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The email wasn’t a challenge but a simple question: Is it possible to run a multivariate analysis in multiple sites? I was going to answer yes, of course, and leave it there but it would be a cruel, non-satisfying answer. We can get a better handle of the question if we use a simple example; let’s assume that we have two traits (call them tree stem diameter and stem density) assessed in two sites (localities).
Because this is genetics we have a family structure, let’s say half-siblings so we only half the mother in common, and we will ignore any experimental design features to keep things simple. We have 100 families, with 30 trees each, in sites A and B, for a total of 6,000 trees (100 x 30 x 2). The data could look like this:
site family tree diam dens A 1 1 20 398 A 1 2 19 400 ... A 100 30 24 375 ... B 1 1 25 396 ... B 100 30 23 403
We can also think of a trait measured in two sites as separate, but correlated, traits. For example, diameter in site 1 (diam1) is a different variable from diameter in site 2 (diam 2). So now we have four response variables (diam1, dens1, diam2, dens2), two of which have only missing values in a given site:
site family tree diam1 dens1 diam2 dens2 A 1 1 20 398 NA NA A 1 2 19 400 NA NA ... A 100 30 24 375 NA NA ... B 1 1 NA NA 25 396 ... B 100 30 NA NA 23 403
All variables are correlated at the genetic level with an unstructured G covariance matrix, while at the residual level are only correlated within-site (same tree was assessed for both traits), but there is zero correlation between sites, because one could be assessed near Christchurch, while the other near Dunedin, for example.
###
### Data generation
###
# Simulate 100 half-sib families with 30 trees each
# for the four traits in two sites
n.fam <- 100
n.prog <- 30
n.trait <- 4
n.site <- 2
# Couple of variance matrices,
# G is for additive genetic effects
# R is for residuals
# All variances are assumed equal to 1 without loss
# of generality
G <- matrix(c(1, 0.7, 0.5, 0.3,
0.7, 1, 0.2, 0.1,
0.5, 0.2, 1, 0.8,
0.3, 0.1, 0.8, 1), 4, 4)
R <- matrix(c(1, 0.3, 0, 0,
0.3, 1, 0, 0,
0, 0, 1, 0.2,
0, 0, 0.2, 1), 4, 4)
# Between-family variances account for 1/4 of additive
# genetic effects. Within-family account for 3/4 of
# additive + residual.
# We also get the Cholesky decomposition in the same step
BFL <- chol(1/4 * G)
WFL <- chol(3/4 * G + R)
# Simulate random family effects for four traits
# Simulate random family effects for four traits
fam.eff <- t(BFL) %*% matrix(rnorm(n.fam*n.trait),
n.trait, n.fam)
fam.eff <- t(fam.eff) # This is 100 x 4
tre.eff <- t(WFL) %*% matrix(rnorm(n.prog*n.fam*n.trait),
n.trait, n.prog*n.fam)
tre.eff <- t(tre.eff) # This is 3000 x 4
# Expand family effects matrix (n.prog each) to match
# dimension of tree effects
pheno <- fam.eff[rep(1:dim(fam.eff)[1], each = n.prog),] + tre.eff
# Now to 2 sites
pheno2s <- matrix(NA, n.prog*n.fam*n.site, n.trait)
colnames(pheno2s) <- c('diam1', 'dens1', 'diam2', 'dens2')
pheno2s[1:3000, 1:2] <- pheno[1:3000, 1:2]
pheno2s[3001:6000, 3:4] <- pheno[1:3000, 3:4]
# Creating data set. Family and tree are shamelessly recycled
sim.data <- data.frame(site = factor(rep(c('A', 'B'), each = n.fam*n.prog)),
family = factor(rep(1:n.fam, each = n.prog)),
tree = factor(1:30),
pheno2s)
Some neat things:
- Data simulation relies on Cholesky decomposition, as explained in this post over a year ago (time flies!). Generation is a bit more complex this time because we have to account for two, rather than one, covariance structures.
