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Genome-Wide Association Study Identifies Novel Loci Associated with Circulating Phospho- and Sphingolipid Concentrations
Open accessAbstract
Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88×10(-204)) and 10 loci for sphingolipids (smallest P-value = 3.10×10(-57)). After a correction for multiple comparisons (P-value<2.2×10(-9)), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits.
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“Human genome-wide association studies link variants in PLEKHH1 with sphingolipid concentrations that, when altered, lead to neurological and psychiatric disease (49).”
From Comparative analysis of the domestic cat genome reveals genetic signatures underlying feline biology and domestication (2014) · cites this paper as [49]
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Cites
Membrane lipids: where they are and how they behave
van Meer, G., Voelker, D. R., & Feigenson, G. W. (2008) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2330 · Cited by 7,157
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487
Self-citationLarge-scale association analysis identifies 13 new susceptibility loci for coronary artery disease
Cardiogenics, Schunkert, H., the CARDIoGRAM Consortium, König, I. R., Kathiresan, S., Reilly, M. P., Assimes, T. L., Holm, H., Preuss, M., Stewart, A. F. R., et al. (2011) · Nature Genetics · doi:10.1038/ng.784 · Cited by 1,866
MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes
Li, Y., Willer, C. J., Ding, J., Scheet, P., & Abecasis, G. R. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20533 · Cited by 1,843
GenABEL: an R library for genome-wide association analysis
Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm108 · Cited by 1,836 · cited 1×
“As all of the studies included related individuals, testing for association between lipid and allele dosage were performed using a mixed model approach as implemented with the ‘mmscore’ option in the GenABEL software [38].”
MethodTwelve type 2 diabetes susceptibility loci identified through large-scale association analysis
The MAGIC investigators, Voight, B. F., The GIANT Consortium, Scott, L. J., Steinthorsdottir, V., Morris, A. P., Dina, C., Welch, R. P., Zeggini, E., Huth, C., et al. (2010) · Nature Genetics · doi:10.1038/ng.609 · Cited by 1,761
Biochemistry of Lipids, Lipoproteins and Membranes
(2021) · Elsevier eBooks · doi:10.1016/c2020-0-00089-7 · Cited by 1,402
Brügger, B., Erben, G., Sandhoff, R., Wieland, F., & Lehmann, W. D. (1997) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.94.6.2339 · Cited by 882
Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum
Gieger, C., Geistlinger, L., Altmaier, E., Hrabé de Angelis, M., Kronenberg, F., Meitinger, T., Mewes, H.-W., Wichmann, H.-E., Weinberger, K. M., Adamski, J., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000282 · Cited by 737
Cited by
Sorted by citations
Items 101–200 of 205
Ried, J. S., Shin, S., Krumsiek, J., Illig, T., Theis, F. J., Spector, T. D., Adamski, J., Wichmann, H. ‐., Strauch, K., Soranzo, N., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu301 · Cited by 27
Metabolomics through the lens of precision cardiovascular medicine
Lam, S. M., Wang, Y., Li, B., Du, J., & Shui, G. (2017) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2017.02.004 · Cited by 26
Marklund, M., Morris, A. P., Mahajan, A., Ingelsson, E., Lindgren, C. M., Lind, L., & Risérus, U. (2018) · Nutrients · doi:10.3390/nu10111791 · Cited by 28
