tensorqtl_cis¶

tensorQTL cis mode output format with permutations

Overview¶

Field	Value
Format key	`tensorqtl_cis`
Spec file	`formats/tensorqtl_cis.json`
Cite name	tensorQTL
Version	20220726
Source	https://raw.githubusercontent.com/broadinstitute/tensorqtl/refs/heads/master/docs/outputs.md
GitHub	https://github.com/broadinstitute/tensorqtl
Citation	Taylor-Weiner, A., Aguet, F., Jones, M., Zaitlen, N., Daly, M., & Ardlie, K. (2019). Scaling computational genomics to millions of individuals with GPUs. Genome Biology, 20, 183.
## Column mapping

Column	Description
`af`	In-sample ALT allele frequency of the variant
`beta_shape1`	Parameter of the fitted Beta distribution
`beta_shape2`	Parameter of the fitted Beta distribution
`end_distance`	Distance between the variant and phenotype end position (only present if different from start position)
`ma_count`	Number of minor alleles
`ma_samples`	Number of samples carrying at least one minor allele
`num_var`	Number of variants in cis-window
`phenotype_id`	Phenotype ID
`pval_beta`	Beta-approximated empirical p-value
`pval_nominal`	Nominal p-value of the association between the phenotype and variant
`pval_nominal_threshold`	Nominal p-value threshold for significant associations with the phenotype
`pval_perm`	Empirical p-value from permutations
`pval_true_df`	Nominal p-value based on true_df
`qval`	Storey q-value corresponding to pval_beta
`slope`	Regression slope
`slope_se`	Standard error of the regression slope
`start_distance`	Distance between the variant and phenotype start position (e.g., TSS)
`true_df`	Degrees of freedom used to compute p-values
`variant_id`	Variant ID
## Coverage

5/9 tier groups satisfied.

Tier	Status	Matched	Missing
Variant ID	yes	SNPID	rsID
Genomic location	no	—	CHR, POS
Effect / other allele	no	—	EA, NEA
Effect size	yes	BETA	OR, HR, Z
Uncertainty	yes	SE	—
P-value	yes	P	MLOG10P
Sample size	no	—	N, N_CASE
Allele frequency	yes	EAF	MAF, NEAF
Imputation / QC	no	—	INFO
## See also