8.3 Population structure and admixture
7 min read•august 20, 2024
and are key concepts in population genetics. They help us understand how genetic variation is distributed within and between populations, shedding light on evolutionary history and demographic processes.
These concepts are crucial for interpreting genetic data in various applications. From to , understanding population structure helps researchers avoid false conclusions and uncover meaningful genetic associations with traits and diseases.
Genetic variation in populations
- Genetic variation refers to the differences in DNA sequences among individuals within a population
- Understanding the patterns and sources of genetic variation is crucial for population genetics studies and has applications in fields such as medicine, conservation biology, and evolutionary biology
- Key concepts in this section include allele frequencies, genotype frequencies, and the factors that influence genetic variation in populations
Hardy-Weinberg equilibrium
Assumptions of HWE
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- Random mating: Individuals in the population mate randomly with respect to their genotypes
- No selection: There is no selective advantage or disadvantage associated with any genotype
- No mutation: No new alleles are introduced into the population through mutation
- No : There is no into or out of the population
- Infinite population size: The population is large enough to minimize the effects of
Deviations from HWE
- ( or inbreeding) can lead to an excess of homozygotes and a deficit of heterozygotes compared to HWE expectations
- Selection can alter allele frequencies if certain genotypes have a selective advantage or disadvantage
- Mutation introduces new alleles into the population, disrupting the equilibrium
- Migration (gene flow) can introduce new alleles or change allele frequencies in the population
- Finite population size leads to genetic drift, causing random fluctuations in allele frequencies over time
Population structure
Subpopulations vs admixture
- Population structure refers to the presence of genetically distinct subpopulations within a larger population
- Subpopulations arise due to factors such as geographic isolation, limited gene flow, and local adaptation
- Admixture occurs when individuals from two or more previously isolated populations interbreed, resulting in the exchange of genetic material between the populations
Factors influencing structure
- Geographic barriers (mountains, rivers, oceans) can limit gene flow and promote population differentiation
- Mating preferences and assortative mating can lead to the formation of genetically distinct subpopulations
- that vary across environments can drive local adaptation and population differentiation
Consequences of structure
- Population structure can lead to spurious associations in genome-wide association studies (GWAS) if not properly accounted for
- Admixture can introduce novel genetic variation and increase genetic diversity in populations
- Population structure can influence the interpretation of genetic variation and the inference of evolutionary history
Admixture
Admixture events
- Admixture events occur when individuals from two or more previously isolated populations interbreed
- Historical admixture events (African-American, Latino populations) have shaped the genetic diversity of many modern populations
- Recent admixture events (due to increased global migration) are becoming more common and can have implications for population genetics studies
Admixture mapping
- Admixture mapping is a method that leverages the admixture in populations to identify genetic regions associated with traits or diseases
- It relies on the principle that admixed individuals inherit chromosomal segments from their ancestral populations
- By comparing the frequency of ancestral alleles in cases and controls, admixture mapping can identify regions harboring disease-associated variants
Local ancestry inference
- is the process of determining the ancestral origin of chromosomal segments in admixed individuals
- Methods for local ancestry inference include hidden Markov models (HMMs) and machine learning approaches
- Accurate local ancestry inference is crucial for admixture mapping and understanding the genetic history of
Measures of population differentiation
Wright's fixation indices
- (, FIS, FIT) are measures of population differentiation and inbreeding
- FST measures the proportion of total genetic variation that is due to differences among subpopulations
- FIS measures the deviation from HWE within subpopulations due to non-random mating
- FIT measures the deviation from HWE in the total population due to both population structure and non-random mating
FST vs FIS vs FIT
- FST ranges from 0 (no differentiation) to 1 (complete differentiation) and is commonly used to quantify population structure
- FIS ranges from -1 (excess of heterozygotes) to 1 (excess of homozygotes) and reflects the level of inbreeding within subpopulations
- FIT ranges from -1 to 1 and combines the effects of population structure (FST) and inbreeding (FIS)
Limitations of FST
- FST assumes a simple model of population structure (island model) and may not capture complex patterns of differentiation
- FST is influenced by the level of genetic diversity within populations, which can lead to biased estimates when comparing populations with different levels of diversity
- FST does not account for the phylogenetic relationships among populations and may not accurately reflect the evolutionary history of populations
Detecting population structure
Principal component analysis (PCA)
- PCA is a dimensionality reduction technique that can be used to visualize patterns of genetic variation among individuals
- It transforms the high-dimensional genotype data into a smaller number of principal components (PCs) that capture the main axes of variation
- Plotting individuals based on their PC scores can reveal clusters corresponding to genetically distinct subpopulations
Model-based clustering methods
