The impact of low-frequency and rare variants on lipid levels

Surakka, Ida; Horikoshi, Momoko; Mägi, Reedik; Sarin, Antti‐Pekka; Mahajan, Anubha; Lagou, Vasiliki; Marullo, Letizia; Ferreira, Teresa; Miraglio, Benjamin; Timonen, Sanna; Kettunen, Johannes; Pirinen, Matti; Karjalainen, Juha; Þorleifsson, Guðmar; Hägg, Sara; Hottenga, Jouke‐Jan; Isaacs, Aaron; Ladenvall, Claes; Beekman, Marian; Esko, Tõnu; Ried, Janina S.; Nelson, Christopher P.; Willenborg, Christina; Gustafsson, Stefan; Westra, Harm-Jan; Blades, Matthew; Craen, Anton J. M. de; Geus, Eco J. C. de; Deelen, Joris; Grallert, Harald; Hamsten, Anders; Havulinna, Aki S.; Hengstenberg, Christian; Houwing-Duistermaat, Jeanine J.; Hyppönen, Elina; Karssen, Lennart C.; Lehtimäki, Terho; Lyssenko, Valeriya; Magnusson, Patrik K. E.; Mihailov, Evelin; Müller‐Nurasyid, Martina; Mpindi, John-Patrick; Pedersen, Nancy L.; Penninx, Brenda W. J. H.; Perola, Markus; Pers, Tune H.; Peters, Annette; Rung, Johan; Smit, Johannes H.; Steinthórsdóttir, Valgerður; Tobin, Martin D.; Tšernikova, Natalia; Leeuwen, Elisabeth M. van; Viikari, Jorma; Willems, Sara M.; Willemsen, Gonneke; Schunkert, Heribert; Erdmann, Jeanette; Samani, Nilesh J.; Kaprio, Jaakko; Lind, Lars; Gieger, Christian; Metspalu, Andres; Slagboom, P. Eline; Groop, Leif; Duijn, Cornelia M. van; Eriksson, Johan G.; Jula, Antti; Salomaa, Veikko; Boomsma, Dorret I.; Power, Christine; Raitakari, Olli T.; Ingelsson, Erik; Järvelin, Marjo‐Riitta; Þorsteinsdóttir, Unnur; Franke, Lude; Ikonen, Elina; Kallioniemi, Olli; Pietiäinen, Vilja; Lindgren, Cecilia M.; Stefánsson, Kári; Palotie, Aarno; McCarthy, Mark I.; Morris, Andrew P.; Prokopenko, Inga; Ripatti, Samuli

doi:10.1038/ng.3300

Cited by 313 publications

(289 citation statements)

References 42 publications

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“…As shown by others (24,(27)(28)(29)(30), we found a substantial increase in the explained variance when we assessed heritability estimates based on the 209 variants (all traits) identified by both the unconditional and conditional analyses compared to the published lead SNPs in the corresponding 135 loci. In some loci, the inclusion of new independent secondary signals contributes only marginally to heritability estimates.…”

Section: Maf -Minormentioning

confidence: 95%

Analysis with the exome array identifies multiple new independent variants in lipid loci

Kanoni¹,

Masca²,

Stirrups³

et al. 2016

Hum. Mol. Genet.

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It has been hypothesized that low frequency (1-5% minor allele frequency (MAF)) and rare (<1% MAF) variants with large effect sizes may contribute to the missing heritability in complex traits. Here, we report an association analysis of lipid traits 4095 Human Molecular Genetics, 2016, Vol. 25, No. 18 | (total cholesterol, LDL-cholesterol, HDL-cholesterol triglycerides) in up to 27 312 individuals with a comprehensive set of low frequency coding variants (ExomeChip), combined with conditional analysis in the known lipid loci. No new locus reached genome-wide significance. However, we found a new lead variant in 26 known lipid association regions of which 16 were >1000-fold more significant than the previous sentinel variant and not in close LD (six had MAF <5%). Furthermore, conditional analysis revealed multiple independent signals (ranging from 1 to 5) in a third of the 98 lipid loci tested, including rare variants. Addition of our novel associations resulted in between 1.5-and 2.5-fold increase in the proportion of heritability explained for the different lipid traits. Our findings suggest that rare coding variants contribute to the genetic architecture of lipid traits.

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Section: Maf -Minormentioning

confidence: 95%

Analysis with the exome array identifies multiple new independent variants in lipid loci

Kanoni¹,

Masca²,

Stirrups³

et al. 2016

Hum. Mol. Genet.

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“…html); GIANT Consortium (https://www.broadinstitute.org/collaboration/giant/index) 17 for body mass index, waist circumference, and WHR; global lipids genetics consortium (http://csg.sph.umich.edu//abecasis/ public/lipids2013) 18 for lipids; ENGAGE 1000 Genome Consortium 19,20 (http://diagram-consortium.org/2015_ENGAGE_1KG/) for fasting insulin, lipids, and WHR; and CARDIoGRAM Consortium and CARDIoGRAMplusC4D (http://www.cardiogramplusc4d.org/) 1000 Genome Consortium 21,22 for CHD. They were used in enrichment analyses and to obtain the effects of genetic variants on TL, metabolic risk factors, and CHD.…”

