Stephen Montgomery
2009-Jan-12 16:36 UTC
[R] Determining variance components of classed covariates
Hi - I am interested in solving variance components for the data below with respect to the response variable, Expression within R. However, the covariates aren't independent and they also have a class (of which the total variance explained by covariates in that class I am most interested in). Very naively, I have tried to look at each individual covariates variance like this> lm<-lmer(Expression ~ 1 + (1|rs11834524) + (1|rs7074431),data=input_new)> lmLinear mixed-effects model fit by REML Formula: Expression ~ 1 + (1 | rs11834524) + (1 | rs7074431) Data: input AIC BIC logLik MLdeviance REMLdeviance 108.4 116.5 -51.22 102.5 102.4 Random effects: Groups Name Variance Std.Dev. rs11834524 (Intercept) 0.485538 0.69681 rs7074431 (Intercept) 0.013720 0.11713 Residual 0.128853 0.35896 number of obs: 109, groups: rs11834524, 3; rs7074431, 3 Fixed effects: Estimate Std. Error t value (Intercept) 9.9524 0.4098 24.29 My assumption is that this is telling me that rs11834524 explains 0.485538 of the variance and rs7074431 explains 0.013720 of the variance in Expression when looked at independently. However, I would like to know how to write a model where I know how much of the total variance (in Expression) is described by covariates rs11834524, rs1682421, rs13383869 and rs9457141 (call it class A) and covariates rs9459617, rs7074431, rs12450785, rs592724 (call it class B). Assuming an additive model within the class. The caveats are that there is missing data and again that there may be correlation between all the covariates. Such that a theoretical result may be that Class A: Explains 60% of the total variance in expression (response) Class B: Explains 10% of the total variance in expression Thanks for the help! I am sorry I am R challenged here...I really appreciate the guidance! Stephen> dump("input_new", file=stdout())input_new <- structure(list(Individual = structure(1:109, .Label = c("NA06984", "NA06985", "NA06986", "NA06989", "NA06993", "NA06994", "NA07000", "NA07022", "NA07037", "NA07045", "NA07051", "NA07055", "NA07056", "NA07345", "NA07346", "NA07347", "NA07357", "NA07435", "NA11829", "NA11830", "NA11831", "NA11832", "NA11839", "NA11840", "NA11843", "NA11881", "NA11882", "NA11892", "NA11893", "NA11894", "NA11917", "NA11918", "NA11919", "NA11920", "NA11930", "NA11931", "NA11992", "NA11993", "NA11994", "NA11995", "NA12003", "NA12005", "NA12006", "NA12043", "NA12044", "NA12056", "NA12057", "NA12144", "NA12145", "NA12146", "NA12154", "NA12155", "NA12156", "NA12234", "NA12239", "NA12248", "NA12249", "NA12264", "NA12272", "NA12273", "NA12274", "NA12275", "NA12282", "NA12283", "NA12286", "NA12287", "NA12340", "NA12341", "NA12342", "NA12343", "NA12347", "NA12348", "NA12383", "NA12399", "NA12400", "NA12414", "NA12489", "NA12546", "NA12716", "NA12718", "NA12748", "NA12749", "NA12750", "NA12751", "NA12760", "NA12761", "NA12762", "NA12763", "NA12775", "NA12776", "NA12777", "NA12778", "NA12812", "NA12813", "NA12814", "NA12815", "NA12827", "NA12828", "NA12829", "NA12830", "NA12842", "NA12843", "NA12872", "NA12873", "NA12874", "NA12875", "NA12889", "NA12891", "NA12892" ), class = "factor"), Expression = c(9.46026823453575, 10.0788903323991, 9.20330296497174, 10.038741467793, 9.33092349416463, 11.0273957217919, 10.5498875891745, 9.81137299592747, 11.2023261987976, 9.90559354069027, 10.1524696609679, 10.3171767665993, 9.02155519577685, 9.84917871051438, 10.658877473136, 9.88895551011107, 8.62335008726357, 9.21529114100886, 10.7896248923916, 10.1302992505869, 8.64584282787018, 9.56057795866654, 9.89810004078774, 10.2557482141576, 8.95588077688637, 9.56452454115857, 9.26525135092154, 10.5438780642797, 9.8468571349548, 10.7416169225352, 10.5623721612979, 10.6565276881443, 9.67758493445612, 9.75385553511462, 8.997797236767, 11.0106882086179, 10.362578597992, 9.2745507212906, 10.7453355016181, 9.75998268015348, 9.45003620116962, 10.055504292376, 10.7072220720564, 10.0934686444392, 10.0472832129727, 10.1185615033486, 10.3340911031131, 9.70618910683157, 10.5953304905529, 10.4246307909547, 9.91463202635336, 10.249081562168, 10.9252022586474, 10.295544143525, 11.4838109797985, 10.5286570234792, 9.78692800868132, 10.0397050809162, 9.27914623343747, 10.37600233389, 9.27341681588134, 9.40195375611303, 10.8979822929135, 9.03922228977389, 10.3911745662505, 10.4345408213054, 9.8548491618724, 10.1897729275437, 10.2881888849609, 8.9656977165014, 9.81595398472166, 10.1856794532084, 9.3763789479684, 10.1712420020647, 10.2964594680427, 10.3515965292101, 8.94492585275159, 11.2529257614993, 9.25146912450726, 10.1904309237525, 10.7490591053023, 10.3883924463568, 10.097023765247, 