The DOI matches its Crossref record; 67 of the 77 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
Items 401–500 of 829
Gong, Z., Wu, D., Ku, Y., Zou, C., Qiu, L., Hao, X., & Liu, L. (2025) · BMC Pulmonary Medicine · doi:10.1186/s12890-025-03527-x · Cited by 4
“Genetic variations located within genes encoding protein targets of lipid-lowering medications (known as cis-vari-ants) were identified using GWAS data from the Global Lipids Genetics Consortium (GLGC) (http:// www. lipid genet ics. org/) [18].”
Baltramonaityte, V., Karhunen, V., Felix, J. F., Penninx, B. W. J. H., Cecil, C. A. M., Fairchild, G. F., Milaneschi, Y., & Walton, E. (2025) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2025.01.024 · Cited by 4
“Specifically, summary data for lipids were obtained from a study in the Global Lipids Genetics Consortium (Graham et al., 2021), whereas data for fasting insulin and fasting glucose were obtained from two different studies in the Meta-Analysis of Glucose and Insulin-related Traits Consortium…”
Self-citationKuang, A., Hivert, M., Hayes, M. G., Lowe, W. L., & Scholtens, D. M. (2025) · BMC Genomics · doi:10.1186/s12864-025-11229-1 · Cited by 6
Self-citationBiswas, S., Hilser, J. R., Woodward, N. C., Wang, Z., Gukasyan, J., Nemet, I., Schwartzman, W. S., Huang, P., Han, Y. Y., Fouladian, Z., et al. (2025) · Nutrients · doi:10.3390/nu17010198 · Cited by 8
Popp, J. M., Rhodes, K., Jangi, R., Li, M., Barr, K., Tayeb, K., Battle, A. J., & Gilad, Y. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100701 · Cited by 22
“Using schizophrenia, 46 DBP, 47 and LDL cholesterol 48 as example traits, we confirmed that HDC eQTLs showed a higher disease risk than random variants, consistent with patterns observed in eQTLs from primary tissue samples (Figure S9).”
Artificial Intelligence Applications in Genomics
Nagarajan, R., Wang, C., Walton, N., & Walton, N. (2024) · Advances in Molecular Pathology · doi:10.1016/j.yamp.2024.08.001 · Cited by 3
Self-citationTengberg, J. F., Russo, F., Benned‐Jensen, T., & Nielsen, J. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106728 · Cited by 4
PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies
Swaminathan, A. D., Hannemann, A., Ünal, A. B., Pfeifer, N., & Akgün, M. (2025) · Nature Communications · doi:10.48550/arxiv.2410.08122 · Cited by 5
“While balanced binary traits are sometimes analyzed as quantitative traits, this is a step back compared to the SF-GWAS approach which implements logistic regression for binary traits.”
Zhu, Y., Wang, Y., Cui, Z., Liu, F., Hu, C., & Hu, J. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114735 · Cited by 4
Self-citationRay, N. R., Bradley, J. L., Yılmaz, E., Kızıl, Ç., Kurup, J. T., Martin, E. R., Klein, H., Kunkle, B. W., Bennett, D. A. A., Jager, P. L. D., et al. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011631 · Cited by 5
Self-citationQiu, Y., Song, B., Yin, Z., Wang, M., Tao, Y., Xie, M., Duan, A., Chen, Z., Si, K., & Wang, Z. (2024) · European Stroke Journal · doi:10.1177/23969873241265019 · Cited by 5
“16 The GWAS summary statistics pertaining to individuals of European descent from the Global Lipids Genetics Consortium (GLGC) study were procured for this MR investigation.”
InfluentialSelf-citationMalinowski, D., Safranow, K., & Pawlik, A. (2024) · Genes · doi:10.3390/genes15070871 · Cited by 4
“Studies have shown that lipid metabolism is regulated by several genes, which can influence the development of coronary artery disease [2,3].”
BackgroundStatin use and fall risk in adults: a cross-sectional survey and mendelian randomization analysis
Zheng, H., Fang, Y., Wang, S.-T., Huang, Y.-B., Tang, T.-C., & Chen, M. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1364733 · Cited by 4
Lalagkas, P. N., & Melamed, R. (2024) · Research Square · doi:10.21203/rs.3.rs-4536370/v1 · Cited by 1
“The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.”
Peng, Y., Shen, H.-D., Li, C.-Y., Zhu, X.-Y., Gao, Y.-Q., Yi, H., Xu, H.-J., Guan, J., Li, X.-Y., & Yin, S. (2024) · Nutrition and Metabolism · doi:10.1186/s12986-024-00805-z · Cited by 4
“Considering the impact of ethnicity on genetic variations, we intentionally sourced SNP data from GWAS conducted on East Asian populations [18, 19].”
