Population structure (genetics)

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Population structure (also called genetic structure and population stratification) is the presence of a systematic difference in allele frequencies between subpopulations. In a randomly mating (or panmictic) population, allele frequencies are expected to be roughly similar between groups. However, mating tends to be non-random to some degree, causing structure to arise. For example, a barrier like a river can separate two groups of the same species and make it difficult for potential mates to cross; if a mutation occurs, over many generations it can spread and become common in one subpopulation while being completely absent in the other.

Contents

Genetic variants do not necessarily cause observable changes in organisms, but can be correlated by coincidence because of population structure—a variant that is common in a population that has a high rate of disease may erroneously be thought to cause the disease. For this reason, population structure is a common confounding variable in medical genetics studies, and accounting for and controlling its effect is important in genome wide association studies (GWAS). By tracing the origins of structure, it is also possible to study the genetic ancestry of groups and individuals.

Description

The basic cause of population structure in sexually reproducing species is non-random mating between groups: if all individuals within a population mate randomly, then the allele frequencies should be similar between groups. Population structure commonly arises from physical separation by distance or barriers, like mountains and rivers, followed by genetic drift. Other causes include gene flow from migrations, population bottlenecks and expansions, founder effects, evolutionary pressure, random chance, and (in humans) cultural factors. Even in lieu of these factors, individuals tend to stay close to where they were born, which means that alleles will not be distributed at random with respect to the full range of the species. [1] [2]

Measures

Population structure is a complex phenomenon and no single measure captures it entirely. Understanding a population's structure requires a combination of methods and measures. [3] [4] Many statistical methods rely on simple population models in order to infer historical demographic changes, such as the presence of population bottlenecks, admixture events or population divergence times. Often these methods rely on the assumption of panmictia, or homogeneity in an ancestral population. Misspecification of such models, for instance by not taking into account the existence of structure in an ancestral population, can give rise to heavily biased parameter estimates. [5] Simulation studies show that historical population structure can even have genetic effects that can easily be misinterpreted as historical changes in population size, or the existence of admixture events, even when no such events occurred. [6]

Heterozygosity

A population bottleneck can result in a loss of heterozygosity. In this hypothetical population, an allele has become fixed after the population repeatedly dropped from 10 to 3. Loss of heterozygosity over time in a bottlenecking population with label.png
A population bottleneck can result in a loss of heterozygosity. In this hypothetical population, an allele has become fixed after the population repeatedly dropped from 10 to 3.

One of the results of population structure is a reduction in heterozygosity. When populations split, alleles have a higher chance of reaching fixation within subpopulations, especially if the subpopulations are small or have been isolated for long periods. This reduction in heterozygosity can be thought of as an extension of inbreeding, with individuals in subpopulations being more likely to share a recent common ancestor. [7] The scale is important — an individual with both parents born in the United Kingdom is not inbred relative to that country's population, but is more inbred than two humans selected from the entire world. This motivates the derivation of Wright's F-statistics (also called "fixation indices"), which measure inbreeding through observed versus expected heterozygosity. [8] For example, measures the inbreeding coefficient at a single locus for an individual relative to some subpopulation : [9]

Here, is the fraction of individuals in subpopulation that are heterozygous. Assuming there are two alleles, that occur at respective frequencies , it is expected that under random mating the subpopulation will have a heterozygosity rate of . Then:

Similarly, for the total population , we can define allowing us to compute the expected heterozygosity of subpopulation and the value as: [9]

If F is 0, then the allele frequencies between populations are identical, suggesting no structure. The theoretical maximum value of 1 is attained when an allele reaches total fixation, but most observed maximum values are far lower. [7] FST is one of the most common measures of population structure and there are several different formulations depending on the number of populations and the alleles of interest. Although it is sometimes used as a genetic distance between populations, it does not always satisfy the triangle inequality and thus is not a metric. [10] It also depends on within-population diversity, which makes interpretation and comparison difficult. [4]

Admixture inference

An individual's genotype can be modelled as an admixture between K discrete clusters of populations. [9] Each cluster is defined by the frequencies of its genotypes, and the contribution of a cluster to an individual's genotypes is measured via an estimator. In 2000, Jonathan K. Pritchard introduced the STRUCTURE algorithm to estimate these proportions via Markov chain Monte Carlo, modelling allele frequencies at each locus with a Dirichlet distribution. [11] Since then, algorithms (such as ADMIXTURE) have been developed using other estimation techniques. [12] [13] Estimated proportions can be visualized using bar plots — each bar represents an individual, and is subdivided to represent the proportion of an individual's genetic ancestry from one of the K populations. [9]

