lme4qtl
lme4qtl extends the lme4 R package for quantitative trait locus (qtl) mapping. It is all about the covariance structure of random effects. lme4qtl supports user-defined matrices for that, e.g. kinship or IBDs.
See slides bit.ly/1UiTZvQ introducing the lme4qtl R package or read our article / preprint.
| Package | Continuous response |
|---|---|
| stats | lm(myTrait ~ myCovariate, myData) |
| lme4 | lmer(myTrait ~ myCovariate + (1|myID), myData) |
| lme4qtl | relmatLmer(myTrait ~ myCovariate + (1|myID), myData, relmat = list(myID = myMatrix)) |
| Package | Binary response |
|---|---|
| stats | glm(myStatus ~ 1, myData, family = binomial) |
| lme4 | glmer(myStatus ~ (1|myID), myData, family = binomial) |
| lme4qtl | relmatGlmer(myStatus ~ (1|myID), myData, relmat = list(myID = myMatrix), family = binomial) |
Note that rownames/colnames of myMatrix have to be values of myID variable, so matching between relationship matrix and grouping variable is possible. The order doesn't matter.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("variani/lme4qtl")The official release on CRAN is pending.
Citation
To cite the lme4qtl package in publications use:
Ziyatdinov et al., lme4qtl: linear mixed models with flexible
covariance structure for genetic studies of related individuals,
BMC Bioinformatics (2018)
Contact
You are welcome to submit suggestions and bug-reports at https://github.com/variani/lme4qtl/issues.
Example
library(lme4)
library(lattice)library(lme4qtl)packageVersion("lme4qtl")
#> [1] '0.2.1'Load simulated data, phenotypes dat40 and the kinship matrix kin2.
data(dat40, package = "lme4qtl")
dim(dat40)
#> [1] 234 10
dim(kin2)
#> [1] 234 234
head(dat40)
#> ID trait1 trait2 AGE FAMID FA MO SEX trait1bin trait2bin
#> 7 101 8.41954 9.67925 50 10 0 0 1 0 0
#> 14 102 5.47141 4.31886 44 10 0 0 2 0 0
#> 21 103 9.66547 7.00735 34 10 101 102 2 0 0
#> 28 104 6.27092 8.59257 41 10 101 102 1 0 0
#> 35 105 7.96814 7.60801 36 10 101 102 1 0 0
#> 42 106 8.29865 8.17634 37 10 101 102 2 0 0
kin2[1:5, 1:5] # nuclear family with 2 parents and 3 kids
#> 5 x 5 sparse Matrix of class "dsCMatrix"
#> 11 12 13 14 15
#> 11 1.0 . 0.5 0.5 0.5
#> 12 . 1.0 0.5 0.5 0.5
#> 13 0.5 0.5 1.0 0.5 0.5
#> 14 0.5 0.5 0.5 1.0 0.5
#> 15 0.5 0.5 0.5 0.5 1.0Fit a model for continuous trait with two random effects, family-grouping (1|FAM) and additive genetic (1|ID).
m1 <- relmatLmer(trait1 ~ AGE + SEX + (1|FAMID) + (1|ID), dat40, relmat = list(ID = kin2))
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
m1
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: trait1 ~ AGE + SEX + (1 | FAMID) + (1 | ID)
#> Data: dat40
#> REML criterion at convergence: 963.3853
#> Random effects:
#> Groups Name Std.Dev.
#> ID (Intercept) 2.2988
#> FAMID (Intercept) 0.0000
#> Residual 0.7856
#> Number of obs: 224, groups: ID, 224; FAMID, 39
#> Fixed Effects:
#> (Intercept) AGE SEX2
#> 7.563248 0.008314 -0.364197
#> convergence code 0; 1 optimizer warnings; 0 lme4 warningsGet a point estimate of heritability (h2), the proportion of variance explained by (1|ID).
lme4::VarCorr(m1)
#> Groups Name Std.Dev.
