基于杂交种群体的玉米产量及其配合力的全基因组关联分析

doi:10.3864/j.issn.0578-1752.2022.09.001

Abstract

Abstract:

【Objective】By analyzing the yield of the hybrids from the inbred lines bred from the Shaan A and Shaan B group, the combining ability of the inbred lines were evaluated, genome-wide association analysis, and mining associated loci for yield and its combining ability conducted. It will provide references for improving maize inbred lines selected from Shaan A group and Shaan B group and applying them in varieties breeding. 【Method】Based on NCⅡ genetic design, 85 excellent inbred lines from Shaan A group and Shaan B group were used to construct a hybrid population containing 246 F₁. Then, the yield of the hybrid population was tested in three environments to evaluate their general combining ability (GCA) and special combining ability (SCA). Using the 6H90K maize array to detect the parental genotypes, 63 879 high-quality SNPs were obtained, which were used to analyze the genetic characteristics of parental lines. According to the parental genotypes, 55 951 high-quality SNPs were inferred in the hybrid population for genome-wide association analysis of hybrid yield, GCA, and SCA using additive model and non-additive model. Meanwhile, candidate genes around the significant SNPs were screened and annotated based on the maize B73 reference genome.【Result】The yield in the three environments accorded to the normal distribution with wide variation, the broad-sense heritability of yield was 59.04%, and the environmental effect was significant. There was significant positive correlation between hybrid yield and combining ability, and the correlation between hybrid yield and SCA (r=0.95) was higher than that between hybrid yield and GCA (r=0.62). The genetic characteristic of Shaan A group and Shaan B group was different, and inbred lines from Shaan A group have higher general combining ability. Totally, five, seven and nine significant SNPs were detected (-log₁₀(P)>3.86) for GCA, hybrid yield and SCA, respectively. Among them, four SNPs were co-located in hybrid yield and SCA. Ultimately, 17 associated SNPs were anchored. Dominant allele analysis of different trait-associated loci showed that four GCA-associated SNPs were controlled by additive effects, and the F₁ BLUE-associated loci could be divided into 4 types mainly by the dominant effect, and the heterozygous genotype is the favorite allele or sub-optimal allele for yield in F₁. Through functional annotation, the candidate genes were specifically expressed in maize growth and kernel establishment, for example, GRMZM2G165828 and GRMZM2G057557 were related to maize kernel development. 【Conclusion】Based on this study, we consider that GCA and SCA jointly affect the yield of hybrids, and the effect of SCA is greater. Moreover, GCA and SCA may have different genetic basis, and GCA can be increased with the accumulation of favorable alleles. Using the genome-wide association analysis in the F₁ hybrid population can carry out genetic analysis related to combining ability, mine the genetic loci related to yield and combining ability, and accelerated the application of the associated loci in molecular breeding.

Key words: maize, hybrids, general combining ability, special combining ability, genome-wide association analysis

LI ZhouShuai,DONG Yuan,LI Ting,FENG ZhiQian,DUAN YingXin,YANG MingXian,XU ShuTu,ZHANG XingHua,XUE JiQuan. Genome-Wide Association Analysis of Yield and Combining Ability Based on Maize Hybrid Population[J].Scientia Agricultura Sinica, 2022, 55(9): 1695-1709.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2022.09.001

https://www.chinaagrisci.com/EN/Y2022/V55/I9/1695

Figures/Tables 12

Supplementary table 1

Table 1

Table 2

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 3

Table 4

Fig. 5

Fig. 6

Table 5

Prediction of candidate gene"

