What is the difference between multiple alleles and additive alleles

An example is human height: we have differences in height down to fractions of an inch, rather than being either 4 ft, 5 ft, or 6ft tall. Quantitative traits are in contrast to discrete traits where the trait has only a few possible phenotypes which fall into discrete classes ie, peas are either round or wrinkly, and there are no in-between phenotypes.

Multiple allelism : where a particular gene has more than two alleles circulating in the population. An example is human blood type described above where the single gene controlling blood type can be have an A, B, or O allele. This video describes the difference between polygenic traits and multiple allelism: And this video works through some real examples of multiple allelism and quantitative traits stop at min : Gene-by-gene interactions : where the phenotype associated with one allele depends on the allele s present at another gene.

This is different from a quantitative trait where alleles at multiple genes are additive. The gene-by-gene inheritance pattern can also be called epistasis. The take home-message on gene-by-gene interactions is that this phenomenon alters the expected phenotypic ratios of a Mendelian dihybrid cross to a different pattern.

This video gives an overview of a gene-by-gene interaction that controls coat color in mice: Pleiotropy is the phenomenon where a single gene influences multiple, seemingly unrelated traits.

Gene by environment interactions : where the environment plays a role in determining phenotype controlled by alleles. An example is human height which is also an example of a quantitative trait where childhood nutrition plays a role in an adult height. We have gotten taller as a species in the last years mostly not because of changes in our alleles but due to access to better nutrition in much of the world. This is also known as the theory of polygeny for evolution. The variations are according to the specific requirement and needs of the individual or species.

These are essentially responsible for adaptive changes. The polygenes are majorly applied or utilized by the plant breeders. The genetic variability is stored in the form of polygenic complexes.

Segregation and recombination of polygenic genes after interbreeding results in the release of hidden variability in a species. Humans are diploid creatures. This means that for every chromosome in the body, there is another one to match it.

However, there are organisms that have more than two sets of chromosomes. The condition is called polyploidy. Know more about this topic through this tutorial Read More. Genes are expressed through the process of protein synthesis. This elaborate tutorial provides an in-depth review of the different steps of the biological production of protein starting from the gene up to the process of secretion.

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Table of Contents. Biology definition: Polygenic inheritance is a non-Mendelian pattern of inheritance in which a particular trait is produced by the interaction of genes at many loci i. Compare: monogenic inheritance. Related term: polygene. Polygenic inheritance is also involved in quantitative traits , in which multiple gene loci each contribute in a similar way to the phenotype so that the total number of contributing alleles determines the phenotype. In humans, height, weight, and skin color are examples of quantitative traits.

For instance, the height of an adult human is determined by not just a single gene but by more than genes apart from the other non-genetic factors such as environment and nutrition.

In quantitative traits, the Mendelian ratios are replaced by a normal distribution curve, with the two ends of the curve defined by the two extremes possible for the phenotype. The trait is a result of the cumulative effects of many genes Mendelian inheritance.

Polygenic inheritance. Monogenic inheritance. Alleles that contribute to continuous variation Allelic pair. Contributing alleles. Non-contributing alleles. Genes producing a phenotype when expressed together Genome. Which of the following shows polygenic inheritance? Free earlobes as a dominant trait vs. Pink flower color trait as a result of a cross between white-flowering and red-flowering plants. Varying skin colors ranging from very dark to very light. In humans, the height is determined by Send Your Results Optional.

Your Name. To Email. Time is Up! Genetics and Evolution Humans are diploid creatures. Several hypotheses have been proposed to explain the possible genetic mechanisms leading to such variance signals. However, little is known about what causes these signals, or whether this genetic variance-heterogeneity reflects mechanisms of importance in natural populations.

Here, fine-mapping of this association reveals that the vGWA emerges from the effects of three independent genetic polymorphisms that all are in strong LD with the markers displaying the genetic variance-heterogeneity. Our results show that an extended LD across a complex locus including multiple functional alleles can lead to a variance-heterogeneity between genotypes in natural populations.

Further, they provide novel insights into the genetic regulation of ion homeostasis in A. Most biological traits vary in natural populations, and understanding the genetic basis of this variation remains an important challenge. Genome-wide association GWA studies have emerged as a powerful tool to address this challenge by dissecting the genetic architecture of trait variation into the contribution of individual genes. This contribution has traditionally been measured as the difference in the phenotypic means between groups of individuals with alternative genotypes at one, or multiple loci.

However, instead of altering the trait mean, certain loci alter the variability of the trait. Here, we describe the genetic dissection of one such variance-controlling locus that drives variation in leaf molybdenum concentrations amongst natural accessions of Arabidopsis thaliana.

