A Mendelian randomization study of the role of lipoprotein subfractions in coronary artery disease

  1. Qingyuan Zhao  Is a corresponding author
  2. Jingshu Wang
  3. Zhen Miao
  4. Nancy R Zhang
  5. Sean Hennessy
  6. Dylan S Small
  7. Daniel J Rader
  1. Statistical Laboratory, University of Cambridge, United Kingdom
  2. Department of Statistics, University of Chicago, United States
  3. Perelman School of Medicine, University of Pennsylvania, United States
  4. Department of Statistics, University of Pennsylvania, United States
  5. Department of Medicine, University of Pennsylvania, United States

Abstract

Recent genetic data can offer important insights into the roles of lipoprotein subfractions and particle sizes in preventing coronary artery disease (CAD), as previous observational studies have often reported conflicting results. We used the LD score regression to estimate the genetic correlation of 77 subfraction traits with traditional lipid profile and identified 27 traits that may represent distinct genetic mechanisms. We then used Mendelian randomization (MR) to estimate the causal effect of these traits on the risk of CAD. In univariable MR, the concentration and content of medium high-density lipoprotein (HDL) particles showed a protective effect against CAD. The effect was not attenuated in multivariable analyses. Multivariable MR analyses also found that small HDL particles and smaller mean HDL particle diameter may have a protective effect. We identified four genetic markers for HDL particle size and CAD. Further investigations are needed to fully understand the role of HDL particle size.

Introduction

Lipoprotein subfractions have been increasingly studied in epidemiological research and used in clinical practice to predict the risk of cardiovascular diseases (CVD) (Rankin et al., 2014; Mora et al., 2009; China Kadoorie Biobank Collaborative Group et al., 2018). Several studies have identified potentially novel subfraction predictors for CVD (Mora et al., 2009; Hoogeveen et al., 2014; Williams et al., 2014; Ditah et al., 2016; Lawler et al., 2017; Fischer et al., 2014) and demonstrated that the addition of subfraction measurements can significantly improve the risk prediction for CVD (Würtz et al., 2012; van Schalkwijk et al., 2014; McGarrah et al., 2016; Rankin et al., 2014). However, these observational studies often provide conflicting evidence on the precise roles of the lipoprotein subfractions. For example, while some studies suggested that small, dense low-density lipoprotein (LDL) particles may be more atherogenic (Lamarche et al., 1997; Hoogeveen et al., 2014), others found that larger LDL size is associated with higher CVD risk (Campos et al., 2001; Mora, 2009). Some recent observational studies found that the inverse association of CVD outcomes with smaller high-density lipoprotein (HDL) particles is stronger than the association with larger HDL particles (Ditah et al., 2016; Kim et al., 2016; McGarrah et al., 2016; Silbernagel et al., 2017), but other studies reached the opposite conclusion in different cohorts (Li et al., 2016; Arsenault et al., 2009). Currently, the utility of lipoprotein subfractions or particle sizes in routine clinical practice remains controversial (Superko, 2009; Mora, 2009; Davidson et al., 2011; Bays et al., 2016), as there is still a great uncertainty about their causal roles in CVD, largely due to a lack of intervention data (Bays et al., 2016).

Mendelian randomization (MR) is an useful causal inference method that avoids many common pitfalls of observational cohort studies (Smith and Ebrahim, 2003). By using genetic variation as instrumental variables, MR asks if the genetic predisposition to a higher level of the exposure (in this case, lipoprotein subfractions) is associated with higher occurrences of the disease outcome (Didelez and Sheehan, 2007). A positive association suggests a causally protective effect of the exposure if the genetic variants satisfy the instrumental variable assumptions (Didelez and Sheehan, 2007; Davey Smith and Hemani, 2014). Since MR can provide unbiased causal estimate even when there are unmeasured confounders, it is generally considered more credible than other non-randomized designs and is quickly gaining popularity in epidemiological research (Gidding et al., 2012; Davies et al., 2018). MR has been used to estimate the effect of several metabolites on CVD, but most prior studies are limited to just one or a few risk exposures at a time (Emdin et al., 2016; Ference et al., 2017).

In this study, we will use recent genetic data to investigate the roles of lipid and lipoprotein traits in the occurrence of coronary artery disease (CAD) and myocardial infarction (MI). In particular, we are interested in discovering lipoprotein subfractions that may be causal risk factors for CAD and MI in addition to the traditional lipid profile (LDL cholesterol, HDL cholesterol, and triglycerides levels). To this end, we will first estimate the genetic correlation of the lipoprotein subfractions and particle sizes with the tradition risk factors and remove the traits that have a high genetic correlation. We will then use MR to estimate the causal effects of the selected lipoprotein subfractions and particle sizes on CAD and MI. Finally, we will explore potential genetic markers for the identified lipoprotein and subfraction traits.

Materials and methods

GWAS summary datasets and lipoprotein particle measurements

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Table 1 describes all GWAS summary datasets used in this study, including two GWAS of the traditional lipid risk factors (Willer et al., 2013; Hoffmann et al., 2018), two recent GWAS of the human lipidome (Kettunen et al., 2016; Davis et al., 2017), and three GWAS of CAD or MI (Nikpay et al., 2015; Nelson et al., 2017; Abbott et al., 2018). In the two GWAS of the lipidome (Kettunen et al., 2016; Davis et al., 2017), high-throughput nuclear magnetic resonance (NMR) spectroscopy was used to measure the circulating lipid and lipoprotein traits (Soininen et al., 2009). We investigated the 82 lipid and lipoprotein traits measured in these studies that are related to very-low-density lipoprotein (VLDL), LDL, intermediate-density lipoprotein (IDL), and HDL subfractions and particle sizes. All the subfraction traits are named with three components that are separated by hyphens: the first component indicates the size (XS, S, M, L, XL, XXL); the second component indicates the fraction according to the lipoprotein density (VLDL, LDL, IDL, HDL); the third component indicates the measurement (C for total cholesterol, CE for cholesterol esters, FC for free cholesterol, L for total lipids, P for particle concentration, PL for phospholipids, TG for triglycerides). For example, M-HDL-P refers to the concentration of medium HDL particles.

Table 1
Information about the GWAS summary datasets used in this article.

The columns are the phenotypes reported by the GWAS studies, the consortium or name of the first author of the publication, PubMed ID, population, sample size, other GWAS datasets with other lapping sample, and URLs we used to download the datasets.

PhenotypeDataset namePubMed IDPopulationSample sizeSample overlap with other datasetsURL to summary dataset
Traditional lipid traitsGERA29507422 Hoffmann et al., 2018Multi-ethnic94,674ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/
GLGC24097068 Willer et al., 2013European188,578Kettunen, CARDIoGRAMplusC4Dhttp://csg.sph.umich.edu/abecasis/public/lipids2013/
Lipoprotein subfraction traitsDavis29084231 Davis et al., 2017Finnish8372http://csg.sph.umich.edu/boehnke/public/metsim-2017-lipoproteins/
Kettunen27005778 Kettunen et al., 2016European24,925GLGC, CARDIoGRAMplusC4Dhttp://www.computationalmedicine.fi/data#NMR_GWAS
Heart disease traitsCARDIoGRAMplusC4D (CAD)26343387 Nikpay et al., 2015Mostly European185,000GLGC, Kettunenhttp://www.cardiogramplusc4d.org/data-downloads/
CARDIoGRAMplusC4D + UK Biobank (CAD)28714975 Nelson et al., 2017Mostly European
UK Biobank (MI)Interim round two release Abbott et al., 2018European360,420http://www.nealelab.is/uk-biobank/

Aside from the concentration and content of lipoprotein subfractions, the two lipidome GWAS also measured the traditional lipid traits (TG, LDL-C, HDL-C), the average diameter of the fractions (VLDL-D, LDL-D, HDL-D) and the concentration of apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB). A full list of the lipoprotein measurements investigated in this article can be found in Appendix 1.

Genetic correlation and phenotypic screening

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Genetic correlation is a measure of association between the genetic determinants of two phenotypes. It is conceptually different from epidemiological correlation that can be directly estimated from cross-sectional data. In this study, we applied the LD-score regression (Bulik-Sullivan et al., 2015) to the lipidome GWAS (Kettunen et al., 2016; Davis et al., 2017) to estimate the genetic correlations between the lipoprotein subfractions, particle sizes, and traditional risk factors. We then removed lipoprotein subfractions and particle sizes that are strongly correlated with the traditional risk factors, defined as an estimated genetic correlation > 0.8 with TG, LDL-C, HDL-C, ApoB, or ApoA1 in the GWAS published by Davis et al., 2017. Because these traits are largely co-determined with the traditional risk factors, they do not represent independent biological mechanisms and may lead to multicollinearity issues in multivariate MR analyses. Finally, we obtained an independent estimate of the genetic correlations between the selected traits by applying the LD score regression to the GWAS published by Kettunen et al., 2016. We used Bonferroni's procedure to correct for multiple testing (familywise error rate at 0.05).

Three-sample Mendelian randomization design

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For MR, we employed a three-sample design (Zhao et al., 2019b) in which one GWAS was used to select independent genetic instruments that are associated with one or several lipoprotein measures. The other two GWAS were then used to obtain summary associations of the selected SNPs with the exposure and the outcome, as in a typical two-sample MR design (Pierce and Burgess, 2013; Hemani et al., 2016). More specifically, the selection GWAS was used to create a set of SNPs that are in linkage equilibrium with each other in a reference panel (distance >10 megabase pairs, r2<0.001). This was done by ordering the SNPs by the p-values of their association with the trait(s) under investigation and then selecting them greedily using the linkage-disequilibrium (LD) clumping function in the PLINK software package (Purcell et al., 2007). To avoid winner's curse, we require the other two GWAS to have no overlapping sample with the selection GWAS.

As the GWAS published by Davis et al., 2017 has a smaller sample size, we used it to select the genetic instruments so the larger dataset can be used for statistical estimation. In univariable MR, associations of the selected SNPs with the exposure trait (a lipoprotein subfraction or a particle size trait) were obtained from the GWAS published by Kettunen et al., 2016 and the associations with MI were obtained using summary data from an interim release of UK BioBank (Abbott et al., 2018). To maximize the statistical power, we used the so-called ‘genome-wide MR’ design. Independent SNPs are selected by using LD clumping, but we do not truncate the list of SNPs by their p-values. More details about this design can be found in a previous methodological article (Zhao et al., 2019b).

To control for potential pleiotropic effects via the traditional risk factors, we performed two multivariable MR analyses for each lipoprotein subfraction or particle size under investigation. The first multivariable MR analysis considers four exposures: TG, LDL-C, HDL-C, and the lipoprotein measurement under investigation. The second multivariable MR analysis replaces LDL-C and HDL-C with ApoB and ApoA1, in accordance with some recent studies (Richardson et al., 2020). SNPs were ranked by their minimum p-values with the four exposures and are selected as instruments only if they were associated with at least one of the four exposures (p-value 10-4). Both multivariable MR analyses used the Davis (Davis et al., 2017) and GERA (Hoffmann et al., 2018) datasets for instrument selection, the Kettunen (Kettunen et al., 2016) and GLGC (Willer et al., 2013) datasets for the associations of the instruments with the exposures, and the CARDIoGRAMplusC4D + UK Biobank (Nelson et al., 2017) dataset for the associations with CAD.

Statistical estimation

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For univariable MR, we used the robust adjusted profile score (RAPS) because it is more efficient and robust than many conventional methods (Zhao et al., 2020; Zhao et al., 2019b). RAPS can consistently estimate the causal effect even when some of the genetic variants violate instrumental variables assumptions. For multivariable MR, we used an extension to RAPS called GRAPPLE to obtain the causal effect estimates of multiple exposures (Wang et al., 2020). GRAPPLE also allows the exposure GWAS to have overlapping sample with the outcome GWAS, while the original RAPS does not. We assessed the strength of the instruments using the modified Cochran's Q statistic (Sanderson et al., 2019). Because many lipoprotein subfraction traits were analyzed simultaneously, we used the Benjamini-Hochberg procedure to correct for multiple testing (Benjamini and Hochberg, 1995) and the false discovery rate was set to be 0.05. More detail about the statistical methods can be found in Appendix 3.

Table 2
Results of some multivariable Mendelian randomization analyses.

Each row in the table corresponds to a multivariable MR analysis with traditional lipid profile and the specified lipoprotein subfraction or particle size trait. Reported numbers are the point estimates and 95% confidence intervals of the exposure effect.

TraitEffect of TGEffect of LDL-CEffect of HDL-CEffect of subfraction/particle size
None0.19 [0.09,0.29]0.38 [0.33,0.44]−0.053 [-0.13,0.03]
M-HDL-P0.37 [0.22,0.52]0.39 [0.32,0.45]0.30 [0.08,0.52]−0.69 [-1.09,–0.3]
S-HDL-P0.23 [0.12,0.33]0.45 [0.38,0.52]−0.11 [-0.2,–0.02]−0.33 [-0.52,–0.15]
HDL-D0.11 [0.00,0.22]0.42 [0.36,0.49]−0.44 [-0.69,–0.2]0.33 [0.11,0.56]
Effect of TGEffect of ApoBEffect of ApoA1Effect of Subfraction/Particle size
None0.05 [-0.05,0.14]0.49 [0.38,0.60]−0.095 [-0.21,0.02]
M-HDL-P−0.00 [-0.18,0.17]0.50 [0.31,0.69]0.13 [-0.06,0.32]−0.47 [-0.80,–0.15]
S-HDL-P0.07 [-0.03,0.17]0.53 [0.41,0.65]−0.13 [-0.25,–0.02]−0.24 [-0.40,–0.08]
HDL-D0.06 [-0.04,0.15]0.61 [0.47,0.76]−0.46 [-0.73,–0.19]0.30 [0.08,0.52]

Genetic markers for lipoprotein subfractions and CAD

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To obtain genetic markers, we selected SNPs that are associated with the lipoprotein measurements identified in the MR (p-value 5×10-8) and CAD (p-value 0.05) but are not associated with LDL-C or ApoB (p-value 10-3). To maximize the power of this exploratory analysis, we meta-analyzed the results of the two lipidome GWAS (Kettunen et al., 2016; Davis et al., 2017) by inverse-variance weighting. For the associations with LDL-C and CAD, we used the GWAS summary data reported by the GLGC (Willer et al., 2013) and CARDIoGRAMplusC4D (Nelson et al., 2017) consortia. We used LD clumping to obtain independent markers (Purcell et al., 2007) and then validate the markers using tissue-specific gene expression data from the GTEx project.

Sensitivity analysis and replicability

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Because we had multiple GWAS summary datasets for the lipoprotein subfractions and CAD/MI (Table 1), we swapped the roles of the GWAS datasets in the three-sample MR design whenever permitted by the statistical methods to obtain multiple statistical estimates. These estimates are not completely independent of the primary results, but they can nonetheless be used to assess replicability. As a sensitivity analysis, We further analyzed univariable MR using inverse-variance weighting (IVW) (Burgess et al., 2013) and weighted median (Bowden et al., 2016) and compared with the primary results obtained by RAPS. We also assessed the assumptions made by RAPS using some diagnostic plots suggested in previous methodological articles (Zhao et al., 2019b).

Results

Genetic correlations and phenotypic screening

We obtained the genetic correlations of the lipoprotein subfractions and particle sizes with the traditional lipid risk factors: TG, LDL-C, HDL-C, ApoB, and ApoA1 (Table 1). We found that almost all VLDL subfractions traits (besides those related to very small VLDL subfraction) and the mean VLDL particle diameter have an estimated genetic correlation with TG very close to 1. Most traits related to the large and very large HDL subfractions also have a high genetic correlation with HDL-C and ApoA1.

After removing traits that are strongly correlated with the traditional risk factors, we obtained 27 traits that may involve independent genetic mechanisms. Figure 1 shows the genetic correlation matrix for these traits and the traditional lipid factors. The selected traits can be divided into two groups based on whether they are related to VLDL/LDL/IDL particles or HDL particles. Within each group, most traits were strongly correlated with the others. In the first group, most traits had a positive genetic correlation with LDL-C and ApoB, while in the second group, most traits had a positive genetic correlation with HDL-C and ApoA1. Exceptions include LDL-D, which had a negative but statistically non-significant genetic correlation with LDL-C and ApoB, and S-HDL-P and S-HDL-L, which showed no or weak genetic correlation with HDL-C and ApoA1.

Genetic correlation matrix of the 27 lipoprotein subfraction traits selected in phenotypic screening and five traditional lipid traits.

White asterisk indicates the correlation is statistically significant after Bonferroni correction for multiple comparisons at level 0.05.

Mendelian randomization

Figure 2 shows the estimated causal effect of the selected lipoprotein measurements on MI or CAD that are statistically significant (false discovery rate = 0.05). The unfiltered results can be found in Appendix 3, which also contains results of the sensitivity and replicability analyses.

Results of the Mendelian randomization analyses (false discover rate = 0.05): Estimated odds ratio [95% confidence interval] per standard deviation increase of the selected lipoprotein measurements on MI or CAD.

The concentration and lipid content of VLDL, LDL, and IDL subfractions showed harmful and nearly uniform effects on MI in univariable MR. However, after adjusting for the traditional lipid risk factors, the effects of these ApoB-related subfractions become close to zero (besides IDL-FC in one multivariable analysis). The mean diameter of LDL particles (LDL-D) showed a harmful effect on MI in univariable MR, though the effect was smaller than those of the LDL subfractions in univariable MR. The estimated effect of LDL-D was attenuated in the multivariable MR analyses.

The concentration and content of medium HDL particles showed protective effects in univariable and multivariable MR analyses. In particular, adjusting for the traditional lipid risk factors did not attenuate the effect of traits related to medium HDL. The concentration of and total lipid in small HDL particles showed protective effects in multivariable MR analyses, though the effect sizes were smaller than those of the medium HDL traits. The mean diameter of HDL particles (HDL-D) had almost no effect on MI in the univariable MR analysis, but after adjusting for the traditional lipid risk factors, it showed a harmful effect.

Table 2 reports the estimated effects of M-HDL-P, S-HDL-P, HDL-D, and traditional lipid traits (TG, LDL-C, HDL-C, ApoB, ApoA1) in the multivariable MR analyses. To better understand the role of HDL subfractions and particle sizes, we also included in the table the results of the multivariate MR analyses for the traditional lipid risk factors only. Those baseline analyses suggested that HDL-C/ApoA1 had a weak, non-significant protective effect on CAD, which is consistent with prior studies (Holmes et al., 2015; Wang et al., 2020). Adding S-HDL-P to the MR analysis did not substantially alter the estimated effects of the traditional lipid traits. However, when M-HDL-P or HDL-D was included in the model, the estimated effects of M-HDL-P and HDL-D changed substantially. In particular, when M-HDL-P was included in the multivariable MR analyses, HDL-C/ApoA1 showed a harmful effect on CAD. When HDL-D was included, HDL-C/ApoA1 showed a protective effect.

Genetic markers associated with HDL subfractions and CAD

We identified four genetic variants that are associated with S-HDL-P, M-HDL-P, or HDL-D, not associated with LDL-C or ApoB, and associated with CAD: rs838880 (SCARB1), rs737337 (DOCK6), rs2943641 (IRS1), and rs6065904 (PLTP) (Figure 3). These SNP-cis gene pairs are also supported by examining expression quantitative trait loci (eQTL) in the tissue-specific GTEx data (Appendix 4). The first three variants were not associated with S-HDL-P. However, they had uniformly positive associations with M-HDL-P, L-HDL-P, XL-HDL-P, HDL-D, ApoA1, and HDL-C, and a negative association with CAD. The last variant rs6065904 had positive associations with S-HDL-P and M-HDL-P, negative associations with L-HDL-P, XL-HDL-P, HDL-D, negative but smaller associations with ApoA1 and HDL-C, and a negative association with CAD.

