What is heritability in biology

Heritability does not indicate what proportion of a trait is determined by genes and what proportion is determined by environment. So, a heritability of 0. Knowing the heritability of a trait does not provide information about which genes or environmental influences are involved, or how important they are in determining the trait.

Heritable is not the same as familial. A trait is described as familial if it is shared by members of a family. Traits can appear in families for many reasons in addition to genetics, such as similarities in lifestyle and environment. For example, the language that is spoken tends to be shared in families, but it has no genetic contribution and so is not heritable. Heritability does not give any information about how easy or difficult it is to change a trait. For example, hair color is a trait with high heritability, but it is very easy to change with dye.

If heritability provides such limited information, why do researchers study it? Heritability is of particular interest in understanding traits that are very complex with many contributing factors.

Moore DS, Shenk D. The heritability fallacy. Wiley Interdiscip Rev Cogn Sci. Epub Dec 1. PubMed: Similarly, it also tells us how well we could predict the trait in you based on that trait in your parents. Actually making this prediction from your DNA would require precisely knowing the effect of every genetic variant, which is very, very far away from being a reality.

But the heritability puts an upper limit on how good that prediction could ever be as we learn more about the genetics of the trait. Heritability measures how important genetics is to a trait. A high heritability, close to 1, indicates that genetics explain a lot of the variation in a trait between different people; a low heritability, near zero, indicates that most of the variation is not genetic.

Heritability is a property of the population not the individual. When the heritability of a trait is described, it reflects how much variability in the population is a consequence of genetic factors. Heritability is specific to how a trait was measured. This can also cause differences in heritability that depend on who measures the trait e.

Heritability is specific to whom the trait was measured in. Heritability is not fate. Heritability is not immutable. Heritability does not measure our ability to affect the trait. High heritability does not mean group differences are genetic.

There is a troubling history of attributing observed group differences, such as reported racial disparities in IQ scores, to genetics.

As noted above, heritability is specific to the choice of measurement, population, and environment, and the heritability of a trait is not immutable. Estimating the heritability of a trait in a given population is a starting point for understanding that trait, rather than an end goal. For geneticists and biologists, heritability is some indication of what traits will be fruitful to study. By providing a metric for how much a trait is related to genetics as opposed to other factors, it tells us how much to consider genetics if we want to learn more about the causes for that trait.

Darwin [] , working without the advantages that genetics would later bring, discussed hereditary traits at the level of phenotypes.

Darwin demonstrated that natural selection sorts among hereditary variations, for example, the height of an organism, its weight, the color of its coat and so on. Most contemporary discussions of heredity constrain hereditary traits to those that can be demonstrated to be passed on genetically. Heritability is usually assessed by complex statistical analysis, careful experimentation or both. Discussions of heredity invite confusions between mechanisms responsible for individual development and mechanisms responsible for the transmission of traits from one generation to the next.

Genes are the standard units of inheritance discussed in biology. Population geneticists study the patterns of transmission of traits in populations from one generation to the next.

Molecular biologists identify coding sequences of DNA and hence the proteins that these sequences produce in the developing organism. Working together, molecular biologists and population geneticists can produce a convergent account of a particular gene, providing both its pattern of transmission and an account of its role in development. For example, medical geneticists may discover a pattern of inheritance for a disease in a family that leads them to hypothesize that there is a gene or a number of genes responsible for the development of the trait in individual humans.

Molecular analysis may then lead to the discovery of a sequence of DNA that codes for an unusual protein that is in part responsible for the development of the symptoms of the disease. Finally, population genetics techniques, such as heritability analysis, may then be applied to mechanisms discovered by molecular biologists. Mendelian genetics provides laws that govern the passing on of discrete traits from one generation to the next. For example, Mendel experimentally demonstrated particular patterns of inheritance for smooth and wrinkled peas in a population of pea plants.

