Heritability

From Wikipedia, the free encyclopedia

 

Studies of heritability ask questions such as how much genetic factors play a role in differences in height between people. This is not the same as asking how much genetic factors influence height in any one person.

 

Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of variation in a phenotypic trait in a population that is due to genetic variation between individuals in that population. In other words, the concept of heritability can alternately be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is not explained by the environment or random chance?"

Other causes of measured variation in a trait are characterized as environmental factors, including measurement error. In human studies of heritability these are often apportioned into factors from "shared environment" and "non-shared environment" based on whether they tend to result in persons brought up in the same household being more or less similar to persons who were not.

Heritability is estimated by comparing individual phenotypic variation among related individuals in a population. Heritability is an important concept in quantitative genetics, particularly in selective breeding and behavior genetics (for instance, twin studies). It is the source of much confusion due to the fact that its technical definition is different from its commonly-understood folk definition. Therefore, its use conveys the incorrect impression that behavioral traits are "inherited" or specifically passed down through the genes. Behavioral geneticists also conduct heritability analyses based on the assumption that genes and environments contribute in a separate, additive manner to behavioral traits.

Overview

Heritability measures the fraction of phenotype variability that can be attributed to genetic variation. This is not the same as saying that this fraction of an individual phenotype is caused by genetics. For example, it is incorrect to say that since the heritability of personality traits is about .6, that means that 60% of your personality is inherited from your parents and 40% comes from the environment. In addition, heritability can change without any genetic change occurring, such as when the environment starts contributing to more variation. As a case in point, consider that both genes and environment have the potential to influence intelligence. Heritability could increase if genetic variation increases, causing individuals to show more phenotypic variation, like showing different levels of intelligence. On the other hand, heritability might also increase if the environmental variation decreases, causing individuals to show less phenotypic variation, like showing more similar levels of intelligence. Heritability increases when genetics are contributing more variation or because non-genetic factors are contributing less variation; what matters is the relative contribution. Heritability is specific to a particular population in a particular environment. High heritability of a trait, consequently, does not necessarily mean that the trait is not very susceptible to environmental influences. Heritability can also change as a result of changes in the environment, migration, inbreeding, or the way in which heritability itself is measured in the population under study. The heritability of a trait should not be interpreted as a measure of the extent to which said trait is genetically determined in an individual.

The extent of dependence of phenotype on environment can also be a function of the genes involved. Matters of heritability are complicated because genes may canalize a phenotype, making its expression almost inevitable in all occurring environments. Individuals with the same genotype can also exhibit different phenotypes through a mechanism called phenotypic plasticity, which makes heritability difficult to measure in some cases. Recent insights in molecular biology have identified changes in transcriptional activity of individual genes associated with environmental changes. However, there are a large number of genes whose transcription is not affected by the environment.

Estimates of heritability use statistical analyses to help to identify the causes of differences between individuals. Since heritability is concerned with variance, it is necessarily an account of the differences between individuals in a population. Heritability can be univariate – examining a single trait – or multivariate – examining the genetic and environmental associations between multiple traits at once. This allows a test of the genetic overlap between different phenotypes: for instance hair color and eye color. Environment and genetics may also interact, and heritability analyses can test for and examine these interactions (GxE models).

A prerequisite for heritability analyses is that there is some population variation to account for. This last point highlights the fact that heritability cannot take into account the effect of factors which are invariant in the population. Factors may be invariant if they are absent and do not exist in the population, such as no one having access to a particular antibiotic, or because they are omni-present, like if everyone is drinking coffee. In practice, all human behavioral traits vary and almost all traits show some heritability.

Definition

Any particular phenotype can be modeled as the sum of genetic and environmental effects:

Phenotype (P) = Genotype (G) + Environment (E).

Likewise the phenotypic variance in the trait – Var (P) – is the sum of effects as follows:

Var(P) = Var(G) + Var(E) + 2 Cov(G,E).

In a planned experiment Cov(G,E) can be controlled and held at 0. In this case, heritability is defined as:

${\displaystyle H^{2}={\frac {\mathrm {Var} (G)}{\mathrm {Var} (P)}}}$ .

H² is the broad-sense heritability. This reflects all the genetic contributions to a population's phenotypic variance including additive, dominant, and epistatic (multi-genic interactions), as well as maternal and paternal effects, where individuals are directly affected by their parents' phenotype, such as with milk production in mammals.

A particularly important component of the genetic variance is the additive variance, Var(A), which is 

the variance due to the average effects (additive effects) of the alleles. Since each parent passes a single allele per locus to each offspring, parent-offspring resemblance depends upon the average effect of single alleles. Additive variance represents, therefore, the genetic component of variance responsible for parent-offspring resemblance. The additive genetic portion of the phenotypic variance is known as Narrow-sense heritability and is defined as

${\displaystyle h^{2}={\frac {\mathrm {Var} (A)}{\mathrm {Var} (P)}}}$

An upper case H² is used to denote broad sense, and lower case h² for narrow sense.

For traits which are not continuous but dichotomous such as an additional toe or certain diseases, the contribution of the various alleles can be considered to be a sum, which past a threshold, manifests itself as the trait, giving the liability threshold model in which heritability can be estimated and selection modeled.

Additive variance is important for selection. If a selective pressure such as improving livestock is exerted, the response of the trait is directly related to narrow-sense heritability. The mean of the trait will increase in the next generation as a function of how much the mean of the selected parents differs from the mean of the population from which the selected parents were chosen. The observed response to selection leads to an estimate of the narrow-sense heritability (called realized heritability). This is the principle underlying artificial selection or breeding.

Example

Figure 1. Relationship of phenotypic values to additive and dominance effects using a completely dominant locus.