- Model-based clustering methods (STRUCTURE, ADMIXTURE) assign individuals to ancestral populations based on their genotypes
- These methods assume that the population is a mixture of K ancestral populations and estimate the admixture proportions for each individual
- The optimal number of ancestral populations (K) can be determined using cross-validation or other model selection criteria
Admixture proportion estimation
- involves quantifying the proportion of an individual's genome that is derived from each ancestral population
- Methods for admixture proportion estimation include maximum likelihood approaches and Bayesian methods
- Accurate estimation of admixture proportions is important for understanding the genetic history of admixed populations and for admixture mapping
Linkage disequilibrium (LD)
LD measures (D, D', r2)
- LD refers to the non-random association of alleles at different loci in a population
- D is the difference between the observed and expected frequencies of haplotypes under linkage equilibrium
- D' is a normalized measure of D that ranges from -1 to 1 and accounts for differences in allele frequencies
- r2 is the squared correlation coefficient between alleles at two loci and ranges from 0 (no LD) to 1 (perfect LD)
LD decay over distance
- LD tends to decay with increasing physical distance between loci due to recombination
- The rate of LD decay varies across the genome and is influenced by factors such as recombination rate, population history, and selection
- The extent of LD in a population determines the resolution of genetic mapping studies and the power to detect associations
LD in admixed populations
- Admixture can create extended regions of LD that span larger distances compared to non-admixed populations
- The pattern of LD in admixed populations is influenced by the timing and extent of admixture events
- Admixture LD can be leveraged for admixture mapping and local ancestry inference
Applications of population structure
Genome-wide association studies (GWAS)
- GWAS aim to identify genetic variants associated with complex traits or diseases by comparing allele frequencies between cases and controls
- Population structure can lead to spurious associations in GWAS if not properly accounted for
- Methods for controlling population structure in GWAS include (PCA) and mixed linear models
Controlling for population stratification
- Population stratification refers to the presence of systematic differences in allele frequencies between cases and controls due to population structure
- Failure to control for population stratification can lead to false-positive associations in GWAS
- Methods for controlling population stratification include genomic control, structured association, and principal component analysis (PCA)
Admixture mapping for disease genes
- Admixture mapping leverages the extended LD in admixed populations to identify genetic regions associated with diseases that differ in prevalence between ancestral populations
- It compares the ancestry proportions of cases and controls at each locus across the genome
- Admixture mapping has been successfully applied to identify disease-associated genes for conditions such as hypertension, type 2 diabetes, and prostate cancer
Challenges in population genetics
Sampling bias and ascertainment
- Sampling bias occurs when the individuals included in a study are not representative of the larger population
- Ascertainment bias arises when the genetic markers used in a study are not randomly selected and may over- or under-represent certain types of variation
- These biases can distort estimates of population parameters and lead to incorrect conclusions about population history and structure
Distinguishing selection vs demography
- Both selection and demographic processes (population bottlenecks, expansions, migrations) can shape patterns of genetic variation in populations
- Distinguishing the effects of selection from those of demography is challenging and requires careful statistical modeling and hypothesis testing
- Methods for detecting selection include tests based on allele frequency spectra, haplotype structure, and population differentiation
Limitations of admixture inference
- Admixture inference methods rely on assumptions about the number of ancestral populations and the admixture model
- The accuracy of admixture inference can be limited by factors such as the genetic similarity of ancestral populations, the timing and complexity of admixture events, and the density of genetic markers
- Admixture inference results should be interpreted with caution and validated using independent sources of information (historical records, archaeological evidence)
Key Terms to Review (31)
Admixed populations: Admixed populations refer to groups of individuals that arise from the mixing of two or more previously distinct populations, resulting in a new genetic composition. This mixing can occur through migration, interbreeding, or colonization, leading to a complex interplay of genetic traits that can reflect the ancestry of the contributing populations. Understanding these populations is vital for studying genetic diversity, population structure, and evolutionary dynamics.
Admixture: Admixture refers to the mixing of different populations, resulting in the incorporation of genetic material from one group into another. This process plays a crucial role in shaping genetic diversity within populations and can provide insights into historical migrations, population structure, and the evolution of species. Understanding admixture helps researchers decipher the complexity of genetic traits and diseases across various groups.
Admixture mapping: Admixture mapping is a method used to identify genetic variants associated with traits or diseases by analyzing populations that are the result of mixing two or more ancestral populations. This approach leverages the genetic structure and markers from these mixed populations to detect associations between specific genes and phenotypes. By understanding how genetic contributions vary among different ancestries, researchers can uncover the genetic basis of complex traits and diseases more effectively.
