Abstract
Throughout the history of sorghum domestication, kernel traits have been subject to extensive selection. Breeding of grain sorghum is highly dependent on kernel morphology, which influences yield and quality. This study examined the genetic variation and architecture of kernel size, shape, and color among two BC1F2 subpopulations, H4 and H6, derived from crossing Sorghum bicolor BTx623 and Sorghum halepense Gypsum 9E. We phenotyped 246 BC1F2 families and their parents in two locations for two years using high-throughput digital imaging techniques, to determine fourteen kernel traits in five broad categories: size (area, length, width, and aspect ratio), shape (circularity and PC1), density (factor form density; FFD), weight (1000-kernel weight) and color (RGB and CIE-L*a*b*). Based on single-trait, interval mapping, we identified 76 and 71 significant QTLs in the H4 and H6 subpopulations, respectively. Both parent genotypes contributed QTL alleles that conferred positive additive effects, indicating that Sorghum halepense contains alleles that may enhance some kernel-related traits of elite sorghums. Some genomic regions affect many traits—for example, a linkage group 6 homolog in the H4 subpopulation was associated with several kernel traits in the region between 0 and 57.3 cM, including FFD, area, length, and width; and in H6 between 56.8 and 185.9 cM contained QTLs associated with kernel color parameters (R, G, L* and B*), FFD, kernel shape (PC1), kernel area, aspect ratio, kernel width, and 1000-kernel weight. These results contribute to a better understanding of genetic factors governing sorghum kernel traits, providing a foundation for improving grain and quality traits in both annual and perennial sorghums using genomic tools.
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References
Alfieri M, Balconi C, Cabassi G et al (2017) Antioxidant activity in a set of sorghum landraces and breeding lines. Maydica 62:1–7
Bai C, Wang C, Wang P et al (2017) QTL mapping of agronomically important traits in sorghum (Sorghum bicolor L.). Euphytica. https://doi.org/10.1007/S10681-017-2075-1
Bholowalia P, Kumar A (2014) EBK-means: a clustering technique based on elbow method and K-means in WSN
Boren B, Waniska RD (1992) Sorghum seed color as an indicator of tannin content. J Appl Poult Res 1:117–121. https://doi.org/10.1093/japr/1.1.117
Boyles R (2017) Quantitative trait loci mapping of agronomic and yield traits in two grain sorghum biparental families. Crop Sci 57:2443–2456. https://doi.org/10.2135/cropsci2016.12.0988
Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177. https://doi.org/10.1534/genetics.105.044586
Brewer MT, Lang L, Fujimura K et al (2006) Development of a controlled vocabulary and software application to analyze fruit shape variation in tomato and other plant species. PLANT Physiol 141:15–25. https://doi.org/10.1104/pp.106.077867
Broman KW, Wu H, Saunak S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinforma Appl NOTE 19:889–890. https://doi.org/10.1093/bioinformatics/btg112
Brown P (2006) Inheritance of inflorescence architecture in sorghum. Theor Appl Genet 113:931–942. https://doi.org/10.1007/s00122-006-0352-9
Brown PJ (2008) Efficient mapping of plant height quantitative trait loci in a sorghum association population with introgressed dwarfing genes. Genetics 180:629–637. https://doi.org/10.1534/genetics.108.092239
Bylesjö M, Segura V, Soolanayakanahally RY et al (2008) LAMINA: a tool for rapid quantification of leaf size and shape parameters. BMC Plant Biol 8:82. https://doi.org/10.1186/1471-2229-8-82
Campbell I, Casady A, Science WC-C (1975) Effects of a single height gene (DW3 of sorghum on certain agronomic characters1. Wiley Online Libr 15:595–597. https://doi.org/10.2135/cropsci1975.0011183X001500040043x
Cavanagh C, Morell M, Mackay I, Powell W (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol 11:215–221. https://doi.org/10.1016/J.PBI.2008.01.002
Chitwood DH, Ranjan A, Kumar R et al (2014) Resolving distinct genetic regulators of tomato leaf shape within a heteroblastic and ontogenetic context. Plant Cell 26:3616–3629. https://doi.org/10.1105/tpc.114.130112
