66 results on '"Alipanahi, B."'
Search Results
2. 100P Predicting tumor ER and HER2 status using a cell-free RNA liquid biopsy assay
- Author
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Schwartzberg, L.S., Yen, J., Boyle, E., Cavazos, T., Karimzadeh, M., Heydari, H., Fish, L., Trivedi, R., Hormozdiari, F., Lazar, A., and Alipanahi, B.
- Published
- 2024
- Full Text
- View/download PDF
3. 65O Cancer treatment monitoring using cell-free DNA fragmentomes
- Author
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Fijneman, R., Rinaldi, L., Skidmore, Z., Alipanahi, B., Keefer, L., Erve, I. van 't, Lumbard, K., Carey, J., Chesnick, B., Cristiano, S., Dracopoli, N., Scharpf, R., Meijer, G., Leal, A.I.C., and Velculescu, V.
- Published
- 2024
- Full Text
- View/download PDF
4. A comprehensive re-assessment of the association between vitamin D and cancer susceptibility using Mendelian randomization
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Ong, Jue-Sheng, Dixon-Suen, Suzanne C., Han, Xikun, An, Jiyuan, Liyanage, Upekha, Dusingize, Jean-Cluade, Schumacher, Johannes, Gockel, Ines, Böhmer, Anne, Jankowski, Janusz, Palles, Claire, O’Mara, Tracy, Spurdle, Amanda, Law, Matthew H., Iles, Mark M., Pharoah, Paul, Berchuck, Andrew, Zheng, Wei, Thrift, Aaron P., Olsen, Catherine, Neale, Rachel E., Gharahkhani, Puya, Webb, Penelope M., MacGregor, Stuart, Fitzgerald, Rebecca, Buas, Matt, Gammon, Marilie D., Corley, Douglas A., Shaheen, Nicholas J., Hardie, Laura J., Bird, Nigel C., Reid, Brian J., Chow, Wong-Ho, Risch, Harvey A., Ye, Weimin, Liu, Geoffrey, Romero, Yvonne, Bernstein, Leslie, Wu, Anna H., Whiteman, David E., Vaughan, Thomas, Agee, M., Alipanahi, B., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., McIntyre, M. H., Mountain, J. L., Noblin, E. S., Northover, C. A. M., Pitts, S. J., Sathirapongsasuti, J. Fah, Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tian, C., Tung, J. Y., Vacic, V., Wilson, C. H., Ong, Jue-Sheng [0000-0002-6062-710X], Dixon-Suen, Suzanne C. [0000-0003-3714-8386], Han, Xikun [0000-0002-3823-7308], Gockel, Ines [0000-0001-7423-713X], Böhmer, Anne [0000-0002-5716-786X], O’Mara, Tracy [0000-0002-5436-3232], Spurdle, Amanda [0000-0003-1337-7897], Law, Matthew H. [0000-0002-4303-8821], Iles, Mark M. [0000-0002-2603-6509], Pharoah, Paul [0000-0001-8494-732X], Zheng, Wei [0000-0003-1226-070X], Thrift, Aaron P. [0000-0002-0084-5308], Olsen, Catherine [0000-0003-4483-1888], Gharahkhani, Puya [0000-0002-4203-5952], Webb, Penelope M. [0000-0003-0733-5930], MacGregor, Stuart [0000-0001-6731-8142], and Apollo - University of Cambridge Repository
- Subjects
631/67/68 ,45/43 ,article ,692/699/67/2195 ,692/4028/67/2324 - Abstract
Previous Mendelian randomization (MR) studies on 25-hydroxyvitamin D (25(OH)D) and cancer have typically adopted a handful of variants and found no relationship between 25(OH)D and cancer; however, issues of horizontal pleiotropy cannot be reliably addressed. Using a larger set of variants associated with 25(OH)D (74 SNPs, up from 6 previously), we perform a unified MR analysis to re-evaluate the relationship between 25(OH)D and ten cancers. Our findings are broadly consistent with previous MR studies indicating no relationship, apart from ovarian cancers (OR 0.89; 95% C.I: 0.82 to 0.96 per 1 SD change in 25(OH)D concentration) and basal cell carcinoma (OR 1.16; 95% C.I.: 1.04 to 1.28). However, after adjustment for pigmentation related variables in a multivariable MR framework, the BCC findings were attenuated. Here we report that lower 25(OH)D is unlikely to be a causal risk factor for most cancers, with our study providing more precise confidence intervals than previously possible.
