Through genome-wide searches for statistical associations between genotypes and phenotypes, quantitative trait locus (QTL) analysis simultaneously locates genetic effects on the trait of interest to positions within the genome and characterizes the relative phenotypic consequences of carrying certain natural alleles at these loci 
. Since the inception of this approach several decades ago, these alluring capabilities have driven innovations in genetic marker technologies, population design and statistical methods, largely targeted at realizing the potential of this method to clone the nucleotide polymorphisms that cause the natural phenotypic variations we observe 
For many QTL studies, genetic resolution remains limited by the number of recombination events and/or the marker density required to fully delineate them. The number of recombination events is a function of population type and size, and has been overcome in several systems by elegant breeding designs, particularly among plants. For example, in maize, three intermated recombinant inbred line (iRIL) populations have been created [3; A. Charcosset, unpublished; N. Lauter and S. Moose, unpublished]
. These are dramatically enriched for the number of recombination events per line, such that genetic resolution is improved by up to 50-fold compared to traditional RIL populations 
. In order to be realized, gains in genetic resolution were accompanied by commensurate improvements in genetic marker density 
. Although none of these maize populations is fully resolved by markers, achievement of appropriately high marker density will fade as a major limitation, since transcript-derived markers have been shown to be reliable for generation of thousands of new markers per experiment 
, and suitable transcript profiling platforms exist for at least 14 crops as well as for all animal model systems. More recently, experimental populations with even higher genetic resolution have been developed for public use 
. Collectively, these advances place the burden on statisticians to evolve methods that accommodate these gains in QTL resolution, such that statistical methodologies are not limiting in this process.
There has been extensive literature written on identifying QTL. Much of this literature is statistical in nature and, as with any statistical problem, it is not enough to simply state an estimate of a parameter of interest without indicating some measure of uncertainty. Especially in cases when fine-mapping is being pursued toward goals such as positional cloning or establishing pleiotropic action, a hard set of bounds that contain a QTL with a certain confidence is of great interest. Thus, the statistical challenge has turned from QTL estimation to the construction of confidence intervals for these locations.
The first and most widely used confidence estimation method is the LOD drop-off, or support interval (SI) method 
. For an estimated QTL, a SI is determined by plotting the LOD score (obtained from a chosen QTL estimation method) along a chromosome to generate the LOD curve and then by following the curve from the peak to its prescribed drop in LOD value on each side. It has been shown in previous studies of standard interval mapping that in order for a SI to have 95% coverage, the LOD drop should be between 1.5 and 2.0 units, depending on the length of the genome and marker density 
. SI widths intimately depend on the shape of the LOD profile, specifically the steepness of the drops flanking the QTL peak.
Another approach to constructing confidence intervals for estimated QTL is to use a non-parametric bootstrap confidence interval (NPCI) method that repeatedly samples n
observations, with replacement, from the original sample of size n 
. For each resampled data set, the location of the QTL of interest is estimated using a particular QTL estimation method. This process is repeated B
times and a 95% NPCI is constructed by ordering the B
estimated QTL peaks along the chromosome and reporting the 2.5 and 97.5 percentiles as α
0.05 positional bounds for the QTL 
. Alternatively, the CI can be assumed to be symmetric and calculated from the replications accordingly 
. NPCI methods are not as dependent as SI methods on the shape of the LOD profile around the maximum LOD value 
Both SI and NPCI methods have previously been used to analyze QTL positions estimated by standard interval mapping (SIM) 
. SIM tests for the presence of a QTL at any location along the genome using the nearest fully informative genetic markers (flanking markers) that capture the position in question. Composite interval mapping (CIM) does this as well, but is much more widely used because it has been shown to localize QTL more precisely 
. When testing a putative QTL, CIM includes additional markers as covariates in the model to help control for effects of other QTL.
There are marked differences in the shapes of LOD profiles generated from SIM versus CIM (). In addition to effects of the number of selected background markers and their minimum statistical significance, adjustable parameters which affect LOD curve shape include the distance between test interval sites and the size of the blockout window in which background markers are excluded from the model 
. When markers selected as covariates for CIM are linked to a test position, drastic changes in the LOD curve shape are caused by moderate adjustments to CIM parameters set by the user, rather than by actual changes in the likelihood of a QTL existing at the test site 
. Naturally, such differences in profile shape alter positional confidence results more dramatically for SIs than for NPCIs.
Figure 1 LOD profile plot for one simulated data set with n=200 individuals.
Since neither SI nor NPCI methods were developed specifically for use with CIM, we developed and investigated a CIM-specific confidence interval estimation method, CIM-NPCI. Here we report an extensive simulation study that compares the two existing confidence interval estimation methods to our proposed method when using composite interval mapping at varying marker densities and at varying distances from the nearest genetic marker to the simulated QTL positions. We show that CIM-NPCIs consistently capture the simulated QTL across all sets of conditions, and that the slightly narrower SIs and NPCIs fail to do so at unacceptably high rates, especially when marker density is high. As high marker densities are essential in studies attempting to finely localize QTL, these findings are significant. Further, in examining the consequences of a known propensity for CIM LOD peaks to gravitate toward nearby genetic markers, we uncovered several trends that are instructive for considering how best to apply the CIM and CIM-NPCI methods to achieve optimal results.