标记位点 SNP ID	性状 Trait	SNPs位置 Position (bp)	候选基因 Candidate genes	功能注释 Annotation
SNP_1	GCA	Chr.1:296696400	AC206957.1_FG005	转座元件Transposable_element
SNP_2	GCA	Chr.2:228845613	GRMZM2G559383	-
SNP_3	GCA	Chr.4:80111618	GRMZM2G175827(Zm00001d050350)	Kan3转录因子Kan3 transcription factor
SNP_4	GCA	Chr.4:155406944	GRMZM2G137029(Zm00001d051447)	F-box蛋白PP2-A13 F-box protein PP2-A13
SNP_5	GCA	Chr.4:176561593	GRMZM2G097081(Zm00001d052102)	AP2-EREBP-转录因子57 AP2-EREBP-transcription factor 57(ereb57)
SNP_6	CO_ADD	Chr.10:146974725	GRMZM2G149932(Zm00001d026580)	外泌素家族蛋白 Exostosin family protein
SNP_7	CO_DOM	Chr.10:4152732	GRMZM2G101928(Zm00001d023342)	-
SNP_8	CO_DOM	Chr.2:24634902	GRMZM2G124371(Zm00001d002876)	具有FYVE锌指结构域的染色体凝聚（RCC1）家族的调控因子 Regulator of chromosome condensation (RCC1) family with FYVE zinc finger domain
SNP_9	CO_DOM	Chr.2:28002879	GRMZM2G133012(Zm00001d002980)	-
SNP_10	F₁BLUE	Chr.2:4377056	GRMZM2G363066(Zm00001d002013)	水稻凝集素蛋白激酶家族蛋白同源基因 OsRLCK168, homologous gene of lectin protein kinase family protein
SNP_11	F₁BLUE	Chr.4:153499376	GRMZM2G101388	转座元件Transposable_element
SNP_12	F₁BLUE	Chr.10:146837618	GRMZM2G464157(Zm00001d026572)	羧酯酶SOBER1 Carboxylesterase SOBER1
SNP_13	SCA	Chr.1:8628332	GRMZM2G474258(Zm00001d027598)	CCT结构域相关基因（cct101） CO CO-LIKE TIMING OF CAB1 protein domain101(cct101)
SNP_14	SCA	Chr.2:194691551	GRMZM2G079109(Zm00001d006153)	1-酰基甘油-3-磷酸O-酰基转移酶 1-acylglycerol-3-phosphate O-ac= yltransferase
SNP_15	SCA	Chr.2:193402735	GRMZM2G378215	-
SNP_16	SCA	Chr.7:805187	GRMZM2G165828(Zm00001d018615)	类似于谷氨酸受体3.4前体（配体门控离子通道3.4）（AtGLR 4） Similar to Glutamate receptor 3.4 precursor (Ligand-gated ion channel 3.4) (AtGLR4)
SNP_17	SCA	Chr.10:1148101	GRMZM2G057557(Zm00001d023220)	外膜OMP85家族蛋白 Outer membrane OMP85 family protein