 

The simplest genetic model involves a single locus with two alleles (b and B) affecting one quantitative phenotype.

The number of B alleles can vary from 0, 1, or 2. For any genotype, B_iB_j, the expected phenotype can then be written as the sum of the overall mean, a linear effect, and a dominance deviation:

${\displaystyle P_{ij}=\mu +\alpha _{i}+\alpha _{j}+d_{ij}}$ = Population mean + 

Additive Effect (${\displaystyle a_{ij}=\alpha _{i}+\alpha _{j}}$) + 

Dominance Deviation (${\displaystyle d_{ij}}$).

The additive genetic variance at this locus is the weighted average of the squares of the additive effects:

${\displaystyle \mathrm {Var} (A)=f(bb)a_{bb}^{2}+f(Bb)a_{Bb}^{2}+f(BB)a_{BB}^{2},}$

where ${\displaystyle f(bb)a_{bb}+f(Bb)a_{Bb}+f(BB)a_{BB}=0.}$

There is a similar relationship for variance of dominance deviations:

${\displaystyle \mathrm {Var} (D)=f(bb)d_{bb}^{2}+f(Bb)d_{Bb}^{2}+f(BB)d_{BB}^{2},}$

where ${\displaystyle f(bb)d_{bb}+f(Bb)d_{Bb}+f(BB)d_{BB}=0.}$

The linear regression of phenotype on genotype is shown in Figure 1.

Assumptions

Estimates of the total heritability of human traits assume the absence of epistasis, which has been called the "assumption of additivity". Although some researchers have cited such estimates in support of the existence of "missing heritability" unaccounted for by known genetic loci, the assumption of additivity may render these estimates invalid. There is also some empirical evidence that the additivity assumption is frequently violated in behavior genetic studies of adolescent intelligence and academic achievement.

Estimating heritability

Since only P can be observed or measured directly, heritability must be estimated from the similarities observed in subjects varying in their level of genetic or environmental similarity. The statistical analyses required to estimate the genetic and environmental components of variance depend on the sample characteristics. Briefly, better estimates are obtained using data from individuals with widely varying levels of genetic relationship - such as twins, siblings, parents and offspring, rather than from more distantly related (and therefore less similar) subjects. The standard error for heritability estimates is improved with large sample sizes.

In non-human populations it is often possible to collect information in a controlled way. For example, among farm animals it is easy to arrange for a bull to produce offspring from a large number of cows and to control environments. Such experimental control is generally not possible when gathering human data, relying on naturally occurring relationships and environments.

In classical quantitative genetics, there were two schools of thought regarding estimation of heritability.

One school of thought was developed by Sewall Wright at The University of Chicago, and further popularized by C. C. Li (University of Chicago) and J. L. Lush (Iowa State University). It is based on the analysis of correlations and, by extension, regression. Path Analysis was developed by Sewall Wright as a way of estimating heritability.

The second was originally developed by R. A. Fisher and expanded at The University of Edinburgh, Iowa State University, and North Carolina State University, as well as other schools. It is based on the analysis of variance of breeding studies, using the intraclass correlation of relatives. Various methods of estimating components of variance (and, hence, heritability) from ANOVA are used in these analyses.

Today, heritability can be estimated from general pedigrees using linear mixed models and from genomic relatedness estimated from genetic markers.

Studies of human heritability often utilize adoption study designs, often with identical twins who have been separated early in life and raised in different environments. Such individuals have identical genotypes and can be used to separate the effects of genotype and environment. A limit of this design is the common prenatal environment and the relatively low numbers of twins reared apart. A second and more common design is the twin study in which the similarity of identical and fraternal twins is used to estimate heritability. These studies can be limited by the fact that identical twins are not completely genetically identical, potentially resulting in an underestimation of heritability.

In observational studies, or because of evocative effects (where a genome evokes environments by its effect on them), G and E may covary: gene environment correlation. Depending on the methods used to estimate heritability, correlations between genetic factors and shared or non-shared environments may or may not be confounded with heritability.

Regression/correlation methods of estimation

The first school of estimation uses regression and correlation to estimate heritability.

Comparison of close relatives

In the comparison of relatives, we find that in general,

${\displaystyle h^{2}={\frac {b}{r}}={\frac {t}{r}}}$

where r can be thought of as the coefficient of relatedness, b is the coefficient of regression and t is the coefficient of correlation.

Parent-offspring regression

Figure 2. Sir Francis Galton's (1889) data showing the relationship between offspring height (928 individuals) as a function of mean parent height (205 sets of parents).

 

Heritability may be estimated by comparing parent and offspring traits (as in Fig. 2). The slope of the line (0.57) approximates the heritability of the trait when offspring values are regressed against the average trait in the parents. If only one parent's value is used then heritability is twice the slope. (Note that this is the source of the term "regression," since the offspring values always tend to regress to the mean value for the population, i.e., the slope is always less than one). This regression effect also underlies the DeFries–Fulker method for analyzing twins selected for one member being affected.

Sibling comparison

A basic approach to heritability can be taken using full-Sib designs: comparing similarity between siblings who share both a biological mother and a father. When there is only additive gene action, this sibling phenotypic correlation is an index of familiarity – the sum of half the additive genetic variance plus full effect of the common environment. It thus places an upper-limit on additive heritability of twice the full-Sib phenotypic correlation. Half-Sib designs compare phenotypic traits of siblings that share one parent with other sibling groups.

Twin studies

Figure 3. Twin concordances for seven psychological traits (sample size shown inside bars), with DZ being fraternal and MZ being identical twins.

