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Size scaling describes the relative growth rates of different body parts of an organism following a positive correlation. Domestication and crop breeding often target the scaling traits in the opposite directions. The genetic mechanism of the size scaling influencing the pattern of size scaling remains unexplored. Here, we revisited a diverse barley (Hordeum vulgare L.) panel with genome-wide single-nucleotide polymorphisms (SNPs) profile and the measurement of their plant height and seed weight to explore the possible genetic mechanisms that may lead to a correlation of the two traits and the influence of domestication and breeding selection on the size scaling. Plant height and seed weight are heritable and remain positively correlated in domesticated barley regardless of growth type and habit. Genomic structural equation modeling systematically evaluated the pleiotropic effect of individual SNP on the plant height and seed weight within a trait correlation network. We discovered seventeen novel SNPs (quantitative trait locus) conferring pleiotropic effect on plant height and seed weight, involving genes with function in diverse traits related to plant growth and development. Linkage disequilibrium decay analysis revealed that a considerable proportion of genetic markers associated with either plant height or seed weight are closely linked in the chromosome. We conclude that pleiotropy and genetic linkage likely form the genetic bases of plant height and seed weight scaling in barley. Our findings contribute to understanding the heritability and genetic basis of size scaling and open a new venue for seeking the underlying mechanism of allometric scaling in plants.
Agrawal AA. A scale-dependent framework for trade-offs, syndromes, and specialization in organismal biology. Ecology. 2020;101(2):e02924.
Freschet GT, Swart EM, Cornelissen JH. Integrated plant phenotypic responses to contrasting above-and below-ground resources: Key roles of specific leaf area and root mass fraction. New Phytol. 2015;206(4):1247–1260.
Armbruster WS, Pélabon C, Bolstad GH, Hansen TF. Integrated phenotypes: Understanding trait covariation in plants and animals. Philos Trans R Soc Lond B Biol Sci. 2014;369(1649):20130245.
West GB, Brown JH. The origin of allometric scaling laws in biology from genomes to ecosystems: Towards a quantitative unifying theory of biological structure and organization. J Exp Biol. 2005;208(Pt.9):1575–1592.
Kleiber M. Body size and metabolic rate. Physiol Rev. 1947;27:511–541.
Kleiber M. Metabolic turnover rate: A physiological meaning of the metabolic rate per unit body weight. J Theor Biol. 1975;53:199–204.
Moles AT, Ackerly DD, Webb CO, Tweddle JC, Dickie JB, Pitman AJ, Westoby M. Factors that shape seed mass evolution. Proc Natl Acad Sci USA. 2005;102:10540–10544.
Novack-Gottshall PM. Ecosystem-wide body-size trends in Cambrian–Devonian marine invertebrate lineages. Paleobiology. 2008;34:210–228.
Vasseur F, Fouqueau L, de Vienne D, Nidelet T, Violle C, Weigel D. Nonlinear phenotypic variation uncovers the emergence of heterosis in Arabidopsis thaliana. PLoS Biol. 2019;17:e3000214.
Blum JJ. On the geometry of four-dimensions and the relationship between metabolism and body mass. J Theor Biol. 1977;64:599–601.
West GB, Brown JH, Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science. 1997;276:122–126.
Darveau CA, Suarez RK, Andrews RD, Hochachka PW. Allometric cascade as a unifying principle of body mass effects on metabolism. Nature. 2002;417:166–170.
Weibel ER. The pitfalls of power laws. Nature. 2002;417:131–132.
Niklas KJ. Plant allometry: Is there a grand unifying theory? Biol Rev. 2004;79(4):871–889.
Moles AT, Ackerly DD, Webb CO, Tweddle JC, Dickie JB, Westoby M. A brief history of seed size. Science. 2005;307:576–580.
Grubb PJ, Metcalfe DJ, Coomes D. Comment on “a brief history of seed size”. Science. 2005;310(5749):783.
Rees M, Venable DL. Why do big plants make big seeds? J Ecol. 2007;95:926–936.
