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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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“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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MethodInfluential
Research works printed but not linked: 1
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“…Consortium, the largest lipid genome-wide association study (GWaS) meta-analysis comprising approximately 173,082 individuals of European ancestry [27,28]. the outcome data for cystic kidney disease was obtained from the FinnGen consortium (website: https://www.finngen.fi/en), which includes 469…”
He, W., Shi, J., Qian, Y., Fan, T., Cai, X., Li, H., Huang, P., & Shi, Q. (2025) · PLoS ONE · doi:10.1371/journal.pone.0320479 · Cited by 2
“We downloaded GWAS data two major subtypes of stroke, including AIS [10] and ICH [23], and ten CRTs, including total cholesterol (TC) [24], HDL [24], low density lipoprotein cholesterol (LDL) [24], logarithm of triglycerides (logTG) [24], BMI [25]; systolic blood pressure (SBP) [26], diastolic…”
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Yousri, N. A., Cai, Y., Guo, B., Peloso, G., Surakka, I., Peters, U., Highland, H., Hajj, N. E., Darst, B., Graff, M., et al. (2025) · medRxiv · doi:10.1101/2025.04.21.25326110 · Cited by 1
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“11,12,14,16,19 We selected traits for inclusion in the multi-trait analysis based on their biological relevance to atherosclerosis, the strength of their relationships with atherosclerosis, and the availability of large, high-quality GWAS datasets.”
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“…association studies (GWAS) and whole-exome sequencing (WES)/whole genome sequencing (WGS) have identified thousands of genetic loci associated with high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), total cholesterol (TC), and triglycerides (TG) [3–5].”
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“This locus has been previously associated through GWAS with Parkinson’s disease [44],response to warfarin treatment [45],and triglycerides levels [46], but not with kidney function or CKD.”
Tangsudjai, S., Fujita, A., Tamura, T., Okuno, T., Oda, M., & Kato, K. (2025) · Metabolic brain disease · doi:10.1007/s11011-025-01551-8 · Cited by 1
“Our results demonstrated that St3gal4 deficiency in mice resulted in alterations in body size and metabolic parameters, which align with human GWAS linking ST3GAL4 to ALP, protein, triglycerides, T-Cho, and lipoproteins (Lin et al. 2014; Sharapov et al. 2019; Graham et al. 2021; Yengo et al. 2022).”
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“…in PGS derived from the AFR meta-analysis ( n = 51,895, partial R 2 = 0.0425) than PGS derived from the EUR meta-analysis, encompassing over ten times more samples ( n = 548,812, partial R 2 = 0.0319), which supports the previously proposed importance of diversity in the derivation dataset 49–52 .”
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“4,5 9 Variant alleles associated with higher concentration of LDL are more abundant among subjects with coronary artery disease than those without.”
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Self-citationThe role of genetically predicted serum iron levels on neurodegenerative and cardiovascular traits
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“…lateral sclerosis 34 ), cardiovascular (coronary artery disease 35 , heart failure 36 ), cerebrovascular (stroke 37 ) and lipid (triglycerides, and the following cholesterol: total, low-density lipoprotein, high-density lipoprotein, non-high-density lipoprotein 38 ) traits (Supplementary Fig.”
Self-citationCo-expression-wide association studies link genetically regulated interactions with complex traits
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“For each outcome trait, we downloaded summary-level data from the largest publicly available GWAS study of European ancestry participants [42, 43, 45].”
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Self-citationRare variant effect estimation and polygenic risk prediction
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“For low-density lipoprotein cholesterol (LDL-C) levels, we adjusted premedication levels by dividing the raw LDL-C level by 0.7 for individuals on cholesterol-lowering medication 32 .”
InfluentialSelf-citationCorrelation-based tests for the formal comparison of polygenic scores in multiple populations
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Clinical impact of genetic testing for lipid disorders
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“association study (GWAS) of blood lipid levels carried out in 1.65 million individuals identified 941 lipid-asso-ciated genomic regions containing more than 1.700 distinct variants [16].”
InfluentialTaniguchi, J., Masuda, T., Iwatani, Y., Yamamoto, K., Sakai, N., Okada, Y., & Watanabe, M. (2023) · Clinical Genetics · doi:10.1111/cge.14443 · Cited by 1
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“𝐺 × 𝐸 The correlations in the first and the third columns represent the variants reported in Graham et al while the correlations in the second and Online Methods”
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“We used ancestry-specific summary-level data from the Global Lipids Genetics Consortium (GLGC) for plasma LDL-cholesterol (1,231,289/82,587 people of European/East Asian ancestry) (21).”
