玉米穗长一般配合力多位点全基因组关联分析和预测

doi:10.3724/SP.J.1006.2023.23042

摘要/Abstract

摘要：

穗长是一个重要的农艺性状, 与产量密切相关。一般配合力(general combining ability, GCA)是评价优异自交系的重要指标。因此, 解析穗长GCA的遗传基础, 制定相应的育种策略对玉米杂交种产量的提高具有重要意义。本研究以123个玉米自交系和8个测验种按照North Carolina II遗传交配设计组配的537个F₁杂交种为试验材料, 在2个环境下进行表型鉴定, 利用玉米5.5 K液相育种芯片鉴定的11,734个SNP (single nucleotide polymorphisms)对2个环境以及综合环境穗长GCA进行多位点全基因组关联分析(multi-locus genome-wide association study, MGWAS)和基因组预测。利用7种MGWAS共检测到11个穗长GCA显著关联SNP标记(P < 8.52E-07), 单个位点解释GCA变异介于8.06%~28.23%之间。不同MGWAS共定位的SNP位点有5个。位点7_178103602在周口和综合环境利用mrMLM (multi-locus random-SNP-effect mixed linear model)方法重复检测到, 可解释穗长GCA变异的26.02%~28.23%, 为环境稳定的主效SNP。共挖掘10个候选基因, 其中auxin amido synthetase 9和EID1-like F-box protein 2可能是控制穗长GCA的关键基因。5种随机效应模型对3个环境穗长GCA的预测准确性介于0.53~0.69之间, 且模型间差异较小。在新乡和周口环境, GBLUP (genomic best linear unbiased prediction)和RKHS (reproducing kernel Hilbert space)整合不同显著位点作为固定效应均可提高穗长GCA基因组估计育种值的准确性, 提高率为2.34%~14.98%, 而在综合环境中除了利用FarmCPU (fixed and random model circulating probability unification)或BLINK (Bayesian-information and linkage-disequilibrium iteratively nested keyway)鉴定的1个显著位点作为固定效应会略降低预测精度外, 其他2种MGWAS方法显著位点的加入均能提高基因组预测力, 提高率为2.80%~6.84%。因此, MGWAS显著位点作为固定效应加入预测模型有利于提高穗长GCA基因组估计育种值的准确性, 可用来对玉米亲本穗长GCA进行有效预测和选择。

关键词: 穗长, 一般配合力, 多位点全基因组关联分析, 固定效应模型, 基因组选择

Abstract:

Ear length is an important agronomic trait, which is closely related with yield. General combining ability (GCA) is an important index to evaluate excellent inbred lines. Therefore, the dissection of genetic basis of ear length GCA and formulation of corresponding breeding strategies is of great significance to improve maize yield. In this study, 537 F₁hybrids as the experimental materials were obtained from 123 maize inbred lines and eight tester lines according to North Carolina II genetic mating design, and phenotyped under two environments. A total of 11,734 single nucleotide polymorphisms (SNPs) identified using the maize 5.5 K liquid breeding chip were used to conduct multi-locus genome-wide association study (MGWAS) and genomic prediction for ear length GCA in two environments and combined environment. A total of 11 SNPs significantly associated with ear length GCA were detected using seven MGWAS, and the variation of GCA effect explained by a single locus was 8.06%-28.23%. Five SNPs were co-located using different MGWAS. Locus 7_178103602 was repeatedly detected using mrMLM (multi-locus random-SNP-effect mixed linear model) in Zhoukou and combined environment, explaining 26.02%-28.23% of variation of ear length GCA, which was an environment-stable and major-effect SNP. 11 candidate genes were identified, among which auxin amido synthetase 9 and EID1-like F-box protein 2 may be key genes for GCA of ear length. The accuracy of five random effect models for predicting ear length GCA ranged from 0.53 to 0.69 in the three environments, and there were minor differences among these models. In Xinxiang and Zhoukou environments, GBLUP (genomic best linear unbiased prediction) and RKHS (reproducing kernel Hilbert space) incorporating different significant loci as fixed effects could improve the accuracy of genomic estimated breeding value for GCA of ear length, with a percentage increase of 2.34%-14.98%. In the combined environment, except that the accuracy was slightly reduced using one significant locus derived from FarmCPU (fixed and random model circulating probability unification) or BLINK (Bayesian-information and linkage-disequilibrium iteratively nested keyway) as fixed effects, the addition of significant loci derived from the other two MGWAS methods could improve the genomic prediction ability, with a percentage increase of 2.80%-6.84%. Therefore, the incorporation of significant loci from MGWAS into the prediction models as fixed effects is helpful to improve the accuracy of the genomic estimated breeding value for ear length GCA, which could be used to effectively predict and select GCA of maize parental ear length.

Key words: ear length, general combining ability, multi-locus genome-wide association study, fixed effect model, genomic selection

马娟, 朱卫红, 刘京宝, 宇婷, 黄璐, 郭国俊. 玉米穗长一般配合力多位点全基因组关联分析和预测[J]. 作物学报, 2023, 49(6): 1562-1572.

MA Juan, ZHU Wei-Hong, LIU Jing-Bao, YU Ting, HUANG Lu, GUO Guo-Jun. Multi-locus genome-wide association study and prediction for general combining ability of maize ear length[J]. Acta Agronomica Sinica, 2023, 49(6): 1562-1572.

图/表 11

图1

表1

表2

附表1

根据MaizeGDB获得的穗长一般配合力候选基因和已知穗长基因的表达量"