Table 5

References 56

[1]	李少昆, 赵久然, 董树亭, 赵明, 李潮海, 崔彦宏, 刘永红, 高聚林, 薛吉全, 王立春, 王璞, 陆卫平, 王俊河, 杨祁峰, 王子明. 中国玉米栽培研究进展与展望. 中国农业科学, 2017, 50(11): 1941-1959.
	LI S K, ZHANG J R, DONG S T, ZHAO M, LI C H, CUI Y H, LIU Y H, GAO J L, XUE J Q, WANG L C, WANG P, LU W P, WANG J H, YANG Q F, WANG Z M. Advances and prospects of maize cultivation in China. Scientia Agricultura Sinica, 2017, 50(11): 1941-1959. (in Chinese)
[2]	李鹏, 白永新, 张润生, 魏振飞, 张建华. 浅议我国玉米育种发展现状与方向. 种子科技, 2019, 37(2): 18-19.
	LI P, BAI Y X, ZHANG R S, WEI Z F, ZHANG J H. Discussion on the development status and direction of maize breeding in China. Seed Science and Technology, 2019, 37(2): 18-19. (in Chinese)
[3]	BIRCHLER J A, AUGER D L, RIDDLE N C. In search of the molecular basis of heterosis. The Plant Cell, 2003, 15(10): 2236-2239. doi: 10.1105/tpc.151030
[4]	BIRCHLER J A, YAO H, CHUDALAYANDI S. Unraveling the genetic basis of hybrid vigor. Proceedings of the National Academy of Sciences of the USA, 2006, 103(35): 12957.
[5]	BIRCHLER J A, YAO H, CHUDALAYANDI S, VAIMAN D, VEITIA R A. Heterosis. The Plant Cell, 2010, 22(7): 2105-2112. doi: 10.1105/tpc.110.076133
[6]	堵纯信, 曹春景, 曹青, 毕蒙蒙, 董战鲲, 张发林. 玉米杂交种郑单958的选育与应用. 玉米科学, 2006(6): 43-45+49.
	DU C X, CAO C J, CAO Q, BI M M, DONG Z K, ZHANG F L. The breeding and application of maize hybrid Zhengdan 958. Journal of Maize Sciences, 2006(6): 43-45+49. (in Chinese)
[7]	钏兴宽. 配合力理论及其在水稻育种中的应用. 种子, 2014, 33(6): 39-41+46.
	CHUAN X K. Combining ability theory and its application in rice breeding. Seed, 2014, 33(6): 39-41+46. (in Chinese)
[8]	SPRAGUE G, TATUM L. General vs. specific combining ability in single crosses of corn. Agronomy Journal, 1942, 34: 923-932. doi: 10.2134/agronj1942.00021962003400100008x
[9]	李明顺, 张世煌, 李新海, 潘光堂, 白丽, 彭泽斌. 根据产量特殊配合力分析玉米自交系杂种优势群. 中国农业科学, 2002, 35(6): 600-605.
	LI M S, ZHANG S H, LI X H, PAN G T, BAI L, PENG Z B. Study on heterotic groups among maize inbred lines based on SCA. Scientia Agricultura Sinica, 2002, 35(6): 600-605. (in Chinese)
[10]	杨爱国, 张世煌, 李明顺, 荣廷昭, 潘光堂. CIMMYT和我国玉米种质群体的配合力及杂种优势分析. 作物学报, 2006, 32(9): 1329-1337.
	YANG A G, ZHANG S H, LI M S, RONG T Z, PAN G T. Combining ability and heterosis of 14 CIMMYT and 13 domestic maize populations in an NCⅡ mating design. Acta Agronomica Sinica, 2006, 32(9): 1329-1337. (in Chinese)
[11]	GRIFFING B. Concept of general and specific combining ability in relation to diallel crossing systems. Australian Journal of Biological Sciences, 1955, 9: 463-493. doi: 10.1071/BI9560463
[12]	LÜ A Z, ZHANG H, ZHANG Z X, TAO Y S, YUE B, ZHENG Y L. Conversion of the statistical combining ability into a genetic concept. Journal of Integrative Agriculture, 2012, 11(1): 43-52. doi: 10.1016/S1671-2927(12)60781-0
[13]	QI H, HUANG J, ZHENG Q, HUANG Y, SHAO R, ZHU L, ZHANG Z, QIU F, ZHOU G, ZHENG Y, YUE B. Identification of combining ability loci for five yield-related traits in maize using a set of testcrosses with introgression lines. Theoretical and Applied Genetics, 2013, 126(2): 369-377. doi: 10.1007/s00122-012-1985-5
[14]	WANG H, XU C, LIU X, GUO Z, XU X, WANG S, XIE C, LI W X, ZOU C, XU Y. Development of a multiple-hybrid population for genome-wide association studies: Theoretical consideration and genetic mapping of flowering traits in maize. Scientific Reports, 2017, 7(1): 40239. doi: 10.1038/srep40239
[15]	ZHOU Z, ZHANG C, LU X, WANG L, HAO Z, LI M, ZHANG D, YONG H, ZHU H, WENG J, LI X. Dissecting the genetic basis underlying combining ability of plant height related traits in maize. Frontiers in Plant Science, 2018, 9: 1117. doi: 10.3389/fpls.2018.01117
[16]	CHEN J, ZHOU H, XIE W, XIA D, GAO G, ZHANG Q, WANG G, LIAN X, XIAO J, HE Y Q. Genome-wide association analyses reveal the genetic basis of combining ability in rice. Plant Biotechnology Journal, 2019, 17(11): 2211-2222. doi: 10.1111/pbi.13134
[17]	XIAO Y, JIANG S, CHENG Q, WANG X, YAN J, ZHANG R, QIAO F, MA C, LUO J, LI W, LIU H, YANG W, SONG W, MENG Y, WARBURTON M, ZHAO J, WANG X, YAN J. The genetic mechanism of heterosis utilization in maize improvement. Genome Biology, 2021, 22(1): 148. doi: 10.1186/s13059-021-02370-7
[18]	HUANG X, YANG S, GONG J, ZHAO Q, FENG Q, ZHAN Q, ZHAO Y, LI W, CHENG B, XIA J, CHEN N, HUANG T, ZHANG L, FAN D, CHEN J, ZHOU C, LU Y, WENG Q, HAN B. Genomic architecture of heterosis for yield traits in rice. Nature, 2016, 537(7622): 629-633. doi: 10.1038/nature19760
