Collapsing (Old)¶

Command examples:¶

atav_4.8.sh --collapsing --sample $SAMPLE_FILE --out $OUTPUT

Command options:¶

--collapsing: a collapsing method will be performed.

--collapsing-comp-het: a collapsing method for Compound Het + Homozygous.

--recessive: add this flag if you want to run a recessive model; by default, a dominant model is run.

--linear: linear regression for continuous traits.

--permute: add this flag if you want to run permutations; by default without permutations.

--nperm: number of permutations if running permutations; the default value is 1000.

--var-missing-rate {1.0}: a variant would be dropped before collapsing in one gene if its missingness rate is greater than or equal to the specified number (the suggested number should be bigger than 0.8).

--test-model: use a flag "--test-model fisher" to test statistics for collapsed variants in a gene by a Fisher's exact test; use a flag "--test-model regression" to test statistics for collapsed variants in a gene by linear/logistic regression framework to do log likelihood ratio test. Note: gender information is used as a covariate in the log likelihood ratio test by performing logistic regression. User can incorporate a specified a number of eigenvectors to adjust population stratification as covariates in regression models. By default, ATAV tests collapsed variants in gene units using a Fisher's exact test.

--covar {$COV_FILE}: specify a covariate file. The $COV_FILE can be a regular covariate file in flat text format (space-delimited, tab-delimited, or mixed both space and tab delimited), or a .evec file if no other covariates are included. When a .evec file is specified, users may specify the number of eigen axes to be included by using a parameter “--ncov $N” where the default of $N is 3. Note: (1) in a covariate file, the first column should be the subject IDs. From the second column on, they should be covariates; (2) adding a number sign “#” before the header line if there is a header line in the covariate file.

--ncov {3}: number of eigenvectors/covariates from a covariate file to be included in multivariate regression.