 

Heritability for traits in humans is most frequently estimated by comparing resemblances between twins. "The advantage of twin studies, is that the total variance can be split up into genetic, shared or common environmental, and unique environmental components, enabling an accurate estimation of heritability". Fraternal or dizygotic (DZ) twins on average share half their genes (assuming there is no assortative mating for the trait), and so identical or monozygotic (MZ) twins on average are twice as genetically similar as DZ twins. A crude estimate of heritability, then, is approximately twice the difference in correlation between MZ and DZ twins, i.e. Falconer's formula H²=2(r(MZ)-r(DZ)). 

The effect of shared environment, c², contributes to similarity between siblings due to the commonality of the environment they are raised in. Shared environment is approximated by the DZ correlation minus half heritability, which is the degree to which DZ twins share the same genes, c²=DZ-1/2h². Unique environmental variance, e², reflects the degree to which identical twins raised together are dissimilar, e²=1-r(MZ).

Analysis of variance methods of estimation

The second set of methods of estimation of heritability involves ANOVA and estimation of variance components.

Basic model

We use the basic discussion of Kempthorne. Considering only the most basic of genetic models, we can look at the quantitative contribution of a single locus with genotype G_i as

${\displaystyle y_{i}=\mu +g_{i}+e}$

where ${\displaystyle g_{i}}$ is the effect of genotype G_i and ${\displaystyle e}$ is the environmental effect.

Consider an experiment with a group of sires and their progeny from random dams. Since the progeny get half of their genes from the father and half from their (random) mother, the progeny equation is

${\displaystyle z_{i}=\mu +{\frac {1}{2}}g_{i}+e}$

Intraclass correlations

Consider the experiment above. We have two groups of progeny we can compare. The first is comparing the various progeny for an individual sire (called within sire group). The variance will include terms for genetic variance (since they did not all get the same genotype) and environmental variance. This is thought of as an error term.

The second group of progeny are comparisons of means of half sibs with each other (called among sire group). In addition to the error term as in the within sire groups, we have an addition term due to the differences among different means of half sibs. The intraclass correlation is

${\displaystyle \mathrm {corr} (z,z')=\mathrm {corr} (\mu +{\frac {1}{2}}g+e,\mu +{\frac {1}{2}}g+e')={\frac {1}{4}}V_{g}}$ ,

since environmental effects are independent of each other.

The ANOVA

In an experiment with ${\displaystyle n}$ sires and ${\displaystyle r}$ progeny per sire, we can calculate the following ANOVA, using ${\displaystyle V_{g}}$ as the genetic variance and ${\displaystyle V_{e}}$ as the environmental variance: 

Table 1: ANOVA for Sire experiment

Source	d.f.	Mean Square	Expected Mean Square
Among sire groups	${\displaystyle n-1}$	${\displaystyle S}$	${\displaystyle {\frac {3}{4}}V_{g}+V_{e}+r({{\frac {1}{4}}V_{g}})}$
Within sire groups	${\displaystyle n(r-1)}$	${\displaystyle W}$	${\displaystyle {\frac {3}{4}}V_{g}+V_{e}}$

The ${\displaystyle {\frac {1}{4}}V_{g}}$ term is the intraclass correlation among half sibs. We can easily calculate ${\displaystyle H^{2}={\frac {V_{g}}{V_{g}+V_{e}}}={\frac {4(S-W)}{S+(r-1)W}}}$. The Expected Mean Square is calculated from the relationship of the individuals (progeny within a sire are all half-sibs, for example), and an understanding of intraclass correlations. 

The use of ANOVA to calculate heritability often fails to account for the presence of gene-environment interactions, because ANOVA has a much lower statistical power for testing for interaction effects than for direct effects.

Model with additive and dominance terms

For a model with additive and dominance terms, but not others, the equation for a single locus is

${\displaystyle y_{ij}=\mu +\alpha _{i}+\alpha _{j}+d_{ij}+e,}$

where is the additive effect of the i^th allele, ${\displaystyle \alpha _{j}}$ is the additive effect of the j^th allele, ${\displaystyle d_{ij}}$ is the dominance deviation for the ij^th genotype, and ${\displaystyle e}$ is the environment. 

Experiments can be run with a similar setup to the one given in Table 1. Using different relationship groups, we can evaluate different intraclass correlations. Using ${\displaystyle V_{a}}$ as the additive genetic variance and ${\displaystyle V_{d}}$ as the dominance deviation variance, intraclass correlations become linear functions of these parameters. In general,

Intraclass correlation${\displaystyle =rV_{a}+\theta V_{d},}$

where ${\displaystyle r}$ and ${\displaystyle \theta }$ are found as

${\displaystyle r=}$P[ alleles drawn at random from the relationship pair are identical by descent], and

${\displaystyle \theta =}$P[ genotypes drawn at random from the relationship pair are identical by descent].

Some common relationships and their coefficients are given in Table 2.

Table 2: Coefficients for calculating variance components

Relationship	${\displaystyle r}$	${\displaystyle \theta }$
Identical Twins	${\displaystyle 1}$	${\displaystyle 1}$
Parent-Offspring	${\displaystyle {\frac {1}{2}}}$	${\displaystyle 0}$
Half Siblings	${\displaystyle {\frac {1}{4}}}$	${\displaystyle 0}$
Full Siblings	${\displaystyle {\frac {1}{2}}}$	${\displaystyle {\frac {1}{4}}}$
First Cousins	${\displaystyle {\frac {1}{8}}}$	${\displaystyle 0}$
Double First Cousins	${\displaystyle {\frac {1}{4}}}$	${\displaystyle {\frac {1}{16}}}$

Linear mixed models

A wide variety approaches using linear mixed models have been reported in literature. Via these methods, phenotypic variance is partitioned into genetic, environmental and experimental design variances to estimate heritability. Environmental variance can be explicitly modeled by studying individuals across a broad range of environments, although inference of genetic variance from phenotypic and environmental variance may lead to underestimation of heritability due to the challenge of capturing the full range of environmental influence affecting a trait. Other methods for calculating heritability use data from genome-wide association studies to estimate the influence on a trait by genetic factors, which is reflected by the rate and influence of putatively associated genetic loci (usually single-nucleotide polymorphisms) on the trait. This can lead to underestimation of heritability, however. This discrepancy is referred to as "missing heritability" and reflects the challenge of accurately modeling both genetic and environmental variance in heritability models.