[19]	WANG H, QIN F. Genome-wide association study reveals natural variations contributing to drought resistance in crops. Frontiers in Plant Science, 2017, 8: 1110. doi: 10.3389/fpls.2017.01110
[20]	LI H, PENG Z, YANG X, WANG W, FU J, WANG J, HAN Y, CHAI Y, GUO T, YANG N, LIU J, WARBURTON M L, CHENG Y, HAO X, ZHANG P, ZHAO J, LIU Y, WANG G, LI J, YAN J. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nature Genetics, 2013, 45(1): 43-50. doi: 10.1038/ng.2484
[21]	LI X, WANG M, ZHANG R, FANG H, FU X, YANG X, LI J. Genetic architecture of embryo size and related traits in maize. The Crop Journal, 2022, 10(1): 204-215. doi: 10.1016/j.cj.2021.03.007
[22]	CHEN Q, HAN Y, LIU H, WANG X, SUN J, ZHAO B, LI W, TIAN J, LIANG Y, YAN J, YANG X, TIAN F. Genome-wide association analyses reveal the importance of alternative splicing in diversifying gene function and regulating phenotypic variation in maize. The Plant Cell, 2018, 30(7): 1404-1423. doi: 10.1105/tpc.18.00109
[23]	LIU N, DU Y, WARBURTON M, XIAO Y, YAN J. Phenotypic plasticity contributes to maize adaptation and heterosis. Molecular Biology and Evolution, 2020, 38(4): 1262-1275. doi: 10.1093/molbev/msaa283
[24]	MENG L, LI H, ZHANG L, WANG J. QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. The Crop Journal, 2015, 3(3): 269-283. doi: 10.1016/j.cj.2015.01.001
[25]	KNAPP S J, STROUP W W, ROSS W M. Exact confidence intervals for heritability on a progeny mean basis. Crop Science, 1985, 25(1): 192-194. doi: 10.2135/cropsci1985.0011183X002500010046x
[26]	Covarrubias-Pazaran G. Genome-assisted prediction of quantitative traits using the R package sommer. PLoS ONE, 2016: e0156744.
[27]	黄远樟, 刘来福. 作物数量遗传学基础: 六、配合力: 不完全双列杂交. 遗传, 1980(2): 43-46.
	HUANG Y Z, LIU L F. Basis of crop quantitative genetics: VI. Combining ability: Incomplete diallel hybridization. Genetics, 1980(2): 43-46. (in Chinese)
[28]	MURRAY M G, THOMPSON W F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Research, 1980, 8(19): 4321-4325. doi: 10.1093/nar/8.19.4321
[29]	DANECEK P, AUTON A, ABECASIS G, ALBERS C A, BANKS E, DEPRISTO M A, HANDSAKER R E, LUNTER G, MARTH G T, SHERRY S T, MCVEAN G, DURBIN R. 1000 genomes project analysis group. The variant call format and VCFtools. Bioinformatics (Oxford, England), 2011, 27(15): 2156-2158. doi: 10.1093/bioinformatics/btr330
[30]	AYRES D L, DARLING A, ZWICKL D J, BEERLI P, HOLDER M T, LEWIS P O, HUELSENBECK J P, RONQUIST F, SWOFFORD D L, CUMMINGS M P, RAMBAUT A, SUCHARD M A. BEAGLE: An application programming interface and high-performance computing library for statistical phylogenetics. Systematic Biology, 2012, 61(1): 170-173. doi: 10.1093/sysbio/syr100
[31]	KUMAR S, STECHER G, LI M, KNYAZ C, TAMURA K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Molecular Biology and Evolution, 2018, 35(6): 1547-1549. doi: 10.1093/molbev/msy096
[32]	SAITOU N, NEI M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 1987, 4(4): 406-425.
[33]	LETUNIC I, BORK P. Interactive tree of life (iTOL): An online tool for phylogenetic tree display and annotation. Bioinformatics, 2007, 23(1): 127-128. doi: 10.1093/bioinformatics/btl529
[34]	YANG J, LEE S H, GODDARD M E, VISSCHER P M. GCTA: A tool for genome-wide complex trait analysis. American Journal of Human Genetics, 2011, 88(1): 76-82. doi: 10.1016/j.ajhg.2010.11.011
[35]	LIPKA A E, TIAN F, WANG Q, PEIFFER J, LI M, BRADBURY P J, GORE M A, BUCKLER E S, ZHANG Z. GAPIT: Genome association and prediction integrated tool. Bioinformatics (Oxford, England), 2012, 28(18): 2397-2399. doi: 10.1093/bioinformatics/bts444
[36]	HUANG M, LIU X, ZHOU Y, SUMMERS R M, ZHANG Z. BLINK: A package for the next level of genome-wide association studies with both individuals and markers in the millions. Giga Science, 2019, 8(2): 154.
[37]	HUANG X, YANG S, GONG J, ZHAO Y, FENG Q, GONG H, LI W, ZHAN Q, CHENG B, XIA J, CHEN N, HAO Z, LIU K, ZHU C, HUANG T, ZHAO Q, ZHANG L, FAN D, ZHOU C, LU Y, WENG Q, WANG Z X, LI J, HAN B. Genomic analysis of hybrid rice varieties reveals numerous superior alleles that contribute to heterosis. Nature Communications, 2015, 6(1): 6258. doi: 10.1038/ncomms7258
[38]	GAO X, STARMER J, MARTIN E R. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genetic Epidemiology, 2008, 32(4): 361-369. doi: 10.1002/gepi.20310