组织 Tissue	Zm00001d028915	Zm00001d031527	Zm00001d026137	Zm00001d026668	Zm00001d006761	Zm00001d017399	Zm00001d036328	Zm00001d036602	Zm00001d020325	Zm00001d020686	Zm00001d022457	Zm00001d022473	Zm00001d010756
组织 Tissue	YIGE1				EDL2	VPE1		KNR6	HAK23	ZmACO2	PX3	AAS9
B73_16DAP_Embryo	23.8	9.3	0	16.2	18.3	45.9	23.8	1.6	3.9	0.7	0	2	0
B73_18DAP_Embryo	18.8	9	0	4.3	17.9	52.5	25.9	1.9	4.8	0.9	0	1.8	0
B73_20DAP_Embryo	20.5	9	0	4.9	16.7	71	32	1.9	4.1	1	0	2.5	0
B73_22DAP_Embryo	23.1	8.2	0.1	5.3	19.4	91	26.6	2.2	3.7	1.4	0	2.7	0
B73_24DAP_Embryo	20.5	6	0	7	16.9	125.5	26.7	2.1	1.4	2.8	0	2.1	0
B73_16DAP_Endosperm	15.2	7.5	0.2	7.1	18.2	0.4	17.5	2.1	3.8	0	0	0.2	0
B73_18DAP_Endosperm	12.9	7.4	0.1	2.4	15.4	0.6	21.3	1.8	3	0.1	0	0.2	0
B73_20DAP_Endosperm	9.8	4	0	1.9	9.5	0.6	14.6	1.6	2.2	0	0	0.2	0
B73_22DAP_Endosperm	10.6	3.1	0	1.7	9.6	0.6	13.5	1.6	2	0	0	0.3	0
B73_24DAP_Endosperm	8.6	3.5	0.2	1.7	8	0.8	11.6	1.7	1.5	0	0	0.1	0
run_0_NOPOL_INT	12.8	6.9	10.9	5.6	9.9	0.1	7.7	9	25.9	46.3	13.2	0.8	0.2
run_12_NOPOL_INT	13.7	6.3	8	7.6	10	0.1	14.8	4.6	10	16.8	0.2	0.7	0.1
run_18_NOPOL_INT	19.9	7.4	11.2	5.8	8.8	0	5.1	5.9	40.3	4.8	10.5	0.3	0
run_24_NOPOL_INT	11.7	8.8	33.2	8.9	13.6	0.1	9	9.3	18.1	8.9	4.7	0.6	0.3
run_30_NOPOL_INT	12.5	6.7	7.5	6.9	9.3	0.1	15.6	4.5	9.3	49.4	1.6	0.8	0
run_6_NOPOL_INT	14.6	6.2	12.6	5.9	12.2	0.1	12.8	3.3	9.9	5.8	2.4	1.1	0.1
run_0_DAP_NOPOL_LEAF	70	2.9	6.2	0.5	6.5	0	2.6	2.1	7.3	24.2	0.7	0.2	0.2
run_12_DAP_NOPOL_LEAF	56.9	7.7	2	0.6	3.6	0	4.8	2.7	8.6	38.2	0.2	0.2	0.1
run_18_DAP_NOPOL_LEAF	51.3	6.8	2.7	1.2	3.3	0.1	6.6	3.6	11.3	27.9	0.4	0.2	0.3
run_24_DAP_NOPOL_LEAF	37.1	11.1	1.8	1.4	7.3	0.1	5.1	4.1	10.3	18	0.1	0.3	0.2
run_30_DAP_NOPOL_LEAF	19.1	31.1	4.2	2.5	6.3	0.1	4.6	9	3.3	7.2	0.1	0.1	1
run_0_POL_INT	14.3	7	13.9	5.8	9.9	0.2	8.5	5.8	27.7	56.2	9.1	0.7	0
run_12_POL_INT	14.6	5.4	23.4	5.1	8.7	0.3	12.5	9.4	10.1	12	21.3	0.8	0.3
run_18_POL_INT	18	5	17.3	3.5	10.7	0.1	9.8	7.3	9.1	38.2	1.4	1.2	0
run_24_POL_INT	18.9	6.7	42.3	4	13	0.1	7.2	9.3	17	6.6	4.7	0.7	0.3
run_30_POL_INT	15.2	7.4	29.6	4.2	13.4	0.1	9.2	6.5	9.6	9.4	9.4	0.7	0.3
run_6_POL_INT	15.5	6.6	21.5	3.9	15.1	0	9.9	5.4	9.8	49	4.3	1	0
run_0_DAP_POL_LEAF	71.1	3.9	6.5	0.4	5.7	0	2.3	1.9	7.8	27.6	0.2	0.2	0.2
run_12_DAP_POL_LEAF	55.2	7.3	2.1	0.8	3	0.1	5.7	2	8.7	41.8	0.5	0.2	0.3
run_18_DAP_POL_LEAF	51.2	7.7	3.5	0.7	3.4	0.1	7.1	3.1	11.2	33	0.7	0.2	0.1
run_24_DAP_POL_LEAF	48.2	7.6	2.1	1.2	4.4	0.1	5.2	2	10	26.7	0.4	0.3	0.1
run_30_DAP_POL_LEAF	52.5	14	4	0.9	3.4	0	6.9	3.8	13.7	22.8	0.4	0.2	0.2
B73_18DAP_Pericarp	14.4	9.8	3.1	7.3	17.7	0.6	9	6	3.1	3.5	0	0.4	0.4
B73_10DAP_Whole_seed	16.9	11	5.4	10.8	15.8	0.4	16	4.2	10.5	4.1	0.9	0.8	0.3
B73_12DAP_Whole_seed	20.4	9.3	3.5	4.9	20	0.4	20.7	2.9	8.6	0.8	0	0.6	0.2
B73_14DAP_Whole_seed	17.7	13.6	2.4	2.6	23.3	0.9	26.5	2.2	6.6	1.1	0	0.3	0.1
B73_18DAP_Whole_Seed	13.1	8.3	0.7	4	15.4	2	20.9	2.7	3.5	0.8	0	0.4	0
B73_2DAP_Whole_seed	20.7	14.1	0.9	23.6	14.4	0.7	15.4	4.2	16.9	2.9	0.2	2.7	0
B73_20DAP_Whole_Seed	11.6	5.7	0.7	2.4	11.1	5.2	16.7	2.2	2.4	0.5	0	0.3	0
B73_22DAP_Whole_Seed	12.6	5.5	0.9	2.7	13.4	8.1	16.5	2	2.5	0.4	0	0.8	0.1
B73_24DAP_Whole_Seed	10.9	6	1.1	2.8	11.8	12.9	15.1	2.5	1.6	0.5	0	0.4	0
B73_4DAP_Whole_Seed	21.8	12.8	1.1	23.7	15.5	0.6	16.1	5	16.4	3.5	0.6	2.3	0
B73_6DAP_Whole_seed	21.8	8.8	3.3	13.6	16.5	0.6	9.8	4.7	15.1	2.8	0.5	2.5	0.1
B73_8DAP_Whole_seed	18.9	11	5.1	11	13.4	0.4	12.7	5	11.8	3.5	0.4	1.6	0.2
B73_R1_Anthers	16.4	43	5	1.8	9.3	0	4.5	1.5	25	2	0.9	5	0.2
BraceRoot_Node6_abvgrnd_V13	12.8	3.9	1.7	8.3	13.8	0.1	15.7	3.5	7.1	15.2	2.1	1.1	0
B73_6_DAS_GH_Coleoptile	10.8	10.6	4	8.1	9	0.2	17.2	4.7	14.9	89	18.5	1	0
CortPar_3d	12.1	8.9	7.5	6.7	11.9	0.1	6.7	7.7	44.5	13	11.2	0.5	0
CrownRoot_Node4_V7	13.5	5	27.2	5.8	10.7	0.4	13	10.3	10.8	6.9	22.1	0.9	0.6
CrownRoot_Node5_V13	13.2	5.6	31.5	9.3	13.8	0.1	8.5	5.6	13.4	14.6	3.7	0.9	0.1
CrownRoot_Node5_V7	12.7	7.6	22.2	7.9	9.9	0.1	8.9	5.3	13.2	3.1	10.4	0.9	0
