CD Genomics Blog

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The study of population evolution relies on advanced genomic techniques like whole-genome resequencing or other sequencing methods. These technologies enable the acquisition of genomic data from diverse subpopulations within a species. By extracting extensive variant information such as single nucleotide polymorphisms (SNP), insertions/deletions (InDel), structural variations (SV), and copy number variations (CNV), researchers delve into exploring the genetic architecture of the population.

This comprehensive approach allows for an in-depth investigation into various aspects, including the population’s genetic structure, the mechanisms of domestication, the historical trajectories of the population, and the dynamic processes involved in population evolution. It provides valuable insights into fundamental biological questions surrounding population dynamics and evolution.

Population Genetics through Resequencing

Resequencing emerges as a potent method for extracting genotypic information from specific samples and pinpointing key loci of variation. This technique enables a thorough analysis of the frequency distribution and variation within certain genes across a population, unraveling the mysteries of population genetics.

When applied to a species with a reference genome, it is termed whole-genome resequencing. In contrast, for species lacking a reference genome, the process involves assembling the genome from scratch during whole genome sequencing. The declining cost of sequencing has facilitated the sequencing and assembly of reference genomes for numerous species.

In the realm of population genetics studies, species with reference genomes take center stage. Notable examples include Arabidopsis thaliana, rice, wheat, and maize, particularly prevalent in plant-focused investigations. As the availability of reference genomes expands, resequencing becomes an increasingly robust tool for delving into the intricacies of population genetics.

Population Genetics and Genotyping by Sequencing

Population genetics, a discipline exploring the genetic structure and dynamic patterns within populations, focuses on natural populations, typically encompassing different groups within a species (e.g., distinct varieties, various geographic regions). Early research predominantly relied on SSR markers or specific genes like COI.

The rapid evolution of high-throughput sequencing technology has revolutionized population genetics. The prevailing approach involves generating a plethora of single nucleotide polymorphism (SNP) markers through whole genome sequencing, followed by comprehensive studies on population genetic evolution, phylogeny, germplasm resource identification, molecular marker development, DNA fingerprinting, and other genomic analyses.

Genotyping by Sequencing, a simplified method of genome sequencing technique, has become mainstream in population analysis. It transcends genome constraints, simplifies complexity, offers efficiency with low data volume, ease of operation, and cost-effectiveness. Particularly suited for large sample studies, simplified genome technology has become integral to advancing our understanding of population genetics.

Methods of Population Genetic Analysis

Principal Component Analysis (PCA)

PCA stands as a purely mathematical algorithm with the capacity to streamline complex data by linearly transforming multiple interrelated variables. Widely applicable across various disciplines, PCA finds particular significance in genetics. Here, it is primarily employed for cluster analysis, leveraging the distinctions in single nucleotide polymorphisms (SNPs) among individual genomes.

In the realm of genetics, PCA plays a pivotal role in cluster analysis, where it assesses the degree of SNP differences among individual genomes. By discerning diverse trait characteristics, PCA facilitates the clustering of individuals into subgroups based on principal components. Simultaneously, it serves as a valuable tool for cross-verification alongside other methods, enhancing the reliability of genetic analyses.

Population Structure Analysis

Structure analysis employs a distinct algorithm, diverging from evolutionary trees or principal component analysis (PCA), enabling it to tackle challenges beyond the scope of the latter two methodologies. It proves invaluable in addressing issues that PCA and evolutionary trees may find challenging or impossible to resolve.

Population structure analysis excels in resolving questions such as determining the optimal number of subpopulations within a larger population, assessing the extent of genetic exchange between these populations, and quantifying the level of admixture in individual samples.

In the population structure analysis, an initial assumption is made regarding the number of subpopulations (k=x). Using a simulation algorithm, the method explores the most plausible classification of samples under the given assumption of k=x. Ultimately, by evaluating the maximum likelihood values derived from each simulation, the analysis identifies the most suitable value of k that best characterizes the group under study.

Selection Clearance Analysis

Selection stands as a formidable influence in the evolutionary trajectory of species, manifesting as a conditional screening process. When exploring plant and animal populations, the force of selection can be distinctly classified into two categories: natural selection and artificial selection, based on the origin of the selective pressure.

Further granularity in understanding selection is achieved by considering the directional shifts in allele or haplotype frequencies. Selection can be delineated into positive selection, negative selection, and balancing selection, depending on the specific changes observed in the frequency of the selected genetic elements. This nuanced approach allows for a more detailed and comprehensive analysis of the intricate forces shaping the genetic makeup of populations.

Population Dynamics Analysis

Pairwise sequentially Markovian coalescent analysis, commonly known as PSMC, stands out as a prominent method for deducing the effective population size throughout the evolutionary timeline of a species. It holds its status as the most prevalent and extensively employed approach in current research. PSMC efficiently utilizes data obtained from individual sequencing to extrapolate the effective population size of the specific population to which an individual belongs, offering insights into historical variations at different points in time.

Ancestral Component Analysis

When studying populations, the delineation into subgroups based on geographic distribution or other criteria reveals a hierarchy of relatedness. Individuals within the same subgroup exhibit closer relationships, while subgroups themselves are slightly more distantly related. Population structure analysis becomes a vital tool in unraveling evolutionary processes, enabling the identification of subgroups to which individuals belong through a combination of genotypic and phenotypic association studies.

The graphical representation, where each color corresponds to a taxon and each individual is represented by a stack of bars, unveils a rich tapestry of genetic diversity. This analysis allows us to discern individuals with purer ancestral components and those with mixed heritage. The vivid colors serve as a key to classifying individuals within the population into distinct subgroups, providing a nuanced understanding of the intricate relationships within the broader genetic landscape.

Gene Flow Analysis

Gene flow, the infusion of fresh genetic material from one population of a species into another, stands as a transformative force reshaping the intricate landscape of the population’s "gene pool." This influx of novel alleles through genetic exchange emerges as a crucial contributor to genetic variation, influencing the overall genetic diversity within the population and giving rise to novel combinations of traits.

Leveraging genome-wide allele frequency data, the analysis of gene flow allows for the discernment of divergence and admixture patterns across multiple populations. This comprehensive approach enables researchers to formulate hypotheses regarding the dynamics of inter-population gene flow, shedding light on the intricate processes that shape the genetic makeup of diverse populations.

Related Services

Genome-wide association study (GWAS)
Pan Genome
Variant Calling
Population Evolution
Genetic Linkage Map
Bulk Segregant Analysis (BSA)
Population Genetics

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