This illustrates that multi-allelic genetic architectures can hide large amounts of additive genetic variation, and that it is possible to uncover this hidden variation using the appropriate experimental designs and statistical methods described here. PLoS Genet 11 11 : e Editor: Gregory P. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

All other relevant genotype data that is not already publicly available are within the paper and its Supporting Information files. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Genome Wide Association GWA analysis is a powerful approach to study the genetic basis of complex traits in natural populations. It is widely used to study the genetics of human disease, but is equally useful in studies of other populations.

For example, it has been used to dissect the genetics of traits of importance in agricultural applications see e. The standard GWA approach screens the genome for loci where the alternative genotypes differ significantly in the mean for the trait or traits of interest. Although hundreds of loci have been found to affect a variety of quantitative traits using this strategy, it has become clear that for most complex traits this additive approach fails to uncover much of the genetics contributing to the phenotypic variation in the populations under study.

It is therefore important to explore the genetics of such traits beyond additivity [ 8 ]. An alternative way that genetic variation can contribute to the phenotypic variability in a population is via direct genetic control of the variance [ 9 ]. To identify an individual locus that makes such direct contributions to the trait variance, a statistical test is used to identify significant differences in the phenotypic variance between the groups of individuals that carry alternative alleles at the locus.

When such a variance difference exists between the genotypes at a locus, the locus displays a genetic variance-heterogeneity. These loci are therefore often referred to as variance-heterogeneity loci or vQTL for short [ 10 ].

By performing genome-wide analyses to identify such variance-heterogeneity loci, novel trait associations and alternative genetic mechanisms involved in shaping the total phenotypic variance in the analyzed populations can be identified [ 8 , 10 ]. The direct genetic control of the phenotypic variance is a topic that has been studied for many years in quantitative genetics with a primary focus on its potential contributions to adaptation in natural populations and agricultural selection programs.

Theoretical and empirical work has increased our understanding of how individual loci that display variance, rather than mean, differences between genotypes might cause phenomena such as fluctuating asymmetry, canalization and genetic robustness [ 9 , 11 ]. Empirical work now also supports the general principle that a direct genetic control of the variance is an inherent feature of biological networks and individual genes see [ 12 ] for a review and that it contributes to both capacitation [ 13 , 14 ] and maintenance of developmental homeostasis [ 15 ].

Although it was already shown in the s that it was possible to map vQTL [ 16 ], this approach has only recently been more widely adopted to explore the role of variance-heterogeneity loci in, for example, environmental plasticity [ 15 ], canalization [ 17 ], developmental stability [ 18 ], and natural variation in stochastic noise [ 19 ]. With the advent of GWA analysis, and the later realization that standard additive models leave much of the genetic variance in the analyzed populations uncovered [ 8 ], there has been an increased interest in exploring the contribution of genetic variance-heterogeneity to the phenotypic variability in complex traits [ 10 , 20 ].

Several recent studies in, for example, humans [ 21 ], plants [ 7 , 19 , 22 ], Drosophilia melanogaster [ 23 ] and yeast [ 24 ] have shown that part of this previously unexplored heritable genetic variation, beyond the narrow-sense heritability, can be uncovered by re-analyzing existing GWA datasets using methods to detect differences in trait variance variance-heterogeneity GWA or vGWA for short between genotypes [ 20 — 22 ].

Previously, we re-analyzed ionomic data from a GWA study based on 93 wild-collected A. This association was found near the MOT1 Molybdate transporter 1 gene [ 22 ].

Importantly, this locus did not affect the mean leaf molybdenum concentrations in this dataset [ 2 , 22 ]. Molybdenum is an essential element for plant growth due to its role as a part of the molybdopterin cofactor that is required by several critical enzymes [ 25 ]. Both deficiency and excess of molybdenum have an impact on plant development [ 26 ]. The ability of plants to acquire minerals from the soil, and regulate their levels in the plant, depends on complex biochemical and regulatory pathways.

The genetic architecture of such ionomics traits is thus complex [ 27 ]. To date, several studies in A. Further, GWA analysis has also been used to identify both candidate loci and functional polymorphisms contributing to natural variation in these ionomics traits [ 2 , 3 , 5 , 6 , 43 ].

Here, we quantified molybdenum concentrations in leaves in a larger collection of natural A. We uncovered that a complex multi-locus, multi-allelic genetic architecture leads to the genetic variance-heterogeneity at this locus. Several polymorphisms in three closely linked loci were significantly associated with the mean molybdenum concentration in the leaf, and due to an extended LD between the minor alleles at these loci, their joint effects cause the genetic variance-heterogeneity at this locus.