Discrete or discontinuous traits contrast with continuous or quantitative traits. Height in humans and leaf number in trees are continuous traits. Continuous traits vary on a continuum that can be represented as a normal distribution, graphed as a bell curve. Most philosophical discussion about heredity and heritability arises from the study of continuous traits. The study of quantitative or continuous traits can be carried out by looking simply at phenotypes.

For example, if a population of plants varies in height we can ask how much of this variation is due to genes. Assessing the proportion of the variation of a trait in a population that is due to genes is achieved by a statistical method called the analysis of variance.

Once this analysis has been carried out a simple formula provides a number between 0 and 1 that is the heritability measure for the trait in question.

We use a few simple examples to illustrate the important concepts involved in producing heritability measures. Before we consider the analysis of variance and its contribution to heritability measures, it is helpful to understand the general concept of heritability. Heritability is a measure of genetic influence on variation. If a trait has high heritability, its varying from individual to individual in a population can be explained genetically.

An imaginary example illustrates one way of assessing heritability. A scenario like 2 is the most likely outcome. We can, however, do this with plants and other kinds of experimental organisms and as a result we can get a good sense of the contribution of genes to variation in a phenotypic trait.

Heritability can be estimated in humans by comparing resemblance in the phenotypic traits of twins See Section 4. For a more detailed discussion of twin studies. Twin studies make the following assumptions: Monozygotic, identical , twins share all their genes and their environment but dizygotic, fraternal , twins share half their genes and their environment. For any given trait, say height, we get the following results:. If heritability is low and variation in height is due mostly to the environment, then monozygotic twins will be as different in height from one another as dizygotic twins.

Finally, we can get a sense of the heritability of a trait by finding the slope of the regression line on the plots of offspring value for a trait graphed with parental value. If the slope is 1, the trait is entirely genetic and if the slope is 0, then the trait is not genetic at all. If the variation among individuals is due to variation in their genes, then offspring ought to resemble their parents. Heritability is always a value between 1 and 0.

In the graph below values for mid-parent height and mid-offspring height are plotted for a small sample population mid-parent height is the average of the height of both parents. The slope of the regression line is.

It should be stressed that this is a very informal presentation of this kind of estimation of heritability and for this approach to provide any useful results important constraints on the nature of the population and the relevant environment would have to be satisfied. So far we have introduced methods of measuring or calculating heritability that are somewhat intuitive.

The problem is that these methods do not acknowledge all that is involved in the production of variation in the quantitative traits of organisms in a population. If we stick to the example of variation in height in a sample population of humans, we will discover that in most representative samples, heights are distributed more or less normally. The variance in height is defined as the average of the squared difference between each measured height and the mean height for the population.

From here until the end of this section we adopt a specific strategy for presenting the equations used in spelling out heritability relations. We start out, with equation 1 below, by presenting the simplest version of the relevant equations. Simple equations such as 1 below are rarely ever satisfied but are routinely presented as adequate in elementary introductions to behavioral genetics. Subsequent equations in the sequence below render the relevant situation more accurately.

For example, variation in height of organisms could result from the contribution of several alleles at a locus where each allele contributes more height to the organism. More precisely, allele A could contribute. An organism with aa is 1. In the simplified case presented here this is.

But this equation still oversimplifies the situation and requires more refining to deal with quantitative traits. This occurs when alleles at one locus have an effect on the phenotype that is dependent upon alleles at one or more other loci.

This occurs when the effect of the environment on the phenotype differs between genotypes. For example, if plants with a genotype that tends to produce large plants also select nutrient- rich environments, by root growth or seed dispersal, and plants with a genotype that tends to produce small plants also select nutrient- poor environments, the variance in height would be increased.

If the relation were switched the variance would decrease Futuyma Factoring all the above in we now have the following:. Psychologists are interested in the contribution of genes to human psychological traits whereas evolutionary biologists use heritability measures to predict and measure the response of a trait to selection.

The relevant equation here is. Heritability in this context is referred to as realized heritability. Much of this discussion takes off from a paper by Lewontin in which he argues that the analysis of variance cannot provide us with answers to questions about how much genes contribute to variance in a given trait.