When a large, complex pedigree or another aforementioned type of data is available, heritability and other quantitative genetic parameters can be estimated by restricted maximum likelihood (REML) or Bayesian methods. The raw data will usually have three or more data points for each individual: a code for the sire, a code for the dam and one or several trait values. Different trait values may be for different traits or for different time points of measurement. 

The currently popular methodology relies on high degrees of certainty over the identities of the sire and dam; it is not common to treat the sire identity probabilistically. This is not usually a problem, since the methodology is rarely applied to wild populations (although it has been used for several wild ungulate and bird populations), and sires are invariably known with a very high degree of certainty in breeding programmes. There are also algorithms that account for uncertain paternity.

The pedigrees can be viewed using programs such as Pedigree Viewer , and analyzed with programs such as ASReml, VCE , WOMBAT  or the BLUPF90 family of programs.

Pedigree models are helpful for untangling confounds such as reverse causality, maternal effects such as the prenatal environment, and confounding of genetic dominance, shared environment, and maternal gene effects.

Genomic heritability

When genome-wide genotype data and phenotypes from large population samples are available, one can estimate the relationships between individuals based on their genotypes and use a linear mixed model to estimate the variance explained by the genetic markers. This gives a genomic heritability estimate based on the variance captured by common genetic variants. There are multiple methods that make different adjustments for allele frequency and linkage disequilibrium.

Response to selection

Figure 4. Strength of selection (S) and response to selection (R) in an artificial selection experiment, h²=R/S.

In selective breeding of plants and animals, the expected response to selection of a trait with known narrow-sense heritability ${\displaystyle (h^{2})}$ can be estimated using the breeder's equation:

${\displaystyle R=h^{2}S}$

In this equation, the Response to Selection (R) is defined as the realized average difference between the parent generation and the next generation, and the Selection Differential (S) is defined as the average difference between the parent generation and the selected parents.

For example, imagine that a plant breeder is involved in a selective breeding project with the aim of increasing the number of kernels per ear of corn. For the sake of argument, let us assume that the average ear of corn in the parent generation has 100 kernels. Let us also assume that the selected parents produce corn with an average of 120 kernels per ear. If h² equals 0.5, then the next generation will produce corn with an average of 0.5(120-100) = 10 additional kernels per ear. Therefore, the total number of kernels per ear of corn will equal, on average, 110. 

Observing the response to selection in an artificial selection experiment will allow calculation of realized heritability as in Fig. 5. 

Note that heritability in the above equation is equal to the ratio ${\displaystyle \mathrm {Var} (A)/\mathrm {Var} (P)}$ only if the genotype and the environmental noise follow Gaussian distributions.

Controversies

Heritability estimates' prominent critics, such as Steven Rose, Jay Joseph, and Richard Bentall, focus largely on heritability estimates in behavioral sciences and social sciences. Bentall has claimed that such heritability scores are typically calculated counterintuitively to derive numerically high scores, that heritability is misinterpreted as genetic determination, and that this alleged bias distracts from other factors that researches have found more causally important, such as childhood abuse causing later psychosis. Heritability estimates are also inherently limited because they do not convey any information regarding whether genes or environment play a larger role in the development of the trait under study. For this reason, David Moore and David Shenk describe the term "heritability" in the context of behavior genetics as "...one of the most misleading in the history of science" and argue that it has no value except in very rare cases. When studying complex human traits, it is impossible to use heritability analysis to determine the relative contributions of genes and environment, as such traits result from multiple causes interacting.

The controversy over heritability estimates is largely via their basis in twin studies. The scarce success of molecular-genetic studies to corroborate such population-genetic studies' conclusions is the missing heritability problem. Eric Turkheimer has argued that newer molecular methods have vindicated the conventional interpretation of twin studies, although it remains mostly unclear how to explain the relations between genes and behaviors. According to Turkheimer, both genes and environment are heritable, genetic contribution varies by environment, and a focus on heritability distracts from other important factors. Overall, however, heritability is a concept widely applicable.

Heredity

From Wikipedia, the free encyclopedia

 

Heredity, also called inheritance or biological inheritance, is the passing on of traits from parents to their offspring; either through asexual reproduction or sexual reproduction, the offspring cells or organisms acquire the genetic information of their parents. Through heredity, variations between individuals can accumulate and cause species to evolve by natural selection. The study of heredity in biology is genetics.

Overview

Heredity of phenotypic traits: Father and son with prominent ears and crowns.

 

DNA structure. Bases are in the centre, surrounded by phosphate–sugar chains in a double helix.

 

In humans, eye color is an example of an inherited characteristic: an individual might inherit the "brown-eye trait" from one of the parents. Inherited traits are controlled by genes and the complete set of genes within an organism's genome is called its genotype.

The complete set of observable traits of the structure and behavior of an organism is called its phenotype. These traits arise from the interaction of its genotype with the environment. As a result, many aspects of an organism's phenotype are not inherited. For example, suntanned skin comes from the interaction between a person's phenotype and sunlight; thus, suntans are not passed on to people's children. However, some people tan more easily than others, due to differences in their genotype: a striking example is people with the inherited trait of albinism, who do not tan at all and are very sensitive to sunburn.

