Browsing by Author "Asselbergs, Folkert W."

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals
    (BioMed Central, 2017) Holzinger, Emily R.; Verma, Shefali S.; Moore, Carrie B.; Hall, Molly; De, Rishika; Gilbert-Diamond, Diane; Lanktree, Matthew B.; Pankratz, Nathan; Amuzu, Antoinette; Burt, Amber; Dale, Caroline; Dudek, Scott; Furlong, Clement E.; Gaunt, Tom R.; Kim, Daniel Seung; Riess, Helene; Sivapalaratnam, Suthesh; Tragante, Vinicius; Van Iperen, Erik P. A.; Brautba, Ariel; Carrell, David S.; Crosslin, David R.; Jarvik, Gail P.; Kuivaniemi, Helena; Kullo, Iftikhar J.; Larson, Eric B.; Rasmussen-Torvik, Laura J.; Tromp, Gerard; Baumert, Jens; Cruickshanks, Karen J.; Farrall, Martin; Hingorani, Aroon D.; Hovingh, G. K.; Kleber, Marcus E.; Klein, Barbara E.; Klein, Ronald; Koenig, Wolfgang; Lange, Leslie A.; Mӓrz, Winfried; North, Kari E.; Onland-Moret, N. Charlotte; Reiner, Alex P.; Talmud, Philippa J.; Van Der Schouw, Yvonne T.; Wilson, James G.; Kivimaki, Mika; Kumari, Meena; Moore, Jason H.; Drenos, Fotios; Asselbergs, Folkert W.; Keating, Brendan J.; Ritchie, Marylyn D.
    Background: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Conclusions: These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.
  • Thumbnail Image
    Item
    Genetic association of lipids and lipid drug targets with abdominal aortic aneurysm : a meta-analysis
    (American Medical Association, 2018) Harrison, Seamus C.; Holmes, Michael V.; Burgess, Stephen; Asselbergs, Folkert W.; Jones, Gregory T.; Baas, Annette F.; Van 't Hof, F. N.; De Bakker, Paul I. W.; Blankensteijn, Jan D.; Powell, Janet T.; Saratzis, Athanasios; De Borst, Gert J.; Swerdlow, Daniel I.; Van der Graaf, Yolanda; Van Rij, Andre M.; Carey, David J.; Elmore, James R.; Tromp, Gerard; Kuivaniemi, Helena; Sayers, Robert D.; Samani, Nilesh J.; Bown, Matthew J.; Humphries, Steve E.
    Importance Risk factors for abdominal aortic aneurysm (AAA) are largely unknown, which has hampered the development of nonsurgical treatments to alter the natural history of disease. Objective To investigate the association between lipid-associated single-nucleotide polymorphisms (SNPs) and AAA risk. Design, Setting, and Participants Genetic risk scores, composed of lipid trait–associated SNPs, were constructed and tested for their association with AAA using conventional (inverse-variance weighted) mendelian randomization (MR) and data from international AAA genome-wide association studies. Sensitivity analyses to account for potential genetic pleiotropy included MR-Egger and weighted median MR, and multivariable MR method was used to test the independent association of lipids with AAA risk. The association between AAA and SNPs in loci that can act as proxies for drug targets was also assessed. Data collection took place between January 9, 2015, and January 4, 2016. Data analysis was conducted between January 4, 2015, and December 31, 2016. Exposures Genetic elevation of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Main Outcomes and Measures The association between genetic risk scores of lipid-associated SNPs and AAA risk, as well as the association between SNPs in lipid drug targets (HMGCR, CETP, and PCSK9) and AAA risk. Results Up to 4914 cases and 48 002 controls were included in our analysis. A 1-SD genetic elevation of LDL-C was associated with increased AAA risk (odds ratio [OR], 1.66; 95% CI, 1.41-1.96; P = 1.1 × 10−9). For HDL-C, a 1-SD increase was associated with reduced AAA risk (OR, 0.67; 95% CI, 0.55-0.82; P = 8.3 × 10−5), whereas a 1-SD increase in triglycerides was associated with increased AAA risk (OR, 1.69; 95% CI, 1.38-2.07; P = 5.2 × 10−7). In multivariable MR analysis and both MR-Egger and weighted median MR methods, the association of each lipid fraction with AAA risk remained largely unchanged. The LDL-C–reducing allele of rs12916 in HMGCR was associated with AAA risk (OR, 0.93; 95% CI, 0.89-0.98; P = .009). The HDL-C–raising allele of rs3764261 in CETP was associated with lower AAA risk (OR, 0.89; 95% CI, 0.85-0.94; P = 3.7 × 10−7). Finally, the LDL-C–lowering allele of rs11206510 in PCSK9 was weakly associated with a lower AAA risk (OR, 0.94; 95% CI, 0.88-1.00; P = .04), but a second independent LDL-C–lowering variant in PCSK9 (rs2479409) was not associated with AAA risk (OR, 0.97; 95% CI, 0.92-1.02; P = .28). Conclusions and Relevance The MR analyses in this study lend support to the hypothesis that lipids play an important role in the etiology of AAA. Analyses of individual genetic variants used as proxies for drug targets support LDL-C lowering as a potential effective treatment strategy for preventing and managing AAA.