The following are just some of the software the Centre has produced and published. For updates on software, you can follow our organisation at GitHub.

Falconer ShinyApp

The aim of this App is 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.


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.


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


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.


EasyGATK is a Python-based Genome Analysis ToolKit (GATK) wrapper to help researchers set-up and perform whole-exome sequencing analyses. It was written by Restuadi under the supervision of Qiong-Yi Zhao.


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 provides A Better Coefficient of Determination for Genetic Profile Analysis. It was written by Konstantin Shakhbazov.


CAGE is a data base of eQTL and heritability results from a large gene expression data set on 2,765 individuals. Please see DOI: for further details. The app was written by Alex Holloway and Luke Lloyd-Jones.


Falconer can be used to investigate the standard model of quantitative genetics and is very useful for teaching and learning. The app is based on the work presented in Falconer’s seminal text, “Introduction to Quantitative Genetics”. The app was written by Luke Lloyd-Jones, Alex Holloway, Matt Robinson and Peter Visscher.

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). It was written by Gibran Hemani and Jian Yang.


genRoc is used for the genetic interpretation of the area under ROC curve in genomic profiling. The app was re-written by Konstantin Shakhbazov.


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’s 2014 Nature Reviews Genetics paper The contribution of genetic variants to disease depends on the ruler. The app was written by Cara Nolan and Beben Benyamin.

Linear (mixed) model effects to odds ratios

LMOR This Shiny app accompanies the manuscript titled ‘Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio’. This 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. The app was written by Luke Lloyd-Jones.

Mendelian Randomisation Power Calculator

mRnd provides power and sample size calculations for two-stage least squares Mendelian Randomization studies. It was written by Konstantin Shakhbazov using formulae developed by Peter Visscher.

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. This app was adapted from Peter Visscher’s original Fortran program by Matthew Robinson and updated by Luke Lloyd-Jones.