Heritable traits are known to be passed from one generation to the next via DNA, a molecule that encodes genetic information. DNA is a long polymer that incorporates four types of bases, which are interchangeable. The sequence of bases along a particular DNA molecule specifies the genetic information: this is comparable to a sequence of letters spelling out a passage of text. Before a cell divides through mitosis, the DNA is copied, so that each of the resulting two cells will inherit the DNA sequence. A portion of a DNA molecule that specifies a single functional unit is called a gene; different genes have different sequences of bases. Within cells, the long strands of DNA form condensed structures called chromosomes. Organisms inherit genetic material from their parents in the form of homologous chromosomes, containing a unique combination of DNA sequences that code for genes. The specific location of a DNA sequence within a chromosome is known as a locus. If the DNA sequence at a particular locus varies between individuals, the different forms of this sequence are called alleles. DNA sequences can change through mutations, producing new alleles. If a mutation occurs within a gene, the new allele may affect the trait that the gene controls, altering the phenotype of the organism.

However, while this simple correspondence between an allele and a trait works in some cases, most traits are more complex and are controlled by multiple interacting genes within and among organisms. Developmental biologists suggest that complex interactions in genetic networks and communication among cells can lead to heritable variations that may underlie some of the mechanics in developmental plasticity and canalization.

Recent findings have confirmed important examples of heritable changes that cannot be explained by direct agency of the DNA molecule. These phenomena are classed as epigenetic inheritance systems that are causally or independently evolving over genes. Research into modes and mechanisms of epigenetic inheritance is still in its scientific infancy, however, this area of research has attracted much recent activity as it broadens the scope of heritability and evolutionary biology in general. DNA methylation marking chromatin, self-sustaining metabolic loops, gene silencing by RNA interference, and the three dimensional conformation of proteins (such as prions) are areas where epigenetic inheritance systems have been discovered at the organismic level. Heritability may also occur at even larger scales. For example, ecological inheritance through the process of niche construction is defined by the regular and repeated activities of organisms in their environment. This generates a legacy of effect that modifies and feeds back into the selection regime of subsequent generations. Descendants inherit genes plus environmental characteristics generated by the ecological actions of ancestors. Other examples of heritability in evolution that are not under the direct control of genes include the inheritance of cultural traits, group heritability, and symbiogenesis. These examples of heritability that operate above the gene are covered broadly under the title of multilevel or hierarchical selection, which has been a subject of intense debate in the history of evolutionary science.

Relation to theory of evolution

When Charles Darwin proposed his theory of evolution in 1859, one of its major problems was the lack of an underlying mechanism for heredity. Darwin believed in a mix of blending inheritance and the inheritance of acquired traits (pangenesis). Blending inheritance would lead to uniformity across populations in only a few generations and then would remove variation from a population on which natural selection could act. This led to Darwin adopting some Lamarckian ideas in later editions of On the Origin of Species and his later biological works. Darwin's primary approach to heredity was to outline how it appeared to work (noticing that traits that were not expressed explicitly in the parent at the time of reproduction could be inherited, that certain traits could be sex-linked, etc.) rather than suggesting mechanisms.

Darwin's initial model of heredity was adopted by, and then heavily modified by, his cousin Francis Galton, who laid the framework for the biometric school of heredity. Galton found no evidence to support the aspects of Darwin's pangenesis model, which relied on acquired traits.

The inheritance of acquired traits was shown to have little basis in the 1880s when August Weismann cut the tails off many generations of mice and found that their offspring continued to develop tails.

History

Aristotle's model of inheritance. The heat/cold part is largely symmetrical, though influenced on the father's side by other factors; but the form part is not.

 

Scientists in Antiquity had a variety of ideas about heredity: Theophrastus proposed that male flowers caused female flowers to ripen; Hippocrates speculated that "seeds" were produced by various body parts and transmitted to offspring at the time of conception; and Aristotle thought that male and female fluids mixed at conception. Aeschylus, in 458 BC, proposed the male as the parent, with the female as a "nurse for the young life sown within her".

Ancient understandings of heredity transitioned to two debated doctrines in the 18th century. The Doctrine of Epigenesis and the Doctrine of Preformation were two distinct views of the understanding of heredity. The Doctrine of Epigenesis, originated by Aristotle, claimed that an embryo continually develops. The modifications of the parent’s traits are passed off to an embryo during its lifetime. The foundation of this doctrine was based on the theory of inheritance of acquired traits. In direct opposition, the Doctrine of Preformation claimed that "like generates like" where the germ would evolve to yield offspring similar to the parents. The Preformationist view believed procreation was an act of revealing what had been created long before. However, this was disputed by the creation of the cell theory in the 19th century, where the fundamental unit of life is the cell, and not some preformed parts of an organism. Various hereditary mechanisms, including blending inheritance were also envisaged without being properly tested or quantified, and were later disputed. Nevertheless, people were able to develop domestic breeds of animals as well as crops through artificial selection. The inheritance of acquired traits also formed a part of early Lamarckian ideas on evolution. 

During the 18th century, Dutch microscopist Antonie van Leeuwenhoek (1632–1723) discovered "animalcules" in the sperm of humans and other animals. Some scientists speculated they saw a "little man" (homunculus) inside each sperm. These scientists formed a school of thought known as the "spermists". They contended the only contributions of the female to the next generation were the womb in which the homunculus grew, and prenatal influences of the womb. An opposing school of thought, the ovists, believed that the future human was in the egg, and that sperm merely stimulated the growth of the egg. Ovists thought women carried eggs containing boy and girl children, and that the gender of the offspring was determined well before conception.

