Comments (2)
wgr implementation is a distributed version of regenie. Most of the perf gain comes in the second step (regression). for phenotype imputation, I'd say you can use either method. the regression side is near linear perf ~ compute cluster size
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@kermany good to hear from you :D.
That makes sense that most of the gains are from the regression step. I am less concerned about the performance since I agree the performance gains, and more concerned about the equivalence of results between the two implementations.
I did an overlap of the pQTLs found by Glow vs Regenie C++ and there was very little overlap. Similarly the effect sizes and pvalues were uncorrelated.
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Related Issues (20)
- Cannot write INFO fields with LongType to VCF HOT 2
- Python tests fail with KeyError: '_glow_regression_values' HOT 1
- Logistic regression ValueError: Null fit failed! HOT 2
- AnalysisException: Column 'num_workers' does not exist. HOT 1
- pipe transformer support for file input/output for command line apps HOT 2
- Interaction Tests with GLOW HOT 1
- Spark version upgrade 3.4, 3.3 HOT 5
- Cannot install+use the python glow package any more HOT 2
- Regular updates to work with cloud providers HOT 1
- Problem importing Glow HOT 3
- glow.normalize_variant fails with NullPointerException in NormalizeVariantExpr.scala:55 HOT 4
- Unexpected behaviour when using the SplitMultiallelics Transformer with unbounded INFO fields HOT 2
- import glow throws error: module 'numpy' has no attribute 'unicode'. Did you mean: 'unicode_'? HOT 2
- Feature Request: add regenie's gene-based testing
- Pandas should be pinned <2.0.0 HOT 1
- Glow.hail - No module found HOT 2
- Numpy == 2.0 fails with glow python package v2.0
- Tried spark.sql("RESET") not worked
- Writing sharded VCF fails on read
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