Single-cross maize hybrids display superior heterosis and are produced from crossing two parental inbred lines belonging to genetically different heterotic groups. Here we assembled 1,604 historically utilized maize inbred lines belonging to various female heterotic groups (FHGs) and male heterotic groups (MHGs), and conducted phenotyping and genomic sequencing analyses. We found that the FHGs and MHGs have undergone both convergent and divergent changes for different sets of agronomic traits. Using genome-wide selection scans and association analyses, we identified a large number of candidate genes that contributed to the improvement of agronomic traits of the FHGs and MHGs. Moreover, we observed increased genetic differentiation between the FHGs and MHGs across the breeding eras, and we found a positive correlation between increasing heterozygosity levels in the differentiated genes and heterosis in hybrids. Furthermore, we validated the function of two selected genes and a differentiated gene. This study provides insights into the genomic basis of modern hybrid maize breeding.
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The raw sequencing data were deposited in the Genome Sequence Archive (https://bigd.big.ac.cn/gsa) under the accession code PRJCA009749 (https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA009749). The phenotype dataset reported here is available from the corresponding authors upon request. Source data are provided with this paper.
The custom codes used in this study are available at https://github.com/jasongit0311/maize_for_Li.
Food and Agriculture Organization of the United Nations Agriculture Databases (FAO, 2019); http://www.fao.org/statistics/databases/en/
Duvick, D. N. The contribution of breeding to yield advances in maize (Zea mays L.). Adv. Agron. 86, 83–145 (2005).
Duvick, D. N. Genetic progress in yield of United States maize (Zea mays L.). Maydica 50, 193–202 (2005).
Mansfield, B. D. & Mumm, R. H. Survey of plant density tolerance in U.S. maize germplasm. Crop Sci. 54, 157–173 (2014).
Andorf, C. et al. Technological advances in maize breeding: past, present and future. Theor. Appl. Genet. 132, 817–849 (2019).
Tracy, W. F. & Chandler, M. A. in Plant Breeding: The Arnel R. Hallauer International Symposium Ch. 16 (eds Lamkey, K. R. & Lee, M.) (Blackwell, 2006).
Mikel, M. A. & Dudley, J. W. Evolution of North American dent corn from public to proprietary germplasm. Crop Sci. 46, 1193–1205 (2006).
Lu, Y. L. et al. Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms. Theor. Appl. Genet. 120, 93–115 (2009).
Melchinger, A. E. & Gumber, R. K. in Concepts and Breeding of Heterosis in Crop Plants pp. 29–44 (eds Larnkey, K. R. & Staub, J. E.) (Crop Science Society of America, 1998).
Reif, J. C., Hallauer, A. R. & Melchinger, A. E. Heterosis and heterotic patterns in maize. Maydica 50, 215–223 (2005).
Lauer, S. et al. Morphological changes in parental lines of pioneer brand maize hybrids in the U.S. Central Corn Belt. Crop Sci. 52, 1033–1043 (2012).
Li, Y. X. et al. Contributions of parental inbreds and heterosis to morphology and yield of single-cross maize hybrids in China. Crop Sci. 54, 76–88 (2014).
Gage, J. L., White, M. R., Edwards, J. W., Kaeppler, S. & de Leon, N. Selection signatures underlying dramatic male inflorescence transformation during modern hybrid maize breeding. Genetics 210, 1125–1138 (2018).
Wang, B. B. et al. Genome-wide selection and genetic improvement during modern maize breeding. Nat. Genet. 52, 565–571 (2020).
Duvick, D. N., Smith, J. S. C. & Cooper, M. in Plant Breeding Reviews, Part 2: Long-Term Selection—Crops, Animals, and Bacteria pp. 109–151 (ed. Janick, J.) (John Wiley & Sons, 2004).
van Heerwaarden, J., Hufford, M. B. & Ross-Ibarra, J. Historical genomics of North American maize. Proc. Natl Acad. Sci. USA 109, 12420–12425 (2012).
Jiao, Y. P. et al. Genome-wide genetic changes during modern breeding of maize. Nat. Genet. 44, 812–815 (2012).
