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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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Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
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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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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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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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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Bayesian joint analysis of heterogeneous genomics data.

Priyadip Ray1, Lingling Zheng, Joseph Lucas

  • 1G.S.Sanyal School of Telecommunications, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India, Computational Biology & Bioinformatics, Duke University, Durham, NC 27705, USA, Quintiles, Durham, NC 27703, USA and Electrical & Computer Engineering Department, Duke University, Durham, NC 27705, USA.

Bioinformatics (Oxford, England)
|February 4, 2014
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Summary
This summary is machine-generated.

This study introduces a novel Bayesian factor model for analyzing multiple genomics data types together. The model helps identify key cancer drivers by analyzing gene expression, copy number variations, and methylation data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Multi-platform genomics data present challenges for integrated analysis.
  • Identifying key drivers in complex diseases like cancer requires sophisticated modeling approaches.

Purpose of the Study:

  • To propose a non-parametric Bayesian factor model for the joint analysis of multi-platform genomics data.
  • To uncover key biological drivers in ovarian cancer by integrating gene expression, copy number variation, and methylation data.

Main Methods:

  • Developed a non-parametric Bayesian factor model.
  • Factorized the latent space into shared and data-specific components.
  • Inferred component dimensionality using a beta-Bernoulli process.

Main Results:

  • Successfully applied the model to jointly analyze gene expression/copy number variations and gene expression/methylation data from ovarian cancer patients.
  • Demonstrated the model's capability to uncover potential key drivers related to cancer.

Conclusions:

  • The proposed Bayesian factor model offers a powerful approach for integrated multi-platform genomics data analysis.
  • This method has the potential to advance our understanding of cancer biology and identify novel therapeutic targets.