Genetic markers for HDL size (with risk alleles) and their associations with various lipid traits.

Sensitivity and replicability analysis

We also investigated the effects of lipoprotein subfractions and particle sizes on MI/CAD using multiple GWAS datasets, MR designs and statistical methods. The results are provided in Appendix 3 and are generally in agreement with the primary results reported above. The diagnostic plots for S-HDL-P and M-HDL-P did not suggest evidence of violations of the instrument strength independent of direct effect (InSIDE) assumption (Bowden et al., 2015) made by RAPS and GRAPPLE (Appendix 4).

Discussion

By using recent genetic data and MR, this study examines whether some lipoprotein subfractions and particle sizes, beyond the traditional lipid risk factors, may play a role in coronary artery disease. We find that VLDL subfractions have extremely high genetic correlations with blood triglyceride level and thus offer little extra value. We find some weak evidence that larger LDL particle size may have a small harmful effect on myocardial infarction and coronary artery disease.

Our main finding is that the size of HDL particles may play an important and previously undiscovered role. Although the concentration and lipid content of small and medium HDL particles appear to be positively correlated with HDL cholesterol and ApoA1, their genetic correlations are much smaller than 1, indicating possible independent biological pathway(s). Moreover, the MR analyses suggested that the small and medium HDL particles may have protective effects on CAD. We also find that larger HDL mean particle diameter may have a harmful effect on CAD. Finally, we identified four potential genetic markers for HDL particle size that are independent of LDL cholesterol and ApoB.

There has been a heated debate on the role of HDL particles in CAD in recent years following the failure of several trials for CETP inhibitors (Barter et al., 2007; Schwartz et al., 2012; Lincoff et al., 2017) and recombinant ApoA1 (Nicholls et al., 2018) targeting HDL cholesterol. Observational epidemiology studies have long demonstrated strong inverse association between HDL cholesterol and the risk of CAD or MI (Miller and Miller, 1975; Lewington et al., 2007; Di Angelantonio et al., 2009), but conflicting evidence has been found in MR studies. In an influential study, Voight and collaborators found that the genetic variants associated with HDL cholesterol had varied associations with CAD and that almost all variants suggesting a protective effect of HDL cholesterol were also associated with LDL cholesterol or triglycerides (Voight et al., 2012). Other MR studies also found that the effect of HDL cholesterol on CAD is heterogeneous (Zhao et al., 2019b) or attenuated after adjusting for LDL cholesterol and triglycerides (Holmes et al., 2017; White et al., 2016).

Notice that the harmful effect of larger HDL particle diameter found in this study relies on including HDL-C or ApoA1 in the multivariable MR analysis. Thus, the role of HDL particles in preventing CAD may be more complicated than, for example, that of LDL cholesterol or ApoB. It is possible that HDL cholesterol, HDL subfractions, and HDL particle size are all phenotypic markers for some underlying causal mechanism. A related theory is the HDL function hypothesis (Rader and Hovingh, 2014). Cholesterol efflux capacity, a measure of HDL function, has been documented as superior to HDL-C in predicting CVD risk (Rohatgi et al., 2014; Saleheen et al., 2015). Recent epidemiologic studies found that HDL particle size is positively associated with cholesterol efflux capacity in post-menopausal women (El Khoudary et al., 2016) and in an asymptomatic older cohort (Mutharasan et al., 2017). However, mechanistic efflux studies showed that small HDL particles actually mediate more cholesterol efflux (Favari et al., 2009; Du et al., 2015). A likely explanation of this seeming contradiction is that a high concentration of small HDL particles in the serum may mark a block in maturation of small HDL particles (Mutharasan et al., 2017). This can also partly explain our finding that small HDL traits have a smaller effect than medium HDL traits, as increased medium HDL might indicate successful maturation of small HDL particles.

Among the reported genetic markers, SCARB1 and PLTP have established relations to HDL metabolism and CAD. SCARB1 encodes a plasma membrane receptor for HDL and is involved in hepatic uptake of cholesterol from peripheral tissues. Recently, a rare mutation (P376L) of SCARB1 was reported to raise HDL-C level and increase CAD risk (Zanoni et al., 2016; Samadi et al., 2019). This is opposite direction to the conventional belief that HDL-C is protective and could be explained by HDL dysfunction. PLTP encodes the phospholipid transfer protein and mediates the transfer of phospholipid and cholesterol from LDL and VLDL to HDL. As a result, PLTP plays a complex but pivotal role in HDL particle size and composition. Several studies have suggested that high PLTP activity is a risk factor for CAD (Schlitt et al., 2003; Schlitt et al., 2009; Zhao et al., 2019a).

Our study should be viewed in the context of its limitations, in particular, the inherent limitations of the summary-data MR design. Any causal inference from non-experimental data makes unverifiable assumptions, so does our study. Conventional MR studies assume that the genetic variants are valid instrumental variables. The statistical methods used by us make less stringent assumptions about the instrumental variables, but those assumptions could still be violated even though our model diagnosis does not suggest evidence against the InSIDE assumption. Our study did not adjust for other risk factors for CAD such as body mass index, blood pressure, and smoking. All the GWAS datasets used in this study are from the European population, so the same conclusions might not generalize to other populations. Furthermore, our study used GWAS datasets from heterogeneous subpopulations, which may also introduce bias (Zhao et al., 2019c). We also did not use more than one subfraction traits as exposures in multivariable MR because of their high genetic correlations. Alternative statistical methods could be used to select the best causal risk factor from high-throughput experiments (Zuber et al., 2019). Finally, as pointed out by revieweres, triglycerides has a greater intra-individual biological variability than HDL particle size. It is likely that triglycerides and HDL size represent a gene/environment interaction with a very large environmental component. Further investigations are needed to fully understand this mechanism.

Recently, a NMR spectroscopy method has been developed to estimate HDL cholesterol efflux capacity from serum (Kuusisto et al., 2019). That method can form the basis of a genetic analysis of HDL cholesterol efflux capacity and may complement the results here. We believe more laboratorial and epidemiological research is needed to clarify the roles of HDL subfractions and particle size in cardiovascular diseases.

Appendix 1

Lipid and lipoprotein traits

Two published GWAS of the human lipidome [Kettunen2016, Davis2017] measured lipoprotein subfractions and particle sizes using NMR spectroscopy. We investigated the 82 lipid and lipoprotein traits measured in these studies that are related to very-low-density lipoprotein (VLDL), LDL, and HDL subfractions and particle sizes. All the subfraction traits are named using three components separated by hyphen: the first indicates the size (XS, S, M, L, XL, XXL); the second indicates the category according to the lipoprotein density (VLDL, LDL, IDL, HDL); the third indicates the measurement (C for total cholesterol, CE for cholesterol esters, FC for free cholesterol, L for total lipids, P for particle concentration, PL for phospholipids, TG for triglycerides). A full list of lipid and lipoprotein traits used in our study can be found in Appendix 1—table 1 below.

Appendix 1—table 1
All 82 traits included in this study and whether they are measured in the Kettunen and Davis GWAS (NA means not available).
TraitDescriptionKettunenDavis
VLDL traits and total triglycerides
TGTotal triglycerides
VLDL-DVLDL diameter
XS-VLDL-LTotal lipids in very small VLDLNA
XS-VLDL-PConcentration of very small VLDL particles
XS-VLDL-PLPhospholipids in very small VLDL
XS-VLDL-TGTriglycerides in very small VLDL
S-VLDL-CTotal cholesterol in small VLDL
S-VLDL-FCFree cholesterol in small VLDL
S-VLDL-LTotal lipids in small VLDLNA
S-VLDL-PConcentration of small VLDL particles
S-VLDL-PLPhospholipids in small VLDL
S-VLDL-TGTriglycerides in small VLDL
M-VLDL-CTotal cholesterol in medium VLDL
M-VLDL-CECholesterol esters in medium VLDL
M-VLDL-FCFree cholesterol in medium VLDL
M-VLDL-LTotal lipids in medium VLDLNA
M-VLDL-PConcentration of medium VLDL particles
M-VLDL-PLPhospholipids in medium VLDL
M-VLDL-TGTriglycerides in medium VLDL
L-VLDL-CTotal cholesterol in large VLDL
L-VLDL-CECholesterol esters in large VLDL
L-VLDL-FCFree cholesterol in large VLDL
L-VLDL-LTotal lipids in large VLDLNA
L-VLDL-PConcentration of large VLDL particles
L-VLDL-PLPhospholipids in large VLDL
L-VLDL-TGTriglycerides in large VLDL
XL-VLDL-LTotal lipids in very large VLDLNA
XL-VLDL-PConcentration of very large VLDL particles
XL-VLDL-PLPhospholipids in very large VLDL
XL-VLDL-TGTriglycerides in very large VLDL
XXL-VLDL-LTotal lipids in chylomicrons and extremely very large VLDLNA
XXL-VLDL-PConcentration of chylomicrons and extremely very large VLDL particles
XXL-VLDL-PLPhospholipids in chylomicrons and extremely very large
XXL-VLDL-TGTriglycerides in chylomicrons and extremely very large
LDL and IDL traits
LDL-CTotal cholesterol in LDL
ApoBApolipoprotein B
LDL-DLDL diameter
S-LDL-CTotal cholesterol in small LDL
S-LDL-LTotal lipids in small LDLNA
S-LDL-PPhospholipids in small LDL
M-LDL-CTotal cholesterol in medium LDL
M-LDL-CECholesterol esters in medium LDL
M-LDL-LTotal lipids in medium LDLNA
M-LDL-PConcentration of medium LDL particles
M-LDL-PLPhospholipids in medium LDL
L-LDL-CTotal cholesterol in large LDL
L-LDL-CECholesterol esters in large LDL
L-LDL-FCFree cholesterol in large LDL
L-LDL-LTotal lipids in large LDLNA
L-LDL-PConcentration of large LDL particles
L-LDL-PLPhospholipids in large LDL
IDL-CTotal cholesterol in IDL
IDL-FCFree cholesterol in IDL
IDL-LTotal lipids in IDLNA
IDL-PConcentration of IDL particles
IDL-PLPhospholipids in IDL
IDL-TGTriglycerides in IDL
HDL traits
HDL-CTotal cholesterol in HDL
ApoA1Apolipoprotein A1
HDL-DHDL diameter
S-HDL-LTotal lipids in small HDLNA
S-HDL-PConcentration of small HDL particles
S-HDL-TGTriglycerides in small HDL
M-HDL-CTotal cholesterol in medium HDL
M-HDL-CECholesterol esters in medium HDL
M-HDL-FCFree cholesterol in medium HDL
M-HDL-LTotal lipids in medium HDLNA
M-HDL-PConcentration of medium HDL particles
M-HDL-PLPhospholipids in medium HDL
L-HDL-CTotal cholesterol in large HDL
L-HDL-CECholesterol esters in large HDL
L-HDL-FCFree cholesterol in large HDL
L-HDL-LTotal lipids in large HDLNA
L-HDL-PConcentration of large HDL particles
L-HDL-PLPhospholipids in large HDL
XL-HDL-CTotal cholesterol in very large HDL
XL-HDL-CECholesterol esters in very large HDL
XL-HDL-FCFree cholesterol in very large HDL
XL-HDL-LTotal lipids in very large HDLNA
XL-HDL-PConcentration of very large HDL particles
XL-HDL-PLPhospholipids in very large HDL
XL-HDL-TGTriglycerides in very large HDL

Appendix 2

Genetic correlations

We estimated the genetic correlation between lipoprotein subfractions, particle sizes, and traditional lipid risk factors using the LD score regression (Li et al., 2016). Appendix 2—figure 13 show the estimated genetic correlation matrix between selected traits using different datasets. Below the figures, Appendix 2—table 1 shows the estimated genetic correlations of the lipoprotein subbfractions with the traditional lipid risk factors using the Davis GWAS. The results in Appendix 2—table 1 were then used to screen the traits as described in Materials and methods.

Appendix 2—figure 1
Genetic correlations computed using the Davis et al., 2017 GWAS summary dataset.
Appendix 2—figure 2
Genetic correlations computed using the Kettunen et al., 2016 GWAS summary dataset.
Appendix 2—figure 3
Genetic correlations computed by meta-analyzing the results in Appendix 2—figures 1 and 2.
Appendix 2—table 1
Estimated genetic correlation (standard error) of the lipoprotein subfractions with the traditional lipid risk factors using the Davis GWAS.

Bolded estimates are above 0.8 and the corresponding traits were removed in phenotypic screening.

TraitApoA1ApoBHDL-CLDL-CTG
S-HDL-L0.31 (0.28)0.34 (0.25)0.13 (0.26)0.27 (0.3)0.2 (0.22)
S-HDL-P0.36 (0.24)0.27 (0.22)−0.01 (0.22)0.1 (0.31)0.48 (0.17)
S-HDL-TG−0.13 (0.25)0.77 (0.13)−0.66 (0.15)0.13 (0.28)1.03 (0.07)
M-HDL-C0.65 (0.14)−0.18 (0.2)0.81 (0.09)−0.09 (0.25)−0.34 (0.17)
M-HDL-CE0.68 (0.14)−0.23 (0.21)0.57 (0.12)−0.24 (0.24)−0.32 (0.18)
M-HDL-FC0.67 (0.12)−0.08 (0.21)0.83 (0.08)0.04 (0.24)−0.28 (0.18)
M-HDL-L0.71 (0.15)0.02 (0.27)0.52 (0.17)−0.03 (0.29)−0.19 (0.25)
M-HDL-P0.75 (0.12)0.15 (0.23)0.46 (0.14)0.08 (0.26)0 (0.19)
M-HDL-PL0.69 (0.13)0.04 (0.22)0.65 (0.11)0.02 (0.25)−0.04 (0.19)
L-HDL-C0.76 (0.11)−0.42 (0.13)0.95 (0.02)−0.1 (0.18)−0.62 (0.09)
L-HDL-CE0.82 (0.1)−0.4 (0.12)0.93 (0.04)−0.16 (0.17)−0.62 (0.09)
L-HDL-FC0.66 (0.12)−0.46 (0.13)0.92 (0.03)−0.13 (0.18)−0.7 (0.08)
L-HDL-L0.81 (0.11)−0.29 (0.15)0.74 (0.07)−0.15 (0.18)−0.56 (0.12)
L-HDL-P0.79 (0.09)−0.35 (0.13)0.82 (0.05)−0.12 (0.16)−0.61 (0.09)
L-HDL-PL0.77 (0.09)−0.34 (0.13)0.79 (0.05)−0.12 (0.17)−0.61 (0.09)
XL-HDL-C0.75 (0.16)−0.25 (0.19)0.9 (0.1)0.4 (0.27)−0.63 (0.13)
XL-HDL-CE0.82 (0.16)−0.17 (0.19)0.82 (0.09)0.41 (0.27)−0.54 (0.12)
XL-HDL-FC0.72 (0.14)−0.37 (0.18)0.94 (0.08)0.17 (0.23)−0.71 (0.11)
XL-HDL-L0.93 (0.16)−0.08 (0.25)0.68 (0.14)0.1 (0.27)−0.35 (0.2)
XL-HDL-P0.81 (0.13)−0.32 (0.16)0.86 (0.08)0.17 (0.21)−0.69 (0.11)
XL-HDL-PL0.76 (0.12)−0.41 (0.15)0.83 (0.07)−0.09 (0.18)−0.7 (0.09)
XL-HDL-TG0.72 (0.13)0.49 (0.17)0.33 (0.13)0.13 (0.26)0.3 (0.15)
HDL-D0.7 (0.11)−0.36 (0.13)0.8 (0.06)−0.08 (0.17)−0.64 (0.09)
IDL-C0.38 (0.21)0.58 (0.19)0.07 (0.19)0.8 (0.14)0.39 (0.17)
IDL-FC0.23 (0.2)0.78 (0.12)−0.05 (0.17)0.61 (0.19)0.44 (0.15)
IDL-L0.38 (0.23)0.65 (0.18)0.05 (0.2)0.64 (0.2)0.47 (0.17)
IDL-P0.31 (0.2)0.66 (0.14)−0.04 (0.17)0.82 (0.13)0.49 (0.14)
IDL-PL0.25 (0.23)0.83 (0.1)−0.12 (0.19)0.7 (0.19)0.64 (0.15)
IDL-TG0.22 (0.18)0.82 (0.08)−0.2 (0.13)0.56 (0.15)0.67 (0.08)
S-LDL-C0.11 (0.28)0.66 (0.18)−0.16 (0.22)0.44 (0.34)0.58 (0.14)
S-LDL-L0.26 (0.23)0.66 (0.17)−0.06 (0.21)0.62 (0.21)0.58 (0.13)
S-LDL-P0.34 (0.2)0.68 (0.15)−0.02 (0.19)0.63 (0.18)0.58 (0.13)
M-LDL-C0.15 (0.26)0.63 (0.18)0.22 (0.22)0.87 (0.08)0.13 (0.23)
M-LDL-CE0.3 (0.23)0.61 (0.2)0.05 (0.21)0.65 (0.2)0.45 (0.16)
M-LDL-L0.29 (0.22)0.63 (0.18)0.01 (0.21)0.66 (0.19)0.5 (0.15)
M-LDL-P0.29 (0.23)0.63 (0.18)−0.01 (0.21)0.65 (0.21)0.51 (0.15)
M-LDL-PL0.2 (0.24)0.69 (0.16)0.11 (0.2)0.89 (0.06)0.18 (0.22)
L-LDL-C0.25 (0.24)0.58 (0.21)0.25 (0.22)0.68 (0.19)0.23 (0.21)
L-LDL-CE0.3 (0.23)0.58 (0.22)0.05 (0.21)0.65 (0.21)0.41 (0.17)
L-LDL-FC0.31 (0.24)0.57 (0.22)0.33 (0.23)0.7 (0.18)0.13 (0.23)
L-LDL-L0.31 (0.23)0.61 (0.2)0.04 (0.21)0.65 (0.21)0.44 (0.17)
L-LDL-P0.31 (0.23)0.63 (0.19)0.02 (0.21)0.65 (0.21)0.47 (0.16)
L-LDL-PL0.27 (0.25)0.61 (0.2)0.24 (0.22)0.67 (0.2)0.27 (0.2)
LDL-D−0.33 (0.25)−0.22 (0.23)−0.15 (0.21)−0.15 (0.29)−0.37 (0.16)
XS-VLDL-L0.25 (0.23)0.8 (0.08)−0.2 (0.17)0.61 (0.14)0.73 (0.09)
XS-VLDL-P0.17 (0.18)0.83 (0.07)−0.26 (0.13)0.57 (0.13)0.71 (0.07)
XS-VLDL-PL0.21 (0.19)0.78 (0.09)−0.15 (0.15)0.74 (0.14)0.57 (0.11)
XS-VLDL-TG0.06 (0.18)0.83 (0.08)−0.37 (0.11)0.56 (0.13)0.85 (0.04)
S-VLDL-FC−0.08 (0.2)0.94 (0.05)−0.49 (0.12)0.59 (0.12)0.92 (0.03)
S-VLDL-L−0.12 (0.24)0.7 (0.08)−0.46 (0.15)0.5 (0.14)0.8 (0.05)
S-VLDL-P−0.09 (0.19)0.78 (0.07)−0.48 (0.11)0.5 (0.14)0.95 (0.02)
S-VLDL-PL−0.03 (0.2)0.82 (0.08)−0.43 (0.12)0.44 (0.17)0.92 (0.03)
S-VLDL-TG−0.1 (0.2)0.9 (0.08)−0.49 (0.11)0.49 (0.15)0.98 (0.01)
S-VLDL-C0.01 (0.2)0.9 (0.06)−0.39 (0.13)0.61 (0.15)0.89 (0.05)
M-VLDL-C−0.01 (0.2)0.8 (0.09)−0.47 (0.12)0.41 (0.18)0.95 (0.02)
M-VLDL-CE0.01 (0.19)0.78 (0.08)−0.43 (0.12)0.5 (0.15)0.9 (0.03)
M-VLDL-FC0 (0.21)0.83 (0.09)−0.48 (0.12)0.4 (0.18)0.97 (0.01)
M-VLDL-L−0.1 (0.24)0.66 (0.11)−0.48 (0.15)0.4 (0.18)0.8 (0.05)
M-VLDL-P−0.06 (0.19)0.78 (0.1)−0.46 (0.12)0.43 (0.16)0.98 (0.02)
M-VLDL-PL0.03 (0.21)0.85 (0.09)−0.48 (0.12)0.4 (0.18)0.98 (0.01)
M-VLDL-TG−0.02 (0.21)0.82 (0.11)−0.5 (0.13)0.33 (0.19)0.98 (0.02)
L-VLDL-C−0.05 (0.2)0.83 (0.12)−0.55 (0.12)0.36 (0.19)1 (0.02)
L-VLDL-CE0 (0.19)0.78 (0.12)−0.44 (0.12)0.43 (0.19)0.93 (0.03)
L-VLDL-FC−0.03 (0.2)0.84 (0.12)−0.53 (0.13)0.36 (0.19)1 (0.02)
L-VLDL-L−0.06 (0.24)0.66 (0.14)−0.47 (0.16)0.36 (0.2)0.86 (0.05)
L-VLDL-P−0.02 (0.21)0.72 (0.12)−0.44 (0.13)0.33 (0.18)0.98 (0.02)
L-VLDL-PL0.01 (0.21)0.86 (0.12)−0.53 (0.13)0.3 (0.2)1.04 (0.03)
L-VLDL-TG−0.06 (0.21)0.78 (0.12)−0.54 (0.13)0.26 (0.19)1 (0.02)
XL-VLDL-L−0.08 (0.24)0.7 (0.15)−0.52 (0.16)0.43 (0.2)0.85 (0.05)
XL-VLDL-P−0.06 (0.2)0.76 (0.12)−0.48 (0.13)0.44 (0.18)0.95 (0.03)
XL-VLDL-PL−0.09 (0.23)0.82 (0.13)−0.62 (0.15)0.32 (0.21)1.06 (0.04)
XL-VLDL-TG−0.14 (0.21)0.86 (0.13)−0.65 (0.13)0.34 (0.19)1.03 (0.04)
XXL-VLDL-L−0.07 (0.25)0.65 (0.16)−0.5 (0.17)0.38 (0.22)0.83 (0.06)
XXL-VLDL-P0.17 (0.2)0.72 (0.15)−0.3 (0.15)0.39 (0.21)0.86 (0.07)
XXL-VLDL-PL−0.3 (0.24)0.66 (0.17)−0.8 (0.16)0.22 (0.21)1.06 (0.06)
XXL-VLDL-TG−0.21 (0.25)0.64 (0.16)−0.7 (0.15)0.22 (0.22)1.08 (0.05)
VLDL-D−0.22 (0.2)0.55 (0.14)−0.53 (0.12)0.12 (0.19)0.86 (0.04)

