Here are links to some of the software from researchers in complex trait genomics from the Division of Genetics and Genomics at the institute for Molecular Bioscience, University of Queensland. Also see a GitHub repository for some of these.


GCTA (Genome-wide Complex Trait Analysis) was initially designed to estimate the proportion of phenotypic variance explained by all genome-wide SNPs for complex traits (i.e., the GREML method). It has been subsequently extended for many other analyses to better understand the genetic architecture of complex traits, including GREML-LDMS, COJO, and fastGWA.

You can find out more about GCTA and download executables and source code here.


GCTB is a software tool that comprises a family of Bayesian linear mixed models for complex trait analyses using genome-wide SNPs. It was developed to simultaneously estimate the joint effects of all SNPs and the genetic architecture parameters for a complex trait, including SNP-based heritability, polygenicity and the joint distribution of effect sizes and minor allele frequencies. Version 2.0 of the GCTB software includes summary-data-based versions of the individual-level data Bayesian linear mixed models previously implemented.

You can find out more about GCTA and download executables and source code here.


BayesR is a Bayesian mixture model implementation that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. Moser et al(2015) Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model


epiGPU is for parallelising exhaustive 2D searches for epistasis across graphics cards using OpenCL. It was written by Gibran Hemani.


GEAR is a GEnetic Analysis Repository that provides implementation for cross-cohort analyses of GWAS summary statistic from complex traits. GEAR is developed by Guo-Bo Chen and Zhi-Xiang Zhu. Chen at al (2016) EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations


SMR is a software tool to test for association between gene expression and a complex trait using summary-level data from GWAS and expression quantitative trait loci (eQTL) studies. It provides a useful tool to prioritize genes underlying GWAS hits for follow-up functional studies. The software is developed by Futao Zhang, Zhihong Zhu and Jian Yang at Queensland Brain Institute, The University of Queensland.


Light-weight tools for performing calculations and generating simple plots on the fly, all within your browser.


ABC converts Nagelkerke's R2 to an R2 in liability at the population level from equation 15 of Lee et al (2012), A Better Coefficient of Determination for Genetic Profile Analysis.


CAGE is a data base of eQTL and heritability results from a large gene expression data set on 2,765 individuals. Please see Lloyd-Jones et al (2017) The Genetic Architecture of Gene Expression in Peripheral Blood, AJHG and Lukowski et al (2017) Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood. Nature Communications.


CHARRGE (Calculates Heritability And Relative Risk and Genetic correlation) provides visualisations and calculations of the relationship between increased risk of disease in relatives of affected probands and heritability (single disease) or genetic correlation (two diseases). See Baselmans et al (2020) Risk in Relatives, Heritability, SNP-Based Heritability, and Genetic Correlations in Psychiatric Disorders: A Review Biological Psychiatry

Falconer ShinyApp

The Falconer ShinyApp aims to show how the combination of gene action and allele frequencies at causal loci translate to genetic variance and genetic variance components for a complex trait. Although the theory underlying the App is more than a century old, it is highly relevant in the current era of genome-wide association studies (GWAS). The App can be used to demonstrate the relationship between a SNP effect size estimated from GWAS and the variation the SNP generates in the population, i.e., how locus-specific effects lead to individual differences. In addition, it can also be used to demonstrate how within and between locus interactions (dominance and epistasis, respectively) usually do not lead to a large amount of non-additive variance relative to additive variance, and therefore that these interactions usually do not explain individual differences in a population. Please see Hivert, Wray & Visscher (2021) Gene action, genetic variation, and GWAS: A user-friendly web tool . PLoS Genetics for further details.

GCTA-GREML Power Calculator

This calculator is designed to calculate the statistical power of estimating genetic variance or genetic correlation using genome-wide SNPs (GREML analysis as implemented in GCTA). See Visscher et al (2014) Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples. PLoS Genetics


genRoc is used for the genetic interpretation of the area under ROC curve in genomic profiling. See Wray et al (2010) The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling PLoS Genetics


INDI-V is used to calculate the contribution to genetic risk of individual disease risk loci from their allele frequencies and estimated odds ratios using various methods. It was based Witte, Visscher and Wray(2014 The contribution of genetic variants to disease depends on the ruler Nature Reviews Genetics.

Linear (mixed) model effects to odds ratios

LMOR This Shiny app accompanies Lloyd-Jones et al (2018) Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio GeneticsThis application is designed to map regression coefficients from a linear (mixed) model (LMM) to the odds ratio from genome-wide association studies (GWAS) on disease traits. It has been shown to be effective at mapping effects generated from a linear mixed model GWAS to the odds ratio. This allows for a comparison between effects generated from logistic regression from other GWAS studies.

Mendelian Randomisation Power Calculator

mRnd provides power and sample size calculations for two-stage least squares Mendelian Randomization studies. See Brion et al (2013 Calculating statistical power in Mendelian randomization studies International Journal of Epidemiology.

TwinPower Calculator

The TwinPower Calculator provides automated power analysis for the detection of additive genetic and common environmental variance components of a quantitative trait in the classical twin design. See Visscher (2004) Power of the Classical Twin Design Revisited, and Visscher et al (2008) Power of the Classical Twin Design Revisited: II Detection of Common Environmental Variance both published in Twin Research and Human Genetics.