Lu, H. & Bernardo, R. Molecular marker diversity among current and historical maize inbreds. Theor. Appl. Genet. 103, 613–617 (2001).
Unterseer, S. et al. A comprehensive study of the genomic differentiation between temperate Dent and Flint maize. Genome Biol. 17, 137 (2016).
Wu, X. et al. Analysis of genetic differentiation and genomic variation to reveal potential regions of importance during maize improvement. BMC Plant Biol. 15, 256 (2015).
Li, C. H. et al. The HuangZaoSi maize genome provides insights into genomic variation and improvement history of maize. Mol. Plant 12, 402–409 (2019).
Mikel, M. A. Genetic composition of contemporary US commercial dent corn germplasm. Crop Sci. 51, 592–599 (2011).
Zhang, R. Y. et al. Patterns of genomic variation in Chinese maize inbred lines and implications for genetic improvement. Theor. Appl. Genet. 131, 1207–1221 (2018).
Salvi, S. et al. Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize. Proc. Natl Acad. Sci. USA 104, 11376–11381 (2007).
Strable, J. & Vollbrecht, E. Maize YABBY genes drooping leaf1 and drooping leaf2 regulate floret development and floral meristem determinacy. Development 146, dev171181 (2019).
Provencher, L. M., Miao, L., Sinha, N. & Lucas, W. J. Sucrose export defective1 encodes a novel protein implicated in chloroplast-to-nucleus signaling. Plant Cell 13, 1127–1141 (2001).
Galli, M. et al. Auxin signaling modules regulate maize inflorescence architecture. Proc. Natl Acad. Sci. USA 112, 13372–13377 (2015).
Wallace, J. G. et al. Association mapping across numerous traits reveals patterns of functional variation in maize. PLoS Genet. 10, e1004845 (2014).
Yang, Q. et al. CACTA-like transposable element in ZmCCT attenuated photoperiod sensitivity and accelerated the postdomestication spread of maize. Proc. Natl Acad. Sci. USA 110, 16969–16974 (2013).
Huang, C. et al. ZmCCT9 enhances maize adaptation to higher latitudes. Proc. Natl Acad. Sci. USA 115, E334–E341 (2018).
Jia, H. T. et al. A serine/threonine protein kinase encoding gene KERNEL NUMBER PER ROW6 regulates maize grain yield. Nat. Commun. 11, 998 (2020).
Aubert, D. et al. EMF1, a novel protein involved in the control of shoot architecture and flowering in Arabidopsis. Plant Cell 13, 1865–1875 (2001).
Melchinger, A. E. in The Genetics and Exploitation of Heterosis in Crops Ch. 10 (eds Coors, J. G. & Pandey, S.) (American Society of Agronomy, Crop Science Society of America and Soil Science Society of America, 1999).
Reif, J. C. et al. Genetic distance based on simple sequence repeats and heterosis in tropical maize populations. Crop Sci. 43, 1275–1282 (2003).
Reif, J. C., Gumpert, F. M., Fischer, S. & Melchinger, A. E. Impact of interpopulation divergence on additive and dominance variance in hybrid populations. Genetics 176, 1931–1934 (2007).
Technow, F. et al. Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics 197, 1343–1355 (2014).
Chen, L. et al. Portrait of a genus: the genetic diversity of Zea. Preprint at bioRxiv https://doi.org/10.1101/2021.04.07.438828 (2021).
Jiang, F. K. et al. Mutations in an AP2 transcription factor-like gene affect internode length and leaf shape in maize. PLoS ONE 7, e37040 (2012).
Sawers, R. J. H. et al. The Elm1 (ZmHy2) gene of maize encodes a phytochromobilin synthase. Plant Physiol. 136, 2771–2781 (2004).
Danilevskaya, O. N. et al. Involvement of the MADS-box gene ZMM4 in floral induction and inflorescence development in maize. Plant Physiol. 147, 2054–2069 (2008).
