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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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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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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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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Copy number variant analysis using genome-wide mate-pair sequencing.

James B Smadbeck1, Sarah H Johnson1, Stephanie A Smoley2

  • 1Center for Individualized Medicine - Biomarker Discovery, Mayo Clinic, Rochester, Minnesota.

Genes, Chromosomes & Cancer
|May 5, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for analyzing copy number variations (CNVs) using mate-pair sequencing (MPseq). The method offers comparable sensitivity to chromosomal microarray (CMA) while providing improved resolution of structural variations in cancer genomes.

Keywords:
bioinformaticscancer geneticschromosomal rearrangementscopy number variant analysisnext-generation sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Copy number variation (CNV) is a significant structural variation in human genomes, detectable through techniques like chromosomal microarray (CMA).
  • Current CMA methods lack the resolution to fully characterize structural variations, necessitating integration with other cytogenetic approaches.
  • Next-generation sequencing (NGS) offers potential for comprehensive analysis of both CNVs and their breakpoint junctions.

Purpose of the Study:

  • To develop and evaluate a next-generation sequencing (NGS) based algorithm for analyzing copy number variations (CNVs) from mate-pair sequencing (MPseq) data.
  • To enhance the resolution of structural variations, including breakpoint junctions, which are limitations of current chromosomal microarray (CMA) techniques.
  • To provide a robust analytical tool for MPseq data, particularly for applications in cancer genomics.

Main Methods:

  • Developed a novel algorithm for copy number analysis using mate-pair sequencing (MPseq) data.
  • The algorithm employs a step-wise approach: normalization, segmentation, and classification.
  • Segmentation integrates read depth and discordant mate-pair reads to improve sensitivity and resolution of CNV detection.

Main Results:

  • The MPseq algorithm demonstrated comparable sensitivity to chromosomal microarray (CMA) for copy number variation detection.
  • Achieved superior resolution of breakpoint junctions compared to standard CMA techniques.
  • Successfully analyzed hematological cancer samples, validating the algorithm's performance.

Conclusions:

  • The developed algorithm provides a powerful tool for analyzing MPseq data, offering enhanced resolution for structural variations.
  • This NGS approach complements existing methods, improving the characterization of CNVs and breakpoint junctions in cancer genomes.
  • The findings suggest a promising direction for high-resolution genomic analysis in cancer research.