\HeaderA{xbat}{Simulated pedigree with genotypes and covariates}{xbat}
\keyword{datasets}{xbat}
\begin{Description}\relax
Simulated dataset for a pedigree of 1000 trios with 50 SNPs, with  8
quantitative traits, 2 binary traits, and 8 covariates."
\end{Description}
\begin{Usage}
\begin{verbatim}data(xbat)\end{verbatim}
\end{Usage}
\begin{Format}\relax
'geneSet' object
\end{Format}
\begin{Details}\relax
This data is the 'xbat' example from Lange and Kraft's "Short Course:
Genetics Associateion Analysis."  It is described there as:

"[This] simulated dataset comprises a pedigree file with genotype
information for 1000 trios with 50 SNPs and a phenotype file that
contains 8 quantitative traits, 2 binary traits, and 8 covariates.

"Genotypes

"The simulation generated complete genotype data for 1000 families
with two parents and one offspring.  The single nucleotide
polymorphism (SNP) frequencies and haplotype blocks were estimated
using real data.  These estimates were fixed and used as parameters
for the simulation of the parental genotypes.  Offspring genotypes
were generated by simulating random Mendelian transmission from their
respective parents.  In total, 50 SNPs were simulated, 28 of which lie
in 1 of 5 variable length haplotype blocks (range: 4 to 10 SNPs per
block).  The blocks were simulated as a function of haplotype block
frequency, assuming no recombination, resulting in varying degrees of
linkage disequilibrium within each block.  The remaining 22 SNPs that
are not in a haplotype block were simulated randomly as a function of
SNP frequency.  The SNPs are indicated in the header line of the
pedigree file, and named SNP1, SNP2, .., and SNP50.  Note that the
affectation status variable in the pedigree file is coded as missing
(0) for all individuals.  All phenotype data comes from the phenotype
file (see below).

"Phenotypes

"Overall, 10 phenotypes ($Y$) were simulated additively as function of
the genetic effect size a, marker score $X$, covariate effect size $b$,
and covariate value $Z$ as follows:

\eqn{Y_i = a_iX_i + b_iZ_i \;  (i = 1, 2, .., 10)}{Y[i] = a[i] X[i] + b[i] Z[i]   (i = 1, 2,.., 10)}

"Quantitative Traits

"Eight quantitative phenotypes were simulated from a random sample from
a normal distribution: $Y~N([aX+ bZ], s2)$, where a is the additive
effect for the phenotype and s2 is the variance.  We measure the
strength of the additive effect relative to the phenotypic variance by
the heritability h2 [Falconer and Mackay, 1997], which is the
proportion of phenotypic variation explained by genetic variation.  We
assume that the environment variance is 1.  SNP23 was simulated as the
"disease SNP" which is the 5th SNP in a 10 SNP haplotype block.  The
heritabilities were simulated from random uniform distribution ranging
from -0.1 to 0.1.  In addition, the simulation produced two correlated
quantitative traits (QTL9 and QTL10; r2 = 0.40). The quantitative
traits are indicated in the header line of the phenotype file and
named QTL1, QTL2, .., and QTL10.

"Binary Traits

"Two binary traits were simulated simply by dichotomizing the first
quantitative trait (QTL1).  For the AFF1 trait, individuals were coded
as affected (1) if their QTL1 value is above the sample mean and
unaffected (0) if their QTL1 value was below the sample mean.  For the
AFF2 trait, individuals were coded as affected (1) if their QTL1 value
is at least one standard deviation above the sample mean, and missing
("-") if their trait value did not reach that criteria.

"Covariates

"In addition to the additive genetic effect, each phenotype was
simulated with one covariate effect.  The quantitative covariates were
sampled from normal distribution (mu = random, s2 = 10).  The effect
size for each covariate was sampled randomly from a uniform
distribution (0, 1).  The covariates are indicated in the header line
of the phenotype file and named COV1, COV2, .., and COV10.  Note that
COV1 corresponds to QTL1, AFF1 and AFF2."

(quoted from Lange and Kraft 2005)
\end{Details}
\begin{Source}\relax
Lange, C. and Kraft, P. (2005). "Short Course: Genetics Association
Analysis."
\end{Source}
\begin{References}\relax
Lange, C. and Kraft, P. (2005). "Short Course: Genetics Association
Analysis." 

DeMeo, D. L., C. Lange, et al. (2002). "Univariate and multivariate
family-based association analysis of the IL-13 ARG130GLN polymorphism
in the Childhood Asthma Management Program." Genet Epidemiol 23(4):
335-48.
\end{References}
\begin{Examples}
\begin{ExampleCode}
library(GeneticsBase)
data(xbat)
head(xbat)
\end{ExampleCode}
\end{Examples}