CrownRoot_Nodes_1_3_V7	15.2	5.3	25.9	5	8	0.2	12.9	10.5	9.4	10.5	19.9	0.7	1.1
DifferentiationZone_3d	10	9.9	13.6	7.5	9	0.2	5.8	7.9	39.5	3.6	20.9	0.4	0.1
Mz_Ez_3d	8.4	8.7	0.6	10	10.5	0.1	18.8	1.8	9.5	0.5	0.7	0.6	0.1
B73_R1_Pre_pollination_cob	24.3	14.4	1	22.2	15.5	0.7	17.6	2.9	20.5	2.6	0	3.7	0
run_6DAS_GH_Primary_Root	7.8	9.6	14.8	7.7	7.8	0.2	12.1	4.2	28.3	1.7	7	0.7	0
Seminal_7d	10.7	8.3	30.8	9.7	10.8	0.1	13.1	6.3	19.2	1.9	13.6	0.6	0.4
B73_R1_Silks	21.9	9	1.6	15.3	11.1	0.1	11.1	5.1	14.7	2.2	0	0.3	0
Stele_3d	11	8.5	12.5	6.2	9.3	0.1	10.9	3.8	10.6	2.5	10.3	1	0
TapRoot_Z1_7d	12.3	7.9	13.2	4.7	11.9	0	15.1	4.9	9.8	5.9	1.8	1.2	0
TapRoot_Z2_7d	12.5	7.2	18.4	8.9	11.4	0.1	11.6	5.9	21	6	13.9	0.8	0.1
TapRoot_Z3_7d	43.2	5.7	31.9	4.3	10.5	0.1	5.6	5.8	12.5	14.5	20.8	1.1	0.1
TapRoot_Z4_7d	14	8.4	29.1	8	9.8	0.3	12.6	9.9	17.7	13	4	1	0.2
B73_R2_Thirteenth_Leaf	57.4	4.6	2.3	3.5	2	0.1	2.9	2.1	10.2	84.6	5.5	0.2	1
B73_V1_4D_PE_Pooled_Leaves	123.1	2	28.1	2.2	6.3	0	1.1	1.2	8.5	18.6	26.4	0	0
B73_V1_4D_PE_Primary_root	22.6	7.8	10.3	17.7	17.4	0.7	12	4.2	16.5	14.7	1.8	2.5	0.2
B73_V1_4D_PE_Stem__SAM	18.7	15	9.8	11	14.2	0.4	18.4	4.3	12.9	16.4	14.7	1.4	0.1
B73_V13_Immature_tassel	28.7	12.7	0.5	9.1	14.2	0.8	14.8	5	15.3	0.3	0	3.9	0
B73_V18_Immature_cob	35.6	18.5	0.3	7.7	15.9	0.9	16.2	3.9	19.8	2	0	4.4	0
B73_V18_Meiotic_tassel	28.5	7.9	15.9	6.4	11.4	0.1	5.6	3.9	19.3	1	0.1	2.6	0.2
V3_Stem_andSAM	17.6	13.7	7.9	18.3	12.9	0.4	16.7	3.8	11	22.2	13.1	1.1	0
B73_V3_Topmost_leaf	93.1	4.2	15.9	3.8	7.7	0	1.7	1.7	10.8	11.7	16.9	0.1	0.1
B73_V5_Bottom_of_transition_leaf	31.3	11.5	1.2	10.3	10.7	0.1	8.2	0.4	12.8	9	0	0.6	0
B73_V5_First_elonagetd_internode	19.1	10.3	7.6	25.9	15.9	0.7	17.7	3.5	12.2	33.5	1.2	1.7	0
B73_V5_Shoot_tip	17.7	15.3	3.4	22.5	16.1	0.7	18.1	2.1	14.1	23.1	0.8	0.9	0
V5_Tip_ofStage_2_Leaf	108.4	2.3	10.6	0.9	2.1	0	0.9	0.6	5.8	15.7	5.9	0.1	0
B73_V7_Bottom_of_transition_leaf	29.7	10.9	2	9.2	12.5	0.2	12.5	1.2	13.2	1.6	0	1.2	0
B73_V7_Tip_of_transition_leaf	139.1	2	22.2	0.5	8.3	0.1	0.6	0.8	9.8	1.2	0	0	0.1
B73_V9_Eighth_Leaf	64.6	3	7.7	1.7	5.5	0.1	1.4	2.4	8	18.1	0.6	0.1	0.6
B73_V9_Eleventh_Leaf	44.1	8.4	28.9	8.9	10.2	0.1	1.3	5.5	9.9	5.4	8.5	0.2	0.6
B73_V9_Fourth_elongated_internode	27.3	10.3	1.7	14	20.4	0.5	15.6	1.5	16	2.7	0	1	0
B73_V9_Immature_Leaves	30.1	5.9	5.3	48.8	11	0.1	4.2	1.6	9.3	1.9	0.1	0.4	0.2
B73_V9_Thirteenth_Leaf	42.6	9.1	21.7	20	7.8	0.1	1.9	2.3	11.1	1.1	2.3	0.2	0.1
B73_VT_Thirteenth_Leaf	68.9	3	2.5	1.8	1.6	0.2	1.7	1.8	11.4	47	0.3	0.3	0.5
WholePrimaryRoot_7d	13.5	7.9	57.3	8.2	10.8	0.1	9.5	10.4	21.5	6.2	31.8	0.7	0.5
WholeRootSystem_3d	9.4	8	9.2	9.4	9.8	0	12	4.4	34.1	1	15.9	0.8	0
WholeRootSystem_7d	12.9	9.9	45.4	8.2	11.3	0.2	8.9	9.7	25.9	3.6	23.4	0.7	0.5
Reproductive_Control	22.1	7.4	1.7	22.9	12	0.7	9	6.8	14.1	0.6	0.2	2.2	0
Reproductive_Drought	23.5	12.6	11.8	6.1	9.8	0.3	5.7	8	30.5	4.8	0	1	0
Vegetative_Control	24.9	4.8	22.4	2.2	13.8	0.1	3	7.3	9.4	19	0.5	0.2	0.7
Vegetative_Drought	20.5	13.1	17.2	2.8	14.5	0.1	2.6	7.6	8.2	10.2	0.1	0.2	0.7
wtL_1_wild_type	7.37	13.14	0.41	7.96	5	0.78	15.75	0.67	5.5	0.02	0	0.94	0
wtL_2_wild_type	11.12	13.59	0.13	6.39	7.75	0.71	14.16	0.56	5.64	0	0	0.95	0
wtL_3_wild_type	10.48	15.97	0.5	5.61	6.55	0.44	15.08	0.51	5.57	0	0	0.71	0
lg1_1_lg1_R_mutant	8.31	10.52	0.4	7.22	3.76	0.69	12.15	0.44	5.31	0	0	1.17	0
lg1_2_lg1_R_mutant	9.23	10.71	0.4	6.95	5.57	0.69	12.94	0.5	5.98	0	0	1.14	0
lg1_3_lg1_R_mutant	10.28	11.1	1.5	8.69	5.7	0.76	13.07	0.69	6.22	0.02	0	1.14	0
B_L1_1_preblade_adaxial	4.48	10.2	0	10.95	0.72	0	20.02	0.5	1.83	0.02	0	1.54	0
B_L1_2_preblade_adaxial	3.39	11.42	0	18.03	2.45	0	12.63	0.3	2.27	0	0	0.7	0
B_L1_3_preblade_adaxial	4.16	7.96	0	12.38	1.35	0.1	10.28	0.24	2.26	0	0	2.9	0
B3_preblade	6.96	13.48	0.55	3.64	3.1	0.26	21.37	0.72	2.02	0	0	0.99	0
B4_preblade	6.02	10.89	0.79	9.61	1.51	0.32	15.05	0.57	2.42	0.06	0	1.38	0
