Gwas Analysis
Gwas Analysis - Related Videos
01:11
Genome-wide Association Studies-GWAS
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2023
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. GWAS does not require the identification of the target gene involved in...
05:01
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Cited by 1 •
2020
By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved.
08:27
Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Cited by 4 •
2021
In this protocol, we present an optimized workflow, which combines an efficient and fast sample preparation of many samples. In addition, we provide a step-by-step guide to reduce analytical variations for high-throughput evaluation of metabolic GWAS studies.
15:04
A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
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Cited by 46 •
2009
This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
FAQs
The central comparison in a GWAS analysis is between genetic patterns observed in individuals with different phenotypes. Researchers may examine allele frequencies, meaning how common particular alleles are, or compare genotypes, the allele combinations carried at a locus. A statistical association test then evaluates whether these patterns differ more than expected by chance.
Population structure can create misleading associations when genetic differences between groups coincide with differences in the trait being studied. GWAS analysis therefore adjusts for this factor before interpreting a variant as relevant. This correction helps separate signals linked to population-related structure from signals more directly associated with disease susceptibility, drug response, or another measured phenotype.
GWAS analysis examines genetic variation across many genomic locations, so numerous statistical tests can produce apparently significant findings by chance alone. Adjusting for multiple comparisons makes the evidence threshold more rigorous. This step helps distinguish signals that are more likely to represent meaningful trait associations from results that arise simply because a large number of variants were tested.
An association indicates that a genetic variant or genomic region is linked statistically with a trait, but it does not by itself establish biological causation. Follow-up studies are needed to investigate whether the signal points to a causal gene or pathway. This distinction prevents researchers from treating an associated location as definitive evidence of mechanism.
A typical workflow begins by comparing genetic variation among individuals classified according to a phenotype, such as disease status or drug response. Researchers perform association tests, account for population structure, and adjust for multiple comparisons. The resulting signals are then interpreted as candidate genomic regions, with follow-up studies used to examine possible causal genes and biological pathways.
GWAS analysis can support investigations of disease susceptibility, responses to drugs, and other complex traits. Its results may identify genomic regions associated with these outcomes, providing starting points for functional research and risk prediction. When follow-up work clarifies causal genes or biological pathways, the findings can also contribute to developing targeted therapies.