Varying K can illustrate different scales of population structure; using a small K for the entire human population will subdivide people roughly by continent, while using large K will partition populations into finer subgroups. [9] Though clustering methods are popular, they are open to misinterpretation: for non-simulated data, there is never a "true" value of K, but rather an approximation considered useful for a given question. [3] They are sensitive to sampling strategies, sample size, and close relatives in data sets; there may be no discrete populations at all; and there may be hierarchical structure where subpopulations are nested. [3] Clusters may be admixed themselves, [9] and may not have a useful interpretation as source populations. [14]

Map of samples and population structure of North Africa and neighboring populations.png
A study of population structure of humans in Northern Africa and neighboring populations modelled using ADMIXTURE and assuming K=2,4,6,8 populations (Figure B, top to bottom). Varying K changes the scale of clustering. At K=2, 80% of the inferred ancestry for most North Africans is assigned to cluster that is common to Basque, Tuscan, and Qatari Arab individuals (in purple). At K=4, clines of North African ancestry appear (in light blue). At K=6, opposite clines of Near Eastern (Qatari) ancestry appear (in green). At K=8, Tunisian Berbers appear as a cluster (in dark blue). [15]

Dimensionality reduction

A map of the locations of genetic samples of several African populations (left) and principal components 1 and 2 of the data superimposed on the map (right). The principal coordinate plane has been rotated 16.11deg to align with the map. It corresponds to the east-west and north-south distributions of the populations. Procrustes-transformed PCA plot of genetic variation of Sub-Saharan African populations.png
A map of the locations of genetic samples of several African populations (left) and principal components 1 and 2 of the data superimposed on the map (right). The principal coordinate plane has been rotated 16.11° to align with the map. It corresponds to the east–west and north–south distributions of the populations.

Genetic data are high dimensional and dimensionality reduction techniques can capture population structure. Principal component analysis (PCA) was first applied in population genetics in 1978 by Cavalli-Sforza and colleagues and resurged with high-throughput sequencing. [9] [17] Initially PCA was used on allele frequencies at known genetic markers for populations, though later it was found that by coding SNPs as integers (for example, as the number of non-reference alleles) and normalizing the values, PCA could be applied at the level of individuals. [13] [18] One formulation considers individuals and bi-allelic SNPs. For each individual , the value at locus is is the number of non-reference alleles (one of ). If the allele frequency at is , then the resulting matrix of normalized genotypes has entries: [9]

PCA transforms data to maximize variance; given enough data, when each individual is visualized as point on a plot, discrete clusters can form. [13] Individuals with admixed ancestries will tend to fall between clusters, and when there is homogenous isolation by distance in the data, the top PC vectors will reflect geographic variation. [19] [13] The eigenvectors generated by PCA can be explicitly written in terms of the mean coalescent times for pairs of individuals, making PCA useful for inference about the population histories of groups in a given sample. PCA cannot, however, distinguish between different processes that lead to the same mean coalescent times. [20]

Multidimensional scaling and discriminant analysis have been used to study differentiation, population assignment, and to analyze genetic distances. [21] Neighborhood graph approaches like t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) can visualize continental and subcontinental structure in human data. [22] [23] With larger datasets, UMAP better captures multiple scales of population structure; fine-scale patterns can be hidden or split with other methods, and these are of interest when the range of populations is diverse, when there are admixed populations, or when examining relationships between genotypes, phenotypes, and/or geography. [23] [24] Variational autoencoders can generate artificial genotypes with structure representative of the input data, though they do not recreate linkage disequilibrium patterns. [25]

Demographic inference

Population structure is an important aspect of evolutionary and population genetics. Events like migrations and interactions between groups leave a genetic imprint on populations. Admixed populations will have haplotype chunks from their ancestral groups, which gradually shrink over time because of recombination. By exploiting this fact and matching shared haplotype chunks from individuals within a genetic dataset, researchers may trace and date the origins of population admixture and reconstruct historic events such as the rise and fall of empires, slave trades, colonialism, and population expansions. [26]

Role in genetic epidemiology

Population structure can be a problem for association studies, such as case-control studies, where the association between the trait of interest and locus could be incorrect. As an example, in a study population of Europeans and East Asians, an association study of chopstick usage may "discover" a gene in the Asian individuals that leads to chopstick use. However, this is a spurious relationship as the genetic variant is simply more common in Asians than in Europeans. [27] Also, actual genetic findings may be overlooked if the locus is less prevalent in the population where the case subjects are chosen. For this reason, it was common in the 1990s to use family-based data where the effect of population structure can easily be controlled for using methods such as the transmission disequilibrium test (TDT). [28]