Falster DS, Moles AT, Westoby M. A general model for the scaling of offspring size and adult size. Am Nat. 2008;172:299–317.
Venable DL, Rees M. The scaling of seed size. J Ecol. 2009;97:27–31.
Westoby M, Moles AT, Falster DS. Evolutionary coordination between offspring size at independence and adult size. J Ecol. 2009;97:23–26.
Saltz JB, Hessel FC, Kelly MW. Trait correlations in the genomics era. Trends Ecol Evol. 2017;32:279–290.
Blows MW, Hoffmann AA. A reassessment of genetic limits to evolutionary change. Ecology. 2005;86:1371–1384.
Hine E, McGuigan K, Blows MW. Evolutionary constraints in high-dimensional trait sets. Am Nat. 2014;184:119–131.
Langridge P, Fleury D. Making the most of ‘omics’ for crop breeding. Trends Biotechnol. 2011;29:33–40.
Xiao Y, Liu H, Wu L, Warburton M, Yan J. Genome-wide association studies in maize: Praise and stargaze. Mol Plant. 2017;10:359–374.
Grotzinger AD, Rhemtulla M, de Vlaming R, Ritchie SJ, Mallard TT, Hill WD, Ip HF, Marioni RE, McIntosh AM, Deary IJ, et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav. 2019;3:513.
Langridge P. Reinventing the green revolution by harnessing crop mutant resources. Plant Physiol. 2014;166:1682–1683.
Milla R, Osborne CP, Turcotte MM, Violle C. Plant domestication through an ecological lens. Trends Ecol Evol. 2015;30:463–469.
Badr A, Müller K, Schäfer-Pregl R, El Rabey H, Effgen S, Ibrahim HH, Pozzi C, Rohde W, Salamini F. On the origin and domestication history of barley (Hordeum vulgare). Mol Biol Evol. 2000;17(4):499–510.
Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, Radchuk V, Dockter C, Hedley PE, Russell J, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017;544:427–433.
Gonzalez MY, Weise S, Zhao Y, Philipp N, Arend D, Börner A, Oppermann M, Graner A, Reif JC, Schulthess AW. Unbalanced historical phenotypic data from seed regeneration of a barley ex situ collection. Sci Data. 2018;5:180278.
He T, Hill CB, Angessa TT, Zhang XQ, Chen K, Moody D, Telfer P, Westcott S, Li C. Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection. J Exp Bot. 2019;70:5603–5616.
Milner SG, Jost M, Taketa S, Mazón ER, Himmelbach A, Oppermann M, Weise S, Knüpffer H, Basterrechea M, König P, et al. Genebank genomics highlights the diversity of a global barley collection. Nat Genet. 2019;51:319–326.
Hammer O, Harper DAT, Ryan PD. PAST: Paleontological statistics software package for education and data analysis. Paleontologica Electronica. 2001;4:9.
Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–1313.
Webb CO, Ackerly DD, Kembel SW. Phylocom: Software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics. 2008;24:2098–2100.
Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, Lee SH, Robinson MR, Perry JRB, Nolte IM, van Vliet-Ostaptchouk JV, et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015;47(10):1114–1120.
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: A tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.
Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience. 2015;4:7.
Zhang C, Dong SS, Xu JY, He WM, Yang TL. PopLDdecay: A fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics. 2019;35:1786–1788.
Zhou G, Broughton S, Zhang XQ, Ma Y, Zhou M, Li C. Genome-wide association mapping of acid soil resistance in barley (Hordeum vulgare L.). Front Plant Sci. 2016;7:406.
He T, Angessa TT, Hill CB, Zhang XQ, Chen K, Luo H, Wang Y, Karunarathne SD, Zhou G, Tan C, et al. Genomic structural equation modelling provides a whole-system approach for the future crop breeding. Theor Appl Genet. 2021;134:2875–2889.