Chai, J. F., Raichur, S., Khor, I. W., Torta, F. T., Chew, W. S., Herr, D. R., Ching, J., Kovalik, J. P., Khoo, C. M., Wenk, M. R., et al. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz246 · Cited by 23
Iakoubov, L. Z., Mossakowska, M., Szwed, M., Duan, Z., Sesti, F., & Puzianowska‐Kuźnicka, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079790 · Cited by 24
Rifkin, S. B., Shrubsole, M. J., Cai, Q., Smalley, W. E., Ness, R. M., Swift, L. L., Zheng, W., & Murff, H. J. (2017) · British Journal Of Nutrition · doi:10.1017/s0007114517001490 · Cited by 25
Parra, E. J., Andrew, M., Gignoux, C. R., Sockell, A., Agostino, M. A. D., Morris, A. P., Petty, L. E., Hanis, C. L., Cox, N. J., Valladares‐Salgado, A., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172880 · Cited by 23
Heritability and family-based GWAS analyses of the N -acyl ethanolamine and ceramide plasma lipidome
McGurk, K. A., Williams, S. G., Guo, H., Watkins, H. C., Farrall, M., Cordell, H. J., Nicolaou, A., & Keavney, B. D. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab002 · Cited by 24
Thomas, G., Brown, A. L., & Brown, J. M. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2014.01.004 · Cited by 22
Klingenberg, R., Leiherer, A., Dobrev, D., Kaski, J. C., Levkau, B., März, W., Sossalla, S. T., von Eckardstein, A., & Drexel, H. (2025) · Cardiovascular Research · doi:10.1093/cvr/cvaf093 · Cited by 21
ABHD4-Regulating RNA Panel: Novel Biomarkers in Acute Coronary Syndrome Diagnosis
Agwa, S. H. A., Elzahwy, S. S., El‐Meteini, M., Elghazaly, H. A., Saad, M., Elsamee, A. M. A., Shamekh, R., & Matboli, M. (2021) · Cells · doi:10.3390/cells10061512 · Cited by 21
Heemskerk, M. M., van Harmelen, V., van Dijk, K. W., & van Klinken, J. B. (2015) · European Journal of Human Genetics · doi:10.1038/ejhg.2015.106 · Cited by 22
Shahin, M. H., Gong, Y., Frye, R. F., Rotroff, D. M., Beitelshees, A. L., Baillie, R., Chapman, A. B., Gums, J. G., Turner, S. T., Boerwinkle, E., et al. (2017) · Journal of the American Heart Association · doi:10.1161/jaha.117.006656 · Cited by 21
Teng, M., Wu, S., Er, L., Hsu, L., Chou, H., & Ko, Y. (2018) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-018-0383-9 · Cited by 20
A multi-omics study of circulating phospholipid markers of blood pressure
Liu, J., de Vries, P. S., Fabiola Del Greco, M., Johansson, Å., Schraut, K. E., Hayward, C., van Dijk, K. W., Franco, O. H., Hicks, A. A., Vitart, V., et al. (2022) · Scientific Reports · doi:10.1038/s41598-021-04446-7 · Cited by 21
Insights into the structural and functional activities of forgotten Kinases: PCTAIREs CDKs
Karimbayli, J., Pellarin, I., Belletti, B., & Baldassarre, G. (2024) · Molecular Cancer · doi:10.1186/s12943-024-02043-6 · Cited by 19
Li, J., Akanno, E. C., Valente, T. S., Abo-Ismail, M. K., Karisa, B., Wang, Z., & Plastow, G. S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.538600 · Cited by 20
Association of Physical Activity With Bioactive Lipids and Cardiovascular Events
Hoshi, R. A., Liu, Y., Luttmann‐Gibson, H., Tiwari, S., Giulianini, F., Andres, A. M., Watrous, J. D., Cook, N. R., Costenbader, K. H., Okereke, O. I., et al. (2022) · Circulation Research · doi:10.1161/circresaha.122.320952 · Cited by 18
Zhao, Y., Yuan, X., Yang, Z. H., Zhang, Y., Zhang, S., Li, S., Zhao, Y., Zheng, W., Liu, J., Xia, Y., et al. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.625072 · Cited by 18
High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software
Fabregat‐Traver, D., Sharapov, S., Hayward, C., Rudan, I., Campbell, H. J., Aulchenko, Y. S., & Bientinesi, P. (2014) · F1000Research · doi:10.12688/f1000research.4867.1 · Cited by 17
