Post by javiermr on Oct 19, 2016 13:41:38 GMT
Hi,
I was wondering what would be the best way to estimate the heritability of a trait in an admixed population. Will the inclusion of PCs (or equivalent) in the mixed models as fixed effects be a possible solution? How could you test if the admixture nature of your sample is affecting the heritability estimates?
Thank you.
UPDATE:
I found this paper: Genome partitioning of genetic variation for complex traits using common SNPs (http://www.nature.com/ng/journal/v43/n6/full/ng.823.html)
They have a section on population structure and if I understood correctly, the idea is the following:
- Population structure causes correlations of SNPs on different chromosomes. Therefore, if you fit the whole genome (or each chromosome separately) you will also capture some of the variance by the other chromosomes and your h2 will biased upwards.
- If you fit the chromosomes jointly, you will "protect" against this chromosomal correlations, because the estimate of your variance for each chromosome will be conditional on the other chromosomes.
If this is correct can I:
- Fit the chromosomes jointly and then just add up the h2 of each chromosome to get a h2 accounting for population structure?
- What is the difference between estimating the variance using the whole genome and using PCs if I know they capture most of the population structure?
Thanks again.
I was wondering what would be the best way to estimate the heritability of a trait in an admixed population. Will the inclusion of PCs (or equivalent) in the mixed models as fixed effects be a possible solution? How could you test if the admixture nature of your sample is affecting the heritability estimates?
Thank you.
UPDATE:
I found this paper: Genome partitioning of genetic variation for complex traits using common SNPs (http://www.nature.com/ng/journal/v43/n6/full/ng.823.html)
They have a section on population structure and if I understood correctly, the idea is the following:
- Population structure causes correlations of SNPs on different chromosomes. Therefore, if you fit the whole genome (or each chromosome separately) you will also capture some of the variance by the other chromosomes and your h2 will biased upwards.
- If you fit the chromosomes jointly, you will "protect" against this chromosomal correlations, because the estimate of your variance for each chromosome will be conditional on the other chromosomes.
If this is correct can I:
- Fit the chromosomes jointly and then just add up the h2 of each chromosome to get a h2 accounting for population structure?
- What is the difference between estimating the variance using the whole genome and using PCs if I know they capture most of the population structure?
Thanks again.