Chopra S, Gevens A, Svabek C, Wood KV, Peterson T, Nicholson RL (2002) Excision of the Candystripe1 transposon from a hyper-mutable Y1-cs allele shows that the sorghum Y1 gene controls the biosynthesis of both 3-deoxyanthocyanidin phytoalexins and phlobaphene pigments. Physiol Mol Plant Pathol 60:321–330. https://doi.org/10.1006/pmpp.2002.0411
Cox S, Nabukalu P, Paterson AH et al (2018) Development of perennial grain sorghum. Sustainability 10:1–8
Crews T, Rumsey B, Crews TE, Rumsey BE (2017) What agriculture can learn from native ecosystems in building soil organic matter: a review. Sustainability 9:578. https://doi.org/10.3390/su9040578
Cuevas HE, Zhou C, Tang H et al (2016) The evolution of photoperiod-insensitive flowering in sorghum, a genomic model for panicoid grasses. Mol Biol Evol 33:2417–2428. https://doi.org/10.1093/molbev/msw120
Doebley J (2004) The genetics of maize evolution. Annu Rev Genet 38:37–59. https://doi.org/10.1146/annurev.genet.38.072902.092425
Falconer DS, Douglas S (1989) Introduction to quantitative genetics, 3rd edn. Longman, Scientific and Technical, Burnt Mill Harlow Essex England, New York
Feltus FA, Hart GE, Schertz KF et al (2006) Alignment of genetic maps and QTLs between inter- and intra-specific sorghum populations. Theor Appl Genet 112:1295–1305. https://doi.org/10.1007/s00122-006-0232-3
Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374. https://doi.org/10.1146/annurev.arplant.54.031902.134907
French A, Ubeda-Tomas S, Holman TJ et al (2009) High-throughput quantification of root growth using a novel image-analysis tool. PLANT Physiol 150:1784–1795. https://doi.org/10.1104/pp.109.140558
Galili T (2015) Dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics 31:3718–3720. https://doi.org/10.1093/bioinformatics/btv428
Gao L-Z, Innan H (2008) Nonindependent domestication of the two rice subspecies, Oryza sativa ssp. indica and ssp. japonica, demonstrated by multilocus microsatellites. Genetics 179:965–976. https://doi.org/10.1534/genetics.106.068072
Gegas VC, Nazari A, Griffiths S et al (2010) A genetic framework for grain size and shape variation in wheat. Plant Cell 22:1046–1056. https://doi.org/10.1105/tpc.110.074153
Gelli M, Mitchell SE, Liu K et al (2016) Mapping QTLs and association of differentially expressed gene transcripts for multiple agronomic traits under different nitrogen levels in sorghum. BMC Plant Biol. https://doi.org/10.1186/S12870-015-0696-X
George-Jaeggli B, Jordan DR, van Oosterom EJ, Hammer GL (2011) Decrease in sorghum grain yield due to the dw3 dwarfing gene is caused by reduction in shoot biomass. F Crop Res 124:231–239. https://doi.org/10.1016/j.fcr.2011.07.005
Glaubitz JC, Casstevens TM, Lu F et al (2014) TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9:e90346. https://doi.org/10.1371/journal.pone.0090346
Golpour I, Amiri Parian J, Chayjan RA (2014) Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network
Gorbet DW (1972) Inheritance and genetic relationships of six endosperm types in sorghum. Crop Sci 12:378–382. https://doi.org/10.2135/cropsci1972.0011183x001200030037x
Groos C, Robert N, Bervas E, Charmet G (2003) Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat. Theor Appl Genet 106:1032–1040. https://doi.org/10.1007/s00122-002-1111-1
Gros-Balthazard M, Newton C, Ivorra S et al (2016) The domestication syndrome in Phoenix dactylifera Seeds: toward the identification of wild date palm populations. PLoS ONE 11:e0152394. https://doi.org/10.1371/journal.pone.0152394
Guindo D (2019) Quantitative trait loci for sorghum grain morphology and quality traits: toward breeding for a traditional food preparation of West-Africa. J Cereal Sci 85:256–272. https://doi.org/10.1016/j.jcs.2018.11.012
Hadley H, Freeman J, Science EJ-C (1965) Effects of height mutations on grain yield in sorghum1. Wiley Online Libr 5:11–14. https://doi.org/10.2135/cropsci1965.0011183X000500010005x
Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity (edinb) 69:315–324
Han L (2015) Fine mapping of qGW1, a major QTL for grain weight in sorghum. Theor Appl Genet 128:1813–1825. https://doi.org/10.1007/s00122-015-2549-2
Higgins RH, Thurber CS, Assaranurak I, Brown PJ (2014) Multiparental mapping of plant height and flowering time QTL in partially isogenic sorghum families. G3 Genes Genomes Genet 4:1593–1602. https://doi.org/10.1534/G3.114.013318
