Plant Quantitative Traits

Sobia Ikram, UMR1318 INRA‐AgroParisTech, Versailles, France
Fabien Chardon, UMR1318 INRA‐AgroParisTech, Versailles, France

Published online: December 2010

DOI: 10.1002/9780470015902.a0002021.pub2

In plants, most of the phenotypic variations are continuously distributed and could be considered as quantitative traits. The complexity of their genetic control is high because the involved genes are numerous, with usually minor effects and very sensitive to environment. The implicated loci are localised by two basic approaches, linkage mapping and association mapping, based on the use of genetic maps and sophisticated statistical analysis. Linkage mapping leads to the identification of small regions of genome but that could contain still several hundred genes. Identification of gene underlying the quantitative trait loci requires positional cloning or direct tests of promising candidates. Association mapping checks directly the relationship between each polymorphism and phenotypic trait variation in wild populations, but physical linkage and population structure are sources of false positives. Finally, validation that an individual gene is responsible for the quantitative trait needs to be performed by using genetic or functional complementation.

Key Concepts:

  • Quantitative traits follow continuous, unbroken quasi-normal distributions whereas qualitative (mendelian) traits are discreetly distributed.
  • Quantitative traits are controlled by several genes, with small additive, dominant or epistatic effects, and in interaction with the environment.
  • A quantitative trait loci (QTL) is defined as an area of genome associated with an effect on a quantitative trait.
  • The combination of alleles at the many genes involved in a quantitative trait leads to constitute the different phenotypes.
  • QTL mapping relies on statistical linkage analysis among quantitative trait and genetic markers using a population that carries combinations of alleles derived from parental lines.
  • Association mapping looks for association between a genetic marker and phenotype in unrelated individuals by exploiting historical recombination events and genetic diversity.
  • Population structure is the presence of hidden subgroups in wild populations that appear because of relatedness and selection with an unequal distribution of alleles.
  • Physical linkage and population structure are sources of linkage disequilibrium and might influence the genome-wide association (GWA) mapping by creation of false marker-trait association.
  • Positional cloning of QTL involves the identification of closely linked recombination events requiring analysis of a large number of segregating progeny with molecular markers covering the critical region.
  • Complete genome sequencing has greatly advanced the use of GWA mapping.

Keywords: natural variation; core-collection; epistasis; linkage disequilibrium; QTL; GWA

