- Additive Genetic Variation refers to the component of genetic variation in a population that results from the cumulative effects of individual alleles and can be transmitted predictably from parents to offspring. It is one of the most important concepts in quantitative genetics because additive genetic variation determines the extent to which differences in a trait can respond to selection across generations.
- Many biological traits are influenced by numerous genes, with each allele contributing a relatively small effect to the phenotype. When the effects of alleles combine approximately additively, their cumulative influence produces differences among individuals. These differences form the basis of additive genetic variation.
- Additive genetic variation is commonly represented by additive genetic variance, denoted as VAV_A. Phenotypic variance in a population can be partitioned into genetic and environmental components, and genetic variance can further include additive, dominance, and epistatic variance. A simplified model can be expressed as VP=VA+VD+VI+VEV_P = V_A + V_D + V_I + V_E, although the exact variance decomposition depends on the population, trait, mating system, and statistical model.
- The key feature of additive genetic variation is that its effects can be transmitted from parents to offspring in a predictable statistical manner. This makes additive variance particularly important for predicting the response of a population to selection.
- The genetic contribution that an individual is expected to transmit to its offspring is closely related to its breeding value. Breeding value represents the sum of the average effects of the alleles carried by an individual and is therefore closely connected to additive genetic variation.
- For example, suppose a quantitative trait is influenced by many genetic loci and individuals differ because they carry different alleles at these loci. If the effects of these alleles are largely additive, an individual’s breeding value can be estimated from the combination of alleles it carries. Individuals with higher breeding values are expected, on average, to produce offspring with higher genetic potential for the trait.
- Additive genetic variation should be distinguished from dominance variation. Dominance occurs when the phenotype associated with a heterozygous genotype differs from what would be expected from the simple sum of the effects of the two alleles. Dominance variance can contribute to phenotypic differences but is not transmitted between generations in the same predictable manner as additive genetic effects.
- Additive genetic variation also differs from epistatic variation, which results from interactions between alleles at different genetic loci. Epistasis can influence phenotypes substantially, but the effects of interactions are generally more difficult to predict and transmit than additive effects.
- The importance of additive genetic variation can be understood through narrow-sense heritability. Narrow-sense heritability, usually denoted h2h^2, is the proportion of phenotypic variance attributable to additive genetic variance:
- h2=VA/VPh^2 = V_A / V_P
- A high narrow-sense heritability indicates that additive genetic differences explain a relatively large proportion of the observed phenotypic variation in a particular population and environment. A low value indicates that additive genetic variation accounts for a smaller proportion of phenotypic variance. Heritability is population- and environment-specific and should not be interpreted as the percentage of an individual’s phenotype that is genetic.
- Additive genetic variation is central to the breeder’s equation, which predicts the response to selection:
- R=h2SR = h^2S
- Here, RR is the response to selection, h2h^2 is narrow-sense heritability, and SS is the selection differential. The equation shows why additive genetic variation is essential for evolutionary and breeding responses. If there is no additive genetic variation for a trait, directional selection based on that trait will generally produce little or no predictable response in the population mean.
- The selection differential measures the difference between the mean phenotype of selected individuals and the mean phenotype of the original population. When the selected phenotype is associated with additive genetic differences, part of this difference can be transmitted to subsequent generations.
- The response to selection is therefore closely related to additive genetic variance. A population may contain substantial phenotypic variation but show a limited response to selection if most of that variation is environmental or due to genetic components that are not predictably transmitted.
- Additive genetic variation is especially important for quantitative traits. Traits such as height, body weight, milk production, crop yield, growth rate, flowering time, and many physiological characteristics are influenced by many genetic loci and environmental factors. Additive effects across these loci can generate continuous distributions of phenotypes.
- The genetic architecture of a quantitative trait can contain alleles with different effect sizes. Some loci may have relatively large effects, while many others may have small effects. The combined contribution of these alleles can produce substantial additive genetic variation even when individual loci have only modest effects.
- Additive genetic variation can also arise from differences in allele frequencies. If different alleles have different average effects on a trait, variation in their frequencies among individuals creates differences in breeding values. Changes in allele frequencies caused by natural selection, artificial selection, genetic drift, mutation, and gene flow can therefore alter the amount and distribution of additive genetic variation.
- The relationship between additive genetic variation and allele frequencies is influenced by the effects of alleles and the genetic structure of the population. For a simple locus with additive allele effects, additive variance depends on allele frequencies and the magnitude of the allele effect. For traits controlled by many loci, the total additive variance reflects the combined contribution of many loci.
- Additive genetic effects are sometimes described using average effects of alleles. The average effect of an allele depends on the population in which it is evaluated because it reflects allele frequencies and genetic background. Consequently, additive genetic variance is not an absolute property of a gene or allele but a statistical property of a population for a particular trait and context.
- The concept of an allelic substitution effect is also important in quantitative genetics. It describes the expected change in a trait associated with replacing one allele with another under a specified population model. These effects contribute to estimates of additive genetic effects and breeding values.