Gregor Mendel: father of genetics

Table showing how the genes exchange according to segregation or independent assortment during meiosis and how this translates into Mendel's laws

 

The idea of particulate inheritance of genes can be attributed to the Moravian monk Gregor Mendel who published his work on pea plants in 1865. However, his work was not widely known and was rediscovered in 1901. It was initially assumed that Mendelian inheritance only accounted for large (qualitative) differences, such as those seen by Mendel in his pea plants – and the idea of additive effect of (quantitative) genes was not realised until R.A. Fisher's (1918) paper, "The Correlation Between Relatives on the Supposition of Mendelian Inheritance" Mendel's overall contribution gave scientists a useful overview that traits were inheritable. His pea plant demonstration became the foundation of the study of Mendelian Traits. These traits can be traced on a single locus.

Modern development of genetics and heredity

In the 1930s, work by Fisher and others resulted in a combination of Mendelian and biometric schools into the modern evolutionary synthesis. The modern synthesis bridged the gap between experimental geneticists and naturalists; and between both and palaeontologists, stating that:

1. All evolutionary phenomena can be explained in a way consistent with known genetic mechanisms and the observational evidence of naturalists.
2. Evolution is gradual: small genetic changes, recombination ordered by natural selection. Discontinuities amongst species (or other taxa) are explained as originating gradually through geographical separation and extinction (not saltation).
3. Selection is overwhelmingly the main mechanism of change; even slight advantages are important when continued. The object of selection is the phenotype in its surrounding environment. The role of genetic drift is equivocal; though strongly supported initially by Dobzhansky, it was downgraded later as results from ecological genetics were obtained.
4. The primacy of population thinking: the genetic diversity carried in natural populations is a key factor in evolution. The strength of natural selection in the wild was greater than expected; the effect of ecological factors such as niche occupation and the significance of barriers to gene flow are all important.

The idea that speciation occurs after populations are reproductively isolated has been much debated. In plants, polyploidy must be included in any view of speciation. Formulations such as 'evolution consists primarily of changes in the frequencies of alleles between one generation and another' were proposed rather later. The traditional view is that developmental biology ('evo-devo') played little part in the synthesis, but an account of Gavin de Beer's work by Stephen Jay Gould suggests he may be an exception.

Almost all aspects of the synthesis have been challenged at times, with varying degrees of success. There is no doubt, however, that the synthesis was a great landmark in evolutionary biology. It cleared up many confusions, and was directly responsible for stimulating a great deal of research in the post-World War II era. 

Trofim Lysenko however caused a backlash of what is now called Lysenkoism in the Soviet Union when he emphasised Lamarckian ideas on the inheritance of acquired traits. This movement affected agricultural research and led to food shortages in the 1960s and seriously affected the USSR.

There is growing evidence that there is transgenerational inheritance of epigenetic changes in humans and other animals.

Common genetic disorders

• Down syndrome
• Sickle cell disease
• Phenylketonuria (PKU)
• Haemophilia

Types

An example pedigree chart of an autosomal dominant disorder.

 

An example pedigree chart of an autosomal recessive disorder.

 

An example pedigree chart of a sex-linked disorder (the gene is on the X chromosome)

Dominant and recessive alleles

An allele is said to be dominant if it is always expressed in the appearance of an organism (phenotype) provided that at least one copy of it is present. For example, in peas the allele for green pods, G, is dominant to that for yellow pods, g. Thus pea plants with the pair of alleles either GG (homozygote) or Gg (heterozygote) will have green pods. The allele for yellow pods is recessive. The effects of this allele are only seen when it is present in both chromosomes, gg (homozygote). 

The description of a mode of biological inheritance consists of three main categories:

1. Number of involved loci

• Monogenetic (also called "simple") – one locus
• Oligogenetic – few loci
• Polygenetic – many loci

2. Involved chromosomes

• Autosomal – loci are not situated on a sex chromosome
• Gonosomal – loci are situated on a sex chromosome
  • X-chromosomal – loci are situated on the X-chromosome (the more common case)
  • Y-chromosomal – loci are situated on the Y-chromosome
• Mitochondrial – loci are situated on the mitochondrial DNA

3. Correlation genotype–phenotype

• Dominant
• Intermediate (also called "codominant")
• Recessive
• Overdominant
• Underdominant

These three categories are part of every exact description of a mode of inheritance in the above order. In addition, more specifications may be added as follows:

4. Coincidental and environmental interactions

• Penetrance
  • Complete
  • Incomplete (percentual number)
• Expressivity
  • Invariable
  • Variable
• Heritability (in polygenetic and sometimes also in oligogenetic modes of inheritance)
• Maternal or paternal imprinting phenomena (also see epigenetics)

5. Sex-linked interactions

• Sex-linked inheritance (gonosomal loci)
• Sex-limited phenotype expression (e.g., cryptorchism)
• Inheritance through the maternal line (in case of mitochondrial DNA loci)
• Inheritance through the paternal line (in case of Y-chromosomal loci)

6. Locus–locus interactions

• Epistasis with other loci (e.g., overdominance)
• Gene coupling with other loci (also see crossing over)
• Homozygotous lethal factors
• Semi-lethal factors

Determination and description of a mode of inheritance is also achieved primarily through statistical analysis of pedigree data. In case the involved loci are known, methods of molecular genetics can also be employed.

Neurogenetics

From Wikipedia, the free encyclopedia

 

Human karyogram

 

Neurogenetics studies the role of genetics in the development and function of the nervous system. It considers neural characteristics as phenotypes (i.e. manifestations, measurable or not, of the genetic make-up of an individual), and is mainly based on the observation that the nervous systems of individuals, even of those belonging to the same species, may not be identical. As the name implies, it draws aspects from both the studies of neuroscience and genetics, focusing in particular how the genetic code an organism carries affects its expressed traits. Mutations in this genetic sequence can have a wide range of effects on the quality of life of the individual. Neurological diseases, behavior and personality are all studied in the context of neurogenetics. The field of neurogenetics emerged in the mid to late 1900s with advances closely following advancements made in available technology. Currently, neurogenetics is the center of much research utilizing cutting edge techniques.

