Description Usage Arguments Details Value Author(s) References See Also Examples
trioGxE
estimates statistical interaction (GxE) between a single nucleotide polymorphism (SNP)
and a continuous environmental or non-genetic attributes
in case-parent trio data by fitting a generalized additive
model (GAM) using a penalized iteratively re-weighted least squares algorithm.
1 2 3 4 5 |
data |
a data frame with columns for parental genotypes, child genotypes and child environmental/non-genetic attribute. See ‘Details’ section for the required format. | ||||||||||||||||
pgenos |
a length-2 vector of character strings specifying the names of the two columns
in | ||||||||||||||||
cgeno |
a character string specifying the name of the column in | ||||||||||||||||
cenv |
a character string specifying the name of the column in | ||||||||||||||||
penmod |
the penetrance mode of the genetic and interaction effects: | ||||||||||||||||
k |
an optional vector or single value specifying the
desired number(s) of knots to be used for the cubic spline basis construction of smooth function(s) representing GxE.
When | ||||||||||||||||
knots |
knot positions for the cubic spline basis construction.
When | ||||||||||||||||
sp |
smoothing parameters for the interaction functions.
When | ||||||||||||||||
lsp0 |
an optional length-2 numeric vector or a single numeric value used for
choosing trial values of log smoothing parameters in the grid search for
the optimal smoothing parameters.
When | ||||||||||||||||
lsp.grid |
trial values of log smoothing parameters used in the grid search for smoothing parameters.
When | ||||||||||||||||
control |
a list of convergence parameters for the penalized iteratively re-weighted least squares (PIRLS) procedure:
| ||||||||||||||||
testGxE |
a logical specifying whether the fitting is for testing interaction.
Default is | ||||||||||||||||
return.data |
a logical specifying whether the original data should be returned.
If | ||||||||||||||||
... |
sets the arguments for |
trioGxE
fits data from case-parent trios to a GAM with smooth functions
representing gene-environment interaction (G \times E).
The data
input must be a data frame containing the following 4 columns (of any order):
[,1] | number (0, 1 or 2) of copies of a putative risk allele carried by the mother |
[,2] | number of copies of a putative risk allele carried by the father |
[,3] | number of copies of a putative risk allele carried by the affected child (G) |
[,4] | value of a continuous environmental/non-genetic attribute measured on the child (E) |
The function trioGxE
does basic error checking to ensure that
only the trios that are consistent with Mendelian segregation law
with complete genotype, environment and parental genotype information.
The function determines which trios are from informative parental mating types.
An informative parental pair has at least one heterozygote; such parental pair can
have offspring that are genetically different.
Under the assumption that the parents transmit the alleles to their child under
Mendel's law, with no mutation, there are three types of informative mating types
G_p=1,2,3:
G_p=1: | if one parent is heterozygous, and the other parent is homozygous for the non-risk allele |
G_p=2: | if one parent is heterozygous, and the other parent is homozygous for the risk allele |
G_p=3: | if both parents are heterozygous |
Since GxE occurs when genotype relative risks vary with non-genetic attribute values E=e, GxE inference is based on the attribute-specific genotype relative risks, GRR_h(e), expressed as
{\rm GRR}_h(e) = \frac{P(D=1|G=h,E=e)}{P(D=1|G=h-1,E=e)} = \exp{(γ_h + f_h(e))}, ~~h=1,2,
where D=1 indicates the affected status, γ_1 and γ_2 represent genetic main effect, and f_1(e) and f_2(e) are unknown smooth functions. The functions f_h(e) represent GxE since GRRs vary with E=e only when f_1(e)\neq 0 or f_2(e)\neq 0 vary with E. The expressions are followed by assuming a log-linear model of disease risk in Shin et al. (2010).
Under the co-dominant penetrance mode (i.e., penetrance="codominant"
),
GRR_1(e) and GRR_2(e) are estimated using the information
in the trios from the informative mating types G_p=1, 3 and those from G_p=2, 3, respectively.
Under a non-co-dominant penetrance mode, only one GRR function, GRR(e)=γ+f(e),
is estimated from an appropriate set of informative trios.
Under the dominant penetrance mode (i.e., penetrance="dominant"
),
because GRR_2(e) is 1 (i.e., γ_2=0 and f_2(e)=0),
GRR(e)\equiv GRR_1(e) is estimated based on the trios from G_p=1 and 3.
Under the recessive penetrance mode (i.e., penetrance="recessive"
),
because GRR_1(e) is 1 (i.e., γ_1=0 and f_1(e)=0),
GRR(e)\equiv GRR_2(e) is estimated based on the trios from G_p=2 and 3.
Under the multiplicative or log-additive penetrance model (penetrance="additive"
),
since GRR_1(e) = GRR_2(e) (i.e., γ_1=γ_2 and f_1=f_2),
GRR(e)\equiv GRR_1(e) \equiv GRR_2(e) is estimated based on all informative trios.
The interaction functions are approximated by cubic regression spline functions defined by
the knots specified through the arguments k
and knots
.
For each interaction function, at least three knots are chosen within the range of
the observed non-genetic attribute values.
Under the co-dominant mode, k[1]
and k[2]
knots, respectively, located at
knots[[1]]
and knots[[2]]
are used to construct the basis for
f_1(e) and f_2(e), respectively.
By default, a total of 5 knots are placed for each interaction function:
three interior knots at the 25th, 50th and 75th quantiles and two boundary knots
at the endpoints of the data in trios from G_p=1 or 3, for f_1(e),
and in trios from G_p=2 or 3, for f_2(e).
