Introduction
The inheritance of many traits in plants, animals and humans cannot be explained by simple Mendelian genetics. While Mendel described inheritance of characters controlled by a single gene pair (monogenic traits), many important characters — such as height, body weight, yield, skin color and intelligence — show continuous variation and are controlled by several genes. The Multiple Factor Hypothesis (also called polygenic inheritance) explains how several genes, each with a small effect, produce continuous variation in a population.
Definition
The Multiple Factor Hypothesis states that quantitative traits are governed by many genes (polygenes), each contributing a small and additive effect to the phenotype, and that the phenotype is further influenced by environmental factors.
- Each gene contributes additively.
- No single gene has a dominant, overriding effect.
- The combined action of all genes plus environment produces continuous variation.
Historical background
Key milestones:
- Francis Galton (late 19th century): Observed continuous variation for traits like height and intelligence and emphasized statistical study of such traits.
- Nilsson-Ehle (1908): Demonstrated polygenic inheritance in wheat kernel color — a classic experimental proof that several genes can produce a continuous range of phenotypes.
- East and others (early 20th century): Extended polygenic concepts to maize and other crops, and helped formulate the multiple factor hypothesis used in quantitative genetics.
Basic principles of the hypothesis
Three central ideas:
- Polygenic control: A trait is controlled by two or more genes (polygenes).
- Additive effects: Each dominant allele contributes a small, usually equal, increment to the trait; recessive alleles contribute little or nothing.
- Environmental influence: Non-genetic factors (nutrition, climate, management) interact with genotype to shape the final phenotype.
Classic example: Nilsson-Ehle’s wheat kernel color experiment
Nilsson-Ehle crossed deep-red kernel wheat (homozygous for several dominant color genes, e.g., AABBCC) with white-kernel wheat (aabbcc). The F1 seeds were intermediate in color (AaBbCc). In the F2, a wide continuous range of kernel colors appeared — from deep red to white — forming a distribution that resembled a normal (bell-shaped) curve.
This result supported the idea that multiple genes contribute additively: more dominant alleles give a deeper red color, fewer dominant alleles give lighter color, and the combination of many such small effects yields continuous variation.
Characteristics of polygenic (multiple-factor) traits
- Continuous variation: Individuals vary along a gradient rather than in discrete classes.
- Normal distribution: Phenotypic values typically approximate a bell-shaped curve when plotted for large populations.
- Quantitative measurement: Traits are measured on a numerical scale (e.g., height in cm, yield in kg/plant).
- Environment and genotype interaction: The same genotype can produce different phenotypes under different environments.
- Multiple genes with small effects: Often tens or even hundreds of loci influence a complex trait.
Genetic architecture and important caveats
While the hypothesis assumes additive and equal effects for simplicity, real traits may show deviations:
- Variable effect sizes: Some loci have larger effects (major genes) while many have minor effects.
- Dominance: Heterozygote effects sometimes differ from the simple additive expectation.
- Epistasis: Interactions between genes can alter trait expression in non-additive ways.
- Gene-environment interaction: Environmental sensitivity may differ among genotypes, complicating selection.
Statistical implications and quantitative genetics
The multiple factor hypothesis laid the foundation for quantitative genetics and biometrical methods. Key concepts include:
- Phenotypic variance (Vp): Total observed variation in a trait, that can be partitioned into genetic (Vg) and environmental (Ve) components.
- Heritability: The proportion of phenotypic variance due to genetic variance; important for predicting response to selection.
- Selection response: Predictable improvement under selection when genetic variance and selection intensity are known.
Applications
- Plant breeding: Improving yield, quality, stress tolerance and other complex traits that are polygenic.
- Animal breeding: Selection for milk yield, growth rate, fertility and carcass traits.
- Human genetics: Understanding complex traits and disease susceptibility (e.g., height, diabetes risk).
- Evolutionary biology: Explaining continuous variation and gradual evolution under selection.
Limitations of the classical multiple factor model
- Assumes equal additive effects for simplicity, which is rarely true in reality.
- Ignores or downplays dominance and epistasis, both of which can be important.
- Environmental influence can be complex and difficult to partition accurately.
- Modern genetics (molecular QTL mapping, GWAS) reveals many loci with varied effect sizes and interactions, requiring more nuanced models.
Conclusion
The Multiple Factor Hypothesis provides a simple, powerful explanation for continuous variation by proposing that many genes additively influence a trait while the environment also plays a role. It remains a cornerstone of quantitative genetics and plant/animal breeding, although modern genomics has refined our understanding by revealing variable effect sizes, gene interactions and complex gene-environment relationships.
Notes on figures / images
- Add an image of the F2 kernel color gradient (Nilsson-Ehle) or a photograph of wheat kernels to visually illustrate how multiple alleles change color intensity. Place it near the Nilsson-Ehle section.
- Add a bell-shaped frequency curve illustrating normal distribution of a quantitative trait — place it near the Characteristics section.
- Include a simple diagram of partitioning variance (Vp = Vg + Ve) next to the Statistical implications section.
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