Shin, S., Petersen, A.-K., Wahl, S., Zhai, G., Römisch‐Margl, W., Small, K. S., Döring, A., Kato, B. S., Peters, A., Grundberg, E., et al. (2014) · Genome Medicine · doi:10.1186/gm542 · Cited by 18
Huybrechts, I., Jacobs, I., Aglago, E. K., Yammine, S. G., Matta, M., Schmidt, J. A., Casagrande, C., Nicolas, G., Biessy, C., Puyvelde, H. V., et al. (2023) · Nutrients · doi:10.3390/nu15173695 · Cited by 16
Veenstra, J., Kalsbeek, A., Westra, J., Disselkoen, C., Smith, C. E., & Tintle, N. (2017) · Nutrients · doi:10.3390/nu9080900 · Cited by 15
Large-scale linear regression: Development of high-performance routines
Frank, A., Fabregat‐Traver, D., & Bientinesi, P. (2015) · Applied Mathematics and Computation · doi:10.1016/j.amc.2015.11.078 · Cited by 15
Zhao, S. J., Lu, L., Liu, Q., Chen, J., Yuan, Q., Qiu, S., & Wang, X. (2019) · Scandinavian Journal of Immunology · doi:10.1111/sji.12832 · Cited by 15
Dharuri, H. K., Henneman, P., Demirkan, A., van Klinken, J. B., Mook-Kanamori, D. O., Wang‐Sattler, R., Gieger, C., Adamski, J., Hettne, K. M., Roos, M., et al. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-865 · Cited by 16
Diabetes adversely affects phospholipid profiles in human carotid artery endarterectomy plaques
Zayed, M. A., Hsu, F., Patterson, B. W., Yan, Y., Naim, U., Darwesh, M., Silva, G. D., Yang, C. Y., & Semenkovich, C. F. (2018) · Journal of Lipid Research · doi:10.1194/jlr.m081026 · Cited by 13
Shashkova, T., Gorev, D. D., Pakhomov, E., Shadrina, A. S., Sharapov, S., Tsepilov, Y. A., Karssen, L. C., & Aulchenko, Y. S. (2020) · Vavilov Journal of Genetics and Breeding · doi:10.18699/vj20.686 · Cited by 15
Demirkan, A., Pool, R., Deelen, J., Beekman, M., Liu, J., Harms, A. C., Vaarhorst, A. A. M., Hagenbeek, F. A., Willemsen, G. A. H. M., Verhoeven, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/621334 · Cited by 15
Genetic variants including markers from the exome chip and metabolite traits of type 2 diabetes
Jäger, S., Wahl, S., Kröger, J., Sharma, S., Hoffmann, P., Floegel, A., Pischon, T., Prehn, C. P., Adamski, J., Müller‐Nurasyid, M., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-06158-3 · Cited by 14
Feitosa, M. F., Lunetta, K. L., Wang, L., Wojczynski, M. K., Kammerer, C. M., Perls, T. T., Schupf, N., Christensen, K., Murabito, J. M., & Province, M. A. (2020) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2020.02.005 · Cited by 11
Investigating the pathogenesis and risk of Type 2 diabetes: clinical applications of metabolomics
Ng, T. W., Khan, A. A., & Meikle, P. J. (2012) · Clinical Lipidology · doi:10.2217/clp.12.75 · Cited by 14
A genome-wide association study identifies 41 loci associated with eicosanoid levels
Rhee, E. P., Surapaneni, A., Schlosser, P., Alotaibi, M., Yang, Y.-N., Coresh, J., Jain, M., Cheng, S. C., Yu, B., & Grams, M. E. (2023) · Communications Biology · doi:10.1038/s42003-023-05159-5 · Cited by 14
Genome scale metabolic network modelling for metabolic profile predictions
Cooke, J., Delmas, M., Wieder, C., Rodríguez-Mier, P., Frainay, C., Vinson, F., Ebbels, T. M. D., Poupin, N., & Jourdan, F. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011381 · Cited by 12
Sequence-Based Analysis of Lipid-Related Metabolites in a Multiethnic Study
Feofanova, E. V., Yu, B., Metcalf, G., Liu, X., Muzny, D. M., Below, J. E., Wagenknecht, L. E., Gibbs, R. A., Morrison, A. C., & Boerwinkle, E. (2018) · Genetics · doi:10.1534/genetics.118.300751 · Cited by 12
Serine Palmitoyltransferase Subunit 3 and Metabolic Diseases
