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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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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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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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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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Related Experiment Video

Updated: Sep 20, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis.

Emile R Chimusa1, Joel Defo1

  • 1Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.

Frontiers in Genetics
|June 6, 2022
PubMed
Summary

This study introduces ancMETA, a new framework for gene/pathway meta-analysis in genome-wide association studies (GWASs). It enhances detection of low-risk genetic variants for complex diseases across diverse populations.

Keywords:
BayesianGWASgenemeta-analysissubnetwork

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWASs) utilize high-throughput technologies to identify genetic variants associated with complex traits.
  • Current GWAS meta-analysis methods face challenges in detecting variants with small effect sizes and understanding cross-ancestry replicability.
  • Identifying gene-gene interactions underlying complex disease pathogenesis across diverse populations remains a significant challenge.

Purpose of the Study:

  • To develop and validate ancMETA, a Bayesian graph-based framework for gene/pathway-specific meta-analysis.
  • To enhance statistical power in detecting genetic associations with small effect sizes by combining SNP and gene-level effects across multiple GWASs.
  • To investigate gene-gene interactions and their role in complex disease pathogenesis across different human populations.

Main Methods:

  • Proposed ancMETA, a Bayesian graph-based framework for gene/pathway-specific meta-analysis.
  • Combined effect sizes of multiple SNPs within genes and genes within pathways across independent population GWASs.
  • Assessed framework performance on simulated datasets and applied it to European bipolar disorder (BD) cohorts.

Main Results:

  • The ancMETA framework demonstrated increased statistical power for meta-analysis of genetic variants.
  • Identified significant associations between variants in the angiotensinogen (AGT) gene and bipolar disorder (BD) across seven European cohorts.
  • Detected a significant BD-specific subnetwork centered around the ESR1 gene, linked to neurotrophin signaling and myometrial relaxation/contraction pathways.

Conclusions:

  • ancMETA offers a novel approach to post-GWAS analysis, improving the detection of genetic associations for complex diseases.
  • The framework facilitates the examination of gene-gene interactions and ethnic differences in disease pathogenesis.
  • ancMETA holds promise for advancing our understanding of the genetic architecture of complex diseases and their variability across populations.