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.

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 1.0

Fit 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 warnings

Get 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 0.1045809

Profile 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 8.4237085

densityplot(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            1.22780606

An 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            0.72728

Another 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            1.4147793

Fit 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