qtlDesign (version 0.92)

Confidence interval expected widths: Calculating expected QTL confidence interval widths

Description

Provides expected confidence interval widths for QTL location when we have dense markers.

Usage

ci.length(cross,n,effect,p=0.95,sigma2=1,env.var,gen.var,bio.reps=1)

Arguments

cross

String indicating cross type which is "bc", for backcross, "f2" for intercross, and "ri" for recombinant inbred lines.

n

Sample size

p

Confidence level for desired confidence interval

effect

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.

sigma2

Error variance; if this argument is absent, env.var and gen.var must be specified.

env.var

Environmental (within genotype) variance

gen.var

Genetic (between genotype) variance due to all loci segregating between the parental lines.

bio.reps

Number of biological replicates per unique genotype. This is usually 1 for backcross and intercross, but may be larger for RI lines.

Value

• Returns the expected confidence interval width in cM assuming dense markers.

Details

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.

References

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.

See Also

powercalc.

Examples

ci.length(cross="bc",n=400,effect=5,p=0.95,sigma2=1)