Appendix 3

Mendelian randomization

We implemented several Mendelian randomization (MR) designs and statistical methods to estimate the causal effect of lipoprotein subfractions and particles sizes on coronary artery disease. In general, we adopted the three-sample summary data MR design described in Zhao et al., 2019b, Wang et al., 2020 and we swapped the roles of the GWAS datasets whenever permitted by the statistical methods. More specifically, the statistical methods we used for univariable MR (RAPS, IVW, weighted median) require that the GWAS datasets for obtaining instruments, SNP effects on the exposure, and SNP effects on the outcome must have no overlapping sample. The multivariable MR method we used (GRAPPLE) allows the exposure and outcome GWAS to be dependent and estimates the proportion of overlapping sample. However, GRAPPLE still requires that the selection GWAS uses an non-overlapping sample.

The MR designs we implemented in this study are summarized in Appendix 3—table 1. We considered two ways of instrument selection for univariable MR. In ‘traditional selection’, the traditional lipid traits were used to select the instruments for the corresponding subfraction traits. That is, HDL-C was used to select SNPs for HDL subfractions and particle size, LDL-C for IDL and LDL subfractions and particle size, and TG for VLDL subfractions and particle size. This tends to select more instruments because the GWAS for traditional lipid traits had a larger sample size. In ‘subfraction selection’, the instrumental SNPs were selected for each lipoprotein subfraction and particle size using the same or closest trait in the selection GWAS. For example, if the exposure under investigation is S-HDL-L but it is not measured in the Davis GWAS (if it is used for selection), S-HDL-P is used instead for instrument selection.

For multivariable MR, we considered two models with different sets of exposures: TG, LDL-C, HDL-C, and the subfraction/particle size under investigation; TG, ApoB, ApoA1, and the subfraction/particle size under investigation. SNPs were selected as potential instruments if they were associated (p-value 10-4) with at least one of the four exposures. LD clumping was then used to obtain independent instruments, as described in Materials and Methods.

We briefly comment on the statistical methods used in univariable MR. All the three methods we used—RAPS, IVW, weighted median—require that the exposure GWAS and outcome GWAS have non-overlapping samples. RAPS and weighted median can provide consistent estimate of the causal effect even when some of the genetic variants are not valid instruments, provided that the direct effects of the genetic variants are independent of the strength of their associations with the exposure. The last condition is called the Instrument Strength Independent of Direct Effect (InSIDE) assumption in the MR literature [bowden2015mendelian]. RAPS is also robust to idiosyncratically large direct effect (Bowden et al., 2015). Because IVW and weighted median can be severely biased by weak instruments (Zhao et al., 2020), we only used them with the set of SNPs that have genome-wide significant association (p-value 5×10-8) with the exposure. In comparison, RAPS does not suffer from weak instrument bias and we used it with all the SNPs obtained by LD clumping without any p-value threshold.

Below, Appendix 3—figure 1 shows the MR results for the 27 lipoprotein measurements selected in phenotypic screening. Estimates that are statistically significant at a false discovery rate of 0.05 are shown in Figure 2 of the main paper. Appendix 3—table 2 shows the estimated effect of all the lipoprotein subfractions and particle sizes on myocardial infarction or coronary artery disease in various MR designs. Full results of the multivariable MR analyses, including the estimated effects of the traditional lipid risk factors, can be found in Appendix 3—tables 5 and 6. The results of the univariable MR analyses using IVW and weighted median estimators can be found in Appendix 3—tables 3 and 4.

Appendix 3—table 1
Three-sample Mendelian randomization designs.
MR designSelectionExposureOutcomeReported in
Univariable(traditional selection)GERADavisCARDIoGRAMplusC4DAppendix 3—table 24
GERADavisUK BiobankAppendix 3—table 24
GERAKettunenUK BiobankAppendix 3—table 24
GLGCDavisUK BiobankAppendix 3—table 24
Univariable(subfraction selection)DavisKettunenUK BiobankFigure 2; Appendix 3—figure 1 and Appendix 3—table 24
KettunenDavisUK BiobankAppendix 3—figure 1 and Appendix 3—table 24
MultivariableDavis, GERAKettunen, GLGCCARDIoGRAMplusC4D+ UK BiobankFigure 2, Table 2; Appendix 3—figure 1 and Appendix 3—table 24

Pooled results

Appendix 3—figure 1
Mendelian randomization results for the 27 lipoprotein measurements selected in phenotypic screening.

In the tables below, Red indicates p-value is significant (at level 0.05) after Bonferroni correction for all the results in the corresponding table and blue indicates p-value ≤ 0.05.

Appendix 3—table 2
Mendelian randomization results using all selected SNPs (univariable MR using RAPS and multivariable MR using GRAPPLE).
Method: RAPS/GRAPPLE + All SNPs
ScreeningGERAGERAGERAGLGCDavisKettunenGERA + DavisGERA + Davis
ExposureDavisDavisKettunenDavisKettunenDavisGLGC + KettunenGLGC + Kettunen
OutcomeCADUKBUKBUKBUKBUKBCAD + UKBCAD + UKB
AdjustedHDL-C + LDL-C + TGApoA1 + ApoB + TG
VLDL traits
TG0.258 (0.053)0.296 (0.075)NA0.262 (0.06)NA0.289 (0.068)NANA
VLDL-D-0.099 (0.049)0.028 (0.074)0.072 (0.073)0.116 (0.065)-0.163 (0.067)-0.204 (0.071)-0.588 (0.094)-0.32 (0.112)
XS-VLDL-LNANA0.368 (0.064)NA0.429 (0.059)NA0.132 (0.119)0.084 (0.141)
XS-VLDL-P0.17 (0.031)0.26 (0.048)0.367 (0.065)0.248 (0.047)0.429 (0.06)0.338 (0.056)0.118 (0.125)0.061 (0.158)
XS-VLDL-PL0.191 (0.034)0.284 (0.055)0.386 (0.069)0.278 (0.052)0.449 (0.049)0.435 (0.049)0.159 (0.12)0.253 (0.135)
XS-VLDL-TG0.201 (0.034)0.3 (0.053)0.388 (0.068)0.283 (0.046)0.372 (0.063)0.326 (0.055)-0.157 (0.187)-0.248 (0.15)
S-VLDL-C0.294 (0.06)0.343 (0.076)NA0.322 (0.063)NA0.424 (0.094)-1.035 (0.323)-1.265 (0.568)
S-VLDL-FC0.243 (0.051)0.303 (0.068)0.389 (0.079)0.286 (0.056)0.489 (0.071)0.416 (0.074)-1.027 (0.337)-0.489 (0.213)
S-VLDL-LNANA0.356 (0.075)NA0.376 (0.072)NA-0.898 (0.28)-1.629 (0.586)
S-VLDL-P0.226 (0.047)0.288 (0.068)0.343 (0.074)0.261 (0.054)0.359 (0.069)0.271 (0.094)-1.245 (0.463)-1.644 (0.606)
S-VLDL-PL0.228 (0.047)0.294 (0.067)0.372 (0.074)0.273 (0.054)0.365 (0.066)0.336 (0.063)-0.613 (0.182)-1.213 (0.478)
S-VLDL-TG0.223 (0.049)0.283 (0.071)0.323 (0.073)0.25 (0.055)0.327 (0.071)0.275 (0.067)NaN-0.301 (0.108)
M-VLDL-C0.253 (0.053)0.304 (0.078)0.327 (0.074)0.276 (0.06)0.368 (0.07)0.312 (0.079)-1.433 (0.451)-0.373 (0.118)
M-VLDL-CE0.248 (0.051)0.309 (0.074)0.344 (0.077)0.285 (0.058)0.369 (0.073)0.295 (0.069)-1.035 (0.293)-0.995 (0.338)
M-VLDL-FC0.245 (0.058)0.283 (0.082)0.31 (0.076)0.259 (0.063)0.341 (0.069)0.341 (0.068)-1.412 (0.444)-0.799 (0.311)
M-VLDL-LNANA0.311 (0.079)NA0.358 (0.078)NA-1.878 (0.75)-0.298 (0.098)
M-VLDL-P0.25 (0.062)0.282 (0.083)0.305 (0.081)0.247 (0.065)0.293 (0.089)0.269 (0.065)-1.974 (0.745)-0.312 (0.096)
M-VLDL-PL0.248 (0.056)0.295 (0.077)0.318 (0.075)0.259 (0.06)0.351 (0.071)0.31 (0.063)-2.012 (0.943)-0.297 (0.106)
M-VLDL-TG0.205 (0.064)0.248 (0.087)0.3 (0.082)0.224 (0.067)0.275 (0.092)0.246 (0.074)-2.133 (0.879)-0.806 (0.455)
L-VLDL-C0.299 (0.067)0.304 (0.1)0.297 (0.081)0.291 (0.077)0.289 (0.085)0.317 (0.077)-1.254 (0.297)-0.609 (0.337)
L-VLDL-CE0.247 (0.061)0.282 (0.088)0.282 (0.082)0.282 (0.072)0.285 (0.082)0.3 (0.112)-1.081 (0.282)-0.673 (0.217)
L-VLDL-FC0.316 (0.076)0.294 (0.108)0.311 (0.083)0.287 (0.081)0.351 (0.087)0.298 (0.078)-1.274 (0.308)-0.619 (0.291)
L-VLDL-LNANA0.36 (0.096)NA0.32 (0.102)NA-1.277 (0.313)-0.532 (0.278)
L-VLDL-P0.268 (0.073)0.287 (0.103)0.281 (0.085)0.262 (0.075)0.219 (0.086)0.255 (0.082)-1.357 (0.344)-0.617 (0.229)
L-VLDL-PL0.322 (0.071)0.318 (0.102)0.346 (0.089)0.283 (0.077)0.397 (0.101)0.351 (0.076)NaN-0.287 (0.104)
L-VLDL-TG0.243 (0.077)0.238 (0.104)0.332 (0.094)0.246 (0.08)0.26 (0.103)0.324 (0.082)-1.428 (0.372)-0.252 (0.091)
XL-VLDL-LNANA0.289 (0.098)NA0.435 (0.14)NA-1.069 (0.203)-0.577 (0.249)
XL-VLDL-P0.27 (0.074)0.262 (0.099)0.281 (0.093)0.279 (0.084)0.404 (0.122)0.251 (0.084)-1.209 (0.238)-0.373 (0.109)
XL-VLDL-PL0.446 (0.09)0.344 (0.13)0.31 (0.093)0.361 (0.118)0.375 (0.12)0.408 (0.102)-1.214 (0.257)-0.583 (0.268)
XL-VLDL-TG0.294 (0.092)0.229 (0.109)0.261 (0.094)0.284 (0.095)0.365 (0.111)0.319 (0.093)-1.071 (0.205)-0.603 (0.248)
XXL-VLDL-LNANA0.397 (0.108)NA0.312 (0.108)NA-1.355 (0.318)-0.402 (0.144)
XXL-VLDL-P0.308 (0.08)0.327 (0.096)0.378 (0.097)0.297 (0.088)0.32 (0.101)0.227 (0.073)-1.639 (0.502)-1.089 (0.449)
XXL-VLDL-PL0.338 (0.091)0.346 (0.103)0.342 (0.103)0.351 (0.103)0.282 (0.114)0.317 (0.086)-1.259 (0.262)-0.814 (0.344)
XXL-VLDL-TG0.384 (0.108)0.374 (0.124)0.348 (0.1)0.433 (0.121)0.304 (0.138)0.359 (0.18)-1.202 (0.262)-1.075 (0.402)
IDL/LDL traits
LDL-C0.523 (0.043)0.512 (0.053)0.514 (0.042)0.473 (0.055)0.435 (0.048)0.464 (0.048)NA0.319 (0.182)
ApoB0.605 (0.056)0.55 (0.062)0.551 (0.052)0.543 (0.069)0.61 (0.066)0.613 (0.06)-0.532 (0.191)NA
LDL-D0.271 (0.215)0.452 (0.299)2.064 (0.233)0.831 (0.684)0.328 (0.073)0.201 (0.055)0.145 (0.061)0.119 (0.071)
S-LDL-C0.624 (0.053)0.589 (0.061)0.539 (0.048)0.537 (0.067)0.474 (0.056)0.48 (0.05)-0.282 (0.152)0.08 (0.238)
S-LDL-LNANA0.561 (0.047)NA0.473 (0.057)NA-0.251 (0.145)-0.005 (0.29)
S-LDL-P0.621 (0.057)0.581 (0.065)0.56 (0.049)0.558 (0.073)0.459 (0.061)0.546 (0.063)-0.266 (0.151)-0.362 (0.596)
M-LDL-C0.648 (0.055)0.607 (0.062)0.545 (0.044)0.545 (0.068)0.455 (0.049)0.557 (0.054)-0.271 (0.162)-0.169 (0.909)
M-LDL-CE0.643 (0.056)0.601 (0.062)0.564 (0.042)0.545 (0.069)0.467 (0.05)0.55 (0.055)-0.088 (0.188)NaN
M-LDL-LNANA0.559 (0.042)NA0.461 (0.049)NA-0.069 (0.191)NaN
M-LDL-P0.638 (0.056)0.597 (0.062)0.557 (0.043)0.54 (0.069)0.472 (0.051)0.46 (0.05)-0.179 (0.174)0.432 (0.31)
M-LDL-PL0.658 (0.063)0.605 (0.067)0.556 (0.047)0.571 (0.077)0.506 (0.053)0.559 (0.057)-0.407 (0.162)-0.566 (0.839)
L-LDL-C0.627 (0.053)0.577 (0.059)0.515 (0.042)0.504 (0.063)0.465 (0.048)0.488 (0.052)-0.059 (0.261)NaN
L-LDL-CE0.638 (0.055)0.589 (0.06)0.555 (0.041)0.514 (0.065)0.463 (0.049)0.493 (0.054)0.116 (0.321)0.461 (0.213)
L-LDL-FC0.609 (0.051)0.557 (0.057)0.503 (0.041)0.491 (0.06)0.468 (0.047)0.457 (0.052)0.223 (0.315)NaN
L-LDL-LNANA0.543 (0.04)NA0.468 (0.047)NA0.167 (0.273)NaN
L-LDL-P0.606 (0.052)0.559 (0.058)0.545 (0.041)0.49 (0.062)0.484 (0.046)0.494 (0.048)0.084 (0.213)NaN
L-LDL-PL0.61 (0.053)0.558 (0.058)0.515 (0.043)0.492 (0.063)0.528 (0.048)0.502 (0.052)-0.036 (0.195)NaN
IDL-C0.596 (0.054)0.55 (0.059)0.562 (0.042)0.481 (0.064)0.511 (0.047)0.423 (0.051)0.192 (0.229)0.769 (0.501)
IDL-FC0.586 (0.054)0.539 (0.059)0.525 (0.044)0.494 (0.063)0.44 (0.044)0.402 (0.05)0.19 (0.156)0.33 (0.127)
IDL-LNANA0.57 (0.043)NA0.494 (0.048)NA0.148 (0.175)0.444 (0.328)
IDL-P0.566 (0.052)0.536 (0.059)0.575 (0.044)0.488 (0.065)0.434 (0.049)0.412 (0.051)0.153 (0.148)0.292 (0.173)
IDL-PL0.583 (0.052)0.533 (0.058)0.532 (0.045)0.489 (0.064)0.471 (0.047)0.396 (0.05)0.153 (0.18)0.406 (0.184)
IDL-TG0.603 (0.066)0.595 (0.075)0.658 (0.063)0.567 (0.085)0.432 (0.056)0.315 (0.053)0.11 (0.103)0.047 (0.135)
HDL traits
HDL-C-0.117 (0.031)-0.199 (0.045)-0.136 (0.055)-0.317 (0.052)-0.045 (0.059)-0.108 (0.05)NANaN
ApoA1-0.119 (0.042)-0.193 (0.06)0.023 (0.058)-0.264 (0.071)0.075 (0.064)-0.13 (0.068)-0.481 (0.271)NA
HDL-D-0.008 (0.027)-0.124 (0.041)0.004 (0.046)-0.092 (0.048)0.067 (0.045)0.007 (0.041)0.333 (0.114)0.296 (0.1)
S-HDL-LNANA-0.098 (0.095)NA-0.037 (0.085)NA-0.312 (0.106)-0.224 (0.087)
S-HDL-P-0.265 (0.084)-0.362 (0.113)-0.13 (0.092)-0.317 (0.119)-0.053 (0.081)-0.08 (0.094)-0.331 (0.095)-0.24 (0.083)
S-HDL-TG0.354 (0.072)0.386 (0.088)0.65 (0.089)0.475 (0.097)0.351 (0.087)0.283 (0.073)0.253 (0.637)-0.044 (0.466)
M-HDL-C-0.323 (0.058)-0.43 (0.079)-0.364 (0.085)-0.376 (0.091)-0.46 (0.104)-0.434 (0.075)-0.508 (0.165)-0.442 (0.143)
M-HDL-CE-0.333 (0.058)-0.458 (0.078)-0.372 (0.09)-0.385 (0.087)-0.542 (0.105)-0.443 (0.071)-0.487 (0.157)-0.413 (0.137)
M-HDL-FC-0.275 (0.065)-0.319 (0.08)-0.262 (0.083)-0.313 (0.092)-0.313 (0.094)-0.409 (0.082)-0.649 (0.225)-0.408 (0.166)
M-HDL-LNANA-0.311 (0.095)NA-0.474 (0.123)NA-0.606 (0.188)-0.485 (0.155)
M-HDL-P-0.298 (0.06)-0.394 (0.086)-0.273 (0.101)-0.373 (0.1)-0.565 (0.131)-0.307 (0.079)-0.694 (0.204)-0.472 (0.166)
M-HDL-PL-0.265 (0.058)-0.346 (0.083)-0.25 (0.09)-0.335 (0.096)-0.358 (0.104)-0.3 (0.072)-0.632 (0.191)-0.486 (0.171)
L-HDL-C-0.067 (0.03)-0.144 (0.044)-0.139 (0.051)-0.144 (0.05)-0.147 (0.052)-0.049 (0.045)0.516 (0.213)0.575 (0.204)
L-HDL-CE-0.063 (0.03)-0.144 (0.044)-0.116 (0.051)-0.149 (0.051)-0.134 (0.051)-0.094 (0.047)0.519 (0.23)0.61 (0.206)
L-HDL-FC-0.082 (0.03)-0.144 (0.045)-0.114 (0.053)-0.128 (0.053)-0.13 (0.051)-0.03 (0.047)0.518 (0.181)0.59 (0.148)
L-HDL-LNANA-0.108 (0.05)NA-0.132 (0.052)NA0.457 (0.189)0.541 (0.184)
L-HDL-P-0.071 (0.028)-0.146 (0.042)-0.111 (0.05)-0.13 (0.049)-0.083 (0.05)-0.1 (0.043)0.422 (0.191)0.476 (0.155)
L-HDL-PL-0.087 (0.029)-0.161 (0.043)-0.141 (0.051)-0.142 (0.051)-0.105 (0.053)-0.092 (0.044)0.443 (0.202)0.51 (0.169)
XL-HDL-C0.055 (0.046)-0.013 (0.068)0.11 (0.066)0.064 (0.073)0.048 (0.069)0.112 (0.068)0.474 (0.223)0.565 (0.196)
XL-HDL-CE0.064 (0.044)0.006 (0.066)0.129 (0.066)0.08 (0.07)0.057 (0.068)0.046 (0.075)0.426 (0.177)0.511 (0.206)
XL-HDL-FC0.009 (0.039)-0.05 (0.059)0.066 (0.058)-0.026 (0.067)0.102 (0.06)0.049 (0.066)0.433 (0.16)0.609 (0.159)
XL-HDL-LNANA0.073 (0.055)NA0.038 (0.058)NA0.358 (0.154)0.481 (0.141)
XL-HDL-P0.038 (0.033)-0.022 (0.049)0.112 (0.057)0.017 (0.056)0.083 (0.055)0.023 (0.057)0.41 (0.139)0.39 (0.135)
XL-HDL-PL0.029 (0.031)-0.031 (0.046)0.037 (0.05)0.005 (0.055)0.038 (0.052)0.013 (0.046)0.343 (0.118)0.466 (0.12)
XL-HDL-TG0.092 (0.027)0.112 (0.041)0.14 (0.047)0.135 (0.047)0.191 (0.042)0.136 (0.039)0.147 (0.074)0.165 (0.086)

