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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The result that cross-population PGS outperformed ancestry-matched PGSs replicated previous studies showing that multi-ancestry PGS demonstrated improved prediction performance relative to ancestry-matched PGSs.
“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]
The ‘Trans-ancestry PRS are most predictive’ section reports that trans-ancestry scores had the best or near-best performance across the groups tested. It describes prediction relative to ancestry-matched scores as ‘improved or comparable,’ so the claim overstates the finding by presenting improvement without that qualification. (LLM, conf=0.84)
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“Considering the results presented here, and those of related studies 38–40 , we consider that future genetic studies will substantially benefit from meta-analyses across participants of diverse ancestries.”
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“Considering the results presented here, and those of related studies 38–40 , we consider that future genetic studies will substantially benefit from meta-analyses across participants of diverse ancestries.”
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“Although many traits show a high degree of shared genetic correlation across ancestries 32,36,37 , others have distinct genetic variants with large effects that are more common in specific ancestry groups 34 , which may limit the utility of multi-ancestry PRS for particular phenotypes in some…”
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“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 .”
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“Of these 159 variants, 31 showed the largest effect in African ancestry analyses, 24 in East Asian, 67 in European, 20 in Hispanic and 17 in South Asian.”
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“Annotation of variants as known or new was done based on manual reviews of previously published variants and with variants reported in the GWAS catalogue 27 for any of the studied lipid traits (accessed May 2020, provided as Supplementary Table 18).”
MethodSelf-citationvan Leeuwen, E. M., Karssen, L. C., Deelen, J., Isaacs, A. J., Medina‐Gómez, C., Mbarek, H., Kanterakis, A., Trompet, S., Postmus, I., Verweij, N., et al. (2015) · Nature Communications · doi:10.1038/ncomms7065 · Cited by 59 · see citing works
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“Although many traits show a high degree of shared genetic correlation across ancestries 32,36,37 , others have distinct genetic variants with large effects that are more common in specific ancestry groups 34 , which may limit the utility of multi-ancestry PRS for particular phenotypes in some…”
BackgroundMethodSelf-citationMajara, L., Kalungi, A., Koen, N., Zar, H. J., Stein, D. J., Kinyanda, E., Atkinson, E. G., & Martin, A. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.12.426453 · Cited by 26 · cited 2× · see citing works
“…many traits show a high degree of shared genetic correlation across ancestries 32,36,37 , others have distinct genetic variants with large effects that are more common in specific ancestry groups 34 , which may limit the utility of multi-ancestry PRS for particular phenotypes in some ancestries.”
BackgroundShah, P. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.727555798.793563055 · Cited by 16 · see citing works
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- [34]Lehmann BCL, Mackintosh M, McVean G & Holmes CC High trait variability in optimal polygenic prediction strategy within multiple-ancestry cohorts. bioRxiv, 2021.2001.2015.426781, doi: 10.1101/2021.01.15.426781 (2021). · DOI
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Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association
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Atherosclerosis: Recent developments
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“GWAS loci for CVD risk factors, including plasma lipid levels (Graham et al., 2021), hypertension (Cabrera et al.”
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“Whereas MR studies of modifiable risk factors generally employ variants from multiple gene regions, MR analyses exploring drug target effects typically utilize variants from a single gene region, specifically variants within the region around the protein-coding gene.”
Genetic drivers of heterogeneity in type 2 diabetes pathophysiology
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Self-citationAlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model
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Advancing regulatory variant effect prediction with AlphaGenome
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“( g ) AlphaGenome predictions for the variant rs12740374, previously found to be associated with low-density lipoprotein cholesterol levels 64 .”
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Self-citationA multi-ancestry polygenic risk score improves risk prediction for coronary artery disease
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Self-citationGenome-wide association analysis of plasma lipidome identifies 495 genetic associations
Ottensmann, L., Tabassum, R., Ruotsalainen, S., Gerl, M., Klose, C., Widén, E., FinnGen, Simons, K., Ripatti, S., & Pirinen, M. (2023) · medRxiv · doi:10.1101/2023.01.21.23284765 · Cited by 239
“For example, the LIPC region has been reported to contain three independent signals for standard lipids [20] and in addition to these a fourth independent signal has been reported to be associated with lipid species [35].”
