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The power of genetic diversity in genome-wide association studies of lipids
Open accessAbstract
Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2, heart disease remains the leading cause of death worldwide 3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25, we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice.
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In the citing paper
“This result replicated previous studies showing that multi-ancestry PGS demonstrated an improved prediction performance relative to ancestry-matched PGSs41,42.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [41]
Experimental — accuracy not yet measuredIn the citing paper
“We found genetic variants significantly associated with plasma high-density lipoprotein cholesterol (HDL-c; p < 5.0E-08; Fig. 6a).”
From Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans (2023) · cites this paper as [28]
In the citing paper
“The top-associated single nucleotide polymorphism (SNP), rs10507274, more specifically the “C” allele, found in ~6% of the general population, is significantly associated with increased plasma levels of HDL-c (p = 8.6E-11, Beta = +0.0183 ( ± 2.8E-03); Fig. 6a, b).”
From Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans (2023) · cites this paper as [28]
In the citing paper
“The Global Lipids Genetics Consortium aggregated GWAS results from 1,654,960 individuals from 201 primary studies representing five genetic ancestry groups28.”
From Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans (2023) · cites this paper as [28]
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Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,724 · cited 1×
“Variants were annotated using WGSA 51 , including the summary of each variant from SnpEff 52 and the closest genes for intergenic variants from ANNOVAR 53 .”
MethodSelf-citationKass, R. E., & Raftery, A. (1995) · doi:10.4135/9781412952644.n45 · Cited by 13,782 · cited 1×
“Bayes factors 56,57 (BFs) for each variant in the ancestry-specific meta-analysis were approximated as follows: where β and s.e. are the effect size and standard error of the effect size estimate from the Raremetal meta-analysis, and N AS is the ancestry-specific sample size.”
MethodCingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., Land, S. J., Lu, X., & Ruden, D. M. (2012) · Fly · doi:10.4161/fly.19695 · Cited by 11,816 · cited 2×
“Variants were annotated using WGSA 51 , including the summary of each variant from SnpEff 52 and the closest genes for intergenic variants from ANNOVAR 53 .”
MethodSelf-citationFast model-based estimation of ancestry in unrelated individuals
Alexander, D. H., Novembre, J., & Lange, K. (2009) · Genome Research · doi:10.1101/gr.094052.109 · Cited by 9,898
Roth, G. A., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)32203-7 · Cited by 8,600
The Ensembl Variant Effect Predictor
McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016) · Genome Biology · doi:10.1186/s13059-016-0974-4 · Cited by 8,099 · cited 1×
“Variants within the credible sets were annotated with SnpEff 52 using WGSA 51 and with VEP 59 .”
MethodSelf-citationdbSNP: the NCBI database of genetic variation
Sherry, S. T. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.308 · Cited by 7,502
Howard, W. J. (2007) · Yearbook of Endocrinology · doi:10.1016/s0084-3741(08)70063-5 · Cited by 6,672 · cited 1×
“Individuals on cholesterol-lowering medication had their pre-medication levels 44 approximated by dividing the LDL-C value by 0.7 and the TC value by 0.8.”
MethodInfluential
Research works printed but not linked: 1
- Lehmann, B. C. L., Mackintosh, M., McVean, G. & Holmes, C. C. High trait variability in optimal polygenic prediction strategy within multiple-ancestry cohorts. Preprint at bioRxiv https://doi.org/10.1101/2021.01.15.426781 (2021). · DOI
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Sorted by citations
Items 101–200 of 829
Wang, Y., He, Y., Shi, Y., Qian, D. C., Gray, K. J., Winn, R. A., & Martin, A. R. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.04.002 · Cited by 34
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Pleić, N., Gunjača, I., Leko, M. B., & Zemunik, T. (2023) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgad371 · Cited by 31
Skampardoni, I., Nasrallah, I. M., Abdulkadir, A., Wen, J., Melhem, R., Mamourian, E., Erus, G., Doshi, J., Singh, A., Yang, Z., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.5599 · Cited by 28
Self-citationAng, M. Y., Takeuchi, F., & Kato, N. (2023) · Journal of Human Genetics · doi:10.1038/s10038-023-01189-3 · Cited by 33
“These drugs are capable of regulating energy metabolism and expenditure, appetite control, glucose homeostasis, and insulin sensitivity, offering promising avenues for the development of effective treatments.”
BackgroundSelf-citationSoremekun, O. S., Karhunen, V., He, Y., Rajasundaram, S., Liu, B., Gkatzionis, A., Soremekun, C., Udosen, B., Musa, H. S., Silva, S., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.103953 · Cited by 42
Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation
Ortega, H. I., Udler, M. S., Gloyn, A. L., & Sharp, S. A. (2025) · Nature Reviews Endocrinology · doi:10.1038/s41574-025-01132-w · Cited by 26
“Multi-ancestry GWAS have now robustly demonstrated that increased heterozygosity through ancestry heterogeneity maximizes power to discover new genetic signals and resolve causal mechanisms 105,193–195 .”
Katki, H. A., Berndt, S. I., Machiela, M. J., Stewart, D. R., García‐Closas, M., Kim, J., Shi, J., Yu, K. F., & Rothman, N. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-01973-x · Cited by 31
Self-citationParcha, V., Pampana, A., Shetty, N. S., Irvin, M. R., Natarajan, P., Lin, H. J., Guo, X., Rich, S. S., Rotter, J. I., Li, P., et al. (2022) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.122.003946 · Cited by 35
Self-citationGenetic architecture of routinely acquired blood tests in a British South Asian cohort
Jacobs, B. M., Stow, D., Hodgson, S., Zöllner, J., Samuel, M., Kanoni, S., Bidi, S., Akhtar, S., Anwar, M., Arciero, E., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53091-x · Cited by 31
Self-citationAlver, M., Kasela, S., Haring, L., Luitva, L. B., Fischer, K., Möls, M., & Milani, L. A. (2024) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2024.100914 · Cited by 22
“…(CHD) 19 , total cholesterol (TC) 20 , LDL-cholesterol (LDL) 20 , HDL-cholesterol (HDL) 20 , nonHDL-cholesterol (nonHDL) 20 , triglycerides (TG) 20 , C-reactive protein (CRP) 21 , glycated haemoglobin (HbA1c) 22 , fasting glucose (FG) 22 , random glucose (RG) 23 , type II diabetes (T2D) 24 ,…”
InfluentialKim, M. S., Song, M., Kim, B., Shim, I., Kim, D. S., Natarajan, P., Do, R., & Won, H. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101112 · Cited by 31
Self-citationDron, J. S., Patel, A. P., Zhang, Y., Jurgens, S. J., Maamari, D. J., Wang, M., Boerwinkle, E., Morrison, A. C., de Vries, P. S., Fornage, M., et al. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2022.5271 · Cited by 30
Self-citationRare genetic variants explain missing heritability in smoking
Jang, S.-K., Evans, L. M., Fialkowski, A., Arnett, D. K., Ashley‐Koch, A. E., Barnes, K. C., Becker, D. M., Bis, J. C., Blangero, J., Bleecker, E. R., et al. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01408-5 · Cited by 29
“We estimated the heritability of four smoking phenotypes in samples of European ancestries: (1) age of smoking initiation (AgeSmk, N = 14,709), which measures the age at which an individual started regularly smoking; (2) cigarettes smoked per day (CigDay, N = 15,384), an index of heaviness of smoking and the average n…”
BackgroundSelf-citationMulti-ancestry genome-wide association analyses of polycystic ovary syndrome
Zhao, H., Xu, Y., Xue, B., Zhao, S., Zhang, M., Wu, X., Wang, T., Wen, Y., Li, S., Zhang, Q., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02393-x · Cited by 28
Self-citationSau, A., Sieliwończyk, E., Patlatzoglou, K., Pastika, L., McGurk, K. A., Ribeiro, A. H., Ribeiro, A. L. P., Ho, J. E., Peters, N. S., Ware, J. S., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2024.12.003 · Cited by 36
Li, L., Chen, Z., von Scheidt, M., Li, S., Steiner, A., Güldener, U., Koplev, S., Ma, A., Hao, K., Pan, C., et al. (2022) · Basic Research in Cardiology · doi:10.1007/s00395-022-00917-8 · Cited by 48
Self-citationA novel classification algorithm for customer churn prediction based on hybrid Ensemble-Fusion model
He, C., & Ding, C. H. Q. (2024) · Scientific Reports · doi:10.1038/s41598-024-71168-x · Cited by 27
A genomics perspective of personalized prevention and management of obesity
Gkouskou, K. K., Grammatikopoulou, M. G., Lazou, E., Vasilogiannakopoulou, T., Sanoudou, D., & Eliopoulos, A. G. (2024) · Human Genomics · doi:10.1186/s40246-024-00570-3 · Cited by 30
“Within this context, genome-wide association studies (GWASs) have provided valuable insights into genetic variants associated with body mass index (BMI), a proxy for obesity, as well as other adiposity traits, such as the waist-to-hip ratio, which is a proxy for body fat distribution [1, 4, 5].”
