An example of a polygenic trait is human skin color. Many genes factor into determining a person's natural skin color, so modifying only one of those genes changes the color only slightly. Many disorders with genetic components are polygenic, including autism, cancer, diabetes and numerous others. Most phenotypic characteristics are the result of the interaction of multiple genes.
Examples of disease processes generally considered to be results of multifactorial etiology:
Adult onset diseases
Multifactorially inherited diseases are said to constitute the majority of all genetic disorders affecting humans which will result in hospitalization or special care of some kind .
The continuous distribution of traits such as height and skin colour described above reflects the action of genes that do not quite show typical patterns of dominance and recessiveness. Instead the contributions of each involved locus are thought to be additive. Writers have distinguished this kind of inheritance as polygenic, or quantitative inheritance.
Thus, due to the nature of polygenic traits, inheritance will not follow the same pattern as a simple monohybrid or dihybrid cross. Polygenic inheritance can be explained as Mendelian inheritance at many loci, resulting in a trait which is normally-distributed. If n is the number of involved loci, then the coefficients of the binomial expansion of (a + b)2n will give the frequency of distribution of all n allele combinations. For a sufficiently high n, this binomial distribution will begin to resemble a normal distribution. From this viewpoint, a disease state will become apparent at one of the tails of the distribution, past some threshold value. Disease states of increasing severity will be expected the further one goes past the threshold and away from the mean.
There are, however, many traits and disease states where many genes are involved, but their contribution is not equal, or additive.
The more genes involved in the cross, the more the distribution of the genotypes will resemble a normal, or Gaussian distribution. This shows that multifactorial inheritance is polygenic, and genetic frequencies can be predicted by way of a polyhybrid Mendelian cross. Phenotypic frequencies are a different matter, especially if they are complicated by environmental factors.
The paradigm of polygenic inheritance as being used to define multifactorial disease has encountered much disagreement. Turnpenny (2004) discusses how simple polygenic inheritance cannot explain some diseases such as the onset of Type I diabetes mellitus, and that in cases such as these, not all genes are thought to make an equal contribution.
The assumption of polygenic inheritance is that all involved loci make an equal contribution to the symptoms of the disease. This should result in a normal curve distribution of genotypes. When it does not, then idea of polygenetic inheritance cannot be supported for that illness.
If multifactorial inheritance is indeed the case, then the chance of the patient contracting the disease is reduced if only cousins and more distant relatives have the disease. It must be stated that while multifactorially-inherited disease tends to run in families, inheritance will not follow the same pattern as a simple monohybrid or dihybrid cross.
If a genetic cause is suspected and little else is known about the illness, then it remains to be seen exactly how many genes are involved in the phenotypic expression of the disease. Once that is determined, the question must be answered: if two people have the required genes, why some people still don't express the disease. Generally, what makes the two individuals different are likely to be environmental factors. Due to the involved nature of genetic investigations needed to determine such inheritance patterns, is not usually the first avenue of investigation one would choose to determine etiology.
Psychiatry has determined, often without sufficient evidence, that mental illness follows this pattern. The problem with mental illness itself is that most diagnoses are largely subjective, and even the nosologies in DSM-IV are not widely agreed upon. The example most often cited as an example of a multifactorial mental illness is schizophrenia, however, no genes have been isolated to date. It has been said that genetic causes of mental illness is being emphasised at the expense of paying sufficient attention to environmental factors, especially in the field of biopsychiatry.
More often than not, investigators will hypothesise that a disease is multifactorially heritable, along with a cluster of other hypotheses when it is not known what causes the disease.
Typically, QTLs underlie continuous traits (those traits that vary continuously - the trait could have any value within a range - e.g., height) as opposed to discrete traits (traits that have two or several character values - e.g., eye colour or smooth vs. wrinkled peas used by Mendel in his experiments).
Moreover, a single phenotypic trait is usually determined by many genes. Consequently, many QTLs are associated with a single trait.