InfluentialSelf-citationGenome-wide characterization of circulating metabolic biomarkers
Karjalainen, M. K., Karthikeyan, S., Oliver‐Williams, C., Sliz, E., Allara, E., Fung, W. T., Surendran, P., Zhang, W., Jousilahti, P., Kristiansson, K., et al. (2024) · Nature · doi:10.1038/s41586-024-07148-y · Cited by 268 · see citing works
“This locus was previously known to be associated with LDL-C, triglycerides and total cholesterol 25–27 , and we found associations at this locus with several lipoprotein subclass measures, lipids and fatty acids (Supplementary Table 5).”
BackgroundInfluentialSelf-citationGenetic control of RNA splicing and its distinct role in complex trait variation
Qi, T., Wu, Y., Fang, H., Zhang, F., Liu, S., Zeng, J., & Yang, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01154-4 · Cited by 237 · see citing works
Self-citationPhysiological and pathological roles of lipogenesis
Jeon, Y. G., Kim, Y. Y., Lee, G., & Kim, J. B. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00786-y · Cited by 248 · see citing works
Self-citationPalaniappan, L. P., Allen, N. B., Almarzooq, Z., Anderson, C. A. M., Arora, P. K., Avery, C. L., Baker-Smith, C. M., Bansal, N., Currie, M. E., Earlie, R. S., et al. (2026) · Circulation · doi:10.1161/cir.0000000000001412 · Cited by 312 · see citing works
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“While > 160 genes are necessary to explain 50 – 70% of the heritability of other lipoproteins [25,26], the LPA gene locus alone explains up to 90% of Lp(a) variance [10,17] .”
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“We selected genetic instruments based on their associations with plasma LDL-cholesterol obtained from ancestry-spe-cific summary-level data from the Global Lipids Genetics Consortium (GLGC) (1,231,289/82,587 people of European/East Asian ancestry) [24 ], but not based on functional information.”
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“…group, a meta-analysis was performed using METAL 57 with inverse variance weighting using the UKB and the following consortia: GIANT 93 for BMI; MAGIC 94 for fasting glucose; Global Lipids Genetics Consortium 95 for HDL, LDL and TG; and Biobank Japan 95,96 for SBP in samples of East Asian ancestry.”
Self-citationPolygenic scores in biomedical research
Kullo, I. J., Lewis, C. M., Inouye, M., Martin, A. R., Ripatti, S., & Chatterjee, N. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00470-z · Cited by 175 · see citing works
Self-citationGenotyping and population characteristics of the China Kadoorie Biobank
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“126 However, efforts are underway to gather such data as well as to develop suitable tools for the analysis of non-European data.”
Spatially resolved mapping of cells associated with human complex traits
Song, L., Chen, W., Hou, J., Guo, M., & Yang, J. (2025) · Nature · doi:10.1038/s41586-025-08757-x · Cited by 141 · see citing works
Self-citationGenetic and molecular architecture of familial hypercholesterolemia
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“Colocalization analysis with the largest available GWAS of lipid 31 , blood pressure 32 and smoking 33 traits was performed by coloc 58 , as described in the eQTL colocalization section.”
MethodSelf-citationReturning integrated genomic risk and clinical recommendations: The eMERGE study
Linder, J. E., Allworth, A., Bland, H. T., Caraballo, P. J., Chisholm, R. L., Clayton, E. W., Crosslin, D. R., Dikilitas, O., DiVietro, A., Esplin, E. D., et al. (2023) · Genetics in Medicine · doi:10.1016/j.gim.2023.100006 · Cited by 123 · see citing works
“Sufficiently diverse GWAS data are lacking to support cross-ancestry scores for many common medical conditions, even though it has been demonstrated how genetically diverse population data are critical to the accuracy of the method.(9,39,40) By limiting clinically implemented scores to those validated in multiple ance…”
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Self-citationDeciphering the impact of genomic variation on function
Writing group (ordered by contribution), Engreitz, J., Lawson, H. A., Singh, H., Starita, L. M., Hon, G. C., Carter, H. K., Sahni, N., Reddy, T. E., Lin, X., et al. (2024) · Nature · doi:10.1038/s41586-024-07510-0 · Cited by 91 · see citing works
Self-citationGlobal Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts
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“43 We also found that prediction performance can be improved when using ancestry-matched tuning cohorts for PRS construction to fine-tune hyper-parameters and avoid overfitting, while other studies have also explored options, such as pseudo-validation, when no additional tuning cohort is available.”