- Additive genetic variation is closely connected to Mendelian sampling. Each offspring receives a random sample of alleles from its parents, meaning that even offspring from the same parents can differ genetically. Mendelian sampling contributes to differences between an individual’s breeding value and the average breeding value expected from its parents.
- Pedigree information can be used to estimate additive genetic relationships among individuals. Closely related individuals are expected to share more additive genetic material than unrelated individuals. Quantitative genetic models can use these relationships to estimate additive genetic variance and predict breeding values.
- Modern genetics also uses genomic information to measure additive relationships. A genomic relationship matrix estimates the realized genetic similarity among individuals using genome-wide markers such as single-nucleotide polymorphisms (SNPs). Genomic relationships can provide more precise information about the actual genetic sharing between individuals than pedigree relationships alone.
- Additive genetic variation is therefore fundamental to genomic selection. Genomic selection uses genome-wide marker information to predict the genetic merit of individuals. Statistical models estimate genomic estimated breeding values (GEBVs) based largely on additive genetic effects, allowing breeders to select individuals before all relevant traits can be directly measured.
- In animal and plant breeding, additive genetic variation determines much of the potential for long-term genetic improvement. Breeders aim to identify individuals with favorable breeding values and use them as parents of the next generation. Over successive generations, this can increase the population mean for economically or biologically important traits.
- The rate of genetic improvement depends not only on additive genetic variation but also on selection intensity, generation interval, population size, mating design, and the accuracy with which breeding values are estimated. These factors are incorporated into concepts such as genetic gain and the breeder’s equation.
- Additive genetic variation also plays a major role in evolutionary biology. Natural selection can change the mean phenotype of a population when phenotypic differences are associated with heritable additive genetic differences. The amount of additive genetic variation therefore influences the capacity of populations to evolve in response to selection.
- The evolutionary response of multiple traits depends on the pattern of additive genetic covariance among those traits. Genetic covariance occurs when additive genetic effects influencing two traits are correlated. This can produce a genetic correlation, meaning that selection on one trait may cause correlated changes in another.
- The matrix describing additive genetic variances and covariances among multiple traits is known as the G-matrix or additive genetic variance–covariance matrix. The G-matrix is an important tool in multivariate quantitative genetics because it describes the genetic constraints and opportunities for evolutionary change across multiple traits.
- Additive genetic variation can also be influenced by linkage disequilibrium (LD). When alleles at different loci are associated non-randomly, their combined effects can influence estimates of additive variance. Recombination changes these associations over generations and can therefore influence the genetic architecture and predictability of quantitative traits.
- The distinction between additive genetic variation and total genetic variation is important. Total genetic variance can include additive, dominance, and epistatic components. Although all of these components may contribute to phenotypic differences, additive genetic variance is particularly important for predicting the resemblance between parents and offspring and the response to selection.
- Environmental conditions can also affect the expression of additive genetic variation. Genotype–environment interaction (G×E) occurs when different genotypes respond differently to environmental conditions. As a result, the magnitude of additive genetic variance and the ranking of breeding values can differ between environments.
- For example, a crop variety may have a high breeding value for yield under favorable growing conditions but perform less well under drought. Another variety may have a lower yield under favorable conditions but maintain relatively high performance under water-limited conditions. Such differences demonstrate why additive genetic effects should be evaluated within relevant environmental contexts.
- Additive genetic variation is also important in conservation genetics. Small populations may lose genetic variation through genetic drift, inbreeding, and population bottlenecks. Loss of additive genetic variation can reduce a population’s capacity to respond evolutionarily to environmental change or selection.
- Effective population size influences the maintenance of genetic variation. Larger effective populations generally retain genetic diversity more effectively than small populations, while small effective populations are more strongly affected by genetic drift and inbreeding.
- In human genetics, additive genetic models are frequently used to study complex traits and estimate genetic contributions to phenotypic differences. Many human traits are influenced by thousands of genetic variants, each contributing a small effect. Genome-wide association studies can identify variants associated with traits, while quantitative genetic and genomic models can estimate aggregate additive effects.
- Polygenic scores and polygenic risk scores use information from many genetic variants to estimate an individual’s genetic predisposition for a trait or disease. These approaches often rely heavily on additive assumptions, although real biological systems can also contain dominance, epistasis, gene–environment interaction, and other complexities.
- Additive genetic variation should therefore be viewed as a statistical and population-level concept rather than a simple statement that genes act independently. Real genomes contain complex networks of genes, regulatory elements, interactions, and environmental influences. Nevertheless, additive models are extremely useful because they provide a practical framework for predicting inheritance and selection response.
- Understanding additive genetic variation provides a bridge between genetic variation, phenotypic variation, heritability, breeding value, quantitative genetics, and evolutionary genetics. It explains why some phenotypic differences are transmitted predictably from parents to offspring and why populations with sufficient additive genetic variance can respond to natural or artificial selection.
- Additive genetic variation is ultimately one of the central concepts in quantitative genetics. It provides the statistical foundation for estimating breeding values, predicting response to selection, understanding genetic resemblance among relatives, improving crops and livestock, studying complex traits, and explaining how populations evolve over generations.
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