BackgroundGagnon, É., Paulin, A., Mitchell, P. L., & Arsenault, B. J. (2023) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2023.117371 · Cited by 26
Self-citationMisra, S., Aguilar-Salinas, C. A., Chikowore, T., Konradsen, F., Ronald, C. W., Mbau, L. K., Mohan, V., Morton, R. W., Nyirenda, M., Tapela, N. M., et al. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(23)00164-x · Cited by 43
Self-citationCronjé, H. T., Karhunen, V., Hovingh, G. K., Coppieters, K. T., Lagerstedt, J. O., Nyberg, M., & Gill, D. (2023) · BMC Medicine · doi:10.1186/s12916-023-02867-x · Cited by 24
“8 Per SD (mg/dL) 80% EUR, 9% EAS, 6% AFR, 3% HIS, 2% SAS 1,654,960 [33, 34]”
Health effects of milk consumption: phenome-wide Mendelian randomization study
Yuan, S., Sun, J., Lu, Y., Xu, F.-Z., Li, D.-D., Jiang, F., Wan, Z., Li, X., Qin, L.-Q., & Larsson, S. (2022) · BMC Medicine · doi:10.1186/s12916-022-02658-w · Cited by 23
Self-citationRay, A., Alabarse, P. V. G., Malik, R., Sargurupremraj, M., Bernhagen, J., Dichgans, M., Baumeister, S., & Georgakis, M. K. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.06.001 · Cited by 39
Fularski, P., Hajdys, J., Majchrowicz, G., Stabrawa, M., Młynarska, E., Rysz, J., & Franczyk, B. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031637 · Cited by 22
“These mutations are leading to insufficient LDL-C clearance, which implies an elevated level of the mentioned lipoproteins in the bloodstream [74].”
BackgroundComplex effects of sequence variants on lipid levels and coronary artery disease
Snæbjarnarson, A. S., Helgadóttir, A., Arnadottir, G. A., Ivarsdottir, E. V., Þorleifsson, G., Ferkingstad, E., Einarsson, G., Sveinbjörnsson, G., Thorgeirsson, T. E., Ulfarsson, M. O., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.012 · Cited by 23
Self-citationCiofani, J. L., Han, D., Allahwala, U. K., Woolf, B. A. R., Gill, D., & Bhindi, R. (2023) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2023.08.007 · Cited by 23
The proteogenomic landscape of the human kidney and implications for cardio-kidney-metabolic health
Hirohama, D., Fadista, J., Ha, E., Liu, H., Abedini, A., Levinsohn, J. L., Vassalotti, A., Zeng, L., Li, C., Mohandes, S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03872-8 · Cited by 28
Self-citationDeForest, N., Wang, Y., Zhu, Z., Dron, J. S., Koesterer, R., Natarajan, P., Flannick, J. A., Amariuta, T., Peloso, G. M., & Majithia, A. R. (2024) · Nature Communications · doi:10.1038/s41467-024-52105-y · Cited by 26
Self-citationJoint genotypic and phenotypic outcome modeling improves base editing variant effect quantification
Ryu, J., Barkal, S., Yu, T., Jankowiak, M., Zhou, Y., Francoeur, M., Phan, Q. V., Li, Z., Tognon, M., Brown, L., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01726-6 · Cited by 33
“Two of these loci (rs35081008 and rs2618566) were also identified as caQTL in a recent analysis of human liver tissue samples 30,57 .”
InfluentialSelf-citationArdissino, M., Slob, E. A. W., Reddy, R. K., Morley, A., Schuermans, A., Hill, P., Williamson, C., Honigberg, M. C., de Marvao, A., & Ng, F. S. (2024) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwad402 · Cited by 27
Dose-Response Associations of Lipid Traits With Coronary Artery Disease and Mortality
Yang, G., Mason, A. M., Wood, A., Schooling, C. M., & Burgess, S. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.52572 · Cited by 27
Self-citationClinical utility of polygenic scores for cardiometabolic disease in Arabs
Shim, I., Kuwahara, H., Chen, N., Hashem, M. O., AlAbdi, L., Abouelhoda, M. I., Won, H., Natarajan, P., Ellinor, P. T., Khera, A. V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41985-1 · Cited by 21
“For some traits, the augmentation was minimal such as for LDL cholesterol, whereby the published score is already known to be highlyperforming owing to its development from massive GWAS datasets 40 .”
Self-citationHeritable polygenic editing: the next frontier in genomic medicine?
Visscher, P. M., Gyngell, C., Yengo, L., & Savulescu, J. (2025) · Nature · doi:10.1038/s41586-024-08300-4 · Cited by 33
Yang, S., Ye, X., Ji, X., Li, Z., Tian, M., Huang, P., & Cao, C. (2025) · Genome Medicine · doi:10.1186/s13073-025-01505-w · Cited by 23
Xiong, J., Xu, Y., Wang, N., Wang, S., Zhang, Y., Lu, S., Zhang, X., Liang, X., Liu, C., Jiang, Q., et al. (2024) · Advanced Science · doi:10.1002/advs.202405955 · Cited by 18
Self-citationOjima, T., Namba, S., Ken, S., Yamamoto, K., Sonehara, K., Narita, A., The Biobank Japan Project, Kamatani, Y., Tamiya, G., Yamamoto, M. P., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01782-y · Cited by 18
“Furthermore, as PRS construction methods integrating meta-population effects have recently improved polygenic prediction 3,14,29 , we investigated interactions between meta-population methods and BMI stratification.”
Self-citationA two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits
Wong, T. H. T., Mo, J. M. Y., Zhou, M., Zhao, J. V., Schooling, C. M., He, B., Luo, S., & Yeung, S. L. A. (2024) · Communications Biology · doi:10.1038/s42003-024-05977-1 · Cited by 21
“…fi chypotheses (e.g., focusing on speci fi c lipid phenotype), coupled with the use of MVMR whichcouldbetteraccountforhorizontalpleiotropyviaotherlipids,andthe useoflargerlipidGWAS 58 , will help ascertain the fi ndings from our reverse Mendelian randomization study 2,59,60 .”
MethodSelf-citationHodonsky, C. J., Turner, A. W., Khan, M. D., Barrientos, N. B., Methorst, R., Ma, L., Lopez, N. G., Mosquera, J. V., Auguste, G. E., Farber, E., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100465 · Cited by 20
“16–18 Furthermore, genes within most CAD loci have not been associated with traditional risk factors (e.g., lipid levels or cholesterol metabolism), suggesting molecular mechanisms underlying physiological effects on the coronary artery vessel wall itself.”
BackgroundDemonstrating paths for unlocking the value of cloud genomics through cross cohort analysis
Deflaux, N. A., Selvaraj, M. S., Condon, H. R., Mayo, K., Haidermota, S., Basford, M., Lunt, C., Philippakis, A., Roden, D. M., Denny, J. C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41185-x · Cited by 21
“However real-world experience as illustrated above and by others 27 – 29 has identi fi ed numerous differences between cohorts including phenotype ascertainment, genetic ancestry and population structure.”
Self-citationThe Impact of Genomic Variation on Function (IGVF) ConsortiumPreprint
IGVF Consortium (2023) · PubMed · doi:10.48550/arxiv.2307.13708 · Cited by 13
EraSOR: a software tool to eliminate inflation caused by sample overlap in polygenic score analyses
Choi, S. W., Mak, T. S. H., Hoggart, C., & O’Reilly, P. F. (2022) · GigaScience · doi:10.1093/gigascience/giad043 · Cited by 26
“In addition, we utilized the latest GWAS summary statistics on LDL from the Global Lipids Genetics Consortium (GLGC) [ 24 ] to e v aluate the performance of Er aSOR.”
A genome-wide association study of imaging-defined atherosclerosis
Gummesson, A., Lundmark, P., Chen, Q. S., Björnson, E., Dekkers, K. F., Hammar, U., Adiels, M., Wang, Y., Andersson, T., Bergström, G. M. L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57457-7 · Cited by 23
“GWAS data from SCAPIS, as well as external consortia, including GLGC 42 , GIANT+UK Biobank 43 , MAGIC 44 , ICBP+UK biobank 45 , and CardiogramPlusC4D 5 , were used for these analyses.”
Self-citationWang, Z., Chen, S., Zhang, F., Akhmedov, S., Weng, J., & Xu, S. (2025) · Research · doi:10.34133/research.0618 · Cited by 16
“Furthermore, although echocardiography, radionuclide angiography, computer tomography (CT), magnetic resonance imaging (MRI), and other techniques are available for heart disease examination, they are costly and unsuitable for dynamic monitoring [ 22 , 24 ].”
