10.5068/D1HX1D
Zou, Jennifer
0000-0002-2045-3272
University of California Los Angeles
Gopalakrishnan, Shyam
University of Copenhagen
Parker, Clarissa
Middlebury College
Nicod, Jerome
The Francis Crick Institute
Mott, Richard
University College London
Cai, Na
Helmoltz Pioneer Campus
Lionikas, Arimantas
University of Aberdeen
Davies, Robert
University of Oxford
Palmer, Abraham
University of California, San Diego
Flint, Jonathan
University of California Los Angeles
Analysis of independent cohorts of outbred CFW mice reveals novel loci for
behavioral and physiological traits and identifies factors determining
reproducibility
Dryad
dataset
2021
Genetics
2021-10-17T00:00:00Z
2021-10-17T00:00:00Z
en
26682083811 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Combining samples for genetic association is standard practice in human
genetic analysis of complex traits, but is rarely undertaken in rodent
genetics. Here, using 23 phenotypes and genotypes from two independent
laboratories, we obtained a sample size of 3,076 commercially available
outbred mice and identified 70 loci, more than double the number of loci
identified in the component studies. Fine-mapping in the combined sample
reduced the number of likely causal variants, with a median reduction in
set size of 51%, and indicated novel gene associations, including Pnpo,
Ttll6 and GM11545 with bone mineral density, and Psmb9 with weight.
However replication at a nominal threshold of 0.05 between the two
component studies was low, with less than a third of loci identified in
one study replicated in the second. In addition to overestimates in the
effect size in the discovery sample (Winner’s Curse), we also found that
heterogeneity between studies explained the poor replication, but the
contribution of these two factors varied among traits. Available methods
to control Winner’s Curse were contingent on the power of the discovery
sample, and depending on the method used, both overestimated and
underestimated the true effect. Leveraging these observations we
integrated information about replication rates, study-specific
heterogeneity, and Winner’s Curse corrected estimates of power to assign
variants to one of four confidence levels. Our approach addresses concerns
about reproducibility, and demonstrates how to obtain robust results from
mapping complex traits in any genome-wide association study.