planting density and light use e ciency. We generated an F 6 population (with two segregating traits) derived from a cross between the inbred lines Pingdu9616 (erect and sequential; ES-type) and Florunner (spreading and alternating; SAtype). The selection of bulks with extreme phenotypes was a key step in this study. Speci cally, 30 individuals with recombinant phenotypes [i.e., spreading and sequential (SS-type) and erect and alternating (EA-type)] were selected to generate two bulks. The transcriptomes of individuals were sequenced and then the loci related to LBA and FBP were simultaneously detected via a ΔSNP-index strategy, which involved the direction of positive and negative peaks in the ∆SNP-index plot. The LBArelated locus was mapped to a 6.82 Mb region (101,743,223-108,564,267 bp) on chromosome B05, whereas the FBP-related locus was mapped to a 2.16 Mb region (117,682,846,824 bp) on chromosome B02. Furthermore, the marker-based classical QTL mapping method was used to analyze the PF-F 6 population, which con rmed our PR-BSA/BSR results. Therefore, the PR-BSA/BSR method produces accurate and reliable data.

A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops

“…The “Breeding 4.0” concept is highlight the integration of life science, information science, and breeding science, which helps the identification of elite polyphenotype genes on the basis of hypothesis-driven passive exploration transition to data-driven active knowledge discovery ( Wallace et al., 2018 ). In addition, the methods of genome-wide association study ( Liu et al., 2023b ), quantitative trait locus analysis ( Jiang et al., 2023 ), and bulk segregant analysis ( Yu et al., 2023 ) were conducted to establish the associations between genes and individual traits, but they were not conducive to mining for polyphenotype genes ( Polderman et al., 2015 ; Sonah et al., 2015 ).…”