Welcome to Acta Agronomica Sinica,

Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (6): 1562-1572.doi: 10.3724/SP.J.1006.2023.23042

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Multi-locus genome-wide association study and prediction for general combining ability of maize ear length

MA Juan*(), ZHU Wei-Hong, LIU Jing-Bao, YU Ting, HUANG Lu, GUO Guo-Jun   

  1. Institute of Cereal Crops, Henan Academy of Agricultural Sciences, Zhengzhou 450002, Henan, China
  • Received:2022-05-18 Accepted:2022-11-25 Online:2023-06-12 Published:2022-12-05
  • Contact: *E-mail: majuanjuan85@126.com
  • Supported by:
    Science and Technology Project of Henan Province(222102110043);Science-Technology Foundation for Outstanding Young Scientists of Henan Academy of Agricultural Sciences(2020YQ04)

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 F1 hybrids 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

图A

Distribution of general combining ability of ear length (A) and correlations among different environments (B) In Fig. 1-A, × represents means. In Fig. 1-B, *** represents there is significant difference at the 0.001 probability level."

Table 1

Analysis of variance and heritability of general combining ability of ear length"

来源
Source
自由度
Degree of freedom
均方
Mean of square
F
F-value
P
P-value
环境 Environment 1 1452.1 1182.02 0
重复 Replicate 1 0.3 0.24 0.62
GCAP1 122 11.4 9.25 0
GCAP2 7 26.7 21.77 0
SCA 407 3.6 2.93 0
环境: GCAP1 Environment: GCAP1 122 3.1 2.54 3.82E-15
环境: GCAP2 Environment: GCAP2 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

Table 2

Significant SNPs and candidate genes for the general combining ability of ear length"

标记
Marker name
方法
Method
P
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)

Table S1

Relative expression level of candidate genes for general combining ability of ear length and known genes for ear length obtained from MaizeGDB"

组织
Tissue
Zm00001d028915 Zm00001d031527 Zm00001d026137 Zm00001d026668 Zm00001d006761 Zm00001d017399 Zm00001d036328 Zm00001d036602 Zm00001d020325 Zm00001d020686 Zm00001d022457 Zm00001d022473 Zm00001d010756
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

Fig. 2

Cluster analysis (A) and gene network analysis (B) of candidate genes and known genes for ear length"

Table S2

Analysis of variance for prediction accuracy of random effect models and environments"

来源
Source
自由度
Degree of freedom
平方和
Sum of square
均方
Mean of square
F
F-value
P
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

Table S3

Duncan multiple comparisons for prediction accuracy of different environments in table S2"

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

Fig. 3

Prediction accuracy of general combining ability of ear length using five random effect models"

Table S4

Analysis of variance for prediction accuracy of fixed effect models and random effect models in different environments"

环境
Environment
来源
Source
自由度
Degree of freedom
平方和
Sum of square
均方
Mean of square
F
F-value
P
P-value
新乡Xinxiang 模型 Model 9 0.17 0.02 0.70 0.71
残差 Residuals 990 26.63 0.03
周口Zhoukou 模型Model 9 0.69 0.08 2.16 0.02
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
残差Residuals 990 17.86 0.02

Table S5

Duncan multiple comparisons for prediction accuracy of different models in table S4"

环境
Environment
模型
Model
预测准确性
Prediction accuracy
0.05水平显著性
0.05 significant level
周口Zhoukou ISIS EMBLASSO-GBLUP 0.62 a
ISIS EMBLASSO-RKHS 0.61 ab
mrMLM-GBLUP 0.58 abc
mrMLM-RKHS 0.58 abc
FASTmrMLM-GBLUP 0.56 bc
pLARmEB-GBLUP 0.56 bc
FASTmrMLM-RKHS 0.55 bc
pLARmEB-RKHS 0.55 bc
RKHS 0.54 c
GBLUP 0.54 c
综合环境Combined environment mrMLM-GBLUP 0.73 a
mrMLM-RKHS 0.72 a
pLARmEB-RKHS 0.71 ab
pLARmEB-GBLUP 0.70 ab
RKHS 0.69 ab
GBLUP 0.68 b
BLINK-RKHS 0.68 b
FarmCPU-RKHS 0.68 b
BLINK-GBLUP 0.67 b
FarmCPU-GBLUP 0.67 b

Fig. 4

Prediction accuracy of GBLUP and RKHS incorporating significant loci as fixed effects A, B, and C represent Xinxiang, Zhoukou, and combined environment, respectively. GBLUP and RKHS represent random effect models. Models with GBLUP and RKHS as suffixes represent fixed effect models."