Admixture proportion estimation: Admixture proportion estimation refers to the process of quantifying the genetic contributions from different ancestral populations within an individual's genome. This estimation is crucial in understanding population structure and the complex history of human migration, as it helps identify how much of an individual's genetic makeup comes from various ancestral groups.
Ancestral polymorphism: Ancestral polymorphism refers to the presence of multiple alleles at a specific genetic locus in a population, inherited from a common ancestor. This phenomenon can lead to genetic diversity within populations and can complicate the understanding of population structure and admixture because it can mimic signals of recent admixture or local adaptation, making it challenging to discern the true evolutionary history of a population.
Assortative mating: Assortative mating refers to the non-random mating pattern where individuals with similar phenotypes or genotypes mate more frequently than would be expected under random mating. This phenomenon can significantly influence the genetic structure of populations and is crucial in understanding population dynamics and evolutionary processes.
Effective population size: Effective population size is a concept that quantifies the number of individuals in a population who contribute to the gene pool of the next generation. It differs from actual population size as it accounts for factors like unequal sex ratios, fluctuating population sizes, and variations in reproductive success. This measure is crucial for understanding genetic diversity, evolutionary potential, and the effects of linkage disequilibrium and population structure, as it influences how genes are passed through generations.
Founder effect: The founder effect is a genetic phenomenon that occurs when a small group of individuals establishes a new population, leading to reduced genetic diversity and an increased frequency of certain alleles. This effect often results in the new population exhibiting traits that may differ significantly from the original population due to the limited gene pool. The founder effect is closely tied to concepts like genetic drift and can heavily influence patterns of linkage disequilibrium as well as population structure and admixture.
Fst: Fst, or fixation index, is a measure of population structure that quantifies genetic differentiation between subpopulations. It ranges from 0 to 1, where 0 indicates no differentiation (i.e., subpopulations are genetically identical) and 1 indicates complete differentiation (i.e., the subpopulations are completely distinct). This metric is crucial for understanding the extent of genetic variation and the historical processes that shape population structure and admixture.
Gene flow: Gene flow refers to the transfer of genetic material between populations, which can occur through processes like migration and reproduction. This movement of genes can alter allele frequencies within a population and is essential for maintaining genetic diversity, allowing populations to adapt to changing environments and influencing evolutionary trajectories.
Genetic drift: Genetic drift is a mechanism of evolution that refers to random changes in the frequency of alleles within a population due to chance events. It often has a more significant impact on smaller populations, leading to the loss or fixation of alleles over time. This random nature of genetic drift can interact with other evolutionary forces like selection, contributing to the overall genetic diversity and structure of populations.
Genome-wide association studies: Genome-wide association studies (GWAS) are research methods used to identify genetic variants linked to specific diseases or traits by scanning the genomes of many individuals. These studies leverage large sample sizes to detect associations between genetic variations, such as single nucleotide polymorphisms (SNPs), and phenotypic traits. GWAS are crucial for understanding the genetic basis of complex diseases, informing population genetics, and guiding personalized medicine approaches.
Haplotype diversity: Haplotype diversity refers to the variety of different haplotypes present within a population, indicating genetic variation and the evolutionary history of that group. This measure is crucial in understanding how genetic differences are distributed among individuals and populations, which can reveal insights into population structure, ancestry, and the effects of admixture over time.
Hardy-Weinberg Equilibrium: Hardy-Weinberg Equilibrium is a fundamental principle in population genetics that describes the condition under which allele and genotype frequencies remain constant from generation to generation in a population, provided that certain assumptions are met. It serves as a baseline for studying evolutionary processes, and deviations from this equilibrium can indicate the effects of factors like selection, mutation, migration, and genetic drift. Understanding this concept helps explain how genetic variation is maintained or altered within populations.
Isolation by distance: Isolation by distance refers to a phenomenon in population genetics where individuals that are geographically closer are more likely to interbreed than those that are farther apart. This concept highlights how physical separation affects gene flow and can lead to genetic differentiation among populations, impacting their overall structure and potential for admixture.
J.B.S. Haldane: J.B.S. Haldane was a British geneticist and evolutionary biologist known for his significant contributions to population genetics and the understanding of evolutionary processes. He played a crucial role in integrating Mendelian genetics with Darwinian evolution, proposing that genetic variations can affect population structure and contribute to admixture between different populations.
Ld measures: Linkage Disequilibrium (LD) measures are statistical metrics used to assess the non-random association of alleles at different loci in a given population. LD measures provide insights into the structure of genetic variation within populations, helping to identify relationships between genetic markers and traits. Understanding these measures is crucial for exploring population structure and admixture, as they can reveal how genetic variants are inherited together more often than expected due to chance.