Univariable MR results

Appendix 3—table 3
Mendelian randomization results using genome-wide significant SNPs and inverse variance weighted (IVW) estimator.
Method: IVW + Significant SNPs
SelectionGERAGERAGERAGLGCDavisKettunen
ExposureDavisDavisKettunenDavisKettunenDavis
OutcomeCADUKBUKBUKBUKBUKB
VLDL traits
TG0.184 (0.051)0.278 (0.076)NA0.309 (0.074)NA0.207 (0.064)
VLDL-D0.044 (0.06)0.052 (0.09)0.038 (0.102)0.118 (0.091)-0.083 (0.16)-0.083 (0.138)
XS-VLDL-LNANA0.353 (0.08)NA0.372 (0.083)NA
XS-VLDL-P0.162 (0.04)0.256 (0.059)0.352 (0.081)0.273 (0.063)0.374 (0.084)0.373 (0.095)
XS-VLDL-PL0.165 (0.046)0.262 (0.069)0.37 (0.088)0.27 (0.075)0.443 (0.048)0.401 (0.07)
XS-VLDL-TG0.179 (0.041)0.277 (0.061)0.362 (0.082)0.288 (0.062)0.335 (0.076)0.314 (0.08)
S-VLDL-C0.237 (0.053)0.343 (0.08)NA0.339 (0.083)NA0.443 (0.116)
S-VLDL-FC0.21 (0.05)0.307 (0.076)0.344 (0.098)0.314 (0.076)0.262 (0.122)0.397 (0.116)
S-VLDL-LNANA0.318 (0.095)NA0.27 (0.106)NA
S-VLDL-P0.188 (0.049)0.274 (0.074)0.311 (0.093)0.29 (0.072)0.266 (0.103)0.331 (0.142)
S-VLDL-PL0.198 (0.048)0.291 (0.072)0.342 (0.091)0.3 (0.072)0.281 (0.089)0.331 (0.125)
S-VLDL-TG0.174 (0.051)0.255 (0.076)0.296 (0.094)0.28 (0.073)0.261 (0.102)0.262 (0.093)
M-VLDL-C0.188 (0.053)0.265 (0.08)0.305 (0.096)0.287 (0.077)0.361 (0.078)0.32 (0.134)
M-VLDL-CE0.203 (0.051)0.285 (0.077)0.32 (0.098)0.295 (0.076)0.264 (0.094)0.291 (0.125)
M-VLDL-FC0.165 (0.056)0.233 (0.084)0.292 (0.098)0.27 (0.08)0.3 (0.084)0.303 (0.104)
M-VLDL-LNANA0.265 (0.104)NA0.357 (0.096)NA
M-VLDL-P0.153 (0.056)0.214 (0.085)0.276 (0.104)0.258 (0.081)0.322 (0.092)0.268 (0.074)
M-VLDL-PL0.163 (0.054)0.23 (0.082)0.296 (0.097)0.266 (0.078)0.302 (0.084)0.289 (0.095)
M-VLDL-TG0.14 (0.058)0.196 (0.087)0.268 (0.107)0.247 (0.083)0.327 (0.093)0.245 (0.091)
L-VLDL-C0.177 (0.06)0.24 (0.091)0.288 (0.106)0.286 (0.089)0.108 (0.223)0.31 (0.084)
L-VLDL-CE0.178 (0.057)0.245 (0.087)0.262 (0.105)0.279 (0.086)0.182 (0.187)0.299 (0.077)
L-VLDL-FC0.176 (0.063)0.242 (0.094)0.295 (0.108)0.298 (0.091)0.321 (0.101)0.314 (0.082)
L-VLDL-LNANA0.291 (0.119)NA0.125 (0.232)NA
L-VLDL-P0.164 (0.062)0.227 (0.093)0.269 (0.108)0.275 (0.09)0.332 (0.127)0.247 (0.076)
L-VLDL-PL0.173 (0.061)0.23 (0.092)0.308 (0.115)0.284 (0.088)0.32 (0.127)0.302 (0.079)
L-VLDL-TG0.149 (0.063)0.202 (0.095)0.268 (0.118)0.267 (0.092)0.33 (0.131)0.302 (0.08)
XL-VLDL-LNANA0.263 (0.123)NA0.365 (0.286)NA
XL-VLDL-P0.149 (0.063)0.206 (0.095)0.247 (0.122)0.268 (0.096)0.346 (0.28)0.245 (0.077)
XL-VLDL-PL0.176 (0.067)0.243 (0.101)0.292 (0.119)0.323 (0.101)0.333 (0.265)0.344 (0.133)
XL-VLDL-TG0.151 (0.066)0.205 (0.1)0.241 (0.12)0.282 (0.1)0.323 (0.272)0.249 (0.081)
XXL-VLDL-LNANA0.356 (0.127)NA-0.165 (0.425)NA
XXL-VLDL-P0.228 (0.067)0.35 (0.099)0.372 (0.119)0.376 (0.098)-0.12 (0.389)0.006 (0.153)
XXL-VLDL-PL0.211 (0.07)0.31 (0.105)0.275 (0.125)0.399 (0.107)-0.145 (0.395)0.071 (0.191)
XXL-VLDL-TG0.221 (0.067)0.3 (0.102)0.292 (0.126)0.415 (0.104)0.09 (0.36)0.349 (0.303)
IDL/LDL traits
LDL-C0.427 (0.049)0.431 (0.054)0.409 (0.077)0.409 (0.054)0.416 (0.099)0.422 (0.063)
ApoB0.506 (0.058)0.525 (0.065)0.474 (0.093)0.473 (0.064)0.636 (0.092)0.569 (0.071)
LDL-D0.217 (0.151)0.423 (0.161)1.121 (0.178)0.271 (0.143)0.309 (0.126)0.211 (0.081)
S-LDL-C0.481 (0.056)0.467 (0.063)0.445 (0.087)0.438 (0.063)0.44 (0.128)0.436 (0.076)
S-LDL-LNANA0.44 (0.09)NA0.456 (0.132)NA
S-LDL-P0.501 (0.059)0.494 (0.068)0.449 (0.093)0.472 (0.067)0.49 (0.139)0.588 (0.097)
M-LDL-C0.475 (0.057)0.457 (0.064)0.426 (0.08)0.427 (0.064)0.418 (0.111)0.436 (0.087)
M-LDL-CE0.485 (0.058)0.47 (0.065)0.432 (0.078)0.436 (0.064)0.43 (0.107)0.444 (0.085)
M-LDL-LNANA0.43 (0.08)NA0.43 (0.11)NA
M-LDL-P0.479 (0.057)0.465 (0.064)0.437 (0.081)0.44 (0.064)0.413 (0.122)0.439 (0.093)
M-LDL-PL0.5 (0.063)0.49 (0.071)0.437 (0.087)0.464 (0.07)0.443 (0.132)0.497 (0.099)
L-LDL-C0.449 (0.055)0.436 (0.061)0.432 (0.076)0.411 (0.061)0.409 (0.106)0.417 (0.076)
L-LDL-CE0.464 (0.056)0.451 (0.062)0.426 (0.075)0.422 (0.062)0.416 (0.102)0.433 (0.077)
L-LDL-FC0.425 (0.054)0.411 (0.059)0.424 (0.074)0.393 (0.059)0.387 (0.105)0.394 (0.078)
L-LDL-LNANA0.427 (0.074)NA0.407 (0.103)NA
L-LDL-P0.448 (0.054)0.442 (0.06)0.435 (0.075)0.421 (0.059)0.413 (0.104)0.424 (0.075)
L-LDL-PL0.444 (0.056)0.438 (0.061)0.441 (0.078)0.423 (0.061)0.42 (0.109)0.429 (0.076)
IDL-C0.447 (0.055)0.455 (0.059)0.451 (0.075)0.433 (0.06)0.439 (0.085)0.422 (0.07)
IDL-FC0.429 (0.055)0.439 (0.059)0.468 (0.075)0.414 (0.059)0.431 (0.081)0.402 (0.074)
IDL-LNANA0.467 (0.075)NA0.445 (0.085)NA
IDL-P0.443 (0.055)0.467 (0.06)0.48 (0.077)0.45 (0.059)0.446 (0.088)0.426 (0.071)
IDL-PL0.429 (0.055)0.443 (0.059)0.473 (0.078)0.427 (0.059)0.435 (0.092)0.407 (0.069)
IDL-TG0.461 (0.07)0.518 (0.076)0.625 (0.098)0.494 (0.073)0.342 (0.085)0.34 (0.123)
HDL traits
HDL-C-0.085 (0.044)-0.156 (0.057)-0.146 (0.085)-0.195 (0.06)-0.082 (0.159)-0.015 (0.109)
ApoA1-0.072 (0.054)-0.155 (0.071)-0.036 (0.09)-0.194 (0.074)0.001 (0.192)0.066 (0.158)
HDL-D-0.027 (0.042)-0.071 (0.058)-0.052 (0.073)-0.092 (0.063)0.073 (0.098)0.074 (0.074)
S-HDL-LNANA-0.064 (0.148)NA-0.033 (0.092)NA
S-HDL-P-0.117 (0.087)-0.172 (0.116)-0.13 (0.146)-0.298 (0.117)-0.033 (0.09)-0.115 (0.174)
S-HDL-TG0.224 (0.063)0.317 (0.082)0.496 (0.107)0.344 (0.085)0.334 (0.096)0.286 (0.17)
M-HDL-C-0.214 (0.062)-0.327 (0.078)-0.48 (0.111)-0.39 (0.079)-0.423 (0.175)-0.39 (0.159)
M-HDL-CE-0.227 (0.062)-0.338 (0.077)-0.497 (0.111)-0.4 (0.078)-0.435 (0.194)-0.341 (0.238)
M-HDL-FC-0.158 (0.065)-0.272 (0.084)-0.341 (0.117)-0.337 (0.085)-0.288 (0.218)-0.278 (0.144)
M-HDL-LNANA-0.436 (0.125)NA-0.514 (0.223)NA
M-HDL-P-0.172 (0.066)-0.292 (0.087)-0.414 (0.132)-0.361 (0.089)-0.386 (0.307)-0.18 (0.118)
M-HDL-PL-0.161 (0.064)-0.275 (0.085)-0.38 (0.126)-0.345 (0.087)-0.419 (0.301)-0.2 (0.099)
L-HDL-C-0.047 (0.044)-0.097 (0.059)-0.124 (0.08)-0.133 (0.063)0.022 (0.106)0.021 (0.105)
L-HDL-CE-0.049 (0.044)-0.098 (0.059)-0.12 (0.079)-0.137 (0.063)0.023 (0.112)0.004 (0.106)
L-HDL-FC-0.044 (0.046)-0.094 (0.062)-0.106 (0.082)-0.127 (0.067)0.038 (0.103)0.017 (0.109)
L-HDL-LNANA-0.106 (0.077)NA0.034 (0.102)NA
L-HDL-P-0.045 (0.043)-0.097 (0.058)-0.102 (0.077)-0.125 (0.063)0.009 (0.111)0.025 (0.11)
L-HDL-PL-0.054 (0.044)-0.11 (0.06)-0.115 (0.079)-0.14 (0.064)0.006 (0.115)0.016 (0.115)
XL-HDL-C0.03 (0.06)-0.012 (0.084)0.014 (0.099)-0.05 (0.088)-0.015 (0.165)0.161 (0.101)
XL-HDL-CE0.03 (0.059)-0.009 (0.081)0.025 (0.098)-0.042 (0.086)-0.001 (0.166)0.221 (0.107)
XL-HDL-FC-0.003 (0.056)-0.05 (0.076)-0.001 (0.089)-0.077 (0.081)0.072 (0.11)0.057 (0.092)
XL-HDL-LNANA0.001 (0.085)NA-0.009 (0.138)NA
XL-HDL-P0.015 (0.049)-0.021 (0.067)0.013 (0.088)-0.042 (0.071)0.103 (0.1)0.135 (0.093)
XL-HDL-PL0 (0.047)-0.037 (0.065)-0.026 (0.079)-0.055 (0.069)0.081 (0.088)0.071 (0.069)
XL-HDL-TG0.086 (0.041)0.103 (0.059)0.14 (0.075)0.13 (0.063)0.165 (0.043)0.126 (0.051)
Appendix 3—table 4
Mendelian randomization results using genome-wide significant SNPs and the weighted median estimator.
Method: Weighted median + Significant SNPs
SelectionGERAGERAGERAGLGCDavisKettunen
ExposureDavisDavisKettunenDavisKettunenDavis
OutcomeCADUKBUKBUKBUKBUKB
VLDL traits
TG0.042 (0.055)0.191 (0.072)NA0.228 (0.069)NA0.195 (0.077)
VLDL-D-0.098 (0.052)0.039 (0.095)0.057 (0.11)0.058 (0.093)-0.107 (0.099)-0.052 (0.115)
XS-VLDL-LNANA0.312 (0.076)NA0.393 (0.078)NA
XS-VLDL-P0.101 (0.037)0.23 (0.052)0.303 (0.079)0.229 (0.052)0.409 (0.08)0.253 (0.059)
XS-VLDL-PL0.096 (0.039)0.242 (0.059)0.352 (0.087)0.228 (0.06)0.422 (0.065)0.319 (0.062)
XS-VLDL-TG0.125 (0.041)0.266 (0.057)0.287 (0.079)0.221 (0.056)0.361 (0.084)0.306 (0.069)
S-VLDL-C0.187 (0.059)0.232 (0.075)NA0.256 (0.074)NA0.303 (0.094)
S-VLDL-FC0.152 (0.057)0.207 (0.069)0.289 (0.093)0.227 (0.069)0.316 (0.109)0.279 (0.077)
S-VLDL-LNANA0.282 (0.083)NA0.306 (0.099)NA
S-VLDL-P0.131 (0.057)0.202 (0.069)0.275 (0.085)0.221 (0.062)0.291 (0.093)0.226 (0.078)
S-VLDL-PL0.137 (0.053)0.205 (0.067)0.283 (0.083)0.218 (0.062)0.305 (0.092)0.263 (0.075)
S-VLDL-TG0.112 (0.057)0.204 (0.067)0.216 (0.088)0.229 (0.064)0.267 (0.099)0.244 (0.073)
M-VLDL-C0.12 (0.058)0.2 (0.07)0.255 (0.088)0.213 (0.066)0.303 (0.099)0.224 (0.081)
M-VLDL-CE0.144 (0.054)0.207 (0.071)0.262 (0.087)0.207 (0.068)0.301 (0.098)0.209 (0.072)
M-VLDL-FC0.081 (0.058)0.188 (0.074)0.221 (0.087)0.218 (0.068)0.272 (0.102)0.231 (0.08)
M-VLDL-LNANA0.227 (0.095)NA0.275 (0.109)NA
M-VLDL-P0.047 (0.06)0.191 (0.072)0.221 (0.096)0.226 (0.069)0.31 (0.104)0.257 (0.079)
M-VLDL-PL0.103 (0.056)0.197 (0.071)0.228 (0.089)0.217 (0.064)0.29 (0.104)0.231 (0.078)
M-VLDL-TG-0.005 (0.06)0.199 (0.075)0.224 (0.089)0.222 (0.068)0.318 (0.113)0.233 (0.085)
L-VLDL-C0.109 (0.068)0.2 (0.078)0.237 (0.093)0.231 (0.075)0.242 (0.122)0.262 (0.088)
L-VLDL-CE0.147 (0.063)0.211 (0.079)0.249 (0.09)0.253 (0.073)0.281 (0.11)0.286 (0.081)
L-VLDL-FC0.045 (0.065)0.199 (0.085)0.225 (0.093)0.224 (0.077)0.252 (0.125)0.228 (0.089)
L-VLDL-LNANA0.243 (0.102)NA0.261 (0.122)NA
L-VLDL-P0.041 (0.064)0.209 (0.082)0.224 (0.092)0.21 (0.079)0.289 (0.122)0.223 (0.086)
L-VLDL-PL0.08 (0.063)0.201 (0.08)0.244 (0.101)0.224 (0.077)0.278 (0.123)0.247 (0.092)
L-VLDL-TG-0.008 (0.061)0.215 (0.084)0.225 (0.103)0.161 (0.077)0.286 (0.13)0.277 (0.093)
XL-VLDL-LNANA0.262 (0.111)NANANA
XL-VLDL-P-0.026 (0.063)0.207 (0.091)0.289 (0.102)0.192 (0.088)NA0.209 (0.101)
XL-VLDL-PL-0.006 (0.067)0.197 (0.094)0.253 (0.094)0.213 (0.088)NA0.24 (0.101)
XL-VLDL-TG-0.026 (0.064)0.214 (0.092)0.229 (0.102)0.191 (0.088)NA0.212 (0.099)
XXL-VLDL-LNANA0.316 (0.114)NA-0.156 (0.22)NA
XXL-VLDL-P0.091 (0.071)0.236 (0.089)0.267 (0.1)0.263 (0.088)-0.104 (0.173)0.185 (0.098)
XXL-VLDL-PL0.153 (0.082)0.283 (0.096)0.267 (0.11)0.332 (0.095)-0.139 (0.178)0.126 (0.124)
XXL-VLDL-TG0.126 (0.078)0.266 (0.096)0.244 (0.108)0.339 (0.097)0.227 (0.171)0.23 (0.123)
IDL/LDL traits
LDL-C0.263 (0.053)0.307 (0.066)0.274 (0.05)0.297 (0.063)0.435 (0.072)0.431 (0.067)
ApoB0.365 (0.073)0.472 (0.078)0.381 (0.063)0.375 (0.081)0.624 (0.08)0.565 (0.094)
LDL-D0.306 (0.09)0.413 (0.157)0.467 (0.163)0.271 (0.142)0.294 (0.075)0.193 (0.06)
S-LDL-C0.271 (0.058)0.342 (0.073)0.343 (0.056)0.273 (0.068)0.498 (0.08)0.274 (0.083)
S-LDL-LNANA0.354 (0.061)NA0.449 (0.081)NA
S-LDL-P0.355 (0.063)0.366 (0.078)0.397 (0.069)0.329 (0.08)0.49 (0.089)0.581 (0.098)
M-LDL-C0.283 (0.055)0.313 (0.073)0.299 (0.05)0.244 (0.07)0.474 (0.074)0.297 (0.074)
M-LDL-CE0.27 (0.055)0.333 (0.077)0.299 (0.051)0.255 (0.071)0.437 (0.081)0.311 (0.077)
M-LDL-LNANA0.303 (0.053)NA0.432 (0.079)NA
M-LDL-P0.251 (0.057)0.32 (0.071)0.309 (0.054)0.278 (0.07)0.409 (0.072)0.325 (0.078)
M-LDL-PL0.343 (0.063)0.337 (0.081)0.316 (0.055)0.318 (0.078)0.457 (0.074)0.353 (0.085)
L-LDL-C0.251 (0.052)0.29 (0.067)0.303 (0.048)0.231 (0.063)0.45 (0.075)0.309 (0.071)
L-LDL-CE0.251 (0.054)0.32 (0.068)0.293 (0.052)0.241 (0.066)0.481 (0.074)0.322 (0.077)
L-LDL-FC0.251 (0.048)0.214 (0.061)0.301 (0.049)0.214 (0.062)0.427 (0.068)0.289 (0.065)
L-LDL-LNANA0.289 (0.051)NA0.412 (0.07)NA
L-LDL-P0.281 (0.053)0.321 (0.067)0.29 (0.053)0.244 (0.066)0.42 (0.072)0.351 (0.072)
L-LDL-PL0.286 (0.05)0.32 (0.067)0.313 (0.052)0.298 (0.065)0.413 (0.074)0.35 (0.076)
IDL-C0.283 (0.056)0.349 (0.068)0.315 (0.053)0.313 (0.07)0.51 (0.072)0.383 (0.068)
IDL-FC0.283 (0.053)0.334 (0.066)0.337 (0.053)0.314 (0.065)0.422 (0.067)0.367 (0.064)
IDL-LNANA0.329 (0.056)NA0.494 (0.069)NA
IDL-P0.331 (0.06)0.44 (0.067)0.343 (0.056)0.371 (0.069)0.463 (0.074)0.328 (0.068)
IDL-PL0.265 (0.055)0.332 (0.066)0.344 (0.056)0.316 (0.066)0.451 (0.072)0.359 (0.066)
IDL-TG0.233 (0.067)0.371 (0.086)0.605 (0.078)0.337 (0.085)0.315 (0.082)0.215 (0.057)
HDL traits
HDL-C-0.017 (0.04)-0.167 (0.058)-0.17 (0.072)-0.167 (0.058)-0.096 (0.077)-0.085 (0.07)
ApoA10.094 (0.049)-0.06 (0.076)-0.069 (0.087)-0.167 (0.07)0.005 (0.083)-0.051 (0.121)
HDL-D0.079 (0.034)0.062 (0.061)0.102 (0.064)0.088 (0.061)0.099 (0.061)0.096 (0.058)
S-HDL-LNANA-0.174 (0.113)NANANA
S-HDL-P-0.173 (0.069)0.018 (0.106)-0.171 (0.109)-0.235 (0.113)NA-0.049 (0.108)
S-HDL-TG0.157 (0.061)0.238 (0.085)0.312 (0.105)0.228 (0.086)0.327 (0.105)0.229 (0.076)
M-HDL-C-0.169 (0.054)-0.236 (0.082)-0.264 (0.097)-0.241 (0.077)-0.392 (0.098)-0.266 (0.084)
M-HDL-CE-0.166 (0.053)-0.23 (0.08)-0.271 (0.099)-0.238 (0.075)-0.394 (0.103)-0.23 (0.085)
M-HDL-FC-0.166 (0.055)-0.254 (0.086)-0.281 (0.098)-0.282 (0.087)-0.28 (0.102)-0.22 (0.1)
M-HDL-LNANA-0.296 (0.113)NA-0.448 (0.122)NA
M-HDL-P-0.157 (0.056)-0.199 (0.09)-0.298 (0.112)-0.231 (0.086)-0.291 (0.136)-0.165 (0.131)
M-HDL-PL-0.143 (0.058)-0.183 (0.088)-0.285 (0.108)-0.183 (0.085)-0.321 (0.114)-0.203 (0.12)
L-HDL-C0.086 (0.037)-0.009 (0.066)0.031 (0.083)-0.032 (0.08)0.003 (0.09)0.006 (0.068)
L-HDL-CE0.086 (0.038)-0.011 (0.067)0.075 (0.077)-0.037 (0.076)0.015 (0.091)-0.006 (0.068)
L-HDL-FC0.09 (0.039)-0.005 (0.067)0.079 (0.081)-0.019 (0.076)0.041 (0.078)0.027 (0.074)
L-HDL-LNANA0.074 (0.077)NA0.068 (0.084)NA
L-HDL-P0.081 (0.036)0.046 (0.062)0.075 (0.074)-0.01 (0.066)0.066 (0.07)0.078 (0.064)
L-HDL-PL0.084 (0.039)0 (0.067)0.051 (0.082)-0.021 (0.071)0.054 (0.075)0.074 (0.071)
XL-HDL-C0.163 (0.047)0.122 (0.091)0.136 (0.087)0.132 (0.09)0.02 (0.098)0.161 (0.096)
XL-HDL-CE0.139 (0.044)0.106 (0.088)0.122 (0.09)0.148 (0.085)0.038 (0.091)0.336 (0.092)
XL-HDL-FC0.135 (0.048)0.065 (0.079)0.133 (0.081)0.027 (0.077)0.159 (0.079)0.052 (0.086)
XL-HDL-LNANA0.119 (0.075)NA0.023 (0.078)NA
XL-HDL-P0.115 (0.035)0.087 (0.07)0.12 (0.073)0.129 (0.067)0.16 (0.071)0.15 (0.073)
XL-HDL-PL0.101 (0.037)0.064 (0.07)0.11 (0.072)0.121 (0.069)0.141 (0.069)0.088 (0.065)
XL-HDL-TG0.074 (0.027)0.107 (0.047)0.126 (0.051)0.118 (0.042)0.156 (0.05)0.114 (0.045)

