Download 3. polygenic traits

Polygenic traits
Monogenic diseases form only a small portion of the total burden of human diseases (2%). A
much larger component of our diseases burden is composed of congenital malformations and
common adult diseases, such as cancer, heart disease, and diabetes. Although, they are not the
result of single-gene mutations, these diseases have significant genetic components. They are the
result of a complex interplay of multiple genetic and environmental factors.
SLIDE 1 Polygenic traits Traits in which variation is thought to be caused by the combined
effects of multiple genes are called polygenic (“many genes”). When environmental factors are
also believed to cause variation in the trait, which is usually the case, the term “multifactorial
trait” is used. Classic quantitative (in other words: continuous, multigenic, multifactorial) traits:
weigh, height, intelligence, etc. The thing is that almost every characteristics is controlled by
multiple genes
SLIDES 2-4 Genetics of quantitative traits Many quantitative traits that are measured on a
continuous numerical scale are multifactorial. Because they are caused by additive effects of
many genetic and environmental factors, these traits tend to follow a normal, or bell-shaped,
distribution in a population. Let us use an example to illustrate this concept. To begin with the
simplest case, suppose (unrealistically) that height is determined by a single gene with two
alleles, A and a. Allele A tends to make people tall, and allele a tends to make them short. If
there is no dominance at this locus, then the three possible genotypes, AA, Aa, and aa, will
produce three possible phenotypes: tall, intermediate, and short. Assume that the gene
frequencies of A and a are each 0.5. If we assemble a population of individuals, we will observe
the height distribution depicted in the slide. Now suppose that height is determined by two loci
instead one. The second locus also has two alleles, B (tall) and b (short), and they affect height in
exactly the same way as alleles A and a do. There are now nine possible genotypes in our
population: aabb, aaBb, aaBB, Aabb, AaBb, AaBB, AAbb, AABb, and AABB. Because an
individual might have zero, one, two, three, or four “tall” alleles, there are now five distinct
phenotypes (see the slide). We now extend our example so that many genes and environmental
factors influence height, each having a small effect. Then there are many possible phenotypes,
each differing slightly, and the height distribution approaches the bell-shaped curve shown in
slide… It should be emphasized that individual genes underlying a multifactorial trait follow the
mendelian principles of segregation and independent assortment, just like any other genes. The
only difference is that many of them act together to influence that trait. As a matter of fact, it is
an extension of Mendelian inheritance since the genes contributing to “small pieces” phenotypes.
The distribution of quantitative traits is continuous. Later, we will see that the effects of genes
are not additive but it is the result of the complicated interaction among genes. Further, we will
see that qualitative traits are also determined by several genes. Quantitative Trait Loci (QTL):
regions of DNA that are associated with particular phenotypic traits – these QTLS are often
found in different chromosomes. Statistical analysis is used to determine whether a particular
gene (allele) contribute to a given phenotype.
SLIDE 5 The Polygenic Threshold Theory A number of traits and diseases do not follow the
bell-shaped distribution. Instead, they appear to be either present or absent in individuals. Yet
they do not follow the patterns expected of single gene diseases. A commonly used explanation
is that there is an underlying liability distribution for these diseases in a population. Those who
are closer to the high end of the distribution have more of the disease causing genes and
environmental factors and are more likely to develop the disease. To be affected with the disease,
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a person must exceed the threshold on the liability distribution. The figure shows two thresholds,
a lower one for males, and a higher one for females.
SLIDE 6 Polygenic diseases This term can be used in 3 senses:
1. A disease is caused by the malfunction of several genes
2. A disease can be caused by the malfunction of anyone of the contributing genes
3. A disease is generally caused by the combined effect of genetic and environmental factors
Polygenic diseases: cancers, heart diseases, autoimmune diseases, diabetes, osteoporosis,
hypertension, obesity, atherosclerosis, depression, schizophrenia
SLIDES 7-10 How can monogenic traits exist? Certain phenotypes undoubtedly correspond to a specific allele
of a normal or malfunctioned gene. First of all, let’s make it clear that this fact would not mean that the given gene
or allele encodes the particular trait. What we can observe is that an allele and a phenotype variant co-occur in a
given cellular and genetic environment. We can raise the question of how it is possible that the 1 gene, 1 phenotype,
or more precisely the 1 allele, 1 phenotype variant relationship can occur in complex organisms such as human. For
the sake of simplicity, we consider a haploid* case.
1. Other genes are functionally non-variable. A particular phenotype is determined by several genes, but only one of
them exhibits functional variance, or if many genes vary functionally, but only one of them contributes to phenotype
variation.
2. The effect of one gene is pronounced (Hub gene*). Many genes are variable, but only one of them has a definitive
effect on the phenotype. Therefore, although, other genes can also cause phenotypic variation, this remains within a
scale and thus, can be considered as identical.
