5.5 Positive and negative selection
9 min read•august 20, 2024
Selection is a powerful evolutionary force shaping genetic variation in populations. It comes in different flavors: favors advantageous alleles, removes harmful ones, and maintains diversity. These processes leave distinct signatures in genomes.
Detecting selection involves various statistical methods comparing genetic variation patterns. These include the , , and . Understanding selection helps us unravel human evolution, combat drug resistance, and inform conservation efforts.
Types of selection
- Selection is a key evolutionary force shaping the genetic variation within populations
- Types of selection include positive, negative, and balancing selection, each with distinct effects on allele frequencies and patterns of genetic diversity
Positive vs negative selection
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- Positive selection favors the spread of advantageous alleles, increasing their frequency in the population over time (lactase persistence)
- Negative selection, also known as , removes deleterious alleles from the population, preventing their accumulation (genetic disorders)
- The strength and direction of selection can vary across different genomic regions and environmental conditions
Balancing selection
- Balancing selection maintains multiple alleles at a locus in the population, often due to heterozygote advantage or frequency-dependent selection
- Examples of balancing selection include:
- Sickle cell anemia and malaria resistance in humans
- Self-incompatibility alleles in plants
- Balancing selection can result in increased genetic diversity and the maintenance of polymorphisms over long evolutionary timescales
Detecting selection
- Various statistical tests and approaches have been developed to detect signatures of selection in genomic data
- These methods rely on comparing patterns of genetic variation within and between species, or across different genomic regions
dN/dS ratio
- The dN/dS ratio compares the rates of nonsynonymous (dN) and synonymous (dS) substitutions in protein-coding genes
- A dN/dS ratio > 1 indicates positive selection, while a ratio < 1 suggests negative selection
- This method is widely used to detect selection acting on individual genes or codons
McDonald-Kreitman test
- The McDonald-Kreitman test compares the ratio of nonsynonymous to synonymous substitutions between species (divergence) to the ratio of nonsynonymous to synonymous polymorphisms within species (polymorphism)
- An excess of nonsynonymous divergence relative to polymorphism indicates positive selection, while an excess of nonsynonymous polymorphism suggests negative selection
- This test is robust to demographic effects and can detect selection over longer evolutionary timescales
Tajima's D statistic
- Tajima's D statistic compares the average number of pairwise differences between sequences (π) to the number of segregating sites (S)
- Negative values of Tajima's D indicate an excess of rare alleles, consistent with positive selection or population expansion
- Positive values of Tajima's D suggest an excess of intermediate frequency alleles, indicative of balancing selection or population bottlenecks
Fay and Wu's H test
- compares the frequency of derived alleles to the frequency expected under neutrality
- An excess of high-frequency derived alleles suggests positive selection, while a deficiency of high-frequency derived alleles indicates negative selection
- This test is particularly sensitive to the effects of recent positive selection
Extended haplotype homozygosity (EHH)
- EHH measures the degree of linkage disequilibrium (LD) surrounding a core haplotype
- Positive selection can result in extended regions of high EHH, as the favored allele rapidly increases in frequency along with linked variants
- EHH is useful for detecting ongoing or incomplete
Integrated haplotype score (iHS)
- The iHS is a statistic that compares the EHH of the derived and ancestral alleles at a given SNP
- Large positive or negative values of iHS indicate positive selection favoring the derived or ancestral allele, respectively
- iHS is more powerful than EHH for detecting selection in regions with low recombination rates
Positive selection
- Positive selection occurs when an allele confers a advantage and increases in frequency within a population
- Positive selection can lead to rapid evolutionary change and adaptation to new environments or selective pressures
Selective sweeps
- A selective sweep occurs when a beneficial allele rapidly increases in frequency, eventually reaching in the population
- During a selective sweep, linked neutral variants hitchhike along with the selected allele, resulting in reduced genetic diversity around the selected site
- Selective sweeps can be classified as hard or , depending on the origin and number of beneficial alleles
Hard vs soft sweeps
- occur when a single beneficial mutation arises and rapidly increases in frequency, leading to a complete loss of genetic variation at linked sites (lactase persistence)
- Soft sweeps involve the simultaneous increase in frequency of multiple beneficial alleles, either from standing genetic variation or recurrent mutation (pesticide resistance)
- Soft sweeps result in a more subtle reduction in genetic diversity compared to hard sweeps
Genetic hitchhiking
- refers to the increase in frequency of neutral alleles linked to a positively selected allele
- Hitchhiking can lead to a reduction in genetic diversity and an excess of rare alleles in the vicinity of the selected site