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Self-citationHeterogeneity-aware integrative regression for ancestry-specific association studies
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“Many recent studies have focused on GWAS for lipid levels (Graham et al., 2021; Ramdas et al., 2022), though PWAS are very limited (Zhang et al., 2022).”
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“INTRODUCTION Genome-wide association studies (GWAS) have been successful at identifying specific genetic variations that associate with particular diseases.(1) GWAS are typically based on human cohorts, members of whom have diverse lifestyles, variable co-morbidities, and different environmental exposures.”
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“65 M individuals, the team showed that the multi-ancestry PRS had the best or near-best performance in each ethnic group tested, with improved or equivalent prediction relative to ancestry-matched scores (Graham et al., 2021).”
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Self-citationA fast linkage method for population GWAS cohorts with related individuals
Zajac, G. J. M., Taliun, S. A. G., Sidore, C., Graham, S. E., Åsvold, B. O., Brumpton, B. M., Nielsen, J. B., Zhou, W., Gabrielsen, M. E., Skogholt, A. H., et al. (2023) · Genetic Epidemiology · doi:10.1002/gepi.22516 · Cited by 1
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“Robust genetic variants have been identified in large GWAS with mtDNA CN (n = 465,809), CHD (n = 361,194) and LDL (n=1,166,583).(61,62,72,73,74) To minimize bias in MR analyses, we removed known pleiotropic SNPs (e.”
BackgroundSelf-citationIntegrating large scale genetic and clinical information to predict cases of heart failure
Wu, K.-H., Wolford, B. N., Du, J., Yu, X., Douville, N. J., Mathis, M. R., Graham, S. E., Surakka, I. L., Hornsby, W., Bian, J., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01198-7 · Cited by 1
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Self-citationGALNT2 rs4846914 SNP Is Associated with Obesity, Atherogenic Lipid Traits, and ANGPTL3 Plasma Level
Qaddoumi, M. G., Hebbar, P., Abu‐Farha, M., Somaly, A. A., Melhem, M., Alkayal, F., Al‐Khairi, I., Cherian, P., Alanbaei, M., Al‐Mulla, F., et al. (2022) · Genes · doi:10.3390/genes13071201 · Cited by 2
“Recent genome-wide association (GWA) investigations have discovered novel genetic determinants for a variety of complicated quantitative characteristics, including dyslipidemia [1,5,6].”
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Beyond HDL-Cholesterol: The Search for Functional Biomarkers of High Density Lipoproteins
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Multiancestry conditional and joint analysis applied to GWAS summary statistics
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Variational Bayes for Variable Selection with Adaptive Sharing across Populations and Responses
Yang, Y.-H., Chen, P.-F., Yang, F., & You, C.-T. (2026) · doi:10.48550/arxiv.2610.39677
“We applied MVP-VB to ancestry-specific GWAS summary statistics from the 2021 Global Lipids Genetics Consortium (GLGC) study (Graham et al., 2021) for four blood lipid traits: high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and…”
Jeong, M., Liu, Z., Ni, J., An, U., Han, S., Anand, A., Anand, P., Boffelli, M., & Sankararaman, S. (2026) · doi:10.64898/2026.10.20.760508
Self-citationmultiomeR: a pipeline for reproducible analysis of large-scale single-nucleus Epi Multiome datasetsPreprint
Koefoed, T. G., He, S., Thodberg, M., Pikkupeura, L. M., Krizic, K., Kapel, B. S., Martínez, R., Rasmussen, S., & Hansen, T. F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.14.759117
“It runs the analysis as many small steps, , Schep et al. 2017), for 8 GWAS traits whose enrichment rests on many loci (Law et al. 2019, Vuckovic et al. 2020, Graham et al. 2021, Ishigaki et al. 2022, Ghouse et al. 2024, Keaton et al. 2024, Roselli et al. 2025).”