BackgroundSelf-citationThe HUNT study: A population-based cohort for genetic research
Brumpton, B. M., Graham, S. E., Surakka, I. L., Skogholt, A. H., Løset, M., Fritsche, L. G., Wolford, B. N., Zhou, W., Nielsen, J. B., Holmen, O. L., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100193 · Cited by 110 · see citing works
Self-citationA new method for multiancestry polygenic prediction improves performance across diverse populations
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Self-citationMeta-analysis fine-mapping is often miscalibrated at single-variant resolution
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Self-citationRecent advances in polygenic scores: translation, equitability, methods and FAIR tools
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“For example, a recent study of blood lipid levels showed that PGS constructed using multi-ancestry GWAS outperforms those constructed using single-ancestry matched data [118].”
Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank
Oliveri, A., Rebernick, R. J., Kuppa, A., Pant, A., Chen, Y., Du, X., Cushing, K. C., Bell, H. N., Raut, C., Prabhu, P., et al. (2024) · Nature Genetics · doi:10.1038/s41588-023-01625-2 · Cited by 99 · see citing works
Self-citationTime to Consider the “Exposome Hypothesis” in the Development of the Obesity Pandemic
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Lipids dysregulation in diseases: core concepts, targets and treatment strategies
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“However, elevated lipid levels in the blood can lead to fat deposition in arterial walls, a condition associated with vascular complications such as atherosclerosis, stroke, and heart disease [1–3].”
Association of Lipid-Lowering Drugs With Risk of Psoriasis
Zhao, S. S., Yiu, Z. Z. N., Barton, A. C., & Bowes, J. (2023) · JAMA Dermatology · doi:10.1001/jamadermatol.2022.6051 · Cited by 88 · see citing works
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Self-citationMulti-ancestry meta-analysis and fine-mapping in Alzheimer’s disease
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Self-citationThe Singapore National Precision Medicine Strategy
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Self-citationLiver ALKBH5 regulates glucose and lipid homeostasis independently through GCGR and mTORC1 signaling
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Self-citationKidney multiome-based genetic scorecard reveals convergent coding and regulatory variants
Liu, H., Abedini, A., Ha, E., Ma, Z., Sheng, X., Dumoulin, B., Qiu, C., Arányi, T., Shen, L., Hosni, N. D., et al. (2025) · Science · doi:10.1126/science.adp4753 · Cited by 75 · see citing works
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“A subsample of 10,894 women in the NHS ( n = 7,479) and NHSII ( n = 3,412) cohorts have Broad Institute lipidomics data with species-level information on the total number of C atoms and double bonds in the fatty acid residuals of lipid metabolites (Supplementary Tables 5–8) 37 .”
Self-citationHuang, Q. Q., Sallah, N., Dunca, D., Trivedi, B., Hunt, K. A., Hodgson, S., Lambert, S. A., Arciero, E., Wright, J., Griffiths, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32095-5 · Cited by 66 · see citing works
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Self-citationHu, X., Cai, M., Xiao, J.-S., Wan, X., Wang, Z.-W., Zhao, H., & Yang, C. (2024) · medRxiv · doi:10.1101/2024.01.03.24300765 · Cited by 64
Shared genetic risk factors and causal association between psoriasis and coronary artery disease
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Mediating Factors in the Association of Maternal Educational Level With Pregnancy Outcomes
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Novel insights into causal effects of serum lipids and lipid-modifying targets on cholelithiasis
Chen, L., Qiu, W., Sun, X., Gao, M., Zhao, Y., Li, M., Fan, Z., & Lv, G. (2023) · Gut · doi:10.1136/gutjnl-2023-330784 · Cited by 65 · see citing works
“These Ivs’ information could be found in online supplemental tables 6–16 and the full results of the positive control analysis were displayed in online supplemental table 17.”
BackgroundKanoni, S., Graham, S. E., Wang, Y., Surakka, I. L., Ramdas, S., Zhu, X., Clarke, S. L., Bhatti, K. F., Vedantam, S., Winkler, T. W., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02837-1 · Cited by 63 · see citing works
“Uniquely, we observed 1750 variants associated with one or more lipid levels [24] (Additional file 2: Table S2).”