- Another point with data simulation is that one could generate one set of correlated values at the time by using something like
t(BFL) %*% diag(rnorm(trait), 3)and loop it or useapply(). This would require much less memory but would also be much slower. - We need to repeat each line of the family effects matrix 30 times so we can add to the individual tree effects. Often we use indexing in matrices or data frames to extract a few rows. Instead here I’m using to repeat a given number of times each row by indexing with
rep().
If we show observations 2990 to 3010 (last 10 for site A and first 10 for site B) we can see the pattern of observations below, which will have the required structure to have a US (or heterogeneous variance correlation matrix) for G and a block diagonal matrix for R. By the way, one could also have block diagonal matrices with repeated measurements, although probably with a different correlation structure:
> pheno2s[2990:3010,]
diam1 dens1 diam2 dens2
[1,] 0.57087250 -0.8521059 NA NA
[2,] 0.94859621 0.6599391 NA NA
[3,] -3.37405451 -0.6093312 NA NA
[4,] 0.93541048 -0.7977893 NA NA
[5,] -0.74758553 0.7962593 NA NA
[6,] 0.51280201 1.4870425 NA NA
[7,] -1.92571147 -0.2554365 NA NA
[8,] -1.15923045 -2.0656582 NA NA
[9,] -0.82145904 -0.3138340 NA NA
[10,] 1.79631670 0.3814723 NA NA
[11,] -0.01604778 -1.1804723 NA NA
[12,] NA NA -0.1436143 -1.97628883
[13,] NA NA -2.7099687 -2.93832962
[14,] NA NA -2.5153420 0.73780760
[15,] NA NA 0.3084056 0.61696714
[16,] NA NA -0.2909500 0.78111864
[17,] NA NA -1.8629862 -2.19346309
[18,] NA NA 0.8673053 -0.07692884
[19,] NA NA -0.1459703 0.36981965
[20,] NA NA -0.7688851 -0.96765799
[21,] NA NA 0.6637173 -0.34924814
Gratuitous picture: driving in rural Japan, the cultural value of wood and breaking preconceptions (Photo: Luis).
For the analysis we will use ASReml, which is the Pro option if you work in a breeding program and require solving much larger systems of equations than this tiny example (say 50-100 traits, large pedigrees, etc). Another option for playing with this small data set would be to use MCMCglmm, which also allows for multivariate evaluation of linear mixed models.
###
### Multivariate analysis
###
require(asreml)
# Assuming zero correlation between traits (equivalent
# to four univariate analyses)
m1 <- asreml(cbind(diam1, dens1, diam2, dens2) ~ trait,
random = ~ diag(trait):family,
rcov = ~ units:diag(trait),
data = sim.data)
summary(m1)$varcomp
# gamma component std.error z.ratio constraint
#trait:family!trait.diam1.var 0.2571274 0.2571274 0.04524049 5.683569 Positive
#trait:family!trait.dens1.var 0.2265041 0.2265041 0.04059742 5.579274 Positive
#trait:family!trait.diam2.var 0.2383959 0.2383959 0.04185982 5.695102 Positive
#trait:family!trait.dens2.var 0.2567999 0.2567999 0.04459246 5.758818 Positive
#R!variance 1.0000000 1.0000000 NA NA Fixed
#R!trait.diam1.var 1.8290472 1.8290472 0.04803313 38.078866 Positive
#R!trait.dens1.var 1.7674960 1.7674960 0.04641672 38.078866 Positive
#R!trait.diam2.var 1.6779793 1.6779793 0.04406589 38.078866 Positive
#R!trait.dens2.var 1.7028171 1.7028171 0.04471817 38.078866 Positive
# The multivariate analysis allowing for correlation
# is a bit more complex, so we start by generating
# a structure for starting values
m2.sv <- asreml(cbind(diam1, dens1, diam2, dens2) ~ trait,
