Last updated:
Author(s):
Linda Ottensmann, Rubina Tabassum, Sanni E. Ruotsalainen, Mathias J. Gerl, Christian Klose, Daniel L. McCartney, Elisabeth Widén, FinnGen, Kai Simons, Samuli Ripatti, Veronique Vitart, Caroline Hayward, Matti Pirinen
Publish date:
28 March 2025
Journal:
EBioMedicine
PubMed ID:
40157129

Abstract

BACKGROUND: Genome-wide association studies of lipid species have identified several loci shared with various diseases, however, the relationship between lipid species and disease risk remains poorly understood. Here we investigated whether the plasma levels of lipid species are causally linked to disease risk.

METHODS: We built genetic predictors of 179 lipid species, measured in 7174 Finnish individuals, by utilising either 11 high-impact genomic loci or genome-wide polygenic scores (PGS). We assessed the impact of the lipid species on seven diseases by performing disease association across FinnGen (n = 500,348), UK Biobank (n = 420,531), and Generation Scotland (n = 20,032). We performed univariable Mendelian randomisation (MR) and multivariable MR (MVMR) analyses to examine whether lipid species impact disease risk independently of standard lipids.

FINDINGS: PGS explained >4% of the variance for 34 lipid species but variants outside the high-impact loci had only a marginal contribution. Variants within the high-impact loci showed association with all seven diseases. MVMR supported a causal role of ApoB in ischaemic heart disease after accounting for lipid species. Phosphatidylethanolamine-increasing LIPC variants seemed to lower age-related macular degeneration risk independently of HDL-cholesterol. MVMR suggested a protective effect of four lipid species containing arachidonic acid on cholelithiasis risk independently of Total Cholesterol.

INTERPRETATION: Our study demonstrates how genetic predictors of lipid species can be utilised to gain insights into disease risk. We report potential links between lipid species and age-related macular degeneration and cholelithiasis risk, which can be explored for their utility in disease risk prediction and therapy.

FUNDING: The funders had no role in the study design, data analyses, interpretation, or writing of this article.

Related projects

We propose to apply statistical fine-mapping methods to the UK Biobank genotype-phenotype data. We study how our recently published summary-data based fine-mapping method works with…

Institution:
University of Helsinki, Finland

All projects