BackgroundMethodResultInfluentialSelf-citationEvaluating Performance and Agreement of Coronary Heart Disease Polygenic Risk Scores.
Abramowitz, S. A., Boulier, K., Keat, K., Cardone, K. M., Shivakumar, M., DePaolo, J., Judy, R., Bermudez, F., Mimouni, N., Neylan, C., et al. (2024) · Journal of the American Medical Association (JAMA) · doi:10.1001/jama.2024.23784 · Cited by 58 · see citing works
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“%) of the causal variants are shared across ancestries with strong effect size correlations 14–16 .”
InfluentialRare coding variant analysis for human diseases across biobanks and ancestries
Jurgens, S. J., Wang, X., Choi, S. H., Weng, L.-C. C., Koyama, S., Pirruccello, J. P., Nguyen, T. T. H., Smadbeck, P., Jang, D., Chaffin, M. D., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01894-5 · Cited by 54 · see citing works
“For common variant genome-wide association studies, it has been shown that ancestry-specific variants may contribute strongly to genetic signal and discovery 37–39 .”
Self-citationSchizophrenia genomics: genetic complexity and functional insights
Sullivan, P. F., Yao, S., & Hjerling‐Leffler, J. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00837-7 · Cited by 62 · see citing works
“In addition, owing to different patterns of linkage disequilibrium, non-European data can help to narrow the set of loci identified in schizophrenia GWAS 30,145,146 .”
Fang, S., Yarmolinsky, J., Gill, D., Bull, C. J., Perks, C. M., the PRACTICAL Consortium, Davey Smith, G., Gaunt, T. R., & Richardson, T. G. (2023) · PLOS Medicine · doi:10.1371/journal.pmed.1003988 · Cited by 55 · see citing works
“In total, 28 genetic variants were used to proxy the inhibition of PCSK9, 4 for NPC1L1, 13 for HMGCR, 36 for the levels of LDLR-mediated LDL-c, and 424 for the levels of total LDL-c identified using the latest GLGC GWAS.”
MethodInfluentialPolygenic prediction of body mass index and obesity through the life course and across ancestries
Smit, R. A. J., Wade, K. H., Hui, Q., Arias, J. D., Yin, X., Christiansen, M. R., Yengo, L., Preuss, M. H., Nakabuye, M., Rocheleau, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03827-z · Cited by 54 · see citing works
“…we used PRS-CS, combining GIANT’s combined multi-ancestral GWAS summary statistics (including Bio Me but excluding the UKBB and the ALSPAC study) with a previously described multi-ancestry 1000G-based LD reference panel created following the same protocol as described by the PRS-CS authors 94 .”
Self-citationChen, J., Li, Y., Zhang, Y., Wang, Y., Zhou, Y., Geng, T., Liu, G., Pan, A., & Liao, Y.-F. (2024) · Metabolism · doi:10.1016/j.metabol.2024.155817 · Cited by 51 · see citing works
Self-citationHoggart, C. J., Choi, S. W., García‐González, J., Souaiaia, T., Preuss, M. H., & O’Reilly, P. F. (2023) · Nature Genetics · doi:10.1038/s41588-023-01583-9 · Cited by 63 · see citing works
“…of causal variant effect sizes of 0.9 is consistent with the results of a multiancestry lipids GWAS in which causal variants were fine-mapped 16 and with a recent study estimating a mean genetic correlation of 0.98 of causal variant effect sizes between ancestries across a range of…”
ResultInfluentialSelf-citationChoudhury, A., Brandenburg, J., Chikowore, T., Sengupta, D., Boua, P. R., Crowther, N. J., Agongo, G., Asiki, G., Gómez-Olivé, F. X., Kisiangani, I., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30098-w · Cited by 51 · see citing works
Self-citationCross-ancestry genome-wide association studies of brain imaging phenotypes
Fu, J., Zhang, Q., Wang, J., Wang, M., Zhang, B., Zhu, W., Qiu, S., Geng, Z., Cui, G., Yu, Y., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01766-y · Cited by 39 · see citing works
“Consistent with prior studies on non-imaging traits 16,17 , brain IDPs also showed more SES associations than DES associations between ancestries, indicating that most genetic associations for IDPs identified in one ancestral population can be extrapolated to other populations.”
