Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers
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Evaluation Summary:
The authors describe their work on an atlas of associations between polygenic scores for 129 different traits representing a variety of quantitative phenotypes and diseases, and a large set of metabolites measured in up to 83,000 participants in the UK Biobank. These associations are all available via a public browser, and may be used to identify candidate intermediate phenotypes, as well as potential biomarkers of disease.
(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 #2 and Reviewer #3 agreed to share their name with the authors.)
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Abstract
Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.
Methods:
We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.
Results:
As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS–metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings.
Conclusions:
We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas .
Funding:
This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.
Article activity feed
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Evaluation Summary:
The authors describe their work on an atlas of associations between polygenic scores for 129 different traits representing a variety of quantitative phenotypes and diseases, and a large set of metabolites measured in up to 83,000 participants in the UK Biobank. These associations are all available via a public browser, and may be used to identify candidate intermediate phenotypes, as well as potential biomarkers of disease.
(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 #2 and Reviewer #3 agreed to share their name with the authors.)
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Reviewer #1 (Public Review):
The authors report a public browser in which users can easily investigate associations between PRSs for a wide range of traits, and a large set of metabolites measured by the Nightingale platform in UKBB. This browser can potentially be used for identifying novel biomarkers for disease traits or, alternatively, for identifying novel causal pathways for traits of interest.
Overall I have no major technical concerns about the study, but I would encourage the authors to revisit whether they can find a more compelling example that can better showcase the work that they have done. I understand that this is partly a resource paper but I think the resource itself can have more impact if the paper provides a clearer use-case for how it can drive novel biological insight.
PRS construction: It's unclear how well the …
Reviewer #1 (Public Review):
The authors report a public browser in which users can easily investigate associations between PRSs for a wide range of traits, and a large set of metabolites measured by the Nightingale platform in UKBB. This browser can potentially be used for identifying novel biomarkers for disease traits or, alternatively, for identifying novel causal pathways for traits of interest.
Overall I have no major technical concerns about the study, but I would encourage the authors to revisit whether they can find a more compelling example that can better showcase the work that they have done. I understand that this is partly a resource paper but I think the resource itself can have more impact if the paper provides a clearer use-case for how it can drive novel biological insight.
PRS construction: It's unclear how well the PRS work. Should the reader prefer the stringent or lenient PRS? Perhaps there could be some validation with traits that have decent sample sizes in UKBB. Was there any filtering to remove traits with few GWS hits, low sample sizes, or low SNP heritability as these are unlikely to produce useful PRSs?
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Reviewer #2 (Public Review):
The authors set out to create an atlas of associations between phenome-wide polygenic scores and circulating lipids, fatty acids, and metabolites. To do so, they utilize GWAS from 129 traits available in the OpenGWAS database to derive polygenic (risk) scores (PRS) along with the recently released NMR metabolomics data containing 249 biomarkers (and ratios) in ~120,000 UK Biobank participants. The authors create a publicly available web portal containing PRS to NMR biomarker associations: http://mrcieu.mrsoftware.org/metabolites_PRS_atlas/.
The strength of this study is in the comprehensive nature of the atlas, containing associations for 129 traits phenome-wide, the large sample size of the UK Biobank NMR data, and the use of PRS for prioritising molecular traits for follow-up experiments, which is an …
Reviewer #2 (Public Review):
The authors set out to create an atlas of associations between phenome-wide polygenic scores and circulating lipids, fatty acids, and metabolites. To do so, they utilize GWAS from 129 traits available in the OpenGWAS database to derive polygenic (risk) scores (PRS) along with the recently released NMR metabolomics data containing 249 biomarkers (and ratios) in ~120,000 UK Biobank participants. The authors create a publicly available web portal containing PRS to NMR biomarker associations: http://mrcieu.mrsoftware.org/metabolites_PRS_atlas/.
The strength of this study is in the comprehensive nature of the atlas, containing associations for 129 traits phenome-wide, the large sample size of the UK Biobank NMR data, and the use of PRS for prioritising molecular traits for follow-up experiments, which is an emerging area of interest (International Common Disease Alliance, 2020; Ritchie et al., 2021a). To our knowledge this study is the first to explore this for circulating metabolites.
