Abstract
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. 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.
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. 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.
| Original language | English |
|---|---|
| Article number | jkab394 |
| Number of pages | 13 |
| Journal | G3: Genes, Genomes, Genetics Mission |
| Volume | 12 |
| Issue number | 1 |
| Early online date | 13 Nov 2021 |
| DOIs | |
| Publication status | Published - 1 Jan 2022 |
Bibliographical note
FundingThis work was partially supported by National Institutes of Health grants [R01MH115979 (J.F.), R01GM097737 and P50DA037844 (A.A.P)]. J.Z. is supported by a National Science Foundation Graduate Research Fellowship under Grant DGE1650604. Publication charges for this article have been funded by
1R01MH115979. J.F., A.A.P., and R.M. conceived the study. J.Z., J.F., and S.G.
performed the bioinformatics analysis. C.P. and J.N. prepared the phenotypes. R.W.D. generated the genotypes. J.Z., C.P., S.G, N.C, A.L. A.A.P., and J.F. wrote the manuscript. All authors read and approved the final manuscript.
Keywords
- GWAS
- CFW
- replication
- Winner's curse
- power
- mega-analysis