MethodSelf-citationInfluence of Polygenic Background on the Clinical Presentation of Familial Hypercholesterolemia
Trinder, M., Cermakova, L., Ruel, I. L., Baass, A. W., Paquette, M., Wang, J., Kennedy, B. A., Hegele, R. A., Genest, J. J., & Brunham, L. R. (2024) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.123.320287 · Cited by 10
Self-citationZhang, J., Dong, C., Lin, Y., Shang, L., Ma, J., Hu, R., & Wang, H. (2024) · Molecular and Clinical Oncology · doi:10.3892/mco.2024.2736 · Cited by 6
“With the public release of large‑scale gene‑wide association data, a large number of reliable genetic variants are available for MR studies (11).”
Self-citationThe Role of Genetics in Advancing Cardiometabolic Drug Development
Abou-Karam, R., Cheng, F., Gady, S., & Fahed, A. C. (2024) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01195-6 · Cited by 3
Unraveling COVID-19 Relationship with Anxiety Disorders and Symptoms
Asgel, Z., Kouakou, M. R., Koller, D., Pathak, G. A., Cabrera‐Mendoza, B., & Polimanti, R. A. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.02.061 · Cited by 5
Search and replication of associations of genome variants with lipid levels in a Russian sample
Zaichenoka, M., Ershova, A., Kiseleva, A., Sotnikova, E., Vyatkin, Y. V., Zharikova, A., Pokrovskaya, M. S., Shalnova, S., Ramensky, V., Meshkov, A., et al. (2024) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2023-3871 · Cited by 4
Dai, Y., Hsu, Y.-C., Fernandes, B. S., Zhang, K., Li, X., Enduru, N., Liu, A., Manuel, A. M., Jiang, X., Zhao, Z., et al. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231020 · Cited by 9
“Gene MUC7 encodes the protein mucin-7 and has been implicated in cholesterol metabolism [90].”
Self-citationLi, Y., & Zhang, Q. (2024) · PLoS ONE · doi:10.1371/journal.pone.0296894 · Cited by 3
Estimation of a genetic Gaussian network using GWAS summary data
Yang, Y., Lorincz‐Comi, N. J., & Zhu, X. (2024) · Biometrics · doi:10.1093/biomtc/ujae148 · Cited by 4
Self-citationHasbani, N. R., Westerman, K. E., Kwak, S. H., Chen, H., Li, X., DICORPO, D. A., Wessel, J., Bis, J. C., Sarnowski, C., Wu, P., et al. (2023) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.123.004176 · Cited by 9
“40 While the interaction with T2D at each of these genes is only nominally significant, both LIPG and EIF2B2 would not have reached the Bonferroni-corrected significance threshold by evaluating the main effects alone.”
BackgroundSelf-citationBergstedt, J., Pasman, J. A., Ma, Z., Harder, A., Yao, S., Parker, N., Treur, J. L., Smit, D. J. A., Frei, O., Shadrin, A. A., et al. (2023) · medRxiv · doi:10.1101/2023.09.01.23294931 · Cited by 7
Early prediction of atherosclerosis diagnosis with medical ambient intelligence
Yang, W., Nie, Q.-L., Sun, Y.-J., Zou, D.-R., Tang, J.-M., & Wang, M. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1225636 · Cited by 4
“The blood-testing methods are the collection of blood from the vein, then analyzed in the laboratory to measure various components, such as plasma lipid levels (Graham et al., 2021) and coagulation tests (Koenen and Binder, 2020).”
MethodSelf-citationAberra, Y. T., Ma, L.-J., Björkegren, J., & Civelek, M. (2023) · bioRxiv · doi:10.1101/2022.04.27.489778 · Cited by 4
Tudor, L., Erjavec, G. N., Perković, M. N., Konjevod, M., Uzun, S., Kozumplik, O., Mimica, N., Lauc, G., Štrac, D. Š., & Pivac, N. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065706 · Cited by 4
“However, FUT8 polymorphisms have been associated with the levels of high-density lipoprotein cholesterol (rs10483776) [53,54], hypertension [55], and chronic obstructive pulmonary disease [56,57], which are common comorbidities in PTSD [1].”