Xia, H., Yandeau-Nelson, M., Thompson, D. B. & Guiltinan, M. J. Deficiency of maize starch-branching enzyme I results in altered starch fine structure, decreased digestibility and reduced coleoptile growth during germination. BMC Plant Biol. 11, 95 (2011).
Bai, L., Kim, E. H., DellaPenna, D. & Brutnell, T. P. Novel lycopene epsilon cyclase activities in maize revealed through perturbation of carotenoid biosynthesis. Plant J. 59, 588–599 (2009).
Wang, X., Jing, Y. J., Zhang, B. C., Zhou, Y. H. & Lin, R. C. Glycosyltransferase-like protein ABI8/ELD1/KOB1 promotes Arabidopsis hypocotyl elongation through regulating cellulose biosynthesis. Plant Cell Environ. 38, 411–422 (2015).
Tognetti, V. B. et al. Perturbation of indole-3-butyric acid homeostasis by the UDP-glucosyltransferase UGT74E2 modulates Arabidopsis architecture and water stress tolerance. Plant Cell 22, 2660–2679 (2010).
Dong, N. Q. et al. UDP-glucosyltransferase regulates grain size and abiotic stress tolerance associated with metabolic flux redirection in rice. Nat. Commun. 11, 2629 (2020).
Duvick, D. N. in Developing Drought and Low N-Tolerant Maize pp. 332-335 (eds Edmeades, G. O. et al.) (CIMMYT, 1997).
Wang, T. Y. et al. Changes in yield and yield components of single-cross maize hybrids released in China between 1964 and 2001. Crop Sci. 51, 512–525 (2011).
Purdy, J. L. & Crane, P. L. Inheritance of drying rate in “Mature” corn (Zea mays L.). Crop Sci. 7, 294–297 (1967).
Zhou, G. F. et al. Genome-wide association study of kernel moisture content at harvest stage in maize. Breed. Sci. 68, 622–628 (2018).
Wang, K. R. & Li, S. K. Analysis of influencing factors on kernel dehydration rate of maize hybrids. Sci. Agric. Sin. 50, 2027–2035 (2017).
Troyer, A. F. Adaptedness and heterosis in corn and mule hybrids. Crop Sci. 46, 528–543 (2006).
Schnable, P. S. & Springer, N. M. Progress toward understanding heterosis in crop plants. Annu. Rev. Plant Biol. 64, 71–88 (2013).
Springer, N. M. & Stupar, R. M. Allelic variation and heterosis in maize: how do two halves make more than a whole? Genome Res. 17, 264–275 (2007).
Gerke, J. P. et al. The genomic impacts of drift and selection for hybrid performance in maize. Genetics 201, 1201–1211 (2015).
Huang, X. H. et al. Genomic analysis of hybrid rice varieties reveals numerous superior alleles that contribute to heterosis. Nat. Commun. 6, 6258 (2015).
Yang, M. et al. Genomic architecture of biomass heterosis in Arabidopsis. Proc. Natl Acad. Sci. USA 114, 8101–8106 (2017).
Li, Y., Shi, Y. S., Cao, Y. S. & Wang, T. Y. Establishment of a core collection for maize germplasm preserved in Chinese National GeneBank using geographic distribution and characterization data. Genet. Resour. Crop Evol. 51, 845–852 (2004).
Jiao, Y. P. et al. Improved maize reference genome with single-molecule technologies. Nature 546, 524–527 (2017).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).
McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Bukowski, R. et al. Construction of the third-generation Zea mays haplotype map. GigaScience 7, gix134 (2018).
Cook, J. P. et al. Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels. Plant Physiol. 158, 824–834 (2012).
Wang, K., Li, M. Y. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
Raj, A., Stephens, M. & Pritchard, J. K. fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197, 573–589 (2014).
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Vilella, A. J. et al. Ensemblcompara genetrees: complete, duplication-aware phylogenetic trees in vertebrates. Genome Res. 19, 327–335 (2009).
Zhang, C., Dong, S. S., Xu, J. Y., He, W. M. & Yang, T. L. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics 35, 1786–1788 (2019).