B5_preblade	7.8	11.63	0.52	9.28	1.43	0.39	17.44	1	3.16	0	0	0.9	0
L_L1_1_preligule_adaxial	3.92	15.24	0	16.14	0.63	0	8.48	0	2.37	0	0	6.53	0
L_L1_2_preligule_adaxial	6.73	14.35	0	17.52	1.53	0	10.74	0.37	2.13	0	0	7.28	0
L_L1_3_preligule_adaxial	8.18	12.46	0	17.1	0.79	0.64	9.54	0.19	2.27	0	0	9.02	0
L3_preligule	10.56	11.2	0.57	4.73	2.65	0.18	13.94	0.65	2.95	0	0	2.68	0
L4_preligule	7.85	10.18	0.56	15.38	2.02	0.32	8.36	0.28	3.79	0	0	2.87	0
L5_preligule	7.09	11.57	0.58	11.11	4.42	0.57	11.75	0.28	3.85	0	0	2.24	0
S_L1_1_presheath_adaxial	2.48	10	0	10.44	0	0	9.49	0.9	2.24	0	0	5.13	0
S_L1_2_presheath_adaxial	5.47	10.34	0	22.09	0.39	0	6.58	0.08	3.88	0	0	5.02	0
S_L1_3_presheath_adaxial	4.4	9.94	0	15.78	0.13	0.04	10.32	0.56	2.9	0	0	4.82	0
S3_presheath	6.18	8.75	1.04	13.21	2.09	0.39	11.88	0.59	3.7	0	0	2.01	0
S4_presheath	6.26	14.43	0.36	3.67	1.04	0.36	17.65	0.42	3.91	0	0	1.38	0
S5_presheath	8.23	12.12	0.92	12.28	4.17	0.27	10.54	0.19	3.65	0	0	1.13	0
rmr6_Control_T0	20.8	2.5	9.4	2.1	4.4	0.2	1.5	1.2	9.3	2.5	0	0.2	0.2
rmr6_Control_T7	19.4	3.3	9.2	1.6	5	0.2	1.5	1.6	7.6	5.1	0	0.3	0.2
rmr6_Drought_Salt_T0	7.4	1.3	2.3	0.4	1.9	0.1	0.7	1	3.5	1.5	0	0.1	0
rmr6_Drought_Salt_T7	19.8	3.6	11.9	2.1	5.3	0.1	2.1	2.1	10.2	3.4	0	0.2	0.5
rmr6_Drought_T0	17.6	3.6	8.7	1.1	4.9	0.2	1.7	2.7	8.6	3.1	0.1	0.3	0.1
rmr6_Drought_T7	18.6	2.4	10.4	1.2	4.9	0.1	1.3	1.6	8.5	3.5	0.1	0.2	0.4
rmr6_Salt_T0	18.9	3.3	11.8	1.8	4.9	0.2	2	2.4	10	2.7	0	0.3	0.5
rmr6_Salt_T7	19.4	3	6.7	1.3	5.9	0.1	1.9	1.7	9	4.6	0	0.2	0.3
wt_Control_T0	31.9	3.8	13.5	2.6	6.3	0.1	2.2	1.3	11	3.7	0	0.2	0.3
wt_Control_T7	29.4	4.8	12.2	1.8	4.8	0.1	1.7	1.3	9.6	3.3	0.1	0.3	0.2
wt_Drought_Salt_T0	28.3	5.1	8.6	1.5	6.7	0.2	1.6	1.7	9.4	2.4	0	0.3	0.1
wt_Drought_Salt_T7	29.8	4.1	11.1	2.4	5.1	0.1	1.6	1.2	9.6	2.6	0.1	0.2	0.3
wt_Drought_T0	28.1	5.4	9.5	1.4	7.3	0.1	1.8	1.9	9.1	4.6	0	0.2	0.1
wt_Drought_T7	28.7	3.8	10.5	1.8	5.4	0.1	1.3	1.1	7.6	2.7	0.1	0.1	0.4
wt_Salt_T0	30.3	5.8	12.4	2.2	6	0.1	2.1	1.4	10	2.7	0.1	0.4	0.2
wt_Salt_T7	31.8	4.4	11.5	1.9	7.5	0.1	1.7	2	9.8	3.7	0	0.2	0.2
B73_cold	47.7	33.1	7.4	8	7.8	0	1.9	1.6	7.1	30	1.1	0.1	0.2
B73_control	43.9	5.1	20.3	10.9	5	0.2	2.9	1.8	12.8	28.3	0.3	0.2	0.1
B73_heat	41.1	1.3	9.5	1.5	2	0.1	0.7	0.8	8.4	37.7	0.4	0.5	0
B73_salt	50.01	4.3	2	4.11	2.17	0.12	1.9	1.63	12.43	13.69	0.24	0.73	0
B73_UV	30.98	5.29	6.63	11.44	2.88	0.23	3.76	1.36	7.82	10.43	52.72	0.69	0.99
endosperm_12DAP	28.9	11.9	1.7	5.1	41.2	1.2	20	3	9.1	0	0	1.7	0.1
pericarp_aleurone	17.2	8.4	12.4	6.3	17.8	137.9	10.5	6.7	1.4	0	0.3	1.8	1.1
endosperm_crown	6.3	3.9	2.3	5.4	19.6	4.3	6.5	1.1	1.5	0	0	0.2	0.5
SYMMETRICAL_DIVISION_ZONE	49.2	24	2.3	17.5	28.1	0.9	16.3	7	30.5	0.1	0	9.6	0
GROWTH_ZONE	43.3	28.5	6	26.9	21.5	0.5	10.6	1.1	21.2	5.3	0	3.9	0
embryos_20DAP	30.4	7.8	0.4	5.9	35.4	145.1	13.5	5.6	12.8	5.6	0	8.8	0
EMBRYOS	33.4	7.6	0.2	26.4	18.7	314.7	36	4.5	2.9	4.9	1.3	12.1	0
MATURE_LEAF_TISSUE_leaf_8	122.3	9.5	36.5	2.1	18.9	0.7	2	5.1	18	19.8	0.5	1.6	0.3
run_6_8_mm_from_tip_of_ear_primordium	51	35.2	2.1	11.2	40.5	2.1	10.5	13.8	35.7	9	0	9.7	0
run_2_4_mm_from_tip_of_ear_primordium	46.1	34.6	1.2	11.7	42.5	1.6	11.7	13.3	37.1	10.6	0	8.1	0
MZ	19	28.7	0.1	11.6	24.5	0.3	59.3	1.9	7.1	0	0.1	2.5	0
EZ	8.8	13.8	0.3	42.6	20.2	0.1	24.3	1.2	7.6	0.9	0.4	1.2	0.2
Cortex	11	12.2	6.8	15.8	27.7	0.1	4.4	12.9	73.4	26.6	41.3	0.3	0.6
PR	12.6	18.5	2.7	38.6	24.4	0.2	36	5.5	32.3	8.4	24.9	0.9	0.1
SR	13.4	21.1	2.6	39.6	23.6	0.1	35.2	6.3	24.3	8	83.5	1.2	0.2
Mature_pollen	0.8	23.8	0	0.1	7.2	0	0.3	0.2	21.2	0	0.1	0.7	0
silks	22.1	11.2	13.2	13.5	30	0.2	5	13.2	9.7	13.5	0.3	0.3	0
mature_female_spikelets	38	29.1	8.1	38	53.3	2.3	13.3	10.8	15.4	3.5	0.2	2.7	0.1
Internode_6_7	36.5	39.8	18	103.5	40.5	0.5	27.4	5.2	20.5	12.1	0	0.7	0
Internode_7_8	41.1	48.9	15.6	103	46.4	1.2	28.5	6.5	25.1	7.4	0	0.7	0
Vegetative_Meristem_and_surrounding_Tissue	37.2	30.6	7	29.2	42.4	1.1	22.2	9.3	20.7	2.9	0	4.9	0

