leap_gwas 0.1

The LEAP pipeline includes the following stages:

1. (optional): Find related individuals to be removed: python findRelated.py –bfilesim <Plink base file> –out <output file>

This script creates a file marking the individuals that need to be removed to eliminate relatedness

2. Compute heritability using the method of Golan and Rosset: python calc_h2.py –bfilesim <Plink base file> –extractSim <SNPs used for heritability estimation> –prev <prevalence> –pheno <phenotype file> –related <relatedness file>

This script outputs the heritability estimate

3. Estimate liabilities: python probit.py –bfilesim <Plink base file> –pheno <phenotype file> –prev <prevalence> –extractSim <SNPs used in the heritability estimation> –out <output base file> –related <relatedness file> –h2 <heritability>

This script creates a file called <output base file>.liabs, with estimated liabilities for every individual. The estimated liabilities can be used directly for GWAS by using them as a standard phenotype file.

4. Compute GWAS: python leap_gwas.py –bfilesim <Plink base file> –pheno <estimated liabilities file> –extractSim <SNPs used in the LMM kinship matrix> –out <output file> –h2 <heritability> –bfile <Plink file with tested SNPs> –extract <SNPs to test>

This script performs GWAS with a prespecified h2. The syntax largely follows that of FaSTLMM C++ version.

General comments and tips

1. Fixed effects can be included in the thresholds, liability estimation and GWAS stages. Please type “python probit.py –help” and “leap_gwas.py” for instructions

2. As described in the main text, it is recommended to perform a different liability estimation for every excluded chromosome, and then testing the SNPs on the excluded chromosome for association with the estimated liabilities. The “-extractSim” flag is useful for this. Please see the example file “leap_pipeline.sh” for a usage example.

3. A complete end-to-end usage example is provided with the LEAP source files, and can be invoked via the script “leap_pipeline.sh”. This example estimates liabilities for a small balanced case-control dataset. The dataset was simulated with 50% heritability and 0.1% prevalence. It included 500 cases, 500 controls, 499 causal SNPs, 100 unusually differentiated SNPs and 10000 SNPs differentiated with FST=0.01. Causal SNPs are called “csnp<i>”, and unusually differentiated SNPs are called “dsnp<i>”. The original liabilities for this file are available in the file dataset1.phe.liab (but this file is not used by LEAP).