Chen, H., Patterson, N. & Reich, D. Population differentiation as a test for selective sweeps. Genome Res. 20, 393–402 (2010).
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).
Tian, T. et al. agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res. 45, W122–W129 (2017).
Yu, G., Wang, L. G., Han, Y. & He, Q.Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Li, Q. Q. et al. CRISPR/Cas9-mediated knockout and overexpression studies reveal a role of maize phytochrome C in regulating flowering time and plant height. Plant Biotechnol. J. 18, 2520–2532 (2020).
The work was supported by the National Key Research and Development Program of China (grant nos 2016YFD0100103 to T.W., Yu Li and C. Li; 2021YFD1200700 to T.W.; 2016YFD0100303 to Yu Li; and 2020YFE0202300 and 2021YFF1000300 to H.W.), the Major Program of Guangdong Basic and Applied Research (grant no. 2019B030302006 to H.W.), the National Natural Science Foundation of China (grant no. 31971891 to C. Li) and the CAAS Innovation Program. J.R.-I. is supported by NSF grant no. 1546719 and USDA Hatch project no. CA-D-PLS-2066-H. We thank H. Lu, M. Yang and L. Huang from the Novogene Bioinformatics Institute for bioinformatics support and Z. Lin (Peking University) for his suggestions on data analysis. We thank Y. Yang of the Beijing Academy of Agricultural & Forestry Science for verifying the maize hybrids. We thank H. Li from the Chinese Academy of Agricultural Sciences and H. Zhou from Huazhong Agricultural University for their suggestions on the calculation of the dominance effect. We also thank Y. Xu and C. Xu from Yangzhou University for their advice on the statistical analyses.
The authors declare no competing interests.
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Extended Data Fig. 1 Geographic distribution and population structure analyses of the 1,604 inbred lines.
a, Geographic distribution of all resequenced inbreds according to their origin. Bar graphs show the number of inbreds released in Era I and Era II in the US and China. The orange and blue pie charts show the proportions of inbreds released in Era I and Era II, respectively. The map was drawn using the R ggmap package (http://maps.stamen.com/, map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL). b, The marginal likelihood begins to plateau at a K = 11, which was considered as the optimal number of subgroups based on the fastStructure analysis. c, Population structure of the panel analyzed from K = 2 to 15. K = 11 clearly divided the panel into eleven subgroups, including PA1, PA2, PB, Lancaster1, Lancaster2, Zi330, Iodent, Amargo, BSSS, SPT1 and SPT2.
