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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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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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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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Related Experiment Video

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci.

Kamil Slowikowski1, Xinli Hu2, Soumya Raychaudhuri1

  • 1Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston MA 02215, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston MA 02215, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA.

Bioinformatics (Oxford, England)
|May 13, 2014
PubMed
Summary

This study introduces a new C++ tool for analyzing single-nucleotide polymorphism (SNP) sets to find cell types, tissues, and pathways linked to genetic risk loci. It offers a robust method for understanding genetic associations with various conditions.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genetic association studies identify regions of the genome linked to traits or diseases.
  • Understanding the biological context of these associated regions is crucial for functional interpretation.
  • Existing methods may lack the speed, robustness, or generality needed for comprehensive analysis.

Purpose of the Study:

  • To develop a fast, robust, and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm.
  • To identify cell types, tissues, and pathways affected by genetic risk loci.
  • To provide a proof of concept through novel applications to various complex traits.

Main Methods:

  • Developed a C++ implementation of a SNP set enrichment algorithm.
  • Employed a non-parametric statistical approach to compute empirical P-values.
  • Utilized comparison with null SNP sets to assess enrichment significance.

Main Results:

  • Created a fast, robust, and general C++ tool for SNP set enrichment analysis.
  • Demonstrated the method's utility in identifying affected cell types, tissues, and pathways.
  • Presented novel applications to SNP sets associated with red blood cell count, multiple sclerosis, celiac disease, and HDL cholesterol.

Conclusions:

  • The developed algorithm provides a powerful and efficient method for interpreting genetic risk loci.
  • It enables the identification of specific biological contexts (cell types, tissues, pathways) influenced by genetic variations.
  • The tool has broad applicability for understanding the genetic architecture of complex traits and diseases.