Multivariable MR results

Appendix 3—table 5
Multivariable Mendelian randomization results (adjusted for HDL-C, LDL-C, and TG).
TraitHDL-CLDL-CTGSubfraction
VLDL traits
VLDL-D-0.251 (0.052)0.29 (0.037)0.6 (0.087)-0.588 (0.094)
XS-VLDL-L-0.086 (0.046)0.286 (0.077)0.089 (0.099)0.132 (0.119)
XS-VLDL-P-0.083 (0.045)0.299 (0.078)0.093 (0.106)0.118 (0.125)
XS-VLDL-PL-0.083 (0.046)0.249 (0.098)0.112 (0.076)0.159 (0.12)
XS-VLDL-TG-0.114 (0.046)0.463 (0.079)0.286 (0.173)-0.157 (0.187)
S-VLDL-C-0.267 (0.084)0.754 (0.112)1.033 (0.28)-1.035 (0.323)
S-VLDL-FC-0.195 (0.068)0.898 (0.163)0.935 (0.26)-1.027 (0.337)
S-VLDL-L-0.25 (0.072)0.755 (0.112)0.876 (0.233)-0.898 (0.28)
S-VLDL-P-0.31 (0.101)0.819 (0.157)1.209 (0.4)-1.245 (0.463)
S-VLDL-PL-0.168 (0.051)0.673 (0.074)0.626 (0.159)-0.613 (0.182)
S-VLDL-TG-0.499 (0.305)0.906 (0.34)2.532 (1.57)-2.628 (1.741)
M-VLDL-C-0.201 (0.068)0.808 (0.127)1.472 (0.424)-1.433 (0.451)
M-VLDL-CE-0.168 (0.061)0.799 (0.111)0.996 (0.249)-1.035 (0.293)
M-VLDL-FC-0.2 (0.072)0.658 (0.089)1.469 (0.417)-1.412 (0.444)
M-VLDL-L-0.355 (0.139)0.602 (0.096)1.787 (0.654)-1.878 (0.75)
M-VLDL-P-0.362 (0.124)0.569 (0.08)1.889 (0.676)-1.974 (0.745)
M-VLDL-PL-0.332 (0.141)0.722 (0.159)1.996 (0.869)-2.012 (0.943)
M-VLDL-TG-0.408 (0.153)0.432 (0.061)1.974 (0.772)-2.133 (0.879)
L-VLDL-C-0.216 (0.063)0.509 (0.046)1.163 (0.254)-1.254 (0.297)
L-VLDL-CE-0.272 (0.072)0.465 (0.04)1.038 (0.242)-1.081 (0.282)
L-VLDL-FC-0.144 (0.059)0.493 (0.044)1.233 (0.27)-1.274 (0.308)
L-VLDL-L-0.228 (0.066)0.414 (0.045)1.17 (0.263)-1.277 (0.313)
L-VLDL-P-0.115 (0.056)0.442 (0.046)1.351 (0.317)-1.357 (0.344)
L-VLDL-PL-0.221 (0.111)0.473 (0.07)2.135 (0.948)-2.316 (1.112)
L-VLDL-TG-0.196 (0.066)0.355 (0.05)1.357 (0.322)-1.428 (0.372)
XL-VLDL-L-0.126 (0.049)0.451 (0.04)0.896 (0.159)-1.069 (0.203)
XL-VLDL-P-0.127 (0.053)0.474 (0.043)1.038 (0.183)-1.209 (0.238)
XL-VLDL-PL-0.138 (0.055)0.5 (0.044)1.052 (0.204)-1.214 (0.257)
XL-VLDL-TG-0.129 (0.049)0.424 (0.04)0.944 (0.167)-1.071 (0.205)
XXL-VLDL-L-0.228 (0.067)0.444 (0.043)0.978 (0.207)-1.355 (0.318)
XXL-VLDL-P0.063 (0.076)0.452 (0.05)1.371 (0.384)-1.639 (0.502)
XXL-VLDL-PL-0.185 (0.056)0.371 (0.042)0.997 (0.185)-1.259 (0.262)
XXL-VLDL-TG-0.152 (0.059)0.41 (0.04)0.966 (0.19)-1.202 (0.262)
LDL/IDL traits
ApoB-0.084 (0.046)0.8 (0.146)0.427 (0.101)-0.532 (0.191)
LDL-D-0.057 (0.042)0.367 (0.03)0.21 (0.053)0.145 (0.061)
S-LDL-C-0.062 (0.043)0.614 (0.126)0.261 (0.062)-0.282 (0.152)
S-LDL-L-0.06 (0.044)0.584 (0.118)0.266 (0.068)-0.251 (0.145)
S-LDL-P-0.033 (0.047)0.589 (0.119)0.29 (0.078)-0.266 (0.151)
M-LDL-C-0.082 (0.044)0.623 (0.146)0.203 (0.054)-0.271 (0.162)
M-LDL-CE-0.074 (0.043)0.485 (0.167)0.169 (0.059)-0.088 (0.188)
M-LDL-L-0.071 (0.044)0.444 (0.171)0.19 (0.063)-0.069 (0.191)
M-LDL-P-0.054 (0.044)0.539 (0.153)0.213 (0.063)-0.179 (0.174)
M-LDL-PL-0.081 (0.045)0.747 (0.134)0.232 (0.062)-0.407 (0.162)
L-LDL-C-0.071 (0.049)0.437 (0.242)0.167 (0.054)-0.059 (0.261)
L-LDL-CE-0.07 (0.048)0.277 (0.301)0.149 (0.065)0.116 (0.321)
L-LDL-FC-0.112 (0.057)0.184 (0.304)0.163 (0.053)0.223 (0.315)
L-LDL-L-0.075 (0.049)0.229 (0.26)0.146 (0.068)0.167 (0.273)
L-LDL-P-0.083 (0.046)0.33 (0.2)0.128 (0.064)0.084 (0.213)
L-LDL-PL-0.101 (0.046)0.446 (0.177)0.155 (0.057)-0.036 (0.195)
IDL-C-0.108 (0.057)0.231 (0.215)0.128 (0.064)0.192 (0.229)
IDL-FC-0.107 (0.05)0.23 (0.147)0.123 (0.056)0.19 (0.156)
IDL-L-0.1 (0.05)0.274 (0.161)0.123 (0.069)0.148 (0.175)
IDL-P-0.101 (0.047)0.269 (0.134)0.109 (0.071)0.153 (0.148)
IDL-PL-0.076 (0.048)0.25 (0.162)0.134 (0.071)0.153 (0.18)
IDL-TG-0.083 (0.046)0.314 (0.069)0.103 (0.089)0.11 (0.103)
HDL traits
ApoA10.345 (0.25)0.544 (0.081)0.334 (0.109)-0.481 (0.271)
HDL-D-0.442 (0.124)0.421 (0.033)0.111 (0.055)0.333 (0.114)
S-HDL-L-0.117 (0.046)0.488 (0.044)0.189 (0.054)-0.312 (0.106)
S-HDL-P-0.112 (0.046)0.453 (0.035)0.225 (0.056)-0.331 (0.095)
S-HDL-TG0.002 (0.145)0.314 (0.156)-0.007 (0.469)0.253 (0.637)
M-HDL-C0.179 (0.097)0.36 (0.038)0.147 (0.054)-0.508 (0.165)
M-HDL-CE0.167 (0.087)0.319 (0.036)0.166 (0.055)-0.487 (0.157)
M-HDL-FC0.339 (0.141)0.436 (0.04)0.247 (0.059)-0.649 (0.225)
M-HDL-L0.27 (0.108)0.362 (0.032)0.299 (0.063)-0.606 (0.188)
M-HDL-P0.302 (0.112)0.386 (0.033)0.371 (0.075)-0.694 (0.204)
M-HDL-PL0.311 (0.117)0.402 (0.033)0.333 (0.07)-0.632 (0.191)
L-HDL-C-0.589 (0.211)0.469 (0.039)0.146 (0.055)0.516 (0.213)
L-HDL-CE-0.602 (0.239)0.477 (0.042)0.137 (0.056)0.519 (0.23)
L-HDL-FC-0.573 (0.177)0.437 (0.034)0.171 (0.054)0.518 (0.181)
L-HDL-L-0.556 (0.193)0.437 (0.034)0.142 (0.055)0.457 (0.189)
L-HDL-P-0.515 (0.198)0.417 (0.03)0.133 (0.056)0.422 (0.191)
L-HDL-PL-0.53 (0.201)0.415 (0.034)0.152 (0.055)0.443 (0.202)
XL-HDL-C-0.447 (0.182)0.342 (0.036)0.071 (0.079)0.474 (0.223)
XL-HDL-CE-0.425 (0.146)0.366 (0.038)0.051 (0.069)0.426 (0.177)
XL-HDL-FC-0.459 (0.147)0.377 (0.031)0.097 (0.062)0.433 (0.16)
XL-HDL-L-0.405 (0.146)0.364 (0.031)0.077 (0.068)0.358 (0.154)
XL-HDL-P-0.451 (0.134)0.374 (0.03)0.078 (0.064)0.41 (0.139)
XL-HDL-PL-0.422 (0.119)0.412 (0.033)0.115 (0.055)0.343 (0.118)
XL-HDL-TG-0.186 (0.073)0.336 (0.035)0.045 (0.086)0.147 (0.074)
Appendix 3—table 6
Multivariable Mendelian randomization results (adjusted for ApoA1, ApoB, and TG).
TraitApoA1ApoBTGSubfraction
VLDL traits
VLDL-D-0.227 (0.067)0.545 (0.092)0.208 (0.139)-0.32 (0.112)
XS-VLDL-L-0.123 (0.063)0.53 (0.163)-0.121 (0.085)0.084 (0.141)
XS-VLDL-P-0.121 (0.064)0.553 (0.17)-0.123 (0.088)0.061 (0.158)
XS-VLDL-PL-0.147 (0.066)0.273 (0.138)0.028 (0.05)0.253 (0.135)
XS-VLDL-TG-0.102 (0.06)0.762 (0.168)0.069 (0.055)-0.248 (0.15)
S-VLDL-C-0.384 (0.141)1.426 (0.354)0.606 (0.351)-1.265 (0.568)
S-VLDL-FC-0.188 (0.077)1.001 (0.235)0.081 (0.053)-0.489 (0.213)
S-VLDL-L-0.46 (0.146)1.776 (0.417)0.7 (0.316)-1.629 (0.586)
S-VLDL-P-0.494 (0.159)1.677 (0.386)0.825 (0.372)-1.644 (0.606)
S-VLDL-PL-0.262 (0.097)1.41 (0.343)0.532 (0.261)-1.213 (0.478)
S-VLDL-TG-0.18 (0.069)0.792 (0.121)0.078 (0.051)-0.301 (0.108)
M-VLDL-C-0.157 (0.062)0.867 (0.132)0.085 (0.051)-0.373 (0.118)
M-VLDL-CE-0.221 (0.069)1.224 (0.223)0.47 (0.21)-0.995 (0.338)
M-VLDL-FC-0.222 (0.074)0.902 (0.133)0.482 (0.251)-0.799 (0.311)
M-VLDL-L-0.174 (0.065)0.76 (0.104)0.073 (0.05)-0.298 (0.098)
M-VLDL-P-0.181 (0.065)0.764 (0.1)0.077 (0.051)-0.312 (0.096)
M-VLDL-PL-0.159 (0.065)0.776 (0.116)0.08 (0.051)-0.297 (0.106)
M-VLDL-TG-0.263 (0.106)0.724 (0.094)0.547 (0.406)-0.806 (0.455)
L-VLDL-C-0.218 (0.084)0.732 (0.101)0.352 (0.278)-0.609 (0.337)
L-VLDL-CE-0.293 (0.079)0.781 (0.096)0.405 (0.189)-0.673 (0.217)
L-VLDL-FC-0.197 (0.069)0.737 (0.094)0.365 (0.25)-0.619 (0.291)
L-VLDL-L-0.194 (0.071)0.666 (0.087)0.289 (0.234)-0.532 (0.278)
L-VLDL-P-0.184 (0.061)0.677 (0.086)0.415 (0.217)-0.617 (0.229)
L-VLDL-PL-0.155 (0.063)0.715 (0.095)0.075 (0.051)-0.287 (0.104)
L-VLDL-TG-0.154 (0.062)0.67 (0.083)0.073 (0.05)-0.252 (0.091)
XL-VLDL-L-0.186 (0.066)0.694 (0.088)0.263 (0.19)-0.577 (0.249)
XL-VLDL-P-0.167 (0.061)0.742 (0.088)0.075 (0.05)-0.373 (0.109)
XL-VLDL-PL-0.191 (0.068)0.712 (0.092)0.271 (0.197)-0.583 (0.268)
XL-VLDL-TG-0.195 (0.068)0.666 (0.087)0.334 (0.21)-0.603 (0.248)
XXL-VLDL-L-0.173 (0.066)0.732 (0.098)0.088 (0.052)-0.402 (0.144)
XXL-VLDL-P-0.071 (0.065)0.705 (0.097)0.607 (0.321)-1.089 (0.449)
XXL-VLDL-PL-0.244 (0.082)0.666 (0.091)0.414 (0.257)-0.814 (0.344)
XXL-VLDL-TG-0.3 (0.091)0.694 (0.095)0.627 (0.306)-1.075 (0.402)
IDL/LDL traits
LDL-C-0.119 (0.062)0.247 (0.167)0.066 (0.054)0.319 (0.182)
LDL-D-0.123 (0.06)0.544 (0.091)-0.036 (0.087)0.119 (0.071)
S-LDL-C-0.097 (0.06)0.438 (0.216)0.044 (0.051)0.08 (0.238)
S-LDL-L-0.097 (0.063)0.503 (0.268)0.043 (0.051)-0.005 (0.29)
S-LDL-P-0.059 (0.103)0.932 (0.597)-0.122 (0.112)-0.362 (0.596)
M-LDL-C-0.099 (0.065)0.78 (1.034)-0.172 (0.425)-0.169 (0.909)
M-LDL-CE-0.157 (0.128)-0.346 (2.587)0.195 (0.855)0.854 (2.221)
M-LDL-L-0.123 (0.095)0.247 (1.479)-0.001 (0.445)0.32 (1.293)
M-LDL-P-0.134 (0.07)0.13 (0.286)0.053 (0.052)0.432 (0.31)
M-LDL-PL-0.075 (0.077)1.165 (0.868)-0.248 (0.253)-0.566 (0.839)
L-LDL-C-0.855 (1.68)-5.337 (13.402)2.405 (5.735)5.257 (11.72)
L-LDL-CE-0.151 (0.065)0.129 (0.193)0.061 (0.052)0.461 (0.213)
L-LDL-FC-0.397 (0.219)-1.139 (1.395)0.786 (0.711)1.531 (1.189)
L-LDL-L-0.265 (0.148)-0.854 (1.42)0.41 (0.51)1.266 (1.188)
L-LDL-P-0.258 (0.153)-0.607 (1.225)0.276 (0.402)1.064 (1.029)
L-LDL-PL-0.312 (0.187)-0.741 (1.411)0.39 (0.518)1.227 (1.245)
IDL-C-0.3 (0.123)-0.334 (0.616)0.276 (0.254)0.769 (0.501)
IDL-FC-0.199 (0.069)0.247 (0.118)0.044 (0.049)0.33 (0.127)
IDL-L-0.215 (0.089)0.021 (0.409)0.101 (0.15)0.444 (0.328)
IDL-P-0.175 (0.075)0.214 (0.172)0.04 (0.051)0.292 (0.173)
IDL-PL-0.183 (0.07)0.159 (0.172)0.031 (0.049)0.406 (0.184)
IDL-TG-0.143 (0.075)0.565 (0.146)-0.119 (0.087)0.047 (0.135)
HDL traits
HDL-C-1.513 (1.109)0.982 (0.314)0.27 (0.291)1.446 (1.112)
HDL-D-0.457 (0.138)0.613 (0.073)0.056 (0.049)0.296 (0.1)
S-HDL-L-0.128 (0.059)0.524 (0.062)0.067 (0.05)-0.224 (0.087)
S-HDL-P-0.132 (0.059)0.531 (0.059)0.071 (0.05)-0.24 (0.083)
S-HDL-TG-0.11 (0.113)0.595 (0.221)-0.057 (0.297)-0.044 (0.466)
M-HDL-C0.091 (0.084)0.459 (0.101)-0.1 (0.083)-0.442 (0.143)
M-HDL-CE0.09 (0.078)0.291 (0.083)0.082 (0.05)-0.413 (0.137)
M-HDL-FC0.148 (0.11)0.378 (0.063)0.066 (0.049)-0.408 (0.166)
M-HDL-L0.133 (0.091)0.491 (0.097)-0.029 (0.086)-0.485 (0.155)
M-HDL-P0.129 (0.097)0.501 (0.097)-0.004 (0.09)-0.472 (0.166)
M-HDL-PL0.162 (0.107)0.519 (0.096)-0.037 (0.087)-0.486 (0.171)
L-HDL-C-0.724 (0.232)0.856 (0.132)0.032 (0.093)0.575 (0.204)
L-HDL-CE-0.761 (0.236)0.899 (0.145)0.004 (0.084)0.61 (0.206)
L-HDL-FC-0.749 (0.174)0.842 (0.102)0.094 (0.05)0.59 (0.148)
L-HDL-L-0.717 (0.217)0.815 (0.12)0.023 (0.089)0.541 (0.184)
L-HDL-P-0.653 (0.191)0.749 (0.104)0.057 (0.049)0.476 (0.155)
L-HDL-PL-0.679 (0.201)0.774 (0.109)0.05 (0.049)0.51 (0.169)
XL-HDL-C-0.639 (0.194)0.692 (0.095)-0.058 (0.086)0.565 (0.196)
XL-HDL-CE-0.576 (0.2)0.667 (0.096)-0.077 (0.086)0.511 (0.206)
XL-HDL-FC-0.734 (0.174)0.674 (0.073)0.094 (0.052)0.609 (0.159)
XL-HDL-L-0.652 (0.168)0.733 (0.097)-0.06 (0.084)0.481 (0.141)
XL-HDL-P-0.52 (0.147)0.691 (0.094)-0.075 (0.084)0.39 (0.135)
XL-HDL-PL-0.652 (0.151)0.687 (0.076)0.079 (0.051)0.466 (0.12)
XL-HDL-TG-0.281 (0.111)0.539 (0.09)-0.152 (0.092)0.165 (0.086)