Self-citationDyslipidemia and Risk of Preeclampsia: A Multiancestry Mendelian Randomization Study
Hosier, H., Lipkind, H. S., Rasheed, H., DeWan, A. T., & Rogne, T. (2023) · Hypertension · doi:10.1161/hypertensionaha.122.20426 · Cited by 55 · see citing works
Self-citationMajara, L., Kalungi, A., Koen, N., Tsuo, K., Wang, Y., Gupta, R., Nkambule, L. L., Zar, H. J., Stein, D. J., Kinyanda, E., et al. (2023) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2023.100184 · Cited by 57 · see citing works
Self-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. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00488-y · Cited by 59 · see citing works
An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction
Zhang, J.-N., Zhan, J., Jin, J., Ma, C., Zhao, R., O'Connell, J., Jiang, Y.-X., Koelsch, B., Zhang, H.-Y., & Chatterjee, N. (2023) · bioRxiv · doi:10.1101/2023.03.15.532652 · Cited by 46
“Furthermore, we evaluate these methods using a variety of real datasets from 23andMe Inc. (23andMe), the Global Lipids Genetics Consortium (GLGC) 37 , All of Us (AoU) 38 , and the UK Biobank study (UKBB) 39 .”
InfluentialSelf-citationThe Physiological and Pathological Role of Acyl-CoA Oxidation
Szrok-Jurga, S., Czumaj, A., Turyn, J., Hebanowska, A., Świerczyński, J., Śledziński, T., & Stelmańska, E. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241914857 · Cited by 52 · see citing works
Girard, D., & Vandiedonck, C. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.991716 · Cited by 51 · see citing works
Chen, C., Chen, T., Feng, Y. A., Yu, M., Lin, S., Longchamps, R. J., Wang, S., Hsu, Y., Yang, H., Kuo, P., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100436 · Cited by 82 · see citing works
Self-citationSofer, T., Kurniansyah, N., Murray, M. T., Ho, Y.-L. A., Huffman, J. E., Cho, K., Wilson, P. W. F., & Gottlieb, D. J. (2022) · medRxiv · doi:10.1101/2022.12.21.22283799 · Cited by 1 · see citing works
“previously reported as associated with BMI, cholesterol, and kidney function (51,56,57).”
BackgroundSelf-citationRazavi, A., Mehta, A., Jain, V., Patel, P., Liu, C., Patel, N., Eisenberg, S. D., Vaccarino, V., Isiadinso, I., Sperling, L. S., et al. (2023) · Current Cardiology Reports · doi:10.1007/s11886-023-01987-3 · Cited by 37 · see citing works
“The most recent large-scale multi-ancestry GWAS that included ~ 1.6 million individuals have identified 562 genomic loci associated with HDL-C at a stringent genome-wide association level [16].”
InfluentialIntegrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes
McAllan, L., Baranas̆ić, D., Villicaña, S., Brown, S., Zhang, W., Lehne, B. C., Adamo, M., Jenkinson, A. D., Elkalaawy, M., Mohammadi, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38439-z · Cited by 45 · see citing works
Self-citationYang, X., Kar, S., Antoniou, A. C., & Pharoah, P. D. P. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00599-x · Cited by 40 · see citing works
Breeze, C. E., Haugen, E., Gutiérrez‐Arcelus, M., Yao, X., Teschendorff, A. E., Beck, S., Dunham, I., Stamatoyannopoulos, J. A., Franceschini, N., Machiela, M. J., et al. (2024) · Genome biology · doi:10.1186/s13059-023-03126-1 · Cited by 43 · see citing works
Current advances in primate genomics: novel approaches for understanding evolution and disease
Juan, D., Santpere, G., Kelley, J. L., Cornejo, O. E., & Marquès‐Bonet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-022-00554-w · Cited by 43 · see citing works
A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids
Ramdas, S., Judd, J., Graham, S. E., Kanoni, S., Wang, Y., Surakka, I. L., Wenz, B. M., Clarke, S. L., Chesi, A., Wells, A. D., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.06.012 · Cited by 47 · see citing works
Self-citationShared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus
Guo, X., Feng, Y., Ji, X., Jia, N., Maimaiti, A., Lai, J., Wang, Z., Yang, S., & Hu, S. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105530 · Cited by 27 · see citing works
Self-citationSecure and federated genome-wide association studies for biobank-scale datasets
Cho, H., Froelicher, D., Chen, J. Y., Edupalli, M., Pyrgelis, A., Troncoso-Pastoriza, J. R., Hubaux, J., & Berger, B. (2025) · Nature Genetics · doi:10.1038/s41588-025-02109-1 · Cited by 37 · see citing works
Calibrated prediction intervals for polygenic scores across diverse contexts
Hou, K., Xu, Z.-Q., Ding, Y., Harpak, A., & Pasaniuc, B. (2023) · medRxiv · doi:10.1101/2023.07.24.23293056 · Cited by 35
“We start by showcasing LDL, an important risk factor of cardiovascular disease 39 .”