- Published
- 2021
- Full Text
- View/download PDF
5. A comprehensive re-assessment of the association between vitamin D and cancer susceptibility using Mendelian randomization
- Author
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Ong, JS, Dixon-Suen, Suzanne, Han, X, An, J, Fitzgerald, R, Buas, M, Gammon, MD, Corley, DA, Shaheen, NJ, Hardie, LJ, Bird, NC, Reid, BJ, Chow, WH, Risch, HA, Ye, W, Liu, G, Romero, Y, Bernstein, L, Wu, AH, Whiteman, DE, Vaughan, T, Agee, M, Alipanahi, B, Auton, A, Bell, RK, Bryc, K, Elson, SL, Fontanillas, P, Furlotte, NA, Hinds, DA, Huber, KE, Kleinman, A, Litterman, NK, McIntyre, MH, Mountain, JL, Noblin, ES, Northover, CAM, Pitts, SJ, Sathirapongsasuti, JF, Sazonova, OV, Shelton, JF, Shringarpure, S, Tian, C, Tung, JY, Vacic, V, Wilson, CH, Liyanage, U, Dusingize, JC, Schumacher, J, Gockel, I, Böhmer, A, Jankowski, J, Palles, C, O’Mara, T, Spurdle, A, Law, MH, Iles, MM, Pharoah, P, Berchuck, A, Zheng, W, Thrift, AP, Olsen, C, Neale, RE, Gharahkhani, P, Webb, PM, MacGregor, S, Ong, JS, Dixon-Suen, Suzanne, Han, X, An, J, Fitzgerald, R, Buas, M, Gammon, MD, Corley, DA, Shaheen, NJ, Hardie, LJ, Bird, NC, Reid, BJ, Chow, WH, Risch, HA, Ye, W, Liu, G, Romero, Y, Bernstein, L, Wu, AH, Whiteman, DE, Vaughan, T, Agee, M, Alipanahi, B, Auton, A, Bell, RK, Bryc, K, Elson, SL, Fontanillas, P, Furlotte, NA, Hinds, DA, Huber, KE, Kleinman, A, Litterman, NK, McIntyre, MH, Mountain, JL, Noblin, ES, Northover, CAM, Pitts, SJ, Sathirapongsasuti, JF, Sazonova, OV, Shelton, JF, Shringarpure, S, Tian, C, Tung, JY, Vacic, V, Wilson, CH, Liyanage, U, Dusingize, JC, Schumacher, J, Gockel, I, Böhmer, A, Jankowski, J, Palles, C, O’Mara, T, Spurdle, A, Law, MH, Iles, MM, Pharoah, P, Berchuck, A, Zheng, W, Thrift, AP, Olsen, C, Neale, RE, Gharahkhani, P, Webb, PM, and MacGregor, S
- Abstract
Previous Mendelian randomization (MR) studies on 25-hydroxyvitamin D (25(OH)D) and cancer have typically adopted a handful of variants and found no relationship between 25(OH)D and cancer; however, issues of horizontal pleiotropy cannot be reliably addressed. Using a larger set of variants associated with 25(OH)D (74 SNPs, up from 6 previously), we perform a unified MR analysis to re-evaluate the relationship between 25(OH)D and ten cancers. Our findings are broadly consistent with previous MR studies indicating no relationship, apart from ovarian cancers (OR 0.89; 95% C.I: 0.82 to 0.96 per 1 SD change in 25(OH)D concentration) and basal cell carcinoma (OR 1.16; 95% C.I.: 1.04 to 1.28). However, after adjustment for pigmentation related variables in a multivariable MR framework, the BCC findings were attenuated. Here we report that lower 25(OH)D is unlikely to be a causal risk factor for most cancers, with our study providing more precise confidence intervals than previously possible
- Published
- 2021
6. A comprehensive re-assessment of the association between vitamin D and cancer susceptibility using Mendelian randomization
- Author
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Ong, Jue Sheng, Dixon-Suen, Suzanne C., Han, Xikun, An, Jiyuan, Fitzgerald, Rebecca, Buas, Matt, Gammon, Marilie D., Corley, Douglas A., Shaheen, Nicholas J., Hardie, Laura J., Bird, Nigel C., Reid, Brian J., Chow, Wong Ho, Risch, Harvey A., Ye, Weimin, Liu, Geoffrey, Romero, Yvonne, Bernstein, Leslie, Wu, Anna H., Whiteman, David E., Vaughan, Thomas, Agee, M., Alipanahi, B., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., McIntyre, M. H., Mountain, J. L., Noblin, E. S., Northover, C. A.M., Pitts, S. J., Sathirapongsasuti, J. Fah, Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tian, C., Tung, J. Y., Vacic, V., Wilson, C. H., Liyanage, Upekha, Dusingize, Jean Cluade, Schumacher, Johannes, Gockel, Ines, Böhmer, Anne, Jankowski, Janusz, Palles, Claire, O’Mara, Tracy, Spurdle, Amanda, Law, Matthew H., Iles, Mark M., Pharoah, Paul, Berchuck, Andrew, Zheng, Wei, Thrift, Aaron P., Olsen, Catherine, Neale, Rachel E., Gharahkhani, Puya, Webb, Penelope M., MacGregor, Stuart, other, and, Ong, Jue Sheng, Dixon-Suen, Suzanne C., Han, Xikun, An, Jiyuan, Fitzgerald, Rebecca, Buas, Matt, Gammon, Marilie D., Corley, Douglas A., Shaheen, Nicholas J., Hardie, Laura J., Bird, Nigel C., Reid, Brian J., Chow, Wong Ho, Risch, Harvey