Hilley JL, Weers BD, Truong SK et al (2017) Sorghum Dw2 encodes a protein kinase regulator of stem internode length. Sci Rep. https://doi.org/10.1038/S41598-017-04609-5
Huang R, Jiang L, Zheng J et al (2013) Genetic bases of rice grain shape: so many genes, so little known. Trends Plant Sci 18:218–226. https://doi.org/10.1016/j.tplants.2012.11.001
Ibraheem F, Gaffoor I, Chopra S (2010) Flavonoid phytoalexin-dependent resistance to anthracnose leaf blight requires a functional yellow seed1 in Sorghum bicolor. Genetics 184:915–926. https://doi.org/10.1534/genetics.109.111831
Iwata H, Ukai Y (2002) SHAPE: a computer program package for quantitative evaluation of biological shapes based on elliptic fourier descriptors. J Hered 93:384–385. https://doi.org/10.1093/jhered/93.5.384
Iwata H, Niikura S, Matsuura S et al (1998) Evaluation of variation of root shape of Japanese radish (Raphanus sativus L.) based on image analysis using elliptic Fourier descriptors. Euphytica 102:143–149. https://doi.org/10.1023/A:1018392531226
Iwata H, Ebana K, Uga Y, Hayashi T (2015) Genomic prediction of biological shape: elliptic fourier analysis and kernel partial least squares (PLS) regression applied to grain shape prediction in rice (Oryza sativa L.). PLoS ONE 10:e0120610. https://doi.org/10.1371/journal.pone.0120610
James Rohlf F, Marcus LF (1993) A revolution morphometrics. Trends Ecol Evol 8:129–132. https://doi.org/10.1016/0169-5347(93)90024-J
Kirigwi FM, Van Ginkel M, Brown-Guedira G et al (2007) Markers associated with a QTL for grain yield in wheat under drought. Mol Breed 20:401–413. https://doi.org/10.1007/s11032-007-9100-3
Klein RR, Mullet JE, Jordan DR et al (2008) The effect of tropical sorghum conversion and inbred development on genome diversity as revealed by high-resolution genotyping. Crop Sci. https://doi.org/10.2135/CROPSCI2007.06.0319TPG
Kong W, Nabukalu P, Cox TS et al (2020) Transmission genetics of a Sorghum bicolor × S. halepense backcross populations. Front Plant Sci 11:467. https://doi.org/10.3389/fpls.2020.00467
Kong WQ, Nabukalu P, Cox TS et al (2021) Quantitative trait mapping of plant architecture in two BC1F2 populations of Sorghum bicolor × S. halepense and comparisons to two other sorghum populations. Theor Appl Genet 1:3. https://doi.org/10.1007/s00122-020-03763-1
Kuhl FP, Giardina CR (1982) Elliptic Fourier features of a closed contour. Comput Graph Image Process 18:236–258. https://doi.org/10.1016/0146-664X(82)90034-X
Kumar N, Kulwal PL, Balyan HS, Gupta PK (2007) QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat. Mol Breed 19:163–177. https://doi.org/10.1007/s11032-006-9056-8
León K, Mery D, Pedreschi F, León J (2006) Color measurement in L*a*b*units from RGB digital images. Food Res Int 39:1084–1091. https://doi.org/10.1016/j.foodres.2006.03.006
Lin YR, Schertz KF, Paterson AH (1995) Comparative analysis of QTLs affecting plant height and maturity across the Poaceae, in reference to an interspecific sorghum population. Genetics 141:391–411. https://doi.org/10.1093/GENETICS/141.1.391
Lockhart J (2013) A quantitative genetic basis for leaf morphology is revealed in a set of precisely defined tomato introgression lines. Plant Cell 25:2379. https://doi.org/10.1105/tpc.113.250710
Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sunderland, MA: Sinauer
Mace E (2019) The Sorghum QTL atlas: a powerful tool for trait dissection, comparative genomics and crop improvement. Theor Appl Genet 132:751–766. https://doi.org/10.1007/s00122-018-3212-5
Maintainer G, Gu Z (2018) Type package title circular visualization
Mason RE, Mondal S, Beecher FW et al (2010) QTL associated with heat susceptibility index in wheat (Triticum aestivum L.) under short-term reproductive stage heat stress. Euphytica 174:423–436. https://doi.org/10.1007/s10681-010-0151-x
Mocoeur A (2015) Stability and genetic control of morphological, biomass and biofuel traits under temperate maritime and continental conditions in sweet sorghum (Sorghum bicolor). Theor Appl Genet 128:1685–1701. https://doi.org/10.1007/s00122-015-2538-5
Moles AT (2005) A brief history of seed size. Science 307:576–580. https://doi.org/10.1126/science.1104863