Self-citationBrotman, S. M., Moustafa, J. S. E.-S., Guan, L., Broadaway, K. A., Wang, D., Jackson, A., Welch, R., Currin, K. W., Tomlinson, M., Vadlamudi, S., et al. (2025) · Nature Genetics · doi:10.1038/s41588-024-01982-6 · Cited by 34
Self-citationLiu, X., Sun, X., Zhang, Y., Jiang, W., Lai, M. M., Wiggins, K. L., Raffield, L. M., Bielak, L. F., Zhao, W., Pitsillides, A., et al. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.122.029090 · Cited by 36
Self-citationUse of Polygenic Risk Scores for Coronary Heart Disease in Ancestrally Diverse Populations
Dikilitas, O., Schaid, D. J., Tcheandjieu, C., Clarke, S. L., Assimes, T. L., & Kullo, I. J. (2022) · Current Cardiology Reports · doi:10.1007/s11886-022-01734-0 · Cited by 15
Self-citationShared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18
“We constructed PGSs for EduYears using two Bayesian polygenic prediction methods, PRS-CS v1.”
InfluentialSelf-citationChalitsios, C. V., Ley, H., Gao, J., Turner, M. R., & Thompson, A. G. (2024) · Journal of Neurology · doi:10.1007/s00415-024-12665-x · Cited by 20
“To create genetic instruments, single-nucleotide polymorphisms (SNPs) that demonstrated genome-wide significant association ( p 5E-08 ) with LDL-c levels were first selected [27].”
MethodLiver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits
Broadaway, K. A., Brotman, S. M., Rosen, J. D., Currin, K. W., Alkhawaja, A. A., Etheridge, A. S., Wright, F. A., Gallins, P. J., Jima, D. D., Zhou, Y., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.07.017 · Cited by 26
Self-citationOptimization of multi-ancestry polygenic risk score disease prediction models
Lerga-Jaso, J., Terpolovsky, A., Novković, B., Osama, A., Manson, C., Bohn, S., Marino, A. D., Kunitomi, M., & Yazdi, P. G. (2025) · Scientific Reports · doi:10.1038/s41598-025-02903-1 · Cited by 21
Lipid levels and risk of acute pancreatitis using bidirectional Mendelian randomization
Wang, B., Dron, J. S., Wang, Y., Choi, S. H., Huffman, J. E., Cho, K., Wilson, P. W. F., Natarajan, P., & Peloso, G. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-56946-x · Cited by 20
Self-citationKarhunen, V., Gill, D., Huang, J. V., Bouras, E., Malik, R., Ponsford, M., Ahola‐Olli, A., Papadopoulou, A., Palaniswamy, S., Sebert, S. P., et al. (2023) · BMJ Medicine · doi:10.1136/bmjmed-2022-000157 · Cited by 23
Self-citationGenetic and modifiable risk factors combine multiplicatively in common disease
Pang, S., Yengo, L., Nelson, C. P., Bourier, F. J., Zeng, L., Li, L., Kessler, T., Erdmann, J., Mägi, R., Läll, K., et al. (2022) · Clinical Research in Cardiology · doi:10.1007/s00392-022-02081-4 · Cited by 21
“These findings offer a rationale for directing preventive efforts to individuals with a particularly high burden of combined genetic and non-genetic risk [31, 32, 41, 42].”
BackgroundSelf-citationImpact of loss-of-function in angiopoietin-like 4 on the human phenome
Gagnon, E., Bourgault, J., Gobeil, É., Thériault, S., & Arsenault, B. J. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.117558 · Cited by 17
Li, J., Hu, J., Yun, H., Mei, Z., Wang, X., Luo, K., Guasch‐Ferré, M., Han, X., Truong, B. M. T., Merino, J., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04105-8 · Cited by 22
“…controls) 27 , fasting insulin ( N = 98,210) 108 , proinsulin ( N = 45,861) 109 , HOMA-IR and HOMA-B ( N = 51,750) 110 , BMI-adjusted insulin sensitivity index (ISI, N = 53,657) and insulin fold-change (IFC; N = 55,124) 111 , BMI and WHR ( N = ∼ 700,000) 112 and lipids ( N = ∼ 1,500,000) 113 .”
Self-citationSadler, M. C., Apostolov, A. M., Cevallos, C., Auwerx, C., Ribeiro, D. M., Altman, R. B., & Kutalik, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-58152-3 · Cited by 32
“Trait GWAS summary statistics were obtained from the following consortia: LDL-C, HDL-C and TC from the Global Lipids Genetics Consortium 69 ( N up to 1,320,016; European ancestry), HbA1c from the UKBB (#30750, N = 344,182), SBP from a meta-analysis of the UKBB and the International Consortium of…”
Self-citationZhang, X., Chang, K., Yu, J. Q., & Loomba, R. S. (2025) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-111523-023430 · Cited by 20
Self-citationLee, M., Park, T., Shin, J.-Y., & Park, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-68541-1 · Cited by 17
Self-citationGeneration Scotland: an update on Scotland’s longitudinal family health study
Milbourn, H., McCartney, D. L., Richmond, A., Campbell, A., Flaig, R., Robertson, S., Fawns‐Ritchie, C., Hayward, C., Marioni, R. E., McIntosh, A. M., et al. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-084719 · Cited by 22
Self-citationSmith, J. L., Tcheandjieu, C., Dikilitas, O., Iyer, K. R., Miyazawa, K., Hilliard, A. T., Lynch, J. A., Rotter, J. I., Chen, Y.-D. I., Sheu, W. H., et al. (2024) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.123.004272 · Cited by 43
“…use of PRS derivation methods that leverage shared information and LD diversity between ancestry groups to improve polygenic risk prediction for CHD. 29, 31 Our findings are consistent with these results in In HIS, OR per 1 SD varied between methods, possibly due to relatively small sample size.”
ResultSelf-citationImpact of loss-of-function in angiopoietin-like 4 on the human phenome
Gagnon, É., Bourgault, J., Gobeil, É., Thériault, S., & Arsenault, B. (2024) · medRxiv · doi:10.1101/2024.02.13.24302776 · Cited by 17
“Specifically there were 20 cardiometabolic traits and 9 cardiometabolic diseases (Richardson et al. 2020; Sinnott-Armstrong et al. 2021; Graham et al. 2021; Aragam et al. 2022; Mishra et al. 2022; Mahajan et al. 2022; Liu et al. 2021; Ward et al. 2021).”
BackgroundMethodInfluentialCardiometabolic and renal phenotypes and transitions in the United States population
Lhoste, V. P. F., Zhou, B., Mishra, A., Bennett, J. E., Filippi, S., Asaria, P., Gregg, E. W., Danaei, G., & Ezzati, M. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00391-y · Cited by 20
Yu, C., Bakshi, A., Watts, G. F., Renton, A. E., Fulton‐Howard, B., Goate, A., Natarajan, P., Chasman, D. I., Robman, L. D., Woods, R. L., et al. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.123.031459 · Cited by 20
Self-citationMulti-trait discovery and fine-mapping of lipid loci in 125,000 individuals of African ancestry
Kamiza, A. B., Touré, S. M., Zhou, F., Soremekun, O. S., Cissé, C., Wélé, M., Touré, A. M., Nashiru, O., Corpas, M., Nyirenda, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41271-0 · Cited by 19
“The GLGC included data from the Million Veteran Program (MVPAFR),UKBiobank(UKBB,AFR)andotherconsortiaofindividuals of African ancestry in the US 16 .”
MethodSelf-citationSurakka, I. L., Wu, K.-H., Hornsby, W., Wolford, B. N., Shen, F. Y., Zhou, W., Huffman, J. E., Pandit, A., Hu, Y., Brumpton, B. M., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100345 · Cited by 37
“…linked to the variants and the region around them in a GWAS catalog, together with summary statistics for some of the well-known heart disease risk-related biomarkers 33,34 and related traits 2,35 (Table S4), to better understand the mechanisms underlying the novel ischemic stroke association.”
BackgroundSelf-citationSystematic elucidation of genetic mechanisms underlying cholesterol uptake
Hamilton, M. C., Fife, J. D., Akinci, E., Yu, T., Khowpinitchai, B., Cha, M., Barkal, S., Thi, T. T., Yeo, G. H. T., Ramos Barroso, J. P., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100304 · Cited by 16
“Blue bars represent ABC predicted enhancer connections with DENND4C.”
InfluentialSelf-citationGenotyping and population structure of the China Kadoorie Biobank
Walters, R., Millwood, I., Lin, K., Valle, D., McDonnell, P., Hacker, A., Avery, D., Cai, N., Kretzschmar, W., Ansar, M., et al. (2022) · medRxiv · doi:10.1101/2022.05.02.22274487 · Cited by 17
Self-citationAmyloid-beta metabolism in age-related neurocardiovascular diseases
Aivalioti, E., Georgiopoulos, G. A., Tual‐Chalot, S., Bampatsias, D., Delialis, D., Sopova, K., Drakos, S. G., Stellos, K., & Stamatelopoulos, K. S. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae655 · Cited by 20
Tsuo, K., Shi, Z., Ge, T., Mandla, R., Hou, K., Ding, Y., Paşaniuc, B., Wang, Y., & Martin, A. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.06.606846 · Cited by 20
“Integrating multiple ancestries for discovery GWAS can improve PRS performance compared to single-ancestry GWAS Building on recommendations from previous studies 13,25,28 , we next investigated how multi-ancestry meta-263 analyses affect PRS accuracy across quantitative and binary phenotypes.”