附表1

图2

附表2

附表3

图3

附表4

附表5

图4

参考文献 63

[1]	张人予. 玉米穗长基因EL3的克隆及我国优良自交系基因组变异分析. 中国农业大学博士学位论文, 北京, 2018.
	Zhang R Y. Cloning of EL3 for Ear Length in Maize and Patterns of Genomic Variation in Chinese Maize Inbred Lines. PhD Dissertation of China Agricultural University, Beijing, China, 2018. (in Chinese with English abstract)
[2]	Jia H T, Li M F, Li W Y, Liu L, Jian Y N, Yang Z X, Shen X M, Ning Q, Du Y F, Zhao R, Jackson D, Yang X H, Zhang Z X. A serine/threonine protein kinase encoding gene KERNEL NUMBER PER ROW6 regulates maize grain yield. Nat Commun, 2020, 11: 988-998. doi: 10.1038/s41467-020-14746-7
[3]	周广飞. 一个控制玉米行粒数、穗长其一般配合力的多效性QTL (qKNR7.2)鉴定. 华中农业大学硕士学位论文, 湖北武汉, 2014.
	Zhou G F. Identification of A Pleitropic QTL (qKNR7.2) for Kernel Row Number Per Row, Ear Length, and General Combining Ability of Maize. MS Thesis of Huazhong Agricultural University, Wuhan, Hubei, China, 2014. (in Chinese with English abstract)
[4]	Liu X G, Hu X X, Li K, Liu Z F, Wu Y J, Feng G, Huang C L, Wang H W. Identifying quantitative trait loci for the general combining ability of yield-relevant traits in maize. Breed Sci, 2021, 71: 217-228. doi: 10.1270/jsbbs.20008
[5]	监立强. 玉米产量相关性状及其一般配合力的关联分析. 河北农业大学硕士学位论文, 河北保定, 2017.
	Jian L Q. Genome-Wide Association Study of Yield-Related Traits and General Combining Ability in Maize (Zea mays L.). MS Thesis of Hebei Agricultural University, Baoding, Hebei, China, 2017. (in Chinese with English abstract)
[6]	刘文童, 监立强, 郭晋杰, 赵永锋, 黄亚群, 陈景堂, 祝丽英. 玉米穗部性状及其一般配合力的关联分析. 植物遗传资源学报, 2020, 21: 706-715.
	Liu W T, Jian L Q, Guo J J, Zhao Y F, Huang Y Q, Chen J C, Zhu L Y. Association analysis of ear-related traits and their general combining ability in maize. J Plant Genetic Resour, 2020, 21: 706-715. (in Chinese with English abstract)
[7]	温阳俊, 冯建英, 张瑾. 多位点关联分析方法学的研究进展. 南京农业大学学报, 2022, 45: 1-10.
	Wen Y J, Feng J, Zhang J. Research progress of mulit-locus genome-wide association study. J Nanjing Agric Univ, 2022, 45: 1-10. (in Chinese with English abstract)
[8]	Liu X L, Huang M, Fan B, Buckler E S, Zhang Z. Iterative usage of fixed and random effect models for powerful and efficient genome wide association studies. PLoS Genet, 2016, 12: e1005767.
[9]	Huang M, Liu X, Zhou Y, Summers R M, Zhang Z W. BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience, 2019, 8: 1-12.
[10]	Wang S B, Feng J Y, Ren W L, Huang B, Zhou L, Wen Y J, Zhang J, Dunwell J M, Xu S, Zhang Y M. Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci Rep, 2016, 6: 19444-19453. doi: 10.1038/srep19444
[11]	Tamba C L, Zhang Y M. A fast mrMLM algorithm for multi-locus genome-wide association studies. BioRxiv, 2018. https://doi.org/10.1101/341784.
[12]	Wen Y J, Zhang H, Ni Y L, Huang B, Zhang J, Feng J Y, Wang S B, Dunwell J M, Zhang Y M, Wu R. Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Brief Bioinform, 2018, 19: 700-712. doi: 10.1093/bib/bbw145
[13]	Zhang J, Feng J Y, Ni Y L, Wen Y J, Niu Y, Tamba C L, Yue C, Song Q, Zhang Y M. pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies. Heredity, 2017, 118: 517-524. doi: 10.1038/hdy.2017.8 pmid: 28295030
[14]	Tamba C L, Ni Y L, Zhang Y M. Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies. PLoS Comput Biol, 2017, 13: e1005357.
[15]	Yang Y, Chai Y M, Zhang X, Lu S, Zhao Z C, Wei D, Chen L, Hu Y G. Multi-locus GWAS of quality traits in bread wheat: mining more candidate genes and possible regulatory network. Front Plant Sci, 2020, 11: 1091-1109. doi: 10.3389/fpls.2020.01091 pmid: 32849679
[16]	Peng Y C, Liu H B, Chen J, Shi T T, Zhang C, Sun D F, He Z H, Hao Y F, Chen W. Genome-wide association studies of free amino acid levels by six multi-locus models in bread wheat. Front Plant Sci, 2018, 9: 1196-1204. doi: 10.3389/fpls.2018.01196 pmid: 30154817
[17]	Su J J, Wang C X, Hao F S, Ma Q, Wang J, Li J L, Ning X Z. Genetic detection of lint percentage applying single-locus and multi-locus genome-wide association studies in Chinese early-maturity upland cotton. Front Plant Sci, 2019, 10: 964-974. doi: 10.3389/fpls.2019.00964 pmid: 31428110
[18]	Cui Y R, Zhang F, Zhou Y L. The application of multi-locus GWAS for the detection of salt-tolerance loci in rice. Front Plant Sci, 2018, 9: 1464-1472. doi: 10.3389/fpls.2018.01464 pmid: 30337936
[19]	Zhou G F, Zhu Q L, Mao Y X, Chen G Q, Xue L, Lu H H, Shi M L, Zhang Z L, Song X D, Zhang H M, Hao D R. Multi-locus genome-wide association study and genomic selection of kernel moisture content at the harvest stage in maize. Front Plant Sci, 2021, 12: 697688-697700. doi: 10.3389/fpls.2021.697688
[20]	Meuwissen T H, Hayes B J, Goddard M E. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157: 1819-1829. doi: 10.1093/genetics/157.4.1819 pmid: 11290733
[21]	Vanraden P M. Efficient methods to compute genomic predictions. J Dairy Sci, 2008, 91: 4414-4423. doi: 10.3168/jds.2007-0980 pmid: 18946147
[22]	de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes J M. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics, 2009, 182: 375-385. doi: 10.1534/genetics.109.101501 pmid: 19293140