Self-citationLi, C., Luo, S., He, B., Lam, H. S., Snetselaar, L. G., Leung, C. Y., Leung, G. M., Schooling, C. M., & Yeung, S. L. A. (2026) · Sci · doi:10.3390/sci8100302
Self-citationInstrumental Variable Analysis in Underrepresented Subpopulations Powered by Knowledge Transfer
Wang, R.-Y., Xu, Z.-J., Guo, Z., Lin, X.-H., & Liu, M.-L. (2026) · doi:10.48550/arxiv.2610.05680
Self-citationNiemiec, P., Nowak, T., Wrona, J., Iwanicka, J., Iwanicki, T., Jarosz, A., Górczyńska-Kosiorz, S., & Balcerzyk-Matić, A. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27208975
“To further evaluate our findings, we examined the full summary statistics from the large multi-ancestry GLGC (Global Lipids Genetics Consortium) meta-analysis reported by Graham et al. [89], which included approximately 1.65 million participants from five ancestry groups.”
InfluentialSutton, K. J., Gervis, J. E., Jatoi, M., Hwang, L., Hendricks, A. E., Ghosh, D., Westerman, K. E., & Cole, J. B. (2026) · Genetic Epidemiology · doi:10.1002/gepi.70059
“Biomarker (ALT, TG) and disease (CVD, cirrhosis) IVs were derived from European‐ancestry GWAS that maximized sample size and avoided sample overlap with the UKB (Aragam et al. 2022; Ghouse et al. 2024; Graham et al. 2021; Pazoki et al. 2021).”
Jiang, L., Chen, X., Qian, J., Fang, K., Dai, C., Chang, D., Jin, L., Huang, W., Wang, Y., Duan, X., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71845
Self-citationBedoya, C., Rejali, P., Mccleary, X., Hall, Z., Hoffmann, M. M., Green, L., Frost, P. H., Malloy, M. J., Pullinger, C. R., Kane, J. P., et al. (2026) · Journal of Lipid Research · doi:10.1016/j.jlr.2026.101168
Xu, C.-Z., Yan, Q.-L., Feng, C., Li, Y.-Z., Li, X.-Y., Zhang, R., He, J.-W., & Wu, J.-H. (2026) · Investigative Ophthalmology and Visual Science · doi:10.1167/iovs.67.12.54
“32 To construct lipid instruments for CETP , single nucleotide polymorphisms (SNPs) were extracted from the CETP gene region (chr16: 56895762– 57117757; GRCh37), corresponding to Ensembl GRCh37 gene annotation.”
Self-citationAl‐Saud, S. A. (2026) · Genes · doi:10.3390/genes17101312
“However, the lesson for cardiomyopathy is uncomfortable: the relevant Arab cardiomyopathy datasets remain too small [40].”
Transcriptional and epigenomic analysis of insulin sensitivity in human adipocytes
Zhang, Q., Zhou, A., Maynard, A. G., Emont, M. P., Jacobs, C. L., Nagesh, S., Batista, T. M., Gibson, R. M., Huang, Y., Kaalia, R., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.118042
Tinworth, A. C., Yao, P., Iona, A., Pozarickij, A., Ende, A. V., Millwood, I. Y., Walters, R. G., Clarke, R., Bragg, F., & Chen, Z. (2026) · Nature Cardiovascular Research · doi:10.1038/s44161-026-00871-x · Cited by 1
“Genome-wide genetic instruments for vascular risk factors were obtained from the largest available consortia for each trait and constructed using the same selection criteria as the smoking instruments 66,85,92–94 .”
Self-citationAll of Us diversity and scale yield context-dependent improvements in polygenic prediction
Tsuo, K., Shi, Z., Ge, T., Mandla, R., Hou, K., Ding, Y., Paşaniuc, B., Wang, Y., & Martin, A. R. (2026) · Nature Genetics · doi:10.1038/s41588-026-02734-4 · Cited by 24
Self-citationChen, T., Zhang, H., Mazumder, R., & Lin, X. (2026) · Nature Methods · doi:10.1038/s41592-026-03235-2 · Cited by 4
“To evaluate this, we used METAL 30 to conduct meta-analyses of GWAS (excluding UKBB samples) from the Global Lipid Genetics Consortium (GLGC) 6 , Genetic Investigation of Anthropometric Traits (GIANT) consortium 31 , and Blood Cell Consortium (BCX) 32 (Supplementary Table 12), testing for effect heterogeneity using Co…”
Self-citationOyibo, K., Zhong, M., Adebamowo, C., Choudhury, A., Fatumo, S., Hazelhurst, S., Mitchell, B. D., Ramsay, M., Tayo, B. O., Zhang, Y., et al. (2026) · Human Molecular Genetics · doi:10.1093/hmg/ddag100
“Associations at CETP, LIPC, LPL, CELSR2/PSRC1/SORT1, LDLR , APOB , and PPP1R3B , have also been reported in other ancestry groups, supporting the established roles of these loci in lipid metabolism [ 8, 11].”