Self-citationObesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer
Ponomarenko, I., Николаевич, П. К., Churnosova, M., Николаевна, С. И., Aristova, I., Churnosov, V., Ponomarenko, M., Reshetnikova, Y., Reshetnikov, E., & Churnosov, M. I. (2024) · Biomedicines · doi:10.3390/biomedicines12040818 · Cited by 17
“…with such BC/hormone/adipocyte/BMI-significant signs as the age of menopause in women [136], lipid profile (triglycerides, HDL, and LDL) [123,133,137–141], as well as with adipocyte-, hormone, and BMI-significant disease characterized by hyperandrogenic status, such as polycystic ovaries [142,143].”
BackgroundAncestry, ethnicity, and race: explaining inequalities in cardiometabolic disease
Eastwood, S. V., Hemani, G., Watkins, S., Scally, A., Smith, G. D., & Chaturvedi, N. (2024) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2024.04.002 · Cited by 19
Self-citationMcGrath, I. M., Montgomery, G. W., & Mortlock, S. (2023) · BMC Medicine · doi:10.1186/s12916-023-03184-z · Cited by 24
Blood DNA methylation signature of diet quality and association with cardiometabolic traits
Domínguez-Barragán, J., Fernández‐Sanlés, A., Hernáez, Á., Llauradó-Pont, J., Marrugat, J., Robinson, O., Tzoulaki, I., Elosúa, R., & Lassale, C. M. (2023) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwad317 · Cited by 25
Mechanisms underlying phenotypic variation in neurogenetic disorders
Burgunder, J. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00811-4 · Cited by 17
Ding, K., Qin, X., Wang, H., Wang, K., Kang, X., Yu, Y., Liu, Y., Gong, H., Wu, T., Chen, D., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-07417-6 · Cited by 14
“This was followed by conditioning on LDL-C, TG, HDL-C, and cIMT, where the numbers of pleiotropic loci for the CVDs-CeVDs-RFs tripletswere 6, 4, 3, and 2, respectively.”
InfluentialGuo, X., Yang, Y., Zhou, R., Tian, G., Shan, C., Liu, J., & Li, R. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02924-w · Cited by 15
Self-citationYang, G., & Schooling, C. M. (2023) · BMC Medicine · doi:10.1186/s12916-023-02903-w · Cited by 18
“These associations were largely similar by sex, despite stronger associations with TG and HDL-cholesterol in women than men (Fig.”
BackgroundMethodInfluentialFamilial Hypercholesterolemia: Challenges for a High-Risk Population
Choi, D., Malick, W. A., Köenig, W., Rader, D. J., & Rosenson, R. S. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.02.038 · Cited by 15
Self-citationMeeks, K. A. C., Bentley, A. R., Agyemang, C. O., Galenkamp, H., van den Born, B. H., Hanssen, N. M. J., Doumatey, A. P., Adeyemo, A., & Rotimi, C. N. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104548 · Cited by 15
Self-citationThe Inflamm-Aging Model Identifies Key Risk Factors in Atherosclerosis
He, Y., Chen, Y., Yao, L., Wang, J., Sha, X., & Wang, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.865827 · Cited by 19
“There is substantial evidence that lipids are related to atherosclerosis progression, as a high cholesterol level is a major risk factor in atherosclerosis and CVD, and fatty acid metabolism is closely related to endothelial function and protein homeostasis (Malekmohammad et al., 2021; Graham et al., 2021; Hasan and F…”
BackgroundSelf-citationCanine genome-wide association study identifies DENND1B as an obesity gene in dogs and humans
Wallis, N. J., McClellan, A., Mörseburg, A., Kentistou, K. A., Jamaluddin, A., Dowsett, G. K. C., Schofield, E. C., Morros-Nuevo, A., Saeed, S., Lam, B., et al. (2025) · Science · doi:10.1126/science.ads2145 · Cited by 19
Self-citationA genetic map of human metabolism across the allele frequency spectrum
Zoodsma, M., Beuchel, C. F., Yasmeen, S., Kohleick, L., Nepal, A., Koprulu, M., Kronenberg, F., Mayr, M., Williamson, A., Pietzner, M., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02355-3 · Cited by 20
“…lipoprotein profiling over standard clinical markers, we classified NMR metabolome association profiles for 1,657 genetic variants reported for commonly measured clinical markers (LDL cholesterol, HDL cholesterol, total cholesterol and triglycerides) obtained in 1.6 million people 16 .”
InfluentialSelf-citationChan, W. C., Liu, L., Bouras, E., Zuber, V., Wen, W., Long, J., Gill, D., Murphy, N., Gunter, M. J., Assimes, T. L., et al. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02900-7 · Cited by 15
Self-citationAlcalde‐Herraiz, M., Xie, J., Newby, D., Prats, C., Gill, D., Gordillo‐Marañón, M., Prieto‐Alhambra, D., Català, M., & Prats‐Uribe, A. (2024) · Nature Communications · doi:10.1038/s41467-024-53623-5 · Cited by 15
“…following outcomes (Supplementary Table 8): cardiovascular events (coronary artery disease 39 , myocardial infarction 40 , ischaemic stroke 41 ); risk factors (hypertension 42 , type 2 diabetes mellitus 43 ); and biomarkers (LDL cholesterol 44 , LDL cholesterol 44 , fasting glucose 45 , HbA1c 45 ).”
Pitfalls in performing genome-wide association studies on ratio traits
McCaw, Z. R., Dey, R., Somineni, H., Amar, D., Mukherjee, S., Sandor, K. P., Karaletsos, T., Koller, D., Aschard, H., Smith, G. D., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100406 · Cited by 14
“…traits compiled from external sources (excluding the UKB cohort where possible): body fat percentage, 44 chronically elevated alanine aminotransferase, 45 coronary artery disease, 46 hip circumference, 47 low-density lipoprotein (LDL) cholesterol 48 ), and type 2 diabetes (MIM: 125853).”
Rosoff, D. B., Bell, A. S., Wagner, J., Mavromatis, L. A., Hamandi, A. M., Park, L. Y., Jung, J., & Lohoff, F. W. (2023) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2023.09.001 · Cited by 16
“71 We included 4 LFTs: ALT, AST, ALP, GGT, and bilirubin.”
Zhou, F., Soremekun, O. S., Chikowore, T., Fatumo, S., Barroso, I., Morris, A. P., & Asimit, J. L. (2023) · Nature Communications · doi:10.1038/s41467-023-43159-5 · Cited by 13
Self-citationBridgePRS: A powerful trans-ancestry Polygenic Risk Score methodPreprint
Hoggart, C., Choi, S., García-González, J., Souaiaia, T., Preuss, M. H., & O'Reilly, P. F. (2023) · bioRxiv · doi:10.1101/2023.02.17.528938 · Cited by 14
“9 is consistent with a recent multi-ancestry lipids GWAS in which causal variants were fine-mapped [15].”
BackgroundSelf-citationBell, A. S., Rosoff, D. B., Mavromatis, L. A., Jung, J., Wagner, J., & Lohoff, F. W. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.122.026122 · Cited by 19
A pragmatic clinical trial of cascade testing for familial hypercholesterolemia
Miller, A. A., Bangash, H., Smith, C. Y., Wood-Wentz, C., Bailey, K., & Kullo, I. (2022) · Genetics in Medicine · doi:10.1016/j.gim.2022.08.026 · Cited by 14
Joint Associations of APOC3 and LDL-C–Lowering Variants With the Risk of Coronary Heart Disease
Wang, W., Li, R. P., Song, Z., Huang, N., Huang, T., Hua, X., & Tang, Y.-D. (2025) · JAMA Cardiology · doi:10.1001/jamacardio.2025.0195 · Cited by 16
Genetic Predisposition to Low-Density Lipoprotein Cholesterol and Incident Type 2 Diabetes
Ravi, A., Koyama, S., Cho, S. M. J., Haidermota, S., Hornsby, W., Ellinor, P. T., & Natarajan, P. (2025) · JAMA Cardiology · doi:10.1001/jamacardio.2024.5072 · Cited by 13
Self-citationWang, M., Daghlas, I., Zhang, Z., Gill, D., & Liu, D. (2025) · Neurology · doi:10.1212/wnl.0000000000210245 · Cited by 13
Multi-omics characterization of type 2 diabetes associated genetic variationPreprint
Mandla, R., Lorenz, K., Yin, X., Bocher, O., Huerta‐Chagoya, A., Arruda, A. L., Piron, A., Horn, S., Ken, S., Hatzikotoulas, K., et al. (2024) · medRxiv · doi:10.1101/2024.07.15.24310282 · Cited by 12
Self-citationYan, P., Zhang, L., Yang, C., Zhang, W., Wang, Y., Zhang, M., Cui, H., Tang, M., Chen, L., Wu, X., et al. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1337071 · Cited by 13
Self-citationLee, J., Kim, J., Kim, J. O., & Kwon, Y. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04291-4 · Cited by 16
Self-citationMultiset correlation and factor analysis enables exploration of multi-omics data
Brown, B. C., Wang, C., Kasela, S., Aguet, F., Nachun, D. C., Taylor, K. D., Tracy, R. P., Durda, P., Liu, Y., Johnson, W. C., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100359 · Cited by 22
Self-citationADAMTS7: a Novel Therapeutic Target in Atherosclerosis
Chung, A. Y., Reilly, M. P., & Bauer, R. C. (2023) · Current Atherosclerosis Reports · doi:10.1007/s11883-023-01115-0 · Cited by 18
Yang, G., & Schooling, C. M. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01714-2 · Cited by 17
“Summary statistics were adjusted for age, age2, sex, principal components of ancestry and studyspecific covariates [27].”