A quantitative trait locus (QTL) is a region of DNA that is associated with a particular phenotypic trait - these QTLs are often found on different chromosomes. Knowing the number of QTLs that explains variation in the phenotypic trait tells us about the genetic architecture of a trait. It may tell us that plant height is controlled by many genes of small effect, or by a few genes of large effect.
Another use of QTLs is to identify candidate genes underlying a trait. Once a region of DNA is identified as contributing to a phenotype, it can be sequenced. The DNA sequence of any genes in this region can then be compared to a database of DNA for genes whose function is already known.
In a recent development, classical QTL analyses are combined with gene expression profiling i.e. by DNA microarrays. Such expression QTLs (e-QTLs) describe cis- and trans-controlling elements for the expression of often disease-associated genes. Observed epistatic effects have been found beneficial to identify the gene responsible by a cross-validation of genes within the interacting loci with metabolic pathway- and scientific literature databases.
QTL mapping is the statistical study of the alleles that occur in a locus and the phenotypes (physical forms or traits) that they produce. Because most traits of interest are governed by more than one gene, defining and studying the entire locus of genes related to a trait gives hope of understanding what effect the genotype of an individual might have in the real world.
Statistical analysis is required to demonstrate that different genes interact with one another and to determine whether they produce a significant effect on the phenotype. QTLs identify a particular region of the genome as containing a gene that is associated with the trait being assayed or measured. They are shown as intervals across a chromosome, where the probability of association is plotted for each marker used in the mapping experiment.
The QTL techniques were developed in the late 1980s and can be performed on inbred strains of any species.
To begin, a set of genetic markers must be developed for the species in question. A marker is an identifiable region of variable DNA. Biologists are interested in understanding the genetic basis of phenotypes (physical traits). The aim is to find a marker that is significantly more likely to co-occur with the trait than expected by chance, that is, a marker that has a statistical association with the trait. Ideally, they would be able to find the specific gene or genes in question, but this is a long and difficult undertaking. Instead, they can more readily find regions of DNA that are very close to the genes in question. When a QTL is found, it is often not the actual gene underlying the phenotypic trait, but rather a region of DNA that is closely linked with the gene.
For organisms whose genomes are known, one might now try to exclude genes in the identified region whose function is known with some certainty not to be connected with the trait in question. If the genome is not available, it may be an option to sequence the identified region and determine the putative functions of genes by their similarity to genes with known function, usually in other genomes.
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.
Lander and Botstein developed interval mapping, which overcomes the three disadvantages of analysis of variance at marker loci. Interval mapping is currently the most popular approach for QTL mapping in experimental crosses. The method makes use of a genetic map of the typed markers, and, like analysis of variance, assumes the presence of a single QTL. Each location in the genome is posited, one at a time, as the location of the putative QTL.
In this method, one performs interval mapping using a subset of marker loci as covariates. These markers serve as proxies for other QTLs to increase the resolution of interval mapping, by accounting for linked QTLs and reducing the residual variation. The key problem with CIM concerns the choice of suitable marker loci to serve as covariates; once these have been chosen, CIM turns the model selection problem into a single-dimensional scan. The choice of marker covariates has not been solved, however. Not surprisingly, the appropriate markers are those closest to the true QTLs, and so if one could find these, the QTL mapping problem would be complete anyway.
The human genetic approach has taken attention to plant scientist to fit in to plant breeders families (for review- Jannik et al., 2001). There are some successful attempts to do so. One of quick method of QTL mapping is recently discussed (Rosyara et al., 2007).
Jannink, J.; Bink, M. C.A.M.; and Jansen, R.C. 2001. Using complex plant pedigrees to map valuable genes. Trends in Plant Science 6: 337-342.
Rosyara, U. R.; K.L. Maxson-Stein; K.D. Glover; J.M. Stein; J.L. Gonzalez-Hernandez. 2007. Family-based mapping of FHB resistance QTLs in hexaploid wheat. Proceedings of National Fusarium head blight forum, 2007, Dec 2-4, Kansas City, MO.