A., Ye, Weimin, Liu, Geoffrey, Romero, Yvonne, Bernstein, Leslie, Wu, Anna H., Whiteman, David E., Vaughan, Thomas, Agee, M., Alipanahi, B., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., McIntyre, M. H., Mountain, J. L., Noblin, E. S., Northover, C. A.M., Pitts, S. J., Sathirapongsasuti, J. Fah, Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tian, C., Tung, J. Y., Vacic, V., Wilson, C. H., Liyanage, Upekha, Dusingize, Jean Cluade, Schumacher, Johannes, Gockel, Ines, Böhmer, Anne, Jankowski, Janusz, Palles, Claire, O’Mara, Tracy, Spurdle, Amanda, Law, Matthew H., Iles, Mark M., Pharoah, Paul, Berchuck, Andrew, Zheng, Wei, Thrift, Aaron P., Olsen, Catherine, Neale, Rachel E., Gharahkhani, Puya, Webb, Penelope M., MacGregor, Stuart, and other, and
- Abstract
Previous Mendelian randomization (MR) studies on 25-hydroxyvitamin D (25(OH)D) and cancer have typically adopted a handful of variants and found no relationship between 25(OH)D and cancer; however, issues of horizontal pleiotropy cannot be reliably addressed. Using a larger set of variants associated with 25(OH)D (74 SNPs, up from 6 previously), we perform a unified MR analysis to re-evaluate the relationship between 25(OH)D and ten cancers. Our findings are broadly consistent with previous MR studies indicating no relationship, apart from ovarian cancers (OR 0.89; 95% C.I: 0.82 to 0.96 per 1 SD change in 25(OH)D concentration) and basal cell carcinoma (OR 1.16; 95% C.I.: 1.04 to 1.28). However, after adjustment for pigmentation related variables in a multivariable MR framework, the BCC findings were attenuated. Here we report that lower 25(OH)D is unlikely to be a causal risk factor for most cancers, with our study providing more precise confidence intervals than previously possible.
- Published
- 2021
7. Human loss-of-function variants suggest that partial LRRK2 reduction is not associated with severe disease
- Author
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Whiffin, N, Armean, IM, Kleinman, A, Marshall, JL, Minikel, EV, Goodrich, JK, Quaife, N, Cole, JB, Wang, Q, Karczewski, KJ, Cummings, BB, Francioli, L, Laricchia, K, Guan, A, Alipanahi, B, Morrison, P, Baptista, MAS, Merchant, KM, Genome Aggregation Database Production Team^, Genome Aggregation Database Consortium, Ware, J, Havulinna, AS, Iliadou, B, Lee, J-J, Nadkarni, GN, Whiteman, C, Daly, M, Esko, T, Hultman, C, Loos, RJF, Milani, L, Palotie, A, Pato, C, Pato, M, Saleheen, D, Sullivan, PF, Alföldi, J, Cannon, P, MacArthur, DG, Wellcome Trust, Imper, and Rosetrees Trust
- Subjects
Immunology ,11 Medical and Health Sciences ,nervous system diseases - Abstract
Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation, and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes1,2. Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson’s disease3,4, suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns5–8, the biological consequences of LRRK2 inhibition have not been well-characterized in humans. Here we systematically analyse pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)9, 49,960 exome sequenced individuals from the UK Biobank, and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with prior work10, confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human LoF carriers for target validation in drug discovery.
- Published
- 2020
8. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease
- Author
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Bryois, J. Skene, N.G. Hansen, T.F. Kogelman, L.J.A. Watson, H.J. Liu, Z. Adan, R. Alfredsson, L. Ando, T. Andreassen, O. Baker, J. Bergen, A. Berrettini, W. Birgegård, A. Boden, J. Boehm, I. Boni, C. Boraska Perica, V. Brandt, H. Breen, G. Bryois, J. Buehren, K. Bulik, C. Burghardt, R. Cassina, M. Cichon, S. Clementi, M. Coleman, J. Cone, R. Courtet, P. Crawford, S. Crow, S. Crowley, J. Danner, U. Davis, O. de Zwaan, M. Dedoussis, G. Degortes, D. DeSocio, J. Dick, D. Dikeos, D. Dina, C. Dmitrzak-Weglarz, M. Docampo Martinez, E. Duncan, L. Egberts, K. Ehrlich, S. Escaramís, G. Esko, T. Estivill, X. Farmer, A. Favaro, A. Fernández-Aranda, F. Fichter, M. Fischer, K. Föcker, M. Foretova, L. Forstner, A. Forzan, M. Franklin, C. Gallinger, S. Gaspar, H. Giegling, I. Giuranna, J. Giusti-Rodríquez, P. Gonidakis, F. Gordon, S. Gorwood, P. Gratacos Mayora, M. Grove, J. Guillaume, S. Guo, Y. Hakonarson, H. Halmi, K. Hanscombe, K. Hatzikotoulas, K. Hauser, J. Hebebrand, J. Helder, S. Henders, A. Herms, S. Herpertz-Dahlmann, B. Herzog, W. Hinney, A. Horwood, L.J. Hübel, C. Huckins, L. Hudson, J. Imgart, H. Inoko, H. Janout, V. Jiménez-Murcia, S. Johnson, C. Jordan, J. Julià, A. Juréus, A. Kalsi, G. Kaminská, D. Kaplan, A. Kaprio, J. Karhunen, L. Karwautz, A. Kas, M. Kaye, W. Kennedy, J. Kennedy, M. Keski-Rahkonen, A. Kiezebrink, K. Kim, Y.