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. 2021 Sep 10;19(1):232.
doi: 10.1186/s12916-021-02087-1.

Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci

Eric L Harshfield  1   2 , Eric B Fauman  3 , David Stacey  4 , Dirk S Paul  4   5   6   7   8   9 , Daniel Ziemek  10 , Rachel M Y Ong  4 , John Danesh  4   5   6   7   8   9 , Adam S Butterworth  4   5   6   7   8   9 , Asif Rasheed  11 , Taniya Sattar  11 , Zameer-Ul-Asar  11 , Imran Saleem  11 , Zoubia Hina  11 , Unzila Ishtiaq  11 , Nadeem Qamar  12 , Nadeem Hayat Mallick  13 , Zia Yaqub  12 , Tahir Saghir  12 , Syed Nadeem Hasan Rizvi  12 , Anis Memon  12 , Mohammad Ishaq  14 , Syed Zahed Rasheed  14 , Fazal-Ur-Rehman Memon  15 , Anjum Jalal  16 , Shahid Abbas  16 , Philippe Frossard  11 , Danish Saleheen #  11   17 , Angela M Wood #  4   5   6   7   8   9 , Julian L Griffin #  18   19 , Albert Koulman #  20
Affiliations

Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci

Eric L Harshfield et al. BMC Med. .

Abstract

Background: Genetic, lifestyle, and environmental factors can lead to perturbations in circulating lipid levels and increase the risk of cardiovascular and metabolic diseases. However, how changes in individual lipid species contribute to disease risk is often unclear. Moreover, little is known about the role of lipids on cardiovascular disease in Pakistan, a population historically underrepresented in cardiovascular studies.

Methods: We characterised the genetic architecture of the human blood lipidome in 5662 hospital controls from the Pakistan Risk of Myocardial Infarction Study (PROMIS) and 13,814 healthy British blood donors from the INTERVAL study. We applied a candidate causal gene prioritisation tool to link the genetic variants associated with each lipid to the most likely causal genes, and Gaussian Graphical Modelling network analysis to identify and illustrate relationships between lipids and genetic loci.

Results: We identified 253 genetic associations with 181 lipids measured using direct infusion high-resolution mass spectrometry in PROMIS, and 502 genetic associations with 244 lipids in INTERVAL. Our analyses revealed new biological insights at genetic loci associated with cardiometabolic diseases, including novel lipid associations at the LPL, MBOAT7, LIPC, APOE-C1-C2-C4, SGPP1, and SPTLC3 loci.

Conclusions: Our findings, generated using a distinctive lipidomics platform in an understudied South Asian population, strengthen and expand the knowledge base of the genetic determinants of lipids and their association with cardiometabolic disease-related loci.

Keywords: Gaussian Graphical Modelling; Genetics; Lipidomics; Network analysis; South Asian.

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Conflict of interest statement

E.B.F. and D.Z. are employees and shareholders of Pfizer, Inc. J.D. has received research funding from the British Heart Foundation, the National Institute for Health Research Cambridge Comprehensive Biomedical Research Centre, the Bupa Foundation, diaDexus, the European Research Council, the European Union, the Evelyn Trust, the Fogarty International Centre, GlaxoSmithKline, Merck, the National Heart, Lung, and Blood Institute, the National Institute for Health Research [Senior Investigator Award], the National Institute of Neurological Disorders and Stroke, NHS Blood and Transplant, Novartis, Pfizer, the UK Medical Research Council, and the Wellcome Trust. J.L.G. has received funding from Agilent, Waters, GlaxoSmithKline, Medimmune, Unilever, AstraZeneca, the Medical Research Council, the Biotechnology and Biological Sciences Research Council, the National Institutes of Health, the British Heart Foundation, and the Wellcome Trust. D.Sa. has received funding from Pfizer, Regeneron Pharmaceuticals, Genentech, and Eli Lilly. All other authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Miami plot of combined association results from genome-wide association analysis for all lipids in PROMIS and INTERVAL. The combined association results are shown for all lipids with each variant in PROMIS (top) and INTERVAL (bottom). P values > 1 x 10-3 have been truncated at 1 x 10-3, and P values < 1 x 10-200 have been truncated at 1 x 10-200. Actual P value for lead SNP in FADS-1-2-3 locus in INTERVAL is 1.6 x 10-286
Fig. 2
Fig. 2
Heat map showing associations of significant loci from conditional analyses with selected lipid metabolites in PROMIS. The effect estimates of the associations between significant variants and selected lipids are plotted as a heat map. Results are shown for selected top lipids with the strongest associations within each subclass (rows) against the most strongly associated genetic variant within each locus (columns). The associations with major lipids from the GLGC (total cholesterol, HDL-C, LDL-C, and triglycerides), DIAGRAM Consortium (type 2 diabetes), and CARDIoGRAMplusC4D Consortium (coronary artery disease) are also shown. The magnitude and direction of the effect estimates (standardised per 1-SD) are indicated by a colour scale, with blue indicating a negative association and red indicating a positive association with respect to the SNP effect on the trait. Asterisks indicate the degree of significance of the P values of association. * = P < 1 x 10-4; ** = P < 5 x 10-8; *** = P < 8.9 x 10-10
Fig. 3
Fig. 3
Combined network graph summarising genetic associations and a Gaussian graphical model (GGM) relating to levels of individual lipid species in PROMIS. Nodes representing genetic loci are each labelled with the most likely “causal” gene at that locus according to our functional annotation (see “Methods” section). In order for an edge to be drawn between a genetic locus and a lipid subclass, there must have been a minimum of one variant at that locus significantly (P < 8.9 x 10-10) associated with a minimum of one lipid species belonging to that lipid subclass. Edges between lipid subclasses indicate whether there was either a significant over- (green) or under- (purple) representation (the magnitude is indicated in the thickness of the edges) of GGM connections between lipid species belonging to different lipid subclasses
Fig. 4
Fig. 4
Combined network graph summarising genetic associations and a Gaussian graphical model (GGM) relating to levels of individual triglycerides in PROMIS. Nodes representing genetic loci are each labelled with the most likely “causal” gene at that locus according to our functional annotation (see “Methods” section). In order for an edge to be drawn between a genetic locus and a triglyceride, there must have been a minimum of one variant at that locus significantly (P < 8.9 x 10-10) associated with at least one triglyceride. Edges between triglycerides indicate whether there was either a significant over- (green) or under- (purple) representation, with the magnitude indicated by the thickness of the edges
Fig. 5
Fig. 5
Association of lipids in PROMIS with PNPLA3 and differences in levels of triglycerides by genotype. a Association of G allele of rs738409 in PNPLA3 locus with levels of various lipids in PROMIS. The black lines denote 95% confidence intervals. Difference in levels of triglycerides in PROMIS by genotype: b [TG(57:10)+NH4]+ (m/z 930.754), c [TG(46:0)+NH4]+ (m/z 796.7393), and d [TG(56:6)+NH4]+ (m/z 924.801). P values are for ANOVA test of difference in mean levels of triglycerides by genotype
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