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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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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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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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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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Human Genetics01:28

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.

Andrew G Sharo1, Zhiqiang Hu2, Shamil R Sunyaev3

  • 1Biophysics Graduate Group, University of California, Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

American Journal of Human Genetics
|January 15, 2022
PubMed
Summary

Whole-genome sequencing identifies genetic variants, but many cases remain unresolved. StrVCTVRE is a new tool that accurately predicts pathogenic structural variants (SVs), helping diagnose rare diseases.

Keywords:
copy-number variantmachine learningrandom forestrare diseasestructural variantvariant interpretation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Clinical Diagnostics

Background:

  • Whole-genome sequencing (WGS) is crucial for diagnosing complex genetic disorders, yet a significant portion of cases remain unresolved.
  • Structural variants (SVs) of uncertain significance are implicated in many unresolved WGS cases.
  • Advancements in long-read sequencing and SV detection generate numerous SVs requiring pathogenicity assessment.

Purpose of the Study:

  • To develop and validate StrVCTVRE, a computational tool to differentiate pathogenic from benign SVs overlapping exons.
  • To improve the diagnostic yield of WGS by prioritizing disease-relevant SVs.

Main Methods:

  • Developed StrVCTVRE, a random forest classifier integrating features like gene importance, conservation, expression, and exon structure.
  • Constructed a size-matched training set of rare, putatively benign and pathogenic SVs using multiple resources.
  • Evaluated StrVCTVRE performance on independent test sets across a wide range of SV sizes.

Main Results:

  • StrVCTVRE accurately distinguishes pathogenic SVs from benign ones.
  • Key features like gene expression and conservation, often overlooked, were integrated into the model.
  • The tool can reduce the number of SVs for consideration by approximately 50% while maintaining 90% sensitivity.

Conclusions:

  • StrVCTVRE offers a reliable method to prioritize SVs in unresolved clinical cases.
  • This tool empowers deeper investigation into novel SVs, potentially resolving cases and uncovering new disease mechanisms.
  • StrVCTVRE is rapid, publicly available, and poised to enhance the diagnostic utility of long-read sequencing.