BackgroundCirculating macrophage colony-stimulating factor levels and stroke: A Mendelian randomization study
Cao, L., Liu, D., Karhunen, V., Ren, Y., Ye, D., Gao, J., Gill, D., & Wang, M. (2023) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2023.107050 · Cited by 5
The clinical utility of polygenic risk scores for combined hyperlipidemia
Dron, J. S. (2022) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000865 · Cited by 4
Honigberg, M., Truong, B., Khan, R., Xiao, B., Bhatta, L., Vy, T. H., Guerrero, R., Schuermans, A., Selvaraj, M., Patel, A., et al. (2022) · medRxiv · doi:10.1101/2022.11.30.22282929 · Cited by 4
Self-citationMeng, X., Navoly, G., Giannakopoulou, O., Levey, D. F., Koller, D., Pathak, G. A., Koen, N., Lin, K., Rentería, M. E., Feng, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.20.500802 · Cited by 4
Self-citationDai, H., Yu, Q., Lv, H. X., Jiang, L., Jiang, H., Shen, M., Chen, H., Chen, Y., Zheng, S., Fu, Q., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.898893 · Cited by 4
“A previous multi-ancestry population-based study has revealed multiple variants associated with serum lipid traits (43).”
BackgroundSelf-citationWu, C., Pu, Q., Zou, Y., Jin, J., Lei, Y., Hu, Y., Chen, Z., & Yu, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-15525-4 · Cited by 3
“…lipoprotein cholesterol (HDL-C) and total cholesterol (TC)) were obtained from the largest Genome-Wide Association Study (GWAS) performed by the Global Lipids Genetics Consortium (GLGC) with over 1 million Europeans, adjusting for sex, age, age squared, and population stratification 19 .”
Self-citationAn antagonistically pleiotropic gene regulates vertebrate growth, maturity, and lifespan
Moses, E., Bergman, M., Atlan, T., Duxbury, E., Franěk, R., Dor, O. B., von Chrzanowski, H., Abu‐Zhayia, E. R., Ayoub, N., Kinreich, S., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72381-0 · Cited by 14
Yang, Y., & Zhu, X. (2026) · Genome biology · doi:10.1186/s13059-026-04051-9 · Cited by 2
Self-citationAdvancing precision health discovery in a genetically diverse health system
Haas, R., Margolis, M. P., Wei, A., Yamaguchi, T. N., Feng, J. A., Tran, T., Tozzo, V., Queen, K. J., Mootor, M. F. E., Patil, V., et al. (2026) · Cell · doi:10.1016/j.cell.2026.03.007 · Cited by 8
Das, V., Narayanan, S., Zhang, X., Bergman, O., Djordjevic, D., Kronqvist, M., Chemaly, M., Karadimou, G., Sundman, S., Prasad, I., et al. (2026) · Genome Medicine · doi:10.1186/s13073-026-01601-5 · Cited by 8
“More recently, advanced large-scale studies have mapped genetic risk loci across diverse populations [12–14], performed in-depth molecular subtyping [13, 15] and dissected cell-specific disease mechanisms [6, 16–23], resulting in identification of several subclinical phenotypes of ASCVD [24].”
Scalable and accurate rare variant meta-analysis with Meta-SAIGE
Park, E., Nam, K., Jeong, S., Keat, K., Kim, D., Bansal, V., Zhou, W., & Lee, S. (2025) · Nature Genetics · doi:10.1038/s41588-025-02403-y · Cited by 4
“For instance, the Global Lipids Genetics Consortium analyzes several lipid traits 25,26 (for example, LDL-C, HDL-C, triglycerides); the Genetic Investigation of Anthropometric Traits consortium examines various anthropometric measures 27,28 (for example, height, body mass index, waist–hip ratio);…”
Self-citationXu, C., Ganesh, S. K., & Zhou, X. (2025) · Nature Genetics · doi:10.1038/s41588-025-02360-6 · Cited by 7
“We used data for four lipid traits (HDL, LDL, TC, TG) from the Global Lipids Genetics Consortium 65 , BMI from the Genetic Investigation of Anthropometric Traits (GIANT) consortium 66 and T2D from the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) consortium 67 .”
Yang, X., Tang, W., Chan, H., Wang, M., Yang, H., & Li, Q. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70144 · Cited by 2
“Lipid-associated SNPs were selected according to the original protocol by Graham et al. 26 The detailed information of data processing on the software, packages and parameters used was provided in Table S2.”