a-c, phenotypic changes in the two female groups (PA and US_SS) and four male groups (PB, SPT, CN_NSS and US_NSS) from Era I to Era II. Group 1 (a), Group 2 (b) and Group 3 (c) include seven, eight and three traits, respectively. The P values of Era I versus Era II in six groups using two-sided Wilcoxon test (left to right, as plotted) are: DA, P = 6.36×10−6, 3.73×10−2, 1.34×10−5, 2.07×10−2, 8.83×10−6 and 2.09×10−2, respectively; DS, P = 7.40×10−8, 1.27×10−2, 5.83×10−5, 9.61×10−2, 1.00×10−6 and 1.83×10−2, respectively; ASI, P = 4.14×10−7, 1.58×10−1, 1.29×10−1, 4.64×10−2, 1.05×10−2 and 2.64×10−1, respectively; EH, P = 5.35×10−3, 3.14×10−4, 3.27×10−2, 6.25×10−1, 7.50×10−6 and 1.81×10−2, respectively; EP, P = 3.94×10−3, 2.93×10−4, 1.39×10−3, 3.71×10−1, 4.12×10−8 and 1.30×10−6, respectively; TBN, P = 1.07×10−7, 1.26×10−4, 6.45×10−1, 9.31×10−1, 2.95×10−12 and 2.62×10−10, respectively; KR, P = 3.55×10−1, 3.55×10−3, 2.58×10−3, 1.55×10−2, 2.21×10−4 and 9.67×10−5, respectively; GYPP, P = 3.67×10−1, 1.68×10−1, 9.67×10−3, 2.13×10−1, 3.26×10−8 and 3.12×10−6, respectively; KWPE, P = 7.93×10−1, 2.56×10−1, 1.76×10−3, 2.09×10−2, 1.46×10−7 and 2.15×10−5, respectively; EW, P = 9.48×10−1, 4.39×10−1, 4.85×10−3, 3.47×10−2, 1.42×10−6 and 4.72×10−4, respectively; KNPR, P = 5.77×10−1, 2.05×10−1, 3.37×10−1, 9.58×10−1, 6.71×10−1 and 1.66×10−4, respectively; HKW, P = 6.13×10−2, 4.09×10−1, 4.53×10−1, 2.59×10−1, 6.90×10−4 and 4.29×10−4, respectively; HKV, P = 2.15×10−2, 5.56×10−1, 2.78×10−2, 3.84×10−2, 8.49×10−7 and 9.94×10−4, respectively; KL, P = 6.53×10−1, 3.14×10−1, 5.53×10−5, 1.17×10−5, 2.71×10−8 and 5.75×10−5, respectively; KW, P = 2.50×10−1, 6.41×10−1, 9.32×10−1, 5.72×10−1, 2.85×10−2 and 2.07×10−4, respectively; ED, P = 1.19×10−2, 1.60×10−1, 7.03×10−3, 1.73×10−2, 2.97×10−2 and 2.99×10−1, respectively; KRN, P = 2.06×10−2, 9.68×10−1, 3.30×10−2, 1.78×10−1, 2.00×10−1 and 2.95×10−1, respectively; CW, P = 1.93×10−1, 1.38×10−1, 3.98×10−1, 4.32×10−2, 2.71×10−3 and 8.35×10−1, respectively. d, Phenotypic changes in the FHGs and MHGs and in the two female groups (PA and US_SS) and four male groups (PB, SPT, CN_NSS and US_NSS) from Era I to Era II for the three traits of Group 4. The P values of FHGsEra I versus FHGsEra II, MHGsEra I versus MHGsEra II, FHGsEra I versus MHGsEra I and FHGsEra II versus MHGsEra II using two-sided Wilcoxon test are presented in Supplementary Table 5. The P values of Era I versus Era II in six groups using two-sided Wilcoxon test (left to right, as plotted) are: PH, P = 1.97×10−1, 3.34×10−1, 8.63×10−2, 1.52×10−1, 1.57×10−1 and 6.20×10−1, respectively; TL, P = 2.87×10−1, 3.53×10−1, 3.45×10−1, 3.78×10−3, 8.49×10−1 and 5.09×10−2, respectively; EL, P = 9.79×10−1, 7.53×10−1, 2.03×10−1, 7.36×10−1, 5.80×10−1 and 9.33×10−5, respectively. For boxplots in a-d, the central lines show the median, the box limits indicate the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles. Exact sample size (n) in FHGsEra I, FHGsEra II, MHGsEra I and MHGsEra II (left to right, as plotted): n = 72, 253, 330 and 267. Exact sample size (n) in PAEra I, PAEra II, US_SSEra I, US_SSEra II, PBEra I, PBEra II, SPTEra I, SPTEra II, CN_NSSEra I, CN_NSSEra II, US_NSSEra I and US_NSSEra II (left to right, as plotted): n = 46, 161, 26, 92, 32, 42, 46, 78, 204, 86, 48 and 61. Significant differences are indicated: ** P < 0.01 and * P < 0.05.