10.0824730785217, 10.0828512817661, 10.6371064852226, 10.5831044752098, 10.4484786486601, 8.50264408341596, 10.3468670812262, 9.46061433005316, 8.90027436167269, 9.73630671555279, 9.40555522408144, 10.3220768104446, 8.55132985773453, 10.1678182524815, 10.6145417864386, 10.4169948161073, 10.0253039670548, 10.2568017077865, 10.5045847076951, 9.75993936712448, 8.99997092895909, 10.6742222414794, 10.8640943324257, 10.4295384371541, 10.1987862649656, 10.6744617172313), rs11834524 structure(c(1L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 3L), .Label = c("AA", "AG", "GG"), class = "factor"), rs1682421 = structure(c(1L, 2L, 1L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 3L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, NA, 3L, 2L, 3L, 2L, 2L), .Label = c("CC", "CT", "TT"), class = "factor"), rs13383869 = structure(c(2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, NA, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 1L, 1L, 2L, 2L, NA, 2L, 1L, 1L, 2L, 2L, 1L, 1L), .Label = c("AA", "AG", "GG"), class = "factor"), rs9457141 = structure(c(1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 3L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, NA, 2L, 1L, 2L, NA, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("CC", "CT", "TT"), class = "factor"), rs9459617 = structure(c(1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 3L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, NA, 1L, 3L, 3L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("CC", "CT", "TT"), class = "factor"), rs7074431 = structure(c(2L, 3L, 2L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L), .Label = c("CC", "CT", "TT"), class = "factor"), rs12450785 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 2L), .Label = c("AA", "AG", "GG"), class = "factor"), rs592724 = structure(c(1L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L), .Label = c("CC", "CT", "TT"), class = "factor"), Grp = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "1", class "factor")), .Names = c("Individual", "Expression", "rs11834524", "rs1682421", "rs13383869", "rs9457141", "rs9459617", "rs7074431", "rs12450785", "rs592724", "Grp"), row.names c(NA, -109L), class = "data.frame") Stephen B. Montgomery Postdoctoral Researcher, Population and Comparative Genomics Wellcome Trust Sanger Institute Hinxton, Cambridge CB10 1SA Skype: stephen.b.montgomery -- The Wellcome Trust Sanger Institute is operated by Genome Research Limited, a charity registered in England with number 1021457 and a company registered in England with number 2742969, whose registered office is 215 Euston Road, London, NW1 2BE.
Gabor Grothendieck
2009-Jan-12 17:53 UTC
[R] Determining variance components of classed covariates
You might want to try the https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models list next time for mixed model questions. At any rate the Variance column figures are variances, not percentages. We can use anova with REML=FALSE to make comparisons among models. Below we find that removing the rs7074431 term makes very little difference so we can drop it but removing the rs11834524 term makes a big difference. Thus modx0 can be used.> modxx <- lmer(Expression ~ 1 + (1|rs11834524) + (1|rs7074431), input_new, REML = FALSE) > modx0 <- lmer(Expression ~ 1 + (1|rs11834524), input_new, REML = FALSE) > mod0x <- lmer(Expression ~ 1 + (1|rs7074431), input_new, REML = FALSE) > anova(modxx, modx0)Data: input_new Models: modx0: Expression ~ 1 + (1 | rs11834524) modxx: Expression ~ 1 + (1 | rs11834524) + (1 | rs7074431) Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) modx0 3 111.386 119.460 -52.693 modxx 4 110.288 121.053 -51.144 3.0986 1 0.07836 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1> anova(modxx, mod0x)Data: input_new Models: mod0x: Expression ~ 1 + (1 | rs7074431) modxx: Expression ~ 1 + (1 | rs11834524) + (1 | rs7074431) Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) mod0x 3 206.652 214.726 -100.326 modxx 4 110.288 121.053 -51.144 98.365 1 < 2.2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 On Mon, Jan 12, 2009 at 11:36 AM, Stephen Montgomery <sm8 at sanger.ac.uk> wrote:> Hi - > > I am interested in solving variance components for the data below with > respect to the response variable, Expression within R. > > However, the covariates aren't independent and they also have a class > (of which the total variance explained by covariates in that class I am > most interested in). > > Very naively, I have tried to look at each individual covariates > variance like this > >> lm<-lmer(Expression ~ 1 + (1|rs11834524) + (1|rs7074431), > data=input_new) >> lm > Linear mixed-effects model fit by REML > Formula: Expression ~ 1 + (1 | rs11834524) + (1 | rs7074431) > Data: input > AIC BIC logLik MLdeviance REMLdeviance > 108.4 116.5 -51.22 102.5 102.4 > Random effects: > Groups Name Variance Std.Dev. > rs11834524 (Intercept) 0.485538 0.69681 > rs7074431 (Intercept) 0.013720 0.11713 > Residual 0.128853 0.35896 > number of obs: 109, groups: rs11834524, 3; rs7074431, 3 > > Fixed effects: > Estimate Std. Error t value > (Intercept) 9.9524 0.4098 24.29 > > My assumption is that this is telling me that rs11834524 explains > 0.485538 of the variance and rs7074431 explains 0.013720 of the variance > in Expression when looked at independently. > > However, I would like to know how to write a model where I know how much > of the total variance (in Expression) is described by covariates > rs11834524, rs1682421, rs13383869 and rs9457141 (call it class A) and > covariates rs9459617, rs7074431, rs12450785, rs592724 (call it class B). > Assuming an additive model within the class. The caveats are that there > is missing data and again that there may be correlation between all the > covariates. > > Such that a theoretical result may be that > Class A: Explains 60% of the total variance in expression (response) > Class B: Explains 10% of the total variance in expression > > Thanks for the help! I am sorry I am R challenged here...I really > appreciate the guidance! > > Stephen > > >> dump("input_new", file=stdout()) > input_new <- > structure(list(Individual = structure(1:109, .Label = c("NA06984", > "NA06985", "NA06986", "NA06989", "NA06993", "NA06994", "NA07000", > "NA07022", "NA07037", "NA07045", "NA07051", "NA07055", "NA07056", > "NA07345", "NA07346", "NA07347", "NA07357", "NA07435", "NA11829", > "NA11830", "NA11831", "NA11832", "NA11839", "NA11840", "NA11843", > "NA11881", "NA11882", "NA11892", "NA11893", "NA11894", "NA11917", > "NA11918", "NA11919", "NA11920", "NA11930", "NA11931", "NA11992", > "NA11993", "NA11994", "NA11995", "NA12003", "NA12005", "NA12006", > "NA12043", "NA12044", "NA12056", "NA12057", "NA12144", "NA12145", > "NA12146", "NA12154", "NA12155", "NA12156", "NA12234", "NA12239", > "NA12248", "NA12249", "NA12264", "NA12272", "NA12273", "NA12274", > "NA12275", "NA12282", "NA12283", "NA12286", "NA12287", "NA12340", > "NA12341", "NA12342", "NA12343", "NA12347", "NA12348", "NA12383", > "NA12399", "NA12400", "NA12414", "NA12489", "NA12546", "NA12716", > "NA12718", "NA12748", "NA12749", "NA12750", "NA12751", "NA12760", > "NA12761", "NA12762", "NA12763", "NA12775", "NA12776", "NA12777", > "NA12778", "NA12812", "NA12813", "NA12814", "NA12815", "NA12827", > "NA12828", "NA12829", "NA12830", "NA12842", "NA12843", "NA12872", > "NA12873", "NA12874", "NA12875", "NA12889", "NA12891", "NA12892" > ), class = "factor"), Expression = c(9.46026823453575, 10.0788903323991, > > 9.20330296497174, 10.038741467793, 9.33092349416463, 11.0273957217919, > 10.5498875891745, 9.81137299592747, 11.2023261987976, 9.90559354069027, > 10.1524696609679, 10.3171767665993, 9.02155519577685, 9.84917871051438, > 10.658877473136, 9.88895551011107, 8.62335008726357, 9.21529114100886, > 10.7896248923916, 10.1302992505869, 8.64584282787018, 9.56057795866654, > 9.89810004078774, 10.2557482141576, 8.95588077688637, 9.56452454115857, > 9.26525135092154, 10.5438780642797, 9.8468571349548, 10.7416169225352, > 10.5623721612979, 10.6565276881443, 9.67758493445612, 9.75385553511462, > 8.997797236767, 11.0106882086179, 10.362578597992, 9.2745507212906, > 10.7453355016181, 9.75998268015348, 9.45003620116962, 10.055504292376, > 10.7072220720564, 10.0934686444392, 10.0472832129727, 10.1185615033486, > 10.3340911031131, 9.70618910683157, 10.5953304905529, 10.4246307909547, > 9.91463202635336, 10.249081562168, 10.9252022586474, 10.295544143525, > 11.4838109797985, 10.5286570234792, 9.78692800868132, 10.0397050809162, > 9.27914623343747, 10.37600233389, 9.27341681588134, 9.40195375611303, > 10.8979822929135, 9.03922228977389, 10.3911745662505, 10.4345408213054, > 9.8548491618724, 10.1897729275437, 10.2881888849609, 8.9656977165014, > 9.81595398472166, 10.1856794532084, 9.3763789479684, 10.1712420020647, > 10.2964594680427, 10.3515965292101, 8.94492585275159, 11.2529257614993, > 9.25146912450726, 10.1904309237525, 10.7490591053023, 10.3883924463568, > 10.097023765247, 10.0824730785217, 10.0828512817661, 10.6371064852226, > 10.5831044752098, 10.4484786486601, 8.50264408341596, 10.3468670812262, > 9.46061433005316, 8.90027436167269, 9.73630671555279, 9.40555522408144, > 10.3220768104446, 8.55132985773453, 10.1678182524815, 10.6145417864386, > 10.4169948161073, 10.0253039670548, 10.2568017077865, 10.5045847076951, > 9.75993936712448, 8.99997092895909, 10.6742222414794, 10.8640943324257, > 10.4295384371541, 