Similarly, under a non-co-dominant penetrance mode, when knots=NULL
,
k
knots are chosen based on the data in trios from
G_p=1 and 3 (dominant mode);
in trios from all informative mating types (log-additive mode);
and in trios from G_p=2 or 3 (recessive mode).
A standard model identifiability constraint is imposed on each interaction function,
which involves the sum of the interaction function over all observed attribute values of cases
in the appropriate set of informative trios.
For smoothing parameter estimation, trioGxE
finds the optimal values using either a double (if co-dominant) or
a single 1-dimensional grid search constructed based on the arguments lsp0
and lsp.grid
.
When lsp0 = NULL
, trioGxE
takes the log smoothing parameter estimates obtained
from a likelihood approach that makes inference of GxE conditional
on parental mating types, non-genetic attribute and partial information on child genotypes (Duke, 2007).
When lsp.grid = NULL
, trioGxE
takes the following 6 numbers to be the trial values of
the log-smoothing parameter for each interaction function:
-20 and 20, lsp0
and the quartiles of the truncated normal distributions
constructed based on lsp0
.
At each trial value in lsp.grid
, the prediction error criterion function,
UBRE (un-biased risk estimator, is minimized to find the optimal smoothing parameter.
For more details on how to estimate the smoothing parameters, see Appendix B.3 in Shin (2012).
For variance estimation, trioGxE
uses a Bayesian approach (Wood, 2006);
the resulting Bayesian credible intervals have been shown to have good frequentist coverage probabilities
as long as the bias is a relatively small fraction of the mean squared error (Marra and Wood, 2012)
coefficients |
a vector holding the spline coefficient estimates for \hat{f}_{1} and/or \hat{f}_{2}. The length of the vector is equal to the total number of knots used for constructing the bases of the interaction curves. For example, under the default basis dimension with co-dominant penetrance mode, the vector has size 10 (i.e., 5 for f_1 and the other 5 for f_2). | ||||||||||||
control |
a list of convergence parameters used for the penalized iteratively re-weighted least squares (PIRLS) procedure | ||||||||||||
data |
original data passed to | ||||||||||||
edf |
a vector of effective degrees of freedom for the model parameters (see page 171 in Wood, 2006 for details). | ||||||||||||
Gp |
a vector containing the values of parental mating types G_p (see ‘Details’ for the definition) | ||||||||||||
lsp0 |
log smoothing parameter(s) used in the grid search.
Not returned (i.e., | ||||||||||||
lsp.grid |
trial values of the log smoothing parameter(s) used in the grid search. Not returned when smoothing parameters were not estimated but provided by the user. | ||||||||||||
penmod |
the penetrance mode under which the data were fitted. | ||||||||||||
qrc |
a list containing the QR-decomposition results used for imposing the identifiability constraints.
See | ||||||||||||
smooth |
a list with components:
| ||||||||||||
sp |
optimal smoothing parameter values calculated from UBRE optimization or smoothing parameter values provided by the user. | ||||||||||||
sp.user |
logical whether or not the smoothing parmeter values were provided by the user.
If | ||||||||||||
terms |
list of character strings of column names in | ||||||||||||
triodata |
Formatted data passed into the internal fitting functions. To be used in | ||||||||||||
ubre |
the minimum value of the un-biased risk estimator (UBRE), measure of predictability for the interaction function estimators \hat{f}_{1} or \hat{f}_{2}. Not returned when smoothing parameters were not estimated but provided by the user. | ||||||||||||
ubre.val |
a list or a vector of ubre values corresponding to the trial values
of smoothing parameter(s) in | ||||||||||||
Vp |
Bayesian posterior variance-covariance matrix for the coefficients.
The size the matrix is the same as that of |
Ji-Hyung Shin <shin@sfu.ca>, Brad McNeney <mcneney@sfu.ca>, Jinko Graham <jgraham@sfu.ca>
Duke, L. (2007):
A graphical tool for exploring SNP-by-environment interaction in
case-parent trios,
M.Sc. Thesis, Statistics and Actuarial Science, Simon Fraser University:
URL http://www.stat.sfu.ca/content/dam/sfu/stat/alumnitheses/Duke-2007.pdf.
Marra, G., Wood, S.N. (2012).
Coverage properties of confidence intervals for generalized additive model components.
Scand J Stat, 39: 53-74.
Shin, J.-H., McNeney, B. and Graham, J. (2010).
On the use of allelic transmission rates for assessing gene-by-environment interaction
in case-parent trios.
Ann Hum Gen, 74: 439-51.
Shin, J.-H. (2012):
Inferring gene-environment interaction from case-parent trio data: evaluation of and
adjustment for spurious G\times E and development of a data-smoothing method to uncover
true G\times E,
Ph.D. Thesis, Statistics and Actuarial Science, Simon Fraser University:
URL https://theses.lib.sfu.ca/sites/all/files/public_copies/etd7214-j-shin-etd7214jshin.pdf.
Wood, S. (2006): Generalized Additive Models: An Introduction with R, Boca Raton, FL: Chapman & Hall/CRC.
trioSim
, plot.trioGxE
, test.trioGxE
1 2 3 4 5 6 7 8 | ## fitting a co-dominant model
data(hypoTrioDat)
simfit <- trioGxE(data=hypoTrioDat,pgenos=c("parent1","parent2"),cgeno="child",cenv="attr",
k=c(5,5),knots=NULL,sp=NULL)
## fitting a dominant model to the hypothetical data
simfit.dom <- trioGxE(data=hypoTrioDat,pgenos=c("parent1","parent2"),cgeno="child",cenv="attr",
penmod="dom",k=5,knots=NULL,sp=NULL)
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