Phenotypes (measurable traits), such as height or risk for heart disease, are the product of some combination of genes and environment. These traits can be predicted using polygenic scores, which seek to isolate and estimate the contribution of genetics to a trait by summing the effects of many individual genetic variants. To construct a score, researchers first enroll participants in an association study to estimate the contribution of each genetic variant. Then, they can use the estimated contributions of each genetic variant to calculate a score for the trait for an individual who was not in the original association study. If structure in the study population is correlated with environmental variation, then the polygenic score is no longer measuring the genetic component alone. [29]

Several methods can at least partially control for this confounding effect. The genomic control method was introduced in 1999 and is a relatively nonparametric method for controlling the inflation of test statistics. [30] It is also possible to use unlinked genetic markers to estimate each individual's ancestry proportions from some K subpopulations, which are assumed to be unstructured. [31] More recent approaches make use of principal component analysis (PCA), as demonstrated by Alkes Price and colleagues, [32] or by deriving a genetic relationship matrix (also called a kinship matrix) and including it in a linear mixed model (LMM). [33] [34]

PCA and LMMs have become the most common methods to control for confounding from population structure. Though they are likely sufficient for avoiding false positives in association studies, they are still vulnerable to overestimating effect sizes of marginally associated variants and can substantially bias estimates of polygenic scores and trait heritability. [35] [36] If environmental effects are related to a variant that exists in only one specific region (for example, a pollutant is found in only one city), it may not be possible to correct for this population structure effect at all. [29] For many traits, the role of structure is complex and not fully understood, and incorporating it into genetic studies remains a challenge and is an active area of research. [37]

Related Research Articles

An allele, or allelomorph, is a variant of the sequence of nucleotides at a particular location, or locus, on a DNA molecule.

Population genetics is a subfield of genetics that deals with genetic differences within and among populations, and is a part of evolutionary biology. Studies in this branch of biology examine such phenomena as adaptation, speciation, and population structure.

<span class="mw-page-title-main">Hardy–Weinberg principle</span> Principle in genetics

In population genetics, the Hardy–Weinberg principle, also known as the Hardy–Weinberg equilibrium, model, theorem, or law, states that allele and genotype frequencies in a population will remain constant from generation to generation in the absence of other evolutionary influences. These influences include genetic drift, mate choice, assortative mating, natural selection, sexual selection, mutation, gene flow, meiotic drive, genetic hitchhiking, population bottleneck, founder effect,inbreeding and outbreeding depression.

Allele frequency, or gene frequency, is the relative frequency of an allele at a particular locus in a population, expressed as a fraction or percentage. Specifically, it is the fraction of all chromosomes in the population that carry that allele over the total population or sample size. Microevolution is the change in allele frequencies that occurs over time within a population.

<span class="mw-page-title-main">Quantitative genetics</span> Study of the inheritance of continuously variable traits

Quantitative genetics is the study of quantitative traits, which are phenotypes that vary continuously—such as height or mass—as opposed to phenotypes and gene-products that are discretely identifiable—such as eye-colour, or the presence of a particular biochemical.

<span class="mw-page-title-main">Founder effect</span> Effect in population genetics

In population genetics, the founder effect is the loss of genetic variation that occurs when a new population is established by a very small number of individuals from a larger population. It was first fully outlined by Ernst Mayr in 1942, using existing theoretical work by those such as Sewall Wright. As a result of the loss of genetic variation, the new population may be distinctively different, both genotypically and phenotypically, from the parent population from which it is derived. In extreme cases, the founder effect is thought to lead to the speciation and subsequent evolution of new species.

In population genetics, linkage disequilibrium (LD) is a measure of non-random association between segments of DNA (alleles) at different positions on the chromosome (loci) in a given population based on a comparison between the frequency at which two alleles are detected together at the same loci and the frequencies at which each allele is detected at that loci overall, whether it occurs with or without the other allele of interest. Loci are said to be in linkage disequilibrium when the frequency of being detected together is higher or lower than expected if the loci were independent and associated randomly.

In population genetics, F-statistics describe the statistically expected level of heterozygosity in a population; more specifically the expected degree of (usually) a reduction in heterozygosity when compared to Hardy–Weinberg expectation.

<span class="mw-page-title-main">Wahlund effect</span>

In population genetics, the Wahlund effect is a reduction of heterozygosity in a population caused by subpopulation structure. Namely, if two or more subpopulations are in a Hardy–Weinberg equilibrium but have different allele frequencies, the overall heterozygosity is reduced compared to if the whole population was in equilibrium. The underlying causes of this population subdivision could be geographic barriers to gene flow followed by genetic drift in the subpopulations.

