interpreting multi-SNP results

[domin...@gmail.com]

Hi,

I run bimbam using the multi-snp model for a candidate region but I would appreciate some help interpreting the results:

1. The posterior probability for a model with 3 SNPs is the highest (I used -l 4). Does it mean that a model with 3 SNPs is the best one for my data?

2. How can I decide on which SNPs are associated with the phenotype? In the manual you mentioned some R code to help looking at the BFs. Is it available?

3. Finally, I were able to run the model with -l 4 but not higher. Is it a limitation of the software? 

Thanks a lot!

Vera

[Matthew Stephens]

I hope the attached files help with q 2.

The answer to your question 1 is Yes.

The answer to question 3 is that more than
4 SNPs is too many combinations.


Best wishes,

Matthew

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