Abstract
BackgroundOver the past decade many linkage studies have defined chromosomal intervals containing polymorphisms that modulate a variety of traits. Many phenotypes are now associated with enough mapping data that meta-analysis could help refine locations of known QTLs and detect many novel QTLs.Methodology/Principal FindingsWe describe a simple approach to combining QTL mapping results for multiple studies and demonstrate its utility using two hippocampus weight loci. Using data taken from two populations, a recombinant inbred strain set and an advanced intercross population we demonstrate considerable improvements in significance and resolution for both loci. 1-LOD support intervals were improved 51% for Hipp1a and 37% for Hipp9a. We first generate locus-wise permuted P-values for association with the phenotype from multiple maps, which can be done using a permutation method appropriate to each population. These results are then assigned to defined physical positions by interpolation between markers with known physical and genetic positions. We then use Fisher's combination test to combine position-by-position probabilities among experiments. Finally, we calculate genome-wide combined P-values by generating locus-specific P-values for each permuted map for each experiment. These permuted maps are then sampled with replacement and combined. The distribution of best locus-specific P-values for each combined map is the null distribution of genome-wide adjusted P-values.Conclusions/SignificanceOur approach is applicable to a wide variety of segregating and non-segregating mapping populations, facilitates rapid refinement of physical QTL position, is complementary to other QTL fine mapping methods, and provides an appropriate genome-wide criterion of significance for combined mapping results.
Highlights
The majority of traits that show variation based on genetic influences are affected by multiple genes of small effect as well as by environment
We use locus-specific P-values because in certain data sources such as advanced intercrosses and small recombinant inbred (RI) sets, the correspondence between the likelihood typically reported in mapping output and the P-value, as determined by generation of permuted QTL maps, is not constant with respect to position
This can be related to missing data patterns, distribution of alleles and non-syntenic association
Summary
The majority of traits that show variation based on genetic influences are affected by multiple genes of small effect as well as by environment. We first generate locus-wise permuted P-values for association with the phenotype from multiple maps, which can be done using a permutation method appropriate to each population. These results are assigned to defined physical positions by interpolation between markers with known physical and genetic positions. We calculate genomewide combined P-values by generating locus-specific P-values for each permuted map for each experiment. Our approach is applicable to a wide variety of segregating and non-segregating mapping populations, facilitates rapid refinement of physical QTL position, is complementary to other QTL fine mapping methods, and provides an appropriate genome-wide criterion of significance for combined mapping results
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