<span class="mw-page-title-main">Genotype frequency</span>

Genetic variation in populations can be analyzed and quantified by the frequency of alleles. Two fundamental calculations are central to population genetics: allele frequencies and genotype frequencies. Genotype frequency in a population is the number of individuals with a given genotype divided by the total number of individuals in the population. In population genetics, the genotype frequency is the frequency or proportion of genotypes in a population.

Genetic load is the difference between the fitness of an average genotype in a population and the fitness of some reference genotype, which may be either the best present in a population, or may be the theoretically optimal genotype. The average individual taken from a population with a low genetic load will generally, when grown in the same conditions, have more surviving offspring than the average individual from a population with a high genetic load. Genetic load can also be seen as reduced fitness at the population level compared to what the population would have if all individuals had the reference high-fitness genotype. High genetic load may put a population in danger of extinction.

Mutation–selection balance is an equilibrium in the number of deleterious alleles in a population that occurs when the rate at which deleterious alleles are created by mutation equals the rate at which deleterious alleles are eliminated by selection. The majority of genetic mutations are neutral or deleterious; beneficial mutations are relatively rare. The resulting influx of deleterious mutations into a population over time is counteracted by negative selection, which acts to purge deleterious mutations. Setting aside other factors, the equilibrium number of deleterious alleles is then determined by a balance between the deleterious mutation rate and the rate at which selection purges those mutations.

<span class="mw-page-title-main">Genetic distance</span> Measure of divergence between populations

Genetic distance is a measure of the genetic divergence between species or between populations within a species, whether the distance measures time from common ancestor or degree of differentiation. Populations with many similar alleles have small genetic distances. This indicates that they are closely related and have a recent common ancestor.

Coalescent theory is a model of how alleles sampled from a population may have originated from a common ancestor. In the simplest case, coalescent theory assumes no recombination, no natural selection, and no gene flow or population structure, meaning that each variant is equally likely to have been passed from one generation to the next. The model looks backward in time, merging alleles into a single ancestral copy according to a random process in coalescence events. Under this model, the expected time between successive coalescence events increases almost exponentially back in time. Variance in the model comes from both the random passing of alleles from one generation to the next, and the random occurrence of mutations in these alleles.

<span class="mw-page-title-main">Fixation index</span> Measure of population differentiation

The fixation index (FST) is a measure of population differentiation due to genetic structure. It is frequently estimated from genetic polymorphism data, such as single-nucleotide polymorphisms (SNP) or microsatellites. Developed as a special case of Wright's F-statistics, it is one of the most commonly used statistics in population genetics. Its values range from 0 to 1, with 0.15 being substantially differentiated and 1 being complete differentiation.

<span class="mw-page-title-main">Human genetic variation</span> Genetic diversity in human populations

Human genetic variation is the genetic differences in and among populations. There may be multiple variants of any given gene in the human population (alleles), a situation called polymorphism.

<span class="mw-page-title-main">Zygosity</span> Degree of similarity of the alleles in an organism

Zygosity is the degree to which both copies of a chromosome or gene have the same genetic sequence. In other words, it is the degree of similarity of the alleles in an organism.

<span class="mw-page-title-main">Polygenic score</span> Numerical score aimed at predicting a trait based on variation in multiple genetic loci

In genetics, a polygenic score (PGS) is a number that summarizes the estimated effect of many genetic variants on an individual's phenotype. The PGS is also called the polygenic index (PGI) or genome-wide score; in the context of disease risk, it is called a polygenic risk score or genetic risk score. The score reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. It gives an estimate of how likely an individual is to have a given trait based only on genetics, without taking environmental factors into account; and it is typically calculated as a weighted sum of trait-associated alleles.

<span class="mw-page-title-main">Reinforcement (speciation)</span> Process of increasing reproductive isolation

Reinforcement is a process of speciation where natural selection increases the reproductive isolation between two populations of species. This occurs as a result of selection acting against the production of hybrid individuals of low fitness. The idea was originally developed by Alfred Russel Wallace and is sometimes referred to as the Wallace effect. The modern concept of reinforcement originates from Theodosius Dobzhansky. He envisioned a species separated allopatrically, where during secondary contact the two populations mate, producing hybrids with lower fitness. Natural selection results from the hybrid's inability to produce viable offspring; thus members of one species who do not mate with members of the other have greater reproductive success. This favors the evolution of greater prezygotic isolation. Reinforcement is one of the few cases in which selection can favor an increase in prezygotic isolation, influencing the process of speciation directly. This aspect has been particularly appealing among evolutionary biologists.

Human genetic clustering refers to patterns of relative genetic similarity among human individuals and populations, as well as the wide range of scientific and statistical methods used to study this aspect of human genetic variation.

References

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