Q-statistics for multivariable Mendelian randomization

Here we provide the list of modified Cochran's Q-statistics for the multivariable MR analyses (Appendix 3—tables 7 and 8).

Appendix 3—table 7
Modified Cochran’s Q-statistics (p-values) for the multivariable Mendelian randomization analyses (adjusted for HDL-C, LDL-C, and TG).

DF is short for degrees of freedom.

TraitDFHDL-CLDL-CTGSubfraction
VLDL traits
VLDL-D4327640.8 (0)1918.9 (7.9e-186)877.6 (1.4e-32)840.2 (1.6e-28)
XS-VLDL-L4367983.9 (0)1104.9 (1.1e-59)1935.8 (2.2e-187)926 (1.9e-37)
XS-VLDL-P4367927.8 (0)1066.6 (1.1e-54)1814 (4.8e-167)893.6 (9.6e-34)
XS-VLDL-PL4358291.5 (0)968.1 (1.4e-42)2771.5 (0)849.8 (4.3e-29)
XS-VLDL-TG4317549.8 (0)894.4 (1.3e-34)739.5 (1.3e-18)682.5 (1.2e-13)
S-VLDL-C4298598.1 (0)652.6 (1.7e-11)1220.7 (4.6e-77)541.3 (0.00018)
S-VLDL-FC4347861.2 (0)576 (5.4e-06)519.4 (0.003)507.9 (0.0082)
S-VLDL-L4387105.3 (0)626 (8.5e-09)525.2 (0.0026)514.3 (0.0069)
S-VLDL-P4386686.5 (0)616.5 (3.6e-08)515.6 (0.0061)507.3 (0.012)
S-VLDL-PL4377589.1 (0)702.8 (1e-14)591.5 (1.1e-06)555.1 (0.00011)
S-VLDL-TG4377658.7 (0)612.7 (5.3e-08)498.9 (0.021)494.5 (0.03)
M-VLDL-C4329167.8 (0)740.8 (1.3e-18)558.9 (3.5e-05)551.5 (8.3e-05)
M-VLDL-CE4328055.2 (0)705.9 (1.6e-15)556.6 (4.6e-05)539.7 (0.00031)
M-VLDL-FC4368272.8 (0)814.8 (2.7e-25)528.3 (0.0016)519.1 (0.0037)
M-VLDL-L4297109.2 (0)1269.2 (5.5e-84)532.6 (0.00047)515.9 (0.0025)
M-VLDL-P4368260.7 (0)2059.5 (2.1e-208)527.5 (0.0017)516.8 (0.0046)
M-VLDL-PL4356849.2 (0)599.6 (2.6e-07)496.8 (0.021)493.5 (0.027)
M-VLDL-TG4366123.7 (0)9854.8 (0)532.3 (0.0011)521 (0.0031)
L-VLDL-C4358617.2 (0)8966 (0)654.7 (4.3e-11)561.5 (3.9e-05)
L-VLDL-CE4346636.6 (0)11134 (0)581.6 (2.6e-06)539.5 (0.00041)
L-VLDL-FC4317779.6 (0)6691 (0)595.1 (2.5e-07)562.7 (1.9e-05)
L-VLDL-L4348104.9 (0)5191.4 (0)560.3 (3.9e-05)548.6 (0.00015)
L-VLDL-P4352308 (5.1e-252)10360.3 (0)545.4 (0.00024)537.9 (0.00054)
L-VLDL-PL4308155.4 (0)1310.8 (8.6e-90)491.8 (0.021)489.7 (0.024)
L-VLDL-TG4388581.8 (0)4800.1 (0)569.1 (2.3e-05)559.2 (7.5e-05)
XL-VLDL-L4378686.8 (0)8322.2 (0)674.7 (1.9e-12)620.2 (1.7e-08)
XL-VLDL-P4318550.2 (0)2459.4 (2e-280)608.3 (3.6e-08)588.6 (6.3e-07)
XL-VLDL-PL4317478.2 (0)5042.5 (0)613.3 (1.7e-08)591.6 (4.1e-07)
XL-VLDL-TG4338237.3 (0)9628.9 (0)651.8 (4.6e-11)618.3 (1.1e-08)
XXL-VLDL-L4398476.2 (0)10436.4 (0)652.9 (1.3e-10)570.7 (2.2e-05)
XXL-VLDL-P4371291.3 (2.8e-85)9987.4 (0)540.3 (0.00053)529.5 (0.0016)
XXL-VLDL-PL4369631.8 (0)11287.1 (0)641.6 (4.8e-10)595.5 (5.3e-07)
XXL-VLDL-TG4297809.4 (0)9476.4 (0)595.6 (1.7e-07)564 (1.2e-05)
LDL/IDL traits
ApoB4359220.8 (0)550.1 (0.00014)1809.7 (1.2e-166)535.1 (0.00072)
LDL-D4292909.2 (0)3918.8 (0)2706 (0)1426.1 (2.9e-107)
S-LDL-C4318189.7 (0)569.8 (7.8e-06)4880.9 (0)564.1 (1.6e-05)
S-LDL-L4358403.8 (0)574.4 (7.8e-06)3931.2 (0)564.3 (2.7e-05)
S-LDL-P4317371.4 (0)547.1 (0.00012)3144.7 (0)537.9 (0.00034)
M-LDL-C4309723.7 (0)570.9 (5.8e-06)6568.6 (0)562.9 (1.6e-05)
M-LDL-CE4328442.1 (0)558.3 (3.8e-05)5773.6 (0)549.1 (0.00011)
M-LDL-L4308801.7 (0)555.4 (4e-05)5176.1 (0)548.2 (9.5e-05)
M-LDL-P4298798.9 (0)541.6 (0.00018)5049.7 (0)535.2 (0.00035)
M-LDL-PL4367981.7 (0)573.9 (9.6e-06)4304.8 (0)558.9 (6e-05)
L-LDL-C4328865.2 (0)567.7 (1.2e-05)6179.8 (0)567 (1.3e-05)
L-LDL-CE4338464.3 (0)558.7 (4.1e-05)5731.3 (0)555.6 (5.9e-05)
L-LDL-FC4317481.1 (0)580.6 (1.9e-06)6760.8 (0)580.2 (2e-06)
L-LDL-L4338486.8 (0)604.5 (8.9e-08)5755.8 (0)601.8 (1.3e-07)
L-LDL-P4348310.7 (0)592.1 (6.3e-07)5553.3 (0)584.9 (1.7e-06)
L-LDL-PL4358341.4 (0)588.5 (1.2e-06)5327.8 (0)577.4 (5.3e-06)
IDL-C4347873.9 (0)645.5 (1.7e-10)6336 (0)642.1 (2.9e-10)
IDL-FC4328036 (0)729.5 (1.4e-17)6630.5 (0)725.6 (3e-17)
IDL-L4347869.8 (0)694.5 (2.4e-14)5198.3 (0)689 (7e-14)
IDL-P4369660.5 (0)736.7 (9e-18)5002 (0)726.6 (7.1e-17)
IDL-PL4318432.6 (0)680.6 (1.7e-13)5023 (0)677.4 (3e-13)
IDL-TG4367741.2 (0)1077.5 (4.2e-56)1992.9 (4.9e-197)931.6 (4.4e-38)
HDL traits
ApoA1434494.1 (0.024)511.5 (0.006)932.1 (1.8e-38)492 (0.028)
HDL-D438783.5 (6.6e-22)8500 (0)5713.2 (0)860.1 (9.4e-30)
S-HDL-L4383067.3 (0)4414.6 (0)3763.2 (0)882.2 (3.7e-32)
S-HDL-P4382592.4 (1.1e-301)7652.1 (0)3097.3 (0)951.1 (4.9e-40)
S-HDL-TG425896.9 (6.9e-36)641.3 (5.2e-11)540.1 (0.00013)523 (8e-04)
M-HDL-C437957.6 (5.5e-41)10172.4 (0)4875.5 (0)628.3 (4.9e-09)
M-HDL-CE434955.3 (3.2e-41)1383.1 (1.7e-99)4355.4 (0)648.3 (1e-10)
M-HDL-FC432759.4 (2.4e-20)2989.1 (0)3512.2 (0)538.2 (0.00037)
M-HDL-L435914.2 (3e-36)11535.3 (0)2327.7 (1.7e-255)570.3 (1.3e-05)
M-HDL-P434997.6 (2.3e-46)10709.6 (0)1942.9 (3.2e-189)561.3 (3.4e-05)
M-HDL-PL434977.8 (6.3e-44)9439.9 (0)2566 (1.8e-298)581.3 (2.7e-06)
L-HDL-C434580 (3.2e-06)1257.1 (4.4e-81)4502.7 (0)604.3 (1.1e-07)
L-HDL-CE434549 (0.00014)930.2 (3e-38)5517.2 (0)557.2 (5.6e-05)
L-HDL-FC441627.6 (1.2e-08)8415.3 (0)3594 (0)658.4 (7.9e-11)
L-HDL-L434603.6 (1.2e-07)6743.8 (0)5314.7 (0)623.7 (5.7e-09)
L-HDL-P432601.1 (1.2e-07)7769.3 (0)6024.6 (0)633.2 (8.6e-10)
L-HDL-PL434584.5 (1.8e-06)9935.5 (0)3544.3 (0)611.3 (3.8e-08)
XL-HDL-C430732.9 (3.9e-18)10426.6 (0)2077.7 (1.4e-213)686.9 (4e-14)
XL-HDL-CE430771.4 (9.3e-22)8564.4 (0)2457 (2.2e-280)711.4 (3.3e-16)
XL-HDL-FC432761.8 (1.4e-20)11265.2 (0)2549.4 (3.1e-296)770.9 (1.9e-21)
XL-HDL-L429767.6 (1.6e-21)11490.7 (0)2355.7 (1.2e-262)784.6 (3.4e-23)
XL-HDL-P433724.9 (4.6e-17)11372.5 (0)2539.9 (3.9e-294)798.5 (4.8e-24)
XL-HDL-PL443809.7 (7.8e-24)10093.1 (0)5762 (0)895.4 (7.5e-33)
XL-HDL-TG4321849.1 (3.9e-174)2635.9 (6.5e-312)2240.8 (2.9e-241)1267.8 (4.4e-83)
Appendix 3—table 8
Modified Cochran’s Q-statistics (p-values) for the multivariable Mendelian randomization analyses (adjusted for ApoA1, ApoB, and TG).

DF is short for degrees of freedom.

