Reviewer #4 (Public review):
The manuscript examines how patterns of selection on gene expression differ between a normal field environment and a field environment with elevated salinity based upon transcript abundances obtained from leaves of a diverse panel of rice germplasm. In addition, the manuscript also maps expression QTL (eQTL) that explains variation in each environment. One highlight from the mapping is that a small group of trans-mapping regulators explains some gene expression variation for large sets of transcripts in each environment.
The overall scope of the datasets is impressive, combining large field studies that capture information about fecundity, gene expression, and trait variation at multiple sites. The finding related to patterns indicating increased LD among eQTLs that have cis-trans compensatory or reinforcing effects in interesting in the context of other recent work finding patterns of epistatic selection. The authors have made some changes that address previous comments. However, some analyses in the manuscript remain less compelling or do not make the most from the value of collected data. Although the authors have made several improvements to the precision with which field-specific terminology is applied and to the language chosen when interpreting analytical findings, additional changes to improve these aspects of the manuscript remain necessary.
Selection of gene expression: One strength of the dataset is that gene expression and fecundity were measured for the same genotypes in multiple environments. However, the selection analyses are largely conducted within environments. Addition of phenotypic selection analyses that jointly analyze gene expression across environments and or selection on reaction norms would be worthwhile.
Gene expression trade-offs: The terminology and possibly methods involved in the section on gene expression trade-offs need amendment. I specifically recommend discontinuing reference to the analysis presented as an analysis of antagonistic pleiotropy (rather than more general as trade-offs) because pleiotropy is defined as a property of a genotype, not a phenotype. Gene expression levels are a molecular phenotype, influenced by both genotype and the environment. By conducting analyses of selection within environments as reported, the analysis does not account for the fact that the distribution of phenotypic values, the fitness surface, or both may differ across environments. Thus, this presents a very different situation than asking whether the genotypic effect of a QTL on fitness differs across environments, which is the context in which the contrasting terms antagonistic pleiotropy and conditional neutrality have been traditionally applied. The results reported do not persuasively support the assertion made in the response to reviewers that the terminology is reasonable due to strong coupling between genotype and phenotype. A more interesting analysis would be to examine whether the covariance of phenotype with fitness has truly changed between environments or whether the phenotypic distribution has just shifted to a different area of a static fitness surface.
Biological processes under selection / Decoherence: In the initial review, it was noted that PCA is likely not the most ideal way to cluster genes to generate consolidated metrics for a selection gradient analysis. Because individual genes will contribute to multiple PCs, the current fractional majority-rule method applied to determine whether a PC is under direct or indirect selection for increased or decreased expression comes across as arbitrary and with the potential for double-counting genes. A gene co-expression network analysis could be more appropriate, as genes only belong to one module and one can examine how selection is acting on the eigengene of a co-expression module. Building gene co-expression modules would also provide a complementary and more concrete framework for evaluating whether salinity stress induces "decoherence" and which functional groups of genes are most impacted. Although results of co-expression network analyses are now briefly discussed in the response to reviewers, the findings and their relationship to the PCA/"decoherence" analyses are not reported in the manuscript.
Selection of traits: Having paired organismal and molecular trait data is a strength of the manuscript, but the organismal trait data are underutilized. The manuscript as written only makes weak indirect inferences based on GO categories or assumed gene functions to connect selection at the organismal and molecular levels. After prompted by the initial reviews to test for correspondence between SNPs that explain organismal and gene expression trait variation or co-variance of co-expression module variation and trait variation, the response to reviewers indicates finding negative results. These findings should be included in the manuscript text and discussed.
Genetic architecture of gene expression variation: More descriptive statistics of the eQTL analysis have been included, although additional information about the variation in these measures within environments would be useful. The motivation for featuring patterns of cis-trans compensation specifically for the results obtained under high salinity conditions remains unclear to me. If the lines sampled have predominantly evolved under low salinity conditions, and the hypothesis being evaluated relates to historical experience of stabilizing selection, then evaluating the eQTL patterns under normal conditions provides the more relevant test of the hypothesis.
Lines 280-282: The revised sentence continues to read as an overstatement and merits additional revision with citations.
Lines 379-381: Following revision, it still remains unclear how the interpretation follows from the above analysis; the inference as written goes significantly beyond what may be specifically inferable from the result.
