Logistic Regression In R including related individuals

Question

[a.papadam]

Dear all,

I am currently trying to perform a logistic regression to assess the association between a disease and measurement. The measurement is computed through genetic data, thus I should exclude genetic related individuals.

However, I am losing a significant number of positive individuals, and I wanted to used a mixed model that will account for relatedness so I don’t have to exclude those.

I am currently working with converting to gds files and use Genesis, but I am wondering if there are easier ways to accomplish.

Maybe if I compute a genetic relationship matrix to introduce it to a mixed model in R.

Thank you in advance

Answer 1

[LauferVA]

you are on the right track - I'd only add that i would try to opt for a software solution developed specifically for genetic data.

your response implies a good deal of understanding on this subject, but for completeness, the theoretical underpinnings for this were laid out mostly between 2008 - 2015, with many pubs showing increased statistical power by retaining those samples but including the genotype matrix as input into a GLMM, which allows for genomic control of relatedness by leveraging shared loci other than the disease associated one(s).

I think you will find this manuscript helpful: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3989144/; it discusses several packages that have been used to do this (EMMAX, FaST-LMM, GEMMA, GRAMMAR-Gamm, GCTA). it also provides accessible explanations as to why this works alongside a rigorous presentation of the underlying mathematical structures that power such GLMMs.

if this does not help, please reply and ill take another look.

VAL

[a.papadam]

Thank you for the response. I will report back once I have complete it.