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5 results on '"Kårhus, L.L."'

1. Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications

Author

Sterenborg, R.B.T.M., Steinbrenner, I., Li, Yong, Bujnis, M.N., Naito, T., Marouli, E., Galesloot, T.E., Babajide, O., Andreasen, L., Astrup, A., Åsvold, B.O., Bandinelli, S., Beekman, M., Beilby, J.P., Bork-Jensen, J., Boutin, T., Brody, J.A., Brown, S.J., Brumpton, B., Campbell, P.J., Cappola, A.R., Ceresini, G., Chaker, L., Chasman, D.I., Concas, M.P., Coutinho de Almeida, Rodrigo, Cross, S.M., Cucca, F., Deary, I.J., Kjaergaard, A.D., Echouffo Tcheugui, J.B., Ellervik, C., Eriksson, J.G., Ferrucci, L., Freudenberg, J., Fuchsberger, C., Gieger, C., Giulianini, F., Gögele, M., Graham, S.E., Grarup, N., Gunjača, I., Hansen, T., Harding, B.N., Harris, S.E., Haunsø, S., Hayward, C., Hui, J., Ittermann, T., Jukema, J.W., Kajantie, E., Kanters, J.K., Kårhus, L.L., Kiemeney, L.A.L.M., Kühnel, B., Lahti, J., Langenberg, C., Lapauw, B., Leese, G., Li, Shuo, Liewald, D.C.M., Linneberg, A., Lominchar, J.V.T., Luan, Jian'an, Martin, N.G., Matana, A., Meima, M.E., Meitinger, T., Meulenbelt, I., Mitchell, B.D., Møllehave, L.T., Mora, S., Naitza, S., Nauck, M., Netea-Maier, R.T., Noordam, R., Nursyifa, C., Okada, Y., Onano, S., Papadopoulou, A., Palmer, C.N.A., Pattaro, C., Pedersen, O., Peters, A., Pietzner, M., Polašek, O., Pramstaller, P.P., Psaty, B.M., Punda, A., Ray, D., Redmond, P., Richards, J.B., Ridker, P.M., Russ, T.C., Ryan, K.A., Olesen, M.S., Schultheiss, U.T., Selvin, E., Siddiqui, M.K., Teumer, A., Medici, M., Sterenborg, R.B.T.M., Steinbrenner, I., Li, Yong, Bujnis, M.N., Naito, T., Marouli, E., Galesloot, T.E., Babajide, O., Andreasen, L., Astrup, A., Åsvold, B.O., Bandinelli, S., Beekman, M., Beilby, J.P., Bork-Jensen, J., Boutin, T., Brody, J.A., Brown, S.J., Brumpton, B., Campbell, P.J., Cappola, A.R., Ceresini, G., Chaker, L., Chasman, D.I., Concas, M.P., Coutinho de Almeida, Rodrigo, Cross, S.M., Cucca, F., Deary, I.J., Kjaergaard, A.D., Echouffo Tcheugui, J.B., Ellervik, C., Eriksson, J.G., Ferrucci, L., Freudenberg, J., Fuchsberger, C., Gieger, C., Giulianini, F., Gögele, M., Graham, S.E., Grarup, N., Gunjača, I., Hansen, T., Harding, B.N., Harris, S.E., Haunsø, S., Hayward, C., Hui, J., Ittermann, T., Jukema, J.W., Kajantie, E., Kanters, J.K., Kårhus, L.L., Kiemeney, L.A.L.M., Kühnel, B., Lahti, J., Langenberg, C., Lapauw, B., Leese, G., Li, Shuo, Liewald, D.C.M., Linneberg, A., Lominchar, J.V.T., Luan, Jian'an, Martin, N.G., Matana, A., Meima, M.E., Meitinger, T., Meulenbelt, I., Mitchell, B.D., Møllehave, L.T., Mora, S., Naitza, S., Nauck, M., Netea-Maier, R.T., Noordam, R., Nursyifa, C., Okada, Y., Onano, S., Papadopoulou, A., Palmer, C.N.A., Pattaro, C., Pedersen, O., Peters, A., Pietzner, M., Polašek, O., Pramstaller, P.P., Psaty, B.M., Punda, A., Ray, D., Redmond, P., Richards, J.B., Ridker, P.M., Russ, T.C., Ryan, K.A., Olesen, M.S., Schultheiss, U.T., Selvin, E., Siddiqui, M.K., Teumer, A., and Medici, M.