TraitDFApoA1ApoBTGSubfraction
VLDL traits
VLDL-D2971194.1 (9.1e-108)550 (2.4e-17)573.7 (8.2e-20)606.7 (2.1e-23)
XS-VLDL-L2951185.1 (6.7e-107)927 (2e-66)1151.3 (2.2e-101)887.9 (1.1e-60)
XS-VLDL-P2951194.9 (1.7e-108)900 (1.9e-62)895.5 (8.7e-62)826.7 (6.4e-52)
XS-VLDL-PL2961148.5 (1.2e-100)973.9 (3.2e-73)2104.2 (1.4e-269)961.4 (2.5e-71)
XS-VLDL-TG3021263.7 (1.1e-117)757.9 (4.7e-41)1308.1 (4.4e-125)976.5 (4.6e-72)
S-VLDL-C290988.8 (4.4e-77)394 (4.5e-05)459.8 (7.8e-10)402.6 (1.3e-05)
S-VLDL-FC2961092 (1.4e-91)904 (8.6e-63)1238.7 (2.1e-115)1010.4 (8.1e-79)
S-VLDL-L3011107.9 (1.1e-92)412.3 (2.1e-05)420.8 (5.9e-06)384.7 (0.00078)
S-VLDL-P3011116.6 (4.6e-94)424.8 (3.3e-06)401.3 (9.4e-05)380.6 (0.0013)
S-VLDL-PL2991096 (2.3e-91)428.9 (1.2e-06)446 (7.1e-08)432.1 (7.1e-07)
S-VLDL-TG3001152.4 (4.3e-100)908.5 (1.8e-62)1453.4 (1.8e-150)1303.1 (7.1e-125)
M-VLDL-C2981171.2 (1e-103)824 (7.3e-51)1480 (8.9e-156)1212.5 (1.8e-110)
M-VLDL-CE2981185.4 (4.9e-106)564.4 (1.1e-18)468.9 (9.2e-10)431.6 (6.3e-07)
M-VLDL-FC2981190.4 (7.4e-107)899.8 (1.1e-61)415.2 (8.1e-06)398.8 (8.4e-05)
M-VLDL-L2981144.1 (2.4e-99)869.8 (2.4e-57)1381 (1e-138)1237.4 (1.4e-114)
M-VLDL-P2971121.3 (5.7e-96)821.1 (1.1e-50)1250.5 (4.6e-117)1206.7 (8.1e-110)
M-VLDL-PL2981149.9 (2.8e-100)843.2 (1.5e-53)1391.8 (1.5e-140)1226.3 (9.8e-113)
M-VLDL-TG2961187.4 (5.8e-107)717.3 (5.8e-37)366.3 (0.0033)360.6 (0.006)
L-VLDL-C2951196.5 (9.1e-109)820 (5.6e-51)462.5 (1.5e-09)376.9 (0.00088)
L-VLDL-CE3021183.1 (1.8e-104)844.6 (7.4e-53)541.8 (7.2e-16)441.7 (2.6e-07)
L-VLDL-FC2951172.3 (8.2e-105)851.6 (1.9e-55)460.8 (2.1e-09)406.2 (1.8e-05)
L-VLDL-L2951163.6 (2.2e-103)797 (8.8e-48)406.5 (1.7e-05)391.5 (0.00014)
L-VLDL-P2931160.2 (2e-103)809.5 (5.9e-50)420.2 (1.5e-06)407.9 (1e-05)
L-VLDL-PL2961292 (2.6e-124)833.4 (1.3e-52)1216.5 (9.7e-112)1098.9 (1.1e-92)
L-VLDL-TG2941150.8 (1.3e-101)1213.6 (7e-112)1262.6 (5.2e-120)1162.8 (1.5e-103)
XL-VLDL-L2941196 (5.4e-109)829.4 (1.6e-52)442 (4.9e-08)423.6 (1.1e-06)
XL-VLDL-P2941265.9 (1.4e-120)1180.9 (1.6e-106)1202.2 (5.2e-110)982.1 (5.4e-75)
XL-VLDL-PL2961199.1 (6.9e-109)874.2 (1.9e-58)421.2 (2.3e-06)405.6 (2.3e-05)
XL-VLDL-TG2961184.3 (1.8e-106)828.6 (5.9e-52)430.8 (4.9e-07)430.1 (5.5e-07)
XXL-VLDL-L3041119.2 (1.2e-93)1041.9 (1.6e-81)900.9 (2e-60)699.6 (3.2e-33)
XXL-VLDL-P3031148 (1.7e-98)876.4 (4e-57)382.2 (0.0013)366 (0.0076)
XXL-VLDL-PL3031203 (2.1e-107)775.1 (4e-43)438.1 (5.8e-07)376.5 (0.0025)
XXL-VLDL-TG3031183 (3.7e-104)881.8 (6.6e-58)393.7 (0.00034)372.7 (0.0039)
LDL/IDL traits
LDL-C2931198.7 (9.6e-110)938.8 (1.1e-68)1060.2 (2.1e-87)917.6 (1.5e-65)
LDL-D2961325.2 (6.7e-130)747.9 (5.9e-41)879.1 (3.7e-59)1163.5 (4.6e-103)
S-LDL-C2961195.3 (2.9e-108)706 (1.6e-35)1426 (4.1e-147)686.4 (4.8e-33)
S-LDL-L2961054.7 (1.1e-85)608 (1e-23)1519.6 (2.2e-163)586.4 (2.5e-21)
S-LDL-P297852.9 (3.6e-55)438.7 (1.6e-07)954.7 (4.5e-70)440.1 (1.3e-07)
M-LDL-C2961210.9 (8e-111)396.2 (8.6e-05)409 (1.4e-05)398.9 (6e-05)
M-LDL-CE2951204.3 (4.8e-110)350.8 (0.014)361.7 (0.0048)351.3 (0.013)
M-LDL-L2961212 (5.3e-111)370 (0.0022)392.3 (0.00015)371.6 (0.0019)
M-LDL-P2971125.4 (1.2e-96)623.9 (2.3e-25)911.4 (1.3e-63)582.4 (9.6e-21)
M-LDL-PL2991172.5 (1.2e-103)399.3 (9.1e-05)434.9 (4.5e-07)396.2 (0.00014)
L-LDL-C3001174.6 (1.1e-103)325.5 (0.15)325.5 (0.15)325.5 (0.15)
L-LDL-CE2991179.5 (9e-105)769.8 (3e-43)902.5 (7.7e-62)743.8 (8.4e-40)
L-LDL-FC2951161 (5.8e-103)322.4 (0.13)323.2 (0.12)322.3 (0.13)
L-LDL-L3001172.3 (2.6e-103)336.9 (0.07)349.6 (0.026)340.3 (0.055)
L-LDL-P3001185.4 (2e-105)352.1 (0.021)378.4 (0.0014)355.4 (0.015)
L-LDL-PL2961155.2 (9.8e-102)343.2 (0.031)360.1 (0.0063)344.5 (0.027)
IDL-C2961181.7 (4.9e-106)426.5 (9.8e-07)427.6 (8.3e-07)427.7 (8.1e-07)
IDL-FC2981096.5 (9.9e-92)986.9 (1.1e-74)1075.8 (1.9e-88)975.4 (6.1e-73)
IDL-L2961176.1 (4e-105)516.7 (3.3e-14)531 (1.4e-15)521.4 (1.2e-14)
IDL-P2971094.8 (9.5e-92)910.9 (1.5e-63)1103.9 (3.5e-93)890.2 (1.6e-60)
IDL-PL2971107.8 (8.3e-94)798.9 (1.3e-47)931.6 (1.3e-66)785.6 (8.6e-46)
IDL-TG3021060.8 (5.4e-85)1052.1 (1.2e-83)1092.6 (5.6e-90)1118.3 (4.7e-94)
HDL traits
HDL-C298318.7 (0.2)336.3 (0.063)329.1 (0.1)318.6 (0.2)
HDL-D300637.4 (1.9e-26)1156.6 (9.1e-101)2305.2 (1.3e-305)1183.8 (3.5e-105)
S-HDL-L2991597.7 (4.8e-176)1222.5 (8.2e-112)1916.4 (1.5e-233)1057 (3.1e-85)
S-HDL-P2991666.8 (2.5e-188)1249.4 (2.9e-116)2146.5 (3.4e-276)1103.3 (1.6e-92)
S-HDL-TG299899 (2.5e-61)464.9 (2.4e-09)464.5 (2.6e-09)457.6 (9.2e-09)
M-HDL-C2991145.2 (3.2e-99)768.2 (4.9e-43)951.8 (4e-69)786.8 (1.5e-45)
M-HDL-CE2991201.9 (2e-108)1183.9 (1.7e-105)2139.7 (6.4e-275)843.9 (1.9e-53)
M-HDL-FC298881.1 (5.6e-59)1252 (5.5e-117)1989.1 (2.4e-247)660.1 (1.8e-29)
M-HDL-L2991059 (1.5e-85)766.4 (8.7e-43)920.6 (1.7e-64)672.5 (8.6e-31)
M-HDL-P298990.2 (3.5e-75)760.4 (3.4e-42)1027.6 (6.2e-81)613.7 (4.7e-24)
M-HDL-PL295929.5 (8.3e-67)763.9 (2.7e-43)1057.2 (2.3e-86)588.3 (1.1e-21)
L-HDL-C299579.3 (4.1e-20)623.2 (5.7e-25)639.6 (7.3e-27)617.8 (2.3e-24)
L-HDL-CE299612.2 (1e-23)650.7 (3.6e-28)690.4 (5.5e-33)644 (2.2e-27)
L-HDL-FC308581.7 (4.4e-19)857.5 (2.6e-53)1213.3 (1.4e-107)915.8 (1.3e-61)
L-HDL-L299655.9 (8.7e-29)747.7 (2.6e-40)670.7 (1.4e-30)713.2 (7.5e-36)
L-HDL-P298591.3 (1.5e-21)934 (9.9e-67)1269.7 (6.2e-120)956.8 (3.9e-70)
L-HDL-PL299580 (3.4e-20)863.5 (3.3e-56)1262.4 (2.1e-118)891.8 (2.8e-60)
XL-HDL-C298475.3 (2.7e-10)734 (1e-38)976.1 (4.9e-73)554 (1.3e-17)
XL-HDL-CE299472.9 (5.4e-10)736.9 (6.7e-39)1117.4 (9e-95)517.5 (6.5e-14)
XL-HDL-FC295527.8 (2.1e-15)1182.8 (1.6e-106)2169.4 (3.1e-282)677.3 (4.3e-32)
XL-HDL-L298555.2 (9.6e-18)701.2 (1.6e-34)1014 (7.9e-79)775.3 (3.4e-44)
XL-HDL-P300578.9 (6.3e-20)744.5 (1.1e-39)1015.5 (1.6e-78)751.3 (1.4e-40)
XL-HDL-PL306604.9 (7.8e-22)1153.9 (1.4e-98)1899 (1.5e-227)909.3 (3.7e-61)
XL-HDL-TG300702.2 (2.8e-34)779.8 (2.2e-44)1140.8 (3.2e-98)1399.2 (3.7e-141)

Appendix 4

Diagnostic plots and the genetic markers

As mentioned above, RAPS is more robust against invalid instruments than other statistical methods for univariable MR, but it still needs the InSIDE assumption to be approximately satisfied. Zhao et al., 2019b described two diagnostic plots RAPS that checks whether there is clear evidence that the InSIDE assumption is violated. Here, we report these plots for HDL-C and M-HDL-P in different studies (Appendix 4—figures 1 and 2). Notice that a lack of evidence to falsify the InSIDE assumption does not mean that it is true.

S-HDL-P

Appendix 4—figure 1
Diagnostic plots for S-HDL-P (selection: Davis; exposure: Kettunen; outcome: UK Biobank).

M-HDL-P

Appendix 4—figure 2
Diagnostic plots for M-HDL-P (selection: Davis; exposure: Kettunen; outcome: UK Biobank).

Genetic markers for M-HDL-P and S-HDL-P

We can further assess the validity of the InSIDE assumption for M-HDL-P and S-HDL-P but examining the associations of their genetic instruments with the traditional lipid risk factors and other subfraction traits. We meta-analyzed the summary results in the two lipidome GWAS (Davis and Kettunen) and obtained SNPs that are associated with S-HDL-P and M-HDL-P (p-value 5×10-8; the results are LD-clumped). The next two Tables show some information about these genetic markers and their associations with other traits (Appendix 4—table 1 and 2).

Appendix 4—figures 3 and 4 shows how adjusting for LDL-C and TG changes the effects of the selected SNPs for S-HDL-P and M-HDL-P on CAD. The adjusted effect on CAD is obtained by original effect on CAD – 0.45 * effect on LDL-C – 0.25 * effect on TG. After the adjustment, the associations of the genetic variants with CAD generally became closer to the fitted lines that correspond to the estimated effects of S-HDL-P and M-HDL-P.

Appendix 4—table 1
List of SNPs associated with M-HDL-P.
SNPChrGeneS-HDL-PM-HDL-PL-HDL-PXL-HDL-PHDL-CLDL-CTGCAD
rs112080041DOCK7-0.039 **-0.075 ***-0.015-0.002-0.015 **-0.050 ***-0.069 ***-0.012
rs48469131GALNT2-0.000-0.061 ***-0.062 ***-0.023 .-0.055 ***-0.006-0.044 ***-0.025 .
rs21262598LOC157273-0.066 ***-0.082 ***-0.063 **-0.025 .-0.075 ***-0.063 ***-0.016 .-0.004
rs20836378LPL-0.001-0.058 ***-0.092 ***-0.053 **-0.105 ***-0.008-0.108 ***-0.047 **
rs1046801715ALDH1A2/LIPC-0.096 ***-0.060 ***-0.209 ***-0.202 ***-0.118 ***-0.002-0.038 ***-0.013
rs24761616CETP-0.058 ***-0.121 ***-0.198 ***-0.129 ***-0.243 ***-0.055 ***-0.039 ***-0.044 **
rs194397318LIPG-0.022-0.108 ***-0.104 ***-0.078 ***-0.077 ***-0.024 **-0.009-0.016
rs73733719DOCK6-0.047 .-0.087 ***-0.081 **-0.058 *-0.056 ***-0.007-0.011-0.038 .
rs76944919APOE-0.016-0.078 ***-0.071 ***-0.015-0.064 ***-0.214 ***-0.042 ***-0.085 ***
rs767920PCIF1/PLTP-0.188 ***-0.071 ***-0.129 ***-0.152 ***-0.059 ***-0.009-0.051 ***-0.025 .
Appendix 4—table 2
List of SNPs associated with S-HDL-P.
SNPChrGeneS-HDL-PM-HDL-PL-HDL-PXL-HDL-PHDL-CLDL-CTGCAD
rs7800942GCKR-0.074 ***-0.034 *-0.04 **-0.034 *-0.011 .-0.021 **-0.110 ***-0.005
rs109354733ST3GAL6-AS1-0.052 ***-0.014-0.029 .-0.031 *-0.009 .-0.003-0.005-0.007
rs493636311SIK3-0.064 ***-0.046 **-0.019-0.006-0.034 **-0.018 .-0.043 ***-0.022
rs204308515ALDH1A2/LIPC-0.092 ***-0.056 ***-0.202 ***-0.197 ***-0.106 ***-0.003-0.033 ***-0.008
rs180058815ALDH1A2/LIPC-0.106 ***-0.050 **-0.215 ***-0.212 ***-0.114 ***-0.002-0.044 ***-0.015
rs28971416CETP-0.077 ***-0.122 ***-0.162 ***-0.102 ***-0.214 ***-0.036 ***-0.035 ***-0.012
rs606590420PLTP-0.171 ***-0.060 ***-0.127 ***-0.149 ***-0.052 ***-0.008-0.040 ***-0.022 .
Appendix 4—figure 3
Scatter-plots for S-HDL-P with the effects on CAD adjusted for LDL-C and TG.

Red lines correspond the fitted effects of S-HDL-P in multivariable MR.

Appendix 4—figure 4
Scatter-plots for M-HDL-P with the effects on CAD adjusted for LDL-C and TG.

Red lines correspond the fitted effects of M-HDL-P in multivariable MR.

Gene expression

Here we provide evidence of variant-gene associations from Quantatitive Trait Locus (QTL) analyses in the GTEx project (Appendix 4—table 3).