History

The field of neurogenetics emerged from advances made in molecular biology, genetics and a desire to understand the link between genes, behavior, the brain, and neurological disorders and diseases. The field started to expand in the 1960s through the research of Seymour Benzer, considered by some to be the father of neurogenetics.

Seymour Benzer in his office at Caltech in 1974 with a big model of Drosophila

 

His pioneering work with Drosophila helped to elucidate the link between circadian rhythms and genes, which led to further investigations into other behavior traits. He also started conducting research in neurodegeneration in fruit flies in an attempt to discover ways to suppress neurological diseases in humans. Many of the techniques he used and conclusions he drew would drive the field forward.

Early analysis relied on statistical interpretation through processes such as LOD (logarithm of odds) scores of pedigrees and other observational methods such as affected sib-pairs, which looks at phenotype and IBD (identity by descent) configuration. Many of the disorders studied early on including Alzheimer's, Huntington's and amyotrophic lateral sclerosis (ALS) are still at the center of much research to this day. By the late 1980s new advances in genetics such as recombinant DNA technology and reverse genetics allowed for the broader use of DNA polymorphisms to test for linkage between DNA and gene defects. This process is referred to sometimes as linkage analysis. By the 1990s ever advancing technology had made genetic analysis more feasible and available. This decade saw a marked increase in identifying the specific role genes played in relation to neurological disorders. Advancements were made in but not limited to: Fragile X syndrome, Alzheimer's, Parkinson's, epilepsy and ALS.

Neurological disorders

While the genetic basis of simple diseases and disorders has been accurately pinpointed, the genetics behind more complex, neurological disorders is still a source of ongoing research. New developments such as the genome wide association studies (GWAS) have brought vast new resources within grasp. With this new information genetic variability within the human population and possibly linked diseases can be more readily discerned. Neurodegenerative diseases are a more common subset of neurological disorders, with examples being Alzheimer's disease and Parkinson's disease. Currently no viable treatments exist that actually reverse the progression of neurodegenerative diseases; however, neurogenetics is emerging as one field that might yield a causative connection. The discovery of linkages could then lead to therapeutic drugs, which could reverse brain degeneration.

Gene sequencing

One of the most noticeable results of further research into neurogenetics is a greater knowledge of gene loci that show linkage to neurological diseases. The table below represents a sampling of specific gene locations identified to play a role in selected neurological diseases based on prevalence in the United States.

Gene loci	Neurological disease
APOE ε4, PICALM	Alzheimer's disease
DR15, DQ6	Multiple sclerosis
LRRK2, PARK2, PARK7	Parkinson's disease
HTT[12]	Huntington's disease

Methods of research

Statistical analysis

Logarithm of odds (LOD) is a statistical technique used to estimate the probability of gene linkage between traits. LOD is often used in conjunction with pedigrees, maps of a family's genetic make-up, in order to yield more accurate estimations. A key benefit of this technique is its ability to give reliable results in both large and small sample sizes, which is a marked advantage in laboratory research.

Quantitative trait loci (QTL) mapping is another statistical method used to determine the chromosomal positions of a set of genes responsible for a given trait. By identifying specific genetic markers for the genes of interest in a recombinant inbred strain, the amount of interaction between these genes and their relation to the observed phenotype can be determined through complex statistical analysis. In a neurogenetics laboratory, the phenotype of a model organisms is observed by assessing the morphology of their brain through thin slices. QTL mapping can also be carried out in humans, though brain morphologies are examined using nuclear magnetic resonance imaging (MRI) rather than brain slices. Human beings pose a greater challenge for QTL analysis because the genetic population cannot be as carefully controlled as that of an inbred recombinant population, which can result in sources of statistical error.

Recombinant DNA

Recombinant DNA is an important method of research in many fields, including neurogenetics. It is used to make alterations to an organism's genome, usually causing it to over- or under-express a certain gene of interest, or express a mutated form of it. The results of these experiments can provide information on that gene's role in the organism's body, and it importance in survival and fitness. The hosts are then screened with the aid of a toxic drug that the selectable marker is resistant to. The use of recombinant DNA is an example of a reverse genetics, where researchers create a mutant genotype and analyze the resulting phenotype. In forward genetics, an organism with a particular phenotype is identified first, and its genotype is then analyzed.

Animal research

Drosophila

 

Zebrafish

 

Model organisms are an important tool in many areas of research, including the field of neurogenetics. By studying creatures with simpler nervous systems and with smaller genomes, scientists can better understand their biological processes and apply them to more complex organisms, such as humans. Due to their low-maintenance and highly mapped genomes, mice, Drosophila, and C. elegans are very common. Zebrafish and prairie voles have also become more common, especially in the social and behavioral scopes of neurogenetics. 

In addition to examining how genetic mutations affect the actual structure of the brain, researchers in neurogenetics also examine how these mutations affect cognition and behavior. One method of examining this involves purposely engineering model organisms with mutations of certain genes of interest. These animals are then classically conditioned to perform certain types of tasks, such as pulling a lever in order to gain a reward. The speed of their learning, the retention of the learned behavior, and other factors are then compared to the results of healthy organisms to determine what kind of an effect – if any – the mutation has had on these higher processes. The results of this research can help identify genes that may be associated with conditions involving cognitive and learning deficiencies.

