Review for "Many long intergenic non-coding RNAs distally regulate mRNA gene expression levels"

Completed on 15 Apr 2016 by David McGaughey, Steve Bond, John Didion and Tony Kirilusha. Sourced from http://biorxiv.org/content/early/2016/03/19/044719.

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We reviewed this paper in our April preprint journal club at NHGRI. Overall, we enjoyed the paper and it fostered good discussion. An interesting point you could bring up in the intro or discussion (completely understandable if there’s not room or it’s beyond-scope) would be the hypothesized biological origins of lncRNAs, and the evolutionary discussion of how and why they might have acquired function, especially since one of the main points of the paper is that pc-mRNA and lncRNA are regulated by the same transcriptional machinery and also in light of the recent preprint from Young et al. (http://biorxiv.org/content/ear....

One item we found questionable was the selection of tissue types to study. Three of four tissues (adipose, artery, lung) have clear links to obesity, so a skeptical reviewer might be suspicious that you were at the outset setting yourself up to discover obesity associations. It might help to allay those suspicions by discussing the criteria you used to select the tissues you studied. Would picking any four GTEx tissues out of a hat give you similar results?

Great to see experimental validation, and agree with Arjun that the MR approach is cool. We’re interested to use the software, but the github URL does not yet resolve.

The last paragraph in section 2.6 seems a bit out of place – it might read better if it were integrated in the discussion.

Figures 1:
• You never define eQTLBMA or SNPTEST in the text.
• It’s not clear how associations are placed on the x-axis. For associations between TSS and TES, are you just normalizing the position of the association to the length of the gene (i.e. position / (TES-TS))? A clarifying note in the legend would be helpful.

Figure 2:
• Put panels A and B in the same orientation (currently pc-eQTLs are on top in A but on the bottom in B).
• Would be helpful if the supplementary figures were included so we could see the skin overlaps.

Figure 3: It seems a bit misleading to say “Both cis-linc-eQTLs and cis-pc-eQTLs were enriched for linkage to TASs” when the odds ratio for best linc-eQTLs is the only one <1. Some discussion of why OR<1 for best linc-eQTLs but >1 for all linc-eQTLs would be welcome.

Figure 4: Not sure what we’re supposed to take away from this plot, other than “hey there’s a lincRNA next to 10 adipose TASs.” The mean expression values are hard to interpret – you’ve taken an estimate (RPKM), log2-transformed it, and then put it in grayscale in a tiny box. Is it highly expressed in adipose? Significantly more than in other tissues? It would be cool if you layered this on top of the chromatin map to see how different the adipose regulatory environment is from the other tissues.

Figure 6: This is the classical MR schematic, but we felt it would be much more informative to see a toy example of a positive MR result in the current context – i.e. replace Z with “SNP”, X with “cis-RNA”, etc. It’s in the legend, but readers will appreciate not having to jump back and forth between the figure and the text to figure out what’s going on.

Figure 7:
• Confused – you talk about the naïve approach first in the text and refer to figure 7A as the naïve results, but the legend says that 7A is the MR results and 7B is the naïve results.
• Might be helpful (and the MR results will also look even more impressive) to put both plots on the same y-axes.

David McGaughey
Steve Bond
John Didion
Tony Kirilusha