Morimoto Y, Maundu P, Fujimaki H, Morishima H (2005) Diversity of landraces of the white-flowered gourd (Lagenaria siceraria) and its wild relatives in kenya: fruit and seed morphology. Genet Resour Crop Evol 52:737–747. https://doi.org/10.1007/s10722-004-6119-8
Morris GP, Ramu P, Deshpande SP et al (2013) Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci 110:453–458. https://doi.org/10.1073/pnas.1215985110
Murray SC, Sharma A, Rooney WL et al (2008) Genetic improvement of sorghum as a biofuel feedstock: I. QTL for stem sugar and grain nonstructural carbohydrates. Crop Sci 48:2165–2179. https://doi.org/10.2135/cropsci2008.01.0016
Nabukalu P, Cox TS (2016) Response to selection in the initial stages of a perennial sorghum breeding program. Euphytica. https://doi.org/10.1007/s10681-016-1639-9
Nabukalu P, Kong W, Cox TS, Paterson AH (2021) Detection of quantitative trait loci regulating seed yield potential in two interspecific S. bicolor2 × S. halepense subpopulations. Euphytica 217:13. https://doi.org/10.1007/s10681-020-02734-3
Nagaraja Reddy R, Madhusudhana R, Murali Mohan S et al (2013) Mapping QTL for grain yield and other agronomic traits in post-rainy sorghum [Sorghum bicolor (L.) Moench]. Theor Appl Genet 126:1921–1939. https://doi.org/10.1007/S00122-013-2107-8
Okamoto Y, Nguyen AT, Yoshioka M et al (2013) Identification of quantitative trait loci controlling grain size and shape in the D genome of synthetic hexaploid wheat lines. Breed Sci 63:423–429. https://doi.org/10.1270/jsbbs.63.423
OuYang A-G, Gao R, Liu Y et al (2010) An automatic method for identifying different variety of rice seeds using machine vision technology. In: 2010 sixth international conference on natural computation. IEEE, pp 84–88
Paterson A, Lin Y-R, Li Z et al (1995) Convergent domestication of cereal crops by independent mutations at corresponding genetic loci. Science 269:1714–1718. https://doi.org/10.1126/science.269.5231.1714
Paterson A, Bowers JE, Bruggmann R et al (2009) The Sorghum bicolor genome and the diversification of grasses. Nature 457:551–556. https://doi.org/10.1038/nature07723
Paterson A, Kong W, Johnston RM et al (2020) The evolution of an invasive plant, Sorghum halepense L. (‘Johnsongrass’). Front Genet 11:317. https://doi.org/10.3389/FGENE.2020.00317
Pinto RS, Reynolds MP, Mathews KL et al (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021. https://doi.org/10.1007/s00122-010-1351-4
Quinby JR (1967) The Maturity genes of sorghum. Adv Agron 19:267–305. https://doi.org/10.1016/S0065-2113(08)60737-3
R Development Core Team (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org
Rajkumar FB, Kavil SP et al (2013) Molecular mapping of genomic regions harbouring QTLs for root and yield traits in sorghum (Sorghum bicolor L. Moench). Physiol Mol Biol Plants 19:409–419. https://doi.org/10.1007/S12298-013-0188-0
Rhodes DH, Hoffmann L, Rooney WL et al (2014) Genome-wide association study of grain polyphenol concentrations in global sorghum [Sorghum bicolor (L.) Moench] germplasm. J Agric Food Chem 62:10916–10927. https://doi.org/10.1021/JF503651T
Rooney LW, Miller FR (1982) Variation in the structure and kernel characteristics of sorghum
Sakhi S, Shehzad T, Rehman S, Okuno K (2013) Mapping the QTLs underlying drought stress at developmental stage of sorghum (Sorghum bicolor (L.) Moench) by association analysis. Euphytica 193:433–450. https://doi.org/10.1007/S10681-013-0963-6
Shiddiq DMF, Nazaruddin YY, Muchtadi FI, Raharja S (2011) Estimation of rice milling degree using image processing and Adaptive Network Based Fuzzy Inference System (ANFIS). In: 2011 2nd international conference on instrumentation control and automation. IEEE, pp 98–103
Shimomura K, Horie H, Sugiyama M et al (2016) Quantitative evaluation of cucumber fruit texture and shape traits reveals extensive diversity and differentiation. Sci Hortic 199:133–141. https://doi.org/10.1016/J.SCIENTA.2015.12.033
Spagnolli FC, Mace E, Jordan D, Borrás L, Gambin BL (2016) Quantitative trait loci of plant attributes related to Sorghum grain number determination. Crop Sci 56:3046–3054. https://doi.org/10.2135/cropsci2016.03.0185
