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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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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Incomplete Dominance01:43

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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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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Related Experiment Video

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Disease variant prediction with deep generative models of evolutionary data.

Jonathan Frazer1, Pascal Notin2, Mafalda Dias1

  • 1Marks Group, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

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|October 28, 2021
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Summary

A new computational model, EVE (evolutionary model of variant effect), predicts protein variant pathogenicity without disease labels. This approach outperforms existing methods and aids in classifying millions of genetic variants of unknown significance.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Area of Science:

  • Genomics
  • Computational Biology
  • Human Genetics

Background:

  • Over 98% of protein variants in human disease genes have unknown clinical consequences, hindering accurate diagnosis and treatment.
  • Current computational methods for variant interpretation rely on limited, biased, and variable-quality disease labels, leading to unreliable predictions.
  • High-throughput experimental methods are increasingly used but are resource-intensive.

Purpose of the Study:

  • To develop a novel computational approach for predicting protein variant pathogenicity that does not require labeled disease data.
  • To leverage deep generative models and evolutionary information to accurately assess variant effects.
  • To provide a scalable and reliable tool for classifying millions of genetic variants of unknown significance.

Main Methods:

  • Developed EVE (evolutionary model of variant effect), a deep generative model trained on evolutionary sequence variation across organisms.
  • Modeled the distribution of sequence variations to implicitly capture protein sequence constraints essential for fitness.
  • Evaluated EVE's performance against existing label-dependent computational methods and high-throughput experimental predictions.

Main Results:

  • EVE outperforms state-of-the-art computational methods that rely on labeled data.
  • EVE's predictive performance is comparable to or better than high-throughput experimental predictions.
  • Predicted pathogenicity for over 36 million variants across 3,219 disease genes, classifying over 256,000 variants of unknown significance.

Conclusions:

  • Deep generative models leveraging evolutionary information offer a powerful, label-free approach for variant pathogenicity prediction.
  • EVE provides valuable, independent evidence for variant interpretation in both research and clinical settings.
  • This method significantly advances the ability to interpret genetic variants and their impact on human health.