How replicable are mRNA expression QTL?
- 170 Downloads
Applying quantitative trait analysis methods to genome-wide microarray-derived mRNA expression phenotypes in segregating populations is a valuable tool in the attempt to link high-level traits to their molecular causes. The massive multiple-testing issues involved in analyzing these data make the correct level of confidence to place in mRNA abundance quantitative trait loci (QTL) a difficult problem. We use a unique resource to directly test mRNA abundance QTL replicability in mice: paired recombinant inbred (RI) and F2 data sets derived from C57BL/6J (B6) and DBA/2J (D2) inbred strains and phenotyped using the same Affymetrix arrays. We have one forebrain and one striatum data set pair. We describe QTL replication at varying stringencies in these data. For instance, 78% of mRNA expression QTL (eQTL) with genome-wide adjusted p ≤ 0.0001 in RI data replicate at a genome-wide adjusted p < 0.05 or better. Replicated QTL are disproportionately putatively cis-acting, and approximately 75% have higher apparent expression levels associated with B6 genotypes, which may be partly due to probe set generation using B6 sequence. Finally, we note that while trans-acting QTL do not replicate well between data sets in general, at least one cluster of trans-acting QTL on distal Chr 1 is notably preserved between data sets.
KeywordsQuantitative Trait Locus Recombinant Inbred Recombinant Inbred Strain Collaborative Cross Quantitative Trait Locus Peak
RWW received support from The Informatics Center for Mouse Neurogenetics; P20-MH62009 from NIMH, NIDA, and NSF; and INIA grants U01AA13499 and U24AA135B from NIAAA for generation of the BXD RI brain data set and development of analysis tools. LL received support from INIA grant U01AA01442501 from NIAAA. JLP received salary support from U01AA01442501 from NIAAA to LL and P20-MH62009 from NIMH, NIDA, and NSF to RWW. Thanks to Arthur Centeno for computer support, Fred Korz for AWK scripting advice, and Pamela Franklin for administrative assistance. RJH received support from NIAAA grants AA11034 and AA11384, and grant MH51372, for generating data from B6D2 F2 data in brain and striatum. TS received support from NIAAA INIA grant U01AA13515 and the W. Harry Feinstone Center for Genome Research for array phenotyping of the brain R1 data set. The B6D2 F2 whole-brain and striatal data were also supported by AA06243, AA10760, and two Merit Review programs from the Department of Veterans Affairs to JKB and RH. GR received support from P20-MH62009 for generating BXD RI striatum data. Thanks to Christopher Pung and Stephanie Chin for technical assistance and the BIDMC Genomics Core for array processing.
- Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57: 289–300Google Scholar
- de Vienne D, Maurice A, Josse JM, Leonardi A, Damerval C (1994) Mapping factors controlling genetic expression. Cell Mol Biol (Noisy-le-grand) 40(1): 29–39Google Scholar
- Rosenthal D (1994) Parametric measures of effect size. In The Handbook of Research Synthesis, Cooper H, Hedges LV (eds.) (New York: Russell Sage), pp 232–244Google Scholar