In its current form the atlas has several limitations, which should be straightforward to address. Notably, results in the current atlas may be confounded by (1) technical variation in the NMR data (Ritchie et al., 2021b), and (2) major biological determinants of biomarker concentrations, including body mass index, fasting time, and statin usage. Further, association results for two (of the 129) PRSs, systolic blood pressure (SBP) and diastolic blood pressure (DBP), are invalid (vastly inflated) as the GWASs used to construct these PRSs included UK Biobank samples.
To demonstrate how one might use these PRS to NMR biomarker associations to prioritise (or deprioritise) findings for follow-up, the authors select a biomarker of interest, glycoprotein acetyls (GlycA), to perform bi-directional Mendelian randomization to orient the direction of causal effects between GlycA and traits of associated PRS. However, the conclusions of this analysis are hampered by the heterogeneous nature of the GlycA biomarker, which captures the levels of five proteins in circulation (Otvos et al., 2015; Ritchie et al., 2019), making it a difficult target to appropriately instrument for Mendelian randomization analysis. This, however, does not detract from the broader point the authors make: that PRS can help prioritize molecular traits for experimental follow-up.
There are also several important limitations to the study which cannot be addressed, which the authors discuss appropriately in the paper. First, the NMR data does not provide a comprehensive view of the metabolome - it is heavily focussed on lipids and fatty acids. Many small metabolites in circulation cannot be measured by NMR spectroscopy, and further insights must wait for data from molecular profiling efforts planned or underway in UK Biobank (e.g. mass spectrometry). Second, the authors restricted analysis to participants of European ancestries. This a pragmatic analysis choice given (1) the PRSs were derived from GWAS performed in European ancestries, (2) PRS associations are particularly susceptible to confounding from genetic stratification and differences in environment, and (3) the very small sample sizes for which NMR data is currently available in UK Biobank participants. Finally, although a large sample size, UK Biobank is not a random sample of the population: healthy adults are over-represented, meaning PRS to metabolite associations may be different in disease cases or less healthy individuals.
Overall this study has strong potential, with straightforward to address limitations, and the resulting atlas will provide a useful characterisation of the relationships between NMR biomarkers and polygenic predisposition to various traits and diseases, which can be used by domain experts to prioritise biomarkers or traits for experimental follow-up.
References
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International Common Disease Alliance. 2020. International Common Disease Alliance Recommendations and White Paper.Otvos JD, Shalaurova I, Wolak-Dinsmore J, Connelly MA, Mackey RH, Stein JH, Tracy RP. 2015. GlycA: A Composite Nuclear Magnetic Resonance Biomarker of Systemic Inflammation. Clin Chem 61:714-723.
Ritchie SC, Kettunen J, Brozynska M, Nath AP, Havulinna AS, Männistö S, Perola M, Salomaa V, Ala-Korpela M, Abraham G, Würtz P, Inouye M. 2019. Elevated serum alpha-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality. PLoS One 14:e0223692.
Ritchie SC, Lambert SA, Arnold M, Teo SM, Lim S, Scepanovic P, Marten J, Zahid S, Chaffin M, Liu Y, Abraham G, Ouwehand WH, Roberts DJ, Watkins NA, Drew BG, Calkin AC, Di Angelantonio E, Soranzo N, Burgess S, Chapman M, Kathiresan S, Khera AV, Danesh J, Butterworth AS, Inouye M. 2021a. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. Nat Metab 3:1476-1483.
Ritchie SC, Surendran P, Karthikeyan S, Lambert SA, Bolton T, Pennells L, Danesh J, Di Angelantonio E, Butterworth AS, Inouye M. 2021b. Quality control and removal of technical variation of NMR metabolic biomarker data in ∼120,000 UK Biobank participants. medRxiv. doi:10.1101/2021.09.24.21264079
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Reviewer #3 (Public Review):
Fang et al. created an atlas for associations between the genetic liability of common risk factors or complex disorders and the abundance of small molecules as well as the characteristics of major apolipoproteins in blood. The whole study is well executed, and the statistical framework is sound. A clear strength of the study is the large array of common risk factors and disease analyzed by means of polygenic risk scores (PRS). Further, the development of an open access platform with appealing graphical display of study results is another strength of the work. Such a reference catalog can help to identify novel biomarkers for diseases and possible causative mechanisms. The authors further show, how such a systematic investigation can also help to distinguish cause from causation. For example, an inflammatory …
Reviewer #3 (Public Review):
Fang et al. created an atlas for associations between the genetic liability of common risk factors or complex disorders and the abundance of small molecules as well as the characteristics of major apolipoproteins in blood. The whole study is well executed, and the statistical framework is sound. A clear strength of the study is the large array of common risk factors and disease analyzed by means of polygenic risk scores (PRS). Further, the development of an open access platform with appealing graphical display of study results is another strength of the work. Such a reference catalog can help to identify novel biomarkers for diseases and possible causative mechanisms. The authors further show, how such a systematic investigation can also help to distinguish cause from causation. For example, an inflammatory molecule readily measured by the NMR platform and strongly associated in observational studies, is likely to be a consequence rather than a cause for common complex diseases.