Self-citationLongitudinal dynamics of gene expression and metabolomics in an aging population cohort
Moustafa, J. S. E.-S., Ramisch, A., Raza, Y. N., Leday, G. G. R., Jiao, Y., Wang, D., Stevens, M., Roberts, A. L., Tomlinson, M., Yan, X., et al. (2026) · Science · doi:10.1126/science.aed6452
Self-citationWang, B. H., Wang, Y., & Yin, Y. (2026) · Genes · doi:10.3390/genes17091061
“…nine comparison traits, comprising six external vascular traits and three overlapping GIGASTROKE stroke-subtype analyses: systolic blood pressure, diastolic blood pressure [20], LDL cholesterol, HDL cholesterol, triglycerides [21], white matter hyperintensities [22], LAS, CES and SVS [3].”
Self-citationWang, S., Tang, Y., Sui, Y., Zhao, H., Khan, A., Yin, W., Chen, Q., Xu, T., Liu, Z., Zhang, Z., et al. (2026) · Nature Cardiovascular Research · doi:10.1038/s44161-026-00865-9 · Cited by 1
“The LDL-C genetic score was constructed using the LDpred2 method 55 , based on the LDL-C GWAS of the Global Lipids Genetics Consortium (GLGC) 33 .”
Self-citationFujimoto, R., Ogawa, K., Namba, S., Ogawa, Y., Edahiro, R., Sonehara, K., Tagawa, S., Watanabe, M., Yata, T., Shirai, Y., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02741-5
“…to MS, we conducted gsMap analyses using GWAS data for other immune-mediated traits (rheumatoid arthritis 47 , systemic lupus erythematosus 48 , ulcerative colitis 49 , Crohn’s disease 49 and allergy 50 ) and control traits (Alzheimer’s disease 51 , depression 52 and low-density lipoprotein 53 ).”
Self-citationZhao, C., Cath, D. C., van Dalfsen, J. H., Milaneschi, Y., Naderi, E., Penninx, B. W. J. H., Bruggeman, R., Schoevers, R. A., & Alizadeh, B. Z. (2026) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2026.106999
Self-citationBankier, S., Talukdar, H. A., Khan, M., Mocci, G., Sukhavasi, K., Hao, K., Ma, A., Ruusalepp, A., Schadt, E. E., Kovacic, J. C., et al. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101735 · Cited by 2
Arruda, A. L., Singh, A., Bocher, O., Voight, B. F., Morris, A. P., & Zeggini, E. (2026) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2026.100667
“38 The GWAS sample size for contributing cohorts similar to reference populations obtained in Europe was 1,320,016, and we used data from the following five blood lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TGs), total…”
Self-citationYang, G., Zhang, D. Y., Fischman, C. J., Cuchel, M., Suszták, K., & Rader, D. J. (2026) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2026.06.015
Self-citationFeatures of Lipid Disorders in Cardiovascular–Kidney–Metabolic Syndrome
Ceasovschih, A., Şener, Y. Z., Arici, M., Cherska, M., Bianconi, V., Ejubović, M., Kotlyarov, S. N., Ristovski, V., Yetkin, A., Golforoush, P. A., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27188317
One million exomes identify FNIP1 as a metabolic brake
Enzan, N., & Koyama, S. (2026) · Trends in Genetics · doi:10.1016/j.tig.2026.09.003
Cholesteryl Ester Transfer Protein Inhibition and Risk of Age-Related Macular Degeneration
Xue, C. C., Li, H., Wang, S., Qiao, F., Yu, M., Chee, M. L., Li‐Gao, R., van Dijk, K. W., Khor, C. C., Tan, A. C. S., et al. (2026) · Ophthalmology · doi:10.1016/j.ophtha.2026.09.027
Self-citationA pre-train and fine-tune framework for adaptive boosting of pre-trained polygenic risk scores
Hu, J., Chen, R., Salvatore, M., Wu, O., Özdemir, O. B., Lu, Y., Murphy, S. N., Karlson, E. W., Khan, A., Kiryluk, K., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-77128-5 · Cited by 1
“…from Jung et al. 41 , for hypertension from Wojcik et al. 42 , for breast cancer from BCAC Zhang et al. 43 , for type 2 diabetes from DIAGRAM of Glucose et al. 44 , for HDL, LDL, and total cholesterol from the GLGC study Graham et al. 45 , and for BMI from the GIANT consortium Locke et al. 46 .”