[23]	González-Recio O, Forni S. Genome-wide prediction of discrete traits using bayesian regressions and machine learning. Genet Sel Evol, 2011, 43: 7-18. doi: 10.1186/1297-9686-43-7 pmid: 21329522
[24]	Guo Z G, Tucker D M, Lu J, Kishore V, Gay G. Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet, 2012, 124: 261-275. doi: 10.1007/s00122-011-1702-9 pmid: 21938474
[25]	Lian L, Jacobson A, Zhong S Q. Genome wide prediction accuracy within 969 maize biparental populations. Crop Sci, 2014, 54: 1514-1522. doi: 10.2135/cropsci2013.12.0856
[26]	Technow F, Schrag T A, Schipprack W, Bauer E, Simianer H, Melchinger A E. Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics, 2014, 197: 1343-1355. doi: 10.1534/genetics.114.165860 pmid: 24850820
[27]	de Oliveira A A, Resende M F R Jr, Ferrão L F V, Amadeu R R, Guimarães L J M, Guimarães C T, Pastina M M, Margarido G R A. Genomic prediction applied to multiple traits and environments in second season maize hybrids. Heredity, 2020, 125: 60-72. doi: 10.1038/s41437-020-0321-0 pmid: 32472060
[28]	Wang X, Zhang Z L, Xu Y, Li P P, Xu C W. Using genomic data to improve the estimation of general combining ability based on sparse partial diallel cross designs in maize. Crop J, 2020, 8: 819-829. doi: 10.1016/j.cj.2020.04.012
[29]	Zhang A, Pérez-Rodríguez P, Vicente F S, Palacios-Rojas N, Dhliwayo T, Liu Y B, Cui Z H, Guan Y, Wang H, Zheng H J, Olsen M, Prasanna B M, Ruan Y Y, Crossa J, Zhang X C. Genomic prediction of the performance of hybrids and the combining abilities for line by tester trials in maize. Crop J, 2021, 10: 109-116. doi: 10.1016/j.cj.2021.04.007
[30]	熊雪航, 段海洋, 李文龙, 李建新, 孙莉, 孙岩, 秦永田, 汤继华, 张雪海. 玉米穗长全基因组关联分析. 分子植物育种, 2022, https://kns.cnki.net/kcms/detail/46.1068.S.20220630.1359.004.html
	Xiong X H, Duan H Y, Li W L, Li J X, Sun L, Sun Y, Qin Y T, Tang J H, Zhang X H. Genome-wide association study of ear length in maize. Mol Plant Breed, 2022, https://kns.cnki.net/kcms/detail/46.1068.S.20220630.1359.004.html. (in Chinese with English abstract)
[31]	秦文萱, 鲍建喜, 王彦博, 马雅杰, 龙艳, 李金萍, 董振营, 万向元. 玉米叶夹角性状的全基因组关联分析与关键位点优异等位变异挖掘. 作物学报, 2022, 48: 2691-2705. doi: 10.3724/SP.J.1006.2022.23019
	Qin W X, Bao J X, Wang Y B, Ma Y J, Long Y, Li J P, Dong Z Y, Wang X Y. Genome-wide association study of leaf angle traits and mining of elite alleles from the major loci in maize. Acta Agron Sin, 2022, 48: 2691-2705 (in Chinese with English abstract).
[32]	彭勃, 赵晓雷, 王奕, 袁文娅, 李春辉, 李永祥, 张登峰, 石云素, 宋燕春, 王天宇, 黎裕. 玉米叶向值的全基因组关联分析. 作物学报, 2020, 46: 819-831. doi: 10.3724/SP.J.1006.2020.93063
	Peng B, Zhao X L, Wang Y, Yuan Y W, Li C H, Li Y X, Zhang D F, Shi S Y, Song C Y, Wang T Y, Li Y. Genome-wide association studies of leaf orientation value in maize. Acta Agron Sin, 2020, 46: 819-831. (in Chinese with English abstract) doi: 10.3724/SP.J.1006.2020.93063
[33]	Pritchard J K, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics, 2000, 155: 945-959. doi: 10.1093/genetics/155.2.945 pmid: 10835412
[34]	Jakobsson M, Rosenberg N A. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics, 2007, 23: 1801-1806. doi: 10.1093/bioinformatics/btm233 pmid: 17485429
[35]	Bradbury P J, Zhang Z W, Kroon D E, Casstevens T M, Ramdoss Y, Buckler E S. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 2007, 23: 2633-2635. doi: 10.1093/bioinformatics/btm308 pmid: 17586829
[36]	Wang J B, Zhang Z W. GAPIT Version 3: boosting power and accuracy for genomic association and prediction. Genom Proteom Bioinf, 2021, 19: 629-640. doi: 10.1016/j.gpb.2021.08.005 pmid: 34492338
[37]	Zhang Y W, Tamba C L, Wen Y J, Li P, Ren W L, Ni Y L, Gao J, Zhang Y M. mrMLM v4.0.2: an R platform for multi-locus genome-wide association studies. Genom Proteom Bioinf, 2020, 18: 481-487. doi: 10.1016/j.gpb.2020.06.006
[38]	Cingolani P, Platts A, Wang L, Coon M, Nguyen T, Wang L, Land S J, Lu X, Ruden D M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin), 2012, 6: 80-92. doi: 10.4161/fly.19695 pmid: 22728672
[39]	Luo Y, Zhang M L, Liu Y, Liu J, Li W Q, Chen G S, Peng Y, Jin M, Wei W, Jian L, Yan J, Fernie A R, Yan J B. Genetic variation in YIGE1 contributes to ear length and grain yield in maize. New Phytol, 2022, 234: 513-526. doi: 10.1111/nph.v234.2
[40]	Ning Q, Jian Y N, Du Y, Li Y F, Shen X M, Jia H T, Zhao R, Zhan J M, Yang F, Jackson D, Liu L, Zhang Z W. An ethylene biosynthesis enzyme controls quantitative variation in maize ear length and kernel yield. Nat Commun, 2021, 12: 5832-5842. doi: 10.1038/s41467-021-26123-z pmid: 34611160
[41]	Pérez P, de los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics, 2014, 198: 483-495. doi: 10.1534/genetics.114.164442 pmid: 25009151
[42]	Zhang Y M, Jia Z, Dunwell J M. The applications of new multi-locus GWAS methodologies in the genetic dissection of complex traits. Front Plant Sci, 2019, 10: 100-105. doi: 10.3389/fpls.2019.00100
[43]	An Y X, Chen L, Li Y X, Li C H, Shi Y S, Zhang D F, Li Y, Wang T Y. Genome-wide association studies and whole-genome prediction reveal the genetic architecture of KRN in maize. BMC Plant Biol, 2020, 20: 490-500. doi: 10.1186/s12870-020-02676-x pmid: 33109077
