粳稻分蘖数全基因组关联分析及候选基因的挖掘  

Genome-Wide Association Study and Candidate Gene Mining of Tillering Number in Japonica Rice

作　　者：张继峰 刘华东 王敬国[1] 刘化龙[1] 孙健[1] 杨洛淼 贾琰[1] 吴文申 郑洪亮[1] 邹德堂[1] ZHANG JiFeng;LIU HuaDong;WANG JingGuo;LIU HuaLong;SUN Jian;YANG LuoMiao;JIAYan;WU WenShen;ZHENG HongLiang;ZOU DeTang(Key Laboratory of Germplasm Enhancement,Physiology and Ecology ofFood Crops in Cold Region,Ministry of Education,Northeast Agricultural University,Harbin 150030)

机构地区：[1]东北农业大学寒地粮食作物种质创新与生理生态教育部重点实验室,哈尔滨150030

出　　处：《中国农业科学》2020年第16期3205-3213,共9页Scientia Agricultura Sinica

基　　金：国家重点研发计划(2017YFD0100503);黑龙江省科学基金(C2016018);“东农”学者计划青年才俊项目(17QC02);黑龙江自然科学基金联合引导项目(LH2019C035)。

摘　　要：【目的】利用全基因组关联分析(genome-wide association study,GWAS)检测与粳稻分蘖数显著相关的SNP位点,筛选影响分蘖数的候选基因,为分子辅助育种提高产量提供理论基础。【方法】利用295份来自世界各地的粳稻品种,于2018和2019年分蘖盛期调查水稻分蘖数,结合高通量重测序获得的788396个高质量多态性SNP,利用TASSEL 5.0软件的MLM模型进行全基因组关联分析,对GEC软件计算的有效独立SNP数目进行阈值确定,判定SNP标记与目标性状关联的显著性。根据2年检测到的峰值SNP和水稻每条染色体LD衰减距离,确定2年共定位分蘖数主效QTL,并进一步提取QTL区间内所有基因外显子区非同义突变SNP和启动子区SNP进行单倍型分析,结合基因注释筛选影响粳稻分蘖数候选基因。【结果】295份粳稻品种的分蘖数在2018和2019年总趋势基本一致,并且均具有较大的表型分布。通过全基因组关联分析,在P<5.46×10^-6阈值条件下,从第8、9和10染色体上共鉴定3个与粳稻分蘖数相关的QTL(qTiller8、qTiller9和qTiller10),其中,在2年中均检测到qTiller9,在2018和2019年的表型贡献率分别为11.86%和10.61%,而qTiller8和qTiller10仅在2018年被检测到,表型贡献率分别为9.36%和9.10%。对qTiller9区间内的全部15个基因进行单倍型分析,结果表明,在qTiller9区间内共有6个基因(LOC_Os09g25090、LOC_Os09g25100、LOC_Os09g25150、LOC_Os09g25190、LOC_Os09g25200和LOC_Os09g25220)的不同单倍型分蘖数之间存在极显著差异,LOC_Os09g25090被启动子区SNP分为2种单倍型,hap2(TAA)分蘖数极显著大于hap1(AGG);LOC_Os09g25100被非同义突变SNP分为2种单倍型,hap2(GAGA)分蘖数极显著大于hap1(AGCC);LOC_Os09g25150被非同义突变SNP分为2种单倍型,hap2(ATG)分蘖数极显著大于hap1(GCC);LOC_Os09g25190被启动子区SNP分为2种单倍型,hap2(GCATCGCATCGACGCCGA)分蘖数极显著大于hap1(ATGCTGATGAAGTCATCC);LOC_Os09g25200被非同义突变SNP分�【Objective】Genome-wide association study(GWAS)was used to detect SNP loci that were significantly related to tillering number in japonica rice,and to screen candidate genes affecting tillering number【Method】This study used 295 japonica rice varieties from around the world,in 2018 and 2019 survey from the tillering stage of rice tillering number,the combination of high throughput sequencing weight gain high quality 788396 polymorphism,SNP,using TASSEL 5.0 software genome-wide association analysis of MLM model,using the GEC software to calculate the number of effective independent SNPS for the determination of threshold value,determine the significance of SNP markers associated with target traits.Based on the peak SNP detected in two years and the LD attenuation distance of each chromosome in rice,the major QTLs for co-localization of biennial number were determined,and the non-synonymous SNPs and promoter regions of all gene exon regions in the QTL interval were further extracted.SNPs were subjected to haplotype analysis,and then combined with gene annotation to screen candidate genes affecting japonica rice tillering number.【Result】The tillering number of 295 japonica rice varieties were basically the same in 2018 and 2019,and they all had a large phenotypic distribution.Under the threshold of P<5.46×10^-6,three QTLs(qTiller8,qTiller9 and qTiller10)related to tillering number of Japonica rice were identified on chromosomes 8,9 and 10 by genome-wide association analysis.q Tiller9 jointly detected that the contribution rate of phenotype in 2018 and 2019 was 11.86%and 10.61%,respectively.qtiller8 and qtiller10 were only detected in 2018,and the contribution rate of phenotype was 10.61%The rates were 9.36%and 9.10%,respectively.Haplotype analysis of all 15 genes in q Tiller9 interval showed that there were 6 genes in qTiller9 interval(LOC_Os09 g25090,LOC_Os09 g25100,LOC_Os09 g25150,LOC_Os09 g25190,LOC_Os09 g25200 and LOC_Os09 g25220).LOC_Os09 g25090 was divided into two haplotypes by promoter SNP,and the

关 键 词：粳稻 分蘖数 全基因组关联分析 单倍型分析 候选基因 

分 类 号：S511.22[农业科学—作物学]