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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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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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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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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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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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Related Experiment Video

Updated: Mar 11, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Bayesian variable selection for post-analytic interrogation of susceptibility loci.

Siying Chen1, Sara Nunez1, Muredach P Reilly2

  • 1Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, U.S.A.

Biometrics
|November 19, 2016
PubMed
Summary
This summary is machine-generated.

We developed a new Bayesian variable selection method to analyze genomic data for coronary artery disease (CAD). This approach identified protein coding genes and long intergenic non-coding RNAs associated with low-density lipoprotein cholesterol levels.

Keywords:
Bayesian variable selection (BVS)Genome-wide association studies (GWAS)Long non-coding RNAs (lncRNAs)Protein coding gene-level testingRegulatory elementsSpike and slab prior

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Dissecting the roles of protein coding genes and regulatory elements in complex diseases like coronary artery disease (CAD) is challenging.
  • Existing analytical tools often struggle to differentiate contributions from overlapping genomic regions.

Purpose of the Study:

  • To introduce a novel application of Bayesian variable selection (BVS) for classifying genomic associations.
  • To leverage large meta-analysis summary statistics for identifying genetic loci related to low-density lipoprotein cholesterol (LDL-C).

Main Methods:

  • Applied the expectation maximization variable selection (EMVS) algorithm to SNP data from 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs).
  • Utilized Global Lipids Genetics Consortium (GLGC) meta-analysis summary statistics for LDL-C across 45 known CAD loci.
  • R version 3.2.1 was used for all analyses, with code provided as supplemental material.

Main Results:

  • Identified 33 PCGs and 3 lncRNAs across 11 loci with >50% posterior probability of association with LDL-C.
  • Findings align with previous research while offering novel insights into LDL-C genetic architecture.
  • The framework is adaptable to include additional genomic elements like enhancers and splicing regions.

Conclusions:

  • The proposed Bayesian variable selection approach effectively identifies key genes and regulatory elements associated with complex traits.
  • This method provides a flexible framework for future genomic analyses using diverse datasets and evolving taxonomies.
  • The findings contribute to a deeper understanding of the genetic underpinnings of coronary artery disease risk factors.