[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.
[1] XU Jia-Bo, WU Peng-Hao, HUANG Bo-Wen, CHEN Zhan-Hui, MA Yue-Hong, REN Jiao-Jiao. QTL locating and genomic selection for tassel-related traits using F2:3 lineage haploids [J]. Acta Agronomica Sinica, 2023, 49(3): 622-633.
[2] YAN Weikai. A critical review on the principles and procedures for cultivar development and evaluation [J]. Acta Agronomica Sinica, 2022, 48(9): 2137-2154.
[3] Jian-Bo HE,Fang-Dong LIU,Guang-Nan XING,Wu-Bin WANG,Tuan-Jie ZHAO,Rong-Zhan GUAN,Jun-Yi GAI. Characterization and Analytical Programs of the Restricted Two-stage Multi- locus Genome-wide Association Analysis [J]. Acta Agronomica Sinica, 2018, 44(9): 1274-1289.
[4] MA Yan-Song, LIU Zhang-Xiong, WEN Zi-Xiang, WEI Shu-Hong, YANG Chun-Ming, WANG Hui-Cai6, YANG Chun-Yan, LU Wei-Guo, XU Ran, ZHANG Wan-Hai, WU Ji-An, HU Guo-Hua, LUAN Xiao-Yan, FU . Effect of Population Structure on Prediction Accuracy of Soybean 100-Seed Weight by Genomic Selection MA Yan-Song1,2,13, LIU Zhang-Xiong1, WEN Zi-Xiang3, WEI Shu-Hong4, YANG Chun-Ming5, WANG Hui-Cai6, YANG [J]. Acta Agron Sin, 2018, 44(01): 43-52.
[5] WANG Bo-Xin,WANG Ya-Hui,CHEN Peng-Fei,LIU Xu-Dong-Yu,FENG Zhi-Qian,HAO Yin-Chuan,ZHANG Ren-He,ZHANG Xing-Hua,XUE Ji-Quan*. Combining Ability of Maize Inbred Lines from ShaanA Group and Shaan B Group under Different Density Conditions [J]. Acta Agron Sin, 2017, 43(09): 1328-1336.
[6] WANG Jian-Jun,YONG Hong-Jun,ZHANG Xiao-Cong,LI Ming-Shun,ZHANG De-Gui,BAI Li,GAO Zhi-Qiang,ZHANG Shi-Huang,LI Xin-Hai. Combining Ability and Heterosis Effects between 12 Exotic Maize Populations and Domestic Germplasm [J]. Acta Agron Sin, 2012, 38(12): 2170-2177.
[7] SHU Yong-Dun, TUN Lei, WANG Dan, GUO Chang-Gong. Application of Artificial Neural Network in Genomic Selection for Crop Improvement [J]. Acta Agron Sin, 2011, 37(12): 2179-2186.
[8] CHEN Hong-Mei, WANG Yan-Fen, YAO Wen-Hua, LUO Li-Ming, LI Jia-Li, XU Chun-Xia, PAN Xin-Meng, GUO Hua-Chun. Utilization Potential of the Temperate Maize Inbreds Integrated with Tropical Germplasm [J]. Acta Agron Sin, 2011, 37(10): 1785-1793.
[9] YANG Jia-Yin,GAI Jun-Yi. Heterosis,Combining Ability and Their Genetic Basis of Yield among Key Parental Materials of soybean in Huang-Huai Valleys [J]. Acta Agron Sin, 2009, 35(4): 620-630.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Li Shaoqing, Li Yangsheng, Wu Fushun, Liao Jianglin, Li Damo. Optimum Fertilization and Its Corresponding Mechanism under Complete Submergence at Booting Stage in Rice[J]. Acta Agronomica Sinica, 2002, 28(01): 115 -120 .
[2] Wang Lanzhen;Mi Guohua;Chen Fanjun;Zhang Fusuo. Response to Phosphorus Deficiency of Two Winter Wheat Cultivars with Different Yield Components[J]. Acta Agron Sin, 2003, 29(06): 867 -870 .
[3] YANG Jian-Chang;ZHANG Jian-Hua;WANG Zhi-Qin;ZH0U Qing-Sen. Changes in Contents of Polyamines in the Flag Leaf and Their Relationship with Drought-resistance of Rice Cultivars under Water Deficiency Stress[J]. Acta Agron Sin, 2004, 30(11): 1069 -1075 .
[4] Yan Mei;Yang Guangsheng;Fu Tingdong;Yan Hongyan. Studies on the Ecotypical Male Sterile-fertile Line of Brassica napus L.Ⅲ. Sensitivity to Temperature of 8-8112AB and Its Inheritance[J]. Acta Agron Sin, 2003, 29(03): 330 -335 .
[5] Wang Yongsheng;Wang Jing;Duan Jingya;Wang Jinfa;Liu Liangshi. Isolation and Genetic Research of a Dwarf Tiilering Mutant Rice[J]. Acta Agron Sin, 2002, 28(02): 235 -239 .
[6] WANG Li-Yan;ZHAO Ke-Fu. Some Physiological Response of Zea mays under Salt-stress[J]. Acta Agron Sin, 2005, 31(02): 264 -268 .
[7] TIAN Meng-Liang;HUNAG Yu-Bi;TAN Gong-Xie;LIU Yong-Jian;RONG Ting-Zhao. Sequence Polymorphism of waxy Genes in Landraces of Waxy Maize from Southwest China[J]. Acta Agron Sin, 2008, 34(05): 729 -736 .
[8] HU Xi-Yuan;LI Jian-Ping;SONG Xi-Fang. Efficiency of Spatial Statistical Analysis in Superior Genotype Selection of Plant Breeding[J]. Acta Agron Sin, 2008, 34(03): 412 -417 .
[9] WANG Yan;QIU Li-Ming;XIE Wen-Juan;HUANG Wei;YE Feng;ZHANG Fu-Chun;MA Ji. Cold Tolerance of Transgenic Tobacco Carrying Gene Encoding Insect Antifreeze Protein[J]. Acta Agron Sin, 2008, 34(03): 397 -402 .
[10] ZHENG Xi;WU Jian-Guo;LOU Xiang-Yang;XU Hai-Ming;SHI Chun-Hai. Mapping and Analysis of QTLs on Maternal and Endosperm Genomes for Histidine and Arginine in Rice (Oryza sativa L.) across Environments[J]. Acta Agron Sin, 2008, 34(03): 369 -375 .