Appendix 4—table 3
Tissue-specific gene expressions associated with the 4 discovered genetic markers in the GTEx project.
SNP.IdTypeGene.SymbolVariant.Idp valueEffectTissue
rs838880eQTLSCARB1chr12_124777047_C_T_b381.5E-08-0.20Cells - Cultured fibroblasts
rs838880sQTLSCARB1chr12_124777047_C_T_b384.1E-06-0.34Testis
rs737337sQTLDOCK6chr19_11236817_T_C_b383.8E-430.99Artery - Tibial
rs737337sQTLDOCK6chr19_11236817_T_C_b386.4E-350.93Adipose - Subcutaneous
rs737337sQTLDOCK6chr19_11236817_T_C_b386.4E-350.93Adipose - Subcutaneous
rs737337sQTLDOCK6chr19_11236817_T_C_b381.6E-270.95Esophagus - Muscularis
rs737337sQTLDOCK6chr19_11236817_T_C_b383.2E-201.10Colon - Sigmoid
rs737337sQTLDOCK6chr19_11236817_T_C_b381.1E-170.93Esophagus - Gastroesophageal Junction
rs737337sQTLDOCK6chr19_11236817_T_C_b381.8E-090.81Artery - Coronary
rs737337sQTLDOCK6chr19_11236817_T_C_b381.2E-07-0.49Thyroid
rs737337sQTLKANK2chr19_11236817_T_C_b384.4E-070.43Artery - Tibial
rs737337sQTLKANK2chr19_11236817_T_C_b383.5E-060.55Heart - Left Ventricle
rs2943641eQTLIRS1chr2_226229029_T_C_b381.4E-16-0.30Adipose - Subcutaneous
rs2943641eQTLIRS1chr2_226229029_T_C_b386.1E-12-0.23Adipose - Visceral (Omentum)
rs2943641eQTLRP11-395N3.2chr2_226229029_T_C_b383.5E-09-0.23Adipose - Subcutaneous
rs2943641eQTLRP11-395N3.1chr2_226229029_T_C_b382.1E-07-0.23Adipose - Subcutaneous
rs2943641eQTLRP11-395N3.2chr2_226229029_T_C_b382.3E-06-0.19Adipose - Visceral (Omentum)
rs6065904eQTLPLTPchr20_45906012_G_A_b384.4E-22-0.27Muscle - Skeletal
rs6065904eQTLPLTPchr20_45906012_G_A_b381.6E-16-0.27Adipose - Subcutaneous
rs6065904eQTLPLTPchr20_45906012_G_A_b381.2E-15-0.28Adipose - Visceral (Omentum)
rs6065904eQTLPLTPchr20_45906012_G_A_b383.2E-15-0.42Heart - Atrial Appendage
rs6065904eQTLPLTPchr20_45906012_G_A_b387.2E-14-0.25Artery - Tibial
rs6065904eQTLPLTPchr20_45906012_G_A_b381.8E-12-0.27Nerve - Tibial
rs6065904eQTLPLTPchr20_45906012_G_A_b387.3E-12-0.26Esophagus - Muscularis
rs6065904eQTLPLTPchr20_45906012_G_A_b382.0E-11-0.29Colon - Transverse
rs6065904eQTLPLTPchr20_45906012_G_A_b384.1E-11-0.32Colon - Sigmoid
rs6065904eQTLPLTPchr20_45906012_G_A_b381.2E-09-0.26Artery - Aorta
rs6065904eQTLPLTPchr20_45906012_G_A_b384.2E-09-0.29Heart - Left Ventricle
rs6065904eQTLPLTPchr20_45906012_G_A_b385.0E-09-0.22Thyroid
rs6065904eQTLPLTPchr20_45906012_G_A_b381.7E-08-0.29Stomach
rs6065904eQTLPLTPchr20_45906012_G_A_b384.3E-08-0.24Lung
rs6065904eQTLNEURL2chr20_45906012_G_A_b386.6E-08-0.26Adipose - Subcutaneous
rs6065904eQTLPLTPchr20_45906012_G_A_b386.8E-08-0.33Liver
rs6065904eQTLCTSAchr20_45906012_G_A_b384.0E-07-0.14Nerve - Tibial
rs6065904eQTLPLTPchr20_45906012_G_A_b385.3E-07-0.37Spleen
rs6065904eQTLNEURL2chr20_45906012_G_A_b385.6E-07-0.26Adipose - Visceral (Omentum)
rs6065904eQTLPLTPchr20_45906012_G_A_b388.9E-07-0.46Small Intestine - Terminal Ileum
rs6065904eQTLRP3-337O18.9chr20_45906012_G_A_b381.8E-06-0.22Adipose - Subcutaneous
rs6065904eQTLWFDC3chr20_45906012_G_A_b382.9E-06-0.31Nerve - Tibial
rs6065904eQTLDNTTIP1chr20_45906012_G_A_b383.1E-06-0.17Artery - Tibial
rs6065904eQTLWFDC3chr20_45906012_G_A_b384.5E-06-0.27Skin - Sun Exposed (Lower leg)
rs6065904eQTLSNX21chr20_45906012_G_A_b384.8E-06-0.15Esophagus - Muscularis
rs6065904eQTLWFDC3chr20_45906012_G_A_b388.9E-06-0.27Skin - Not Sun Exposed (Suprapubic)
rs6065904eQTLDNTTIP1chr20_45906012_G_A_b381.0E-05-0.14Nerve - Tibial
rs6065904eQTLPLTPchr20_45906012_G_A_b381.1E-05-0.27Prostate
rs6065904eQTLPLTPchr20_45906012_G_A_b381.3E-05-0.26Pituitary
rs6065904eQTLPLTPchr20_45906012_G_A_b381.4E-05-0.21Esophagus - Gastroesophageal Junction
rs6065904eQTLSNX21chr20_45906012_G_A_b381.5E-05-0.16Esophagus - Mucosa
rs6065904eQTLSNX21chr20_45906012_G_A_b381.7E-05-0.23Colon - Sigmoid
rs6065904eQTLSNX21chr20_45906012_G_A_b381.7E-05-0.17Thyroid
rs6065904eQTLPLTPchr20_45906012_G_A_b382.6E-05-0.21Breast - Mammary Tissue
rs6065904eQTLWFDC3chr20_45906012_G_A_b382.9E-05-0.23Artery - Tibial
rs6065904eQTLNEURL2chr20_45906012_G_A_b383.2E-05-0.21Thyroid
rs6065904eQTLPLTPchr20_45906012_G_A_b383.7E-05-0.17Testis
rs6065904eQTLCTSAchr20_45906012_G_A_b384.4E-05-0.11Skin - Not Sun Exposed (Suprapubic)
rs6065904eQTLWFDC3chr20_45906012_G_A_b385.8E-05-0.23Muscle - Skeletal
rs6065904eQTLNEURL2chr20_45906012_G_A_b388.2E-05-0.27Heart - Atrial Appendage
rs6065904eQTLSNX21chr20_45906012_G_A_b388.4E-05-0.17Artery - Aorta
rs6065904eQTLNEURL2chr20_45906012_G_A_b389.5E-05-0.24Artery - Aorta
rs6065904eQTLWFDC3chr20_45906012_G_A_b389.5E-05-0.31Artery - Aorta
rs6065904eQTLRP3-337O18.9chr20_45906012_G_A_b389.5E-05-0.29Heart - Atrial Appendage
rs6065904eQTLPLTPchr20_45906012_G_A_b381.2E-04-0.15Skin - Sun Exposed (Lower leg)
rs6065904eQTLWFDC13chr20_45906012_G_A_b381.5E-040.28Esophagus - Muscularis
rs6065904eQTLDNTTIP1chr20_45906012_G_A_b382.1E-04-0.12Cells - Cultured fibroblasts
rs6065904sQTLZNF335chr20_45906012_G_A_b383.3E-11-0.65Testis
rs6065904sQTLACOT8chr20_45906012_G_A_b381.3E-090.58Heart - Left Ventricle
rs6065904sQTLPLTPchr20_45906012_G_A_b384.5E-08-0.32Whole Blood
rs6065904sQTLPLTPchr20_45906012_G_A_b384.8E-080.53Spleen
rs6065904sQTLACOT8chr20_45906012_G_A_b381.3E-070.42Esophagus - Mucosa
rs6065904sQTLACOT8chr20_45906012_G_A_b382.6E-070.49Heart - Atrial Appendage
rs6065904sQTLCTSAchr20_45906012_G_A_b381.0E-06-0.41Artery - Aorta
rs6065904sQTLACOT8chr20_45906012_G_A_b381.2E-060.33Nerve - Tibial
rs6065904sQTLACOT8chr20_45906012_G_A_b381.2E-060.67Brain - Spinal cord (cervical c-1)
rs6065904sQTLTNNC2chr20_45906012_G_A_b382.1E-060.54Brain - Cerebellum
rs6065904sQTLACOT8chr20_45906012_G_A_b382.1E-060.54Brain - Cerebellum
rs6065904sQTLWFDC3chr20_45906012_G_A_b385.5E-060.23Skin - Sun Exposed (Lower leg)
rs6065904sQTLWFDC3chr20_45906012_G_A_b389.4E-06-0.28Skin - Not Sun Exposed (Suprapubic)

Data availability

GWAS data used in the data are publicly available. Details can be found in Table 1.

References

    1. Nikpay M
    2. Goel A
    3. Won HH
    4. Hall LM
    5. Willenborg C
    6. Kanoni S
    7. Saleheen D
    8. Kyriakou T
    9. Nelson CP
    10. Hopewell JC
    11. Webb TR
    12. Zeng L
    13. Dehghan A
    14. Alver M
    15. Armasu SM
    16. Auro K
    17. Bjonnes A
    18. Chasman DI
    19. Chen S
    20. Ford I
    21. Franceschini N
    22. Gieger C
    23. Grace C
    24. Gustafsson S
    25. Huang J
    26. Hwang SJ
    27. Kim YK
    28. Kleber ME
    29. Lau KW
    30. Lu X
    31. Lu Y
    32. Lyytikäinen LP
    33. Mihailov E
    34. Morrison AC
    35. Pervjakova N
    36. Qu L
    37. Rose LM
    38. Salfati E
    39. Saxena R
    40. Scholz M
    41. Smith AV
    42. Tikkanen E
    43. Uitterlinden A
    44. Yang X
    45. Zhang W
    46. Zhao W
    47. de Andrade M
    48. de Vries PS
    49. van Zuydam NR
    50. Anand SS
    51. Bertram L
    52. Beutner F
    53. Dedoussis G
    54. Frossard P
    55. Gauguier D
    56. Goodall AH
    57. Gottesman O
    58. Haber M
    59. Han BG
    60. Huang J
    61. Jalilzadeh S
    62. Kessler T
    63. König IR
    64. Lannfelt L
    65. Lieb W
    66. Lind L
    67. Lindgren CM
    68. Lokki ML
    69. Magnusson PK
    70. Mallick NH
    71. Mehra N
    72. Meitinger T
    73. Memon FU
    74. Morris AP
    75. Nieminen MS
    76. Pedersen NL
    77. Peters A
    78. Rallidis LS
    79. Rasheed A
    80. Samuel M
    81. Shah SH
    82. Sinisalo J
    83. Stirrups KE
    84. Trompet S
    85. Wang L
    86. Zaman KS
    87. Ardissino D
    88. Boerwinkle E
    89. Borecki IB
    90. Bottinger EP
    91. Buring JE
    92. Chambers JC
    93. Collins R
    94. Cupples LA
    95. Danesh J
    96. Demuth I
    97. Elosua R
    98. Epstein SE
    99. Esko T
    100. Feitosa MF
    101. Franco OH
    102. Franzosi MG
    103. Granger CB
    104. Gu D
    105. Gudnason V
    106. Hall AS
    107. Hamsten A
    108. Harris TB
    109. Hazen SL
    110. Hengstenberg C
    111. Hofman A
    112. Ingelsson E
    113. Iribarren C
    114. Jukema JW
    115. Karhunen PJ
    116. Kim BJ
    117. Kooner JS
    118. Kullo IJ
    119. Lehtimäki T
    120. Loos RJF
    121. Melander O
    122. Metspalu A
    123. März W
    124. Palmer CN
    125. Perola M
    126. Quertermous T
    127. Rader DJ
    128. Ridker PM
    129. Ripatti S
    130. Roberts R
    131. Salomaa V
    132. Sanghera DK
    133. Schwartz SM
    134. Seedorf U
    135. Stewart AF
    136. Stott DJ
    137. Thiery J
    138. Zalloua PA
    139. O'Donnell CJ
    140. Reilly MP
    141. Assimes TL
    142. Thompson JR
    143. Erdmann J
    144. Clarke R
    145. Watkins H
    146. Kathiresan S
    147. McPherson R
    148. Deloukas P
    149. Schunkert H
    150. Samani NJ
    151. Farrall M
    (2015) A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease
    Nature Genetics 47:1121.
    https://doi.org/10.1038/ng.3396
    1. Voight BF
    2. Peloso GM
    3. Orho-Melander M
    4. Frikke-Schmidt R
    5. Barbalic M
    6. Jensen MK
    7. Hindy G
    8. Hólm H
    9. Ding EL
    10. Johnson T
    11. Schunkert H
    12. Samani NJ
    13. Clarke R
    14. Hopewell JC
    15. Thompson JF
    16. Li M
    17. Thorleifsson G
    18. Newton-Cheh C
    19. Musunuru K
    20. Pirruccello JP
    21. Saleheen D
    22. Chen L
    23. Stewart A
    24. Schillert A
    25. Thorsteinsdottir U
    26. Thorgeirsson G
    27. Anand S
    28. Engert JC
    29. Morgan T
    30. Spertus J
    31. Stoll M
    32. Berger K
    33. Martinelli N
    34. Girelli D
    35. McKeown PP
    36. Patterson CC
    37. Epstein SE
    38. Devaney J
    39. Burnett MS
    40. Mooser V
    41. Ripatti S
    42. Surakka I
    43. Nieminen MS
    44. Sinisalo J
    45. Lokki ML
    46. Perola M
    47. Havulinna A
    48. de Faire U
    49. Gigante B
    50. Ingelsson E
    51. Zeller T
    52. Wild P
    53. de Bakker PI
    54. Klungel OH
    55. Maitland-van der Zee AH
    56. Peters BJ
    57. de Boer A
    58. Grobbee DE
    59. Kamphuisen PW
    60. Deneer VH
    61. Elbers CC
    62. Onland-Moret NC
    63. Hofker MH
    64. Wijmenga C
    65. Verschuren WM
    66. Boer JM
    67. van der Schouw YT
    68. Rasheed A
    69. Frossard P
    70. Demissie S
    71. Willer C
    72. Do R
    73. Ordovas JM
    74. Abecasis GR
    75. Boehnke M
    76. Mohlke KL
    77. Daly MJ
    78. Guiducci C
    79. Burtt NP
    80. Surti A
    81. Gonzalez E
    82. Purcell S
    83. Gabriel S
    84. Marrugat J
    85. Peden J
    86. Erdmann J
    87. Diemert P
    88. Willenborg C
    89. König IR
    90. Fischer M
    91. Hengstenberg C
    92. Ziegler A
    93. Buysschaert I
    94. Lambrechts D
    95. Van de Werf F
    96. Fox KA
    97. El Mokhtari NE
    98. Rubin D
    99. Schrezenmeir J
    100. Schreiber S
    101. Schäfer A
    102. Danesh J
    103. Blankenberg S
    104. Roberts R
    105. McPherson R
    106. Watkins H
    107. Hall AS
    108. Overvad K
    109. Rimm E
    110. Boerwinkle E
    111. Tybjaerg-Hansen A
    112. Cupples LA
    113. Reilly MP
    114. Melander O
    115. Mannucci PM
    116. Ardissino D
    117. Siscovick D
    118. Elosua R
    119. Stefansson K
    120. O'Donnell CJ
    121. Salomaa V
    122. Rader DJ
    123. Peltonen L
    124. Schwartz SM
    125. Altshuler D
    126. Kathiresan S
    (2012) Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study
    The Lancet 380:572–580.
    https://doi.org/10.1016/S0140-6736(12)60312-2
    1. Willer CJ
    2. Schmidt EM
    3. Sengupta S
    4. Peloso GM
    5. Gustafsson S
    6. Kanoni S
    7. Ganna A
    8. Chen J
    9. Buchkovich ML
    10. Mora S
    11. Beckmann JS
    12. Bragg-Gresham JL
    13. Chang HY
    14. Demirkan A
    15. Den Hertog HM
    16. Do R
    17. Donnelly LA
    18. Ehret GB
    19. Esko T
    20. Feitosa MF
    21. Ferreira T
    22. Fischer K
    23. Fontanillas P
    24. Fraser RM
    25. Freitag DF
    26. Gurdasani D
    27. Heikkilä K
    28. Hyppönen E
    29. Isaacs A
    30. Jackson AU
    31. Johansson Å
    32. Johnson T
    33. Kaakinen M
    34. Kettunen J
    35. Kleber ME
    36. Li X
    37. Luan J
    38. Lyytikäinen LP
    39. Magnusson PKE
    40. Mangino M
    41. Mihailov E
    42. Montasser ME
    43. Müller-Nurasyid M
    44. Nolte IM
    45. O'Connell JR
    46. Palmer CD
    47. Perola M
    48. Petersen AK
    49. Sanna S
    50. Saxena R
    51. Service SK
    52. Shah S
    53. Shungin D
    54. Sidore C
    55. Song C
    56. Strawbridge RJ
    57. Surakka I
    58. Tanaka T
    59. Teslovich TM
    60. Thorleifsson G
    61. Van den Herik EG
    62. Voight BF
    63. Volcik KA
    64. Waite LL
    65. Wong A
    66. Wu Y
    67. Zhang W
    68. Absher D
    69. Asiki G
    70. Barroso I
    71. Been LF
    72. Bolton JL
    73. Bonnycastle LL
    74. Brambilla P
    75. Burnett MS
    76. Cesana G
    77. Dimitriou M
    78. Doney ASF
    79. Döring A
    80. Elliott P
    81. Epstein SE
    82. Ingi Eyjolfsson G
    83. Gigante B
    84. Goodarzi MO
    85. Grallert H
    86. Gravito ML
    87. Groves CJ
    88. Hallmans G
    89. Hartikainen AL
    90. Hayward C
    91. Hernandez D
    92. Hicks AA
    93. Holm H
    94. Hung YJ
    95. Illig T
    96. Jones MR
    97. Kaleebu P
    98. Kastelein JJP
    99. Khaw KT
    100. Kim E
    101. Klopp N
    102. Komulainen P
    103. Kumari M
    104. Langenberg C
    105. Lehtimäki T
    106. Lin SY
    107. Lindström J
    108. Loos RJF
    109. Mach F
    110. McArdle WL
    111. Meisinger C
    112. Mitchell BD
    113. Müller G
    114. Nagaraja R
    115. Narisu N
    116. Nieminen TVM
    117. Nsubuga RN
    118. Olafsson I
    119. Ong KK
    120. Palotie A
    121. Papamarkou T
    122. Pomilla C
    123. Pouta A
    124. Rader DJ
    125. Reilly MP
    126. Ridker PM
    127. Rivadeneira F
    128. Rudan I
    129. Ruokonen A
    130. Samani N
    131. Scharnagl H
    132. Seeley J
    133. Silander K
    134. Stančáková A
    135. Stirrups K
    136. Swift AJ
    137. Tiret L
    138. Uitterlinden AG
    139. van Pelt LJ
    140. Vedantam S
    141. Wainwright N
    142. Wijmenga C
    143. Wild SH
    144. Willemsen G
    145. Wilsgaard T
    146. Wilson JF
    147. Young EH
    148. Zhao JH
    149. Adair LS
    150. Arveiler D
    151. Assimes TL
    152. Bandinelli S
    153. Bennett F
    154. Bochud M
    155. Boehm BO
    156. Boomsma DI
    157. Borecki IB
    158. Bornstein SR
    159. Bovet P
    160. Burnier M
    161. Campbell H
    162. Chakravarti A
    163. Chambers JC
    164. Chen YI
    165. Collins FS
    166. Cooper RS
    167. Danesh J
    168. Dedoussis G
    169. de Faire U
    170. Feranil AB
    171. Ferrières J
    172. Ferrucci L
    173. Freimer NB
    174. Gieger C
    175. Groop LC
    176. Gudnason V
    177. Gyllensten U
    178. Hamsten A
    179. Harris TB
    180. Hingorani A
    181. Hirschhorn JN
    182. Hofman A
    183. Hovingh GK
    184. Hsiung CA
    185. Humphries SE
    186. Hunt SC
    187. Hveem K
    188. Iribarren C
    189. Järvelin MR
    190. Jula A
    191. Kähönen M
    192. Kaprio J
    193. Kesäniemi A
    194. Kivimaki M
    195. Kooner JS
    196. Koudstaal PJ
    197. Krauss RM
    198. Kuh D
    199. Kuusisto J
    200. Kyvik KO
    201. Laakso M
    202. Lakka TA
    203. Lind L
    204. Lindgren CM
    205. Martin NG
    206. März W
    207. McCarthy MI
    208. McKenzie CA
    209. Meneton P
    210. Metspalu A
    211. Moilanen L
    212. Morris AD
    213. Munroe PB
    214. Njølstad I
    215. Pedersen NL
    216. Power C
    217. Pramstaller PP
    218. Price JF
    219. Psaty BM
    220. Quertermous T
    221. Rauramaa R
    222. Saleheen D
    223. Salomaa V
    224. Sanghera DK
    225. Saramies J
    226. Schwarz PEH
    227. Sheu WH
    228. Shuldiner AR
    229. Siegbahn A
    230. Spector TD
    231. Stefansson K
    232. Strachan DP
    233. Tayo BO
    234. Tremoli E
    235. Tuomilehto J
    236. Uusitupa M
    237. van Duijn CM
    238. Vollenweider P
    239. Wallentin L
    240. Wareham NJ
    241. Whitfield JB
    242. Wolffenbuttel BHR
    243. Ordovas JM
    244. Boerwinkle E
    245. Palmer CNA
    246. Thorsteinsdottir U
    247. Chasman DI
    248. Rotter JI
    249. Franks PW
    250. Ripatti S
    251. Cupples LA
    252. Sandhu MS
    253. Rich SS
    254. Boehnke M
    255. Deloukas P
    256. Kathiresan S
    257. Mohlke KL
    258. Ingelsson E
    259. Abecasis GR
    260. Global Lipids Genetics Consortium
    (2013) Discovery and refinement of loci associated with lipid levels
    Nature Genetics 45:1274.
    https://doi.org/10.1038/ng.2797

Article and author information

Author details

  1. Qingyuan Zhao

    Statistical Laboratory, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration
    For correspondence
    qyzhao@statslab.cam.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9902-2768
  2. Jingshu Wang

    Department of Statistics, University of Chicago, Chicago, United States
    Contribution
    Conceptualization, Data curation, Software, Validation, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Zhen Miao

    Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Investigation, Visualization, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3255-9517
  4. Nancy R Zhang

    Department of Statistics, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Supervision, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Sean Hennessy

    Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Resources, Supervision, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Dylan S Small

    Department of Statistics, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Supervision, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4928-2646
  7. Daniel J Rader

    1. Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    2. Department of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Validation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared

Funding

No external funding was received for this work.

Version history

  1. Received: April 28, 2020
  2. Accepted: April 23, 2021
  3. Accepted Manuscript published: April 26, 2021 (version 1)
  4. Version of Record published: May 28, 2021 (version 2)

Copyright

© 2021, Zhao et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Qingyuan Zhao
  2. Jingshu Wang
  3. Zhen Miao
  4. Nancy R Zhang
  5. Sean Hennessy
  6. Dylan S Small
  7. Daniel J Rader
(2021)
A Mendelian randomization study of the role of lipoprotein subfractions in coronary artery disease
eLife 10:e58361.
https://doi.org/10.7554/eLife.58361

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