- The extent of hitchhiking depends on the strength of selection, recombination rate, and population size
Examples of positive selection
- Lactase persistence in humans, enabling adult milk consumption
- Malaria resistance conferred by the sickle cell allele in African populations
- Coat color variations in domesticated animals, such as white spots in horses
- in bacteria, driven by the use of antibiotics in medicine and agriculture
Negative selection
- Negative selection, also known as purifying selection, removes deleterious alleles from a population, preventing their accumulation over time
- Negative selection is a key force maintaining the function and stability of genes and gene networks
Purifying selection
- Purifying selection eliminates that negatively impact fitness
- The strength of purifying selection depends on the fitness effect of the mutation and the effective population size
- Purifying selection is more effective in large populations and for mutations with severe fitness consequences
Background selection
- refers to the reduction in genetic diversity at neutral sites linked to deleterious mutations
- As deleterious mutations are purged from the population, linked neutral variants are also removed, resulting in a decrease in genetic variation
- The impact of background selection is more pronounced in regions of low recombination and high gene density
Deleterious mutations
- Deleterious mutations are genetic changes that reduce an individual's fitness, such as mutations disrupting protein function or gene regulation
- The majority of new mutations are deleterious, and their accumulation can lead to a decline in population fitness
- Deleterious mutations are the primary target of negative selection
Fitness effects of mutations
- The fitness effect of a mutation determines its fate in the population and the strength of selection acting upon it
- Mutations can be classified as lethal, deleterious, neutral, or beneficial, based on their impact on fitness
- The of new mutations is a key parameter in population genetics and is shaped by the action of selection
Evolutionary consequences
- Selection has profound consequences for the evolution of populations and species, shaping patterns of genetic variation and adaptation
Adaptation vs constraint
- Adaptation refers to the process by which populations evolve to become better suited to their environment through the action of positive selection
- Evolutionary constraint, imposed by negative selection, limits the range of viable genetic variation and maintains the function of essential genes and pathways
- The balance between adaptation and constraint determines the rate and direction of evolutionary change
Maintenance of genetic variation
- Selection can maintain genetic variation in populations through various mechanisms, such as balancing selection and spatially or temporally varying selection
- Genetic variation is essential for the ability of populations to respond to changing environmental conditions and evolve over time
- The maintenance of genetic variation is influenced by factors such as population size, mutation rate, and the strength and type of selection
Impact on genome evolution
- Selection leaves distinct signatures in the genome, such as reduced diversity around selected sites and an excess of rare or high-frequency alleles
- The efficiency of selection depends on factors such as recombination rate, gene density, and the effective population size
- Selection can drive the evolution of genome architecture, including the arrangement of genes and regulatory elements, and the distribution of recombination hotspots
Computational methods
- Computational methods play a crucial role in detecting and quantifying selection from genomic data, enabling the study of evolutionary processes at unprecedented scales
Simulation of selection
- Simulations of selection are used to generate expected patterns of genetic variation under different selective scenarios
- These simulations help to establish null models and assess the power and accuracy of statistical tests for detecting selection
- Simulation frameworks, such as SLiM and msprime, allow for the incorporation of complex demographic histories and selection models
Coalescent theory
- is a mathematical framework that describes the ancestry of genetic lineages back in time
- The coalescent is used to model the effects of selection on genealogies and patterns of genetic variation
- Coalescent simulations are widely used to generate null distributions for statistical tests of selection and to infer demographic histories
Ancestral recombination graph
- The is an extension of the coalescent that incorporates recombination events
- ARGs are used to model the complex ancestry of genetic sequences in the presence of recombination and selection
- Inference methods based on ARGs, such as ARGweaver, can be used to detect selection and estimate historical recombination rates
Machine learning approaches
- , such as support vector machines and deep learning, are increasingly being applied to detect selection from genomic data
- These methods can integrate multiple summary statistics and leverage patterns across the genome to improve the power and accuracy of selection scans
- Machine learning approaches are particularly useful for detecting soft sweeps and selection on polygenic traits, which may be difficult to detect using traditional methods
Applications
- The study of selection has numerous applications across diverse fields, from understanding human evolution to managing agricultural and natural resources
Human evolution
