Dissection of the Genetic Basis of Rice Panicle Architecture Using a Genome-wide Association Study

Abstract

Panicle architecture is one of the major factors influencing productivity of rice crops. The regulatory mechanisms underlying this complex trait are still unclear and genetic resources for rice breeders to improve panicle architecture are limited. Here, we have performed a genome-wide association study (GWAS) to analyze and identify genetic determinants underlying three panicle architecture traits. A population of 340 rice accessions from the 3000 Rice Genomes Project was phenotyped for panicle length, primary panicle number and secondary branch number over two years; GWAS was performed across the whole panel, and also across the japonica and indica sub-panels. A total of 153 quantitative trait loci (QTLs) were detected, of which 5 were associated with multiple traits, 8 were unique to either indica or japonica sub-panels, while 37 QTLs were stable across both years. Using haplotype and expression analysis, we reveal that genetic variations in the OsSPL18 promoter significantly affect gene expression and correlate with panicle length phenotypes. Three new candidate genes with putative roles in determining panicle length were also identified. Haplotype analysis of OsGRRP and LOC_Os03g03480 revealed high association with panicle length variation. Gene expression of DSM2, involved in abscisic acid biosynthesis, was up-regulated in long panicle accessions. Our results provide valuable information and resources for further unravelling the genetic basis determining rice panicle architecture. Identified candidate genes and molecular markers can be used in marker-assisted selection to improve rice panicle architecture through molecular breeding.

Genetic control of branching patterns in grass inflorescences

“…Primary and secondary branches are controlled independently in rice, even though all branches are morphologically similar ( Harrop et al, 2019 ; Bai et al, 2021 ). For example, double mutants of LAX1LAX2 (described above), have no visible defect in primary branches but lack secondary branches and spikelets altogether ( Tabuchi et al, 2011) .…”

Genetic basis underlying tiller angle in rice (Oryza sativa L.) by genome-wide association study

“…Novel alleles in PAY1 and TIG1 affect tiller angle in rice Some alleles of the genes related to yield-related QTLs were selected during domestication or breeding programs (Jin et al 2008;Tan et al 2008;Jiang et al 2012). The allele mining technique has been successfully employed to nd important variations at various locus for understanding mechanisms underlying domestication and for breeding (Huang et al 2018;Guo et al 2020;Bai et al 2021). Present study identi ed novel alleles of PAY1 and TIG1 through GWAS in a japonica panel that are associated with variable tiller angle, and provided genetic resource for further understanding mechanisms underlying rice tiller angle.…”

“…Fortunately, advances in next-generation sequencing (NGS) technology and bioinformatics tools, providing large-scale SNP arrays in natural groups in rice become reality (Alexandrov et al 2015;Wang et al 2020). Genome-wide association studies (GWAS), as a powerful method for studying the genetics of natural variation based on a linkage disequilibrium mapping approach, have widely been applied to detection of complex agronomic traits in plants (Cockram et al 2010;Wang et al 2016Wang et al , 2020Luján Basile et al 2019;Okada et al 2019;Bai et al 2021). However, so far, only have TAC3 and DWARF2 (D2), been identi ed as rice tiller angle regulators by GWAS (Dong et al 2016b).…”

Genome-Wide Association Study Reveals Novel QTLs and Candidate Genes for Grain Number in Rice

“…GNPP is determined by the rate and duration of spikelet differentiation from a developmental perspective. In addition, morphologically, GNPP is directly influenced by the branch number as grains or spikelets are grown on branches [ 28 ]. Here, we investigated the PBN, SBN, and GNPP traits of 468 accessions and found a high positive correlation between them, with correlation coefficients of 0.79 between GNPP and PBN and 0.96 between GNPP and SBN ( Figure 1 E–G).…”

“…When comparing our GWAS results with other GWAS results on GNPP-related traits, we found qSBNi2 co-located with recently reported qSBN4-3 , a QTL for the secondary branch number. qSBN4-3 was identified for two consecutive years in the experimental fields of Sanya, Hainan Province, China, by GWAS in the indica subpanel [ 28 ]. However, other GWAS signals do not overlap with the previous GWAS results for GNPP-related traits [ 35 , 36 , 37 , 38 , 39 ].…”