-R. Kirk, K. Klareskog, L. Klump, K. Knudsen, G.P. La Via, M. Landén, M. Larsen, J. Le Hellard, S. Leppä, V. Levitan, R. Li, D. Lichtenstein, P. Lilenfeld, L. Lin, B.D. Lissowska, J. Luykx, J. Magistretti, P. Maj, M. Mannik, K. Marsal, S. Marshall, C. Martin, N. Mattheisen, M. Mattingsdal, M. McDevitt, S. McGuffin, P. Medland, S. Metspalu, A. Meulenbelt, I. Micali, N. Mitchell, J. Mitchell, K. Monteleone, P. Monteleone, A.M. Montgomery, G. Mortensen, P.B. Munn-Chernoff, M. Nacmias, B. Navratilova, M. Norring, C. Ntalla, I. Olsen, C. Ophoff, R. O’Toole, J. Padyukov, L. Palotie, A. Pantel, J. Papezova, H. Parker, R. Pearson, J. Pedersen, N. Petersen, L. Pinto, D. Purves, K. Rabionet, R. Raevuori, A. Ramoz, N. Reichborn-Kjennerud, T. Ricca, V. Ripatti, S. Ripke, S. Ritschel, F. Roberts, M. Rotondo, A. Rujescu, D. Rybakowski, F. Santonastaso, P. Scherag, A. Scherer, S. Schmidt, U. Schork, N. Schosser, A. Seitz, J. Slachtova, L. Slagboom, P.E. Slof-Op ‘t Landt, M. Slopien, A. Sorbi, S. Strober, M. Stuber, G. Sullivan, P. Świątkowska, B. Szatkiewicz, J. Tachmazidou, I. Tenconi, E. Thornton, L. Tortorella, A. Tozzi, F. Treasure, J. Tsitsika, A. Tyszkiewicz-Nwafor, M. Tziouvas, K. van Elburg, A. van Furth, E. Wade, T. Wagner, G. Walton, E. Watson, H. Werge, T. Whiteman, D. Widen, E. Woodside, D.B. Yao, S. Yilmaz, Z. Zeggini, E. Zerwas, S. Zipfel, S. Anttila, V. Artto, V. Belin, A.C. de Boer, I. Boomsma, D.I. Børte, S. Chasman, D.I. Cherkas, L. Christensen, A.F. Cormand, B. Cuenca-Leon, E. Davey-Smith, G. Dichgans, M. van Duijn, C. Esko, T. Esserlind, A.L. Ferrari, M. Frants, R.R. Freilinger, T. Furlotte, N. Gormley, P. Griffiths, L. Hamalainen, E. Hiekkala, M. Ikram, M.A. Ingason, A. Järvelin, M.-R. Kajanne, R. Kallela, M. Kaprio, J. Kaunisto, M. Kogelman, L.J.A. Kubisch, C. Kurki, M. Kurth, T. Launer, L. Lehtimaki, T. Lessel, D. Ligthart, L. Litterman, N. Maagdenberg, A. Macaya, A. Malik, R. Mangino, M. McMahon, G. Muller-Myhsok, B. Neale, B.M. Northover, C. Nyholt, D.R. Olesen, J. Palotie, A. Palta, P. Pedersen, L. Pedersen, N. Posthuma, D. Pozo-Rosich, P. Pressman, A. Raitakari, O. Schürks, M. Sintas, C. Stefansson, K. Stefansson, H. Steinberg, S. Strachan, D. Terwindt, G. Vila-Pueyo, M. Wessman, M. Winsvold, B.S. Zhao, H. Zwart, J.A. Agee, M. Alipanahi, B. Auton, A. Bell, R. Bryc, K. Elson, S. Fontanillas, P. Furlotte, N. Heilbron, K. Hinds, D. Huber, K. Kleinman, A. Litterman, N. McCreight, J. McIntyre, M. Mountain, J. Noblin, E. Northover, C. Pitts, S. Sathirapongsasuti, J. Sazonova, O. Shelton, J. Shringarpure, S. Tian, C. Tung, J. Vacic, V. Wilson, C. Brueggeman, L. Bulik, C.M. Arenas, E. Hjerling-Leffler, J. Sullivan, P.F. International Headache Genetics Consortium Eating Disorders Working Group of the Psychiatric Genomics Consortium
- Abstract
Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson’s disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson’s disease. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
- Published
- 2020
9. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson's disease
- Author
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Bryois J., Skene N. G., Hansen T. F., Kogelman L. J. A., Watson H. J., Liu Z., Adan R., Alfredsson L., Ando T., Andreassen O., Baker J., Bergen A., Berrettini W., Birgegard A., Boden J., Boehm I., Boni C., Boraska Perica V., Brandt H., Breen G., Buehren K., Bulik C., Burghardt R., Cassina M., Cichon S., Clementi M., Coleman J., Cone R., Courtet P., Crawford S., Crow S., Crowley J., Danner U., Davis O., de Zwaan M., Dedoussis G., Degortes D., DeSocio J., Dick D., Dikeos D., Dina C., Dmitrzak-Weglarz M., Docampo Martinez E., Duncan L., Egberts K., Ehrlich S., Escaramis G., Esko T., Estivill X., Farmer A., Favaro A., Fernandez-Aranda F., Fichter M., Fischer