- Detecting selection in the human genome has provided insights into the genetic basis of adaptation to different environments, diets, and disease pressures
- Examples of selection in humans include lactase persistence, high-altitude adaptation, and resistance to infectious diseases (malaria, HIV)
- Studying selection in ancient human genomes has shed light on the timing and nature of key evolutionary events, such as the out-of-Africa migration and the interbreeding with archaic hominins
Domestication and artificial selection
- Domestication involves the artificial selection of desirable traits in plants and animals, leading to rapid evolutionary change
- Detecting selection in domesticated species has revealed the genetic basis of traits such as increased yield, altered morphology, and behavioral changes (docility, tameness)
- Examples of selection during domestication include the evolution of non-shattering seeds in cereals, increased milk production in cattle, and changes in coat color and pattern in dogs
Drug resistance in pathogens
- The evolution of drug resistance in pathogens is a major public health concern, driven by the strong imposed by antimicrobial use
- Detecting selection in pathogen genomes can identify the genetic basis of resistance and inform strategies for managing the spread of resistant strains
- Examples of drug resistance evolution include the emergence of multidrug-resistant tuberculosis, methicillin-resistant Staphylococcus aureus (MRSA), and HIV resistance to antiretroviral therapy
Conservation genetics
- Detecting selection in threatened or endangered species can inform conservation efforts by identifying adaptive variation and potential risks associated with inbreeding and
- Selection scans can help to prioritize populations or genomic regions for conservation, based on their evolutionary potential and resilience to environmental change
- Examples of selection in conservation contexts include the identification of locally adapted populations in salmonids, the detection of inbreeding depression in island fox populations, and the assessment of genetic diversity in critically endangered species (black rhinoceros, giant panda)
Key Terms to Review (34)
Ancestral Recombination Graph (ARG): An ancestral recombination graph (ARG) is a graphical representation that depicts the ancestral relationships and recombination events among a set of sampled DNA sequences. This graph illustrates how genetic material has been reshuffled across generations, capturing the complexity of evolutionary processes like recombination, mutation, and selection. Understanding ARGs is crucial as they provide insights into how both positive and negative selection pressures can shape the genetic variation observed in populations.
Antibiotic resistance: Antibiotic resistance occurs when bacteria evolve and develop the ability to survive exposure to antibiotics that once effectively eliminated them. This phenomenon is a significant concern in medicine, as it limits treatment options for bacterial infections and can lead to increased morbidity and mortality. Understanding antibiotic resistance is crucial for developing effective strategies to combat bacterial infections and improve public health outcomes.
Background selection: Background selection refers to the process through which deleterious mutations are removed from a population due to the effects of natural selection, impacting the genetic variation at linked neutral sites. This phenomenon can reduce the overall genetic diversity of a population by constraining the evolutionary potential of neutral alleles that are located near harmful mutations on the same chromosome. Understanding this concept is essential for grasping how both positive and negative selection shape genetic landscapes over time.
Balancing selection: Balancing selection is a type of natural selection that maintains genetic diversity in a population by favoring the survival of multiple alleles, rather than just the dominant or the most advantageous allele. This process often occurs in heterogeneous environments where different alleles confer advantages under varying conditions. It can lead to the preservation of genetic variations that might otherwise be lost, resulting in a stable equilibrium of allele frequencies over time.
Coalescent Theory: Coalescent theory is a retrospective model of population genetics that traces the genealogical lineage of alleles in a population back to a common ancestor. It provides a framework for understanding genetic variation and can be used to infer the demographic history of populations, estimate evolutionary rates, identify the effects of selection, and understand the patterns of genetic linkage between loci.
Deleterious mutations: Deleterious mutations are genetic changes that negatively affect an organism's fitness, making them less likely to survive and reproduce. These mutations can disrupt normal biological functions or processes, leading to harmful traits or diseases. The impact of deleterious mutations is crucial in understanding the dynamics of evolution, particularly in relation to how natural selection acts on different alleles within a population.
Distribution of Fitness Effects (DFE): The distribution of fitness effects (DFE) refers to the range and frequency of different effects that mutations can have on the fitness of an organism. It highlights how some mutations may be beneficial, neutral, or harmful, and it is crucial for understanding the dynamics of evolution. The DFE influences how populations adapt over time and provides insights into the balance of positive and negative selection operating on genetic variations.