#> ID (Intercept) 2.29880
#> FAMID (Intercept) 0.00000
#> Residual 0.78562
lme4qtl::VarProp(m1)
#> grp var1 var2 vcov sdcor prop
#> 1 ID (Intercept) <NA> 5.2845002 2.2988041 0.8954191
#> 2 FAMID (Intercept) <NA> 0.0000000 0.0000000 0.0000000
#> 3 Residual <NA> <NA> 0.6172059 0.7856245 0.1045809Profile the variance components (h2) to get the 95% confidence intervals. The method functions profile and confint are implemented in lme4. Note that a different model m2 is used, because profiling is prone to errors/warnings if model fit is poor.
m2 <- relmatLmer(trait2 ~ (1|ID), dat40, relmat = list(ID = kin2))
VarProp(m2)
#> grp var1 var2 vcov sdcor prop
#> 1 ID (Intercept) <NA> 5.573272 2.360778 0.7723589
#> 2 Residual <NA> <NA> 1.642638 1.281654 0.2276411
prof <- profile(m2)
#> Warning in zetafun(np, ns): NAs detected in profiling
prof_prop <- lme4qtl::varpropProf(prof) # convert to proportions
confint(prof_prop)
#> 2.5 % 97.5 %
#> .sigprop01 0.5292158 0.9157910
#> .sigmaprop 0.0655726 0.4652175
#> (Intercept) 7.2745157 8.4237085densityplot(prof)
densityplot(prof_prop)
try(splom(prof))
#> Error in if (singfit) warning("splom is unreliable for singular fits") :
#> missing value where TRUE/FALSE needed
prof_clean <- na.omit(prof) # caution: NAs are indicators of poor fits
splom(prof_clean)
Fit a model with genetic and residual variances that differ by gender (sex-specificity model). The formula syntax with dummy (see ?lme4::dummy) is applied to the residual variance (1|RID) to cancel the residual correlation.
dat40 <- within(dat40, RID <- ID) # replicate ID
m4 <- relmatLmer(trait2 ~ SEX + (0 + SEX|ID) + (0 + dummy(SEX)|RID), dat40, relmat = list(ID = kin2))
VarCorr(m4)
#> Groups Name Std.Dev. Corr
#> ID SEX1 1.94400138
#> SEX2 2.64404940 0.826
#> RID dummy(SEX) 0.00050224
#> Residual 1.22780606An example of parameter constraints that make the genetic variance between genders equal.
m4_vareq <- relmatLmer(trait2 ~ SEX + (0 + SEX|ID) + (0 + dummy(SEX)|RID), dat40, relmat = list(ID = kin2),
vcControl = list(vareq = list(id = c(1, 2, 3))))
VarCorr(m4_vareq)
#> Groups Name Std.Dev. Corr
#> ID SEX1 2.47777
#> SEX2 2.47777 0.746
#> RID dummy(SEX) 0.95827
#> Residual 0.72728Another example of parameter constraint that implies the genetic correlation between genders equal to 1.
m4_rhog1 <- relmatLmer(trait2 ~ SEX + (0 + SEX|ID) + (0 + dummy(SEX)|RID), dat40, relmat = list(ID = kin2),
vcControl = list(rho1 = list(id = 3)))
VarCorr(m4_rhog1)
#> Groups Name Std.Dev. Corr
#> ID SEX1 1.7627785
#> SEX2 2.5782330 1.000
#> RID dummy(SEX) 0.0014823
#> Residual 1.4147793Fit a model for binary trait.
m3 <- relmatGlmer(trait1bin ~ (1|ID), dat40, relmat = list(ID = kin2), family = binomial(probit))
m3
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#> Approximation) [glmerMod]
#> Family: binomial ( probit )
#> Formula: trait1bin ~ (1 | ID)
#> Data: dat40
#> AIC BIC logLik deviance df.resid
#> 218.1325 225.0432 -107.0663 214.1325 232
#> Random effects:
#> Groups Name Std.Dev.
#> ID (Intercept) 0.7669
#> Number of obs: 234, groups: ID, 234
#> Fixed Effects:
#> (Intercept)
#> -1.242