Lone, M. A., Bourquin, F., & Hornemann, T. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-981-19-0394-6_4 · Cited by 11
Lipidomic Profiling in Synovial Tissue
Coras, R., Murillo‐Saich, J., Singh, A. G., Kavanaugh, A., & Gumá, M. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.857135 · Cited by 13
Shen, C., Suo, Y., Guo, J., Su, W., Zhang, Z., Yang, S., Wu, Z., Fan, Z., Zhou, X., & Hu, H. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1430583 · Cited by 10
Teng, M., Wu, S., Hsu, L., Tzeng, I., Chou, H., Su, C.-W., & Ko, Y. (2019) · Lipids in Health and Disease · doi:10.1186/s12944-019-1057-9 · Cited by 10
Zhu, J., Manichaikul, A. W., Hu, Y., Chen, Y. I., Liang, S., Steffen, L. M., Rich, S. S., Tsai, M. Y., Siscovick, D. S., Lemaître, R. N., et al. (2016) · European Journal of Nutrition · doi:10.1007/s00394-016-1193-1 · Cited by 10
Xie, Q., Zhang, Z., Chen, Z., Sun, J., Li, M., Wang, Q., & Pan, Y. (2023) · Biology · doi:10.3390/biology12040500 · Cited by 10
Tabassum, R., Rämö, J. T., Ripatti, P., Koskela, J. T., Kurki, M., Karjalainen, J. M., Hassan, S., Núñez-Fontarnau, J., Kiiskinen, T., Söderlund, S., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/457960 · Cited by 10
An integrative approach to prioritize candidate causal genes for complex traits in cattle
Ghoreishifar, S. M., MacLeod, I. M., Chamberlain, A. J., Liu, Z., Lopdell, T., Littlejohn, M. D., Xiang, R., Pryce, J. E., & Goddard, M. E. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011492 · Cited by 9
Lipidomics, Atrial Conduction, and Body Mass Index
Fabiola Del Greco, M., Foco, L., Teumer, A., Verweij, N., Paglia, G., Meraviglia, V., Melotti, R., Vuković, V., Rauhe, W., Joshi, P. K., et al. (2019) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.118.002384 · Cited by 9
Miranda, D. N., Coletta, D. K., Mandarino, L. J., & Shaibi, G. Q. (2014) · Obesity · doi:10.1002/oby.20711 · Cited by 9
Clinical Update: Ceramides As Novel Biomarkers of Cardiovascular Disease Risk
Bosak, K., Sauer, A., & Meeusen, J. W. (2023) · The Journal for Nurse Practitioners · doi:10.1016/j.nurpra.2023.104838 · Cited by 8
Moseholm, K. F., Cronjé, H. T., Koch, M., Fitzpatrick, A. L., López, O. L., Otto, M. C. D. O., Longstreth, W. T., Hoofnagle, A. N., Mukamal, K. J., Lemaître, R. N., et al. (2024) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12623 · Cited by 8
SPTLC3 regulates plasma membrane sphingolipid composition to facilitate hepatic gluconeogenesis
Montefusco, D. J., Jamil, M., Canals, D., Saligrama, S., Yang, Y., Allegood, J. C., & Cowart, L. A. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.115054 · Cited by 8
Hornemann, T. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.119091 · Cited by 8
Wu, J., Chen, D., Li, R., Tang, X., Xu, C., Guo, Y., Qi, L., Li, S., & Ren, Q. (2021) · Recent Patents on Anti-Cancer Drug Discovery · doi:10.2174/1574892816666210729125802 · Cited by 8
Vyncke, K. E., Huybrechts, I., Winckel, M. V., Cuenca‐García, M., Labayen, I., Gottrand, F., Widhalm, K., Leclercq, C., Libuda, L., Μanios, Y., et al. (2014) · Lipids · doi:10.1007/s11745-014-3935-2 · Cited by 9
Genetics of Familial Combined Hyperlipidemia (FCHL) Disorder: An Update
Taghizadeh, E., Farahani, N., Mardani, R., Taheri, F., Taghizadeh, H., & Hayat, S. M. G. (2021) · Biochemical Genetics · doi:10.1007/s10528-021-10130-2 · Cited by 8
Park, T. J., Lee, H.-S., Kim, Y. J., & Kim, B. (2019) · Bioscience Reports · doi:10.1042/bsr20190078 · Cited by 7
Huang, M., Kelly, R. S., Kachroo, P., Chu, S. H., Lee‐Sarwar, K. A., Chawes, B., Bisgaard, H. F., Litonjua, A. A., Weiss, S. T., & Lasky-Su, J. A. (2020) · Metabolites · doi:10.3390/metabo10040151 · Cited by 6