Abstract

Contains fulltext : 304858.pdf (Publisher’s version ) (Open Access), To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Published
2024

2. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Author

Kanoni, S., Graham, S.E., Wang, Yuxuan, Surakka, I., Ramdas, S., Zhu, X, Clarke, S.L., Bhatti, K.F., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Zeyuan, Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, Wei, Weir, D.R., Turman, C., Huang, H., Graff, M, Choudhury, A., Sengupta, D., 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., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Yousri, N.A., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Hwu, C.M., Hung, Y.J., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Galesloot, T.E., Lores-Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Graaf, J. de, Assimes, T.L., Peloso, G.M., Kanoni, S., Graham, S.E., Wang, Yuxuan, Surakka, I., Ramdas, S., Zhu, X, Clarke, S.L., Bhatti, K.F., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Zeyuan, Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, Wei, Weir, D.R., Turman, C., Huang, H., Graff, M, Choudhury, A., Sengupta, D., 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., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Yousri, N.A., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Hwu, C.M., Hung, Y.J., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Galesloot, T.E., Lores-Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Graaf, J. de, Assimes, T.L., and Peloso, G.M.

Abstract

Item does not contain fulltext, BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Published
2022

3. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author

Ramdas, S., Judd, J., Graham, S.E., Kanoni, S., Wang, Y, Surakka, I., Wenz, B., Clarke, S.L., Chesi, A., Wells, A., Bhatti, K.F., Vedantam, S., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Z, Xue, C., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, W, Weir, D.R., Turman, C., Huang, H., Graff, Mariaelisa, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., 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., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Lee, W.J., Hsiung, C.A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Galesloot, T.E., Lores de Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Zhu, Xiang, Brown, C.D., Ramdas, S., Judd, J., Graham, S.E., Kanoni, S., Wang, Y, Surakka, I., Wenz, B., Clarke, S.L., Chesi, A., Wells, A., Bhatti, K.F., Vedantam, S., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Z, Xue, C., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, W, Weir, D.R., Turman, C., Huang, H., Graff, Mariaelisa, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., 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., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Lee, W.J., Hsiung, C.A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Galesloot, T.E., Lores de Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Zhu, Xiang, and Brown, C.D.

Abstract

Item does not contain fulltext, A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Published
2022

4. Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight

Author

Iurilli, M.L.C. Zhou, B. Bennett, J.E. Carrillo-Larco, R.M. Sophiea, M.K. Rodriguez-Martinez, A. Bixby, H. Solomon, B.D. Taddei, C. Danaei, G. Di Cesare, M. Stevens, G.A. Riley, L.M. Savin, S. Cowan, M.J. Bovet, P. Damasceno, A. Chirita-Emandi, A. Hayes, A.J. Ikeda, N. Jackson, R.T. Khang, Y.-H. Laxmaiah, A. Liu, J. Miranda, J.J. Saidi, O. Sebert, S. Sorić, M. Starc, G. Gregg, E.W. Abarca-Gómez, L. Abdeen, Z.A. Abdrakhmanova, S. Ghaffar, S.A. Rahim, H.F.A. Abu-Rmeileh, N.M. Garba, J.A. Acosta-Cazares, B. Adams, R.J. Aekplakorn, W. Afsana, K. Afzal, S. Agdeppa, I.A. Aghazadeh-Attari, J. Aguilar-Salinas, C.A. Agyemang, C. Ahmad, M.H. Ahmad, N.A. Ahmadi, A. Ahmadi, N. Ahmed, S.H. Ahrens, W. Aitmurzaeva, G. Ajlouni, K. Al-Hazzaa, H.M. Al-Lahou, B. Al-Raddadi, R. Alarouj, M. AlBuhairan, F. AlDhukair, S. Ali, M.M. Alkandari, A. Alkerwi, A. Allin, K. Alvarez-Pedrerol, M. Aly, E. Amarapurkar, D.N. Amiri, P. Amougou, N. Amouyel, P. Andersen, L.B. Anderssen, S.A. Ängquist, L. Anjana, R.M. Ansari-Moghaddam, A. Aounallah-Skhiri, H. Araújo, J. Ariansen, I. Aris, T. Arku, R.E. Arlappa, N. Aryal, K.K. Aspelund, T. Assah, F.K. Assunção, M.C.F. Aung, M.S. Auvinen, J. Mária Avdicová Avi, S. Azevedo, A. Azimi-Nezhad, M. Azizi, F. Azmin, M. Babu, B.V. Bæksgaard Jørgensen, M. Baharudin, A. Bahijri, S. Baker, J.L. Balakrishna, N. Bamoshmoosh, M. Banach, M. Bandosz, P. Banegas, J.R. Baran, J. Barbagallo, C.M. Barceló, A. Barkat, A. Barros, A.J.D. Barros, M.V.G. Basit, A. Bastos, J.L.D. Bata, I. Batieha, A.M. Batista, R.L. Battakova, Z. Batyrbek, A. Baur, L.A. Beaglehole, R. Bel-Serrat, S. Belavendra, A. Romdhane, H.B. Benedics, J. Benet, M. Bergh, I.H. Berkinbayev, S. Bernabe-Ortiz, A. Bernotiene, G. Bettiol, H. Bezerra, J. Bhagyalaxmi, A. Bharadwaj, S. Bhargava, S.K. Bhutta, Z.A. Bi, H. Bi, Y. Bia, D. Lele, E.C.B. Bikbov, M.M. Bista, B. Bjelica, D.J. Bjerregaard, P. Bjertness, E. Bjertness, M.B. Björkelund, C. Bloch, K.V. Blokstra, A. Bo, S. Bobak, M. Boddy, L.M. Boehm, B.O. Boeing, H. Boggia, J.G. Bogova, E. Boissonnet, C.P. Bojesen, S.E. Bonaccio, M. Bongard, V. Bonilla-Vargas, A. Bopp, M. Borghs, H. Braeckevelt, L. Braeckman, L. Bragt, M.C.E. Brajkovich, I. Branca, F. Breckenkamp, J. Breda, J. Brenner, H. Brewster, L.M. Brian, G.R. Brinduse, L. Brophy, S. Bruno, G. Bueno-de-Mesquita, H.B. Bugge, A. Buoncristiano, M. Burazeri, G. Burns, C. de León, A.C. Cacciottolo, J. Cai, H. Cama, T. Cameron, C. Camolas, J. Can, G. Candido, A.P.C. Cañete, F. Capanzana, M.V. Capková, N. Capuano, E. Capuano, V. Cardol, M. Cardoso, V.C. Carlsson, A.C. Carmuega, E. Carvalho, J. Casajús, J.A. Casanueva, F.F. Celikcan, E. Censi, L. Cervantes-Loaiza, M. Cesar, J.A. Chamukuttan, S. Chan, A.W. Chan, Q. Chaturvedi, H.K. Chaturvedi, N. Rahim, N.C.A. Chee, M.L. Chen, C.-J. Chen, F. Chen, H. Chen, S. Chen, Z. Cheng, C.-Y. Cheraghian, B. Chetrit, A. Chikova-Iscener, E. Chiolero, A. Chiou, S.-T. Chirlaque, M.