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sgkit.regenie review. Work in progress #1182

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sgkit.regenie review. Work in progress #1182

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69a62f5
update reference for regenie
jdstamp Jan 29, 2024
422d393
regenie overview
jdstamp Jan 29, 2024
0a5a549
name ridge parameter
jdstamp Jan 29, 2024
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18 changes: 11 additions & 7 deletions sgkit/stats/regenie.py
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Original file line number Diff line number Diff line change
Expand Up @@ -262,7 +262,7 @@ def _stage_1(G: Array, X: Array, Y: Array, alphas: Optional[NDArray] = None) ->
these predictions, a technique commonly referred to as stacking.

For more details, see the level 0 regression model described in step 1
of [Mbatchou et al. 2020](https://www.biorxiv.org/content/10.1101/2020.06.19.162354v2).
of [Mbatchou et al. (2021) Nat Genet](https://doi.org/10.1038/s41588-021-00870-7).
"""
assert G.ndim == 2
assert X.ndim == 2
Expand Down Expand Up @@ -766,7 +766,7 @@ def regenie(
tests. These estimates are subtracted from trait values and
sampling statistics (p-values, standard errors, etc.) are evaluated
against the residuals. See the REGENIE preprint [1] for more details.
For a simpler technical overview, see [2] for a detailed description
For a technical overview, see [2, 3] for a detailed description
of the individual stages and separate regression models involved.

Parameters
Expand Down Expand Up @@ -799,9 +799,11 @@ def regenie(
Defaults to 10 sample blocks split roughly across all possible
samples or the number of samples, if that number is < 10.
alphas
List of alpha values to use for regularization, by default None.
List of ridge parameter values to use for regularization, by default None.
If not provided, these will be set automatically based on
datasize and apriori heritability assumptions.
datasize and apriori heritability assumptions. See the description of
level 0 ridge regressions in the Supporting Information of [1] for more
details.
add_intercept
Whether or not to add intercept to covariates, by default True.
normalize
Expand Down Expand Up @@ -870,11 +872,13 @@ def regenie(

References
----------
[1] - Mbatchou, J., L. Barnard, J. Backman, and A. Marcketta. 2020.
“Computationally Efficient Whole Genome Regression for Quantitative and Binary
Traits.” bioRxiv. https://www.biorxiv.org/content/10.1101/2020.06.19.162354v2.abstract.
[1] - Mbatchou, J., Barnard, L., Backman, J. et al. Computationally efficient
whole-genome regression for quantitative and binary traits.
Nat Genet 53, 1097–1103 (2021). https://doi.org/10.1038/s41588-021-00870-7

[2] - https://glow.readthedocs.io/en/latest/tertiary/whole-genome-regression.html

[3] - https://rgcgithub.github.io/regenie/overview/
"""
if isinstance(covariates, Hashable):
covariates = [covariates]
Expand Down
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