K., Focker M., Foretova L., Forstner A., Forzan M., Franklin C., Gallinger S., Gaspar H., Giegling I., Giuranna J., Giusti-Rodriquez P., Gonidakis F., Gordon S., Gorwood P., Gratacos Mayora M., Grove J., Guillaume S., Guo Y., Hakonarson H., Halmi K., Hanscombe K., Hatzikotoulas K., Hauser J., Hebebrand J., Helder S., Henders A., Herms S., Herpertz-Dahlmann B., Herzog W., Hinney A., Horwood L. J., Hubel C., Huckins L., Hudson J., Imgart H., Inoko H., Janout V., Jimenez-Murcia S., Johnson C., Jordan J., Julia A., Jureus A., Kalsi G., Kaminska D., Kaplan A., Kaprio J., Karhunen L., Karwautz A., Kas M., Kaye W., Kennedy J., Kennedy M., Keski-Rahkonen A., Kiezebrink K., Kim Y. -R., Kirk K., Klareskog L., Klump K., Knudsen G. P., La Via M., Landen M., Larsen J., Le Hellard S., Leppa V., Levitan R., Li D., Lichtenstein P., Lilenfeld L., Lin B. D., Lissowska J., Luykx J., Magistretti P., Maj M., Mannik K., Marsal S., Marshall C., Martin N., Mattheisen M., Mattingsdal M., McDevitt S., McGuffin P., Medland S., Metspalu A., Meulenbelt I., Micali N., Mitchell J., Mitchell K., Monteleone P., Monteleone A. M., Montgomery G., Mortensen P. B., Munn-Chernoff M., Nacmias B., Navratilova M., Norring C., Ntalla I., Olsen C., Ophoff R., O'Toole J., Padyukov L., Palotie A., Pantel J., Papezova H., Parker R., Pearson J., Pedersen N., Petersen L., Pinto D., Purves K., Rabionet R., Raevuori A., Ramoz N., Reichborn-Kjennerud T., Ricca V., Ripatti S., Ripke S., Ritschel F., Roberts M., Rotondo A., Rujescu D., Rybakowski F., Santonastaso P., Scherag A., Scherer S., Schmidt U., Schork N., Schosser A., Seitz J., Slachtova L., Slagboom P. E., Slof-Op 't Landt M., Slopien A., Sorbi S., Strober M., Stuber G., Sullivan P., Swiatkowska B., Szatkiewicz J., Tachmazidou I., Tenconi E., Thornton L., Tortorella A., Tozzi F., Treasure J., Tsitsika A., Tyszkiewicz-Nwafor M., Tziouvas K., van Elburg A., van Furth E., Wade T., Wagner G., Walton E., Watson H., Werge T., Whiteman D., Widen E., Woodside D. B., Yao S., Yilmaz Z., Zeggini E., Zerwas S., Zipfel S., Anttila V., Artto V., Belin A. C., de Boer I., Boomsma D. I., Borte S., Chasman D. I., Cherkas L., Christensen A. F., Cormand B., Cuenca-Leon E., Davey-Smith G., Dichgans M., van Duijn C., Esserlind A. L., Ferrari M., Frants R. R., Freilinger T., Furlotte N., Gormley P., Griffiths L., Hamalainen E., Hiekkala M., Ikram M. A., Ingason A., Jarvelin M. -R., Kajanne R., Kallela M., Kaunisto M., Kubisch C., Kurki M., Kurth T., Launer L., Lehtimaki T., Lessel D., Ligthart L., Litterman N., Maagdenberg A., Macaya A., Malik R., Mangino M., McMahon G., Muller-Myhsok B., Neale B. M., Northover C., Nyholt D. R., Olesen J., Palta P., Pedersen L., Posthuma D., Pozo-Rosich P., Pressman A., Raitakari O., Schurks M., Sintas C., Stefansson K., Stefansson H., Steinberg S., Strachan D., Terwindt G., Vila-Pueyo M., Wessman M., Winsvold B. S., Zhao H., Zwart J. A., Agee M., Alipanahi B., Auton A., Bell R., Bryc K., Elson S., Fontanillas P., Heilbron K., Hinds D., Huber K., Kleinman A., McCreight J., McIntyre M., Mountain J., Noblin E., Pitts S., Sathirapongsasuti J., Sazonova O., Shelton J., Shringarpure S., Tian C., Tung J., Vacic V., Wilson C., Brueggeman L., Bulik C. M., Arenas E., Hjerling-Leffler J., Sullivan P. F., Functional Genomics, APH - Methodology, APH - Mental Health, Biological Psychology, APH - Personalized Medicine, Amsterdam Neuroscience - Complex Trait Genetics, Complex Trait Genetics, Bryois, Julien, Hansen, Thomas Folkmann, Kogelman, Lisette J A, Watson, Hunna J, Breen, Gerome, Bulik, Cynthia M, Micali, Nadia, van Duijn, C, Kas lab, Bryois, J., Skene, N. G., Hansen, T. F., Kogelman, L. J. A., Watson, H. 