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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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...

Evolutionary Relationships through Genome Comparisons

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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...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

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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Related Experiment Video

Updated: Jun 21, 2025

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

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

Published on: July 27, 2021

PathGPS: discover shared genetic architecture using GWAS summary data.

Zijun Gao¹, Qingyuan Zhao², Trevor Hastie³

¹Marshall Business School, University of Southern California, Los Angeles CA, 90089, United States.

|July 15, 2024

Summary

This summary is machine-generated.

PathGPS is a new tool for analyzing genetic data from Genome Wide Association Studies (GWAS). It helps uncover genetic pathways influencing traits by separating genetic and environmental factors, aiding biological mechanism discovery.

Keywords:

GWAS pathway analysis structural equation model summary data

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Area of Science:

Genomics and Bioinformatics
Systems Biology
Statistical Genetics

Background:

Large-scale biobanks and omics datasets offer unprecedented opportunities for biological discovery.
Understanding complex trait regulation requires dissecting genetic and environmental influences.
Existing methods may not fully leverage the structure within Genome Wide Association Studies (GWAS) summary data.

Purpose of the Study:

To introduce PathGPS, an exploratory data analysis tool for discovering genetic architectures from GWAS summary data.
To enable the decoupling of genetic and environmental components influencing biological traits.
To identify and extract key genetic pathways underlying trait variation.

Main Methods:

PathGPS employs a linear structural equation model to differentiate genetic and environmental pathway contributions.
It contrasts GWAS associations of 'signal' genes with 'noise' genes to estimate the genetic component.
Principal component and factor analysis are utilized to extract genetic pathways, exploiting low-rank and sparse properties.
A bootstrap aggregating (bagging) algorithm enhances stability and aids hyperparameter tuning.

Main Results:

Application of PathGPS to a metabolomics dataset and the UK Biobank successfully identified known gene-trait clusters.
The tool generated novel hypotheses for future research into gene-trait associations.
PathGPS demonstrated robustness and stability through the bagging algorithm.

Conclusions:

PathGPS is an effective tool for exploratory analysis of GWAS summary data to uncover genetic architectures.
The method provides a robust framework for dissecting genetic pathways and their role in trait regulation.
PathGPS facilitates the generation of new research hypotheses in human genetics and complex diseases.