Extended Data Fig. 3 Accumulation of favorable alleles in the female and male heterotic groups during modern hybrid maize breeding.
a, Profile of favorable allele frequencies changes at GWAS associated SNPs in the FHGs and MHGs. Orange indicates an increase, while green indicates a decrease in the frequency of a favorable allele from Era I to Era II during modern breeding. Each row represents an associated SNP. Blue and gray colors (in the first column) mark rows representing the associated SNPs at the threshold of P < 10−6 and 10−5, respectively. b, The number of favorable alleles accumulated in the FHGs and MHGs across different breeding eras. The P values of FHGEra I versus FHGEra II, MHGEra I versus MHGEra II, FHGEra I versus MHGEra I and FHGEra II versus MHGEra II using two-sided Wilcoxon test are: DA, P = 3.70×10−1, 3.71×10−2, 2.10×10−1 and 2.04×10−4, respectively; DS, P = 5.60×10−2, 3.62×10−4, 3.36×10−1 and 7.53×10−1, respectively; ASI, P = 1.73×10−1, 2.54×10−15, 1.42×10−13 and 9.62×10−7, respectively; EH, P = 4.12×10−1, 3.52×10−1, 7.55×10−1 and 2.63×10−2, respectively; EP, P = 5.15×10−1, 1.77×10−1, 8.08×10−1 and 1.50×10−1, respectively; TBN, P = 8.11×10−7, 2.68×10−8, 2.34×10−19 and 4.83×10−41, respectively; KWPE, P = 5.30×10−1, 9.80×10−18, 1.70×10−15 and 2.63×10−10, respectively; EW, P = 6.43×10−1, 3.22×10−15, 2.61×10−15 and 9.41×10−10, respectively; KNPR, P = 2.67×10−1, 3.74×10−15, 1.15×10−17 and 7.94×10−17, respectively; HKW, P = 6.51×10−2, 2.50×10−13, 9.47×10−5 and 3.28×10−3, respectively; HKV, P = 2.22×10−1, 1.44×10−13, 4.83×10−4 and 7.16×10−2, respectively; KL, P = 2.43×10−1, 8.35×10−23, 4.71×10−17 and 5.82×10−3, respectively; KW, P = 8.62×10−1, 3.82×10−7, 2.95×10−1 and 1.65×10−4, respectively; KRN, P = 2.81×10−2, 2.16×10−1, 1.19×10−8 and 4.32×10−4, respectively; CW, P = 9.11×10−1, 7.04×10−3, 1.09×10−5 and 1.20×10−5, respectively. For boxplots, the central lines show the median, the box limits indicate the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles. Significant differences are indicated: *** P < 0.001, ** P < 0.01 and * P < 0.05. Exact sample size (n) are 72, 253, 330 and 267 for FHGsEra I, FHGsEra II, MHGsEra I and MHGsEra II, respectively. c, Correlation between the number of accumulated favorable alleles and improved trait in the FHGs and MHGs. The six traits of Group 1 include DA (days to anthesis), DS (days to silking), ASI (anthesis to silking interval), EH (ear height), EP (relative ear height), TBN (tassel branch number); the seven traits of Group 2 include KWPE (kernel weight per ear), EW (ear weight), KNPR (kernel number per row), HKW (hundred kernel weight), HKV (hundred kernel volume), KL (kernel length) and KW (kernel width); and the two traits of Group 3 include KRN (kernel row number) and CW (cob weight). The Pearson correlation coefficient (r) and P value are presented.
Extended Data Fig. 4 Distribution and sharing of selective sweeps and profile of allele frequency change for the selected genes in the FHGs and MHGs.
a, The distribution ratio of selective sweeps located in genic and intergenic regions in the six heterotic groups. b, Distribution of the distances from intergenic selective sweeps to the nearest gene. c, Enrichment of shared genes (below the diagonal) and GO terms (above the diagonal) in the female heterotic groups (FHGs) and/or male heterotic groups (MHGs). Squares are marked with an asterisk (P < 0.05) and two asterisks (P < 0.01) if the observed number shared was significantly higher than the background number under permutation test conditions. d, Profile of allele frequency change of the 589 selected genes exhibiting co-directional change in allele frequencies between the FHGs and MHGs from Era I to Era II. e, Profile of allele frequency change of the 28 selected genes exhibiting anti-directional changes between the FHGs and MHGs from Era I to Era II. f, Profile of allele frequency change of the 400 selected genes exhibiting convergent increase or reduction in allele frequencies in the MHGs, but not in the FHGs, from Era I to Era II. For d-f, Orange indicates an increase of the reference allele frequency of nonsynonymous SNPs, whereas green indicates an increase of the alternative allele frequency from Era I to Era II. Each row represents a nonsynonymous SNP of the selected genes.