Before we turn to this and related discussions, we provide more detail on how heritability is measured in humans. He proposed the distinction between nature and nurture, arguing that traits due to nature were the products of inherited biological material. He also was the first to propose the study of human twins as a way of understanding the contribution of nature, as opposed to nurture, to human traits Burbridge ; see also Kronfeldner In studies of plants and other organisms, genetic makeup and environment can be systematically varied to assess the relative impact of genes and environment on phenotypic traits.

By tracking similarities and differences between groups of genetically related individuals reared in similar environments, twin and family studies permit some scientific investigation into sources of variance attributable to genetics and environment.

The ACE model is a factor analytic model that partitions phenotypic variance represented with boxes into three latent components represented with circles. For example, because both MZ and DZ twin pairs live in the same home, the correlation between their shared environment equals 1.

The formula is derived from an observed population statistic: the phenotypic correlation between pairs of MZ and DZ twins. Perhaps unsurprisingly, genetically identical twins are more similar than fraternal twins.

The implication is that the greater the genetic similarity, the greater the phenotypic similarity. Thus, narrow-sense, quantitative genetic heritability is intuitively defined as the difference between MZ and DZ correlations: the greater the difference, the higher the heritability. On the flip side, if there is minimal difference between MZ and DZ correlations, it would imply that genetics have little to do with variance in the trait in question, and result in a low heritability estimate.

Thus, MZ correlation are treated as the sum of A and C:. Thus, the DZ correlation is the sum of half additive genetic variance and common environment:. With narrow-sense heritability defined, one may deduce the formulas for estimating shared C :. Finally, non-shared environment may be derived from the assumption that genetic similarity, shared environment, and non-shared environment comprise the total variance in an observed trait:.

Consider the following example. Starting with heritability:. To estimate non-shared environment C , recall the formula derived earlier:. Research on heritability is currently undergoing a major transition. Too much has happened since the discovery of DNA to detail here, such as linkage analysis and candidate gene studies, but a few key developments are integral to understanding the most recent approaches to estimating heritability.

First, all modern approaches to heritability estimation are constructed from empirical data on Single Nucleotide Polymorphisms SNPs. In effect, SNPs are the fundamental units of genetic differences that occur between individuals. Early SNP chips tracked , variants across the genome; recent technology allows SNP detection in the excess of millions. With the development of fast and cheap SNP chips within the past ten years, one now has the ability to assess large populations of minute DNA-based differences between individuals, which confers a new approach to estimating heritability.

Historically, GWAS are conducted as case-control studies: SNP profiles are collected from one population of individuals who share a trait such as high ADHD or macular degeneration and are compared to SNP profiles of a control population lacking in the trait under investigation. If it turns out that populations, on average, with the trait of interest also share genetic similarities, then GWAS will flag the relevant SNPs as statistically significant.

After nearly a century of twin and family studies consistently demonstrating relatively high heritability of traits, there was some expectation that early GWAS would find a few genes of large effect. GWAS results in this respect have been disappointing. The first efforts to do this were met with disappointing results as well. Weedon et al. These meager results inspired an impetus in the GWAS community to conduct bigger and better studies that would be required to power the small effect sizes of individual SNPs.

Even when summing the small effects of hundreds of genome-wide significant SNPs, variance explained by GWAS results are still quite small. In an effort to increase the amount of phenotypic variance accounted for by GWAS results, Yang et al. While traditional heritability is estimated from coarse-grain genetic similarity of related individuals, SNP heritability is estimated from fine-grain genetic similarity of unrelated individuals. SNP heritability now comprises a family of highly sophisticated statistical techniques that seek to maximize proportions of phenotypic variance attributable to observed or imputed SNP variants.

SNP heritability exhibits a few features worth attention. First, instead of limiting analysis to SNPs who meet the strict p -value GWAS significance threshold, SNP heritability is derived by analyzing the complete set of SNPs for each participant sample—even those that are not associated with the trait of interest.

To this end, SNP heritability is biologically non-obvious.