Provides expected confidence interval widths for QTL location when we have dense markers.
ci.length(cross,n,effect,p=0.95,sigma2=1,env.var,gen.var,bio.reps=1)Returns the expected confidence interval width (scalar) in cM assuming dense markers.
String indicating cross type which is "bc", for backcross, "f2" for intercross, and "ri" for recombinant inbred lines.
Sample size
Confidence level for desired confidence interval
The QTL effect we want to detect. For
powercalc and samplesize this is a numeric (vector).
For detectable it specifies the relative magnitude of the
additive and dominance components for the intercross.
The specification of effect depends on the cross. For
backcross, it is the difference in means the heterozygote and
homozygote. For RI lines it is half the difference in means of the
homozygotes, for intercross, it is a two component vector of the form
c(a,d), where a is the additive effect (half the
difference between the homozygotes), and d is the dominance
effect (difference between the heterozygote and the average of the
homozygotes). The genotype means will be -a-d/2, d/2,
and a-d/2. For detectable, optionally for the
intercross, one can use a string to specify the QTL effect type.
The strings "add" or "dom" are used to denote an additive or
dominant model respectively for the phenotype. It may be
it can be a numerical vector of the form c(a,d) indicating
the relative magnitudes of the additive and dominance components (as
defined above). The default is "add".
Error variance; if this argument is absent,
env.var and gen.var must be specified.
Environmental (within genotype) variance
Genetic (between genotype) variance due to all loci segregating between the parental lines.
Number of biological replicates per unique genotype. This is usually 1 for backcross and intercross, but may be larger for RI lines.
Saunak Sen
With dense markers, the log likelihood follows a compound process. Approximate expected confidence intervals can be calculated by pretending the log likelihood decays linearly with a drift rate that depends on the effect size and cross type.
Dupuis J and Siegmund D (1999) Statistical methods for mapping quantitative trait loci from a dense set of markers. Genetics 151:373-386.
Darvasi A (1998) Experimental strategies for the genetic dissection of complex traits in animal models. Nature Genetics 18:19-24.
Kong A and Wright FA (1994) Asymptotic theory for gene mapping. Proceedings of the National Academy of Sciences of the USA 91:9705-9709.
powercalc.
ci.length(cross="bc",n=400,effect=5,p=0.95,sigma2=1)
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