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Nervous system ,Netherlands Twin Register (NTR) ,Aging ,Parkinson's disease ,Medizin ,Genome-wide association study ,Disease ,Neurodegenerative ,Medical and Health Sciences ,ddc:616.89 ,Mice ,0302 clinical medicine ,Malaltia de Parkinson ,Monoaminergic ,Eating Disorders Working Group of the Psychiatric Genomics Consortium ,2.1 Biological and endogenous factors ,Aetiology ,Cervell ,ALZHEIMERS ,NEURONS ,Animals ,Brain ,Genome-Wide Association Study ,Humans ,Neurons ,Parkinson Disease ,Transcriptome ,11 Medical and Health Sciences ,Genetics & Heredity ,0303 health sciences ,Parkinson Disease/etiology/genetics/pathology ,HERITABILITY ,International Headache Genetics Consortium ,Biological Sciences ,Transcriptome/genetics ,medicine.anatomical_structure ,Neurological ,Genome-Wide Association Study/methods ,Alzheimer's disease ,Life Sciences & Biomedicine ,Gens ,Cell type ,TISSUES ,1.1 Normal biological development and functioning ,Biology ,IMMUNITY ,23andMe Research Team ,Article ,03 medical and health sciences ,ENTERIC NERVOUS-SYSTEM ,SDG 3 - Good Health and Well-being ,Underpinning research ,medicine ,Genetics ,Brain/pathology ,GENOME-WIDE ASSOCIATION ,NUCLEUS ,METAANALYSIS ,030304 developmental biology ,Science & Technology ,Neurons/pathology ,Human Genome ,Neurosciences ,06 Biological Sciences ,medicine.disease ,RISK LOCI ,Brain Disorders ,Genes ,Enteric nervous system ,Neuroscience ,030217 neurology & neurosurgery ,Developmental Biology - Abstract
Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson’s disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson’s disease. Eating Disorders Working Group of the Psychiatric Genomics Consortium Roger Adan17,18,19, Lars Alfredsson20, Tetsuya Ando21, Ole Andreassen22, Jessica Baker9, Andrew Bergen23,24, Wade Berrettini25, Andreas Birgegård26,27, Joseph Boden28, Ilka Boehm29, Claudette Boni30, Vesna Boraska Perica31,32, Harry Brandt33, Gerome Breen13,14, Julien Bryois1, Katharina Buehren34, Cynthia Bulik1,9,15, Roland Burghardt35, Matteo Cassina36, Sven Cichon37, Maurizio Clementi36, Jonathan Coleman13,14, Roger Cone38, Philippe Courtet39, Steven Crawford33, Scott Crow40, James Crowley16,26, unna Danner18, Oliver Davis41,42, Martina de Zwaan43, George Dedoussis44, Daniela Degortes45, Janiece DeSocio46, Danielle Dick47, Dimitris Dikeos48, Christian Dina49,50, Monika Dmitrzak-Weglarz51, Elisa Docampo Martinez52,53,54, Laramie Duncan55, Karin Egberts56, Stefan Ehrlich29, Geòrgia Escaramís52,53,54, Tõnu Esko57,58, Xavier Estivill52,53,54,59, Anne Farmer13, Angela Favaro45, Fernando Fernández-Aranda60,61, Manfred Fichter62,63, Krista Fischer57, Manuel Föcker64, Lenka Foretova65, Andreas Forstner37,66,67,68,69, Monica Forzan36, Christopher Franklin31, Steven Gallinger70, Héléna Gaspar13,14, Ina Giegling71, Johanna Giuranna64, Paola Giusti-Rodríquez16, Fragiskos Gonidakis72, Scott Gordon73, Philip Gorwood30,74, Monica Gratacos Mayora52,53,54, Jakob Grove75,76,77,78, Sébastien Guillaume39, Yiran Guo79, Hakon Hakonarson79,80, Katherine Halmi81, Ken Hanscombe82, Konstantinos Hatzikotoulas31, Joanna Hauser83, Johannes Hebebrand64, Sietske Helder13,84, Anjali Henders85, Stefan Herms37,69, Beate Herpertz-Dahlmann34, Wolfgang Herzog86, Anke Hinney64, L. John Horwood28, Christopher Hübel1,13, Laura Huckins31,87, James Hudson88, Hartmut Imgart89, Hidetoshi Inoko90, Vladimir Janout91, Susana Jiménez-Murcia60,61, Craig Johnson92, Jennifer Jordan93,94, Antonio Julià95, Anders Juréus1, Gursharan Kalsi13, Deborah Kaminská96, Allan Kaplan97, Jaakko Kaprio98,99, Leila Karhunen100, Andreas Karwautz101, Martien Kas17,102, Walter Kaye103, James Kennedy97, Martin Kennedy104, Anna Keski-Rahkonen98, Kirsty Kiezebrink105, Youl-Ri Kim106, Katherine Kirk73, Lars Klareskog107, Kelly Klump108, Gun Peggy Knudsen109, Maria La Via9, Mikael Landén1,19, Janne Larsen76,110,111, Stephanie Le Hellard112,113,114, Virpi Leppä1, Robert Levitan115, Dong Li79, Paul Lichtenstein1, Lisa Lilenfeld116, Bochao Danae Lin17, Jolanta Lissowska117, Jurjen Luykx17, Pierre Magistretti118,119, Mario Maj120, Katrin Mannik57,121, Sara Marsal95, Christian Marshall122, Nicholas Martin73, Manuel Mattheisen26,27,75,123, Morten Mattingsdal22, Sara McDevitt124,125, Peter McGuffin13, Sarah Medland73, Andres Metspalu57,126, Ingrid Meulenbelt127, Nadia Micali128,129, James Mitchell130, Karen Mitchell131, Palmiero Monteleone132, Alessio Maria Monteleone120, Grant Montgomery73,85,133, Preben Bo Mortensen76,110,111, Melissa Munn-Chernoff9, Benedetta Nacmias134, Marie Navratilova65, Claes Norring26,27, Ioanna Ntalla44, Catherine Olsen73, Roel Ophoff17,135, Julie O’Toole136, Leonid Padyukov107, Aarno Palotie58,99,137, Jacques Pantel30, Hana Papezova96, Richard Parker73, John Pearson138, Nancy Pedersen1, Liselotte Petersen76,110,111, Dalila Pinto87, Kirstin Purves13, Raquel Rabionet139,140,141, Anu