GENESIS: Statistical methods for analyzing genetic data from samples

Maintainer: Analysis Commons
Version: 0.1

Description:

Runs single variant and aggregate test for genetic data. Implements Single-variant, Burden, SKAT and SMMAT tests for Continuous or Dichotomous outcomes. All tests may account for familial relatedness through kinship matrices. Underlying functions adapted from: Conomos MP and Thornton T (2016). GENESIS: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness. http://bioconductor.org/packages/devel/bioc/html/GENESIS.htmlhttps://github.com/smgogarten/GENESIS

What do these apps do?

Runs phenotype-genotype association analyses on sequence data. The workflow genesis_nullmodel will run the null model. This is fitting your model with your outcome, adjustments and kinship matrix, but does not use the genotypes. The second workflow genesis_tests takes the null model object from the first step and runs your association analysis. The same null model can be used for single-variant or aggregate tests.

What are typical use cases for this app?

This workflow can sun single-variant, SKAT and burden tests. It will account for relatedness using a kinship matrix and implements GMMAT for logistic regression.

What data are required for this app to run?

This workflow requires genotype files in GDS format (*.gds) and a phenotype file. Rare variant tests require a gene-aggregation file and an annotation file.

What does this app output?

genesis_null model will output a copy of your null model as an R object. The results of the genesis_tests workflow will be a compressed CSV file of association results

How does this app work?

This workflow uses the GENESIS package developed by Matt Conomos, Tim Thornton and Stephanie Gogarten (TOPmed DCC). This workflow has been optimized to allow genotype random access from the GDS genotype format files and to parallelize over a number of cores.

Inputs

genesis_nullmodel

• this_outcome_name: [string] Column name of outcome variable
• this_outcome_type: [string] Continuous or Dichotomous
• this_covariate_list: [string] Covariates Comma separated list that match column names in the phenotype file. Leave blank for no adjustments
• this_pheno_file: [file] Phenotypes for each sample in delimited text file
• this_genotype_file: [file] Genotypes Any chromosome of the dataset you are using. This is used to subset your phenotype to only the samples in the GDS data set. This is a GDS formatted genotype file
• this_results_file: [string] Output Name prefix for output file name, no spaces.
• this_kinship_matrix: [file] Kinship/GRM Kinship matrix with sample ids as the row and column names. Matrices save as R data will load faster, but csv is accepted as well. Matrix can contain pedigree or empirical kinship values
• this_pheno_id: [string] Sample ID Column name that contains the sample IDs. These IDs should match the genotype file IDs and the kinship file IDs.
• this_test_stat: [string] Valid tests statistic types are: Score, Wald, or Firth.
• this_conditional: [string] chr:pos:ref:alt format for the SNP that will be added to the model. Multiple snps in a comma delimited list can be added. (e.g. '22:16425814:C:T' or '22:16425814:C:T,22:17808063:TA:T,22:18096610:G:T')
• this_het_vars: [string] Heterogenous Variances Fits model allowing for heterogeneous variances by group. Provide column name in phenotype file that defines the group.
• this_disk: [int] disk space in GB
• this_memory: [int] memory in GB

genesis_tests

• this_agg_file: [file] Variant aggregation file CSV file listing which variants should be grouped together for aggregate tests (e.g. SKAT). The file contains 'group_id' ( window, gene etc.), 'chr' ('1' or 'chr1'), 'pos', 'ref' and 'alt'.
• this_top_maf: [float] Max MAF Maximum minor allele frequency ( generally used for aggregate tests )
• this_test_stat: [string] Valid tests statistic types are: Score, Wald and Firth
• this_test_type: [string] Test type Valid tests are one of the collapsing tests SKAT, SMMAT. Burden or Single
• this_min_mac: [int] Minimim MAC Minimum minor allele count for threshold ( only used for single variant tests )
• this_weights: Weights function Beta weights set to flat weights (e.g. set to 'c(1,1)' for unweighted, 'c(1,25)' for Wu weights or 'c(0.5,0.5)' for Madsen-Browning weights). Not used in single var analyses.
• this_weights_col: [string] Name of column in agg_file that contains the variant weights
• this_user_cores: [int] defaults to 2 less than the number of cores available. memory needed will depend on test configuration and sample size. if your analysis is terminating with an out-of-memory error, reduce the number of cores
• this_window: [int] window size for sliding window test
• this_step: [int] step size for sliding window test
• these_genotype_files: [file] Genotypes GDS formatted genotype files
• this_null_model: [file] Null model object that is the output of genesis_nullmodel. The same null model can be used for single variant and aggregate tests.
• this_results_file: [string] Output Name prefix for output file name, no spaces
• this_disk: [int] disk space in GB
• this_memory: [int] memory in GB