Understanding the effects of linkage and pleiotropy on evolutionary adaptation
Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies
Genetic correlations among traits are ubiquitous in nature. However, we still have a limited understanding of the genetic architecture of trait correlations. Some genetic correlations among traits arise because of pleiotropy - single mutations or genotypes that have effects on multiple traits. Other genetic correlations among traits arise because of linkage among mutations that have independent effects on different traits. Teasing apart the differential effects of pleiotropy and linkage on trait correlations is difficult, because they result in very similar genetic patterns. However, understanding these differential effects gives important insights into how ubiquitous pleiotropy may be in nature.
In the preprint "Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies", Chebib and Guillaume  explore the conditions under which trait correlations caused by pleiotropy result in similar and different genetic patterns than trait correlations caused by linkage. Their main finding is that pleiotropic architectures result in higher trait correlations than do architectures in which completely linked mutations affect different traits. This results clarifies and goes against a previous theoretical study that predicted that pleiotropic architectures could not be distinguished from completely linked mutations that affect independent traits.
In genome-wide association studies (GWAS), it is difficult to know if a significant signal is a causal variant that truly affects the trait, a false positive neutral variant linked to a causal variant, or a false positive causal variant that affects a different trait but is significant because of trait correlations. In their study, Chebib and Guillaume  show that this latter category can be a common source of false positives in GWAS studies when mutations affecting different traits are linked. One of the main limitation of this aspect of their analysis is the lack of simulation of neutral loci, which would likely show even higher rates of false positives than reported in their study.
The main limitation in their study is the restrictive assumptions about the genetic architectures (e.g. all pairs of loci have a fixed recombination rate among them). In reality, new causal mutations that arise near another causal mutation may have higher or lower establishment probabilities depending on the direction of effects on the trait and the parameters for selection and demography. Their study still deserves a recommendation, however, because of the new insights it gives into the genetic architecture of trait correlations.
 Chebib, J. and Guillaume, F. (2019). Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies. bioRxiv, 656413, v3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/656413
Kathleen Lotterhos (2019) Understanding the effects of linkage and pleiotropy on evolutionary adaptation. Peer Community in Evolutionary Biology, 100087. 10.24072/pci.evolbiol.100087
Revision round #12019-07-19
Decision round #1
Both reviewers point out the merit of this simulation study, which tests verbal arguments that linked loci should behave similarly to a single pleiotropic locus. Both reviewers suggested clarifications to the text and/or extensions to the mathematics, with which I agree are necessary before this manuscript would be recommended.
Specifically, clarification is needed for the model parameters throughout the manuscript, the figures need to be presented more clearly, and the explanation for the difference between two fully linked loci and a single pleiotropic locus needs to be made more explicit earlier in the paper. Reviewer 2 points out that there may be some important differences in the the joint distribution of mutational effects, and this need to be clarified in the manuscript. This reviewer also points out how the influence of migration may be predicted from the law of total covariance, which is worth incorporating into the manuscript. Also, clarification on the demography is needed. Is this an island-mainland model? Or a 2-patch model with asymmetrical migration? What is the population size in each patch?
Both reviewers point out that the GWAS results are not clearly presented and I agree. Major revisions will be needed in this section if the paper is going to earn a recommendation. Firstly, it seems strange to do a GWAS analysis only on causal loci and not to include simulated neutral loci for the calculation of false positive rates. Second, it is below the standard of the field to conduct a GWAS without a correction for population structure. If structure is corrected for in the model it is unclear in the manuscript, and if it is not then false positive rates could be inflated. In the context of fully linked loci, “false positives” are linked loci that have an effect on a different trait other than the one being analyzed, so they are not truly neutral and this needs to be clarified. Finally, the presentation of results in Figure 8 is not intuitive, especially for the linked architectures - is locus 1 linked to locus 121 on the same linkage group? Linkage architectures should still have Type II error rates reported (even if these are zero) in Table 1. It’s hard to figure out what the main message from Table 1 is, so a figure here might be warranted.
Overall, I agree with the reviewer that said it’s easy to get lost in the results, especially in Figures 3-6. Streamlining the message would strengthen the manuscript.
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