By dissecting this variance-heterogeneity locus in detail, we both reveal the genetic complexity of an adaptive locus for molybdenum homeostasis in A. The first GWA analysis searching for genetic effects on mean leaf molybdenum concentrations [ 2 ] did not uncover any genome-wide significant associations for this trait.

This was surprising as it was known from earlier QTL studies that a strong polymorphism affecting this trait was segregating in the analyzed population [ 36 ]. To investigate this further we measured the molybdenum concentration in leaves from at least six replicate plants of natural A. In this larger dataset, we detected several SNPs associated with the mean leaf molybdenum concentrations in, or near, the MOT1 locus Fig 1.

The minor alleles for some associated SNPs increased the mean phenotype, whereas others decreased it relative to the major allele Table 1 ; Fig 1B. In our earlier study we identified a genome-wide significant genetic variance-heterogeneity for leaf molybdenum concentrations at this same locus containing MOT1 [ 22 ]. Here, we therefore aim to functionally dissect this region further to obtain a deeper understanding of the genetic mechanisms controlling the range of leaf molybdenum concentrations observed in A.

B Region on chromosome 2 where a highly significant genetic variance-heterogeneity was detected for the leaf molybdenum concentrations. A vGWA analysis of leaf molybdenum concentrations in the accessions, searching for genetic effects on the between accession variance heterogeneity S1 Text , revealed several SNP markers that displayed a genome-wide significant genetic variance-hetereogeneity in the region of the reported vQTL near the MOT1 gene [ 22 ].

A 53 bp deletion in the promoter-region of this gene has earlier been shown to decrease MOT1 expression, leading to low concentrations of molybdenum in the plant [ 36 , 44 ]. To complement our SNP-marker dataset with this known, and other potentially functional, structural promoter polymorphisms segregating in the analyzed population, we screened the promoter region of MOT1 using PCR fragment size differentiation see Methods for details and identified in total six non-coding structural polymorphisms Fig 2 , S1 Table.

These were then genotyped in of the phenotyped accessions. Two of the six segregating MOT1 promoter polymorphisms were significantly associated with mean leaf molybdenum concentration.

Baxter et al. The duplication exists in two distinct variants alleles differing by four polymorphisms: three point-mutations and one 4bp insertion DUP and DUP in Fig 2B.

To our knowledge, this duplication has not previously been described in the literature. Using the same assay, we tested 6 accessions carrying the high-molybdenum DUP allele. Although these results do not provide direct functional evidence that the DUP allele increases the molybdenum concentration in the leaves via an increased expression of MOT1 in the roots, it suggests this as a plausible mechanism worth further explorations.

Together, our results provide further evidence that allelic heterogeneity at MOT1 is an important component of the genetic architecture of natural variation in leaf molybdenum concentrations. Multiple associations to loci with either mean- or variance differences between genotypes for leaf molybdenum concentrations were uncovered in the single-locus GWA and vGWA analyses. To confirm the independence of these effects, and evaluate their joint contributions to leaf molybdenum, we fitted all markers SNPs and structural variants on chromosome 2 in a generalized linear model to the mean leaf molybdenum concentration using the LASSO method [ 45 ].

This penalized maximum likelihood regresses the effects of polymorphisms that make no, or only a minor, independent contribution to the trait towards zero and highlights the markers that jointly make the largest contribution to the trait variation. The penalty in the analyses was chosen so that all highlighted polymorphisms in the final model also have a genome-wide significant effect in the earlier GWA or vGWA analyses S1 Fig ; see Methods section for details. In this way, the LASSO method picks up the genome-wide significant polymorphisms that have independent effects on the trait.

Under certain conditions, multi-allelic genetic architectures can lead to a genetic variance-heterogeneity in association-analyses based on bi-allelic SNPs see e. Here, we will show that the genetic variance-heterogeneity we detected for vBLOCK is due to a multi-allelic genetic architecture that closely resembles this example.

This results in a situation similar to that in the example above: multiple alleles with different directional phenotypic effects are unevenly distributed across the two variants of vBLOCK. This estimate is similar to that reported in earlier studies 0. The first GWA analysis for leaf molybdenum concentrations by Atwell et al.

The later vGWA study by Shen et al. Using the variance decomposition proposed by Shen et al. The total amount of genetic variance associated with the vGWA signal here is thus comparable to that of Shen et al. Here, we functionally explore the associations outside of the coding and regulatory regions of MOT1 in more detail to identify additional functional candidate polymorphisms and genes for the regulation of molybdenum homeostasis.

