Hi,
I understand that comparing own sequence data with unselected controls from ancestry-matched population examined by projects such as the 1000 Genomes and HapMap can give an indication as to whether there is any variation in allele frequencies, provided that the disease is rare and these populations can be considered as unselected controls. Does anyone have any recommendation on how to carry out this comparison having allele frequencies and functional prediction (e.g. synonymous or nonsynonymous)? What are the considerations you keep in mind when comparing own data for cases with publicly available control data as opposed to own genotyped controls?
Many thanks,
Silvia
Hi Xu,
Thank you for your reply. I had a look at ANNOVAR, I will surely give it a go. For the moment, I think I have obtained the same information using a variety of other tools (e.g. Expasy, Ensembl VEP). I guess mine is more of a methodological question, can database frequencies be used as a mean of identifying candidate risk variants that are then studied in a set of sequenced controls? Do you compare your case frequencies with database frequencies directly and how? In my point of view, although I can select ancestry-matched database subsets to minimise the impact of population structure, database controls have not been genotyped using the same pipelines as individual lab projects, so an association test can't be carried out. Thank you for your time
Sorry, now I understand the question. Eliminating the technical differences between cases and controls is the goal in association test, so controls genotyped on the same platform is generally preferred, so you will be sure that any significant allele frequency difference is really from the phenotypes but not technical issues. If what you have are array genotyped data, you will find that it is not that difficult to find a matched control set. If you have sequencing data, I would say it is OK to use controls from sequencing projects like 1000 genomes for a preliminary analysis, as things are more complex. You would need to look into the sequencing preparation kit, sequencing platform, mapping software, variant calling algorithm and variant calls filtering criteria, etc,trying to match those can be a nightmare, so you may end up with insuficient number of controls. Any significant hit can be from a slightly technical difference, and disease-related differences can be missed.
Depth of sequencing is also crucial, as it will drastically influence the frequency of picking up alternative alleles. The other issue is adjusting for genetic background, which you can do with any of a number of PCA methods, assuming that your own data are also genome-wide or exome wide. If you only have a handful of candidate markers that were genotyped on your own sample, you risk false positives from population stratification.
- Badges
- Science topic
- Similar topics
- Biological Science
- Genetics
- Genetic Analysis