However, in its current form, the study suffers from some weakness that would need to be addressed to improve the applicability of the 'atlas'. This includes a distinction of locus-specific versus real polygenic effects, that is, to what extend are findings for a PRS driven by strong single genetic variants that have been shown to have dramatic impact on small molecule concentrations in blood. Further, it is unclear how much NMR spectroscopy adds over and above established clinical biomarkers, such as LDL-cholesterol or total triglycerides. This is in particular important, since the authors do not adequately distinguish between small molecules, such as amino acids, and characteristics of lipoprotein particles, e.g., the cholesterol content of VLDL, LDL or HDL particles, the latter presenting the vast majority of measures provided by the NMR platform. Finally, the study would benefit from more intriguing or novel examples, how such an atlas could help to identify novel biomarkers or potential causal metabolites, or lipoprotein measures other than the long-established markers named in the manuscript, such as creatinine or lipoproteins.
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Author Response
Reviewer #1 (Public Review):
The authors report a public browser in which users can easily investigate associations between PGSs for a wide range of traits, and a large set of metabolites measured by the Nightingale platform in UKBB. This browser can potentially be used for identifying novel biomarkers for disease traits or, alternatively, for identifying novel causal pathways for traits of interest.
Overall I have no major technical concerns about the study, but I would encourage the authors to revisit whether they can find a more compelling example that can better showcase the work that they have done. I understand that this is partly a resource paper but I think the resource itself can have more impact if the paper provides a clearer use-case for how it can drive novel biological insight.
Many thanks for your …
Author Response
Reviewer #1 (Public Review):
The authors report a public browser in which users can easily investigate associations between PGSs for a wide range of traits, and a large set of metabolites measured by the Nightingale platform in UKBB. This browser can potentially be used for identifying novel biomarkers for disease traits or, alternatively, for identifying novel causal pathways for traits of interest.
Overall I have no major technical concerns about the study, but I would encourage the authors to revisit whether they can find a more compelling example that can better showcase the work that they have done. I understand that this is partly a resource paper but I think the resource itself can have more impact if the paper provides a clearer use-case for how it can drive novel biological insight.
Many thanks for your comments. We have undertaken a new application of bi-directional Mendelian randomization to demonstrate how users may use this approach to disentangle whether associations in our atlas likely reflect either causes or consequences of PGS traits/diseases. This example is described on page 9:
‘For example, we applied Mendelian randomization (MR) to further evaluate associations highlighted in our atlas with triglyceride-rich very low density lipoprotein (VLDL) particles. For instance, both VLDL particle average diameter size and concentration were associated with the PGS for body mass index (BMI) (Beta=0.04, 95% CI=0.033 to 0.046, P<1x10-300 & Beta=0.012, 95% CI=0.006 to 0.019, P=2.7x104 respectively) and coronary heart disease (CHD) (Beta=0.026, 95% CI=0.019 to 0.032, P<1x10-300 & Beta=0.035, 95% CI=0.028 to 0.042, P<1x10-300 respectively). Conducting bi-directional MR suggested that the associations with average diameter of VLDL particles are likely attributed to a consequence of BMI and CHD liability as opposed to the size of VLDL particles having a causal influence on these outcomes (Supplementary Table 6). In contrast, MR analyses suggested that the concentration of VLDL particles increases risk of CHD (Beta=1.28 per 1-SD change in VLDL particle concentration, 95% CI=1.25 to 1.65, P=2.8x10-7) which may explain associations between the CHD PGS and this metabolic trait within our atlas.’
and discussed in the discussion on page 21:
‘We likewise conducted bi-directional MR to demonstrate that associations between the CHD PGS and VLDL particle size likely reflect an effect of CHD liability on this metabolic trait. In contrast, the association between the CHD PGS and VLDL concentrations are likely attributed to the causal influence of this metabolic trait on CHD risk, suggesting that it is the concentration of these triglyceride-rich particles that are important in terms of the aetiology of CHD risk as opposed to their actual size. We envisage that findings from our atlas, as well as other ongoing efforts which leverage the large-scale NMR data within UKB, should facilitate further granular insight into lipoprotein lipid biology.’