Self-citationChen, Y.-C., Liu, T.-Y., Liao, C., Yang, J., Lin, W., Liao, W., Chen, S., & Tsai, F. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1823599
“This dataset includes more than 1.65 million individuals from five genetic ancestry groups: Admixed African or African ( n = 99,432), EAS ( n = 146,492), European ( n = 1,320,016), Hispanic ( n = 48,057), and South Asian ( n = 40,963) (Graham et al., 2021).”
InfluentialSelf-citationWood, A. J., Lovegrove, C., Furniss, D., & Wiberg, A. (2026) · International Journal of Epidemiology · doi:10.1093/ije/dyag173
Self-citationHui, P. S. C., Devlin, B., Evans, D., & Hwang, L.-D. (2026) · International Journal of Epidemiology · doi:10.1093/ije/dyag186
“…artery disease (CAD) [20], high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, total triglycerides [21], systolic blood pressure (SBP), diastolic blood pressure (DBP) [22], type 2 diabetes (T2D) [23], haemoglobin A1c (HbA1c), fasting glucose, and…”
Toward personalized medicine in AD/ADRD through genetic-exposome dementia risk assessments
Tolosa‐Tort, P., Okorie, M., Chatterjee, A., Boeriu, A. I., Sharma, R. U., & Andrews, S. J. (2026) · npj Dementia · doi:10.1038/s44400-026-00087-7
“Because most GWAS have been conducted in European ancestry cohorts, PRS derived from them often have poor transportability to ancestrally diverse and admixed populations due to differences in linkage disequilibrium, minor allele frequency, and SNP effect sizes 73,74 .”
Mandla, R., Shi, Z., Hou, K., Wang, Y., Mies, G., Aw, A. J., Cullina, S., BioBank, P. M., Aw, A., Kenny, E. E., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-75515-6 · Cited by 4
“Interestingly, the effect sizes are largely similar between AFR-like and EUR-like populations in the paper which reported this association 62 , suggesting unique LD structures in AoU or non-genetic factors such as cohort differences.”
Self-citationD’Urso, F., & Broccolo, F. (2026) · Applied Sciences · doi:10.3390/app16168007
Yapp, T.-A. J., Krishnan, M., Liu, S.-W., Manna, S. L., Cheng, H., Naseri, T., Reupena, M. S., Viali, S., Tuitele, J., Deka, R., et al. (2026) · HGG advances · doi:10.1016/j.xhgg.2026.100663
“Graham et al. 7 and Kanoni et al. 8 generated multi-ancestry PGSs for all four major lipid traits from approximately 1.65 million individuals across diverse ancestries, representing the most promising existing tools for cross-ancestry prediction.”
InfluentialThe role of polygenic risk in Alzheimer's disease prediction for African Americans
Selkirk, C. G., Beck, T. L., Evans, D. A., & Rajan, K. B. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71712
“14,43 These findings underscore the urgent need for more diverse GWAS representation and development of ancestry-inclusive PRS models to ensure equitable genomic health care across populations.”
Anthropometric and cardio-metabolic trait variation and genetic associations in sub-Saharan Africa
Hansen, M. E. B., Hazra, U., Kim, M., Raj, S. M., Fan, S., Beggs, W. R., Mpoloka, S. W., Mokone, G. G., Nyambo, T., Meskel, D. W., et al. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.07.006 · Cited by 1
Cheung, N., Cheung, H. K., Yu, Y.-W., & Tsang, Y. C. Y. (2026) · Cureus · doi:10.7759/cureus.114952
“The Global Lipids Genetics Consortium has shown that genetic diversity can improve the discovery and localization of lipid-associated loci, while also highlighting the importance of including populations that have historically been underrepresented in genome-wide association studies [14].”
