Post by lcweng on Mar 24, 2016 1:01:58 GMT
Hi,
I am a beginner of GCTA, and I am working on a project for heritability estimation on disease outcomes. In my testing population, I got a different h2g estimation (higher) after LD-pruning, so I am wondering what would be the better dataset for h2g estimation (whole imputation dataset or LD-prunned dataset).
1. Should I removed all SNPs in LD (e.g., r2>0.3, 0.6, 0.8, etc) from my dataset?
2. If there are different LD properties for causal SNPs, but the study sample size is too small for GREML-LDMS. Should I use a LD-prunned data in h2g estimation?
Thank you.
I am a beginner of GCTA, and I am working on a project for heritability estimation on disease outcomes. In my testing population, I got a different h2g estimation (higher) after LD-pruning, so I am wondering what would be the better dataset for h2g estimation (whole imputation dataset or LD-prunned dataset).
1. Should I removed all SNPs in LD (e.g., r2>0.3, 0.6, 0.8, etc) from my dataset?
2. If there are different LD properties for causal SNPs, but the study sample size is too small for GREML-LDMS. Should I use a LD-prunned data in h2g estimation?
Thank you.