Extended Data Fig. 5 Representative selected genes related to plant growth and development and abiotic stress responses in at least two heterotic groups.
a-l, Genes functionally characterized in maize or their homologous genes have been functionally characterized in rice. Each gene includes three plots: XP-CLR plot, physical position of the gene (indicated by vertical gray dotted line), top 5% score of XP-CLR in individual heterotic group (shown by horizontal dotted line). Haplotype table plot, the type and number of haplotypes formed by nonsynonymous SNPs of gene were counted. The haplotypes with at least 20 inbred lines were listed in the table. Haplotype frequency bar plot, different haplotype frequency change from Era I to Era II was shown within each of the six heterotic groups.
Extended Data Fig. 6 Representative differentiated genes related to abiotic stress responses and plant growth and development.
a-k, Genes functionally characterized in maize or their homologous genes have been functionally characterized in rice. Each gene includes three plots: FST and allele frequency difference (AFD) plot, physical position of the gene (indicated by vertical gray dotted line), top 5% score of FST and AFD between the FHGs and MHGs were shown by horizontal orange dotted line and green dotted line, respectively. Haplotype table plot, the type and number of haplotypes formed by nonsynonymous SNPs of gene were counted. The haplotypes with at least 10 inbred lines were listed in the table. Haplotype frequency bar plot, different haplotype frequency change from Era I to Era II was shown in the female and male heterotic groups.
Extended Data Fig. 7 The relationships between heterozygosity levels and heterosis and between accumulated superior heterozygous alleles and heterosis for yield-related traits in four testcross populations.
a-d, Each plot group includes six plots for two traits (kernel weight per ear (KWPE) and ear weight (EW)): Left plot, correlation between heterozygosity levels of whole-genome nonsynonymous SNPs in non-divergent regions and better parent heterosis (BPH) of KWPE and EW in the testcross population. Middle plot, correlation between heterozygosity levels of nonsynonymous SNPs located in genes continuously selected in divergent regions and BPH of KWPE and EW in the testcross population. A total of 2,563 and 2,136 nonsynonymous SNPs contained in 478 and 375 genes continuously selected in PA × SPT and US_SS × US_NSS, were used to calculate the heterozygosity levels for the testcross populations from PA × SPT and SS × NSS heterotic patterns, respectively. Right plot, correlation between the number of accumulated superior heterozygous alleles and BPH of KWPE and EW in the testcross population. Four testcross populations include 88 hybrids derived from CNH3754 (a PA inbred) × 88 SPT inbreds, 91 hybrids derived from 91 PA inbreds × Jing2416 (a SPT inbred), 106 hybrids derived from Xunshi104-8 (a SS inbred) × 106 US_NSS inbreds, and 101 hybrids derived from 101 US_SS inbreds × F62 (a NSS inbred). The Pearson correlation coefficient (r) and P value are presented.
Extended Data Fig. 8 Identification of ZmKOB1 as a differentiated gene between the PA and SPT heterotic groups.
a, FST (above the axis) and allele frequency difference (AFD) of ZmKOB1. The candidate gene was visualized by green shadow. b, Gene structure and haplotype analyses of ZmKOB1. c, Box plots for kernel weight per ear (KWPE), kernel number per row (KNPR) and ear length (EL) for the four haplotypes. Center line, medium; box limits, upper and lower quartiles; whiskers, 1.5 × interquartile range. n indicates the number of inbred lines for each haplotype. The significance of difference was analyzed using two-sided Wilcoxon test. d, Haplotype frequency of ZmKOB1 in different breeding eras in the PA and SPT heterotic groups.
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Li, C., Guan, H., Jing, X. et al. Genomic insights into historical improvement of heterotic groups during modern hybrid maize breeding. Nat. Plants 8, 750–763 (2022). https://doi.org/10.1038/s41477-022-01190-2