random = ~ corgh(trait):family,
rcov = ~ units:us(trait),
data = sim.data,
start.values = TRUE)
# Now we'll constraint some R variance components
# to zero and fix them there. These are values for
# which we now they are 0
sv <- m2.sv$gammas.table
sv$Value1 <- 0
sv$Constraint1 <- 'F'
# Run the analyses with those constraints for the R matrix
# using the restricted values in R.param
m2 <- asreml(cbind(diam1, dens1, diam2, dens2) ~ trait,
random = ~ corgh(trait):family,
rcov = ~ units:us(trait, init = r.init),
data = sim.data,
R.param = sv)
summary(m2)$varcomp
# gamma component std.error
# trait:family!trait.dens1:!trait.diam1.cor 0.72535514 0.72535514 0.06381776
# trait:family!trait.diam2:!trait.diam1.cor 0.49100215 0.49100215 0.10067768
# trait:family!trait.diam2:!trait.dens1.cor 0.21445972 0.21445972 0.12085780
# trait:family!trait.dens2:!trait.diam1.cor 0.37476119 0.37476119 0.10975970
# trait:family!trait.dens2:!trait.dens1.cor 0.05221773 0.05221773 0.12439924
# trait:family!trait.dens2:!trait.diam2.cor 0.85356318 0.85356318 0.04321683
# trait:family!trait.diam1 0.25712744 0.25712744 0.04524049
# trait:family!trait.dens1 0.22650410 0.22650410 0.04059742
# trait:family!trait.diam2 0.23839593 0.23839593 0.04185982
# trait:family!trait.dens2 0.25679989 0.25679989 0.04459246
# R!variance 1.00000000 1.00000000 NA
# R!trait.diam1:diam1 1.82904721 1.82904721 0.04803313
# R!trait.dens1:diam1 0.90450432 0.90450432 0.03737490
# R!trait.dens1:dens1 1.76749598 1.76749598 0.04641672
# R!trait.diam2:diam1 0.00000000 0.00000000 NA
# R!trait.diam2:dens1 0.00000000 0.00000000 NA
# R!trait.diam2:diam2 1.67797927 1.67797927 0.04406589
# R!trait.dens2:diam1 0.00000000 0.00000000 NA
# R!trait.dens2:dens1 0.00000000 0.00000000 NA
# R!trait.dens2:diam2 0.71249532 0.71249532 0.03406354
# R!trait.dens2:dens2 1.70281710 1.70281710 0.04471817
#
# z.ratio constraint
# trait:family!trait.dens1:!trait.diam1.cor 11.3660390 Unconstrained
# trait:family!trait.diam2:!trait.diam1.cor 4.8769710 Unconstrained
# trait:family!trait.diam2:!trait.dens1.cor 1.7744796 Unconstrained
# trait:family!trait.dens2:!trait.diam1.cor 3.4143789 Unconstrained
# trait:family!trait.dens2:!trait.dens1.cor 0.4197593 Unconstrained
# trait:family!trait.dens2:!trait.diam2.cor 19.7507102 Unconstrained
# trait:family!trait.diam1 5.6835685 Positive
# trait:family!trait.dens1 5.5792738 Positive
# trait:family!trait.diam2 5.6951016 Positive
# trait:family!trait.dens2 5.7588182 Positive
# R!variance NA Fixed
# R!trait.diam1:diam1 38.0788655 Positive
# R!trait.dens1:diam1 24.2008477 Positive
# R!trait.dens1:dens1 38.0788655 Positive
# R!trait.diam2:diam1 NA Fixed
# R!trait.diam2:dens1 NA Fixed
# R!trait.diam2:diam2 38.0788655 Positive
# R!trait.dens2:diam1 NA Fixed
# R!trait.dens2:dens1 NA Fixed
# R!trait.dens2:diam2 20.9166565 Positive
# R!trait.dens2:dens2 38.0788655 Positive
In model 1 each of the variances was supposed to be ~0.25 (1/4 * 1) and the residual variances ~1.75 (3/4*1 + 1). Once we move to model 2 we also get values similar to the correlations we were trying to simulate. And this is the end of the long answer.
P.S. If, for some bizarre reason, you would like to use SAS for this type of analyses, Fikret Isik has proc mixed code to run multivariate genetic analyses.
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