[39]	ZHOU H, XIA D, ZENG J, JIANG G, HE Y. Dissecting combining ability effect in a rice NCII-III population provides insights into heterosis in indica-japonica cross. Rice, 2017, 10(1): 39. doi: 10.1186/s12284-017-0179-9
[40]	SHULL G H. The composition of a field of maize. Journal of Heredity, 1908, 4(1): 296-301.
[41]	梁文科, 张世煌, 戚廷香, 邱法展, 庹洪章, 刘永忠, 郑用琏, 徐尚忠. 热带温带玉米群体产量性状遗传力及遗传方差分量的剖析. 中国农业科学, 2006, 39(11): 2178-2185.
	LIANG W K, ZHANG S H, QI T X, QIU F Z, TUO H Z, LIU Y Z, ZHENG Y L, XU S Z. Dissection of heritability and genetic variance components for yield traits in tropical and temperate maize populations. Scientia Agricultura Sinica, 2006, 39(11): 2178-2185. (in Chinese)
[42]	MAKUMBI D, BETRÁN J F, BÄNZIGER M, RIBAUT J M. Combining ability, heterosis and genetic diversity in tropical maize (Zea mays L.) under stress and non-stress conditions. Euphytica, 2011, 180(2): 143-162. doi: 10.1007/s10681-010-0334-5
[43]	ZHANG X, LV L, LV C, GUO B, XU R. Combining ability of different agronomic traits and yield components in hybrid barley. PLoS ONE, 2015: e0126828.
[44]	倪先林, 张涛, 蒋开锋, 杨莉, 杨乾华, 曹应江, 文春阳, 郑家奎. 杂交稻特殊配合力与杂种优势、亲本间遗传距离的相关性. 遗传, 2009, 31(8): 849-854.
	NI X L, ZHANG T, JIANG K F, YANG L, YANG Q H, CAO Y J, WEN C Y, ZHENG J K. Correlations between specific combining ability, heterosis and genetic distance in hybrid rice. Genetics, 2009, 31(8): 849-854. (in Chinese)
[45]	JIANG Y, SCHMIDT R H, ZHAO Y, REIF J C. A quantitative genetic framework highlights the role of epistatic effects for grain- yield heterosis in bread wheat. Nature Genetics, 2017, 49(12): 1741-1746. doi: 10.1038/ng.3974
[46]	ROMERO N J A, WILLCOX M, BURGUEÑO J, ROMAY C, SWARTS K, TRACHSEL S, PRECIADO E, TERRON A, DELGADO H V, VIDAL V, ORTEGA A, BANDA A E, MONTIEL N O G, ORTIZ-MONASTERIO I, VICENTE F S, ESPINOZA A G, ATLIN G, WENZL P, HEARNE S, BUCKLER E S. A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nature Genetics, 2017, 49(3): 476-480. doi: 10.1038/ng.3784
[47]	ESHED Y, BAUM S F, PEREA J V, BOWMAN J L. Establishment of polarity in lateral organs of plants. Current Biology, 2001, 11(16): 1251-1260. doi: 10.1016/S0960-9822(01)00392-X
[48]	ESHED Y, IZHAKI A, BAUM S F, FLOYD S K, BOWMAN J L. Asymmetric leaf development and blade expansion in Arabidopsis are mediated by KANADI and YABBY activities. Development, 2004, 131(12): 2997-3006. doi: 10.1242/dev.01186
[49]	HOOPES G M, HAMILTON J P, WOOD J C, ESTEBAN E, PASHA A, VAILLANCOURT B, PROVART N J, BUELL C R. An updated gene atlas for maize reveals organ-specific and stress-induced genes. The Plant Journal, 2019, 97(6): 1154-1167. doi: 10.1111/tpj.14184
[50]	STELPFLUG S C, SEKHON R S, VAILLANCOURT B, HIRSCH C N, BUELL C R, DE LEON N, KAEPPLER S M. An expanded maize gene expression atlas based on RNA sequencing and its use to explore root development. Plant Genome, 2016, 9(1): 38-54.
[51]	WU X, LI Y, SHI Y, SONG Y, ZHANG D, LI C, BUCKLER E S, LI Y, ZHANG Z, WANG T. Joint-linkage mapping and GWAS reveal extensive genetic loci that regulate male inflorescence size in maize. Plant Biotechnology Journal, 2016, 14(7): 1551-1562. doi: 10.1111/pbi.12519
[52]	PEIFFER J A, ROMAY M C, GORE M A, FLINT-GARCIA S A, ZHANG Z, MILLARD M J, GARDNER C A, MCMULLEN M D, HOLLAND J B, BRADBURY P J, BUCKLER E S. The genetic architecture of maize height. Genetics, 2014, 196(4): 1337-1356. doi: 10.1534/genetics.113.159152
[53]	PEIFFER J A, FLINT-GARCIA S A, DE LEON N, MCMULLEN M D, KAEPPLER S M, BUCKLER E S. The genetic architecture of maize stalk strength. PLoS ONE, 2013, 8(6): e67066. doi: 10.1371/journal.pone.0067066
[54]	WANG Q J, YUAN Y, LIAO Z, JIANG Y, WANG Q, ZHANG L, GAO S, WU F, LI M, XIE W, LIU T, XU J, LIU Y, FENG X, LU Y. Genome-wide association study of 13 traits in maize seedlings under low phosphorus stress. Plant Genome, 2019, 12(3): 1-13.
[55]	DONG M Y, LEI L, FAN X W, LI Y Z. Analyses of open-access multi-omics data sets reveal genetic and expression characteristics of maize ZmCCT family genes. AoB Plants, 2021, 13(5): plab048. doi: 10.1093/aobpla/plab048
[56]	ZHU X M, SHAO X Y, PEI Y H, GUO X M, LI J, SONG X Y, ZHAO M A. Genetic diversity and genome-wide association study of major ear quantitative traits using high-density SNPs in maize. Front Plant Science, 2018, 9: 966. doi: 10.3389/fpls.2018.00966

