README.md

In naglemi/mtmcskat: Multi-Threaded Monte Carlo Sequence Kernel Association Test (MTMC-SKAT)

Overview of MTMC-SKAT

Introduction to MTMC-SKAT

Multi-Threaded Monte Carlo Sequence Kernel Association Test (MTMC-SKAT) provides high-level R and command line interfaces for SKAT (Wu et al., 2011; Lee et al., 2016), most notably with support for adaptive resampling with multi-threading using the future parallel framework to rapidly calculate empirical p-values. These features can facilitate high-powered and accurate GWAS for traits that do not satisfy linear model assumptions.

Resampling is performed using an algorithm that allows calculation of empirical p-values to a user-specified number of significant figures. The efficiency of parallelization over SNP windows or permutations depends on a given dataset and resources of a given computer. In the automated workflow, permutations are continually generated and batches of SNP windows are tested to calculate empirical p-values as sufficient permutations become available for each batch. The appropriate mode (multithreading over windows or permutations) is automatically detected for each batch of SNP windows. See the MTMC-SKAT Workflows vignette for details on this automatic workflow and information for designing custom workflows.

Running over a single scaffold

Workflows are applied over scaffolds independently, with parallelization over scaffolds or sub-scaffolds on high-performance clusters as described later. Each scaffold is submitted as a .traw file, which can be prepared from more common SNP data formats using PLINK. The standard workflow can be run over a given scaffold using the following command.

MTMCSKAT_workflow(phenodata = "poplar_shoot_sample.csv",
                  covariates = "poplar_PCs_covariates.csv",
                  raw_file_path = "poplar_Chr10_portion.traw",
                  window_size = 3000,
                  window_shift = 1000,
                  output_dir = "Results/",
                  job_id = "my_sample_analysis",
                  ncore = "AllCores",
                  max_accuracy = 5,
                  sig_figs = 2)

Inputs include file paths to phenotype, covariate and genotype file paths (in standard PLINK formats for phenotypes and covariates and .traw for genotypes data), the sizes of overlapping SNP windows to be tested and the shift in position between windows, and the limit for accuracy (recommended to be set beyond the FDR or Bonferroni correction threshold) given as 10^-x.

Information for additional, option parameters can be found by typing ?MTMCSKAT_workflow in R after installing and loading the package.

Running over an array of scaffolds using HPC

The following command can be used to submit a job to a batch scheduler such as SGE or SLURMS. This notebook shows an example of how we use this command to produce an array of jobs and submit them on a high-performance cluster.

Rscript wrappers/mtmcskat_wrapper.R \
--phenodata <my_phenotype_file> \
--covariates <my_covariate_file> \
--raw_file_path <my_scaffold> \
--window_size 3000 \ # Window size of 3kb suggested for P. trichocarpa
--window_shift 1000 \ # Windows will overlap by 1kb
--output_dir <my_output_dir> \
--pre_allocated_dir <pre_allocated_dir> \ # path to save pre-processed scaffolds, saving time in future
--job_id <my_job_id> \
--desired_sig_figs 2 \
--min_accuracy 4 \ # Perform permutations sufficient for no fewer than 4 decimal places
--max_accuracy 5 \ # Perform permutations for no more than 5 decimal places
--plot 0 \ # Do not make plots while running on cluster
--RAM 64000000000 \
--n_thread <my_n_thread_one_job>
--top_N 2 # Perform permutation validation for two top associations on scaffold
--missing_cutoff 0.15 # Exclude SNPs missing from >15% of samples

Acknowledgements

We thank the National Science Foundation Plant Genome Research Program for support (IOS #1546900, Analysis of genes affecting plant regeneration and transformation in poplar), and members of GREAT TREES Research Cooperative at OSU for its support of the Strauss laboratory.

This work used EXPANSE at the San Diego Supercomputer Center at the University of California, San Diego. An EXPANSE allocation was provided first via allocation MCB200174 from the Extreme Science and Engineering Discovery Environment (XSEDE), which was supported by National Science Foundation grant number #1548562. This allocation was then transitioned to the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

References

Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M. and Lin, X., 2011. Rare-variant association testing for sequencing data with the sequence kernel association test. The American Journal of Human Genetics, 89(1), pp.82-93.

Lee, S., Fuchsberger, C., Kim, S. and Scott, L., 2016. An efficient resampling method for calibrating single and gene-based rare variant association analysis in case–control studies. Biostatistics, 17(1), pp.1-15.

naglemi/mtmcskat documentation built on Aug. 23, 2023, 5:35 p.m.