PGS construction: It's unclear how well the PGS work. Should the reader prefer the stringent or lenient PGS? Perhaps there could be some validation with traits that have decent sample sizes in UKBB. Was there any filtering to remove traits with few GWS hits, low sample sizes, or low SNP heritability as these are unlikely to produce useful PGSs?
An example of validation was previously included for the chronic kidney disease PGS and its association with circulating creatinine, although this has now been removed due to the feedback you provided in your comments below. However, we have now provided the weights for all of the PGS included in our web atlas should users want to use these scores for prediction purposes (page 7):
‘The specific weights for clumped variants used in all PGS can be found at https://tinyurl.com/PGSweights.’
On page 8 we have mentioned that in this work we have used a more lenient threshold to facilitate endeavours in a ‘reverse gear Mendelian randomization’ framework. However, the option to use the more stringent threshold remains an option for users interested in this as an alternative:
‘In this paper, we have discussed findings using PGS that were derived using the more lenient criteria (i.e., P<0.05 & r2<0.1), although all findings based on both thresholds can be found in the web atlas.’
‘Specifically, we believe our findings can facilitate a ‘reverse gear Mendelian randomization’ approach to disentangle whether associations likely reflect metabolic traits acting as a cause or consequence of disease risk (Holmes and Davey Smith, 2019) as illustrated using triglyceride-rich very low density lipoprotein (VLDL) particles in the next section.’
We have not filtering based on other criteria such as the number as SNPs given that certain scores, despite only been constructed using few SNPs, may still provide useful to users. For example, our score for ‘Drinks per day’ based on the more stringent threshold (i.e. P<5x10-8) consists of only 6 SNPs. However, one of these is rs1229984, a missense variant located at the alcohol dehydrogenase ADH1B gene region and known to be a strong predictor of alcohol use (e.g. https://pubmed.ncbi.nlm.nih.gov/31745073/).
Reviewer #2 (Public Review):
The authors set out to create an atlas of associations between phenome-wide polygenic scores and circulating lipids, fatty acids, and metabolites. To do so, they utilize GWAS from 129 traits available in the OpenGWAS database to derive polygenic (risk) scores (PGS) along with the recently released NMR metabolomics data containing 249 biomarkers (and ratios) in ~120,000 UK Biobank participants. The authors create a publicly available web portal containing PGS to NMR biomarker associations:
http://mrcieu.mrsoftware.org/metabolites_PGS_atlas/.
The strength of this study is in the comprehensive nature of the atlas, containing associations for 129 traits phenome-wide, the large sample size of the UK Biobank NMR data, and the use of PGS for prioritising molecular traits for follow-up experiments, which is an emerging area of interest (International Common Disease Alliance, 2020; Ritchie et al., 2021a). To our knowledge this study is the first to explore this for circulating metabolites.
In its current form the atlas has several limitations, which should be straightforward to address. Notably, results in the current atlas may be confounded by (1) technical variation in the NMR data (Ritchie et al., 2021b), and (2) major biological determinants of biomarker concentrations, including body mass index, fasting time, and statin usage.
Firstly, thank you for the suggestion to use your ‘ukbnmr’ R package to help remove technical variations from the UK Biobank NMR metabolites data. We have applied it to remove outliers and variation in the individual data due to (1) the duration between sample preparation and sample measurement, (2) position of samples on shipment plates, (3) different equipment (spectrometers) used. This meant that we needed to re-run our entire analysis pipeline for this project from scratch to the updated dataset. Results do not appear to have drastically changed, although nonetheless we have updated results from all downstream analyses in our online web atlas using this updated dataset provided by ‘ukbnmr’.
Secondly, the reviewer is correct that biological factors, such as body mass index (BMI) and statin usage, are indeed strongly correlated with metabolites levels. However, we are not able to adjust for such biological factors directly in our analyses, given that they are potential colliders in the causal relationship between diseases/traits and metabolites. Statin usage may be caused by both the high genetic liability to coronary artery disease as well as abnormal lipoprotein lipid levels. Likewise, obesity (and changes in BMI) may result from a high genetic predisposition to cardiometabolic disorders and disrupted metabolism. Thus, adjusting for statin usage and BMI will induce collider bias (https://jamanetwork.com/journals/jama/fullarticle/2790247), which creates spurious associations between the disease/trait PGS and metabolites.