Section: Summary Of Gwas Datamentioning

confidence: 99%

Exploring the Causal Pathway From Telomere Length to Coronary Heart Disease

Zhan

Karlsson

et al. 2017

Circ Res

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In this study, we aim to examine metabolic risk factors as possible mediators in the causal relationship between genetically determined TL and CHD using a network MR method 12 from summarized genome-wide association study (GWAS) data. The rationale for focusing on metabolic risk factors is based on the following: (1) these metabolic risk factors explain a large proportion Molecular Medicine© 2017 American Heart Association, Inc. Rationale: Observational studies have found shorter leukocyte telomere length (TL) to be a risk factor for coronary heart disease (CHD), and recently the association was suggested to be causal. However, the relationship between TL and common metabolic risk factors for CHD is not well understood. Whether these risk factors could explain pathways from TL to CHD warrants further attention.Objective: To examine whether metabolic risk factors for CHD mediate the causal pathway from short TL to increased risk of CHD using a network Mendelian randomization design. Methods and Results:Summary statistics from several genome-wide association studies were used in a 2-sample Mendelian randomization study design. Network Mendelian randomization analysis-an approach using genetic variants as the instrumental variables for both the exposure and mediator to infer causality-was performed to examine the causal association between telomeres and CHD and metabolic risk factors. Summary statistics from the ENGAGE Telomere Consortium were used (n=37 684) as a TL genetic instrument, CARDIoGRAMplusC4D Consortium data were used (case=22 233 and control=64 762) for CHD, and other consortia data were used for metabolic traits (fasting insulin, triglyceride, total cholesterol, low-density lipoprotein cholesterol, fasting glucose, diabetes mellitus, glycohemoglobin, body mass index, waist circumference, and waist:hip ratio). One-unit increase of genetically determined TL was associated with −0.07 (95% confidence interval, −0.01 to −0.12; P=0.01) lower log-transformed fasting insulin (pmol/L) and 21% lower odds (95% confidence interval, 3-35; P=0.02) of CHD. Higher genetically determined log-transformed fasting insulin level was associated with higher CHD risk (odds ratio, 1.86; 95% confidence interval, 1.01-3.41; P=0.04). Conclusions:

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“…Estimated from genome-wide association studies, the common genetic variants analyzed in the present study only explain 8-10% of the total variance of baseline serum triglyceride levels (8,9). Thus, the translation of our genetic findings into direct clinical application will not be relevant until a sufficient number of genetic variants that can explain a larger proportion of the variance in triglycerides are identified in the future.…”

Section: Interactionsmentioning

confidence: 98%

“…The participants did not give information on the type of lipid lowering medication, but Danish guidelines state that statins should be the first-line drug of choice (26). Sensitivity analyses were performed by excluding individuals influencing levels of triglycerides (8)(9)(10), which together explain up to 9.3-9.6% of the variance in fasting serum triglyceride concentrations estimated from a random sample of adults from the FINRISK cohort (8) and in the Framingham Heart study (9).…”

Section: Biochemical and Anthropometric Measuresmentioning

confidence: 99%

Increasing insulin resistance accentuates the effect of triglyceride-associated loci on serum triglycerides during 5 years

Justesen

Andersson

Allin

et al. 2016

Journal of Lipid Research

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A high level of circulating triglycerides is a risk factor for type 2 diabetes, myocardial infarction, ischemic heart disease, and all-cause mortality (1-4). A recent Mendelian randomization study supports a causal effect of triglycerides on coronary heart disease risk (5). Regulation of blood levels of triglycerides involves a complex interplay implicating both the liver and pancreas. Some of the important players in this regard are body adiposity, insulin resistance, and glucose homeostasis (6). In addition, circulating triglyceride levels are heritable, as shown in a recent population-based study of twins (H 2 = 0.59; h 2 = 0.33) (7). Genetic association studies have identified more than 40 SNPs Abstract Blood concentrations of triglycerides are influenced by genetic factors as well as a number of environmental factors, including adiposity and glucose homeostasis. The aim was to investigate the association between a serum triglyceride weighted genetic risk score (wGRS) and changes in fasting serum triglyceride level over 5 years and to test whether the effect of the wGRS was modified by 5 year changes of adiposity, insulin resistance, and lifestyle factors. A total of 3,474 nondiabetic individuals from the Danish Inter99 cohort participated in both the baseline and 5 year follow-up physical examinations and had information on the wGRS comprising 39 genetic variants. In a linear regression model adjusted for age, sex, and baseline serum triglyceride, the wGRS was associated with increased serum triglyceride levels over 5 years [per allele effect = 1.3% (1.0-1.6%); P = 1.0 × 10 17 ]. This triglyceride-increasing effect of the wGRS interacted with changes in insulin resistance (P interaction = 1.5 × 10 6). This interaction indicated that the effect of the wGRS was stronger in individuals who became more insulin resistant over 5 years. In conclusion, our findings suggest that increased genetic risk load is associated with a larger

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The impact of low-frequency and rare variants on lipid levels

Cited by 313 publications

References 42 publications

Analysis with the exome array identifies multiple new independent variants in lipid loci

Analysis with the exome array identifies multiple new independent variants in lipid loci

Exploring the Causal Pathway From Telomere Length to Coronary Heart Disease

Increasing insulin resistance accentuates the effect of triglyceride-associated loci on serum triglycerides during 5 years

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