Wagner GP, Zhang J. The pleiotropic structure of the genotype–phenotype map: The evolvability of complex organisms. Nat Rev Genet. 2011;12:204–213.
Lin M, Lucas HC Jr, Smueli G. Research commentary—Too big to fail: Large samples and the p-value problem. Inf Syst Res. 2013;24:906–917.
Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23:2633–2635.
Komatsuda T, Pourkheirandish M, He C, Azhaguvel P, Kanamori H, Perovic D, Stein N, Graner A, Wicker T, Tagiri A, et al. Six-rowed barley originated from a mutation in a homeodomain-leucine zipper I-class homeobox gene. Proc Natl Acad Sci USA. 2007;104(4):1424–1429.
Moles AT, Falster DS, Leishman MR, Westoby M. Small seeded species produce more seeds per square metre of canopy per year, but not per individual per lifetime. J Ecol. 2004;92:384–396.
Rollinson N, Nilsson-Örtman V, Rowe L. Density-dependent offspring interactions do not explain macroevolutionary scaling of adult size and offspring size. Evolution. 2019;73:2162–2174.
Gross LJ, Beckage B. Toward a metabolic scaling theory of crop systems. Proc Natl Acad Sci USA. 2012;109:15535–15536.
Wu R, Ma CX, Littell RC, Casella G. A statistical model for the genetic origin of allometric scaling laws in biology. J Theor Biol. 2002;219:121–135.
Li H, Huang Z, Gai J, Wu S, Zeng Y, Li Q, Wu R. A conceptual framework for mapping quantitative trait loci regulating ontogenetic allometry. PLOS ONE. 2007;2(11):e1245.
Gupta M, Abu-Ghannam N, Gallaghar E. Barley for brewing: Characteristic changes during malting, brewing and applications of its by-products. Compr Rev Food Sci Food Saf. 2010;9:318–328.
Sakuma S, Lundqvist U, Kakei Y, Thirulogachandar V, Suzuki T, Hori K, Wu J, Tagiri A, Rutten T, Koppolu R, et al. Extreme suppression of lateral floret development by a single amino acid change in the VRS1 transcription factor. Plant Physiol. 2017;175:1720–1731.
Gardner KM, Latta RG. Shared quantitative trait loci underlying the genetic correlation between continuous traits. Mol. Ecol. 2007;16:4195–4209.
Gianola D, de los Campos G, Toro MA, Naya H, Schön CC, Sorensen D. Do molecular markers inform about pleiotropy? Genetics. 2015;201(1):23–29.
Chen F, Dahal P, Bradford KJ. Two tomato expansin genes show divergent expression and localization in embryos during seed development and germination. Plant Physiol. 2001;127:928–936.
Ma N, Wang Y, Qiu S, Kang Z, Che S, Wang G, Huang J. Overexpression of OsEXPA8, a root-specific gene, improves rice growth and root system architecture by facilitating cell extension. PLOS ONE. 2013;8:e75997.
Stark H, Rodnina MV, Wieden HJ, van Heel M, Wintermeyer W. Large-scale movement of elongation factor G and extensive conformational change of the ribosome during translocation. Cell. 2000;100:301–309.
Liu JH, Li YC, Zhang J, Gao PZ, Wang AB, Zhang N, Xu BY, Jin ZQ. Banana MaEF1A facilitates plant growth and development. Biol Plant. 2016;60:435–442.
Odunuga OO, Longshaw VM, Blatch GL. Hop: More than an Hsp70/Hsp90 adaptor protein. BioEssays. 2016;26(10):1058–1068.
Sangster TA, Salathia N, Lee HN, Watanabe E, Schellenberg K, Morneau K, Wang H, Undurraga S, Queitsch C, Lindquist S. HSP90-buffered genetic variation is common in Arabidopsis thaliana. Proc Natl Acad Sci USA. 2008;105:2969–2974.
Delker C, Quint M. Expression level polymorphisms: Heritable traits shaping natural variation. Trends Plant Sci. 2011;16:481–488.
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