Peer review of: Accounting for diverse evolutionary forces reveals mosaic patterns of selection on human (2020)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
In this manuscript, LaBella et al use various methods to infer the strength and direction and direction 
of natural selection acting at single nucleotide polymorphisms (SNPs) associated with pre-term birth( 
PTB) in a previous GWAS. PTB is a serious medical issue, effecting ~10% of women yet the molecular 
and genetic causes of PTB remain elusive. The study is well-done, and is a nice combination of human 
population genetics and evolutionary genetics. I do, however, have two concerns one minor the other 
less so: 
 
1) The minor one – One of the rationales of the study is that human pregnancy is odd for various 
reasons, for example, humans suffer from the so-called obstetrical dilemma which postulates that the 
human birth canal narrowed during evolution to facilitate up-right walking at the same time the 
human brain enlarged. As a consequence of the narrow birth canal and big heads, babies tend to get 
stuck which makes human birth risky and should have led to selection for shorter gestations. But it is 
not entirely clear that the obstetrical dilemma is much of a dilemma: 
 
https://www.ncbi.nlm.nih.gov/pubmed/30146522 
 
https://www.ncbi.nlm.nih.gov/pubmed/31251927 
 
This is a conceptual concern, if the dilemma doesn't exist why is there positive or balancing selection 
on SNPs associated with birth timing? A bit of explanation in the intro or discussion should suffice to 
address the counter factual of no dilemma 
 
2a) My bigger concern is that the authors use a nearest gene approach to associate SNPs with genes. 
But previous studies combining GWAS and chromatin interaction data suggests that the nearest gene 
approach is only correct about half the time. So, how many SNPs are expected to interact with genes 
related to pregnancy by chance? Is that number more or less than you observe? It seems this is an 
important null model to test whether what you observed is compatible with the null. 
 
2b) While I obviously dont expect you to perform HiC on all the relevant cell-types related to 
pregnancy to link SNPs to genes, the nearest gene approach may be a bit too naive. Perhaps you 
could use GREAT to associate SNPs to genes, while also a prediction it is at least one that is a little 
more complex than the closest gene approach and has set of defined rules. 
 
I think the combination of GWAS and evolution used by the authors is an important step toward 
integrating evolution and medicine that can contribute to determining what causes (pre)term birth. 
But I worry that many candidate genes have been associated with pregnancy, so observing that some 
are also nearby PTB associated SNPs may occur just by chance, it is important to test how many one 
would expect to know whether the authors observations are expected by chance or not. 
 
Best (and sorry for the late review!), 
Vinny Lynch 
 
 
 
Reviewer #2: 
Remarks to the Author: 
Authors evaluated evolutionary forces and natural selection pressure on the variants associated with 

perterm birth (PTB). Authors assessed a set of methods to infer variants’ and regional evolutionary 
forces, considering minor allele frequency and linkage disequilibrium. They found signature of 
evolution on around half of the PTB-associated regions. They made classification of the variants into 
five categories, and provided biological interpretation for each of them. While this study is potentially 
interesting, the rationale and logic of the study design was not clear for this reviewer. 
 
1. The main question is why the authors only assessed PTB. Since GWAS summary statistics of much 
larger numbers of the phenotypes are now released (e.g., by international consortium and nation-wide 
biobanks such as UK Biobank), this type of analysis should be applied in a phenome-wide way. 
 
2. Readers may want to see assessments in the traits which has been reported to be linked with 
selection pressure first (e.g., anthropometric traits). Then, readers want to see overall test statics 
distribution in a phenome-wide way, to empirically infer the null distribution. Then, one can evaluate 
how PTB is strongly or weakly under evolutionary forces. 
 
3. The evolutionary force methods used in the study is based on a small number of the individuals 
(mostly 1KG populations). Since large-scale GWAS data is publicly available, these selection pressure 
should also be estimated using such big data, with matched ancestry with those used in the phenome-
wide analysis. 
 
4. The descriptions on PTB-associated variant classification is redundant. Please note that one can 
make any similar biological descriptions when a certain set of genes and regions are provided. This 
section should be more concisely summarized, what was specific to the findings in PTB, and other 
traits. 
 
5. Overall, while the theme of this study could potentially be interesting, what was the novel finding 
and conclusion was not clear for this reviewer. 
 
 
 
Reviewer #3: 
Remarks to the Author: 
In their manuscript, the authors aim at characterizing the multiple evolutionary forces experienced by 
preterm birth-associated genes to the greatest extent possible. The authors introduce an empirical 
matching control approach that makes a lot of sense to identify the genes of interest that have 
experienced different selective forces. 
 
I have one main concern about the analysis conducted by the authors, but it is a serious one that in 
my opinion requires careful attention. 
 
The approach used to get empirical p-values is similar to the classic outlier approach in that, even if 
there was no positive, or balancing, or negative selection at all in the human genome, you would still 
get a top 20 or a top 10 candidates that you can write a story about. I will not remind the authors of 
the repeated warnings in the literature about excessive story-telling in the context of the outlier 
approach. 
 
What I am getting at is that unless the authors show that they have a significant enrichment in 
diverse selective signals in the preterm birth genes compared to the rest of the genome, then they do 
not have a strong result. This is because if there is no overall enrichment of some kind compared to 
the rest of the genome (positive selection or balancing, etc.), then that means that there is nothing 
special about those genes compared to other genes in the genome, and there is therefore no 

remarkable evolution to tell a story about. 
 
So I believe that the authors should run the same analysis that they did on the preterm birth genes, 
but on the same number of ramdomly picked genes in the human genome. They should repeat that 
process 100 or even 1,000 times, and see each time if they get less, as many, or more significant hits 
than they do in the real gene set of interest. This would represent a proper, unbiased test for the 
overall enrichment in selection signals, and would also provide unbiased empirical overall false 
discovery rates for the whole analysis. Even better, the authors could for each random set, see if there 
are is less, as much, or more enrichment in the random set compared to the corresponding “random” 
rest of the genome. 
 
The unbiased false discovery rates are important, because the matching process used by the authors 
means that only parts of the genome can be used as control. The smaller the part of the genome that 
can be used as control, the more “noisy” and far from an ideal control (if there were infinite numbers 
of control loci) the actual control can get. This can create either a situation where the controls are too 
conservative, or too liberal, depending on the case. Building random sets and estimating FDRs that 
take the size of the control into account solves these important issues. 
 
With respect to the matching made by the authors, I noticed that the matching intervals are very 
large (+ or – 500%) for factors like gene density for example. This is very large and thus likely to get 
enough control regions, but in fact so large that it may not really end up matching similar regions at 
all. The authors need to address this important problem in their approach. Again, having a proper FDR 
analysis would allow using more narrow matching, and thus smaller control sets, while still being able 
to evaluate what the effect of the smaller control sets is on the overall power and accuracy of the test.