Tai, E. Shyong

The mutational constraint spectrum quantified from variation in 141,456 humans

Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., et al. (2020) · Nature

Genomics and Rare DiseasesGenetic Associations and Epidemiology

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Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants

Zhou, B., Lu, Y., Hajifathalian, K., Bentham, J., Di Cesare, M., Danaei, G., Bixby, H., Cowan, M., Ali, M., Taddei, C., et al. (2016) · The Lancet

Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Management and Research

Cited by 4,311

Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants

Zhou, B., Carrillo-Larco, R. M., Danaei, G., Riley, L. M., Paciorek, C. J., Stevens, G. A., Gregg, E. W., Bennett, J. E., Solomon, B., Singleton, R. K., et al. (2021) · The Lancet

Blood Pressure and Hypertension StudiesSodium Intake and Health

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Biological, clinical and population relevance of 95 loci for blood lipids

Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature

Genetic Associations and EpidemiologyLipoproteins and Cardiovascular Health

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Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants

Zhou, B., Bentham, J., Di Cesare, M., Bixby, H., Danaei, G., Cowan, M. J., Paciorek, C. J., Singh, G., Hajifathalian, K., Bennett, J. E., et al. (2017) · The Lancet

Blood Pressure and Hypertension StudiesSodium Intake and Health

Cited by 2,579

A genomic mutational constraint map using variation in 76,156 human genomes

Chen, S., Francioli, L. C., Goodrich, J. K., Collins, R. L., Kanai, M., Wang, Q., Alföldi, J., Watts, N. A., Vittal, C., Gauthier, L. D., et al. (2024) · Nature

Genomics and Rare DiseasesGenetic Associations and Epidemiology

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Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans

Kathiresan, S., Melander, O., Guiducci, C., Surti, A., Burtt, N. P., Rieder, M. J., Cooper, G. M., Roos, C., Voight, B. F., Havulinna, A. S., et al. (2008) · Nature Genetics

Genetic Associations and EpidemiologyLipoproteins and Cardiovascular Health

Cited by 1,386

The genetic architecture of type 2 diabetes

Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D. J., et al. (2016) · Nature

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 1,051

A structural variation reference for medical and population genetics

Collins, R. L., Brand, H., Karczewski, K. J., Zhao, X., Alföldi, J., Francioli, L. C., Khera, A. V., Lowther, C., Gauthier, L. D., Wang, H., et al. (2020) · Nature

Genomic variations and chromosomal abnormalitiesGenomics and Rare Diseases

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Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility

DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Mahajan, A., Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Consortium, Go, M. J., Zhang, W., Below, J. E., Gaulton, K. J., et al. (2014) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 1,034

The power of genetic diversity in genome-wide association studies of lipids

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., et al. (2021) · Nature

Genetic Associations and EpidemiologyLipoproteins and Cardiovascular Health

Cited by 972

Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity

Wahl, S., Drong, A., Lehne, B., Loh, M., Scott, W. R., Kunze, S., Tsai, P.-C., Ried, J. S., Zhang, W., Yang, Y., et al. (2017) · Nature

Epigenetics and DNA MethylationGenetic Associations and Epidemiology

Cited by 945

A saturated map of common genetic variants associated with human height

Yengo, L., Vedantam, S., Marouli, E., Sidorenko, J., Bartell, E., Sakaue, S., Graff, M., Eliasen, A. U., Jiang, Y., Raghavan, S., et al. (2022) · Nature

Genetic Associations and EpidemiologyGenetics and Physical Performance

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Rising rural body-mass index is the main driver of the global obesity epidemic in adults

NCD Risk Factor Collaboration (NCD-RisC), Bixby, H., Bentham, J., Zhou, B., Di Cesare, M., Paciorek, C. J., Bennett, J. E., Taddei, C., Stevens, G. A., Rodriguez-Martinez, A., et al. (2019) · Nature

Obesity, Physical Activity, DietIndigenous Health and Education

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SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations

Unoki, H., Takahashi, A., Kawaguchi, T., Hara, K., Horikoshi, M., Andersen, G., Ng, D. P. K., Holmkvist, J., Borch-Johnsen, K., Jørgensen, T., et al. (2008) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 725

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Mahajan, A., Spracklen, C. N., Zhang, W., Ng, M. C. Y., Petty, L. E., Kitajima, H., Yu, G. Z., Rüeger, S., Speidel, L., Kim, Y. J., et al. (2022) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Suzuki, K., Hatzikotoulas, K., Southam, L., Taylor, H. J., Yin, X., Lorenz, K. M., Mandla, R., Huerta-Chagoya, A., Melloni, G. E. M., Kanoni, S., et al. (2024) · Nature

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

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Loss-of-function mutations in SLC30A8 protect against type 2 diabetes

Go-T2D Consortium, Flannick, J., T2D-GENES Consortium, Thorleifsson, G., Beer, N. L., Jacobs, S. B. R., Grarup, N., Burtt, N. P., Mahajan, A., Fuchsberger, C., et al. (2014) · Nature Genetics

Genetic Associations and EpidemiologyDiabetes and associated disorders

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Genome-wide meta-analyses of multiancestry cohorts identify multiple new susceptibility loci for refractive error and myopia

Consortium for Refractive Error and Myopia (CREAM), Verhoeven, V. J. M., The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Research Group, Wellcome Trust Case Control Consortium 2 (WTCCC2), The Fuchs' Genetics Multi-Center Study Group, Hysi, P. G., Wojciechowski, R., Fan, Q., Guggenheim, J. A., Höhn, R., et al. (2013) · Nature Genetics

Ophthalmology and Visual Impairment StudiesGenetic Associations and Epidemiology

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Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Rodriguez-Martinez, A., Zhou, B., Sophiea, M. K., Bentham, J., Paciorek, C. J., Iurilli, M. L., Carrillo-Larco, R. M., Bennett, J. E., Di Cesare, M., Taddei, C., et al. (2020) · The Lancet

Obesity, Physical Activity, DietHuman Health and Disease

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Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis

Wheeler, E., Leong, A., Liu, C.-T., Hivert, M.-F., Strawbridge, R. J., Podmore, C., Li, M., Yao, J., Sim, X., Hong, J., et al. (2017) · PLOS Medicine

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

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Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Platform, B. G., Flannick, J., DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, et al. (2019) · Nature

Genetic Associations and EpidemiologyGenomics and Rare Diseases

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A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Sabanayagam, C., Xu, D., Ting, D. S. W., Nusinovici, S., Banu, R., Hamzah, H., Lim, C., Tham, Y.-C., Cheung, C. Y., Tai, E. S., et al. (2020) · The Lancet Digital Health

Retinal Imaging and AnalysisChronic Kidney Disease and Diabetes

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Low copy number of the salivary amylase gene predisposes to obesity

Falchi, M., El-Sayed Moustafa, J. S., Takousis, P., Pesce, F., Bonnefond, A., Andersson-Assarsson, J. C., Sudmant, P. H., Dorajoo, R., Al-Shafai, M. N., Bottolo, L., et al. (2014) · Nature Genetics

Genomic variations and chromosomal abnormalitiesGenetic Associations and Epidemiology

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