BackgroundMethodInfluentialvan der Graaf, A., Warmerdam, R., Auwerx, C., Boltz, T., Boomsma, D. I., Brown, A., Cheruiyot, E. K., Davenport, E. E., Dupuis, T., Esko, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60868-1 · Cited by 17
“The infinitesimal genetic architecture assumption: All genetic variants G in a locus have a non-zero genetic effect on the exposure with zero mean 4.”
Self-citation
Metadata
- Authors
- Graham, S. E., Clarke, S. L., Wu, K.-H. H., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Hwang, M. Y., Han, S., Narita, A., Choudhury, A., Bentley, A. R., Ekoru, K., Verma, A., Trivedi, B., Martin, H. C., Hunt, K. A., Hui, Q., Klarin, D., Zhu, X., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D. F., Holm, H., Olafsson, I., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B. M., Rasheed, H., Ruotsalainen, S. E., Havulinna, A. S., Veturi, Y., Feng, Q., Rosenthal, E. A., Lingren, T., Pacheco, J. A., Pendergrass, S. A., Haessler, J., Giulianini, F., Bradford, Y., Miller, J. E., Campbell, A., Lin, K., Millwood, I. Y., Hindy, G., Rasheed, A., Faul, J. D., Zhao, W., Weir, D. R., Turman, C., Huang, H., Graff, M., Mahajan, A., Brown, M. R., Zhang, W., Yu, K., Schmidt, E. M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J.-H., Matsuda, F., Jang, H.-M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J. J., Willemsen, G., Wood, A. R., Ji, Y., Gao, Z., Haworth, S., Mitchell, R. E., Chai, J. F., Aadahl, M., Yao, J., Manichaikul, A., Warren, H. R., Ramirez, J., Bork-Jensen, J., Kårhus, L. L., Goel, A., Sabater-Lleal, M., Noordam, R., Sidore, C., Fiorillo, E., McDaid, A. F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Møllehave, L. T., Thuesen, B. H., Munz, M., Zeng, L., Huang, J., Yang, B., Poveda, A., Kurbasic, A., Lamina, C., Forer, L., Scholz, M., Galesloot, T. E., Bradfield, J. P., Daw, E. W., Zmuda, J. M., Mitchell, J. S., Fuchsberger, C., Christensen, H., Brody, J. A., Feitosa, M. F., Wojczynski, M. K., Preuss, M., Mangino, M., Christofidou, P., Verweij, N., Benjamins, J. W., Engmann, J., Kember, R. L., Slieker, R. C., Lo, K. S., Zilhao, N. R., Le, P., Kleber, M. E., Delgado, G. E., Huo, S., Ikeda, D. D., Iha, H., Yang, J., Liu, J., Leonard, H. L., Marten, J., Schmidt, B., Arendt, M., Smyth, L. J., Cañadas-Garre, M., Wang, C., Nakatochi, M., Wong, A., Hutri-Kähönen, N., Sim, X., Xia, R., Huerta-Chagoya, A., Fernandez-Lopez, J. C., Lyssenko, V., Ahmed, M., Jackson, A. U., Yousri, N. A., Irvin, M. R., Oldmeadow, C., Kim, H.-N., Ryu, S., Timmers, P. R. H. J., Arbeeva, L., Dorajoo, R., Lange, L. A., Chai, X., Prasad, G., Lorés-Motta, L., Pauper, M., Long, J., Li, X., Theusch, E., Takeuchi, F., Spracklen, C. N., Loukola, A., Bollepalli, S., Warner, S. C., Wang, Y. X., Wei, W. B., Nutile, T., Ruggiero, D., Sung, Y. J., Hung, Y.-J., Chen, S., Liu, F., Yang, J., Kentistou, K. A., Gorski, M., Brumat, M., Meidtner, K., Bielak, L. F., Smith, J. A., Hebbar, P., Farmaki, A.-E., Hofer, E., Lin, M., Xue, C., Zhang, J., Concas, M. P., Vaccargiu, S., van der Most, P. J., Pitkänen, N., Cade, B. E., Lee, J., van der Laan, S. W., Chitrala, K. N., Weiss, S., Zimmermann, M. E., Lee, J. Y., Choi, H. S., Nethander, M., Freitag-Wolf, S., Southam, L., Rayner, N. W., Wang, C. A., Lin, S.-Y., Wang, J.-S., Couture, C., Lyytikäinen, L.-P., Nikus, K., Cuellar-Partida, G., Vestergaard, H., Hildalgo, B., Giannakopoulou, O., Cai, Q., Obura, M. O., van Setten, J., Li, X., Schwander, K., Terzikhan, N., Shin, J. H., Jackson, R. D., Reiner, A. P., Martin, L. W., Chen, Z., Li, L., Highland, H. M., Young, K. L., Kawaguchi, T., Thiery, J., Bis, J. C., Nadkarni, G. N., Launer, L. J., Li, H., Nalls, M. A., Raitakari, O. T., Ichihara, S., Wild, S. H., Nelson, C. P., Campbell, H., Jäger, S., Nabika, T., Al-Mulla, F., Niinikoski, H., Braund, P. S., Kolcic, I., Kovacs, P., Giardoglou, T., Katsuya, T., Bhatti, K. F., de Kleijn, D., de Borst, G. J., Kim, E. K., Adams, H. H. H., Ikram, M. A., Zhu, X., Asselbergs, F. W., Kraaijeveld, A. O., Beulens, J. W. J., Shu, X.-O., Rallidis, L. S., Pedersen, O., Hansen, T., Mitchell, P., Hewitt, A. W., Kähönen, M., Pérusse, L., Bouchard, C., Tönjes, A., Chen, Y.-D. I., Pennell, C. E., Mori, T. A., Lieb, W., Franke, A., Ohlsson, C., Mellström, D., Cho, Y. S., Lee, H., Yuan, J.-M., Koh, W.-P., Rhee, S. Y., Woo, J.-T., Heid, I. M., Stark, K. J., Völzke, H., Homuth, G., Evans, M. K., Zonderman, A. B., Polasek, O., Pasterkamp, G., Hoefer, I. E., Redline, S., Pahkala, K., Oldehinkel, A. J., Snieder, H., Biino, G., Schmidt, R., Schmidt, H., Chen, Y. E., Bandinelli, S., Dedoussis, G., Thanaraj, T. A., Kardia, S. L. R., Kato, N., Schulze, M. B., Girotto, G., Jung, B., Böger, C. A., Joshi, P. K., Bennett, D. A., De Jager, P. L., Lu, X., Mamakou, V., Brown, M., Caulfield, M. J., Munroe, P. B., Guo, X., Ciullo, M., Jonas, J. B., Samani, N. J., Kaprio, J., Pajukanta, P., Adair, L. S., Bechayda, S. A., de Silva, H. J., Wickremasinghe, A. R., Krauss, R. M., Wu, J.-Y., Zheng, W., den Hollander, A. I., Bharadwaj, D., Correa, A., Wilson, J. G., Lind, L., Heng, C.-K., Nelson, A. E., Golightly, Y. M., Wilson, J. F., Penninx, B., Kim, H.-L., Attia, J., Scott, R. J., Rao, D. C., Arnett, D. K., Hunt, S. C., Walker, M., Koistinen, H. A., Chandak, G. R., Yajnik, C. S., Mercader, J. M., Tusié-Luna, T., Aguilar-Salinas, C. A., Villalpando, C. G., Orozco, L., Fornage, M., Tai, E. S., van Dam, R. M., Lehtimäki, T., Chaturvedi, N., Yokota, M., Liu, J., Reilly, D. F., McKnight, A. J., Kee, F., Jöckel, K.-H., McCarthy, M. I., Palmer, C. N. A., Vitart, V., Hayward, C., Simonsick, E., van Duijn, C. M., Lu, F., Qu, J., Hishigaki, H., Lin, X., März, W., Parra, E. J., Cruz, M., Gudnason, V., Tardif, J.-C., Lettre, G., ’t Hart, L. M., Elders, P. J. M., Damrauer, S. M., Kumari, M., Kivimaki, M., van der Harst, P., Spector, T. D., Loos, R. J. F., Province, M. A., Psaty, B. M., Brandslund, I., Pramstaller, P. P., Christensen, K., Ripatti, S., Widén, E., Hakonarson, H., Grant, S. F. A., Kiemeney, L. A. L. M., de Graaf, J., Loeffler, M., Kronenberg, F., Gu, D., Erdmann, J., Schunkert, H., Franks, P. W., Linneberg, A., Jukema, J. W., Khera, A. V., Männikkö, M., Jarvelin, M.-R., Kutalik, Z., Cucca, F., Mook-Kanamori, D. O., van Dijk, K. W., Watkins, H., Strachan, D. P., Grarup, N., Sever, P., Poulter, N., Rotter, J. I., Dantoft, T. M., Karpe, F., Neville, M. J., Timpson, N. J., Cheng, C.-Y., Wong, T.-Y., Khor, C. C., Sabanayagam, C., Peters, A., Gieger, C., Hattersley, A. T., Pedersen, N. L., Magnusson, P. K. E., Boomsma, D. I., de Geus, E. J. C., Cupples, L. A., van Meurs, J. B. J., Ghanbari, M., Gordon-Larsen, P., Huang, W., Kim, Y. J., Tabara, Y., Wareham, N. J., Langenberg, C., Zeggini, E., Kuusisto, J., Laakso, M., Ingelsson, E., Abecasis, G., Chambers, J. C., Kooner, J. S., de Vries, P. S., Morrison, A. C., North, K. E., Daviglus, M., Kraft, P., Martin, N. G., Whitfield, J. B., Abbas, S., Saleheen, D., Walters, R. G., Holmes, M. V., Black, C., Smith, B. H., Justice, A. E., Baras, A., Buring, J. E., Ridker, P. M., Chasman, D. I., Kooperberg, C., Wei, W.-Q., Jarvik, G. P., Namjou, B., Hayes, M. G., Ritchie, M. D., Jousilahti, P., Salomaa, V., Hveem, K., Åsvold, B. O., Kubo, M., Kamatani, Y., Okada, Y., Murakami, Y., Thorsteinsdottir, U., Stefansson, K., Ho, Y.-L., Lynch, J. A., Rader, D. J., Tsao, P. S., Chang, K.-M., Cho, K., O’Donnell, C. J., Gaziano, J. M., Wilson, P., Rotimi, C. N., Hazelhurst, S., Ramsay, M., Trembath, R. C., van Heel, D. A., Tamiya, G., Yamamoto, M., Kim, B.-J., Mohlke, K. L., Frayling, T. M., Hirschhorn, J. N., Kathiresan, S., Program, V. M. V., Global Lipids Genetics Consortium*, Boehnke, M., Natarajan, P., Peloso, G. M., Brown, C. D., Morris, A. P., Assimes, T. L., Deloukas, P., Sun, Y. V., & Willer, C. J.