[44]	Zhou B, Zhou Z J, Ding J Q, Zhang X C, Mu C, Wu Y, Gao J Y, Song Y X, Wang S W, Ma J L, Li X T, Wang R X, Xia Z L, Chen J F, Wu J Y. Combining three mapping strategies to reveal quantitative trait loci and candidate genes for maize ear length. Plant Genome, 2018, 11: 1-8.
[45]	Li D D, Zhou Z Q, Lu X H, Jiang Y, Li G L, Li J H, Wang H Y, Chen S J, Li X H, Würschum T, Reif J C, Xu S Z, Li M S, Liu W X. Genetic dissection of hybrid performance and heterosis for yield-related traits in maize. Front Plant Sci, 2021, 12: 774478-774496. doi: 10.3389/fpls.2021.774478
[46]	Su C F, Wang W, Gong S L, Zuo J H, Li S J, Xu S Z. High density linkage map construction and mapping of yield trait QTLs in maize (Zea mays) using the genotyping-by-sequencing (GBS) technology. Front Plant Sci, 2017, 8: 706-719. doi: 10.3389/fpls.2017.00706
[47]	Chen L, An Y X, Li Y X, Li C H, Shi Y S, Song Y C, Zhang D F, Wang T Y, Li Y. Candidate loci for yield-related traits in maize revealed by a combination of MetaQTL analysis and regional association mapping. Front Plant Sci, 2017, 8: 2190-2203. doi: 10.3389/fpls.2017.02190 pmid: 29312420
[48]	Zhou Z P, Li G L, Tan S Y, Li D D, Liu W X. A QTL atlas for grain yield and its component traits in maize (Zea mays). Plant Breed, 2020, 139: 562-574. doi: 10.1111/pbr.v139.3
[49]	Zhao Y M, Su C F. Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize. Sci Rep, 2019, 9: 16112-16121. doi: 10.1038/s41598-019-52222-5 pmid: 31695075
[50]	Lu X, Zhou Z Q, Yuan Z H, Zhang C S, Hao Z F, Wang Z H, Li M S, Zhang D G, Yong H J, Han J N, Li X H, Weng J F. Genetic dissection of the general combining ability of yield-related traits in maize. Front Plant Sci, 2020, 11: 788-802. doi: 10.3389/fpls.2020.00788 pmid: 32793248
[51]	Galli M, Liu Q J, Moss B L, Malcomber S, Li W, Gaines C, Federici S, Roshkovan J, Meeley R, Nemhauser J L, Gallavotti A. Auxin signaling modules regulate maize inflorescence architecture. Proc Natl Acad Sci USA, 2015, 112: 13372-13377. doi: 10.1073/pnas.1516473112 pmid: 26464512
[52]	Koops P, Pelser S, Ignatz M, Klose C, Marrocco-Selden K, Kretsch T. EDL3 is an F-box protein involved in the regulation of abscisic acid signalling in Arabidopsis thaliana. J Exp Bot, 2011, 62: 5547-5560. doi: 10.1093/jxb/err236
[53]	Zhang H H, Yin L L, Wang M Y, Yuan X H, Liu X L. Factors affecting the accuracy of genomic selection for agricultural economic traits in maize, cattle, and pig populations. Front Genet, 2019, 10: 189-198. doi: 10.3389/fgene.2019.00189 pmid: 30923535
[54]	Tehseen M M, Kehel Z, Sansaloni C P, Lopes M D S, Amri A, Kurtulus E, Nazari K. Comparison of genomic prediction methods for yellow, stem, and leaf rust resistance in wheat landraces from Afghanistan. Plants, 2021, 10: 558-572. doi: 10.3390/plants10030558
[55]	Ma J, Cao Y Y. Genetic dissection of grain yield of maize and yield-related traits through association mapping and genomic prediction. Front Plant Sci, 2021, 12: 690059-690069. doi: 10.3389/fpls.2021.690059
[56]	Lozada D N, Mason R E, Sarinelli J M, Brown-Guedira G. Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat. BMC Genet, 2019, 20: 82. doi: 10.1186/s12863-019-0785-1 pmid: 31675927
[57]	Odilbekov F, Armoniené R, Koc A, Svensson J, Chawade A. GWAS-assisted genomic prediction to predict resistance to Septoria tritici Blotch in Nordic winter wheat at seedling stage. Front Genet, 2019, 10: 1224-1233. doi: 10.3389/fgene.2019.01224 pmid: 31850073
[58]	马娟, 朱卫红, 丁俊强. 玉米重要农艺性状的基因组预测分析. 玉米科学, 2022, 30(1): 48-52.
	Ma J, Zhu W H, Ding J Q. Genomic prediction analysis for maize important agronomic traits. J Maize Sci, 2022, 30(1): 48-52. (in Chinese with English abstract)
[59]	Arruda M, Lipka A, Brown P, Krill A, Thurber C, Brown-Guedira G, Dong Y, Foresman B J, Kolb F L. Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum). Mol Breed, 2016, 36: 1-11. doi: 10.1007/s11032-015-0425-z
[60]	Bernardo R. Genomewide selection when major genes are known. Crop Sci, 2014, 54: 68-75. doi: 10.2135/cropsci2013.05.0315
[61]	Yuan Y, Cairns J E, Babu R, Gowda M, Makumbi D, Magorokosho C, Zhang A, Liu Y B, Wang N, Hao Z F, San V F, Olsen M S, Prasanna B M, Lu Y L, Zhang X C. Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and flowering time under drought and heat stress conditions in maize. Front Plant Sci, 2019, 9: 1919-1933. doi: 10.3389/fpls.2018.01919
[62]	Liu Y B, Hu G H, Zhang A, Loladze A, Hu Y X, Wang H, Qu J T, Zhang X C, Olsen M, Vicente F S, Crossa J, Lin F, Prasanna B M. Genome-wide association study and genomic prediction of Fusarium ear rot resistance in tropical maize germplasm. Crop J, 2021, 9: 325-341. doi: 10.1016/j.cj.2020.08.008
[63]	Cericola F, Jahoor A, Orabi J, Andersen J R, Janss L L, Jensen J. Optimizing training population size and genotyping strategy for genomic prediction using association study results and pedigree information: a case of study in advanced wheat breeding lines. PLoS One, 2017, 12: e0169606.

