Nature CommunicationsGeneticsPeer-reviewed journal article
The transferability of lipid loci across African, Asian and European cohorts
Karoline B. KuchenbaeckeriD, Nikita TelkariD, Theresa Reiker, Robin G. WaltersiD, Kuang LiniD, Anders ErikssoniD, Deepti GurdasaniiD, Arthur GillyiD, Lorraine SouthamiD, Emmanouil Tsafantakis, Maria Karaleftheri, Janet SeeleyiD, Anatoli Kamali, Gershim AsikiiD, Iona Y. MillwoodiD, Michael Vaclav HolmesiD, Huaidong DuiD, Yu GuoiD, Meena Kumari BiD, George V. DedoussisiD, Liming LiiD, Zhengming CheniD, Manjinder S. SandhuiD, Eleftheria ZegginiiD, Understanding Society Scientific Group, , Dieter Wolke CiD
Abstract
The under-representation of non-European samples in genome-wide association studies could ultimately restrict who benefits from medical advances through genomic science. Our aim was therefore to address the fundamental question whether causal variants for blood lipids are shared across populations.\n\nA polygenic score based on established LDL-cholesterol-associated loci from European discovery samples had consistent effects on serum levels in samples from the UK, Uganda and Greek population isolates (correlation coefficient r=0.23 to 0.28 per LDL standard deviation, p60% of major lipid loci displayed evidence of replication with one exception. There was evidence for an effect on serum levels in the Ugandan samples for only 10% of major triglyceride loci. The PRS was only weakly associated in this group (r=0.06, SE=0.013). We establish trans-ethnic colocalization as a method to distinguish shared from population-specific trait loci.\n\nOur results provide evidence for high levels of consistency of genetic associations for cholesterol biomarkers across populations. However, we also demonstrate that the degree of shared causal genetic architecture can be population-, trait- and locus-specific. Efforts to implement genetic risk prediction in clinical settings should account for this.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- research focus: fine-mapping
- approach: statistical model
- Citations
- 96
- FWCI
- 7.21
- Field percentile
- top 2%
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