- Affiliations
- University of Michigan (US)
- VA Palo Alto Health Care System (US)
- Queen Mary University of London (GB)
- University of Pennsylvania (US)
- Broad Institute (US)
- University of Regensburg (DE)
- Washington University in St. Louis (US)
- Korea National Institute of Health (KR)
- Tohoku University (JP)
- University of the Witwatersrand (ZA)
- National Institutes of Health (US)
- Wellcome Sanger Institute (GB)
- Emory University (US)
- Pennsylvania State University (US)
- deCODE Genetics (Iceland) (IS)
- National University Hospital of Iceland (IS)
- Kyushu University (JP)
- RIKEN Center for Integrative Medical Sciences (JP)
- Norwegian University of Science and Technology (NO)
- University of Helsinki (FI)
- Vanderbilt University Medical Center (US)
- University of Washington (US)
- Cincinnati Children's Hospital Medical Center (US)
- Northwestern University (US)
- Genentech (US)
- Fred Hutch Cancer Center (US)
- Brigham and Women's Hospital (US)
- Western General Hospital (GB)
- University of Oxford (GB)
- Qatar University (QA)
- Center for Non-Communicable Diseases (PK)
- Harvard University (US)
- University of North Carolina at Chapel Hill (US)
- Centre for Human Genetics (GB)
- Imperial College Healthcare NHS Trust (GB)
- Uppsala University (SE)
- University of Cambridge (GB)
- Kyoto University (JP)
- Boston University (US)
- Amsterdam Public Health (NL)
- Vrije Universiteit Amsterdam (NL)
- University of Exeter (GB)
- Nanjing University of Chinese Medicine (CN)
- University of Bristol (GB)
- National University of Singapore (SG)
- Frederiksberg Hospital (DK)
- The Lundquist Institute (US)
- University of Virginia (US)
- Leiden University Medical Center (NL)
- Institute of Genetic and Biomedical Research (IT)
- SIB Swiss Institute of Bioinformatics (CH)
- Centre Hospitalier Universitaire Vaudois (CH)
- MRC Centre for Environment and Health (GB)
- University of Glasgow (GB)
- Humboldt-Universität zu Berlin (DE)
- German Centre for Cardiovascular Research (DE)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Lund University (SE)
- Innsbruck Medical University (AT)
- Leipzig University (DE)
- Radboud University Medical Center (NL)
- Quantinuum (United States) (US)
- University of Pittsburgh (US)
- Eurac Research (IT)
- Vejle Sygehus (DK)
- Icahn School of Medicine at Mount Sinai (US)
- King's College London (GB)
- University Medical Center Groningen (NL)
- University College London (GB)
- Montreal Heart Institute (CA)
- University of Toronto (CA)
- Heidelberg University (DE)
- Shanghai Institute of Nutrition and Health (CN)
- Rush University Medical Center (US)
- Agency for Science, Technology and Research (SG)
- University of Duisburg-Essen (DE)
- Dortmund University of Applied Sciences and Arts (DE)
- Queen's University Belfast (GB)
- Nagoya University (JP)
- MRC Unit for Lifelong Health and Ageing (GB)
- Brown Foundation Institute of Molecular Medicine for the Prevention of Human Diseases (US)
- Centre for Cellular and Molecular Biology (IN)
- Weill Cornell Medical College in Qatar (QA)
- University of Alabama at Birmingham (US)
- Hunter Medical Research Institute (AU)
- Ewha Womans University (KR)
- Kangbuk Samsung Hospital (KR)
- University of Colorado Anschutz (US)
- Institute of Genomics and Integrative Biology (IN)
- Radboud University Nijmegen (NL)
- University of California, San Francisco (US)
- National Center for Global Health and Medicine (JP)
- University of Leicester (GB)
- Beijing Tongren Hospital (CN)
- Institute of Genetics and Biophysics (IT)
- Istituto Neurologico Mediterraneo (IT)
- Tri-Service General Hospital Songshan Branch (TW)
- University of Edinburgh (GB)
- University Hospital Regensburg (DE)
- University of Trieste (IT)
- German Center for Diabetes Research (DE)
- Dasman Diabetes Institute (KW)
- Harokopio University of Athens (GR)
- Medical University of Graz (AT)
- University of North Carolina at Charlotte (US)
- IRCCS Materno Infantile Burlo Garofolo (IT)
- University of Turku (FI)
- Utrecht University (NL)
- Universitätsmedizin Greifswald (DE)
- Hallym University (KR)
- University of Gothenburg (SE)
- Christian-Albrechts-Universität zu Kiel (DE)
- Taichung Veterans General Hospital (TW)
- Université Laval (CA)
- Tampere University (FI)
- University of Copenhagen (DK)
- Erasmus MC (NL)
- The Ohio State University (US)
- George Washington University (US)
- Peking University (CN)
- University Hospital Leipzig (DE)
- Jichi Medical University (JP)
- Shimane University (JP)
- The University of Osaka (JP)
- Yonsei University (KR)
- University Medical Center Utrecht (NL)
- National and Kapodistrian University of Athens (GR)
- The University of Sydney (AU)
- University of Tasmania (AU)
- Pennington Biomedical Research Center (US)
- University of Newcastle Australia (AU)
- The University of Western Australia (AU)
- Sahlgrenska University Hospital (SE)
- Kyung Hee University (KR)
- Istituto di Genetica Molecolare (IT)
- University of Potsdam (DE)
- University of California, Los Angeles (US)
- University of San Carlos (PH)
- University of Kelaniya (LK)
- Institute of Biomedical Sciences, Academia Sinica (TW)
- Jawaharlal Nehru University (IN)
- University of Mississippi Medical Center (US)
- Amsterdam University Medical Centers (NL)
- University of Kentucky (US)
- Newcastle University (GB)
- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (MX)
- National Institute of Genomic Medicine (MX)
- Kurume University (JP)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- University of Dundee (GB)
- National Institute on Aging (US)
- Wenzhou Medical University (CN)
- Mexican Social Security Institute (MX)
- University of Iceland (IS)
- Philadelphia VA Medical Center (US)
- Kaiser Permanente Washington Health Research Institute (US)
- University of Southern Denmark (DK)
- Children's Hospital of Philadelphia (US)
- University of Oulu (FI)
- University of Sassari (IT)
- St George's, University of London (GB)
- Imperial College London (GB)
- Churchill Hospital (GB)
- Singapore National Eye Center (SG)
- Helmholtz Munich (DE)
- Karolinska Institutet (SE)
- Mashhad University of Medical Sciences (IR)
- Chinese National Human Genome Center at Shanghai (CN)
- University of Eastern Finland (FI)
- The University of Texas Health Science Center at Houston (US)
- University of Illinois Chicago (US)
- QIMR Berghofer Medical Research Institute (AU)
- Columbia University Irving Medical Center (US)
- National Institute for Health and Care Research (GB)
- University of Aberdeen (GB)
- Geisinger Health System (US)
- Regeneron (United States) (US)
- University of Washington Medical Center (US)
- Finnish Institute for Health and Welfare (FI)
- Institute of Medical Science, The University of Tokyo (JP)
- VA Boston Healthcare System (US)
- University of Massachusetts Boston (US)
- National Health Laboratory Service (ZA)
- University of Manchester (GB)
- King Abdulaziz University (SA)
- Corresponding author
- Sun, Yan V.