Chen, W., Ma, H., Li, B., Yang, F. W., Xiao, Y., Yan Fang, G. O. N. G., Li, Z., Li, T., Zeng, Q., Xu, K., et al. (2022) · Genes · doi:10.3390/genes13050746 · Cited by 6
Annevelink, C. E., Westra, J., Sala‐Vila, A., Harris, W. S., Tintle, N., & Shearer, G. C. (2023) · Journal of Nutrition · doi:10.1016/j.tjnut.2023.12.035 · Cited by 5
Genomic Influence in the Prevention of Cardiovascular Diseases with a Sterol-Based Treatment
Martín, I. S. M., Olivares, J. A. B., Arruche, E. P., Delgado, E. A., Cabañas, M. C., Vilar, E. G., & Yurrita, L. C. (2018) · Diseases · doi:10.3390/diseases6020024 · Cited by 5
Polyunsaturated Fatty Acid - mediated Cellular Rejuvenation for Reversing Age-related Vision DeclinePreprint
Gao, F.-Y., Tom, E., Rydz, C., Cho, W., Kolesnikov, A. V., Sha, Y.-T., Papadam, A., Jafari, S., Joseph, A., Ahanchi, A., et al. (2024) · bioRxiv · doi:10.1101/2024.07.01.601592 · Cited by 7
Yamakawa, P. E., Fonseca, A. R., da Silva, I. D. C. G., Gonçalves, M. V., Marchioni, D. M. L., Carioca, A. A. F., Michonneau, D., & Arrais‐Rodrigues, C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289285 · Cited by 4
Metabolomic associations of impaired awareness of hypoglycaemia in type 1 diabetes
Varkevisser, R. D. M., Cecil, A., Prehn, C. P., Mul, D., Aanstoot, H., Paterson, A. D., Wolffenbuttel, B. H. R., & van der Klauw, M. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-55032-6 · Cited by 4
Examining the link between 179 lipid species and 7 diseases using genetic predictors
Ottensmann, L., Tabassum, R., Ruotsalainen, S., Gerl, M. J., Klose, C. D., McCartney, D. L., Widén, E., Simons, K., Ripatti, S., Vitart, V., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105671 · Cited by 5
Sex-specific response of the human plasma lipidome to short-term cold exposure
Höring, M., Brunner, S. P., Scheiber, J., Honecker, J., Liebisch, G., Seeliger, C., Schinhammer, L., Claussnitzer, M., Burkhardt, R., Hauner, H., et al. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2024.159567 · Cited by 4
Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods
Lehtimäki, M., Mishra, B., Del-Val, C., Lyytikäinen, L., Kähönen, M., Cloninger, C., Raitakari, O., Laaksonen, R., Zwir, I., Lehtimäki, T., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-30168-z · Cited by 2
Pervjakova, N., Kukushkina, V., Haller, T., Kasela, S., Joensuu, A., Kristiansson, K., Annilo, T., Perola, M., Salomaa, V. V., Jousilahti, P., et al. (2018) · Biomarkers in Medicine · doi:10.2217/bmm-2018-0020 · Cited by 4
Karjalainen, M. K., Karthikeyan, S., Oliver‐Williams, C., Sliz, E., Allara, E., Surendran, P., Zhang, W., Jousilahti, P., Kristiansson, K., Salomaa, V. V., et al. (2022) · medRxiv · doi:10.1101/2022.10.20.22281089 · Cited by 3
Martín, I. S. M., Vilar, E. G., Rojo, S. S., Yurrita, L. C., Arruche, E. P., Delgado, E. A., & Olivares, J. A. B. (2019) · Nutrients · doi:10.3390/nu11102538 · Cited by 2
Shi, L., Li, H., Zhou, C., & Wang, L. (2023) · Agriculture · doi:10.3390/agriculture14010043 · Cited by 2
Park, S. (2024) · Journal of Experimental & Biomedical Sciences/Biomedical Science Letters · doi:10.15616/bsl.2024.30.3.173 · Cited by 2
Enfoque genómico en la enfermedad cardiovascular
Martín, I. S. M., la Rosa, L. D. L. C.-D., Rojo, S. S., Vilar, E. G., Ciudad-Cabañas, M. J., & Collado-Yurrita, L. (2016) · Nutrición Hospitalaria · doi:10.20960/nh.23 · Cited by 3
Brial, F., Hedjazi, L., Sonomura, K., Hageh, C. A., Zalloua, P., Matsuda, F., & Gauguier, D. (2022) · Metabolites · doi:10.3390/metabo12070596 · Cited by 2