To better illustrate this issue, we have added additional text on page 14 to justify this study design decision as well as added a new figure (Figure 3) to help demonstrate this clearly to the readers. Fasting time on the other hand we believe is unlikely to act as a collider and was adjusted as a covariate in all linear regression models in this work. This is mentioned on page 25.
…Further, association results for two (of the 129) PGSs, systolic blood pressure (SBP) and diastolic blood pressure (DBP), are invalid (vastly inflated) as the GWASs used to construct these PGSs included UK Biobank samples.
Many thanks for your suggestion. We have now removed the SBP and DBP PGS from our atlas due to overlapping samples in UKB. Furthermore, our colleagues at the University of Bristol have notified us that the Glioma GWAS data obtained from the OpenGWAS platform was uploaded with incorrect effect alleles. This PGS has also been subsequently removed from the atlas. Additionally, we removed the Alzheimer’s disease (without APOE) PGS because the pleiotropic effect of lipid associated genes is now systematically examined using lipid gene excluded PGS.
To demonstrate how one might use these PGS to NMR biomarker associations to prioritise (or deprioritise) findings for follow-up, the authors select a biomarker of interest, glycoprotein acetyls (GlycA), to perform bi-directional Mendelian randomization to orient the direction of causal effects between GlycA and traits of associated PGS. However, the conclusions of this analysis are hampered by the heterogeneous nature of the GlycA biomarker, which captures the levels of five proteins in circulation (Otvos et al., 2015; Ritchie et al., 2019), making it a difficult target to appropriately instrument for Mendelian randomization analysis. This, however, does not detract from the broader point the authors make: that PGS can help prioritize molecular traits for experimental follow-up.
We have now conducted further sensitivity analyses to evaluate the genetically predicted effects of each of the five proteins in the reference you have provided. This is discussed on page 11:
‘We also conducted further sensitivity analyses given that the NMR signal of GlycA is a composite signal contributed by the glycan N-acetylglucosamine residues on five acute-phase proteins, including alpha1-acid glycoprotein, haptoglobin, alpha1-antitrypsin, alpha1-antichymotrypsin, and transferrin (Otvos et al., 2015). Using cis-acting plasma protein (where possible) and expression quantitative trait loci (pQTLs and eQTLs) as instrumental variables for these proteins (Supplementary Table 12) did not provide convincing evidence that they play a role in disease risk for associations between PGS and GlycA (Supplementary Table 13). The only effect estimate robust to multiple testing was found for higher genetically predicted alpha1-antitrypsin levels on gamma glutamyl transferase (GGT) levels (Beta=0.05 SD change in GGT per 1 SD increase in protein levels, 95% CI=0.03 to 0.07, FDR=3.6x10-3), although this was not replicated when using estimates of genetic associations with GGT levels from a larger GWAS conducted in the UK Biobank data (Beta=1.6x10-3, 95% CI=-6.9 x10-3 to 0.01, P=0.71). For details of pleiotropy robust analysis and replication results see Supplementary Table 14.’
There are also several important limitations to the study which cannot be addressed, which the authors discuss appropriately in the paper. First, the NMR data does not provide a comprehensive view of the metabolome - it is heavily focused on lipids and fatty acids. Many small metabolites in circulation cannot be measured by NMR spectroscopy, and further insights must wait for data from molecular profiling efforts planned or underway in UK Biobank (e.g. mass spectrometry). Second, the authors restricted analysis to participants of European ancestries. This a pragmatic analysis choice given (1) the PGSs were derived from GWAS performed in European ancestries, (2) PGS associations are particularly susceptible to confounding from genetic stratification and differences in environment, and (3) the very small sample sizes for which NMR data is currently available in UK Biobank participants. Finally, although a large sample size, UK Biobank is not a random sample of the population: healthy adults are over-represented, meaning PGS to metabolite associations may be different in disease cases or less healthy individuals.
Overall this study has strong potential, with straightforward to address limitations, and the resulting atlas will provide a useful characterisation of the relationships between NMR biomarkers and polygenic predisposition to various traits and diseases, which can be used by domain experts to prioritise biomarkers or traits for experimental follow-up.
