My first thought was to suggest a mixed effects model, so I will describe that first. But having had a Google there are models known as "Conditional logistic regression" models in which you can include any data structure as a covariate, which may be more appropriate, but I cannot vouch for them as I haven't used them. I would suggest reading the documentation.
Mixed effects models
I only know how to do this in R (stats language - www.r-project.org), but if you're using PLINK (so presumably also UNIX) R is pretty straightforward.
There are a couple of packages available for mixed effects models, my personal preference is function
lme in package
nlme (CRAN link). This type of model allows you to specify 2 types of independent variable (e.g. age, BMI, ethnicity... in your case);
- Fixed effects - such as age, sex (something that is a phenotype of the samples),
- Random effects - such as batch or other "technical" consideration,
This means you can format a model as you would before, but include an additional "random effects" term for your "matched" variable;
model = lme( fixed= outcome ~ exposure + covariate1, random= 1|matched )
Conditional logistic regression models
survival package for R there is a function called
clogit (CRAN link) which seems to do exactly what you want. Although I have never used it myself.
From what I can gather this allows you to run a logistic regression with an additional covariate
strata(matched), so your model may look like
model = clogit( outcome ~ exposure + covariate1 + strata(matched) )
This may not be exactly what you are after, as I'm not familiar with your data, so I would suggest heading over to https://stats.stackexchange.com/ as well, and searching for questions about regression analysis and paired observations - if none answer your question then ask a fresh one.