Linkage disequilibrium: Linkage disequilibrium refers to the non-random association of alleles at different loci in a population, meaning certain combinations of alleles occur together more often than would be expected under random mating. This concept is important for understanding how genetic variants are inherited and can indicate underlying genetic structures, population history, and evolutionary dynamics.
Local ancestry inference: Local ancestry inference is a computational method used to determine the ancestral origins of specific segments of an individual's genome. This technique helps identify the contributions of different ancestral populations to an individual's genetic makeup, providing insights into population structure and admixture processes. By analyzing genetic variation, researchers can reconstruct how different ancestries have influenced an individual's genetic profile over time.
Microsatellites: Microsatellites are short, repetitive sequences of DNA, typically consisting of 1 to 6 base pairs repeated multiple times, which can vary in length among individuals. They are highly polymorphic and serve as important genetic markers for studying population structure and admixture, allowing researchers to assess genetic diversity, migration patterns, and evolutionary relationships among different groups.
Migration: Migration is the movement of individuals or groups from one location to another, often resulting in changes in genetic diversity and population structure. It plays a crucial role in shaping populations, as it can introduce new genetic material, leading to admixture and influencing evolutionary processes. The study of migration helps us understand how populations adapt and evolve over time.
Non-random mating: Non-random mating is a mating pattern where individuals choose mates based on specific traits or characteristics rather than randomly. This behavior can lead to certain alleles becoming more common in a population, influencing genetic diversity and evolutionary processes. Non-random mating can occur through mechanisms such as assortative mating, where similar phenotypes mate more frequently, or disassortative mating, where different phenotypes are preferred.
Population Bottleneck: A population bottleneck refers to a sharp reduction in the size of a population due to environmental events or human activities, leading to a decrease in genetic diversity. This reduction can significantly alter the genetic structure of a population, making it more susceptible to diseases and reducing its ability to adapt to future changes. Understanding how bottlenecks affect population structure and admixture is crucial in studying evolutionary processes and conservation biology.
Population structure: Population structure refers to the composition of a population in terms of its genetic variation, demographic characteristics, and spatial distribution. Understanding population structure is crucial for studying how different groups within a population may experience varying evolutionary pressures, leading to differences in allele frequencies, which are essential in applications such as conservation genetics and human health.
Principal Component Analysis: Principal Component Analysis (PCA) is a statistical technique used to simplify complex datasets by reducing their dimensionality while retaining the most important features. By transforming the data into a new set of variables called principal components, PCA helps in uncovering patterns, identifying structure, and visualizing high-dimensional data. This technique plays a crucial role in analyzing population structure, examining gene expression differences, exploring gene co-expression networks, and integrating multi-omics datasets.
Selection pressures: Selection pressures are environmental factors that influence the survival and reproduction of individuals within a population, acting as a driving force in natural selection. These pressures can lead to changes in allele frequencies over time, shaping the genetic makeup of populations and influencing evolutionary processes. By determining which traits are advantageous or disadvantageous in a given environment, selection pressures play a crucial role in population structure and admixture.
SNPs: Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation among individuals. They occur when a single nucleotide in the genome sequence is altered, which can impact gene function and contribute to differences in traits, diseases, and responses to drugs. SNPs are particularly useful in genetic studies for linking genes to traits and understanding population diversity.
Structure software: Structure software refers to a category of computational tools and algorithms specifically designed to analyze and model the genetic structure of populations, focusing on the relationships and genetic variation within and between groups. This type of software often incorporates statistical methods to identify population structure, assess admixture events, and visualize genetic data, making it crucial for understanding evolutionary dynamics and demographic history.
Substructure: In the context of population genetics, substructure refers to the presence of distinct groups within a larger population that exhibit genetic differentiation. These subpopulations may have limited gene flow between them, often due to geographic, behavioral, or ecological barriers, which can lead to unique genetic signatures that reflect their specific histories and adaptations.
Wright-Sewall: The Wright-Sewall model is a foundational framework in population genetics that describes the genetic structure of populations and the processes of evolution through natural selection, mutation, migration, and genetic drift. It provides a mathematical basis for understanding how allele frequencies change over time in a population and plays a key role in explaining population structure and admixture.
Wright's fixation indices: Wright's fixation indices are statistical measures used to quantify the genetic structure of populations and the extent of genetic differentiation between them. These indices provide insights into how much genetic variation exists within populations compared to that between different populations, helping to understand the influence of factors like population structure, migration, and admixture on genetic diversity.