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A polygene, multiple factor, multiple gene inheritance, or quantitative gene is a group of non-epistatic genes that together influence a phenotypic trait. The precise loci or identities of the non-allelic genes are often unknown to biologists. Advances in statistical methodology and high throughout sequencing are, however, allowing researchers to locate candidate genes for the trait. These genes are generally pleiotropic as well. The genes that contribute to type 2 diabetes are thought to be mostly polygenes.[1]

Traits with polygenic determinism correspond to the classical quantitative characters, as opposed to the qualitative characters with monogenic or oligogenic determinism. In essence instead of two options, such as freckles or no freckles, there are many variations. Like the color of skin, hair, or even eyes.


Polygenic inheritance occurs when one characteristic is controlled by two or more genes. Often the genes are large in quantity but small in effect.[2] Examples of human polygenic inheritance are height, skin color, eye color and weight. Polygenes exist in other organisms, as well. Drosophila, for instance, display polygeny with traits such as wing morphology,[3] bristle count[4] and many others.

Trait distribution[edit]

The frequency of the phenotypes of these traits generally follows a normal continuous variation distribution pattern. This results from the many possible allelic combinations. When the values are plotted, a bell-shaped curve is obtained. The mode of the distribution represents the optimal, or fittest, phenotype. The more genes are involved, the smoother the estimated curve. However, in this model all genes must code for alleles with additive effects. This assumption is often unrealistic as many genes display epistasis effects which can have unpredictable effects on the distribution of outcomes, especially when looking at the distribution on a fine scale.[5]

Mapping polygenes[edit]

Example of a genome-wide scan for QTL of osteoporosis

Traditionally, mapping polygenes requires statistical tools available to help measure the effects of polygenes as well as narrow in on single genes. One of these tools is QTL-mapping. QTL-mapping utilizes a phenomenon known as linkage disequilibrium by comparing known marker genes with correlated phenotypes. Often, researchers will find a large region of DNA, called a locus, that accounts for a significant amount of the variation observed in the measured trait. This locus will usually contain a large number of genes that are responsible. A new form of QTL has been described as expression QTL (eQTL). eQTLs regulate the amount of expressed mRNA, which in turn regulates the amount of protein within the organism.[6]

Another interest of statistical geneticists using QTL mapping is to determine the complexity of the genetic architecture underlying a phenotypic trait. For example, they may be interested in knowing whether a phenotype is shaped by many independent loci, or by a few loci, and do those loci interact. This can provide information on how the phenotype may be evolving.


  1. ^ Emerging epidemic of type 2 diabetes in youth
  2. ^ Falconer, D. S. & Mackay TFC (1996). Introduction to Quantitative Genetics. Fourth edition. Addison Wesley Longman, Harlow, Essex, UK.
  3. ^ Quantitative Trait Loci Affecting Components of Wing Shape in Drosophila melanogaster
  4. ^ http://service004.hpc.ncsu.edu/mackay/Good_Mackay_site/PUB_files/Mackay%202005%20Trends%20Genet.pdf
  5. ^ Ricki Lewis (2003), Multifactorial Traits, McGraw-Hill Higher Education 
  6. ^ Consoli L, Lefèvre A, Zivy M, de Vienne D, Damerval C (Apr 2002). "QTL analysis of proteome and transcriptome variations for dissecting the genetic architecture of complex traits in maize.". Plant Mol Biol. 48 (5-6): 575–581. doi:10.1023/A:1014840810203. PMID 11999835.