MESA

MESA is a method to call ethnicity-specific associations for biobank-scale multi-ethnic GWAS. This repository stores the C++ implementation of the statistical method used in my MPhil thesis "Identification of Population-specific Associations in Multi-ethnic Genome-wide Association Studies".

Why should you use MESA?

Here are the reasons of using MESA

• You want to detect ethnicity-specific associations for GWAS with admixture populations
• The data set you are analyzing comes from a biobank-scale GWAS
• You want to analyze multiple binary or continuous phenotypes
• You want to estimate admixture proportions of individuals
• You want to estimate population-specific allele frequencies
• You want a computationally efficient method to do all sorts of things mentioned above simultaneously

Requirements

1. A Linux environment
2. Intel CPU that supports OpenMP and AVX (Tested on Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz)
3. GCC >= 9 and build-essential
4. Eigen >= 3.3.7 from here

Installation

1. Specify variables BUILD_DIR, SRC_DIR, EIGEN_DIR in makefile
2. Specify the user options in common.h if necessary.
3. Run make at the project root to build MESA

How-to-use

1. Export the environment variable OMP_NUM_THREADS before using MESA. e.g.

   export OMP_NUM_THREADS=4

2. Run ./build/mesa to see available options
3. Follow the instructions here to see how you may apply MESA to the data set you would like to analyze

Help

Usage: mesa -i INPUT [--cov COV_FILE] -N NUM_INDIVIDUALS -K NUM_ANCESTRIES -G NUM_SNPS -o OUTPUT_PREFIX [OPTION]...

Required arguments:
   -i, --in INPUT              Path of genotype matrix file (String). 
                               Accepted file type: tsv, ttsv, bed
                               e.g. -i /home/User/genotype.bed
   -N NUM_INDIVIDUALS          Number of individuals to be tested
                               (Positive integer)
   -K NUM_ANCESTRIES           Number of ancestries to be fitted
                               (Positive integer)
   -G NUM_SNPS                 Number of SNPs to be tested (Positive
                               integer)
   -o, --out OUTPUT_PREFIX     Prefix of path of output file
                               (String). For example, option
                               '-o /home/User/output' will produce
                               files such as '/home/User/output_p.tsv'Optional arguments:
   --cov COV_FILE              Path of phenotype matrix file (String)
   --batch-size                Number of subsampled SNPs (Positive
                               integer, 1000
   --newton-iter X             Maximum number of Newton's steps = X
                               (Positive integer, default: 10)
   --em-iter X                 Maximum number of E-steps and M-steps = X
                               (Positive integer, default: 30)
   --epochs X                  Number of passes through whole data = X
                               set (Positive integer, default: 1)
   --continue                  Whether to use existing estimates
                               extracted from OUTPUT_PREFIX_p.tsv, etc
   --no-pretrain               Start training with no warm-up
   --se-only                   Calculate standard errors using
                               existing estimates extracted from
                               OUTPUT_PREFIX_p.tsv,
                               OUTPUT_PREFIX_q.tsv and 
                               OUTPUT_PREFIX_effect.tsv

Output

• *_p.tsv: a G-by-K matrix of estimated baseline allele frequency intercept terms. The (g, k)-th entry corresponds to in the literature
• *_q.tsv: a N-by-K matrix of estimated ancestry proportions. The (i, k)-th entry corresponds to in the literature
• *_effect.tsv: a G-by-(K * cov_num) matrix of estimated effect sizes, where 'cov_num' is the number of phenotypes tested. The (g, (k-1)*cov_num + r)-th entry corresponds to the estimate of in the literature
• *_effect_se.tsv: a G-by-(K * cov_num) matrix of standard errors of estimated effect sizes, where 'cov_num' is the number of phenotypes tested. The (g, (k-1)*cov_num + r)-th entry corresponds to the standard error estimate of in the literature