- ORCID
- Graham, Sarah E.
- Clarke, Shoa L.
- Wu, Kuan-Han H.
- Kanoni, Stavroula
- Zajac, Greg J. M.
- Ramdas, Shweta
- Surakka, Ida
- Vedantam, Sailaja
- Winkler, Thomas W.
- Locke, Adam E.
- Marouli, Eirini
- Hwang, Mi Yeong
- Han, Sohee
- Narita, Akira
- Choudhury, Ananyo
- Bentley, Amy R.
- Ekoru, Kenneth
- Verma, Anurag
- Martin, Hilary C.
- Hunt, Karen A.
- Hui, Qin
- Klarin, Derek
- Zhu, Xiang
- Gudbjartsson, Daniel F.
- Holm, Hilma
- Akiyama, Masato
- Sakaue, Saori
- Terao, Chikashi
- Kanai, Masahiro
- Zhou, Wei
- Brumpton, Ben M.
- Rasheed, Humaira
- Ruotsalainen, Sanni E.
- Havulinna, Aki S.
- Veturi, Yogasudha
- Feng, QiPing
- Rosenthal, Elisabeth A.
- Lingren, Todd
- Pacheco, Jennifer Allen
- Pendergrass, Sarah A.
- Miller, Jason E.
- Campbell, Archie
- Millwood, Iona Y.
- Hindy, George
- Faul, Jessica D.
- Zhao, Wei
- Weir, David R.
- Turman, Constance
- Huang, Hongyan
- Graff, Mariaelisa
- Mahajan, Anubha
- Zhang, Weihua
- Yu, Ketian
- Schmidt, Ellen M.
- Pandit, Anita
- Gustafsson, Stefan
- Yin, Xianyong
- Luan, Jian’an
- Matsuda, Fumihiko
- Jang, Hye-Mi
- Yoon, Kyungheon
- Medina-Gomez, Carolina
- Pitsillides, Achilleas
- Hottenga, Jouke Jan
- Willemsen, Gonneke
- Wood, Andrew R.
- Gao, Zishan
- Haworth, Simon
- Mitchell, Ruth E.
- Chai, Jin Fang
- Aadahl, Mette
- Manichaikul, Ani
- Warren, Helen R.
- Ramirez, Julia
- Kårhus, Line L.
- Goel, Anuj
- Sabater-Lleal, Maria
- Noordam, Raymond
- Sidore, Carlo
- Fiorillo, Edoardo
- McDaid, Aaron F.
- Marques-Vidal, Pedro
- Wielscher, Matthias
- Trompet, Stella
- Sattar, Naveed
- Møllehave, Line T.
- Thuesen, Betina H.
- Munz, Matthias
- Zeng, Lingyao
- Huang, Jianfeng
- Yang, Bin
- Poveda, Alaitz
- Kurbasic, Azra
- Lamina, Claudia
- Forer, Lukas
- Scholz, Markus
- Galesloot, Tessel E.
- Zmuda, Joseph M.
- Fuchsberger, Christian
- Brody, Jennifer A.
- Feitosa, Mary F.
- Wojczynski, Mary K.
- Preuss, Michael
- Mangino, Massimo
- Christofidou, Paraskevi
- Verweij, Niek
- Benjamins, Jan W.
- Engmann, Jorgen
- Kember, Rachel L.
- Slieker, Roderick C.
- Lo, Ken Sin
- Zilhao, Nuno R.
- Le, Phuong
- Kleber, Marcus E.
- Delgado, Graciela E.
- Huo, Shaofeng
- Ikeda, Daisuke D.
- Iha, Hiroyuki
- Yang, Jian
- Liu, Jun
- Leonard, Hampton L.
- Marten, Jonathan
- Schmidt, Börge
- Smyth, Laura J.
- Wang, Chaolong
- Nakatochi, Masahiro
- Wong, Andrew
- Sim, Xueling
- Xia, Rui
- Fernandez-Lopez, Juan Carlos
- Lyssenko, Valeriya
- Jackson, Anne U.
- Yousri, Noha A.
- Irvin, Marguerite R.
- Oldmeadow, Christopher
- Ryu, Seungho
- Timmers, Paul R. H. J.
- Arbeeva, Liubov
- Dorajoo, Rajkumar
- Chai, Xiaoran
- Prasad, Gauri
- Pauper, Marc
- Long, Jirong
- Li, Xiaohui
- Theusch, Elizabeth
- Takeuchi, Fumihiko
- Spracklen, Cassandra N.
- Loukola, Anu
- Bollepalli, Sailalitha
- Warner, Sophie C.
- Wang, Ya Xing
- Wei, Wen B.
- Nutile, Teresa
- Ruggiero, Daniela
- Hung, Yi-Jen
- Chen, Shufeng
- Liu, Fangchao
- Yang, Jingyun
- Kentistou, Katherine A.
- Gorski, Mathias
- Brumat, Marco
- Meidtner, Karina
- Bielak, Lawrence F.
- Smith, Jennifer A.
- Hebbar, Prashantha
- Farmaki, Aliki-Eleni
- Hofer, Edith
- Lin, Maoxuan
- Xue, Chao
- Zhang, Jifeng
- Concas, Maria Pina
- Vaccargiu, Simona
- van der Most, Peter J.
- Pitkänen, Niina
- Cade, Brian E.
- Lee, Jiwon
- van der Laan, Sander W.
- Chitrala, Kumaraswamy Naidu
- Weiss, Stefan
- Zimmermann, Martina E.
- Lee, Jong Young
- Nethander, Maria
- Southam, Lorraine
- Rayner, Nigel W.
- Wang, Carol A.
- Wang, Jun-Sing
- Lyytikäinen, Leo-Pekka
- Nikus, Kjell
- Cuellar-Partida, Gabriel
- Vestergaard, Henrik
- Giannakopoulou, Olga
- Cai, Qiuyin
- van Setten, Jessica
- Schwander, Karen
- Jackson, Rebecca D.
- Reiner, Alexander P.
- Martin, Lisa Warsinger
- Chen, Zhengming
- Li, Liming
- Highland, Heather M.
- Young, Kristin L.
- Kawaguchi, Takahisa
- Thiery, Joachim
- Bis, Joshua C.
- Nadkarni, Girish N.
- Launer, Lenore J.
- Nalls, Mike A.
- Raitakari, Olli T.
- Ichihara, Sahoko
- Wild, Sarah H.
- Nelson, Christopher P.
- Jäger, Susanne
- Nabika, Toru
- Braund, Peter S.
- Kovacs, Peter
- de Kleijn, Dominique
- de Borst, Gert J.
- Kim, Eung Kweon
- Adams, Hieab H. H.
- Ikram, M. Arfan
- Zhu, Xiaofeng
- Asselbergs, Folkert W.
- Kraaijeveld, Adriaan O.
- Beulens, Joline W. J.
- Shu, Xiao-Ou
- Pedersen, Oluf
- Hansen, Torben
- Hewitt, Alex W.
- Kähönen, Mika
- Pérusse, Louis
- Bouchard, Claude
- Tönjes, Anke
- Chen, Yii-Der Ida
- Pennell, Craig E.
- Mori, Trevor A.
- Lieb, Wolfgang
- Ohlsson, Claes
- Mellström, Dan
- Cho, Yoon Shin
- Lee, Hyejin
- Koh, Woon-Puay
- Rhee, Sang Youl
- Woo, Jeong-Taek
- Heid, Iris M.
- Stark, Klaus J.
- Homuth, Georg
- Evans, Michele K.
- Zonderman, Alan B.
- Pasterkamp, Gerard
- Hoefer, Imo E.
- Redline, Susan
- Pahkala, Katja
- Oldehinkel, Albertine J.
- Snieder, Harold
- Biino, Ginevra
- Schmidt, Reinhold
- Schmidt, Helena
- Chen, Y. Eugene
- Bandinelli, Stefania
- Dedoussis, George
- Thanaraj, Thangavel Alphonse
- Kato, Norihiro
- Schulze, Matthias B.
- Girotto, Giorgia
- Joshi, Peter K.
- Bennett, David A.
- De Jager, Philip L.
- Lu, Xiangfeng
- Mamakou, Vasiliki
- Brown, Morris
- Caulfield, Mark J.
- Munroe, Patricia B.
- Guo, Xiuqing
- Ciullo, Marina
- Jonas, Jost B.