Smith, C. J., Sinnott-Armstrong, N. A., Cichońska, A., Julkunen, H., Fauman, E., Würtz, P., & Pritchard, J. (2022) · bioRxiv · doi:10.1101/2022.04.02.486791 · Cited by 35
Lee, D. S. M., Cardone, K. M., Zhang, D. Y., Tsao, N. L., Abramowitz, S. A., Sharma, P., DePaolo, J. S., Conery, M., Aragam, K. G., Biddinger, K. J., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02140-2 · Cited by 44 · see citing works
“As with other complex disease traits, common less-penetrant variants influencing these core genes likely contribute to heart failure risk 4,6,9 ; yet the cata-log of such variants and their cumulative contribution to heart failure remains incomplete.”
Self-citationFrick, E. A., Emilsson, V., Jónmundsson, Þ., Steindorsdottir, A. E., Johnson, E. C. B., Puerta, R., Dammer, E. B., Shantaraman, A., Cano, A. C., Boada, M., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00693-1 · Cited by 45 · see citing works
Self-citationNAT2 global landscape: Genetic diversity and acetylation statuses from a systematic review
Gutiérrez-Virgen, J. E., Piña‐Pozas, M., Hernández-Tobías, E. A., Taja‐Chayeb, L., López-González, M. D. L., Meraz‐Ríos, M. A., & Gómez, R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283726 · Cited by 44 · see citing works
SDPRX: A statistical method for cross-population prediction of complex traits
Zhou, G., Chen, T., & Zhao, H. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.11.007 · Cited by 34 · see citing works
Zhu, X., Yang, Y., Lorincz‐Comi, N. J., Li, G., Bentley, A. R., de Vries, P. S., Brown, M., Morrison, A. C., Rotimi, C. N., Gauderman, W. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47806-3 · Cited by 34 · see citing works
“SMARCA4 was reported to be associated with LDL-C in the lipids GWAS in Africans 44 but not in the recent largest lipids GWAS which is predominantly European ancestry 3 .”
Self-citationCross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes
Liu, N., Zhang, L. J., Tian, T., Cheng, J., Zhang, B., Qiu, S., Geng, Z., Cui, G., Zhang, Q., Liao, W., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01425-8 · Cited by 39 · see citing works
Self-citationGenetic and epigenetic background of diabetic kidney disease
Sandholm, N., Dahlström, E. H., & Groop, P. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1163001 · Cited by 39 · see citing works
Guo, G., Wu, Y., Liu, Y., Wang, Z., Xu, G., Wang, X., Liang, F., Lai, W., Xiao, X., Zhu, Q., et al. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1113334 · Cited by 32 · see citing works
“Ancestry-specific GWAS summary statistics for the four lipid traits in a European cohort were used in our MR analysis (Graham et al., 2022).”
MethodH3Africa: a model for implementing biobank-based genomic research in resource-constrained settings
Sengupta, D., Choudhury, A., & Ramsay, M. (2025) · Human Molecular Genetics · doi:10.1093/hmg/ddaf113 · Cited by 34 · see citing works
Self-citationCalibrated prediction intervals for polygenic scores across diverse contexts
Hou, K., Xu, Z., Ding, Y., Mandla, R., Shi, Z., Boulier, K. M., Harpak, A., & Paşaniuc, B. (2024) · Nature Genetics · doi:10.1038/s41588-024-01792-w · Cited by 61 · see citing works
“We focused on LDL, an important risk factor of cardiovascular disease 47 .”
Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans
Hendrix, S., Kingma, J., Ottenhoff, R., Valiloo, M., Svecla, M., Zijlstra, L., Sachdev, V., Kovač, K., Levels, J., Jongejan, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40943-1 · Cited by 30 · see citing works
“The Global Lipids Genetics Consortium aggregated GWAS results from 1,654,960 individuals from 201 primary studies representing fi ve genetic ancestry groups 28 .”
InfluentialJin, J., Zhan, J., Zhang, J.-N., Zhao, R., O'Connell, J., Jiang, Y.-X., Aslibekyan, S., Auton, A., Babalola, E., Bell, R., et al. (2023) · bioRxiv · doi:10.1016/j.xgen.2024.100539 · Cited by 30 · see citing works
“9,25 However, given the high computational scalability of MUSS, when the number of ancestry groups is not too large ( K % 5), prior information on genetic correlation can used to specify additional genetic correlation parameter settings to cover a wider range of potential genetic architectures of…”
Self-citationCai, M., Wang, Z., Xiao, J., Hu, X., Chen, G., & Yang, C. (2023) · Nature Communications · doi:10.1038/s41467-023-42614-7 · Cited by 26 · see citing works
“For EUR, we considered two GWAS datasets: the UKBB GWAS summary data released by the Neale Lab with a sample size of 343,621 (http://www.nealelab.is/uk-biobank/), and the EUR GWAS data from GLGC with a sample size of 664,450 34 .”
InfluentialMRBEE: A bias-corrected multivariable Mendelian randomization method
Lorincz‐Comi, N. J., Yang, Y., Li, G., & Zhu, X. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100290 · Cited by 31 · see citing works
Self-citationMartschenko, D. O., Wand, H., Young, J. L., & Wojcik, G. L. (2023) · Nature Genetics · doi:10.1038/s41588-023-01394-y · Cited by 33 · see citing works
Wünnemann, F., Tadjo, T. F., Beaudoin, M., Lalonde, S., Lo, K. S., Kleinstiver, B. P., & Lettre, G. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010680 · Cited by 39 · see citing works
Self-citationFast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference
Zabad, S., Gravel, S., & Li, Y. (2022) · bioRxiv · doi:10.1101/2022.05.10.491396 · Cited by 29
Towards a global view of multiple sclerosis genetics
Jacobs, B. M., Peter, M., Giovannoni, G., Noyce, A. J., Morris, H. R., & Dobson, R. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-022-00704-y · Cited by 28 · see citing works
Li, D., Lin, J., Yang, H.-X., Zhou, L.-H., Li, Y.-J., Xu, Z., Sun, L., Zhang, X.-Y., Xu, W.-L., & Wang, Y.-G. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02790-w · Cited by 28 · see citing works
Self-citation
- 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)
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- Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (MX)
- National Institute of Genomic Medicine (MX)
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- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
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- 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
- 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.
- Krauss, Ronald M.
- Zheng, Wei
- 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
- Lehtimäki, Terho
- Chaturvedi, Nish
- Liu, Jianjun
- Reilly, Dermot F.
- McKnight, Amy Jayne
- Kee, Frank
- Jöckel, Karl-Heinz
- McCarthy, Mark I.
- Hayward, Caroline
- Simonsick, Eleanor
- 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.
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- 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
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- Karpe, Fredrik
- Neville, Matt J.
- Timpson, Nicholas J.
- Cheng, Ching-Yu
- Wong, Tien-Yin
- Khor, Chiea Chuen
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- 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
- Ghanbari, Mohsen
- Gordon-Larsen, Penny
- Huang, Wei
- Kim, Young Jin
- Wareham, Nicholas J.
- Langenberg, Claudia
- Zeggini, Eleftheria
- Kuusisto, Johanna
- Laakso, Markku
- Ingelsson, Erik
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- North, Kari E.
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- 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
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- Ritchie, Marylyn D.
- Jousilahti, Pekka
- Salomaa, Veikko
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- 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 · 2012: 1
- FWCI
- 21.98
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-05-26)
- Open access
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- doi.org
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- 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)
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- 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)
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- 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 › Surgery › 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
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