I am currently performing regression analysis on a large dataset. I had my data annotated with phenotypes.
However, after performing regression, the phenotypes aren’t preserved in the model. It helps in analysing which phenotype is not falling on the regression curve.
The only way to do is to annotate phenotypes again after regression.
gwas_log = hl.logistic_regression_rows(test=test, y=y, x=x, covariates=covariates, pass_through=pass_through)
gwas_log = gwas_log.annotate(pheno=pheno_keys)
There is a pass_through method but it only works for variants. Is there any method for samples or phenotypes?
Hi, @Abhishek ! Thanks for your question. As far as I have run my analysis, I have merged/annotated the phenotypes again post regression. But perhaps, @johnc1231 may have a clearer answer to this which I have not observed and could learn from 
What is pheno_keys in this case? I don’t really understand what you’re trying to do.
It was a sample code for reference but I will the keys here as requested:
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test=firth
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x=mt.add2 where add2 is the additive model for gene(burden) matrix
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y=mt.pheno.values() which results in a list of phenotypes [SuperPopulation, Population, CoffeeAddiction]
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covariates=[1.0]
In summary, I am trying to perform All by All GWAS(therefore a list of phenotypes).