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

环境 Env.	重复 Rep.	最小值 Minimum (kg·hm^-2)	最大值 Maximum (kg·hm^-2)	均值 Mean (kg·hm^-2)	标准差 SD	偏度 Skewness	峰度 Kurtosis	变异系数 CV	广义遗传力 H²(%)	F值 F value
环境 Env.	重复 Rep.	最小值 Minimum (kg·hm^-2)	最大值 Maximum (kg·hm^-2)	均值 Mean (kg·hm^-2)	标准差 SD	偏度 Skewness	峰度 Kurtosis	变异系数 CV	广义遗传力 H²(%)	基因型 Gen.	环境 Env.	基因×环境 Gen.×Env.
杨凌 Yangling	1	5095.60	12917.48	9527.95	1243.25	-0.60	0.96	0.13	59.04	6.60**	6318.72**	3.89**
杨凌 Yangling	2	4286.95	12709.47	9286.99	1437.99	-0.64	0.63	0.15
榆林 Yulin	1	12325.73	20036.29	15794.53	1367.89	0.17	0.20	0.09
榆林 Yulin	2	12066.87	20488.13	15533.07	1441.90	0.20	0.36	0.09
旬邑 Xunyi	1	9401.12	20342.91	14038.85	1889.22	0.41	0.25	0.13
旬邑 Xunyi	2	10093.17	19664.45	14115.04	1783.91	0.26	-0.04	0.13
BLUE		10775.45	17180.75	13831.91	988.61	-0.28	0.59	0.07

性状 Trait	数量 Number	最小值 Minimum	最大值 Maximum	标准差 SD	偏度 Skewness	峰度 Kurtosis	相关系数 Correlation coefficient
性状 Trait	数量 Number	最小值 Minimum	最大值 Maximum	标准差 SD	偏度 Skewness	峰度 Kurtosis	GCA	SCA	BLUE
GCA	82	-565.87	316.87	172.54	-0.68	0.49		0.51***	0.62***
SCA	246	-1772.11	1621.62	520.94	-0.30	0.65			0.95***

性状 Trait	标记名称 SNPs	染色体 Chr.	物理位置 Position (bp)	基因型 Genotype	最小等位基因频率 MAF	P值 P value	表型解释率 PVE (%)
GCA	Affx-291398318	1	296696400	A/G	0.36	1.02E-04	23.37
GCA	Affx-291395842	2	228845613	T/C	0.22	1.13E-04	11.91
GCA	Affx-291375443	4	80111618	G/A	0.12	7.12E-05	12.29
GCA	Affx-291425877	4	155406944	G/A	0.29	2.77E-05	8.26
GCA	Affx-159192088	4	176561593	C/A	0.41	1.31E-04	7.97
F₁_ADD	●Affx-291424805	10	146974725	T/C	0.24	6.27E-05	1.55
F₁_DOM	Affx-88979942	2	4377056	G/A	0.31	1.07E-04	1.75
F₁_DOM	■Affx-291385286	2	24634902	T/G	0.37	8.43E-05	4.12
F₁_DOM	▲Affx-88980445	2	28002879	G/A	0.42	1.36E-04	0.43
F₁_DOM	Affx-158919359	4	153499376	C/T	0.08	9.46E-05	5.96
F₁_DOM	Affx-291394192	10	146837618	A/C	0.43	8.24E-05	3.30
F₁_DOM	★Affx-291431456	10	4152732	C/T	0.47	3.52E-05	3.57
SCA_ADD	●Affx-291424805	10	146974725	T/C	0.24	2.82E-05	1.77
SCA_DOM	Affx-291423507	1	8628332	C/T	0.44	3.13E-05	1.21
SCA_DOM	■Affx-291385286	2	24634902	T/G	0.37	3.78E-05	3.26
SCA_DOM	▲Affx-88980445	2	28002879	G/A	0.42	6.80E-06	1.22
SCA_DOM	Affx-291382512	2	194691551	G/A	0.49	9.30E-05	0.92
SCA_DOM	Affx-158945854	2	193402735	A/C	0.49	3.64E-05	1.76
SCA_DOM	Affx-291445414	7	805187	G/C	0.42	1.13E-04	1.42
SCA_DOM	Affx-291393021	10	1148101	C/T	0.31	1.06E-04	6.31
SCA_DOM	★Affx-291431456	10	4152732	C/T	0.47	6.97E-05	3.15