Wilson, E. P., Broadaway, K. A., Parsons, V. A., Vadlamudi, S., Narisu, N., Brotman, S. M., Currin, K. W., Stringham, H. M., Erdos, M. R., Welch, R., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.09.003 · Cited by 2
Self-citationZou, J., Zhang, Y., Sun, X., Wang, Y., Li, Y., & Zhao, Z. H. (2025) · Virulence · doi:10.1080/21505594.2025.2553786 · Cited by 3
“Genetic associations with lipid traits were extracted from the Global Lipids Genetics Consortium (GLGC) consisting of 1,320,016 European individuals [19].”
Self-citationLiu, Z., Sun, X., Liu, C., Wang, C., Zheng, R., Du, X., Song, W., & Lu, C. (2025) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-025-01883-6 · Cited by 3
“For HDL-C and TG, we extracted the GWAS summary data from the Global Lipids Genetics Consortium (GLGC), which included 1,320,016 subjects with European ancestry [19].”
Self-citationWang, C., Yu, X., Guo, X., Yu, X., & Wang, F. (2025) · Respiratory Medicine · doi:10.1016/j.rmed.2025.108271 · Cited by 3
Self-citationWang, X., Zhang, W.-G., Lin, T., Mao, R., Lin, Y., Yu, J., Ruan, L., Lin, J., Gao, L., Zhang, P., et al. (2025) · Discover Oncology · doi:10.1007/s12672-025-03094-x · Cited by 3
“Data on low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) were obtained from the Global Lipids Genetics Consortium (GLGC, http://www.lipidgenetics.org/#locuszoom) [27].”
Self-citationWu, S., Meena, D., Smith, A., Huang, J., Otto, G., Ko, Y.-H., Yarmolinsky, J., Gill, D., Rohatgi, A. K., Dehghan, A., et al. (2025) · The Lancet Regional Health - Southeast Asia · doi:10.1016/j.lansea.2025.100621 · Cited by 4
“21 The GWAS statistics for CAD were obtained from the East London Genes & Health (ELGH) study for South Asians.”
Self-citationJamshidi, S., Eskandari, Z., Ojaroodi, A. F., Keramat, S., & Stanek, A. (2025) · Nutrients · doi:10.3390/nu17142303 · Cited by 6
“Important risk factors for atherosclerosis include lipoprotein metabolism [45], hypertension [46], diabetes [47], obesity and nutrition [48], exercise and physical activity [49], stress and sleep [49,50], smoking [51], pollution [41], gut microbiota [52], alcohol consumption [53], and infection…”
Sutton, K. J., Gervis, J. E., Jatoi, M., Hwang, L., Hendricks, A. E., Ghosh, D., Westerman, K. E., & Cole, J. B. (2025) · medRxiv · doi:10.1101/2025.06.26.25330002 · Cited by 2
A Spanish-Portuguese GWAS of progressive supranuclear palsy reveals a novel risk locus in NFASC
García‐González, P., Lara, H. R., Compta, Y., Fernandez, M., van der Lee, S. J., de Rojas, I., Saiz-Aúz, L., Painous, C., Camara, A., Muñoz, E., et al. (2025) · European Journal of Human Genetics · doi:10.1038/s41431-025-01872-3 · Cited by 9
Hur, H. J., Yang, H. J., Kim, M. J., Jang, H., Kim, M., & Park, S. (2025) · Journal of clinical lipidology · doi:10.1016/j.jacl.2025.04.202 · Cited by 1
Self-citationUsing Genomics to Develop Personalized Cardiovascular Treatments
Sanghvi, M. M., Young, W. J., Naderi, H., Burns, R., Ramírez, J., Bell, C. G., & Munroe, P. B. (2025) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.125.319221 · Cited by 5
Self-citationYang, T., Chen, Z., Cao, D.-Y., Song, W., Jiang, J., Ma, X., Mao, Z., Guan, X.-D., Wu, J.-F., Shi, R., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-95812-2 · Cited by 3
“…et al. 6 showed a significant increase in the risk of surgical site infections post-colorectal cancer surgery in obese patients, and Chakaroun et al. 4 indicated that bariatric surgery could reduce the risk of postoperative complications and infections, which aligns with our findings of a…”
Self-citationExamining 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
“For the LIPC region, three independent association signals have been reported for the standard lipids 19 and an additional signal has been discovered for lipid species.”
Self-citationNorland, K., Schaid, D. J., & Kullo, I. J. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100427 · Cited by 1
“To develop PGS, we used GWAS summary statistics from FinnGen for binary traits and BMI and multi-ancestry meta-analysis GWAS summary statistics from the Global Lipids Genetics Consortium 22 for lipid traits (excluding the UKB samples).”
