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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Hardy-Weinberg Principle01:49

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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Genetic Drift03:33

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Related Experiment Video

Updated: Jul 14, 2025

Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains
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Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains

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Simulation-based Benchmarking of Ancient Haplotype Inference for Detecting Population Structure.

Jazeps Medina Tretmanis1, Flora Jay2, María C Avila-Árcos3

  • 1Center for Computational Molecular Biology, Brown University.

Biorxiv : the Preprint Server for Biology
|October 9, 2023
PubMed
Summary

Ancient DNA (aDNA) analysis is challenging but crucial for understanding human evolution. This study shows that accurate haplotype phasing is achievable with ancient DNA, even with population history events, improving demographic reconstructions.

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Area of Science:

  • Paleogenomics
  • Human Evolutionary Genetics
  • Bioinformatics

Background:

  • Ancient DNA (aDNA) provides insights into ancient population dynamics and medically relevant adaptations through introgression.
  • Analyzing aDNA is challenging due to degradation, fragmentation, contamination, and low DNA yields.
  • Haplotype estimation (phasing) in aDNA is underexplored despite its potential for demographic inference.

Approach:

  • Developed a software tool to simulate aDNA, incorporating aDNA-specific features and population evolutionary history.
  • Evaluated the impact of aDNA quality (contamination, read depth) and demographic events on haplotype phasing accuracy.
  • Quantified phasing error rates across various ancient DNA quality metrics and demographic scenarios.

Key Points:

  • Low phasing error is achievable for ancient individuals up to ~400 generations ago, provided adequate contamination control and read depth.
  • Population splits and bottlenecks significantly affect phasing quality, with bottlenecks causing the highest error rates.
  • Estimated haplotypes, even if imperfect, outperform simulated genotype data for reconstructing post-split population structures.

Conclusions:

  • Haplotype phasing is a viable method for analyzing ancient DNA, enhancing our understanding of human demographic history.
  • The developed simulation tool aids in evaluating aDNA analysis methods and interpreting genomic data from ancient populations.
  • Accurate haplotype data from aDNA is superior to genotype data for resolving ancient population dynamics and evolutionary events.