标记位点 SNP ID	性状 Trait	标记名称 SNPs	染色体位置 Position (bp)	基因型 Genotype	不同性状下的有利等位基因 Favorable bases in different traits			主效应模式 SNP effect
标记位点 SNP ID	性状 Trait	标记名称 SNPs	染色体位置 Position (bp)	基因型 Genotype	GCA	F₁	SCA	主效应模式 SNP effect
SNP_1	GCA	Affx-291398318	Chr.1:296696400	A/G	AA***	AA***	AA***	A
SNP_2	GCA	Affx-291395842	Chr.2:228845613	T/C	CC**	CC**	CC**	A
SNP_3	GCA	Affx-291375443	Chr.4:80111618	G/A	GG**	\	\	\
SNP_4	GCA	Affx-291425877	Chr.4:155406944	G/A	AA**	GA/GG	GA	A=D
SNP_5	GCA	Affx-159192088	Chr.4:176561593	C/A	CC	CC	CC	A
SNP_6	CO_ADD	Affx-291424805	Chr.10:146974725	T/C	CC***	TC	TC*	D
SNP_7	CO_DOM	Affx-291431456	Chr.10:4152732	C/T	TT	TT/CC*	TT/CC*	A
SNP_8	CO_DOM	Affx-291385286	Chr.2:24634902	T/G	GG	TG*	TG**	D
SNP_9	CO_DOM	Affx-88980445	Chr.2:28002879	G/A	AA	GA*	GA*	D
SNP_10	F₁BLUE	Affx-88979942	Chr.2:4377056	G/A	GG**	GG*	AA/GG	A
SNP_11	F₁BLUE	Affx-158919359	Chr.4:153499376	C/T	CC**	CC***	CC**	A
SNP_12	F₁BLUE	Affx-291394192	Chr.10:146837618	A/C	CC*	AC**	AC*	D
SNP_13	SCA	Affx-291423507	Chr.1:8628332	C/T	TT*	CT	CT*	D
SNP_14	SCA	Affx-291382512	Chr.2:194691551	G/A	AA/GG	GA*	GA**	D
SNP_15	SCA	Affx-158945854	Chr.2:193402735	A/C	CC	AC*	AC**	D
SNP_16	SCA	Affx-291445414	Chr.7:805187	G/C	CC	GC*	GC**	D
SNP_17	SCA	Affx-291393021	Chr.10:1148101	C/T	CC	CC/CT***	CC/CT***	A=D

Genome-Wide Association Analysis of Yield and Combining Ability Based on Maize Hybrid Population