A multiparametric anti-aging CRISPR screen uncovers a role for BAF in protein synthesis regulation
Breusegem, S. Y., Houghton, J. W., Romero-Bueno, R., Fragoso-Luna, A., Kentistou, K. A., Ong, K. K., Janssen, A. F. J., Bright, N. A., Riedel, C. G., Perry, J. R. B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56916-5 · Cited by 4
“…on body mass index (BMI, n =806,834) and waist-hip ratio (WHR) adjusted for BMI ( n =694,649) from the GIANT study 54 , the GLGC triglycerides study 55 ( n =1,253,275) and the GEFOS estimated bone mineral density (eBMD, n = 426,824) study 56 and only variants with a minor allele frequency >0.1%.”
InfluentialSelf-citationJin, J., Li, B., Wang, X., Yang, X., Li, Y., Wang, R., Ye, C., Shu, J., Fan, Z., Xue, F., et al. (2025) · medRxiv · doi:10.1101/2025.02.07.25321875 · Cited by 5
“The performance was evaluated on UKB validation individuals of EUR, AFR, AMR, EAS, and SAS ancestries 54,55 .”
Self-citationA multi-ancestry genome-wide association study and evaluation of polygenic scores of LDL-C levels
Umlai, U. I., Toor, S. M., Al-Sarraj, Y. A., Mohammed, S., Hail, M. S. H. A., Ullah, E., Kunji, K. B., El-Menyar, A. A., Gomaa, M., Jayyousi, A., et al. (2025) · Journal of Lipid Research · doi:10.1016/j.jlr.2025.100752 · Cited by 6
“Notably, a recent comprehensive GWAS conducted by the Global Lipids Genetics Consortium (GLGC), which represents the largest published GWAS and meta-analysis on LDL-C levels (Graham et al. ) was performed on a cohort of around 1.65 million individuals (11).”
InfluentialHu, Y., Cui, X., Lu, M., Guan, X., Li, Y., Zhang, L., Lin, L., Zhang, M., Zhang, M., Hao, J., et al. (2025) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2024.10.018 · Cited by 2
Ansari, A., Yadav, P. K., Zhou, L., Prakash, B., Ijaz, L., Christiano, A., Ahmad, S., Rimbert, A., & Hussain, M. M. (2025) · JCI Insight · doi:10.1172/jci.insight.182260 · Cited by 3
“These associations were further replicated in the Global Lipid Genetic Consortium (GLGC) dataset (16) (Supplemental Figure 5, D and E).”
Hakim, A., Connally, N. J., Schnitzler, G. R., Cho, M., Jiang, Z. G., Sunyaev, S. R., & Gupta, R. M. (2025) · Genes · doi:10.3390/genes16010084 · Cited by 3
Li, M., Duan, H., Luo, J., Tan, Y. T., Liu, M., Zhao, X., Shi, D., & Ma, X. (2024) · Medicine · doi:10.1097/md.0000000000040770 · Cited by 4
“[23] We identified single nucleotide polymorphisms (SNPs) associated with key lipid properties at a significant genome-wide threshold ( P 5.0 × 10 ‐ 8 ) to mimic the lipid-modifying influence of chosen drug targets.”
InfluentialFrom Genetic Findings to new Intestinal Molecular Targets in Lipid Metabolism
May, C. L., Ducheix, S., Cariou, B., & Rimbert, A. (2025) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01264-w · Cited by 3
“Additionally, genome-wide association studies (GWAS) have identified many independent variants associated with plasma lipid levels in the general population [26].”
Yarmolinsky, J., Lee, M. A., Lau, E., Moratalla-Navarro, F., Vincent, E. E., Li‐Gao, R., Rensen, P. C. N., van Dijk, K. W., Tsilidis, K. K., Sangphukieo, A., et al. (2025) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djaf137 · Cited by 5
“…targets were constructed using circulating plasma protein levels (ie, as compared with downstream biomarkers of drug target perturbation) to increase instrument strength given that circulating plasma protein levels are expected to be more proximal to cis -acting variants influencing these traits.”
Lin, Y., Li, X., Hu, M., Zhao, J., & Zhu, C. (2024) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003601 · Cited by 6
“…D137), including body fat percentage (BFP), body mass index (BMI), waist-to-hip ratio (WHR), waist circumference, hip circumference, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, apolipoprotein A-1, and apolipoprotein B (34 – 36).”
Self-citationZhao, J. V., & Zhang, J. (2024) · Nutrients · doi:10.3390/nu16213674 · Cited by 3
“We derived the genetic associations for lipids from the Global Lipids Genetics Consortium (GLGC), a large-scale consortium of over 0.84 million participants of European ancestry, excluding the UK Biobank [32].”
