Covariate models of genetic polymorphisms

From: "Tham Lai San, PHCV2" phcv2@nus.edu.sg Subject: [NMusers] Covariate models of genetic polymorphisms Date: Thu, 6 Jul 2006 15:37:25 +0800 Dear Colleagues, I am trying to model the variants of a few genes as covariates. On their own, when modelled as GRPCL=POP_CL*SizeDescriptor*FGene1 where by the Fgene1(theta) has 3 values for wild type, heterozygous mutant and homozygous mutant respectively, most have shown some functional effect on CL. Would like to know if anybody has some prior experience putting in multiple genes as covariates into the model and how best to approach this? E.g. GRPCL=POP_CL*SizeDescriptor*FGene1+FGene2 or GRPCL=POP_CL*SizeDescriptor*FGene1*FGene2? What if the genes are also known to cross-talk? Best Regards, Lai San Tham

From: "Bill Bachman" bachmanw@comcast.net Subject: RE: [NMusers] Covariate models of genetic polymorphisms Date: Thu, July 06, 2006 8:28 am The following comments apply to any covariates (as I have no experience with genetic markers as covariates): An argument can be made that multiplicative parameterizations are sometimes found to be easier to get to converge than the linear counterpart (possibly they are more descriptive of the mathematical relationships found in nature or that they code for a fractional change in the parameter rather than a strictly additive component to the parameter). Many go one step further and use multiplicative exponential parameterization that better codes for nonlinear relationships. If you use the linear parameterization and want to test for interaction, add another term: something like this GRPCL=POP_CL*SizeDescriptor+FGene1+FGene2+FGene1*FGene2 Possibly the best advice I could give is: try the variations yourself. Others experience is nice to know, but nothing beats testing your hypotheses yourself.

From: Mark Sale - Next Level Solutions mark@nextlevelsolns.com Subject: RE: [NMusers] Covariate models of genetic polymorphisms Date: Fri, 07 Jul 2006 09:16:49 -0700 Bill, Lai San Tham As you seem to suspect, genetic markers (and IMHO many covariates) frequently exert their effect only when present in combinations. So, Bill is correct that the interaction between the genes ought to be tested, as in Bill code GRPCL=POP_CL*SizeDescriptor+FGene1+FGene2*FGene1 It is entirely possible that you would find that FGene1, FGene2 etc individually have little to no effect. However, if you put in GRPCL=POP_CL*SizeDescriptor+FGene1*FGen2 ; if gene1 and gene2 need to be mutant or perhaps GRPCL=POP_CL*SizeDescriptor+FGene1*(1-FGen2) ; if gene1 is to be mutant and gene2 wild type or perhaps GRPCL=POP_CL*SizeDescriptor+(1-FGene1)*FGene2 etc etc you might find a very important effect. An effect is seen only when Gene1 and Gene2 are both mutants, but having a wild type Gene1 compensates for a mutant Gene1, with no effect on the phenotype. That is, the effects are not independent, but very dependent. Testing each efffect individually is likely not adequate. However, the number of combination of even a modest number of genes, with only 2 or 3 polymorph very quickly becomes prohibitively large. Even in the model that Bill proposed, I'd be concerned that it was over parameterized and would have convergence problems if all the effects were put in together, rather than the usual method of one at a time. I'm not sure if this is what you mean by cross-talk, perhaps it is this sort of interaction. I usually think of cross talk (in biology at least) as activation/inhibition of one receptor by some activity of another receptor, even though they aren't heterodimers. This receptor cross talk can be mediated by changes in gene expression, but I haven't heard the term cross talk used when refering to polymorphisms. Those who read this list server already know about my proposed solution, so I won't bore them with that again. Mark Sale MD Next Level Solutions, LLC www.NextLevelSolns.com

From: "Tham Lai San, PHCV2" phcv2@nus.edu.sg Subject: RE: [NMusers] Covariate models of genetic polymorphisms Date: Mon, 10 Jul 2006 08:18:53 +0800 Dear Mark, Bill and David, Being a relatively inexperienced NM user coming from an island where few people speak the same lingo, you advice is very much appreciated. I am investigating the SNP polymorphims for a bunch of transciptional regulators of CYP450, so to be specific, Mark is right about calling them receptors rather than genes. This is a preliminary screening exercise to see if my hypothesis holds because I am not examining the effect of CYP polymorphims on CL but effect of receptor polymorphims on CYP expression. However, I did notice while screening through the polymorphisms of individual SNPs that using a binary covariate for mutants is not as good as separating heterozygous from homzygous mutants. Meanwhile, still working through the permutations, via the brute force method! :-) Lai-San Tham, PharmD Department of Hematology-Oncology National University Hospital, Singapore _______________________________________________________