Genetic influence on the metabolome
Rhee, E. P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-812784-1.00006-2 · Cited by 2
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Lee, S., Kelly, R. S., Chen, Y., Waqas, M., Mendez, K. M., Hecker, J., Hahn, G., Lutz, S. M., Celedon, J. C., Clish, C. B., et al. (2025) · Metabolomics · doi:10.1007/s11306-025-02256-w · Cited by 1
Wang, Y., Shi, S., & Cai, J. (2025) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.10157 · Cited by 1
Bangera, A., Lubaba, F., Gopalakrishnan, A. P., Shivamurthy, P. B., Raju, R., & Ramesh, P. (2026) · DNA and Cell Biology · doi:10.1177/10445498261433751 · Cited by 1
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Martín, I. S. M., Rojo, S. S., Vilar, E. G., Yurrita, L. C., & Olivares, J. A. B. (2019) · Annals of Pediatric Cardiology · doi:10.4103/apc.apc_86_18 · Cited by 1
Radhika, A., Burgula, S., Badapanda, C., Hussain, T., & Naushad, S. (2023) · Mammalian Genome · doi:10.1007/s00335-023-10017-0 · Cited by 1
Hirano, S., & Imai-Okano, K. (2016) · Springer eBooks · doi:10.1007/978-4-431-56033-3_15 · Cited by 1
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Large-scale linear regression: Development of high-performance routinesPreprint
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Hu, Q. (2018) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r30k26t35
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Ilham, A. Z., & Utami, W. (2020) · Scholars Bulletin · doi:10.36348/sb.2020.v06i01.004
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Huang, N., Hong, X., Cao, W., Lv, J., Yu, C., Sun, D., Liao, C., Pang, Y., Hu, R., Pang, Z., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4618842
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Visentin, M., Gai, Z., Kovilakath, A., Luca, F. D., Panteloglou, G., Othman, A., Liu, M., Barone, F., Garrelfs, I., Verheij, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.25.672208
Metadata
- Authors
- Demirkan, A., van Duijn, C. M., Ugocsai, P., Isaacs, A., Pramstaller, P. P., Liebisch, G., Wilson, J. F., Johansson, Å., Rudan, I., Aulchenko, Y. S., Kirichenko, A. V., Janssens, A. C. J. W., Jansen, R. C., Gnewuch, C., Domingues, F. S., Pattaro, C., Wild, S. H., Jonasson, I., Polasek, O., Zorkoltseva, I. V., Hofman, A., Karssen, L. C., Struchalin, M., Floyd, J., Igl, W., Biloglav, Z., Broer, L., Pfeufer, A., Pichler, I., Campbell, S., Zaboli, G., Kolcic, I., Rivadeneira, F., Huffman, J., Hastie, N. D., Uitterlinden, A., Franke, L., Franklin, C. S., Vitart, V., DIAGRAM Consortium, Nelson, C. P., Preuss, M., CARDIoGRAM Consortium, Bis, J. C., O'Donnell, C. J., Franceschini, N., CHARGE Consortium, Witteman, J. C. M., Axenovich, T., Oostra, B. A., Meitinger, T., Hicks, A. A., Hayward, C., Wright, A. F., Gyllensten, U., Campbell, H., Schmitz, G., & on behalf of the EUROSPAN consortium
- Affiliations
- Erasmus MC (NL)
- Eurac Research (IT)
- University Hospital Regensburg (DE)
- University of Edinburgh (GB)
- Uppsala University (SE)
- University of Split (HR)
- Russian Academy of Sciences (RU)
- University of Groningen (NL)
- Wellcome Sanger Institute (GB)
- University of Zagreb (HR)
- Institute of Genetics and Cancer (GB)
- University Medical Center Groningen (NL)
- University of Leicester (GB)
- University of Lübeck (DE)
- University of Washington (US)
- University of North Carolina at Chapel Hill (US)
- Helmholtz Munich (DE)
- Corresponding author
- Isaacs, Aaron
- ORCID
- Demirkan, Ayşe
- Isaacs, Aaron
- Pramstaller, Peter P.