Raevuori98, Nicolas Ramoz30, Ted Reichborn-Kjennerud109,142, Valdo Ricca134,143, Samuli Ripatti144, Stephan Ripke145,146,147, Franziska Ritschel29,148, Marion Roberts13, Alessandro Rotondo149, Dan Rujescu62,71, Filip Rybakowski150, Paolo Santonastaso151, André Scherag152, Stephen Scherer153, ulrike Schmidt13, Nicholas Schork154, Alexandra Schosser155, Jochen Seitz34, Lenka Slachtova156, P. Eline Slagboom127, Margarita Slof-Op ‘t Landt157,158, Agnieszka Slopien159, Sandro Sorbi134,160, Michael Strober161,162, Garret Stuber9,163, Patrick Sullivan1,16, Beata Świątkowska164, Jin Szatkiewicz16, Ioanna Tachmazidou31, Elena Tenconi45, Laura Thornton9, Alfonso Tortorella165,166, Federica Tozzi167, Janet Treasure13, Artemis Tsitsika168, Marta Tyszkiewicz-Nwafor150, Konstantinos Tziouvas169, Annemarie van Elburg18,170, Eric van Furth157,158, Tracey Wade171, Gudrun Wagner101, Esther Walton29, Hunna Watson9,10,11, Thomas Werge172, David Whiteman73, Elisabeth Widen99, D. Blake Woodside173,174, Shuyang Yao1, Zeynep Yilmaz9,16, Eleftheria Zeggini31,175, Stephanie Zerwas9 and Stephan Zipfel176 17Brain Center Rudolf Magnus, Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands. 18Center for Eating Disorders Rintveld, Altrecht Mental Health Institute, Zeist, the Netherlands. 19Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 20Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 21Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan. 22NORMENT KG Jebsen Centre, Division of Mental Health and Addiction, University of Oslo, Oslo University Hospital, Oslo, Norway. 23BioRealm, LLC, Walnut, CA, USA. 24Oregon Research Institute, Eugene, OR, USA. 25Department of Psychiatry, Center for Neurobiology and Behavior, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. 26Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. 27Center for Psychiatry Research, Stockholm Health Care Services, Stockholm City Council, Stockholm, Sweden. 28Christchurch Health and Development Study, University of Otago, Christchurch, New Zealand. 29Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany. 30INSERM U894, Centre of Psychiatry and Neuroscience, Paris, France. 31Wellcome Sanger Institute, Hinxton, Cambridge, UK. 32Department of Medical Biology, School of Medicine, University of Split, Split, Croatia. 33The Center for Eating Disorders at Sheppard Pratt, Baltimore, MD, USA. 34Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, Germany. 35Klinikum Frankfurt/Oder, Frankfurt, Germany. 36Clinical Genetics Unit, Department of Woman and Child Health, University of Padova, Padua, Italy. 37Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland. 38Life Sciences Institute and Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA. 39Department of Emergency Psychiatry and Post-Acute Care, CHRU Montpellier, University of Montpellier, Montpellier, France. 40Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA. 41MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. 42School of Social and Community Medicine, University of Bristol, Bristol, UK. 43Department of Psychosomatic Medicine and Psychotherapy, Hannover Medical School, Hannover, Germany. 44Department of Nutrition and Dietetics, Harokopio University, Athens, Greece. 45Department of Neurosciences, University of Padova, Padua, Italy. 46College of Nursing, Seattle University, Seattle, WA, USA. 47Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA. 48Department of Psychiatry, Athens University Medical School, Athens University, Athens, Greece. 49L’institut du thorax, INSERM, CNRS, UNIV Nantes, Nantes, France. 50L’institut du thorax, CHU Nantes, Nantes, France. 51Department of Psychiatric Genetics, Poznań University of Medical Sciences, Poznań, Poland. 52Barcelona Institute of Science and Technology, Barcelona, Spain. 53Universitat Pompeu Fabra, Barcelona, Spain. 54Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. 55Department of Psychiatry and Behavioral Sciences, Stanford University Stanford, CA, USA. 56Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, Centre for Mental Health, Würzburg, Germany. 57Estonian Genome Center, University of Tartu, Tartu, Estonia. 58Program in Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. 59Genomics and Disease, Bioinformatics and Genomics Programme, Centre for Genomic Regulation, Barcelona, Spain. 