Post by a29 on Aug 1, 2023 23:19:25 GMT
Hi everyone!
I did imputation with WGS data, and GWAS. I got many genome-wide significant variants and decided to proceed only with top 50 variants from each chromosome.
Therefore, I was interested in how much of variance was explained by those top 50 SNPs.
I made a grm using all the top SNPs from all chromosomes (~1000) and another grm from 50K chip (~45000), put them together into a model and run GREML.
Commands:
--reml --mgrm 2_grms_together.txt --pheno --out
I am interested if this is a right approach, given the imputed WGS data and fact that I am interested only in variance explained by 1000 top SNPs?
I did imputation with WGS data, and GWAS. I got many genome-wide significant variants and decided to proceed only with top 50 variants from each chromosome.
Therefore, I was interested in how much of variance was explained by those top 50 SNPs.
I made a grm using all the top SNPs from all chromosomes (~1000) and another grm from 50K chip (~45000), put them together into a model and run GREML.
Commands:
--reml --mgrm 2_grms_together.txt --pheno --out
I am interested if this is a right approach, given the imputed WGS data and fact that I am interested only in variance explained by 1000 top SNPs?