Reviewer #3 (Public Review):
Fang et al. created an atlas for associations between the genetic liability of common risk factors or complex disorders and the abundance of small molecules as well as the characteristics of major apolipoproteins in blood. The whole study is well executed, and the statistical framework is sound. A clear strength of the study is the large array of common risk factors and disease analyzed by means of polygenic risk scores (PGS). Further, the development of an open access platform with appealing graphical display of study results is another strength of the work. Such a reference catalog can help to identify novel biomarkers for diseases and possible causative mechanisms. The authors further show, how such a systematic investigation can also help to distinguish cause from causation. For example, an inflammatory molecule readily measured by the NMR platform and strongly associated in observational studies, is likely to be a consequence rather than a cause for common complex diseases.
However, in its current form, the study suffers from some weakness that would need to be addressed to improve the applicability of the 'atlas'. This includes a distinction of locus-specific versus real polygenic effects, that is, to what extent are findings for a PGS driven by strong single genetic variants that have been shown to have dramatic impact on small molecule concentrations in blood.
Thank you for your suggestions to help refine our work. In line with this comment, we have repeated all analyses 1) after applying the ‘ukbnmr’ R package as recommending by reviewer #2 to remove technical variations and outliers and 2) conducted sensitivity analyses to remove an established list of lipid gene loci from PGS construction. Full results can be interrogated in the web atlas to evaluate whether PGS association may be driven by locus-specific effects at these regions, which may be particularly informative given the representation of lipoprotein lipid metabolites on the NMR panel. Findings are reported on page 19:
‘The polygenic nature of complex traits means that the inclusion of highly weighted pleiotropic genetic variants in PGS may introduce bias into genetic associations within our atlas. To provide insight into this issue, we constructed PGS excluding variants within the regions of the genome which encode the genes for 14 major regulators of NMR lipoprotein lipids signals which captured 75% of the gene-metabolite associations in the Finnish Metabolic Syndrome In Men (METSIM) cohort (Gallois et al., 2019). For details of these genes see Supplementary Table 5).
For PGS with these lipid loci excluded, anthropometric traits such as waist-to-hip ratio (N=209), waist circumference (N=206) and body mass index (N=205) still provided strong evidence of association with the majority of metabolic measurements on the NMR panel based on multiple testing corrections. Elsewhere however, the Alzheimer’s disease PGS, which was associated with 60 metabolic traits robust to P<0.05/19 in the initial analysis including these lipid loci (Supplementary Table 17), provided no convincing evidence of association with the 249 circulating metabolites after excluding the lipid loci based on the same multiple testing threshold (Supplementary Table 18). Further inspection suggested that the likely explanation for this attenuation of evidence were due to variants located within the APOE locus which are recognised to exert their influence on phenotypic traits via horizontally pleiotropic pathways (Ferguson et al., 2020).’
…Further, it is unclear how much NMR spectroscopy adds over and above established clinical biomarkers, such as LDL-cholesterol or total triglycerides. This is in particular important, since the authors do not adequately distinguish between small molecules, such as amino acids, and characteristics of lipoprotein particles, e.g., the cholesterol content of VLDL, LDL or HDL particles, the latter presenting the vast majority of measures provided by the NMR platform. Finally, the study would benefit from more intriguing or novel examples, how such an atlas could help to identify novel biomarkers or potential causal metabolites, or lipoprotein measures other than the long-established markers named in the manuscript, such as creatinine or lipoproteins.
To address these comments, we have added a new example focusing on the granular measures of VLDL particles provided by the NMR data (on top of the examples listed at the start of the response to reviewer document), which as the review points out is one of its strengths of the measures generated by this platform over long-established biomarkers (page 21):
‘We likewise conducted bi-directional MR to demonstrate that associations between the CHD PGS and VLDL particle size likely reflect an effect of CHD liability on this metabolic trait. In contrast, the association between the CHD PGS and VLDL concentrations are likely attributed to the causal influence of this metabolic trait on CHD risk, suggesting that it is the concentration of these triglyceride-rich particles that are important in terms of the aetiology of CHD risk as opposed to their actual size. We envisage that findings from our atlas, as well as other ongoing efforts which leverage the large-scale NMR data within UKB, should facilitate further granular insight into lipoprotein lipid biology.’
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