Description Usage Arguments Details Value Acknowledgments Author(s) References See Also Examples
This function transforms caseparent data into a format suitable as input for trio logic regression. The function can also be used for the imputation of missing genotypes in caseparent data, while taking the existing SNP block structure into account.
1  trio.prepare(trio.dat, freq=NULL, blocks=NULL, logic=TRUE, ...)

trio.dat 
An object returned from the function

freq 
An optional data frame specifying haplotype blocks and
frequencies. For an example, see the data frame The object must have three columns in the following order: block
identifiers ( 
blocks 
An optional vector of integers, specifying (in sequence)
the lengths of the linkage disequilibrium blocks. The sum of these
integers must be equal to the total numbers of SNPs in the data set
used as input. Using the integer 1 for SNPs not contained in LD
blocks is required if this argument is used. If both arguments

logic 
A logical value indicating whether the trio data are
returned with genotypes in dominant and recessive coding, suitable
as input for trio logic regression ( 
... 
Optional arguments that can be passed to function

To create the genotypes for the pseudocontrols it is
necessary to take the LD structure of the SNPs into account. This
requires information on the LD blocks. It is assumed that the user
has already delineated the block structure according to his or her
method of choice. The function trio.prepare
, which operates on an
output object of trio.check
, accepts the block length
information as an argument. If this argument is not specified, a
uniform block length of 1 (i.e., no LD structure) is assumed. If the
haplotype frequencies are not specified, they are estimated from the
parents' genotypes using the function haplo.em
. The
function then returns a list that contains the genotype information in
binary format, suitable as input for trio logic regression. Since
trio logic regression requires complete data, the function trio.prepare
also performs an imputation of the missing genotypes. The imputation
is based on the estimated or supplied haplotype information.
bin 
A matrix suitable as input for trio logic regression. The
first column specifies the cases and pseudocontrols as required by
logic regression using conditional logistic regression (the integer
3 for the probands followed by three zeros indicating the
pseudocontrols). The following columns specify the (possibly
imputed) genotypes in dominant and recessive coding, with two binary
variables for each SNP. This is returned only if 
trio 
A data frame with imputed SNPs in genotype format derived
from the input. This is returned only if 
miss 
A data frame with five columns indicating the missing genotypes in the input object.
The five columns of the data frame refer to the family id ( 
freq 
The estimated or supplied haplotype information, in the same format as described in the Arguments above. 
Support was provided by NIH grants R01 DK061662 and HL090577.
Qing Li, mail2qing@yahoo.com
Li, Q., Fallin, M.D., Louis, T.A., Lasseter, V.K., McGrath, J.A., Avramopoulos, D., Wolyniec, P.S., Valle, D., Liang, K.Y., Pulver, A.E., and Ruczinski, I. (2010). Detection of SNPSNP Interactions in Trios of Parents with Schizophrenic Children. Genetic Epidemiology, 34, 396406.
1 2 3 4  data(trio.data)
trio.tmp < trio.check(dat=trio.ped1)
trio.bin < trio.prepare(trio.dat=trio.tmp, blocks=c(1,4,2,3))
trio.bin$bin[1:8,]

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