Abstract
BackgroundImproved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs) for QTL. Support interval (SI) methods based on the shape of the QTL likelihood curve have proven adequate for standard interval mapping, but have not been shown to be appropriate for use with composite interval mapping (CIM), which is one of the most commonly used QTL mapping methods.ResultsBased on a non-parametric confidence interval (NPCI) method designed for use with the Haley-Knott regression method for mapping QTL, a CIM-specific method (CIM-NPCI) was developed to appropriately account for the selection of background markers during analysis of bootstrap-resampled data sets. Coverage probabilities and interval widths resulting from use of the NPCI, SI, and CIM-NPCI methods were compared in a series of simulations analyzed via CIM, wherein four genetic effects were simulated in chromosomal regions with distinct marker densities while heritability was fixed at 0.6 for a population of 200 isolines. CIM-NPCIs consistently capture the simulated QTL across these conditions while slightly narrower SIs and NPCIs fail at unacceptably high rates, especially in genomic regions where marker density is high, which is increasingly common for real studies. The effects of a known CIM bias toward locating QTL peaks at markers were also investigated for each marker density case. Evaluation of sub-simulations that varied according to the positions of simulated effects relative to the nearest markers showed that the CIM-NPCI method overcomes this bias, offering an explanation for the improved coverage probabilities when marker densities are high.ConclusionsExtensive simulation studies herein demonstrate that the QTL confidence interval methods typically used to positionally evaluate CIM results can be dramatically improved by accounting for the procedural complexity of CIM via an empirical approach, CIM-NPCI. Confidence intervals are a critical measure of QTL utility, but have received inadequate treatment due to a perception that QTL mapping is not sufficiently precise for procedural improvements to matter. Technological advances will continue to challenge this assumption, creating even more need for the current improvement to be refined.
Highlights
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 [1]
This pattern is only extensible to composite interval mapping (CIM)-non-parametric confidence interval (NPCI) for the sub-simulation with QTLs at 10% distance from the left marker, where all three methods show their largest spreads in coverage probabilities for QTL capture across the four marker densities (Figure 2)
CIM-NPCIs perform well, with Q1, Q2, Q3 and Q4 clustering more tightly in the coverage probability plots than they do for Support interval (SI) and NPCIs, indicating that they are less variable as a function of the local marker density (Figure 2)
Summary
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 [1]. 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 [4]. In order to be realized, gains in genetic resolution were accompanied by commensurate improvements in genetic marker density [5,6] None of these maize populations is fully resolved by markers, achievement of appropriately high marker density will fade as a major limitation, since transcriptderived markers have been shown to be reliable for generation of thousands of new markers per experiment [7], and suitable transcript profiling platforms exist for at least 14 crops as well as for all animal model systems. Support interval (SI) methods based on the shape of the QTL likelihood curve have proven adequate for standard interval mapping, but have not been shown to be appropriate for use with composite interval mapping (CIM), which is one of the most commonly used QTL mapping methods
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