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Related Experiment Video

Updated: Jun 5, 2026

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

GCTA: a tool for genome-wide complex trait analysis.

Jian Yang¹, S Hong Lee, Michael E Goddard

¹Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, 300 Herston Road, Brisbane, Queensland 4006, Australia. jian.yang@qimr.edu.au

American Journal of Human Genetics

|December 21, 2010

Summary

This summary is machine-generated.

Genome-Wide Complex Trait Analysis (GCTA) is a new software tool that addresses the missing heritability problem in complex diseases. GCTA estimates genetic variance explained by all SNPs, offering a comprehensive approach to understanding trait heritability.

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

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Last Updated: Jun 5, 2026

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

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Area of Science:

Genetics
Bioinformatics
Statistical Genetics

Background:

Genome-wide association studies (GWAS) often explain a small portion of heritability for complex human diseases and traits.
The "missing heritability" problem highlights a gap in understanding genetic contributions to complex traits.

Purpose of the Study:

To introduce Genome-Wide Complex Trait Analysis (GCTA), a user-friendly software tool designed to address the "missing heritability" problem.
To provide a method for estimating the total variance explained by all single nucleotide polymorphisms (SNPs) for complex traits.

Main Methods:

GCTA utilizes a method to estimate the genetic relationships from SNPs.
It employs mixed linear model analysis to determine the variance explained by SNPs across the genome or specific chromosomes.
The software includes functions for data management, linkage disequilibrium estimation, and GWAS simulation.

Main Results:

GCTA estimates the variance explained by all SNPs for complex traits, rather than focusing on individual SNP associations.
The tool is particularly effective for analyzing large GWAS datasets and can estimate variance explained by SNPs on the X chromosome.
It facilitates testing hypotheses related to dosage compensation.

Conclusions:

GCTA is a versatile software tool for estimating and partitioning complex trait variation using large GWAS datasets.
It offers a robust approach to tackling the "missing heritability" challenge in genetic research.
The software enhances the analysis of genetic architecture for complex diseases and traits.