Abstract
The Churchill-Doerge approach toward constructing empirical thresholds has received widespread use in the genetic mapping literature through the past 16 years. The method is valued for both its simplicity and its ability to preserve the genome-wide error rate at a prespecified level. However, the Churchill-Doerge method is not designed to maintain the local (comparison-wise) error rate at a constant level except in situations that are unlikely to occur in practice. In this article, we introduce the objective of preserving the local error rate at a constant level in the context of mapping quantitative trait loci in linkage populations. We derive a method that preserves the local error rate at a constant level, provide an application via simulation on a Hordeum vulgare population, and demonstrate evidence of the relationship between recombination and location bias. Furthermore, we indicate that this method is equivalent to the Churchill-Doerge method when several assumptions are satisfied.
Highlights
The Churchill-Doerge approach toward constructing empirical thresholds has received widespread use in the genetic mapping literature through the past 16 years
The process of performing a genome scan to detect quantitative trait loci (QTL) may be interpreted within a hypothesis-testing framework
At each point in the genome a statistical test is performed, resulting in a P-value representing the evidence for the presence of a QTL at that location
Summary
The Churchill-Doerge approach toward constructing empirical thresholds has received widespread use in the genetic mapping literature through the past 16 years. We introduce the objective of preserving the local error rate at a constant level in the context of mapping quantitative trait loci in linkage populations. We derive a method that preserves the local error rate at a constant level, provide an application via simulation on a Hordeum vulgare population, and demonstrate evidence of the relationship between recombination and location bias. At each point in the genome a statistical test is performed, resulting in a P-value representing the evidence for the presence of a QTL at that location. As the number of analysis points grows, so does the number of statistical tests performed, and the chance of incorrectly declaring a QTL at a given marker (committing a Type-I error) increases. The method can be demonstrated to strongly control the GWER at a prespecified level
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