Summary: run variance components analysis No Arguments
This command looks for evidence of quantitative trait loci (QTLs). At each scan position, the program determines whether a significant amount of the variance in a quantitative trait can be attributed to a QTL at that position. Specifically, it calculates maximum likelihood values for the mean trait value (separately for each sex, if desired), additive and dominance variance components for the QTL, additive and dominance variance components for other, unlinked loci, and an environmental variance component. One or both of the dominance components can be optionally excluded. In addition, the program can incorporate covariate effects by estimating the regression of the trait value on a given covariate value. The significance of the QTL effects is tested by comparing the maximum likelihood model with another one in which the QTL variance components are constrained to equal zero. The likelihood ratio of the two models is used to calculate a LOD score which can be compared to a chi-squared distribution as in classical methods of QTL analysis.
Data preparation
Phenotype values should be included in the pedigree file, after the
genotype values for each individual. Covariates should be listed
immediately after the phenotypes. Multiple values may be entered, up to
the numbers given by the constants MAX_PHENOTYPES and MAX_COVARIATES in
npl.h. Each phenotype should be indicated in the map file by a single
line reading "0 2" followed by five empty lines (These are needed to
maintain consistency with the LINKAGE file format. The data expected by
LINKAGE in these lines are not used by Genehunter and can be excluded).
Each covariate should be indicated with a single line reading "4 0". In
addition, the total number of loci (the first number on the first line
of the map file) should include the number of phenotypes and
covariates, as well as the number of markers and qualitative traits.
Running the program
When the "variance components" command is entered, the user is prompted
for names for the output files and is then asked whether to include dominance
variance components for the unlinked loci and for the QTL. The user is then
given the option of entering starting estimates for the model's
parameters, rather than letting the program come up with it's own
estimates. This option is provided because the program's ability to
converge on the maximum likelihood values is sometimes sensitive to the
starting guesses. Trying out different starting values and seeing
whether the same result is obtained provides a check on the correctness
of the results. This should probably be done with all analyses, but is
especially needed if the program is yielding odd results, such as
negative or unrealistically high LOD scores. If manual input is chosen,
the program first displays the total variance of the trait value being
examined, as well as the mean trait value (separately for males and
females if this option has been chosen). These figures can be helpful
in choosing starting values.
Output
The output file shows a LOD score for each scan position, along with
estimates of the means, variance components, and covariate regression
coefficients for that position. The corresponding estimates for the
null model are also reported. Because the program can sometimes fail to
converge on an estimate for some positions, the output indicates for
each position whether convergence occurred. When it does not occur, the
output shows the estimates for the last position which did converge. If
the program frequently fails to converge, it may be necessary to raise
the maximum number of iterations allowed in the estimating algorithm
(MAXITS in the file varcom.c). If postscript output is switched on, the
program also produces two graphics files. One contains a plot of LOD
score versus position, and the other a plot of the proportion of total
phenotypic variance accounted for by each component of the maximum
likelihood model, versus position.