Four T-DNA alleles of five different genes in the region around SNP 1 10,, bp; S2 Fig ; S4 Table were evaluated for leaf molybdenum concentrations, but in none of these did the leaf molybdenum concentrations differ from that of the wild-type Col We also evaluated 19 mutants with T-DNA insertions in 14 genes around SNP 2 11,, bp; Fig 4 ; S4 Table , and identified two with significantly altered leaf molybdenum concentrations compared to the wild-type Col-0 Table 3.

Interestingly, as well as low molybdenum, the T-DNA knockout allele of this gene has a slightly increased leaf copper concentration compared to wild-type 3.

From the literature it is known that copper and molybdenum homeostasis are related and that copper depleted Brassica napus plants have up-regulated expression of both copper transporter genes and MOT1 [ 46 ].

We measured the mean leaf molybdenum concentrations for available T-DNA insertional alleles and compared them to the wild-type Col Common approaches to dissect the genetics of complex traits in segregating populations are linkage mapping and association studies. These studies aim to identify the loci in the genome where genetic polymorphisms control the phenotypic variance in the studied populations. This is achieved by screening for significant genotype-phenotype associations across a large number of genotyped polymorphic markers in the genome.

The most common statistical models used in such analyses aim to identify loci with significant mean phenotype differences between the genotypes at individual loci. Although such models are powerful for capturing much genetic variance in populations, they have limited power when challenged with more complex genetic architectures including multiple-alleles, variance-heterogeneity and genetic interactions [ 8 , 47 ].

It is therefore important to also develop, and test, methods that explore statistical genetic models reaching beyond additivity when aiming for a more complete dissection of the genetic architecture of complex traits.

The genetic architecture of variation in mean leaf molybdenum concentrations has earlier been explored using GWA analyses in a smaller set of 93 wild collected A. No genome-wide significant associations were found for leaf molybdenum, which was surprising given that the trait has a high heritability [ 36 , 43 ] and that several polymorphisms in MOT1 are known to contribute to natural variation in this trait [ 36 , 37 ].

When we re-analyzed this data using a method to detect variance differences between genotypes, a strong genetic variance-heterogeneity was identified near the MOT1 gene [ 22 ]. Here, we studied a larger set of A. This is the first successful fine-mapping and replication of a variance-heterogeneity locus on a genome-wide significance scale and in an independent dataset.

In this larger dataset we also identified four loci that independently alter the mean concentration of leaf molybdenum. The minor allele at one of these DEL 53 was a deletion in the promoter region of MOT1 previously identified using an F 2 bi-parental mapping population. This deletion allele decreases the concentration of molybdenum in leaves by down-regulating MOT1 transcription [ 36 ].

One allele DUP was an insertion polymorphism in the promoter region of MOT1 , and our analyses revealed that accessions carrying this polymorphism have higher expression of MOT1 compared to the Col-0 accession that does not carry this polymorphism. The regulation of molybdenum concentrations in the leaves is hence due to multiple alleles in a gene known to regulate molybdenum uptake, MOT1 , but also alleles at other neighboring loci that have earlier not been found to contribute to molybdenum homeostasis in A.

These results support and refine earlier results from QTL and functional analyses of the MOT1 region that highlighted the central importance of the MOT1 region in the regulation of molybdenum homeostasis in natural populations and also suggested that the natural variation in this trait might have a multi-allelic background [ 36 , 37 ].

As it is well known that major loci affecting traits under selection often evolve multiple mutations affecting the phenotype, and that allelic heterogeneity is an important driver of evolution in natural A.

Striking examples of allelic heterogeneity in natural A. Multi-allelic loci are, however, a major challenge in traditional GWA analyses [ 48 ]. It is therefore valuable to note that such loci, under certain conditions, can lead to a genetic variance-heterogeneity see e.

The following two examples illustrate how genetic variance-heterogeneity can arise under i classic allelic heterogeneity where multiple loss-of-function alleles have evolved independently at a locus, and ii general multi-allelic architectures where the alleles affect the phenotype to various degree and hence either increase or decrease the phenotype relative to that of the major allele.

Below, we illustrate how the distribution of the minor alleles across the SNP genotypes will alter the differences in phenotypic mean and variances between the genotypes, and hence affect the power to detect them in GWA and vGWA analyses. Hence, the vGWA analysis is likely to be useful for identifying loci under a set of different scenarios ranging from classic allelic heterogeneity to loci with multiple alleles having distinct effects on the phenotype.

As shown here, the genetic variance-heterogeneity for vBLOCK was detected based on its genetic variance-heterogeneity due to its close resemblance to scenario c above Fig 2A. Here, we dissected a locus displaying a genetic variance-heterogeneity for the molybdenum concentration in A.