MethodInfluentialSelf-citationHouse, J. S., Breeyear, J. H., Akhtari, F. S., Evans, V., Buse, J. B., Hempe, J. M., Doria, A., Mychaleckyj, J. C., Fonseca, V. A., Shi, M., et al. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1473329 · Cited by 4
“Variants in and near PROCA1 have been associated with cholesterol levels (46) and with reticulocyte count (39), further implicating blood cell counts and morphometric traits in the severity of T2D comorbidities.”
Dudek, M. F., Wenz, B. M., Brown, C., Voight, B., Almasy, L., & Grant, S. (2024) · bioRxiv · doi:10.1101/2024.09.24.614730 · Cited by 3
“…associated with liver-65 related traits including metabolic associated steatotic liver disease (MASLD, formerly NAFLD) 29– 31 , type 2 diabetes (T2D) 32 , hyperlipidemia 13,33 , enzyme levels 34 , and T2D risk factors such as obesity 35–37 , most of which have unknown functional mechanisms 38 .”
Self-citationAlwan, H., Luan, J., Williamson, A., Carrasco-Zanini, J., Stewart, I. D., Wareham, N. J., Langenberg, C., & Pietzner, M. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105306 · Cited by 3
“43,44 We further observed inverse associations with polydipsia (OR = 0.57, 95% CI: 0.42 – 0.78, p-value = 3.35 × 10 − 2 ), and pemphigus (OR = 0.51, 95% CI: 0.35 – 0.75, p-value = 4.52 × 10 − 2 ).”
InfluentialSelf-citationZheng, L., Zhang, C., Bu, S., Guo, W., Li, T., Xu, Y., Liu, Y., Yuan, C., Feng, C., Zong, G., et al. (2024) · Vascular Health and Risk Management · doi:10.2147/vhrm.s459075 · Cited by 5
“16 We used the results excluding Finnish samples to avoid largely overlap with the cohort for outcome.”
MethodFish Oil Supplementation Modifies the Genetic Potential for Blood Lipids
Sun, Y., McDonald, T., Baur, A., Xu, H., Bateman, N., Shen, Y., Li, C., & Ye, K. (2024) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2024.07.009 · Cited by 7
“For the primary analysis using PGS from Graham et al. [19] with UKB excluded, there was strong evidence that fi sh oil supplementation modi fi es the associations between PGS and the corresponding observed concentration for 4 lipids in either the partially or fully adjusted model (Figure 2, Table 2)…”
BackgroundMethodResultInfluentialSelf-citationCharacterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry
Jiang, L., Gangireddy, S., Dickson, A. L., Xin, Y., Yan, C., Kawai, V. K., Cox, N. J., Linton, M. F., Wei, W., Stein, C., et al. (2024) · Journal of Lipid Research · doi:10.1016/j.jlr.2024.100569 · Cited by 3
“While genetic studies on interindividual TG variability have begun to include ancestries beyond those of European patients, (9, 12) relatively little is known about the contribution of genetic variation to high TG levels in people of African ancestry (AA).”
BackgroundInfluentialSelf-citationCohort Profile Update: The Glostrup Population Studies 1964–2024
Møllehave, L. T., Madsen, A. L., Kampmann, F. B., Bjerregaard, A. A., Dantoft, T. M., Leth-Møller, K., Thysen, S. M., Schovsbo, S. U., Jacobsen, R. K., Aadahl, M., et al. (2024) · International Journal of Epidemiology · doi:10.1093/ije/dyae051 · Cited by 4
Self-citationJi, X., Guo, H., Han, M., Peng, H., & Yuan, H. (2024) · International Journal of Rheumatic Diseases · doi:10.1111/1756-185x.15106 · Cited by 3
“15 To proxy PCSK9, instrumental variables in the form of single nucleotide polymorphisms (SNPs) were utilized.”
MethodJaso-Vera, M. E., Takaoka, S., Patel, I., & Ruan, X. (2024) · Human Genetics · doi:10.1007/s00439-024-02654-5 · Cited by 4
“2B) and GWAS data (Graham et al. 2021; Klarin .”
Arunachalam, V., Lea, R., Hoy, W. E., Lee, S., Mott, S., Savige, J., Mathews, J. D., McMorran, B., & Nagaraj, S. (2024) · Genome Medicine · doi:10.1186/s13073-024-01299-3 · Cited by 3
“Furthermore, a GWAS demonstrated that MEIS2 variants were associated with triglycerides using a mixed ancestry of millions of individuals [68, 69], as triglycerides are known to be one of the major risk factors for CKD.”