Figure 1. Natural variation in Arabidopsis thaliana. (a) Seed colour and seed size variation. (b) Inflorescence architecture variation. (c) Leaf shape and rosette size variation.
Figure 2. (a) Frequency distribution of stem colour in Nicotiana rustica, a qualitative trait evaluated by classification rather than by measurement. (b) Frequency distribution of wheat biomass showing the bell-shaped or normal distribution typical of quantitative traits.
Figure 3. Difference between additive, dominant and epistatic gene actions. (a) Additive gene action. Trait values of genotypes depend of alleles A1 and A2 at one locus A. The heterozygous genotype A1A2 has an average phenotypic value of the two homozygous genotypes A1A1 and A2A2. (b) Dominance gene action. The trait value of heterozygous genotype A1A2 deviates from the mean. Dominance effect is equal to this deviation d. (c) Epistatic gene action. Allelic effect at the locus A depends on the alleles at the locus B. If the alleles B1 are fixed at locus B, then the allele A2 has an additive effect at locus A on the trait. If the alleles B2 are fixed at locus B, then the allele A2 has no effect.
Figure 4. Principle of QTL detection by linkage analysis. (a) Preparation of mapping population. Two parental lines (white P1 and black P2) are used to create a RIL population. Segments derived from parental chromosome are represented by white and black boxes. RIL population is obtained by six generations of single seed descent from the F2 population. As a consequence, the genetic background of RIL is almost completely homozygous. (b) RILs are genotyped with markers covering all chromosomes and scored for trait of interest (deficient – or performing+lines). Statistical combination of genotypes with phenotypes leads to draw the LOD score curve among the chromosome providing an estimation of the QTL presence. When the LOD score exceeds a threshold (blue hashed line), a QTL is localised.
Figure 5. Principle of association mapping with tomato fruit size. During species history, many recombination events have been appeared to make the current haplotypes from a few ancestral lines. Genotype of present day lines is investigated by using genetic markers (SNP1, SNP2 and SNP3), whereas their phenotypic performance (fruit size) is scored. Correlation between genetic markers and phenotype among collections of diverse germplasm tests association and leads to map locus closed to SNP2 involved in the trait variation with two positive (+) and one negative (–) alleles.
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 References
    Abdurakhmonov IY and Abdukarimov A (2008) Application of association mapping to understanding the genetic diversity of plant germplasm resources. International Journal of Plant Genomics 2008: 1–18.
    Alonso-Blanco C, Aarts MGM, Bentsink L et al. (2009) What has natural variation taught us about plant development, physiology, and adaptation? Plant Cell 21(7): 1877–1896.
    Atwell S, Huang YS, Vilhjálmsson BJ et al. (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465(7298): 627–631.
    Baxter I, Muthukumar B, Park HC et al. (2008) Variation in molybdenum content across broadly distributed populations of Arabidopsis thaliana is controlled by a Mitochondrial Molybdenum Transporter (MOT1). PLoS Genetics 4(2): e1000004.
    Beló A, Zheng P, Luck S et al. (2008) Whole genome scan detects an allelic variant of fad2 associated with increased oleic acid levels in maize. Molecular Genetics and Genomics 279(1): 1–10.
    Blair MW, Diaz LM, Buendia HF and Duque MC (2009) Genetic diversity, seed size associations and population structure of a core collection of common beans (Phaseolus vulgaris L). Theoretical and Applied Genetics 119: 955–972.
    Brachi B, Faure N, Horton M et al. (2010) Linkage and association mapping of Arabidopsis thaliana flowering time in nature. PLoS Genetics 6: e1000940.
    Brown AHD (1989) Core collection: a practical approach to genetic resources management. Genome 31: 818–824.
    Chardon F, Virlon B, Moreau L et al. (2004) Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta analysis and synteny conservation with the rice genome. Genetics 168(4): 2169–2185.
    Clark RM, Wagler TN, Quijada P and Doebley J (2006) A distant upstream enhancer at the maize domestication gene tb1 has pleiotropic effects on plant and inflorescent architecture. Nature Genetics 38(5): 594–597.
    Damerval C, Maurice A, Josse JM and De-Vienne D (1994) Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics 137(1): 289–301.
    Doi K, Izawa T, Fuse T et al. (2004) Ehd1, a B-type response regulator in rice, confers short-day promotion of flowering and controls FT-like gene expression independently of Hd1. Genes & Development 18(8): 926–936.
    Druka A, Potokina E, Luo Z et al. (2010) Expression quantitative trait loci analysis in plants. Plant Biotechnology Journal 8: 10–27.
    El-Din El-Assal S, Alonso-Blanco C, Peeters AJ, Raz V and Koornneef M (2001) A QTL for flowering time in Arabidopsis reveals a novel allele of CRY2. Nature Genetics 29(4): 435–440.
    Frary A, Nesbitt TC, Grandillo S et al. (2000) Fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science 289(5476): 85–88.
    Flint-Garcia SA, Thornsberry JM and Buckler ES (2003) Structure of linkage disequilibrium in plants. Annual Review of Plant Biology 54: 357–374.
    Fu J, Keurentjes JJB, Bouwmeester H et al. (2009) System-wide molecular evidence for phenotypic buffering in Arabidopsis. Nature Genetics 41(2): 166–167.
    Gouesnard B, Dallard J, Bertin P, Boyat A and Charcosset A (2005) European maize landraces: genetic diversity, core collection definition and methodology of use. Maydica 50: 225–234.