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来源 Source	自由度 Degree of freedom	均方 Mean of square	F值 F-value	P值 P-value
环境 Environment	1	1452.1	1182.02	0
重复 Replicate	1	0.3	0.24	0.62
GCA_P1	122	11.4	9.25	0
GCA_P2	7	26.7	21.77	0
SCA	407	3.6	2.93	0
环境: GCA_P1Environment: GCA_P1	122	3.1	2.54	3.82E-15
环境: GCA_P2 Environment: GCA_P2	7	3	2.46	0.017
环境: SCA Environment: SCA	358	3	2.44	0
残差 Residuals	1020	1.2
GCA遗传力 Heritability of GCA	0.82

标记 Marker name	方法 Method	P值 P-value	r² (%)	环境 Environment	候选基因 Candidate gene
7_178103514	mrMLM	3.09E-08	24.29	新乡Xinxiang	Zm00001d022457 (peroxidase 3, PX3)
2_216138581	FASTmrEMMA	6.12E-10	23.09		Zm00001d006761 (EID1-like F-box protein 2, EDL2)
8_126983650	FASTmrEMMA	6.73E-08	19.25		Zm00001d010756
2_216138581	pLARmEB	1.41E-07	15.63		Zm00001d006761 (EID1-like F-box protein 2, ELD2)
8_126983650	pLARmEB	3.43E-09	16.01		Zm00001d010756
6_84476172	BLINK	3.99E-10	—		Zm00001d036328
6_84476172	FarmCPU	3.86E-08	—		Zm00001d036328
7_178103602	mrMLM	5.24E-07	26.02	周口Zhoukou	Zm00001d022457 (peroxidase 3, PX3)
7_178327031	FASTmrMLM	1.68E-08	10.64		Zm00001d022473 (auxin amido synthetase 9, AAS9)
7_178327031	pLARmEB	3.91E-07	11.29		Zm00001d022473 (auxin amido synthetase 9, AAS9)
10_139481397	ISIS EM-BLASSO	4.56E-08	11.69		Zm00001d026137
10_149653708	ISIS EM-BLASSO	7.45E-07	11.25		Zm00001d026668
1_192956360	FarmCPU	1.43E-07	—	综合环境Combined environment	Zm00001d031527
1_192956360	BLINK	1.57E-08	—		Zm00001d031527
7_178103602	mrMLM	7.14E-12	28.23		Zm00001d022457 (peroxidase 3, PX3)
7_106761824	mrMLM	1.82E-11	18.49		Zm00001d020325 (potassium high-affinity transporter23, HAK23)
5_194917385	pLARmEB	7.33E-08	8.06		Zm00001d017399 (vacuolar processing enzyme 1, VPE1)

来源 Source	自由度 Degree of freedom	平方和 Sum of square	均方 Mean of square	F值 F-value	P值 P-value
模型Model	4	0.01	0.0034	0.125	0.97
环境Environment	2	4.97	2.49	91.03	<2E-16
残差Residuals	1493	40.79	0.027

环境 Environment	准确性均值 Mean of accuracy	0.05水平显著性 0.05 significant level
综合环境 Combined environment	0.68	a
新乡Xinxiang	0.61	b
周口Zhoukou	0.54	c

环境 Environment	来源 Source	自由度 Degree of freedom	平方和 Sum of square	均方 Mean of square	F值 F-value	P值 P-value
新乡Xinxiang	模型 Model	9	0.17	0.02	0.70	0.71
新乡Xinxiang	残差 Residuals	990	26.63	0.03
周口Zhoukou	模型Model	9	0.69	0.08	2.16	0.02
周口Zhoukou	残差Residuals	990	35.34	0.04
综合环境Combined environment	模型Model	9	0.38	0.04	2.32	0.01
综合环境Combined environment	残差Residuals	990	17.86	0.02