BackgroundShi, Y., Xiang, Y., Ye, Y., He, T., Sham, P. C., & So, H. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105616 · Cited by 3
The Intersection between Pharmacogenomics and Health Equity: A Case Example
Paetznick, C., & Okoro, O. N. (2023) · Pharmacy · doi:10.3390/pharmacy11060186 · Cited by 3
“AMP plans to update the recommendations as new data or reference materials become available [37–42].”
BackgroundZhao, S. S., Alton, P., Rogers, K., & Hughes, D. M. (2023) · Clinical Therapeutics · doi:10.1016/j.clinthera.2023.10.018 · Cited by 9
DUAN, Y., Ke, X., Wu, H., Yao, S., Shi, W., Han, J., Zhu, R., Wang, J., Jia, Y., Yang, T., et al. (2023) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15298 · Cited by 4
“42 Specific to this study, causal effect inference was performed using multiple GWAS summary data (HOMA-IR, ISI, fasting insulin, HDL cholesterol, triglycerides, LDL cholesterol and total cholesterol) 29 – 32 and eQTL data from GTEx.”
BackgroundMethodInfluentialSelf-citationDai, Y.-L., Hsu, Y.-C., Fernandes, B., Zhang, K., Li, X.-Y., Enduru, N., Liu, A.-D., Manuel, A., Jiang, X.-Q., & Zhao, Z.-M. (2023) · Research Square · doi:10.21203/rs.3.rs-3328861/v1 · Cited by 3
Self-citationBerry, A. S. F., Jones, L. K., Sijbrands, E. J. G., Gidding, S. S., & Oetjens, M. T. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.123.319341 · Cited by 9
Examining the relationship between genetic risk for depression and youth episodic stress exposure
Harrison, T., Docherty, A. R., Finsaas, M., Kotov, R., Shabalin, A., Waszczuk, M., Katz, B. A., Davila, J., & Klein, D. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.08.088 · Cited by 3
Le, N. N., Tran, T., du Toit, C., Gill, D., & Padmanabhan, S. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1116799 · Cited by 3
“Genes related to DBP eQTLs, WHR eQTLs, and T2DM eQTLs resulted in the same enriched pathways as those for SBP, with extra pathways involving in (6) immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell; (7) SAR-CoV-2 infections (WHR, T2DM); (8) adaptive immune system (T2DM); (9) cytokine signalling…”
BackgroundMethodCholesteryl ester transfer protein inhibition is associated with reduced risk of Sjögren’s syndrome
Zhao, S. S., & Dyball, S. (2023) · Lara D. Veeken · doi:10.1093/rheumatology/kead115 · Cited by 3
“8 ) from a genome-wide association study (GWAS) of (cid:4) 1.3 million European individuals [3].”
InfluentialPatel, A., Wang, M., Ruan, Y., Koyama, S., Clarke, S., Yang, X., Tcheandjieu, C., Agrawal, S., Fahed, A., Ellinor, P., et al. (2023) · medRxiv · doi:10.1101/2023.03.03.23286649 · Cited by 3
“…, peripheral artery disease (PAD) 57,61,110 , chronic kidney disease (CKD) 57,63 , systolic blood pressure (SBP) 57,113 , diastolic blood pressure (DBP) 57,113 , low-density lipoprotein cholesterol (LDL) 57,62,79 , high-density lipoprotein cholesterol (HDL) 57,62,79 , triglycerides (TG) 57,62,79 .”
Self-citationKlotho protein and atherosclerotic cardiovascular diseases: prolonging the thread of life
Alieva, A. M., Reznik, E. V., Teplova, N. V., Voronkova, K., Khachirova, E. A., Sarakaeva, L. R., Valiev, R., Kotikova, I. A., & Nikitin, I. (2022) · Medical Journal of the Russian Federation · doi:10.17816/medjrf110823 · Cited by 3
“Как известно, сердечно-сосудистые заболевания (ССЗ) обусловлены генетической предрасположенностью [35].”
A multiparametric anti-aging CRISPR screen uncovers a role for BAF in protein translation
Breusegem, S., Houghton, J., Romero-Bueno, R., Fragoso-Luna, A., Kentistou, K., Ong, K., Janssen, A. F. J., Bright, N. A., Riedel, C. G., Perry, J., et al. (2022) · bioRxiv · doi:10.1101/2022.10.07.509469 · Cited by 3
Self-citationGrand challenges in stroke genomics
Fornage, M. (2022) · Frontiers in Stroke · doi:10.3389/fstro.2022.984176 · Cited by 4
“Increasing ancestral/genetic diversity is essential for the fine-mapping of functional variants (Asimit et al., 2016; Graham et al., 2021).”
