Tibor V Varga1, Azra Kurbasic1, Mattias Aine2, Pontus Eriksson2, Ashfaq Ali1, George Hindy3, Stefan Gustafsson4, Jian'an Luan5, Dmitry Shungin1,6,7, Yan Chen1, Christina-Alexandra Schulz3, Peter M Nilsson8, Göran Hallmans9, Inês Barroso10,11,12, Panos Deloukas13,14, Claudia Langenberg5, Robert A Scott5, Nicholas J Wareham5, Lars Lind15, Erik Ingelsson4,16, Olle Melander17, Marju Orho-Melander3, Frida Renström1,9, Paul W Franks1,7,18. 1. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden. 2. Division of Oncology and Pathology, Skåne University Hospital, Lund University, Lund, Sweden. 3. Diabetes and Cardiovascular Disease - Genetic Epidemiology, Skåne University Hospital, Malmö, Sweden. 4. Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 5. Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK. 6. Department of Odontology. 7. Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden. 8. Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden. 9. Department of Biobank Research, Umeå University, Umeå, Sweden. 10. Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. 11. Metabolic Research Laboratories. 12. NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge, UK. 13. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, London, UK. 14. Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia. 15. Department of Medical Sciences, Uppsala University, Uppsala, Sweden. 16. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. 17. Department of Clinical Sciences, Hypertension and Cardiovascular Diseases, Skåne University Hospital, Malmö, Sweden. 18. Department of Nutrition, Harvard T.H Chan School of Public Health, Boston, MA, USA.
Abstract
Background: Cross-sectional genome-wide association studies have identified hundreds of loci associated with blood lipids and related cardiovascular traits, but few genetic association studies have focused on long-term changes in blood lipids. Methods: Participants from the GLACIER Study (Nmax = 3492) were genotyped with the MetaboChip array, from which 29 387 SNPs (single nucleotide polymorphisms; replication, fine-mapping regions and wildcard SNPs for lipid traits) were extracted for association tests with 10-year change in total cholesterol (ΔTC) and triglycerides (ΔTG). Four additional prospective cohort studies (MDC, PIVUS, ULSAM, MRC Ely; Nmax = 8263 participants) were used for replication. We conducted an in silico look-up for association with coronary artery disease (CAD) in the Coronary ARtery DIsease Genome-wide Replication and Meta-analysis (CARDIoGRAMplusC4D) Consortium (N ∼ 190 000) and functional annotation for the top ranking variants. Results: In total, 956 variants were associated (P < 0.01) with either ΔTC or ΔTG in GLACIER. In GLACIER, chr19:50121999 at APOE was associated with ΔTG and multiple SNPs in the APOA1/A4/C3/A5 region at genome-wide significance (P < 5 × 10-8), whereas variants in four loci, DOCK7, BRE, SYNE1 and KCNIP1, reached study-wide significance (P < 1.7 × 10-6). The rs7412 variant at APOE was associated with ΔTC in GLACIER (P < 1.7 × 10-6). In pooled analyses of all cohorts, 139 SNPs at six and five loci were associated with ΔTC and for ΔTG, respectively (P < 10-3). Of these, a variant at CAPN3 (P = 1.2 × 10-4), multiple variants at HPR (Pmin = 1.5 × 10-6) and a variant at SIX5 (P = 1.9 × 10-4) showed evidence for association with CAD. Conclusions: We identified seven novel genomic regions associated with long-term changes in blood lipids, of which three also raise CAD risk.
Background: Cross-sectional genome-wide association studies have identified hundreds of loci associated with blood lipids and related cardiovascular traits, but few genetic association studies have focused on long-term changes in blood lipids. Methods: Participants from the GLACIER Study (Nmax = 3492) were genotyped with the MetaboChip array, from which 29 387 SNPs (single nucleotide polymorphisms; replication, fine-mapping regions and wildcard SNPs for lipid traits) were extracted for association tests with 10-year change in total cholesterol (ΔTC) and triglycerides (ΔTG). Four additional prospective cohort studies (MDC, PIVUS, ULSAM, MRC Ely; Nmax = 8263 participants) were used for replication. We conducted an in silico look-up for association with coronary artery disease (CAD) in the Coronary ARtery DIsease Genome-wide Replication and Meta-analysis (CARDIoGRAMplusC4D) Consortium (N ∼ 190 000) and functional annotation for the top ranking variants. Results: In total, 956 variants were associated (P < 0.01) with either ΔTC or ΔTG in GLACIER. In GLACIER, chr19:50121999 at APOE was associated with ΔTG and multiple SNPs in the APOA1/A4/C3/A5 region at genome-wide significance (P < 5 × 10-8), whereas variants in four loci, DOCK7, BRE, SYNE1 and KCNIP1, reached study-wide significance (P < 1.7 × 10-6). The rs7412 variant at APOE was associated with ΔTC in GLACIER (P < 1.7 × 10-6). In pooled analyses of all cohorts, 139 SNPs at six and five loci were associated with ΔTC and for ΔTG, respectively (P < 10-3). Of these, a variant at CAPN3 (P = 1.2 × 10-4), multiple variants at HPR (Pmin = 1.5 × 10-6) and a variant at SIX5 (P = 1.9 × 10-4) showed evidence for association with CAD. Conclusions: We identified seven novel genomic regions associated with long-term changes in blood lipids, of which three also raise CAD risk.
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