Post by tacrolimus on Mar 7, 2023 12:26:42 GMT
Dear GCTA team,
I have plink files as well as summary stats from a WGS GWAS I have conducted in a case/control cohort. I have also performed collapsing rare variant analysis in SAIGE-GENE and pulled out the people who have a qualifying variant in my top and only significantly associated gene. I would like to know the heritability of my common variants to the phenotype and then add in the absence/presence of having a qualifying variant in our top gene into the association test to see how much it boosts the heritability. I have done this in R using logistic regression and the scores from a PRS applied to the cohort and then added the absence/presence of a qualifying variant as a binary covariate and and then looked at the changes to the pseudoR2 (liability adjusted) but given I have WGS GWAS data I thought GREML might be a better option. Is this possible?
Many thanks for your time
I have plink files as well as summary stats from a WGS GWAS I have conducted in a case/control cohort. I have also performed collapsing rare variant analysis in SAIGE-GENE and pulled out the people who have a qualifying variant in my top and only significantly associated gene. I would like to know the heritability of my common variants to the phenotype and then add in the absence/presence of having a qualifying variant in our top gene into the association test to see how much it boosts the heritability. I have done this in R using logistic regression and the scores from a PRS applied to the cohort and then added the absence/presence of a qualifying variant as a binary covariate and and then looked at the changes to the pseudoR2 (liability adjusted) but given I have WGS GWAS data I thought GREML might be a better option. Is this possible?
Many thanks for your time