Decreased recent adaptation at human mendelian disease genes as a possible consequence of interference between advantageous and deleterious variants

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   eLife

   Jul 18, 2022

   Author Response

   Reviewer #1 (Public Review):

   The relationship between genetic disease and adaptation is important for biomedical research as well as understanding human evolution. This topic has received considerable attention over the past several decades in human genetics research. The present manuscript provides a much more comprehensive and rigorous analysis of this topic. Specifically, the authors select a set of ~4000 human Mendelian disease genes and examine patterns of recent positive selection in these genes using the iHS and nSL tests (both haplotype test) for selection. They then compare the signals of sweeps to control genes. Importantly, they match the control set to the disease genes based upon many different genomic variables, such as recombination rate, amount of background selection, expression level, etc. The …

   Author Response

   Reviewer #1 (Public Review):

   The relationship between genetic disease and adaptation is important for biomedical research as well as understanding human evolution. This topic has received considerable attention over the past several decades in human genetics research. The present manuscript provides a much more comprehensive and rigorous analysis of this topic. Specifically, the authors select a set of ~4000 human Mendelian disease genes and examine patterns of recent positive selection in these genes using the iHS and nSL tests (both haplotype test) for selection. They then compare the signals of sweeps to control genes. Importantly, they match the control set to the disease genes based upon many different genomic variables, such as recombination rate, amount of background selection, expression level, etc. The authors find that there is a deficit of selective sweeps in disease genes. They test several hypotheses for this deficit. They find that the deficit of sweeps is stronger in disease genes at low recombination rate and those that have more disease mutations. From this, the authors conclude that strongly deleterious mutations could be impeding selective sweeps.

   Strengths

   The manuscript includes a number of important strengths:

   1. It tackles an important question in the field. The question of selection in disease genes has been very well-studied in the past, with conflicting viewpoints. The present study examines this topic in a rigorous way and finds a deficit of sweeps in disease genes.

   1. The statistical analyses are rigorously done. The genome is a confusing place and there can often be many reasons why a certain set of genes could differ from another set of genes, unrelated to the variable of interest. Di et al. carefully match on these genomic confounders. Thus, they rigorously demonstrate that sweeps are depleted in disease genes relative to control genes. Further, the pipeline for ranking the genes and testing for significance is solid.

   1. The Introduction of the manuscript nicely relates different evolutionary models and explanations to patterns that could be seen in the data. As such, the present manuscript isn't just merely an exploratory analysis of patterns of sweeps in disease genes. Rather, it tests specific evolutionary scenarios.

   Weaknesses

   1. The authors did not discuss or test a basic explanation for the deficit of sweeps in disease genes. Namely, certain types of genes, when mutated, give rise to strong Mendelian phenotypes. However, mutations in these genes do not result in variation that gives rise to a phenotype on which positive selection could occur. In other words, there are just different types of genes underlying disease and positive selection. I could think that such a pattern would be possible if humans are close to the fitness optimum and strong effect mutations (like those in Mendelian disease genes) result in moving further away from the fitness optimum. On the other hand, more weak effect mutations could be either weakly deleterious or beneficial and subject to positive selection. I'm not sure whether these patterns would necessarily be captured by the overall measures of constraint which the disease and non-disease genes were matched on.

   We thank the reviewer for suggesting that alternative explanation. It is indeed important that we compare it with our own explanation. To rephrase the reviewer’s suggestion, it is possible that disease genes may just have a different distribution of fitness effects of new mutations. Specifically, mutations in disease genes might have such large effects that they will consistently overshoot the fitness optimum, and thus not get closer to this optimum. This would prevent them from being positively selected. Two predictions can be derived from this potential scenario. First, we can predict a sweep deficit at disease genes, which is what we report. Second, we can also predict that disease genes should exhibit a deficit of older adaptation, not just recent adaptation detected by sweep signals. Indeed, the decrease in adaptation due to (too) large effect mutations would be a generic, intrinsic feature of disease genes regardless of evolutionary time. This means that under this explanation, we expect a test of long-term adaptation such as the McDonald-Kreitman test to also show a deficit at disease genes.

   This latter prediction differs from the prediction made by our favored explanation of interference between deleterious and advantageous variants. In this scenario, the sweep deficit at disease genes is caused by the presence of deleterious, and most importantly currently segregating disease variants. Because the presence of the segregating variants is transient during evolution, our explanation does not predict a deficit of long-term adaptation. We can therefore distinguish which explanation (the reviewer’s or ours) is the most likely based on the presence or absence of a long-term adaptation deficit at disease genes.

   To test this, we now compare protein adaptation in disease and control genes with two versions of the MK test called ABC-MK and GRAPES (refs). ABC-MK estimates the overall rate of adaptation, and also the rates of weak and strong adaptation,and is based on Approximate Bayesian Computation. GRAPES is based on maximum likelihood. Both ABC-MK and GRPES have shown to provide robust estimates of the rate of protein adaptation thanks to evaluations with forward population simulations (refs). We find no difference in long-term adaptation between disease and control non-disease genes, as shown in new figure 4. This shows that the explanation put forward by the reviewer of an intrinsically different distribution of mutation effects at disease genes is less likely than an interference between currently segregating deleterious variants with recent, but not with older long-term adaptation. We even show in the new figure 4 that disease genes and their controls have more, not less strong long-term adaptation compared to the whole human genome baseline (new figure 4C). Also, disease genes in low recombination regions and with many disease variants have experienced more, not less strong long-term adaptation than their controls. Therefore, far from overshooting the fitness optimum due to stronger fitness effects of mutations, it looks like that these stronger fitness effects might in fact be more frequently positively selected in these disease genes.

   We now provide these new results P15L418:
   “Disease genes do not experience constitutively less long-term adaptive mutations
   A deficit of strong recent adaptation (strong enough to affect iHS or 𝑛𝑆!) raises the question of what creates the sweep deficit at disease genes. As already discussed, purifying selection and other confounding factors are matched between disease genes and their controls, which excludes that these factors alone could possibly explain the sweep deficit. Purifying selection alone in particular cannot explain this result, since we find evidence that it is well matched between disease and control genes (Figures 2 and Figure 4-figure supplement 1). Furthermore, we find that the 1,000 genes in the genome with the highest density of conserved elements do not exhibit any sweep deficit (bootstrap test + block-randomized genomes FPR=0.18; Methods). Association with mendelian diseases, rather than a generally elevated level of selective constraint, is therefore what matters to observe a sweep deficit. What then might explain the sweep deficit at disease genes?