Tan YF, Xing YZ, Li JX et al (2000) Genetic bases of appearance quality of rice grains in Shanyou 63, an elite rice hybrid. TAG Theor Appl Genet 101:823–829. https://doi.org/10.1007/s001220051549
Tanksley SD (2004) The genetic, developmental, and molecular bases of fruit size and shape variation in tomato. Plant Cell 16(Suppl):S181–S189. https://doi.org/10.1105/tpc.018119
Tao Y (2019) Large-scale GWAS in sorghum reveals common genetic control of grain size among cereals. Plant Biotechnol J 18:1093–1105. https://doi.org/10.1111/pbi.13284
Tao Y, Mace E, George-Jaeggli B et al (2018) Novel grain weight loci revealed in a cross between cultivated and wild sorghum. Plant Genome 11:170089. https://doi.org/10.3835/PLANTGENOME2017.10.0089
Tian F, Bradbury PJ, Brown PJ et al (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162. https://doi.org/10.1038/ng.746
van der Knaap E, Lippman ZB, Tanksley SD (2002) Extremely elongated tomato fruit controlled by four quantitative trait loci with epistatic interactions. TAG Theor Appl Genet 104:241–247. https://doi.org/10.1007/s00122-001-0776-1
Wickham H (2009) ggplot2. ggplot2. https://doi.org/10.1007/978-0-387-98141-3
Williams K, Sorrells ME (2014) Three-dimensional seed size and shape QTL in hexaploid wheat (L.) populations. Crop Sci 54:98. https://doi.org/10.2135/cropsci2012.10.0609
Worzella WW, Khalidy R, Badawi Y, Daghir S (1965) Inheritance of Beta-carotene in grain sorghum hybrids. Crop Sci 5:591–592. https://doi.org/10.2135/cropsci1965.0011183x000500060032x
Wu Y (2012) Presence of tannins in sorghum grains is conditioned by different natural alleles of Tannin1. Proc Natl Acad Sci U S A 109:10281–10286. https://doi.org/10.1073/pnas.1201700109
Xie X, Jin F, Song M-H et al (2008) Fine mapping of a yield-enhancing QTL cluster associated with transgressive variation in an Oryza sativa × O. rufipogon cross. Theor Appl Genet 116:613–622. https://doi.org/10.1007/s00122-007-0695-x
Zhang D, Kong W, Robertson J et al (2015) Genetic analysis of inflorescence and plant height components in sorghum (Panicoidae) and comparative genetics with rice (Oryzoidae). BMC Plant Biol 15:107. https://doi.org/10.1186/s12870-015-0477-6
Zhou L, Wang S-B, Jian J et al (2015) Identification of domestication-related loci associated with flowering time and seed size in soybean with the RAD-seq genotyping method. Sci Rep 5:9350. https://doi.org/10.1038/srep09350
Zou G (2012) Identification of QTLs for eight agronomically important traits using an ultra-high-density map based on SNPs generated from high-throughput sequencing in sorghum under contrasting photoperiods. J Exp Bot 63:5451–5462. https://doi.org/10.1093/jxb/ers205
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The authors would like to thank Sheila Cox and all the interns at The Land Institute who assisted in the data collection.
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This work was funded in whole or part by the United States Agency for International Development (USAID) Bureau for Resilience and Food Security under Agreement # AID-OAAA-13-00044 as part of Feed the Future Innovation Lab for Climate-Resilient Sorghum. Any opinions, findings, conclusions, or recommendations expressed here are those of the authors alone.
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All authors contributed to and approved the final manuscript. SC conceived the study, generated the population, and contributed to experimental setup and phenotyping, manuscript revision; PN contributed to phenotyping, data analyses, and manuscript write-up, WK contributed to phenotyping, mapping, marker identification, and data analyses; VG, GP, CL, JR, and RC collected phenotypic and genotypic data. HT performed GBS sequencing and provided analytical support for the GRABSEED software; AP co-conceived the study, contributed to phenotyping, marker identification, data interpretation, and manuscript revision.
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Nabukalu, P., Kong, W., Cox, T.S. et al. Genetic variation underlying kernel size, shape, and color in two interspecific S. bicolor2 × S. halepense subpopulations. Genet Resour Crop Evol 69, 1261–1281 (2022). https://doi.org/10.1007/s10722-021-01303-4
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DOI: https://doi.org/10.1007/s10722-021-01303-4