3. The genes contributing to the development of a specific phenotype are located in close vicinity in a chromosome,
therefore a particular set of alleles is linked. Genes located closely to each other in a chromosome are often
contribute to the development of the same phenotype or processes, such as Hox genes. Physical proximity results
that recombination occurs very rarely within the gene cluster during meiosis, and therefore, certain allele
combinations remain relatively stable during successive generations.
SLIDE 11 Relationships between gene and phenotype
2 problems:
1. The fact: an allele of a single gene can unequivocally be corresponded to a phenotype variant. Does it mean that
this gene encode the given phenotype?
- In a strict sense NO, a gene can exert its effect in the context of other genes and the whole body.
- In a wider sense YES, but it is only a correlation
2. Are there many monogenic traits? - NO, more and more monogenic traits turn out to be polygenic
SLIDE 12 Message:
Monogenic traits are under the control of several genes, but in special circumstances, some of
them can appear as monogenic. More precisely, the 1 gene, 1 phenotype relation is never true,
the 1 allele, 1 phenotype variant relation rarely occurs.
SLIDE 13 The common variant hypothesis proposes that many familiar human maladies might be explained by
the inheritance of a relative small number of common gene variants. The belief that common variants would be
helpful in understanding disease had a certain evolutionary logic. The rapid and recent population explosion of
ancestral humans tens of thousands of years ago „locked” many variants in the human gene pool. The bet was that
these common variants („common” usually meaning appearing in at least 5 percent of a given population) would be
fairly easy to find and that a relatively small number of them (from several to perhaps dozens) would shape our
susceptibility to hypertension, dementias, and many other widespread diseases. The idea of Common
Disease/Common Variant Hypothesis, drove major developments in biology over the last five years. The
International HapMap Project*, cataloged the common genetic variations in the human population. Companies like
Affymetrix and Illumina developed powerful gene chips for scanning the human genome. Medical statisticians
designed the genome-wide association study, a robust methodology for discovering true disease genes.
SLIDE 14 The Rare Genetic Variant hypothesis The HapMap and other techniques produced just a handful of
genes that account for very little of the overall genetic risk. After doing comprehensive studies for common
diseases, we can explain only a few percent of the genetic component of most of these traits. For schizophrenia and
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bipolar disorders, we get almost nothing; for Type 2 diabetes, 20 variants, but they explain only 2 to 3 percent of
familial clustering, and so on. The reason for this disappointing outcome is that natural selection has been far more
efficient than many researchers expected at screening out disease-causing variants. The common disease/common
variant idea is largely wrong. What has happened is that a multitude of rare variants lie at the root of most common
diseases, being rigorously pruned away as soon as any starts to become widespread. It takes large, expensive trials
with hundreds of patients in different countries to find even common variants behind a disease. Rare variants lie
beyond present reach. It’s an astounding thing that we have cracked open the human genome and can look at the
entire complement of common genetic variants, and what do we find? Almost nothing. That is absolutely beyond
belief. If rare variants account for most of the genetic burden of disease, then the idea of decoding everyone’s
genome to see to what diseases they are vulnerable to will not work, at least not in the form envisaged. “I don’t
believe we should do more and more genome-wide association studies for common diseases,” Dr. Goldstein said.
Instead, he suggested, the “missing heritability” might be tracked by thoroughly studying the genome of specific
patients. Researchers hunting for disease genes strongly disagree. They say genome-wide association studies with
larger numbers of patients will bring more disease-promoting variants to light. The HapMap project was started
amid much skepticism but has proved a technical success, even if it has brought to light fewer common disease
variants than hoped. “There is no doubt it has succeeded far beyond what some skeptics proposed,” said Dr. David
Altshuler, an architect of the HapMap at the Harvard Medical School. He defends the common disease/common
variant hypothesis, saying it meant only that some, not all, of the genetic burden of a disease would be conveyed by
common variants. And the genome-wide association study, currently the gold standard of gene discovery, is in his
view only one step along the road.
The International HapMap Project is an organization whose goal is to develop a haplotype
map (HapMap) of the human genome, which will describe the common patterns of human
genetic variation. The HapMap is expected to be a key resource for researchers to find genetic
variants affecting health, disease and responses to drugs and environmental factors.
The haplotype is a set of single-nucleotide polymorphisms (SNPs) on a single chromatid (half a
chromosome pair) that are statistically associated. It is thought that these associations, and the
identification of a few alleles of a haplotype block, can unambiguously identify all other
polymorphic sites in its region. Such information is very valuable for investigating the genetics
behind common diseases, and has been investigated in the human species by the International
HapMap Project.
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