   As mentioned in the introduction, it could be that mendelian disease genes experience constitutively less adaptive mutations. This could be the case for example because mendelian disease genes tend to be more pleiotropic (Otto, 2004), and/or because new mutations in mendelian are large effect mutations (Quintana-Murci, 2016) that tend to often overshoot the fitness optimum, and cannot be positively selected as a result. Regardless of the underlying processes, a constitutive tendency to experience less adaptive mutations predicts not only a deficit of recent adaptation, but also a deficit of more long-term adaptation during evolution. The iHS and nSL signals of recent adaptation we use to detect sweeps correspond to a time window of at most 50,000 years, since these statistics have very little statistical power to detect older adaptation (Sabeti et al., 2006). In contrast, approaches such as the McDonald-Kreitman test (MK test) (McDonald and Kreitman, 1991) capture the cumulative signals of adaptative events since humans and chimpanzee had a common ancestor, likely more than six million years ago. To test whether mendelian disease genes have also experienced less long-term adaptation, in addition to less recent adaptation, we use the MK tests ABC-MK (Uricchio et al., 2019) and GRAPES (Galtier, 2016) to compare the rate of protein adaptation (advantageous amino acid changes) in mendelian disease gene coding sequences, compared to confounding factors-matched non-disease controls (Methods). We find that overall, disease and control non-disease genes have experienced similar rates of protein adaptation during millions of years of human evolution, as shown by very similar estimated proportions of amino acid changes that were adaptive (Figure 5A,B,C,D,E). This result suggests that disease genes do not have constitutively less adaptive mutations. This implies that processes that are stable over evolutionary time such as pleiotropy, or a tendency to overshoot the fitness optimum, are unlikely to explain the sweep deficit at disease genes. If disease genes have not experienced less adaptive mutations during long-term evolution, then the process at work during more recent human evolution has to be transient, and has to has to have limited only recent adaptation. It is also noteworthy that both disease genes and their controls have experienced more coding adaptation than genes in the human genome overall (Figure 5A), especially more strong adaptation according to ABC-MK (Figure 5C). The fact that the baseline long-term coding adaptation is lower genome-wide, but similarly higher in disease and their control genes, also shows that the matched controls do play their intended role of accounting for confounding factors likely to affect adaptation. The fact that long-term protein adaptation is not lower at disease genes also excludes that purifying selection alone can explain the sweep deficit at disease genes, because purifying selection would then also have decreased long-term adaptation. A more transient evolutionary process is thus more likely to explain our results.”

   Then P22L613: “More importantly, the fact that constitutively less adaptation at disease genes combined to more power to detect sweeps in low recombination regions does not explain our results, is made even clearer by the fact that disease genes in low recombination regions and with many disease variants have in fact experienced more, not less long-term adaptation according to an MK analysis using both ABC-MK and GRAPES (Figure 5F,G,H,I,J). ABC-MK in particular finds that there is a significant excess of long-term strong adaptation (Figure 4H, P<0.01) in disease genes with low recombination and with many disease variants, compared to controls, but similar amounts of weak adaptation (Figure 5G, P=0.16). It might be that disease genes with many disease variants are genes with more mutations with stronger effects that can generate stronger positive selection. The potentially higher supply of strongly advantageous variants at these disease genes makes it all the more notable that they have a very strong sweep deficit in recent evolutionary times. This further strengthens the evidence in favor of interference during recent human adaptation: the limiting factor does not seem to be the supply of strongly advantageous variants, but instead the ability of these variants to have generated sweeps recently by rising fast enough in frequency.”

   1. While I think the authors did a superb job of controlling for genome differences between disease and non-disease genes, the analysis of separating regions by recombination rate and number of disease mutations does not seem as rigorous. Specifically, the authors tested for enrichment of sweeps in disease genes vs control and then stratified that comparison by recombination rate and/or number of disease mutations. While this nicely matches the disease genes to the control genes, it is not clear whether the high recombination rate genes differ in other important attributes from the low recombination rate genes. Thus, I worry whether there could be a confounder that makes it easier/harder to detect an enrichment/deficit of sweeps in regions of low/high recombination.

   We thank the reviewer for emphasizing the need for more controls when comparing our results in low or high recombination regions. We have now compared the confounding factors between low recombination disease genes and high recombination disease genes, as classified in the manuscript. As shown in new supp table Figure 6 figure supplement 1, confounding factors do not differ substantially between low and high recombination disease genes, and are all within a range of +/- 25% of each other. It would take a larger difference for any confounding factor to explain the sharp sweep deficit difference observed between the low and high recombination disease genes. The only factor with a 35% difference between low and high recombination mendelian disease genes is McVicker’s B, but this is completely expected; B is expected to be lower in low recombination regions.

   We now write P20L569: “Further note that only moderate differences in confounding factors between low and high recombination mendelian disease genes are unlikely to explain the sweep deficit difference (Figure 6-figure supplement 1).”

   Regarding the potential confounding effect of statistical power to detect sweeps differing in low and high recombination regions, please see our earlier response to main point 2.

   Reviewer #2 (Public Review):

   This paper seeks to test the extent to which adaptation via selective sweeps has occurred at disease-associated genes vs genes that have not (yet) been associated with disease. While there is a debate regarding the rate at which selective sweeps have occurred in recent human history, it is clear that some genes have experienced very strong recent selective sweeps. Recent papers from this group have very nicely shown how important virus interacting proteins have been in recent human evolution, and other papers have demonstrated the few instances in which strong selection has occurred in recent human history to adapt to novel environments (e.g. migration to high altitude, skin pigmentation, and a few other hypothesized traits).

   One challenge in reading the paper was that I did not realize the analysis was exclusively focused on Mendelian disease genes until much later (the first reference is not until the end of the introduction on pages 7-8 and then not at all again until the discussion, despite referring to "disease" many times in the abstract and throughout the paper). It would be preferred if the authors indicated that this study focused on Mendelian diseases (rather than a broader analysis that included complex or infectious diseases). This is important because there are many different types of diseases and disease genes. Infectious disease genes and complex disease genes may have quite different patterns (as the authors indicate at the end of the introduction).

   We want to apologize profusely for this avoidable mistake. We have now made it clearer from the very start of the manuscript that we focus on mendelian non-infectious disease genes. We have modified the title and the abstract accordingly, specifying mendelian and non-infectious as required.

   The abstract states "Understanding the relationship between disease and adaptation at the gene level in the human genome is severely hampered by the fact that we don't even know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during recent human evolution." This seems to diminish a large body of work that has been done in this area. The authors acknowledge some of this literature in the introduction, but it would be worth toning down the abstract, which suggests there has been no work in this area. A review of this topic by Lluis Quintana-Murci1 was cited, but diminished many of the developments that have been made in the intersection of population genetics and human disease biology. Quintana-Murci says "Mendelian disorders are typically severe, compromising survival and reproduction, and are caused by highly penetrant, rare deleterious mutations. Mendelian disease genes should therefore fit the mutation-selection balance model, with an equilibrium between the rate of mutation and the rate of risk allele removal by purifying selection", and argues that positive selection signals should be rare among Mendelian disease genes. Several other examples come to mind. For example, comparing Mendelian disease genes, complex disease genes, and mouse essential genes was the major focus of a 2008 paper2, which pointed out that Mendelian disease genes exhibited much higher rates of purifying selection while complex disease genes exhibited a mixture of purifying and positive selection. This paper was cited, but only in regard to their findings of complex diseases. A similar analysis of McDonald-Kreitman tables3 was performed around Mendelian disease genes vs non-disease genes, and found "that disease genes have a higher mean probability of negative selection within candidate cis-regulatory regions as compared to non-disease genes, however this trend is only suggestive in EAs, the population where the majority of diseases have likely been characterized". Both of these studies focused on polymorphism and divergence data, which target older instances of selection than iHS and nSL statistics used in the present study (but should have substantial overlap since iHS is not sensitive to very recent selection like the SDS statistic). Regardless, the findings are largely consistent, and I believe warrant a more modest tone.

   We thank the reviewer for their recommendation. We should have written more about what is currently well known or unknown about recent adaptation in disease genes, and in more nuanced terms. Instead of writing “Understanding the relationship between disease and adaptation at the gene level in the human genome is severely hampered by the fact that we don't even know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during recent human evolution”, we now write in the new abstract:

   “Despite our expanding knowledge of gene-disease associations, and despite the medical importance of disease genes, their recent evolution has not been thoroughly studied across diverse human populations. In particular, recent genomic adaptation at disease genes has not been characterized as well as long-term purifying selection and long-term adaptation. Understanding the relationship between disease and adaptation at the gene level in the human genome is hampered by the fact that we don’t know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during the last ~50,000 years of recent human evolution.”

   We also toned down the start of the introduction. We now write P3L74:

   “Despite our expanding knowledge of mendelian disease gene associations, and despite the fact that multiple evolutionary processes might connect disease and genomic adaptation at the gene level, these connections are yet to be studied more thoroughly, especially in the case of recent genomic adaptation.”

   Although we agree that others have made extensive efforts to characterize older adaptation or purifying selection at disease genes compared to non-disease genes, we still believe that our results are novel and more conclusive about recent positive selection. Our initial statement was however poorly phrased. To our knowledge, our study is the first to look at the issue using specifically sweep statistics that have been shown to be robust to background selection, while also controlling for confounding factors. These sweep statistics have sensitivity for selection events that occurred in the past 30,000 or at most 50,000 years of human evolution (Sabeti et al. 2006). This is a very different time scale compared to the millions of years of adaptation (since divergence between humans and chimpanzees) captured by MK approaches.

   We also want to note that we did cite the Blekhman et al. paper for their result of stronger purifying selection in our initial manuscript. It is true however that we did not specify mendelian disease genes, which was confusing. We want to apologize again for it:

   From the earlier manuscript: “Multiple recent studies comparing evolutionary patterns between human disease and non-disease genes have found that disease genes are more constrained and evolve more slowly (lower ratio of nonsynonymous to synonymous substitution rate, dN/dS, in disease genes) (Blekhman et al., 2008; Park et al., 2012; Spataro et al., 2017)”

   “Among other confounding factors, it is particularly important to take into account evolutionary constraint, i.e the level of purifying selection experienced by different genes. A common intuition is that disease genes may exhibit less adaptation because they are more constrained (Blekhman et al., 2008)”

   It is important to remember that, as we mention in the introduction, previous comparisons did not take potential confounding factors at all into account. It is therefore unclear whether their conclusions were specific to disease genes, or due to confounding factors. We have now made this point clearer in the introduction, as we believe that we have made a substantial effort to control for confounding factors, and that it is a substantial departure from previous efforts:

   P7L201: “In contrast with previous studies, we systematically control for a large number of confounding factors when comparing recent adaptation in human mendelian disease and nondisease genes, including evolutionary constraint, mutation rate, recombination rate, the proportion of immune or virus-interacting genes, etc. (please refer to Methods for a full list of the confounding factors included).”.

   P9L253: “These differences between disease and non-disease genes highlight the need to compare disease genes with control non-disease genes with similar levels of selective constraint. To do this and compare sweeps in mendelian disease genes and non-disease genes that are similar in ways other than being associated with mendelian disease (as described in the Results below, Less sweeps at mendelian disease genes), we use sets of control non-disease genes that are built by a bootstrap test to match the disease genes in terms of confounding factors (Methods)”.

   Furthermore, we have now added a comparison of older adaptation in disease and non-disease genes using a recent version of the MK test called ABC-MK, that can take background selection and other biases such as segregating weakly advantageous variants into account. Also controlling for confounding factors, we find no difference in older adaptation between disease and non-disease genes (please see our response to main point 2).

   Therefore, contrary to the reviewer’s claim that the sweep statistics and MK approaches should have substantial overlap, we now show that it is clearly not the case. We further show that the lack of overlap is expected under our explanation of our results based on interference between recessive deleterious and advantageous variants (see our responses to main point 1 and to reviewer 1 weakness 1).

   Previous analyses were using much smaller mendelian disease gene datasets, less recent polymorphism datasets and, critically, did not control for confounding factors. We also note that reference 3 (Torgerson et al. Plos Genetics 2009) does not make any claim about recent positive selection in mendelian disease genes compared to other genes. Their dataset at the time also only included 666 mendelian disease genes, versus the ~4,000 currently known.

   In short, we do think that we have a claim for novelty, but the reviewer is entirely right that we did a poor job of giving due credit to previous important work. These previous studies deserved much better credit than no credit at all. We want to thank the reviewer from avoiding us the embarrassment of not citing important work.

   We now cite the papers referenced by the reviewer as appropriate in the introduction, based on the scope of their results:

   P3L93: “Multiple recent studies comparing evolutionary patterns between human mendelian disease and non-disease genes have found that mendelian disease genes are more constrained and evolve more slowly (Blekhman et al., 2008; Quintana-Murci, 2016; Spataro et al., 2017; Torgerson et al., 2009). An older comparison by Smith and Eyre-Walker (Smith and Eyre-Walker, 2003) found that disease genes evolve faster than non-disease genes, but we note that the sample of disease genes used at the time was very limited.”

   P5L134 “Among possible confounding factors, it is particularly important to take into account evolutionary constraint, i.e the level of purifying selection experienced by different genes. A common intuition is that mendelian disease genes may exhibit less adaptation because they are more constrained (Blekhman et al., 2008; Spataro et al., 2017; Torgerson et al., 2009),”

   There are some aspects of the current study that I think are highly valuable. For example, the authors study most of the 1000 Genomes Project populations (though the text should be edited since the admixed and South Asian populations are not analyzed, so all 26 populations are not included, only the populations from Africa, East Asia, and Europe are analyzed; a total of 15 populations are included Figures 2-3). Comparing populations allows the authors to understand how signatures of selection might be shared vs population-specific. Unfortunately, the signals that the authors find regarding the depletion of positive selection at Mendelian disease genes is almost entirely restricted to African populations. The signal is not significant in East Asia or Europe (Figure 2 clearly shows this). It seems that the mean curve of the fold-enrichment as a function of rank threshold (Figure 3) trends downward in East Asian and European populations, but the sampling variance is so large that the bootstrap confidence intervals overlap 1). The paper should therefore revise the sentence "we find a strong depletion in sweep signals at disease genes, especially in Africa" to "only in Africa". This opens the question of why the authors find the particular pattern they find. The authors do point out that a majority of Mendelian disease genes are likely discovered in European populations, so is it that the genes' functions predate the Out-of-Africa split? They most certainly do. It is possible that the larger long-term effective population size of African populations resulted in stronger purifying selection at Mendelian disease genes compared to European and East Asian populations, where smaller effective population sizes due to the Out-of-Africa Bottleneck diminished the signal of most selective sweeps and hence there is little differentiation between categories of genes, "drift noise"). It is also surprising to note that the authors find selection signatures at all using iHS in African populations while a previous study using the same statistic could not differentiate signals of selection from neutral demographic simulations4.

   We want to thank the reviewer profusely for putting us on the right track thanks to their insightful suggestion. As described in our response to reviewer 1 weakness 1, we have now shown with simulations that the interference of deleterious variants on advantageous variants is strongly decreased during a bottleneck of a magnitude similar to the Out of Africa bottlenecks experienced by East Asian and European populations. This decrease of interference is likely strong enough to not require any other explanation, even if other processes may also be at work, such as a decrease of the sweeps signals as suggested by the reviewer.

   About the Granka et al. paper, the last author of the current manuscript has already shown in a previous paper (ref) that the type of approaches used to quantify recent adaptation is likely to be severely underpowered due to a number of confounding factors, notably including comparing genic and non-genic windows that are not sufficiently far from each other to not overlap the same sweep signals. Our result are also based on much more recent and less biased sets of SNPs used to measure the sweeps statistics.

   The authors find that there is a remarkably (in my view) similar depletion across all but one MeSH disease classes. This suggests that "disease" is likely not the driving factor, but that Mendelian disease genes are a way of identifying where there are strongly selected deleterious variants recurrently arising and preventing positively selected variants. This is a fascinating hypothesis, and is corroborated by the finding that the depletion gets stronger in genes with more Mendelian disease variants. In this sense, the authors are using Mendelian disease genes as a proxy for identifying targets of strong purifying selection, and are therefore not actually studying Mendelian disease genes. The signal could be clearer if the test set is based on the factor that is actually driving the signal.

   Based on the reviewer’s comment, we have now better explained why our results are unlikely to be a generic property of purifying selection alone. As we explain in our response to main point 3, our results cannot be explained by purifying selection alone, because we match purifying selection between disease genes and the controls. Indeed, we now show with additional MK analyses and GERP-based analyses that our controls for confounding factors already account for purifying selection. This is shown by the fact that disease genes and their controls have similar distributions of deleterious fitness effects.

   In addition, we added a comparison that shows that purifying selection alone does not explain our results. Instead of comparing sweeps at disease and non-disease genes, we compared sweeps (in Africa) between the 1,000 genes with the highest density of conserved, constrained elements and other genes in the genome. If purifying selection is the factor that drives the sweep deficit at disease genes, then we should see a sweep deficit among the genes with the most conserved, constrained elements compared to other genes in the genome. However, we see no such sweep deficit at genes with a high density of conserved, selectively constrained elements (boostrap test + block randomization of genomes, FPR=0.18). See P15L424. Note that for this comparison we had to remove the matching of confounding factors corresponding to functional and purifying selection densities (new Methods P40L1131).

   Again, our results are better explained not just by purifying selection alone, but more specifically by the presence of interfering, segregating deleterious variants. It is perfectly possible to have highly constrained parts of the genome without having many deleterious segregating variants at a given time in evolution.

   The similarity across MeSH classes can be readily explained if what matters is interference with deleterious segregating variants. Because all types of diseases have deleterious segregating variants, then it is not surprising that different MeSH disease categories have a similar sweep deficit. We make that point clearer in the revised manuscript:

   P26L707: “The sweep deficit is comparable across MeSH disease classes (Figure 8), suggesting that the evolutionary process at the origin of the sweep deficit is not diseasespecific. This is compatible with a non-disease specific explanation such as recessive deleterious variants interfering with adaptive variants, irrespective of the specific disease type.”.

   One of the most important steps that the authors undertake is to control for possible confounding factors. The authors identify 22 possible confounding factors, and find that several confounding factors have different effects in Mendelian disease genes vs non-disease genes. The authors do a great job of implementing a block-bootstrap approach to control for each of these factors. The authors talk specifically about some of these (e.g. PPI), but not others that are just as strong (e.g. gene length). I am left wondering how interactions among other confounding factors could impact the findings of this paper. I was surprised to see a focus on disease variant number, but not a control for CDS length. As I understand it, gene length is defined as the entire genomic distance between the TSS and TES. Presumably genes with larger coding sequence have more potential for disease variants (though number of disease variants discovered is highly biased toward genes with high interest). CDS length would be helpful to correct for things that pS does not correct for, since pS is a rate (controlling for CDS length) and does not account for the coding footprint (hence pS is similar across gene categories).

   Based on our response to the previous point, it is clear that a high density of coding sequences, or conserved constrained sequence in general are not enough to explain our results. Furthermore, we want to remind the reviewer that we already control for coding sequence length through controlling for coding density, since we use windows of constant sizes.

   The authors point out that it is crucial to get the control set right. This group has spent a lot of time thinking about how to define a control set of genes in several previous papers. But it is not clear if complex disease genes and infectious disease genes are specifically excluded or not. Number of virus interactions was included as a confounding factor, so VIPs were presumably not excluded. It is clear that the control set includes genes not yet associated with Mendelian disease, but the focus is primarily on the distance away from known Mendelian disease genes.

   We are sorry that we were not more explicit from the start of the manuscript. We now make it clearer what the set disease genes includes or not throughout the entire manuscript, by repeating that we focus specifically on mendelian, non-infectious disease genes. By noninfectious, we mean that we excluded genes with known infectious disease-associated variants. This does not exclude most virus-interacting genes since most of them are not associated at the genetic variant level with infectious diseases. It is also important to note that the effect of virus interactions is accounted for by matching the number of interacting viruses between mendelian disease genes and controls.

   We write P29L818: “By non-infectious, we mean that we excluded genes with known infectious disease-associated variants. This does not exclude most VIPs since most of them are not associated at the genetic variant level with infectious diseases. It is important to note that the effect of virus interactions is accounted for by matching the number of interacting viruses between mendelian disease genes and controls.”

   Minor comments:

   On page 13, the authors say "This artifact is also very unlikely due to the fact that recombination rates are similar between disease and non-disease genes (Figure 1)." However, Figure 1 shows that "deCode recombination 50kb" is clearly higher in disease genes and comparable at 500kb. The increased recombination rate locally around disease genes seems to contradict the argument formulated in this paragraph.

   We apologize for the lack of precision in this sentence. What we meant is that the recombination rates are not different enough that the mentioned hypothetical artifact would be able to explain our results. We also forgot to remind at this point in the manuscript that we match recombination between disease genes and controls. We now use more precise language:

   P28L772 “The recombination rate at disease genes is also only slightly different from the recombination rate at non-disease genes (Figure 1), and we match the recombination rate between disease genes and controls.”.

   Reviewer #3 (Public Review):

   In this paper, the authors ask whether selective sweeps (as measured by the iHS and nSL statistics) are more or less likely to occur in or near genes associated with Mendelian diseases ("disease genes") than those that are not ("non-disease genes"). The main result put forward by the authors is that genes associated with Mendelian diseases are depleted for sweep signatures, as measured by the iHS and nSL statistics, relative to those which are not.

   The evidence for this comes from an empirical randomization scheme to assess whether genes with signatures of a selective sweep are more likely to be Mendelian disease genes that not. The analysis relies on a somewhat complicated sliding threshold scheme that effectively acts to incorporate evidence from both genes with very large iHS/nSL values, as well as those with weaker signals, while upweighting the signal from those genes with the strongest iHS/nSL values. Although I think the anlaysis could be presented more clearly, it does seem like a better analysis than a simple outlier test, if for no other reason than that the sliding threshold scheme can be seen as a way of averaging over uncertainty in where one should set the threshold in an outlier test (along with some further averaging across the two different sweeps statistics, and the size of the window around disease associated genes that the sweep statistics are averaged over). That said, the particular approach to doing so is somewhat arbitrary, but it's not clear that there's a good way to avoid that.

   In addition to reporting that extreme values of iHS/nSL are generally less likely at Mendelian disease genes, the authors also report that this depletion is strongest in genes from low recombination regions, or which have >5 specific variants associated with disease.

   Drawing on this result, the authors read this evidence to imply that sweeps are generally impeded or slowed in the vicinity of genes associated with Mendelian diseases due to linkage to recessive deleterious variants, which hitchhike to high enough frequencies that the selection against homozygotes becomes an important form of interference. This phenomenon was theoretically characterized by Assaf et al 2015, who the authors point to for support. That such a phenomenon may be acting systematically to shape the process of adaptation is an interesting suggestions. It's a bit unclear to me why the authors specifically invoke recessive deleterious mutations as an explanation though. Presumably any form of interference could create the patterns they observe? This part of the paper is, as the authors acknowledge, speculative at this point.

   We thank the reviewer for their comments. We are sorry that we did not provide a clear explanation of why only recessive deleterious mutations are expected to interfere more than other types of deleterious variants. This was shown by Assaf et al. (2015), and we should have stated it explicitly. The reason why recessive deleterious variants interfere more than additive or dominant ones is that they can hitchhike together with an adaptive variant to substantial frequencies before negative selection actually happens, when a significant number of homozygous individuals for the deleterious mutation start happening in the population. On the contrary dominant mutations do not make it to the same high frequencies linked to an adaptive variant, because they start being selected negatively as soon as they appear in the population.

   We now write P18L496: “In diploid species including humans, recessive deleterious mutations specifically have been shown to have the ability to slow down, or even stop the frequency increase of advantageous mutations that they are linked with (Assaf et al., 2015). Dominant variants do not have the same interfering ability, because they do not increase in frequency in linkage with advantageous variants as much as recessive deleterious do, before the latter can be “seen” by purifying selection when enough homozygous individuals emerge in a population (Assaf et al., 2015).”

   We have also confirmed with SLiM forward simulations that recessive deleterious variants interfere with adaptive variants much more than dominant ones (Table 1).

   I'm also a bit concerned by the fact that the signal is only present in the African samples studied. The authors suggest that this is simply due to stronger drift in the history of European and Asian samples. This could be, but as a reader it's a bit frustrating to have to take this on faith.

   We thank the reviewer for pointing out this issue with our manuscript. We have now shown, as detailed above in our response to main point 1, reviewer 1 weakness 1, that a weaker sweep deficit at disease genes in Europe and East Asia is an expected feature under the interference explanation, due to the weakened interference of recessive deleterious variants during bottlenecks of the magnitude observed in Europe and East Asia. We therefore believe that these new results strengthen our previous claim regarding the role interference between deleterious and advantageous variants. We want to thank the reviewer for forcing us to examine the difference between results in Africa and out of Africa, as the manuscript is now more consistent and our results substantially better explained.

   There are other analyses that I don't find terribly convincing. For example, one of the anlayses shows that iHS signals are no less depleted at genes associated with >5 diseases than with 1 does little to convince me of anything. It's not particularly clear that # of associated disease for a given gene should predict the degree of pleiotropy experienced by a variant emerging in that gene with some kind of adaptive function. Failure to find any association here might just mean that this is not a particularly good measure of the relevant pleiotropy.

   We agree with the reviewer that the number of associated disease may not be a good measure of pleiotropy. Unfortunately to our knowledge there is currently no good measure of gene pleiotropy in human genomes. Given that the evidence in favor of interference of deleterious variants is now strengthened, we have chosen to remove this analysis from the manuscript. As we now explain throughout the manuscript, pleiotropy is an unlikely explanation in the first place because of the fact that disease genes have not experienced less long-term adaptation (see the details on our new MK test results in the response to main point 2).

   P16L447: “We find that overall, disease and control non-disease genes have experienced similar rates of protein adaptation during millions of years of human evolution, as shown by very similar estimated proportions of amino acid changes that were adaptive (Figure 5A,B,C,D,E). This result suggests that disease genes do not have constitutively less adaptive mutations. This implies that processes stable over evolutionary time such as pleiotropy, or a tendency to overshoot the fitness optimum, are unlikely to explain the sweep deficit at disease genes.”.

   A last parting thought is that it's not clear to me that the authors have excluded the hypothesis that adaptive variants simply arise less often near genes associated with disease. The fact that the signal is strongest in regions of low recombination is meant to be evidence in favor of selective interference as the explanation, but it is also the regime in which sweeps should be easiest to detect, so it may be just that the analysis is best powered to detect a difference in sweep initiation, independent of possible interference dynamics, in that regime.

   We thank the reviewer for stating these important alternative explanations that needed more attention in our manuscript. In our response to main point 2 above, we explain that higher statistical power in low recombination regions is unlikely to explain our results alone, because we also show that the sweep deficit is substantially present not only in low recombination regions, but also requires the presence of a higher number of disease variants. We also describe in our response to main point 2 how our new MK-test results on long-term adaptation make it very unlikely that mendelian disease genes experience constitutively less adaptation. We want to thank the reviewer again for pointing out this issue with our manuscript, since it was indeed an important missing piece.

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2. 

   eLife

   Jun 14, 2022

   Evaluation Summary:

   This paper is of interest for scientists studying human genetic adaptation and disease. The work improves on previous studies addressing the question of recent positive selection on genes underlying Mendelian diseases, by examining larger datasets of disease genes as well as carefully controlling for confounding factors that could result in disease genes and non-disease genes showing different patterns of genetic variation. The authors suggest that interference between strongly deleterious recessive mutations can reduce adaptation at disease genes, although this conclusion is weakened by the fact that the signal is only observed in Africa.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the …

   Evaluation Summary:

   This paper is of interest for scientists studying human genetic adaptation and disease. The work improves on previous studies addressing the question of recent positive selection on genes underlying Mendelian diseases, by examining larger datasets of disease genes as well as carefully controlling for confounding factors that could result in disease genes and non-disease genes showing different patterns of genetic variation. The authors suggest that interference between strongly deleterious recessive mutations can reduce adaptation at disease genes, although this conclusion is weakened by the fact that the signal is only observed in Africa.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

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3. 

   eLife

   Jun 14, 2022

   Reviewer #1 (Public Review):

   The relationship between genetic disease and adaptation is important for biomedical research as well as understanding human evolution. This topic has received considerable attention over the past several decades in human genetics research. The present manuscript provides a much more comprehensive and rigorous analysis of this topic. Specifically, the authors select a set of ~4000 human Mendelian disease genes and examine patterns of recent positive selection in these genes using the iHS and nSL tests (both haplotype test) for selection. They then compare the signals of sweeps to control genes. Importantly, they match the control set to the disease genes based upon many different genomic variables, such as recombination rate, amount of background selection, expression level, etc. The authors find that there …

   Reviewer #1 (Public Review):

   The relationship between genetic disease and adaptation is important for biomedical research as well as understanding human evolution. This topic has received considerable attention over the past several decades in human genetics research. The present manuscript provides a much more comprehensive and rigorous analysis of this topic. Specifically, the authors select a set of ~4000 human Mendelian disease genes and examine patterns of recent positive selection in these genes using the iHS and nSL tests (both haplotype test) for selection. They then compare the signals of sweeps to control genes. Importantly, they match the control set to the disease genes based upon many different genomic variables, such as recombination rate, amount of background selection, expression level, etc. The authors find that there is a deficit of selective sweeps in disease genes. They test several hypotheses for this deficit. They find that the deficit of sweeps is stronger in disease genes at low recombination rate and those that have more disease mutations. From this, the authors conclude that strongly deleterious mutations could be impeding selective sweeps.

   Strengths

   The manuscript includes a number of important strengths:

   1. It tackles an important question in the field. The question of selection in disease genes has been very well-studied in the past, with conflicting viewpoints. The present study examines this topic in a rigorous way and finds a deficit of sweeps in disease genes.

   2. The statistical analyses are rigorously done. The genome is a confusing place and there can often be many reasons why a certain set of genes could differ from another set of genes, unrelated to the variable of interest. Di et al. carefully match on these genomic confounders. Thus, they rigorously demonstrate that sweeps are depleted in disease genes relative to control genes. Further, the pipeline for ranking the genes and testing for significance is solid.

   3. The Introduction of the manuscript nicely relates different evolutionary models and explanations to patterns that could be seen in the data. As such, the present manuscript isn't just merely an exploratory analysis of patterns of sweeps in disease genes. Rather, it tests specific evolutionary scenarios.

   Weaknesses

   1. The authors did not discuss or test a basic explanation for the deficit of sweeps in disease genes. Namely, certain types of genes, when mutated, give rise to strong Mendelian phenotypes. However, mutations in these genes do not result in variation that gives rise to a phenotype on which positive selection could occur. In other words, there are just different types of genes underlying disease and positive selection. I could think that such a pattern would be possible if humans are close to the fitness optimum and strong effect mutations (like those in Mendelian disease genes) result in moving further away from the fitness optimum. On the other hand, more weak effect mutations could be either weakly deleterious or beneficial and subject to positive selection. I'm not sure whether these patterns would necessarily be captured by the overall measures of constraint which the disease and non-disease genes were matched on.

   2. While I think the authors did a superb job of controlling for genome differences between disease and non-disease genes, the analysis of separating regions by recombination rate and number of disease mutations does not seem as rigorous. Specifically, the authors tested for enrichment of sweeps in disease genes vs control and then stratified that comparison by recombination rate and/or number of disease mutations. While this nicely matches the disease genes to the control genes, it is not clear whether the high recombination rate genes differ in other important attributes from the low recombination rate genes. Thus, I worry whether there could be a confounder that makes it easier/harder to detect an enrichment/deficit of sweeps in regions of low/high recombination.

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4. 

   eLife

   Jun 14, 2022

   Reviewer #2 (Public Review):

   This paper seeks to test the extent to which adaptation via selective sweeps has occurred at disease-associated genes vs genes that have not (yet) been associated with disease. While there is a debate regarding the rate at which selective sweeps have occurred in recent human history, it is clear that some genes have experienced very strong recent selective sweeps. Recent papers from this group have very nicely shown how important virus interacting proteins have been in recent human evolution, and other papers have demonstrated the few instances in which strong selection has occurred in recent human history to adapt to novel environments (e.g. migration to high altitude, skin pigmentation, and a few other hypothesized traits).

   One challenge in reading the paper was that I did not realize the analysis was …

   Reviewer #2 (Public Review):

   This paper seeks to test the extent to which adaptation via selective sweeps has occurred at disease-associated genes vs genes that have not (yet) been associated with disease. While there is a debate regarding the rate at which selective sweeps have occurred in recent human history, it is clear that some genes have experienced very strong recent selective sweeps. Recent papers from this group have very nicely shown how important virus interacting proteins have been in recent human evolution, and other papers have demonstrated the few instances in which strong selection has occurred in recent human history to adapt to novel environments (e.g. migration to high altitude, skin pigmentation, and a few other hypothesized traits).

   One challenge in reading the paper was that I did not realize the analysis was exclusively focused on Mendelian disease genes until much later (the first reference is not until the end of the introduction on pages 7-8 and then not at all again until the discussion, despite referring to "disease" many times in the abstract and throughout the paper). It would be preferred if the authors indicated that this study focused on Mendelian diseases (rather than a broader analysis that included complex or infectious diseases). This is important because there are many different types of diseases and disease genes. Infectious disease genes and complex disease genes may have quite different patterns (as the authors indicate at the end of the introduction).

   The abstract states "Understanding the relationship between disease and adaptation at the gene level in the human genome is severely hampered by the fact that we don't even know whether disease genes have experienced more, less, or as much adaptation as non-disease genes during recent human evolution." This seems to diminish a large body of work that has been done in this area. The authors acknowledge some of this literature in the introduction, but it would be worth toning down the abstract, which suggests there has been no work in this area. A review of this topic by Lluis Quintana-Murci1 was cited, but diminished many of the developments that have been made in the intersection of population genetics and human disease biology. Quintana-Murci says "Mendelian disorders are typically severe, compromising survival and reproduction, and are caused by highly penetrant, rare deleterious mutations. Mendelian disease genes should therefore fit the mutation-selection balance model, with an equilibrium between the rate of mutation and the rate of risk allele removal by purifying selection", and argues that positive selection signals should be rare among Mendelian disease genes. Several other examples come to mind. For example, comparing Mendelian disease genes, complex disease genes, and mouse essential genes was the major focus of a 2008 paper2, which pointed out that Mendelian disease genes exhibited much higher rates of purifying selection while complex disease genes exhibited a mixture of purifying and positive selection. This paper was cited, but only in regard to their findings of complex diseases. A similar analysis of McDonald-Kreitman tables3 was performed around Mendelian disease genes vs non-disease genes, and found "that disease genes have a higher mean probability of negative selection within candidate cis-regulatory regions as compared to non-disease genes, however this trend is only suggestive in EAs, the population where the majority of diseases have likely been characterized". Both of these studies focused on polymorphism and divergence data, which target older instances of selection than iHS and nSL statistics used in the present study (but should have substantial overlap since iHS is not sensitive to very recent selection like the SDS statistic). Regardless, the findings are largely consistent, and I believe warrant a more modest tone.

   There are some aspects of the current study that I think are highly valuable. For example, the authors study most of the 1000 Genomes Project populations (though the text should be edited since the admixed and South Asian populations are not analyzed, so all 26 populations are not included, only the populations from Africa, East Asia, and Europe are analyzed; a total of 15 populations are included Figures 2-3). Comparing populations allows the authors to understand how signatures of selection might be shared vs population-specific. Unfortunately, the signals that the authors find regarding the depletion of positive selection at Mendelian disease genes is almost entirely restricted to African populations. The signal is not significant in East Asia or Europe (Figure 2 clearly shows this). It seems that the mean curve of the fold-enrichment as a function of rank threshold (Figure 3) trends downward in East Asian and European populations, but the sampling variance is so large that the bootstrap confidence intervals overlap 1). The paper should therefore revise the sentence "we find a strong depletion in sweep signals at disease genes, especially in Africa" to "only in Africa". This opens the question of why the authors find the particular pattern they find. The authors do point out that a majority of Mendelian disease genes are likely discovered in European populations, so is it that the genes' functions predate the Out-of-Africa split? They most certainly do. It is possible that the larger long-term effective population size of African populations resulted in stronger purifying selection at Mendelian disease genes compared to European and East Asian populations, where smaller effective population sizes due to the Out-of-Africa Bottleneck diminished the signal of most selective sweeps and hence there is little differentiation between categories of genes, "drift noise"). It is also surprising to note that the authors find selection signatures at all using iHS in African populations while a previous study using the same statistic could not differentiate signals of selection from neutral demographic simulations4.

   The authors find that there is a remarkably (in my view) similar depletion across all but one MeSH disease classes. This suggests that "disease" is likely not the driving factor, but that Mendelian disease genes are a way of identifying where there are strongly selected deleterious variants recurrently arising and preventing positively selected variants. This is a fascinating hypothesis, and is corroborated by the finding that the depletion gets stronger in genes with more Mendelian disease variants. In this sense, the authors are using Mendelian disease genes as a proxy for identifying targets of strong purifying selection, and are therefore not actually studying Mendelian disease genes. The signal could be clearer if the test set is based on the factor that is actually driving the signal.

   One of the most important steps that the authors undertake is to control for possible confounding factors. The authors identify 22 possible confounding factors, and find that several confounding factors have different effects in Mendelian disease genes vs non-disease genes. The authors do a great job of implementing a block-bootstrap approach to control for each of these factors. The authors talk specifically about some of these (e.g. PPI), but not others that are just as strong (e.g. gene length). I am left wondering how interactions among other confounding factors could impact the findings of this paper. I was surprised to see a focus on disease variant number, but not a control for CDS length. As I understand it, gene length is defined as the entire genomic distance between the TSS and TES. Presumably genes with larger coding sequence have more potential for disease variants (though number of disease variants discovered is highly biased toward genes with high interest). CDS length would be helpful to correct for things that pS does not correct for, since pS is a rate (controlling for CDS length) and does not account for the coding footprint (hence pS is similar across gene categories).

   The authors point out that it is crucial to get the control set right. This group has spent a lot of time thinking about how to define a control set of genes in several previous papers. But it is not clear if complex disease genes and infectious disease genes are specifically excluded or not. Number of virus interactions was included as a confounding factor, so VIPs were presumably not excluded. It is clear that the control set includes genes not yet associated with Mendelian disease, but the focus is primarily on the distance away from known Mendelian disease genes.

   Minor comments:

   On page 13, the authors say "This artifact is also very unlikely due to the fact that recombination rates are similar between disease and non-disease genes (Figure 1)." However, Figure 1 shows that "deCode recombination 50kb" is clearly higher in disease genes and comparable at 500kb. The increased recombination rate locally around disease genes seems to contradict the argument formulated in this paragraph.

   1. Quintana-Murci L. Understanding rare and common diseases in the context of human evolution. Genome Biol. 2016 Nov 7;17(1):225. PMCID: PMC5098287
   2. Blekhman R, Man O, Herrmann L, Boyko AR, Indap A, Kosiol C, Bustamante CD, Teshima KM, Przeworski M. Natural selection on genes that underlie human disease susceptibility. Curr Biol. Elsevier BV; 2008 Jun 24;18(12):883-889. PMCID: PMC2474766
   3. Torgerson DG, Boyko AR, Hernandez RD, Indap A, Hu X, White TJ, Sninsky JJ, Cargill M, Adams MD, Bustamante CD, Clark AG. Evolutionary processes acting on candidate cis-regulatory regions in humans inferred from patterns of polymorphism and divergence. PLoS Genet. Public Library of Science (PLoS); 2009 Aug;5(8):e1000592. PMCID: PMC2714078
   4. Granka JM, Henn BM, Gignoux CR, Kidd JM, Bustamante CD, Feldman MW. Limited evidence for classic selective sweeps in African populations. Genetics. Oxford University Press (OUP); 2012 Nov;192(3):1049-1064. PMCID: PMC3522151

   Read the original source
5. 

   eLife

   Jun 14, 2022

   Reviewer #3 (Public Review):

   In this paper, the authors ask whether selective sweeps (as measured by the iHS and nSL statistics) are more or less likely to occur in or near genes associated with Mendelian diseases ("disease genes") than those that are not ("non-disease genes"). The main result put forward by the authors is that genes associated with Mendelian diseases are depleted for sweep signatures, as measured by the iHS and nSL statistics, relative to those which are not.

   The evidence for this comes from an empirical randomization scheme to assess whether genes with signatures of a selective sweep are more likely to be Mendelian disease genes that not. The analysis relies on a somewhat complicated sliding threshold scheme that effectively acts to incorporate evidence from both genes with very large iHS/nSL values, as well as those …

   Reviewer #3 (Public Review):

   In this paper, the authors ask whether selective sweeps (as measured by the iHS and nSL statistics) are more or less likely to occur in or near genes associated with Mendelian diseases ("disease genes") than those that are not ("non-disease genes"). The main result put forward by the authors is that genes associated with Mendelian diseases are depleted for sweep signatures, as measured by the iHS and nSL statistics, relative to those which are not.

   The evidence for this comes from an empirical randomization scheme to assess whether genes with signatures of a selective sweep are more likely to be Mendelian disease genes that not. The analysis relies on a somewhat complicated sliding threshold scheme that effectively acts to incorporate evidence from both genes with very large iHS/nSL values, as well as those with weaker signals, while upweighting the signal from those genes with the strongest iHS/nSL values. Although I think the anlaysis could be presented more clearly, it does seem like a better analysis than a simple outlier test, if for no other reason than that the sliding threshold scheme can be seen as a way of averaging over uncertainty in where one should set the threshold in an outlier test (along with some further averaging across the two different sweeps statistics, and the size of the window around disease associated genes that the sweep statistics are averaged over). That said, the particular approach to doing so is somewhat arbitrary, but it's not clear that there's a good way to avoid that.

   In addition to reporting that extreme values of iHS/nSL are generally less likely at Mendelian disease genes, the authors also report that this depletion is strongest in genes from low recombination regions, or which have >5 specific variants associated with disease.

   Drawing on this result, the authors read this evidence to imply that sweeps are generally impeded or slowed in the vicinity of genes associated with Mendelian diseases due to linkage to recessive deleterious variants, which hitchhike to high enough frequencies that the selection against homozygotes becomes an important form of interference. This phenomenon was theoretically characterized by Assaf et al 2015, who the authors point to for support. That such a phenomenon may be acting systematically to shape the process of adaptation is an interesting suggestions. It's a bit unclear to me why the authors specifically invoke recessive deleterious mutations as an explanation though. Presumably any form of interference could create the patterns they observe? This part of the paper is, as the authors acknowledge, speculative at this point.

   I'm also a bit concerned by the fact that the signal is only present in the African samples studied. The authors suggest that this is simply due to stronger drift in the history of European and Asian samples. This could be, but as a reader it's a bit frustrating to have to take this on faith.

   There are other analyses that I don't find terribly convincing. For example, one of the anlayses shows that iHS signals are no less depleted at genes associated with >5 diseases than with 1 does little to convince me of anything. It's not particularly clear that # of associated disease for a given gene should predict the degree of pleiotropy experienced by a variant emerging in that gene with some kind of adaptive function. Failure to find any association here might just mean that this is not a particularly good measure of the relevant pleiotropy.

   A last parting thought is that it's not clear to me that the authors have excluded the hypothesis that adaptive variants simply arise less often near genes associated with disease. The fact that the signal is strongest in regions of low recombination is meant to be evidence in favor of selective interference as the explanation, but it is also the regime in which sweeps should be easiest to detect, so it may be just that the analysis is best powered to detect a difference in sweep initiation, independent of possible interference dynamics, in that regime.

   Read the original source
6. 

   Version published to 10.7554/elife.69026 on eLife

   Oct 12, 2021
7. 

   Version published to 10.1101/2021.03.31.437959v1 on bioRxiv

   Mar 31, 2021