RichHTML

PDF

Abstract

Cite this article

share this article

Figures/Tables 12

References 56

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZHAO ZhengXin,WANG XiaoYun,TIAN YaJie,WANG Rui,PENG Qing,CAI HuanJie. Effects of Straw Returning and Nitrogen Fertilizer Types on Summer Maize Yield and Soil Ammonia Volatilization Under Future Climate Change [J]. Scientia Agricultura Sinica, 2023, 56(1): 104-117.
[2]	CHAI HaiYan,JIA Jiao,BAI Xue,MENG LingMin,ZHANG Wei,JIN Rong,WU HongBin,SU QianFu. Identification of Pathogenic Fusarium spp. Causing Maize Ear Rot and Susceptibility of Some Strains to Fungicides in Jilin Province [J]. Scientia Agricultura Sinica, 2023, 56(1): 64-78.
[3]	XIONG WeiYi,XU KaiWei,LIU MingPeng,XIAO Hua,PEI LiZhen,PENG DanDan,CHEN YuanXue. Effects of Different Nitrogen Application Levels on Photosynthetic Characteristics, Nitrogen Use Efficiency and Yield of Spring Maize in Sichuan Province [J]. Scientia Agricultura Sinica, 2022, 55(9): 1735-1748.
[4]	LI YiLing,PENG XiHong,CHEN Ping,DU Qing,REN JunBo,YANG XueLi,LEI Lu,YONG TaiWen,YANG WenYu. Effects of Reducing Nitrogen Application on Leaf Stay-Green, Photosynthetic Characteristics and System Yield in Maize-Soybean Relay Strip Intercropping [J]. Scientia Agricultura Sinica, 2022, 55(9): 1749-1762.
[5]	MA XiaoYan,YANG Yu,HUANG DongLin,WANG ZhaoHui,GAO YaJun,LI YongGang,LÜ Hui. Annual Nutrients Balance and Economic Return Analysis of Wheat with Fertilizers Reduction and Different Rotations [J]. Scientia Agricultura Sinica, 2022, 55(8): 1589-1603.
[6]	LI Qian,QIN YuBo,YIN CaiXia,KONG LiLi,WANG Meng,HOU YunPeng,SUN Bo,ZHAO YinKai,XU Chen,LIU ZhiQuan. Effect of Drip Fertigation Mode on Maize Yield, Nutrient Uptake and Economic Benefit [J]. Scientia Agricultura Sinica, 2022, 55(8): 1604-1616.
[7]	ZHANG JiaHua,YANG HengShan,ZHANG YuQin,LI CongFeng,ZHANG RuiFu,TAI JiCheng,ZHOU YangChen. Effects of Different Drip Irrigation Modes on Starch Accumulation and Activities of Starch Synthesis-Related Enzyme of Spring Maize Grain in Northeast China [J]. Scientia Agricultura Sinica, 2022, 55(7): 1332-1345.
[8]	ZHI Lei,ZHE Li,SUN NanNan,YANG Yang,Dauren Serikbay,JIA HanZhong,HU YinGang,CHEN Liang. Genome-Wide Association Analysis of Lead Tolerance in Wheat at Seedling Stage [J]. Scientia Agricultura Sinica, 2022, 55(6): 1064-1081.
[9]	TAN XianMing,ZHANG JiaWei,WANG ZhongLin,CHEN JunXu,YANG Feng,YANG WenYu. Prediction of Maize Yield in Relay Strip Intercropping Under Different Water and Nitrogen Conditions Based on PLS [J]. Scientia Agricultura Sinica, 2022, 55(6): 1127-1138.
[10]	LIU Miao,LIU PengZhao,SHI ZuJiao,WANG XiaoLi,WANG Rui,LI Jun. Critical Nitrogen Dilution Curve and Nitrogen Nutrition Diagnosis of Summer Maize Under Different Nitrogen and Phosphorus Application Rates [J]. Scientia Agricultura Sinica, 2022, 55(5): 932-947.
[11]	QIAO Yuan,YANG Huan,LUO JinLin,WANG SiXian,LIANG LanYue,CHEN XinPing,ZHANG WuShuai. Inputs and Ecological Environment Risks Assessment of Maize Production in Northwest China [J]. Scientia Agricultura Sinica, 2022, 55(5): 962-976.
[12]	HUANG ZhaoFu, LI LuLu, HOU LiangYu, GAO Shang, MING Bo, XIE RuiZhi, HOU Peng, WANG KeRu, XUE Jun, LI ShaoKun. Accumulated Temperature Requirement for Field Stalk Dehydration After Maize Physiological Maturity in Different Planting Regions [J]. Scientia Agricultura Sinica, 2022, 55(4): 680-691.
[13]	FANG MengYing,LU Lin,WANG QingYan,DONG XueRui,YAN Peng,DONG ZhiQiang. Effects of Ethylene-Chlormequat-Potassium on Root Morphological Construction and Yield of Summer Maize with Different Nitrogen Application Rates [J]. Scientia Agricultura Sinica, 2022, 55(24): 4808-4822.
[14]	DU WenTing,LEI XiaoXiao,LU HuiYu,WANG YunFeng,XU JiaXing,LUO CaiXia,ZHANG ShuLan. Effects of Reducing Nitrogen Application Rate on the Yields of Three Major Cereals in China [J]. Scientia Agricultura Sinica, 2022, 55(24): 4863-4878.
[15]	YI YingJie,HAN Kun,ZHAO Bin,LIU GuoLi,LIN DianXu,CHEN GuoQiang,REN Hao,ZHANG JiWang,REN BaiZhao,LIU Peng. The Comparison of Ammonia Volatilization Loss in Winter Wheat- Summer Maize Rotation System with Long-Term Different Fertilization Measures [J]. Scientia Agricultura Sinica, 2022, 55(23): 4600-4613.