# What will be an appropriate mathematical distribution for SNP data?

I found that several papers describe SNPs as a binomial distribution with the probability of "success" equals to minor allele frequency.

However, in my experiments, when I generate SNP array following this distribution, the simulation results behave very differently from the SNP array generated following the random mating procedures. I feel like there is some missing information about the description of a binomial distribution.

I wonder what will be a better mathematical description of the SNP array data? Besides a direct answer, any thoughts, or any suggestion of papers are also highly welcome.

EDIT:

Thanks for these suggestions, after reading relevant materials, I think what I observe is about the Hardy-Weinberg equilibrium affected by evolutionary influences like mutation and migration. I will continue to look and list findings here if I find anything interesting. Please feel free to suggest some mathematical descriptions.

Also thanks for the suggestion of tools. They look very helpful, but I really hope to understand some mathematics behind it.

• Are you familiar with linkage disequilibrium and Hardy-Weinberg equilibrium? Looking up review papers on those might give more insights.
– gringer
Commented Sep 1, 2017 at 5:43
• Re the edit: given the very low mutation rates in eukaryotes, mutation effect on HWE in SNPs should be negligible - or at least, much lower than the effect of genotyping errors.
– juod
Commented Sep 2, 2017 at 9:00

On the other hand, indepedent SNPs should behave as draws from $Binomial(2, MAF)$. Note that the standard QC procedure of filtering out genotypes that deviate from Hardy-Weinberg equilibrium is just a goodness-of-fit test against $Binomial(2, \hat{MAF})$. If you employ this filter, some LD-pruning, and still get different results, consider posting more details about the discrepancies you see - that would be indeed unexpected.