Meng, T.-Z., & Nielsen, D. E. (2026) · Journal of NutriLife · doi:10.1016/j.tjnut.2026.101798
Discovery of functional variants regulating lipid metabolism in an East Asian population
Moon, S., Park, Y. J., Choi, J., Kim, J. M., Yi, S. H., Kim, M., Lee, J., Kim, J., 손호영, Kim, J., et al. (2026) · Metabolism · doi:10.1016/j.metabol.2026.156751 · Cited by 1
Self-citationBody Mass Index and Facial Aging: Mendelian Randomization and Exploratory Target Prioritization
Hu, Y., Li, K.-H., He, M.-J., Yu, C.-S., & Wang, S. (2026) · Clinical, Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s624227
The COPI coatomer influences LDL receptor activity, hepatic lipid storage, and apoB secretion
Panteloglou, G., Robert, J., Smit, M., Huijkman, N., Kloosterhuis, N., Law, C. S., Woods, B., Othman, A., Kleber, M. E., Delgado, G., et al. (2026) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2026.121887
Self-citationZhu, W., Chen, H., Highland, H. M., Giles, C., Meikle, P. J., Lin, P., Petty, L. E., Polikowsky, H. G., Petty, A. S., Anwar, M. Y., et al. (2026) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2026.100664
“One such signal was near DPP3 , which has previously been implicated in GWASs of conventional lipids LDL, TC, and Trig 84,89,95,96 but not lipid species.”
Self-citationGu, X.-L., Wen, X.-B., Ma, L., Wang, D., Li, Z.-Q., Chen, M.-S., & Ye, G.-F. (2026) · Neurosurgical review · doi:10.1007/s10143-026-04420-9
“The LDL-C GWAS data ( n = 132,065) were obtained from the Global Lipids Genetics Consortium, the largest publicly available dataset for this trait [24 ].”
Zhang, W., Yoshiji, S., Sladek, R., Dupuis, J. E., & Lu, T.-Y. (2026) · bioRxiv · doi:10.1101/2024.10.14.617627
Self-citationOkorie, M. C., Jonson, C., Oddi, A. P., Castruita, P. A., Fulton‐Howard, B., Yaffe, K. S., Yokoyama, J. S., Udeh‐Momoh, C. T., Andrews, S. J., & for the Alzheimer's Disease Sequencing Project and the Healthy Aging Brain Study—Health Disparities (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71529 · Cited by 6
Zhang, J.-W., Hong, S.-H., Wang, X., Jurgens, S. J., Liu, C.-T., Dupuis, J. E., Ellinor, P., O'Connor, G. T., Mei, Q.-S., & Choi, S.-H.-A. (2026) · Nature Communications · doi:10.1038/s41467-026-75569-6
“This is consistent with prior fi ndings near PPARG from a multi-ancestry genome-wide association (GWAS) meta-analysis of lipid traits 25 .”
Self-citationSEC16B identified as a regulator of VLDL secretion and lipid accumulation in the liver
Ardehali, H. (2026) · Journal of Clinical Investigation · doi:10.1172/jci208521
“Previous GWAS identified an association between the minor allele of a SNP (rs6682862, chr1:177969302) located in the promoter region of SEC16B and serum cholesterol levels (17, 18) and determined that SEC16B SNPs are strongly associated with obesity and BMI (19).”
Local ancestry-informed rare variant burden testing improves gene discovery in admixed populations
Kore, P., Tan, T.-T., Lu, W.-X., Manuel-Friedman, A., Hu, L.-F., Chatterjee, N., Zhou, W., Dhindsa, R., & Atkinson, E. G. (2026) · medRxiv · doi:10.64898/2026.07.13.26357993
“…of LDL cholesterol metabolism with well-established effects across diverse ancestral groups, including large multi-292 ancestry sequencing and association studies that have consistently implicated both common and rare variation at this locus in lipid regulation and cardiovascular disease risk 28 .”
Self-citationGenetic architecture and cross-platform validation of apolipoprotein A-IV concentrations
Koller, A., Schnitzer, F., Kollerits, B., Lamina, C., Moix, S., Claringbould, A., Mishra, B., Lu, H.-J., Schachtl-Riess, J., Forer, L., et al. (2026) · Genome Biology · doi:10.1186/s13059-026-04196-7
“…genetic correlation (Linkage Disequilibrium Score Regression [56, 57]) and colocalization analyses [58] as well as included summary statistics [51, 59–65] is provided in the supplemental materials. investigators and provide guidance regarding the applicable access procedures and requirements.”
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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