Submitted to: PLoS One
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/5/2010
Publication Date: 2/9/2010
Citation: Crossett, A., Lauter, N.C., Love, T.M. 2010. An Empirical Method for Establishing Positional Confidence Intervals Tailored for Composite Interval Mapping of QTL. PLoS One. 5:e9039.
Interpretive Summary: QTL (quantitative trait locus) mapping is the most effective way to discover and dissect natural genetic variation because it identifies and characterizes causal genetic effects. As genetic resolution has improved, a trend toward overextending narrow localization results has led to inaccurate reporting. In genomics, avoidance of false-positive reporting is paramount; the decision of "to clone, or not to clone" must be made carefully and with adequate statistical certainty. The methods introduced in this paper are shown to be better than the previous methods used for assessing statistical certainty for QTL positions. Crop geneticists benefit from these improvements, as QTL analysis is such an indispensible tool for these researchers. Use of these methods will result in reduction of research expenses because false-positive results will be avoided.
Technical Abstract: Improved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL (quantitative trait loci) realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs). Support interval (SI) methods based on the shape of the QTL likelihood curve have proven adequate for standard interval mapping (SIM), but have not been shown to be appropriate for use with composite interval mapping (CIM), which is preferred for its precision. Based on an existing resampling approach, a CIM-specific non-parametric confidence interval 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 CIM-NPCI, NPCI and SI 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 lines. Across these conditions, CIM-NPCIs consistently capture the simulated QTL, while slightly narrower SIs and NPCIs fail at unacceptably high rates, especially when marker density is high, which is increasingly common for real studies. To examine the effects of a known CIM bias toward locating QTL peaks at markers, all four marker density cases were investigated in four sub-simulations that varied according to the positions of simulated effects relative to the nearest markers. We found that the CIM-NPCI method overcomes the known bias, further supporting adoption of this method for establishment of accurate CIs.