    Hagenblad J and Nordborg M (2002) Sequence variation and haplotype structure surrounding the flowering time locus FRI in Arabidopsis thaliana. Genetics 161(1): 289–298.
    Harjes CE, Rocheford TR, Bai L et al. (2008) Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science 319: 330–333.
    Hasan M, Friedt W, Pons-Kühnemann J et al. (2008) Association of gene-linked SSR markers to seed glucosinolate content in oilseed rape (Brassica napus ssp. napus). Theoretical and Applied Genetics 116(8): 1035–1049.
    Jansen RC and Stam P (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics 136: 1447–1455.
    Kao CH, Zeng ZB and Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152(3): 1203–1216.
    Kearsey MJ (1998) The principles of QTL analysis (a minimal mathematics approach). Journal of Experimental Botany 49(327): 1619–1623.
    Kojima S, Takahashi Y, Kobayashi Y et al. (2002) Hd3a, a rice ortholog of the Arabidopsis FT gene, promotes transition to flowering downstream of Hd1 under short-day conditions. Plant & Cell Physiology 43(10): 1096–1105.
    Koornneef M, Alonso-Blanco C and Vreugdenhil D (2004) Naturally occurring genetic variation in Arabidopsis thaliana. Annual Review of Plant Biology 55: 141–172.
    Lander ES and Botstein D (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121: 185–199.
    Lisec J, Meyer RC, Steinfath M et al. (2008) Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations. Plant Journal 53(6): 960–972.
    Loudet O, Saliba-Colombani V, Camilleri C et al. (2007) Natural variation for sulfate content in Arabidopsis thaliana is highly controlled by APR2. Nature Genetics 39: 896–900.
    Mackay TFC (2009) Q&A: genetic analysis of quantitative traits. Journal of Biology 8(3): 23.
    McKhann HI, Camilleri C, Bérard A et al. (2004) Nested core collections maximizing genetic diversity in Arabidopsis thaliana. Plant Journal 38(1): 193–202.
    Nemri A, Atwell S, Tarone AM et al. (2010) Genome-wide survey of Arabidopsis natural variation in downy mildew resistance using combined association and linkage mapping. Proceedings of the National Academy of Sciences of the USA 107(22): 10302–10307.
    Nordborg M and Weigel D (2008) Next-generation genetics in plants. Nature 456(7223): 720–723.
    Palaisa KA, Morgante M, Williams M and Rafalskia A (2003) Contrasting effects of selection on sequence diversity and linkage disequilibrium at two phytoene synthase loci. Plant Cell 15: 1795–1806.
    Pflieger S, Lefebvre V and Causse M (2001) The candidate gene approach in plant genetics: a review. Molecular Breeding 7: 275–291.
    Quesada T, Gopal V, Cumbie WP et al. (2010) Association mapping of quantitative disease resistance in a natural population of loblolly pine (Pinus taeda L). Genetics.
    Rafalski A (2010) Applications of single nucleotide polymorphisms in crop genetics. Current Opinion in Plant Biology 5(2): 94–100.
    Ren Z, Zheng Z, Chinnusamy V et al. (2010) RAS1, a quantitative trait locus for salt tolerance and ABA sensitivity in Arabidopsis. Proceedings of the National Academy of Sciences of the USA 107(12): 5669–5674.
    Sillanpää MJ and Arjas E (1998) Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data. Genetics 148: 1373–1388.
    Stich B, Piepho HP, Schulz B and Melchinger AE (2008) Multi-trait association mapping in sugar beet (Beta vulgaris L). Theoretical and Applied Genetics 117(6): 947–954.
    Thornsberry JM, Goodman MM, Doebley J et al. (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nature Genetics 28(3): 286–289.
    Tuinstra MR, Ejeta G and Goldsbrough PB (1997) Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic lines that differ at quantitative trait loci. Theoretical and Applied Genetics 95: 1005–1011.
    Vallejos CE and Tanksley SD (1983) Segregation of isozyme markers and cold tolerance in an interspecific backcross of tomato. Theoretical and Applied Genetics 66: 241–247.
    Yan L, Loukoianov A, Tranquilli G et al. (2003) Positional cloning of the wheat vernalization gene VRN1. Proceedings of the National Academy of Sciences of the USA 100(10): 6263–6268.
    Yano M, Katayose Y, Ashikari M et al. (2000) Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the Arabidopsis flowering time gene CONSTANS. Plant Cell 12(12): 2473–2484.
    Yu J and Buckler ES (2006) Genetic association mapping and genome organization of maize. Current Opinion in Biotechnology 17(2): 155–160.
    Zong XX, Guan JP, Wang SM and Liu QC (2008) Genetic diversity and core collection of alien Pisum sativum L. germplasm. Acta Agronomica Sinica 34: 1518–1528.
    Zhao W, Park EJ, Chung JW et al. (2008) Association analysis of the amino acid contents in rice. Journal of Integrative Plant Biology 51(12): 1126–1137.
    Zhu C, Gore M, Buckler ES and Yu J (2008) Status and prospects of association mapping in plants. Plant Genome 1(1): 5–20.
 Further Reading
    book Acquaah G (2007) Principles of Plant Genetics and Breeding. Victoria, Australia: Blackwell Publishing.
    book De Vienne D (2002) Molecular Markers in Plant Genetics and Biotechnology. Enfield, NH: Science Publishers.
    book Kang MS (2002) Quantitative Genetics, Genomics, and Plant Breeding. Wallingford Oxon, UK: CABI.
    book Lynch M and Walsh B (1998) Genetics and Analysis of Quantitative Traits. Sunderland, MA: Sinauer.
    book Wu R, Ma CX and George Casella G (2007) Statistical Genetics of Quantitative Traits: Linkage, Maps, And QTL. New York: Springer Science.

Related Sub-Topics:

Plant Development | Plant Genetics and Molecular Biology | Quantitative Genetics
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Article Title: Plant Quantitative Traits
 
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Ikram, Sobia, and Chardon, Fabien(Dec 2010) Plant Quantitative Traits. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0002021.pub2]