10.1987862649656, 10.6744617172313), rs11834524 > structure(c(1L, > 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, > 1L, 3L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, > 1L, 1L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, > 2L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, > 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 3L, > 2L, 3L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, > 2L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 3L), .Label = c("AA", > "AG", "GG"), class = "factor"), rs1682421 = structure(c(1L, 2L, > 1L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, > 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 3L, 2L, 3L, 1L, 1L, > 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, > 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 2L, > 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, > 3L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, > 1L, 2L, 2L, 1L, 1L, NA, 3L, 2L, 3L, 2L, 2L), .Label = c("CC", > "CT", "TT"), class = "factor"), rs13383869 = structure(c(2L, > 2L, 2L, 2L, 2L, NA, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 3L, > 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, > 2L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, > 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, NA, 2L, 2L, 3L, 2L, > 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, > 2L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, > 1L, 1L, 2L, 2L, NA, 2L, 1L, 1L, 2L, 2L, 1L, 1L), .Label = c("AA", > "AG", "GG"), class = "factor"), rs9457141 = structure(c(1L, 2L, > 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, > 3L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, > 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, > 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, NA, 2L, 1L, 2L, NA, 1L, > 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("CC", > "CT", "TT"), class = "factor"), rs9459617 = structure(c(1L, 2L, > 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 3L, 1L, > 3L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, > 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, NA, 1L, 3L, 3L, 2L, 1L, > 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, > 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("CC", > "CT", "TT"), class = "factor"), rs7074431 = structure(c(2L, 3L, > 2L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, > 2L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 2L, > 1L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 1L, > 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, > 1L, 1L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, > 1L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, > 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L), .Label = c("CC", > "CT", "TT"), class = "factor"), rs12450785 = structure(c(2L, > 2L, 2L, 2L, 2L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, > 3L, 1L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, > 2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, > 1L, 2L, 2L, 1L, 1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, > 2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 3L, 2L, 2L, > 1L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 1L, > 2L, 2L, 3L, 2L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 2L), .Label = c("AA", > "AG", "GG"), class = "factor"), rs592724 = structure(c(1L, 2L, > 1L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 2L, > 2L, 2L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 3L, > 1L, 3L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, > 3L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 3L, 3L, > 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, > 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, > 1L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L), .Label = c("CC", > "CT", "TT"), class = "factor"), Grp = structure(c(1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "1", class > "factor")), .Names = c("Individual", > "Expression", "rs11834524", "rs1682421", "rs13383869", "rs9457141", > "rs9459617", "rs7074431", "rs12450785", "rs592724", "Grp"), row.names > c(NA, > -109L), class = "data.frame") > > > > Stephen B. Montgomery > Postdoctoral Researcher, Population and Comparative Genomics > Wellcome Trust Sanger Institute > Hinxton, Cambridge CB10 1SA > Skype: stephen.b.montgomery > > > > > -- > The Wellcome Trust Sanger Institute is operated by Genome Research > Limited, a charity registered in England with number 1021457 and a > company registered in England with number 2742969, whose registered > office is 215 Euston Road, London, NW1 2BE. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >