Peer review of: Multivariate genomic scan implicates novel loci and haem metabolism in human ageing (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

The relationship between healthspan and lifespan is an interesting and important area of study: 
why some people live a long time in good health where others don’t. Understanding the biological 
mechanisms at play here will inform future research in prediction and treatment. Timmers et al. 
have performed an analysis of genetic variants associated with healthspan and lifespan (and 
longevity, the extreme lifespan phenotype) in multivariate analysis to identify the variants (and 
genes and pathways) associated with healthy ageing. They identify 10 genetic loci reaching 
nominal significance in all three phenotypes: some well-known (APOE, 9p21, LDLR, and LPA) in 
cardiovascular disease, among other phenotypes, and others less-so. This is the first time, to my 
knowledge, the FOXO3 has convincingly appeared at genome-wide significance, despite the 
pathway being prominent in model organism work, and that the haem pathway has been 
implicated in healthy ageing. Overall this is an interesting and timely study, combining three 
analyses of related but different phenotypes to identify shared causes that should be of general 
interest. I have no major concerns with the approach or conclusions drawn, but have some minor 
comments: 

• I have always had some concerns about the healthspan GWAS because although it includes a 
number of disease outcomes the results are dominated by the SNPs affecting the most common 
diseases, as is clearly demonstrated in Figure 1C of this paper (Healthspan GWAS correlates very 
strongly with cardiometabolic disease). To some degree this is also true of lifespan/longevity: the 
results are dominated by cardiometabolic SNPs because it is these diseases that kill most people. I 
realise the authors somewhat discuss this in a sentence in the discussion (end of page 11, lines 
395-398) but I think this warrants much more emphasis/recognition: that GWAS in a population 
with different rates of disease/causes of death would likely find different loci. It is therefore not 
surprising that cardiovascular loci dominate the results, and confounding these with mechanisms 
of ageing (not that the authors said this explicitly) is potentially a mistake. 

• Cancer is likely less prominent only because each cancer is actually quite distinct, genetically. 
The hope that loci affecting “biological age” or similar would emerge has (so far) not really come 
to pass, perhaps with the exception of APOE and 9p21 (CDKN2B-AS1). The evidence for haem 
synthesis as an ageing pathway is compelling and an interesting novel aspect to this paper, 
although it is intriguing that the only GWAS locus linked to this pathway is LDLR, which clearly has 
a CVD-link to healthy ageing. Have the authors also considered the link between iron and infection 
susceptibility? Ferritin is a store for iron and is raised during infection as iron availability is a key 
rate-limiting step for some bacteria (i.e. if none is available, proliferation not possible). Infection 
susceptibility increases with age and this may be part of the story (i.e. decreased haem synthesis 
> increased iron availability > infection). 

• This GWAS, like so many, is limited by the additive nature of the analyses. For example on page 
8 when describing the APOE locus “an average increase in lifespan of 12.7 months per allele.” Yet 
surely the a priori for this locus is a non-additive effect, given the known recessive Alzheimer’s 
effect. I wonder what else is missed by this omission. My comment here is not suggesting the 
authors repeat the 3 published GWAS looking for non-additive effect (although, that would be 
great to see) but to acknowledge this in the paper and especially conclusions when discussing the 
low heritability of healthspan/lifespan (much is probably missed by just focussing on additive, 
mostly common, effects). 

Reviewer #2 (Remarks to the Author): 

Timmers et al present an investigation of loci affecting ageing in humans. By comparing the results 

of multiple association studies into ageing, they determine that three common measures of 
healthy ageing (healthspan, lifespan, and longevity) share substantial genetics, and are linked to 
multiple other plausible phenotypes. Given the shared genetics, Timmers et al combinine these 
ageing studies in a meta-analysis to identify multiple loci linked to a healthy / long-lived 
phenotype, some of which they report for the first time at genome-wide significance. Timmers et 
al conclude with an investigation into the possible biology underlying these loci, based on eQTL 
and gene overrepresentation analysis. This paper is an incremental advance on previous work in 
the field by the authors and others, but nonetheless its increased power over past studies and 
novel findings of genome-wide significance make it a valuable contribution. 

This paper makes heavy use of statistical genetics methodology. This is an area in which I am not 
an expert, so I have focused my review on the statistical and biological reasoning of the paper. My 
conclusion is that this is a solid body of work which is publishable if one major concern is 
addressed. 

Major concern 

The final section of the paper aims to assign biological mechanism to the loci identified in the 
earlier sections. The authors do this by first performing an eQTL colocalisation analysis vs their 
identified loci, and then running a pathway enrichment scan for the eQTL gene hits. This is a valid 
hypothesis-generating approach, however I do have a significant concern about the exact 
procedure used for the pathway enrichment analysis: as it is used currently, I believe that there is 
a high likelihood of false positive results from this procedure. In a similiar vein, due to the solely 
correlative nature of gene enrichment analysis, the discussion of this result would also benefit from 
a little moderation. 

The hypergeometric test employed by the authors relies on two assumptions which I strongly 
suspect are violated by these data: equal opportunity for eQTL colocalisation tagging across genes, 
and independence of gene selection. The authors acknowledge the former (lines 452-455) but do 
not seem to account for it in the test; for more details of the latter see eg 
https://doi.org/10.1093/bioinformatics/btm051. The overall effect of these assumptions being 
violated is a high likelihood of a false positive result. Given that the results of this analysis lead to 
a central conclusion of this paper (the association of haem metabolism with ageing), a more 
careful analysis here is needed, particularly as the haem metabolism finding seems driven almost 
exclusively by the eQTLs colocalising with a single age-associated locus. The authors should either 
undertake an analysis that is more robust to these two issues (perhaps employing permutation?), 
apply an orthogonal approach to confirm the findings of the hypergeometric test, or de-emphasise 
the pathway association and haem findings, which I see as highly tentative without additional 
evidence. 

Lines 404-410: This wording is too strong given the evidence presented. Even if my technical 
concern about the hypergeometric test is addressed, gene enrichment analysis provides at most 
suggestive evidence of a biological link. A reader might take from this section and the title that 
haem metabolism is important in human ageing, which is a conclusion that in my opinion is not 
completely supported by the evidence presented. This section should be moderated to make it 
clear that although the gene enrichment analysis is suitable as a hypothesis generating tool, it is 
premature to link haem metabolism with ageing on the basis of these results alone. 

Minor concerns 

Line 115: The citation for Therneau, Grambsch, & Fleming (1990) has suffered a typo. 

Line 150: The authors tested gender-specific survival association for lead SNPs by assuming that 

paternal and maternal effects are independent. This is a surprising assumption to me, and indeed 
my reading of Timmers 2019 suggests that many loci have sex-concordant effects. It would be 
helpful if the authors could explain in a little more detail why the assumption of independence was 
made. 

Lines 159-168: It seems a lot of tests were done here -- at least one per locus, plus the combined 
test of the loci without a significant trend. To aid interpretation of these results the authors should 
include details of what multiple testing control was applied. 

Lines 304-321: Unfortunately confidence interval lines for Figures S7 and S8 were not visible to 
me, as such I could not fully review this section. 

Lines 386-398: The argument about antagonistic pleiotropy is interesting, but I think that the 
results of the study are suggestive in this regard, not conclusive. Thus the statement that "it is 
clear that loci exhibiting antagonistic pleiotropy exist in humans" is a little strong if based on this 
study alone, and I suggest a moderation of the claims here. The authors could perhaps address 
this by discussing with more reference to the recent literature, eg dx.doi.org/10.1038/s41559-
016-0055 and dx.doi.org/10.1371/journal.pgen.1006328. 

Lines 411-419: In my opinion this paragraph adds little, and strays rather far from the questions 
posed in the Introduction. Unless a deeper investigation of haem metabolism and ageing is added 
to the results, I think the paper would be clearer without this paragraph. 

Other comments and suggestions 

It would be interesting if the authors could link their eQTL findings to the existing body of 
literature on associations between gene expression and ageing / longevity in humans and model 
organisms. 

Mark Pinese 

Reviewer #3 (Remarks to the Author): 

This manuscript performed multiple traits analysis of healthspan, lifespan and longevity and 
identified 10 loci that influencing all three traits by using summary statistics of three large cohorts: 
the HealthSpan, the Parental Lifespan and the Longevity. Among them, five are novel. There is 
suggestion of antagonistic pleiotropy. The manuscript was well written. I have some comments. 

Major 
1) The multivariate analysis of the three GWAS summary statistics resulted an inflation parameter 
1.064 from LD regression. After adjusting for this inflation parameter, it is apparently that the two 
novel loci (SLC4A7 and LINCO2513) are no longer genome wide significant. In addition, the P 
values of the novel loci are all close to 5E-8. Additional replication analysis is necessary to verify 
these novel loci. 
2) The manuscript concluded that there is antagonistic pleiotropy among the loci identified. 
However, this is just a speculation. There is no clear evidence from the current analysis. Although 
combining multiple traits will improve statistical power for detecting associated loci, the MANOVA 
analysis cannot differentiate pleiotropy or colocalization. The evidence is quite weak to claim that a 
genetic variants are associated with all three traits by restricting the variants with a P value < 0.05 

for all three traits. 
Minor: 
1) In figure 1, how the tree in c) was obtained? The sample size of the Longevity GWAS was the 
smallest. This can affect the estimation of genetic correlations. 
2) Page 4, line 113 and 114, β1,2,...,n should be β1, β 2,..., β n and X1,2,...,n should be 
X1,X2,...,Xn.