- Liebisch, Gerhard
- Wilson, James F.
- Johansson, Åsa
- Rudan, Igor
- Aulchenko, Yurii S.
- Janssens, A. Cecile J. W.
- Domingues, Francisco S.
- Pattaro, Cristian
- Wild, Sarah H.
- Zorkoltseva, Irina V.
- Karssen, Lennart C.
- Struchalin, Maksim
- Igl, Wilmar
- Biloglav, Zrinka
- Broer, Linda
- Pichler, Irene
- Rivadeneira, Fernando
- Huffman, Jennifer
- Hastie, Nicholas D.
- Franke, Lude
- Franklin, Christopher S.
- Nelson, Christopher P.
- Preuss, Michael
- Bis, Joshua C.
- Franceschini, Nora
- Axenovich, Tatiana
- Meitinger, Thomas
- Hicks, Andrew A.
- Hayward, Caroline
- Gyllensten, Ulf
- Schmitz, Gerd
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-02-16
- Volume
- 8
- Issue
- 2
- Pages
- e1002490
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3280968
- OpenAlex
- W2132495615
- Cited by
- 205
- References
- 39
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 11 · 2023: 14 · 2022: 14 · 2021: 12 · 2020: 13 · 2019: 20 · 2018: 16 · 2017: 17 · 2016: 16 · 2015: 14 · 2014: 22 · 2013: 13 · 2012: 10
- FWCI
- 2.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (acceptedVersion, other-oa)
- Leicester Research Archive (University of Leicester) (acceptedVersion, other-oa)
- Research Explorer (The University of Manchester) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion)
- Figshare (submittedVersion, cc-by)
- IRIS (submittedVersion)
- Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- SERVAL (Université de Lausanne) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- Open Research (University of Surrey) (submittedVersion)
- Hirsla (Landspítali University Hospital) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Apollo (cc-by)
- Funders
- Wellcome Trust (202272, 090532)
- Royal Society
- European Commission (018947, LSHG-CT-2006-01947, 202272)
- Russian Foundation for Basic Research (047.017.043, NWORFBR 047.017.043)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (047.016.009, NWO-RFBR 047.017.043, RFBR 047.017.043, 047.017.043)
- Centre for Medical Systems Biology
- Provincia autonoma di Bolzano - Alto Adige
- National Institutes of Health (UL1TR000124)
- Medical Research Council
- National Heart, Lung, and Blood Institute (R01HL105756)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK063491)
- National Center for Advancing Translational Sciences
- H2020 European Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Sphingolipid Metabolism and Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biochemistry › Lipid metabolism and biosynthesis
- Keywords
- phospholipids, sphingolipids, plasma lipid levels, genetic loci, sphingomyelin, ceramide, phosphatidylcholine, lysophosphatidylcholine, phosphatidylethanolamine, plasmalogens, PKD2L1, phospholipase D2, apolipoprotein E, fatty acid elongation, fatty acid saturation, glycerophospholipids
- MeSH terms
- Delta-5 Fatty Acid Desaturase, Diabetes Mellitus, Type 2, Humans, Phospholipids, Sphingolipids, Genome, Human, Polymorphism, Single Nucleotide, Databases, Genetic, White People, Genome-Wide Association Study, Genetic Loci, Carotid Intima-Media Thickness
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