BackgroundSelf-citationDing, Y., Hong, G., Wang, Z. X., Huang, L., Chen, Q. H., Yang, Y., Yang, J., & Ning, W. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.06.034 · Cited by 2
Self-citationIntegrated drivers of basal and acute immunity in diverse human populations
Souquette, A. H., Allen, E. K., Oshansky, C. M., Rampersaud, E., Walker, E. V., Tang, L., Wong, S., Jeevan, T., Shi, L., Pounds, S. B., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102690 · Cited by 8
“24,50,51 The results presented here suggest a complex and multidimensional immune space with dynamic landscapes depending on the immune pressure and interactions between variables that change the position of an individual, in addition to single-variable influences.”
Self-citationData-driven RNA phenotyping captures genetically regulated dimensions of the transcriptome
Munro, D. B., Gusev, A., Palmer, A. A., & Mohammadi, P. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.06.005 · Cited by 1
“37 • The “fasting glucose” trait had a data-driven xTWAS hit for DGKB (diacylglycerol kinase beta) in pancreas (lead variants chr7:14887046 for xQTL and chr7: 15025378 for GWAS).”
Pascat, V., Zudina, L., Maurin, L., Ulrich, A., Maina, J. G., Demirkan, A., Balkhiyarova, Z., Pupko, I., Sharhorodska, Y., Pattou, F. N., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-67449-2 · Cited by 8
Self-citationFarm animal evolution demonstrates hidden molecular basis of human traitsPreprint
Connally, N. J., & Sunyaev, S. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703413
“0 26 and meta-analyzed data from the Global Lipids Genetics Consortium, a dataset with over 1.6 million samples 84 .”
InfluentialGenetic associations of circulating plasma proteins with cardiometabolic diseases
Zhou, R., Qiao, J., Zhang, X., Yang, X., Hong, W., Cai, L., Zhang, P., Pauklin, S., Yang, Y., Xu, Z., et al. (2026) · Genome biology · doi:10.1186/s13059-026-03962-x · Cited by 2
“…cholesterol (HDL-C; N = 1,244,580), tri-glyceride (TG; N = 1,253,277) and total cholesterol (TC; N = 1,320,016) for lipidemic phenotypes [60]; alanine aminotransferase (ALT; N = 437,267), alkaline phosphatase (ALP; N = 437,438) and γ-glutamyl transferase (GGT; N = 437,194) for…”
Sevilla-González, M. D. R., Wang, N., Hanson, P. A., Bebo, A., Hitchcock, D. S., Hsu, S. C., Westerman, K. E., CROMER, S. J., Barry, V. G., Borns-Weil, Y., et al. (2025) · medRxiv · doi:10.1101/2025.11.26.25341007 · Cited by 1
“34,35 We next investigated causality using complementary inference approaches.”
Self-citationMirmira, T., Ma, N., Margoliash, J., Rivera, W. G. G., Amariuta, T., Frazer, K. A., Goren, A., & Gymrek, M. (2025) · medRxiv · doi:10.1101/2025.11.13.25339614 · Cited by 2
Targeting Endothelial KDM5A to Attenuate Aging and Ameliorate Age‐Associated Metabolic Abnormalities
Gao, R., Liang, L.-F., Yang, L., Lyu, C., Lyu, Y., Yang, W.-J., Shi, J.-R., Wei, W., Cheng, J.-H., Sun, X.-L., et al. (2025) · Advancement of science · doi:10.1002/advs.202512657 · Cited by 2
Self-citationEngaging migrants and immigrants in genetics research
Valdez-Aguilar, M., Conklin, J., & Abebe, D. (2025) · Nature Genetics · doi:10.1038/s41588-025-02373-1 · Cited by 2
“The historical focus on individuals of European ancestry in genetic studies further restricts the generalizability of findings and reduces the predictive accuracy of polygenic risk tools for non-European populations 25,26 .”
Chung, R., Wang, C., Onthoni, D. D., Liao, B., Hsu, T.-S., Martin, E. R., Hsiung, C. A., Sheu, W. H., & Chiou, H. (2025) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2025.0041 · Cited by 3
“We accessed GWAS summary statistics for these traits in European populations from the GWAS Catalog [29-33].”
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 ›
Are you an author, or do you see a problem with this page? Request a review
