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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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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Genome Size and the Evolution of New Genes03:21

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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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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A Fast and Flexible Framework for Network-Assisted Genomic Association.

Daniel E Carlin1, Samson H Fong2, Yue Qin3

  • 1Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.

Iscience
|June 8, 2019
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Summary

We developed Network Assisted Genomic Association (NAGA), a fast system for analyzing genetic data. NAGA improves the discovery of disease genes by leveraging biological networks.

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BioinformaticsBiological SciencesGenomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Genome-wide association studies (GWAS) identify genetic variants associated with diseases.
  • Interpreting GWAS results often requires integrating biological network information.
  • Existing methods can be slow and lack customizability for network-based analysis.

Purpose of the Study:

  • To present Network Assisted Genomic Association (NAGA), an accessible and efficient system for pathway boosting and interpretation of GWAS.
  • To leverage the NDEx biological network resource for enhanced genomic association analysis.
  • To improve the recovery and replicability of disease-associated genes.

Main Methods:

  • NAGA utilizes the NDEx resource to access and select relevant protein networks for specific association studies.
  • The system performs genome-wide analysis efficiently, completing analysis in under 5 minutes on a laptop.
  • Protein interactions are visualized and annotated using Cytoscape for downstream analysis.

Main Results:

  • NAGA successfully recovered known disease genes from schizophrenia genetic data.
  • The system identified novel associations with genes like amyloid beta precursor.
  • NAGA outperformed conventional approaches in recovering known disease genes and improving result replicability across eight gene-disease association tasks.

Conclusions:

  • NAGA provides a fast, customizable, and effective approach for network-assisted genomic association studies.
  • The system enhances the interpretation of GWAS by integrating biological network data.
  • NAGA offers a valuable tool for discovering and validating disease-associated genes.