Dn/ds ratio: The dn/ds ratio is a measure used in molecular evolution to compare the rates of nonsynonymous (dn) and synonymous (ds) substitutions in a gene. It helps determine whether a gene has evolved under the influence of natural selection by quantifying the extent of changes that affect protein function versus those that do not. A ratio greater than 1 suggests positive selection, while a ratio less than 1 indicates negative selection, providing insights into evolutionary pressures acting on genes.
Extended Haplotype Homozygosity (EHH): Extended haplotype homozygosity (EHH) refers to the phenomenon where a specific haplotype, or a combination of alleles at adjacent loci, is found to be identical across a significant region of the genome within a population. This pattern often indicates that a region has undergone selective pressure, either positive or negative, which can be crucial for understanding how certain genes are maintained or eliminated in populations over time.
Fay and Wu's H Test: Fay and Wu's H Test is a statistical method used to detect the presence of positive or negative selection in genetic data by analyzing the distribution of polymorphism and divergence at a given locus. This test focuses on the ratio of polymorphism within a population to divergence between populations, helping to identify deviations from neutral expectations that might suggest adaptive evolution or purifying selection. The test is particularly valuable in understanding how evolutionary forces shape genetic variation.
Fitness: In biology, fitness refers to the ability of an organism to survive and reproduce in its environment, thereby passing on its genetic material to the next generation. This concept is crucial in understanding how traits are selected for or against in populations over time, influencing evolutionary processes. Fitness can be affected by various factors, including environmental conditions, competition, and available resources, making it a central idea in the study of natural selection.
Fixation: Fixation refers to the process by which a genetic variant becomes the only allele present at a particular locus in a population, effectively meaning that it is 'fixed' in the population. This process can occur through various mechanisms, including natural selection, genetic drift, and mutation. In the context of evolution, fixation is a critical aspect of how alleles spread through populations, influencing genetic diversity and adaptation over time.
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.
Genetic hitchhiking: Genetic hitchhiking is a phenomenon where an allele increases in frequency not because it is beneficial itself, but because it is located near a positively selected allele on the same chromosome. This occurs during the process of selection, as the advantageous allele causes nearby alleles to be carried along with it through generations, resulting in their increased frequency even if they provide no advantage. This effect can also lead to negative consequences when deleterious alleles are unintentionally swept along with beneficial ones.
Genome-wide association studies (GWAS): Genome-wide association studies (GWAS) are research methods used to identify genetic variants associated with specific traits or diseases by scanning the genomes of many individuals. These studies analyze the entire genome to find single nucleotide polymorphisms (SNPs) that correlate with phenotypic traits, shedding light on the genetic basis of diseases and traits, as well as how evolutionary processes like positive and negative selection can influence genetic variation over time. Additionally, genome browsers are tools that visualize and explore the data generated from GWAS, allowing researchers to access and interpret the complex relationships between genetics and phenotypes.
Hard sweeps: Hard sweeps refer to a process in evolutionary biology where a beneficial allele increases in frequency rapidly in a population due to strong positive selection, resulting in a significant reduction of genetic diversity at linked loci. This phenomenon occurs when the advantageous trait provides a substantial survival or reproductive benefit, leading to a rapid fixation of the allele and the surrounding genetic material. The consequence is often a clear signal in genetic data, allowing researchers to identify regions of the genome that have undergone intense selection.
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.
Integrated Haplotype Score (iHS): The Integrated Haplotype Score (iHS) is a statistical measure used to detect recent positive selection in genomic data by analyzing the distribution of haplotypes in a population. It compares the observed frequency of haplotypes around a selected allele to the expected frequency under neutral evolution, helping researchers identify signatures of selection at specific loci. This measure is particularly useful in understanding how certain alleles have increased in frequency due to adaptive advantages in changing environments.
Loss of function: Loss of function refers to a type of genetic mutation that results in the complete or partial inactivation of a gene, preventing it from producing a functional protein. This kind of mutation can impact an organism's phenotype by disrupting normal biological processes, often leading to disease or altered traits. The effect of loss of function mutations can be influenced by natural selection, where advantageous mutations are favored, while detrimental ones may be eliminated from the population.
Machine learning approaches: Machine learning approaches refer to methods that enable computers to learn patterns from data and make predictions or decisions without being explicitly programmed. In the context of genomics, these techniques can be applied to analyze vast biological datasets, uncover functional relationships between genes and proteins, and identify evolutionary pressures such as positive and negative selection.