-D. Cho, B. Christensen, K. Christofaro, D.G. Chudek, J. Cifkova, R. Cilia, M. Cinteza, E. Claessens, F. Clarke, J. Clays, E. Cohen, E. Concin, H. Confortin, S.C. Cooper, C. Coppinger, T.C. Corpeleijn, E. Costanzo, S. Cottel, D. Cowell, C. Craig, C.L. Crampin, A.C. Crujeiras, A.B. Csilla, S. Cucu, A.M. Cui, L. Cureau, F.V. Czenczek-Lewandowska, E. D’Arrigo, G. d’Orsi, E. Dacica, L. Dal Re Saavedra, M.A. Dallongeville, J. Damsgaard, C.T. Dankner, R. Dantoft, T.M. Dasgupta, P. Dastgiri, S. Dauchet, L. Davletov, K. De Backer, G. De Bacquer, D. de Gaetano, G. De Henauw, S. de Oliveira, P.D. De Ridder, D. De Ridder, K. de Rooij, S.R. De Smedt, D. Deepa, M. Deev, A.D. DeGennaro, V., Jr Dehghan, A. Delisle, H. Delpeuch, F. Demarest, S. Dennison, E. Dereń, K. Deschamps, V. Dhimal, M. Di Castelnuovo, A.F. Dias-da-Costa, J.S. Díaz-Sánchez, M.E. Diaz, A. Dika, Z. Djalalinia, S. Djordjic, V. Do, H.T.P. Dobson, A.J. Donati, M.B. Donfrancesco, C. Donoso, S.P. Döring, A. Dorobantu, M. Dorosty, A.R. Doua, K. Dragano, N. Drygas, W. Duan, J.L. Duante, C.A. Duboz, P. Duda, R.B. Duleva, V. Dulskiene, V. Dumith, S.C. Dushpanova, A. Dzerve, V. Dziankowska-Zaborszczyk, E. Eddie, R. Eftekhar, E. Egbagbe, E.E. Eggertsen, R. Eghtesad, S. Eiben, G. Ekelund, U. El-Khateeb, M. Ati, J.E. Eldemire-Shearer, D. Eliasen, M. Elliott, P. Engle-Stone, R. Enguerran, M. Erasmus, R.T. Erbel, R. Erem, C. Eriksen, L. Eriksson, J.G. Escobedo-de la Peña, J. Eslami, S. Esmaeili, A. Evans, A. Faeh, D. Fakhretdinova, A.A. Fall, C.H. Faramarzi, E. Farjam, M. Sant’Angelo, V.F. Farzadfar, F. Fattahi, M.R. Fawwad, A. Felix-Redondo, F.J. Ferguson, T.S. Fernandes, R.A. Fernández-Bergés, D. Ferrante, D. Ferrao, T. Ferrari, M. Ferrario, M.M. Ferreccio, C. Ferrer, E. Ferrieres, J. Figueiró, T.H. Fijalkowska, A. Fink, G. Fischer, K. Foo, L.H. Forsner, M. Fouad, H.M. Francis, D.K. Maria do Carmo Franco Frikke-Schmidt, R. Frontera, G. Fuchs, F.D. Fuchs, S.C. Fujiati, I.I. Fujita, Y. Fumihiko, M. Furusawa, T. Gaciong, Z. Gafencu, M. Galbarczyk, A. Galenkamp, H. Galeone, D. Galfo, M. Galvano, F. Gao, J. Garcia-de-la-Hera, M. García-Solano, M. Gareta, D. Garnett, S.P. Gaspoz, J.-M. Gasull, M. Gaya, A.C.A. Gaya, A.R. Gazzinelli, A. Gehring, U. Geiger, H. Geleijnse, J.M. Ghanbari, A. Ghasemi, E. Gheorghe-Fronea, O.-F. Giampaoli, S. Gianfagna, F. Gill, T.K. Giovannelli, J. Gironella, G. Giwercman, A. Gkiouras, K. Godos, J. Gogen, S. Goldberg, M. Goldsmith, R.A. Goltzman, D. Gómez, S.F. Gomula, A. da Silva, B.G.C. Gonçalves, H. Gonzalez-Chica, D.A. Gonzalez-Gross, M. González-Leon, M. González-Rivas, J.P. González-Villalpando, C. González-Villalpando, M.-E. Gonzalez, A.R. Gottrand, F. Graça, A.P. Graff-Iversen, S. Grafnetter, D. Grajda, A. Grammatikopoulou, M.G. Gregor, R.D. Grodzicki, T. Grøholt, E.K. Grøntved, A. Grosso, G. Gruden, G. Gu, D. Gualdi-Russo, E. Guallar-Castillón, P. Gualtieri, A. Gudmundsson, E.F. Gudnason, V. Guerrero, R. Guessous, I. Guimaraes, A.L. Gulliford, M.C. Gunnlaugsdottir, J. Gunter, M.J. Guo, X.-H. Guo, Y. Gupta, P.C. Gupta, R. Gureje, O. 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Subjects
nutritional and metabolic diseases, sense organs, skin and connective tissue diseases
Abstract

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions. © Copyright.

Published
2021

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

Author

Graham, S.E., Clarke, S.L., Wu, K.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., Galesloot, T.E., Kiemeney, B., Graaf, J. de, Lores de Motta, L., Pauper, M., Hollander, A.I. den, Sun, Y.V., Willer, C.J., Graham, S.E., Clarke, S.L., Wu, K.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., Galesloot, T.E., Kiemeney, B., Graaf, J. de, Lores de Motta, L., Pauper, M., Hollander, A.I. den, Sun, Y.V., and Willer, C.J.

Abstract

Item does not contain fulltext, 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.

Published
2021

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