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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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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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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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Updated: Feb 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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FGWAS: Functional genome wide association analysis.

Chao Huang1, Paul Thompson2, Yalin Wang3

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Neuroimage
|July 24, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new functional genome-wide association analysis (FGWAS) framework to improve the detection of genes linked to brain disorders by analyzing functional phenotypes. FGWAS enhances the power of genetic discovery for complex brain conditions.

Keywords:
Computational complexityFunctional genome wide association analysisMultivariate varying coefficient modelWild bootstrap

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Area of Science:

  • Neuroimaging Genetics
  • Statistical Genomics
  • Computational Neuroscience

Background:

  • Functional phenotypes from neuroimaging are crucial for identifying genes in complex brain disorders.
  • Current statistical methods often overlook functional features like smoothness and correlation.
  • A need exists for advanced analytical frameworks to fully leverage functional phenotype data in genetic studies.

Purpose of the Study:

  • To develop a novel functional genome-wide association analysis (FGWAS) framework for efficient whole-genome analysis of functional phenotypes.
  • To integrate functional features into genetic association studies for enhanced discovery power.
  • To improve the detection of genetic variants influencing brain structure and function.

Main Methods:

  • Developed a FGWAS framework comprising a multivariate varying coefficient model, global sure independence screening, and a testing procedure.
  • The multivariate varying coefficient model incorporates smooth coefficient functions and functional principal component analysis to model functional phenotypes.
  • Applied the FGWAS framework to analyze neuroimaging and genetic data from the Alzheimer's Disease Neuroimaging Initiative.

Main Results:

  • FGWAS significantly boosts detection power for genetic variants influencing brain structure and function compared to standard genome-wide association studies (GWAS).
  • Simulation studies confirmed FGWAS's superior performance in identifying sparse signals within large search spaces while controlling the family-wise error rate.
  • Successful application to a large dataset revealed the framework's efficacy in real-world neurogenetic research.

Conclusions:

  • The developed FGWAS framework offers a statistically robust and powerful approach for analyzing functional phenotypes in genome-wide association studies.
  • FGWAS advances the field by effectively integrating functional data, leading to more potent genetic discovery for neuropsychiatric and neurodegenerative disorders.
  • This method holds significant promise for future research in brain structure, function, and disease genetics.