McDonald-Kreitman Test: The McDonald-Kreitman test is a method used to analyze the patterns of genetic variation within and between species to infer the presence of natural selection. By comparing the ratio of polymorphisms (within species) to fixed differences (between species) at a specific locus, this test helps differentiate between positive selection and neutral evolution, shedding light on evolutionary processes.
Molecular adaptation: Molecular adaptation refers to the changes at the molecular level that allow organisms to adjust to their environments and improve their survival and reproduction. These adaptations often involve alterations in DNA sequences, leading to changes in protein function and expression, which can be influenced by selective pressures such as positive and negative selection. Understanding molecular adaptation provides insights into how species evolve and adapt over time.
Negative selection: Negative selection is a process in evolutionary biology where detrimental alleles or traits are removed from a population through natural selection. This process ensures that individuals with harmful genetic variations are less likely to survive and reproduce, thereby maintaining the overall health and fitness of the population. Negative selection plays a crucial role in shaping genetic diversity and is an important mechanism for preventing the accumulation of deleterious mutations over time.
Positive Selection: Positive selection is a process in evolution where advantageous genetic variants increase in frequency within a population due to the benefits they confer in terms of survival and reproduction. This process is crucial for understanding how certain traits become more common over generations, often shaping the evolutionary trajectory of species. It is also an important concept when estimating evolutionary rates and analyzing the balance between different forms of selection.
Purifying selection: Purifying selection is a type of natural selection that acts to remove deleterious alleles from a population, preserving the functional integrity of genes and preventing the accumulation of harmful mutations. This form of selection is crucial for maintaining the overall fitness of a species, as it ensures that only beneficial or neutral genetic variations are passed on through generations. Purifying selection contrasts with positive selection, where advantageous traits increase in frequency.
Selection Coefficient: The selection coefficient is a measure of the relative fitness of a particular genotype compared to others in a population. It quantifies the advantage or disadvantage that a genotype experiences due to natural selection, playing a crucial role in determining how quickly certain alleles can spread or diminish within a population over time. Understanding the selection coefficient helps explain patterns of genetic variation and adaptation in response to environmental pressures.
Selective Pressure: Selective pressure refers to environmental factors that influence the survival and reproduction of individuals within a population. These pressures can lead to changes in allele frequencies over generations, driving the process of evolution. Selective pressures can be positive, favoring advantageous traits, or negative, acting against detrimental traits, shaping the genetic landscape of species over time.
Selective sweeps: Selective sweeps refer to a process in evolutionary biology where a favorable allele increases in frequency within a population, leading to a reduction in genetic variation at nearby loci due to linked selection. This phenomenon often occurs during positive selection, where advantageous traits become more common as individuals carrying them have higher reproductive success. The result is that alleles associated with the beneficial trait are 'swept' to higher frequencies, impacting the genetic landscape of the population.
Sequencing: Sequencing is the process of determining the precise order of nucleotides within a DNA or RNA molecule. This crucial technique enables scientists to decode genetic information, identify mutations, and understand the relationships between genes. By revealing the genetic blueprint of organisms, sequencing plays a key role in various biological research areas, including evolutionary studies and disease research.
Sickle Cell Trait: Sickle cell trait refers to the condition in which an individual carries one copy of the sickle cell gene (HbAS) but does not exhibit the symptoms of sickle cell disease. This trait can provide a protective advantage against malaria, making it beneficial in regions where malaria is prevalent, while also being a carrier of the genetic mutation that can lead to sickle cell disease if two copies are inherited.
Soft sweeps: Soft sweeps refer to a genetic phenomenon where a beneficial allele increases in frequency in a population, but the process involves multiple distinct beneficial mutations at the same locus rather than a single mutation fixing. This means that rather than one mutation sweeping through the population, several similar mutations can rise in frequency simultaneously or sequentially, allowing for genetic diversity and adaptation.
Tajima's D Statistic: Tajima's D Statistic is a measure used in population genetics to assess the difference between the number of segregating sites and the average number of nucleotide differences in a sample. This statistic helps identify deviations from neutrality in genetic variation, which can indicate whether a population has undergone positive or negative selection. A significant Tajima's D value can provide insights into the evolutionary pressures acting on a gene or genomic region.
Wright-Fisher Model: The Wright-Fisher model is a foundational concept in population genetics that describes how allele frequencies in a population change over generations due to random sampling of individuals. It assumes a finite population size and considers the effects of genetic drift, where allele frequencies fluctuate randomly, which is essential for understanding the dynamics of evolution under various selection pressures and the concept of linkage disequilibrium.