Quantitative Genetics

  • P. M. Priyadarshan


Most of the traits improved through breeding like yield, height, drought resistance, disease resistance in many species, etc. are quantitative. They are also called polygenic, continuous, multifactorial or complex traits. Quantitative traits are the result of cumulative action of many genes and their interactions with the environment. Thus, it can create a range of individuals that vary among themselves with continuous distribution of phenotypes. A quantitative trait is assumed to be controlled by the cumulative effect of numerous genes, known as quantitative trait loci (QTLs), as per multiple-factor hypothesis by Nilsson-Ehle (a Swedish geneticist in 1909) and East (an American in 1916). Hence, a single phenotypic trait is regulated by several QTLs.


Multiple-factor hypothesis (Nilsson-Ehle) Models, Assumptions and predictions Partition of variance components Linearity The infinitesimal model Types of gene action Quantifying gene action Population mean Phenotypic variance Breeding value Heritability Estimating additive variance and heritability Models for combining ability analysis Biparental progenies (BIP) Polycross Topcross North Carolina designs Diallels Multiple regression analysis Stability analysis Regression approaches Genetic architecture of quantitative traits 

Further Reading

  1. Bazakos C et al (2017) New strategies and tools in quantitative genetics: how to go from the Phenotype to the Genotype. Annu Rev Plant Biol 68:435–455CrossRefGoogle Scholar
  2. Barrett RDH et al (2005) Experimental evolution of Pseudomonas fluorescens in simple and complex environments. Am Nat 166:470–480CrossRefGoogle Scholar
  3. Etterson JR (2004) Evolutionary potential of Chamaecrista fasciculata in relation to climate change. I. Clinical patterns of selection along an environmental gradient in the Great Plains. Evolution 58:1446–1458CrossRefGoogle Scholar
  4. Falconer DS, Mackay TCF (1966) Introduction to quantitative genetics. Longman, LondonGoogle Scholar
  5. Fisher K et al (2004) Genetic and environmental sources of egg size variation in the butterfly Bicyclus anynana. Heredity 92:163–169CrossRefGoogle Scholar
  6. Gienapp P et al (2008) Climate change and evolution: disentangling environmental and genetic responses. Mol Ecol 17:167–178CrossRefGoogle Scholar
  7. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, SunderlandGoogle Scholar
  8. Merilä J et al (2004) Variation in the degree and costs of adaptive phenotypic plasticity among Rana temporaria populations. J Evol Biol 17:1132–1140CrossRefGoogle Scholar
  9. Mousseau TA, Fox CW (eds) (1998) Maternal effects as adaptations. Oxford University Press, New YorkGoogle Scholar
  10. Saastamoinen M (2008) Heritability of dispersal rate and other life history traits in the Glanville fritillary butterfly. Heredity 100:39–46CrossRefGoogle Scholar
  11. Via S, Hawthorne DJ (2005) Back to the future: genetic correlations, adaptation and speciation. Genetica 123:147–156CrossRefGoogle Scholar
  12. Waldmann P (2001) Additive and non-additive genetic architecture of two different-sized populations of Scabiosa canescens. Heredity 86:648–657CrossRefGoogle Scholar
  13. Charmantier A, Garant D (2005) Environmental quality and evolutionary potential: lessons from wild populations. Proc R Soc Biol Sci 272:1415–1425CrossRefGoogle Scholar
  14. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Longman, HarlowGoogle Scholar
  15. Hill WG et al (2008) Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet 4:e1000008CrossRefGoogle Scholar
  16. Macgregor S et al (2006) Bias, precision and heritability of self-reported and clinically measured height in Australian twins. Hum Genet 120:571–580CrossRefGoogle Scholar
  17. Visscher PM et al (2006) Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. Public Libr Sci Genet 2:e41Google Scholar
  18. Visscher PM, Hill WG, Wray NR (2008) Heritability in the genomics era—concepts and misconceptions. Nat Rev Genet 9:255–266CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • P. M. Priyadarshan
    • 1
  1. 1.Erstwhile Deputy DirectorRubber Research Institute of IndiaKottayamIndia

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