Human research

Many research facilities seek out volunteers with certain conditions or illnesses to participate in studies. Model organisms, while important, cannot completely model the complexity of the human body, making volunteers a key part to the progression of research. Along with gathering some basic information about medical history and the extent of their symptoms, samples are taken from the participants, including blood, cerebrospinal fluid, and/or muscle tissue. These tissue samples are then genetically sequenced, and the genomes are added to current database collections. The growth of these data bases will eventually allow researchers to better understand the genetic nuances of these conditions and bring therapy treatments closer to reality. Current areas of interest in this field have a wide range, spanning anywhere from the maintenance of circadian rhythms, the progression of neurodegenerative disorders, the persistence of periodic disorders, and the effects of mitochondrial decay on metabolism.

Behavioral neurogenetics

Advances in molecular biology techniques and the species-wide genome project have made it possible to map out an individual's entire genome. Whether genetic or environmental factors are primarily responsible for an individual's personality has long been a topic of debate. Thanks to the advances being made in the field of neurogenetics, researchers have begun to tackle this question by beginning to map out genes and correlate them to different personality traits. There is little to no evidence to suggest that the presence of a single gene indicates that an individual will express one style of behavior over another; rather, having a specific gene could make one more predisposed to displaying this type of behavior. It is starting to become clear that most genetically influenced behaviors are due to the effects of many variants within many genes, in addition to other neurological regulating factors like neurotransmitter levels. Due to fact that many behavioral characteristics have been conserved across species for generations, researchers are able to use animal subjects such as mice and rats, but also fruit flies, worms, and zebrafish, to try to determine specific genes that correlate to behavior and attempt to match these with human genes.

Cross-species gene conservation

While it is true that variation between species can appear to be pronounced, at their most basic they share many similar behavior traits which are necessary for survival. Such traits include mating, aggression, foraging, social behavior and sleep patterns. This conservation of behavior across species has led biologists to hypothesize that these traits could possibly have similar, if not the same, genetic causes and pathways. Studies conducted on the genomes of a plethora of organisms have revealed that many organisms have homologous genes, meaning that some genetic material has been conserved between species. If these organisms shared a common evolutionary ancestor, then this might imply that aspects of behavior can be inherited from previous generations, lending support to the genetic causes – as opposed to the environmental causes – of behavior. Variations in personalities and behavioral traits seen amongst individuals of the same species could be explained by differing levels of expression of these genes and their corresponding proteins.

Aggression

There is also research being conducted on how an individual's genes can cause varying levels of aggression and aggression control.

Outward displays of aggression are seen in most animals

 

Throughout the animal kingdom, varying styles, types and levels of aggression can be observed leading scientists to believe that there might be a genetic contribution that has conserved this particular behavioral trait. For some species varying levels of aggression have indeed exhibited direct correlation to a higher level of Darwinian fitness.

Development

Shh and BMP gradient in the neural tube

 

A great deal of research has been done on the effects of genes and the formation of the brain and the central nervous system. The following wiki links may prove helpful:

• Neural development
• Neurogenesis
• The Brain
• Neural tube

There are many genes and proteins that contribute to the formation and development of the central nervous system, many of which can be found in the aforementioned links. Of particular importance are those that code for BMPs, BMP inhibitors and SHH. When expressed during early development, BMP's are responsible for the differentiation of epidermal cells from the ventral ectoderm. Inhibitors of BMPs, such as NOG and CHRD, promote differentiation of ectoderm cells into prospective neural tissue on the dorsal side. If any of these genes are improperly regulated, then proper formation and differentiation will not occur. BMP also plays a very important role in the patterning that occurs after the formation of the neural tube. Due to the graded response the cells of the neural tube have to BMP and Shh signaling, these pathways are in competition to determine the fate of preneural cells. BMP promotes dorsal differentiation of pre-neural cells into sensory neurons and Shh promotes ventral differentiation into motor neurons. There are many other genes that help to determine neural fate and proper development include, RELN, SOX9, WNT, Notch and Delta coding genes, HOX, and various cadherin coding genes like CDH1 and CDH2.

Some recent research has shown that the level of gene expression changes drastically in the brain at different periods throughout the life cycle. For example, during prenatal development the amount of mRNA in the brain (an indicator of gene expression) is exceptionally high, and drops to a significantly lower level not long after birth. The only other point of the life cycle during which expression is this high is during the mid- to late-life period, during 50–70 years of age. While the increased expression during the prenatal period can be explained by the rapid growth and formation of the brain tissue, the reason behind the surge of late-life expression remains a topic of ongoing research.

Current research

Neurogenetics is a field that is rapidly expanding and growing. The current areas of research are very diverse in their focuses. One area deals with molecular processes and the function of certain proteins, often in conjunction with cell signaling and neurotransmitter release, cell development and repair, or neuronal plasticity. Behavioral and cognitive areas of research continue to expand in an effort to pinpoint contributing genetic factors. As a result of the expanding neurogenetics field a better understanding of specific neurological disorders and phenotypes has arisen with direct correlation to genetic mutations. With severe disorders such as epilepsy, brain malformations, or mental retardation a single gene or causative condition has been identified 60% of the time; however, the milder the intellectual handicap the lower chance a specific genetic cause has been pinpointed. Autism for example is only linked to a specific, mutated gene about 15–20% of the time while the mildest forms of mental handicaps are only being accounted for genetically less than 5% of the time. Research in neurogenetics has yielded some promising results, though, in that mutations at specific gene loci have been linked to harmful phenotypes and their resulting disorders. For instance a frameshift mutation or a missense mutation at the DCX gene location causes a neuronal migration defect also known as lissencephaly. Another example is the ROBO3 gene where a mutation alters axon length negatively impacting neuronal connections. Horizontal gaze palsy with progressive scoliosis (HGPPS) accompanies a mutation here. These are just a few examples of what current research in the field of neurogenetics has achieved.