- Samani, Nilesh J.
- Kaprio, Jaakko
- Pajukanta, Päivi
- Adair, Linda S.
- de Silva, H. Janaka
- Krauss, Ronald M.
- Zheng, Wei
- den Hollander, Anneke I.
- Bharadwaj, Dwaipayan
- Correa, Adolfo
- Wilson, James G.
- Lind, Lars
- Heng, Chew-Kiat
- Nelson, Amanda E.
- Golightly, Yvonne M.
- Wilson, James F.
- Penninx, Brenda
- Kim, Hyung-Lae
- Attia, John
- Scott, Rodney J.
- Rao, D. C.
- Arnett, Donna K.
- Hunt, Steven C.
- Koistinen, Heikki A.
- Chandak, Giriraj R.
- Yajnik, Chittaranjan S.
- Mercader, Josep M.
- Aguilar-Salinas, Carlos A.
- Orozco, Lorena
- Fornage, Myriam
- Tai, E. Shyong
- van Dam, Rob M.
- Lehtimäki, Terho
- Chaturvedi, Nish
- Liu, Jianjun
- Reilly, Dermot F.
- McKnight, Amy Jayne
- Kee, Frank
- Jöckel, Karl-Heinz
- McCarthy, Mark I.
- Hayward, Caroline
- Simonsick, Eleanor
- van Duijn, Cornelia M.
- Qu, Jia
- März, Winfried
- Parra, Esteban J.
- Cruz, Miguel
- Gudnason, Vilmundur
- Tardif, Jean-Claude
- Lettre, Guillaume
- Elders, Petra J. M.
- Damrauer, Scott M.
- Kivimaki, Mika
- van der Harst, Pim
- Spector, Tim D.
- Loos, Ruth J. F.
- Province, Michael A.
- Psaty, Bruce M.
- Brandslund, Ivan
- Pramstaller, Peter P.
- Christensen, Kaare
- Ripatti, Samuli
- Widén, Elisabeth
- Hakonarson, Hakon
- Grant, Struan F. A.
- Kiemeney, Lambertus A. L. M.
- Loeffler, Markus
- Kronenberg, Florian
- Gu, Dongfeng
- Erdmann, Jeanette
- Schunkert, Heribert
- Franks, Paul W.
- Linneberg, Allan
- Jukema, J. Wouter
- Khera, Amit V.
- Männikkö, Minna
- Cucca, Francesco
- van Dijk, Ko Willems
- Watkins, Hugh
- Strachan, David P.
- Grarup, Niels
- Poulter, Neil
- Rotter, Jerome I.
- Dantoft, Thomas M.
- Karpe, Fredrik
- Neville, Matt J.
- Timpson, Nicholas J.
- Cheng, Ching-Yu
- Wong, Tien-Yin
- Khor, Chiea Chuen
- Sabanayagam, Charumathi
- Peters, Annette
- Gieger, Christian
- Hattersley, Andrew T.
- Pedersen, Nancy L.
- Magnusson, Patrik K. E.
- Boomsma, Dorret I.
- de Geus, Eco J. C.
- Cupples, L. Adrienne
- van Meurs, Joyce B. J.
- Ghanbari, Mohsen
- Gordon-Larsen, Penny
- Huang, Wei
- Kim, Young Jin
- Wareham, Nicholas J.
- Langenberg, Claudia
- Zeggini, Eleftheria
- Kuusisto, Johanna
- Laakso, Markku
- Ingelsson, Erik
- Chambers, John C.
- Kooner, Jaspal S.
- de Vries, Paul S.
- Morrison, Alanna C.
- North, Kari E.
- Daviglus, Martha
- Kraft, Peter
- Martin, Nicholas G.
- Whitfield, John B.
- Saleheen, Danish
- Walters, Robin G.
- Holmes, Michael V.
- Black, Corri
- Smith, Blair H.
- Justice, Anne E.
- Baras, Aris
- Buring, Julie E.
- Ridker, Paul M.
- Chasman, Daniel I.
- Kooperberg, Charles
- Wei, Wei-Qi
- Jarvik, Gail P.
- Namjou, Bahram
- Hayes, M. Geoffrey
- Ritchie, Marylyn D.
- Jousilahti, Pekka
- Salomaa, Veikko
- Hveem, Kristian
- Åsvold, Bjørn Olav
- Kubo, Michiaki
- Okada, Yukinori
- Murakami, Yoshinori
- Ho, Yuk-Lam
- Lynch, Julie A.
- Rader, Daniel J.
- Tsao, Philip S.
- Chang, Kyong-Mi
- Cho, Kelly
- O’Donnell, Christopher J.
- Gaziano, John M.
- Wilson, Peter
- Rotimi, Charles N.
- Hazelhurst, Scott
- Ramsay, Michèle
- Trembath, Richard C.
- van Heel, David A.
- Tamiya, Gen
- Yamamoto, Masayuki
- Kim, Bong-Jo
- Mohlke, Karen L.
- Frayling, Timothy M.
- Hirschhorn, Joel N.
- Kathiresan, Sekar
- Boehnke, Michael
- Natarajan, Pradeep
- Peloso, Gina M.
- Brown, Christopher D.
- Morris, Andrew P.
- Assimes, Themistocles L.
- Deloukas, Panos
- Sun, Yan V.
- Willer, Cristen J.
- Venue
- Nature
- Published
- 2021-12-09
- Volume
- 600
- Issue
- 7890
- Pages
- 675-679
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8730582
- OpenAlex
- W3205644245
- Cited by
- 972
- References
- 77
- Citations by year
- 2026: 164 · 2025: 246 · 2024: 274 · 2023: 192 · 2022: 89 · 2021: 6
- FWCI
- 21.98
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-05-26)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
- University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- VU Research Portal (publishedVersion, other-oa)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by-nc)
- PubMed Central (acceptedVersion)
- Discovery Research Portal (University of Dundee) (acceptedVersion, other-oa)
- St George's Online Research Archive (St George's University of London) (acceptedVersion)
- Open Research Exeter (University of Exeter) (acceptedVersion)
- Open Access at Essex (University of Essex) (acceptedVersion)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion, other-oa)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by-nc-nd)
- IRIS (submittedVersion, cc-by-nc)
- Explore Bristol Research (submittedVersion, other-oa)
- UTAS Research Repository (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- UnissResearch (Università degli Studi di Sassari) (submittedVersion)
- CNR ExploRA (submittedVersion)
- publish.UP (University of Potsdam) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Research Portal (Queen's University Belfast) (submittedVersion, other-oa)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Open Research (University of Surrey) (submittedVersion, cc-by)
- STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- U.S. Department of Veterans Affairs (IK2-CX001780, I01-BX004821, I01-BX003362)
- McKnight Foundation
- Office of Research and Development
- Wellcome Trust (212946/Z/18/Z, 212945/Z/18/Z, 210561/Z/18/Z, 217065/Z/19/Z, 202802/Z/16/Z, 221854/Z/20/Z)
- Barts Charity (MGU0284)
- Cancer Research UK (16896)
- National Institute for Health and Care Research (NF-SI-0611-10170, NF-SI-0512-10113, NIHR-INF-0987, 16/136/102, NF-SI-0617-10154)
- British Heart Foundation (FS/19/62/34901D, RG/14/5/30893)
- Public Health Agency (STL/5569/19)
- Lundbeckfonden (R155-2013-14070)
- Novo Nordisk Fonden (NNF14OC0010513, NNF19OC0055250, NNF18OC0033898, NNF13OC0005785)
- National Institutes of Health (5k01hl135405-04, 24460, 85,86)
- Medical Research Council (MR/S019669/1, MC_PC_14135, MC_UU_00017/1, MR/S011676/1, MR/R024227/1, MC_PC_15018, MC_PC_20026, MC_PC_19009, MC_UU_00007/10, MC_PC_13049)
- Economic and Social Research Council (ES/N00812X/1, ES/L008459/1)
- Japan Society for the Promotion of Science (21H03206, 21K10439, 19K19434, 21H04850)
- National Institute on Aging (R01AG017917, R01AG015819, P30AG010161)
- National Heart, Lung, and Blood Institute (R01HL133218, R01HL127564, R03HL154284, K01-HL135405, R35HL135818, R01HL149683, R35HL135824, R01HL105756, R01HL109946, R01HL153805)
- National Human Genome Research Institute (K08HG010155, R01HG010297, T32HG000040, U01HG011723)
- National Cancer Institute (R01CA144034, R01CA080205, UM1CA182876)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK072193, U01DK062370, P30DK020572, R01DK093757, P30DK020541, R01DK062370)
- National Center for Chronic Disease Prevention and Health Promotion (U01DP006266)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD030880)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- serum lipids, genetic diversity, non-European ancestry, polygenic risk score, fine mapping, cardiovascular risk, LDL cholesterol, triglycerides, HDL cholesterol, genetic ancestry, sample size and statistical power, linkage disequilibrium
- MeSH terms
- Cardiovascular Diseases, Humans, Linkage Disequilibrium, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Population Groups, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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