I created an algorithm to generate SNPs for random people of different descents - based on HapMap data. While this works good, there is something else I want to take into consideration. So if a SNP has a MAF of 0.01, with an LD of 1, the generated results will, in most cases be sufficient. This is not the case for a MAF of 0.4 and LD of 0.8.
The HapMap LD files have, besides the LD data, also LOD and D-prime. The question is, is there a set of parameters so that as much of the relevant LDs are filtered out of the total set?
I want to filter this out, since, for example, there are LDs described with a value of 0, or an LOD < 1, and problematic D-primes.
Trying to rephrase the question:
Given a huge set of LD information (250GB), which are not filtered on statistical power, I want to make a subselection of LD information that are 'relatively' descriptive of the link with respect to another SNP. So I need to have a set of parameters to filter the LD information from HapMap. Of which possible values are LD, LOD, and D-prime.
Eyeballing the LD dataset, it doesn't gives a good instinctive boundary. A problem with using the data as is, is that both the D' as LOD can be both high and low. I know the parameters have an interdependence, but not a one-to-one mapping.
Does anybody have experience in filtering non-descriptive LD values from (for instance) the HapMap LD dataset?