60Department of Psychiatry, University Hospital of Bellvitge –IDIBELL and CIBERobn, Barcelona, Spain. 61Department of Clinical Sciences, School of Medicine, University of Barcelona, Barcelona, Spain. 62Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University (LMU), Munich, Germany. 63Schön Klinik Roseneck affiliated with the Medical Faculty of the University of Munich (LMU), Munich, Germany. 64Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany. 65Department of Cancer, Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic. 66Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany. 67Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany. 68Department of Psychiatry (UPK), University of Basel, Basel, Switzerland. 69Department of Biomedicine, University of Basel, Basel, Switzerland. 70Department of Surgery, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. 71Department of Psychiatry, Psychotherapy and Psychosomatics, Martin Luther University of Halle-Wittenberg, Halle, Germany. 721st Psychiatric Department, National and Kapodistrian University of Athens, Medical School, Eginition Hospital, Athens, Greece. 73QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. 74CMME (Groupe Hospitalier Sainte-Anne), Paris Descartes University, Paris, France. 75Department of Biomedicine, Aarhus University, Aarhus, Denmark. 76The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSyCH), Aarhus, Denmark. 77Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark. 78Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark. 79Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA. 80Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 81Department of Psychiatry, Weill Cornell Medical College, New york, Ny, USA. 82Department of Medical and Molecular Genetics, King’s College London, Guy’s Hospital, London, UK. 83Department of Adult Psychiatry, Poznań University of Medical Sciences, Poznań, Poland. 84Zorg op Orde, Leidschendam, the Netherlands. 85Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia. 86Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany. 87Department of Psychiatry, and Genetics and Genomics Sciences, Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New york, Ny, USA. 88Biological Psychiatry Laboratory, McLean Hospital/Harvard Medical School, Boston, MA, USA. 89Eating Disorders Unit, Parklandklinik, Bad Wildungen, Germany. 90Department of Molecular Life Science, Division of Basic Medical Science and Molecular Medicine, School of Medicine, Tokai University, Isehara, Japan. 91Faculty of Health Sciences, Palacky University, Olomouc, Czech Republic. 92Eating Recovery Center, Denver, CO, USA. 93Department of Psychological Medicine, University of Otago, Christchurch, New Zealand. 94Canterbury District Health Board, Christchurch, New Zealand. 95Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain. 96Department of Psychiatry, First Faculty of Medicine, Charles University, Prague, Czech Republic. 97Center for Addiction and Mental Health, Department of Psychiatry, Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada. 98Department of Public Health, University of Helsinki, Helsinki, Finland. 99Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland. 100Institute of Public Health and Clinical Nutrition, Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland. 101Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria. 102Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, the Netherlands. 103Department of Psychiatry, University of California San Diego, San Diego, CA, USA. 104Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand. 105Health Services Research Unit, University of Aberdeen, Aberdeen, UK. 106Department of Psychiatry, Seoul Paik Hospital, Inje University, Seoul, Korea. 107Rheumatology Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden. 108Department of Psychology, Michigan State University, East Lansing, MI, USA. 109Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway. 110National Centre for Register-Based Research, Aarhus BSS, Aarhus University, Aarhus, Denmark. 111Centre for Integrated Register-based Research (CIRRAU), Aarhus University, Aarhus, Denmark. 112Department of Clinical Science, K.G. Jebsen Centre for Psychosis Research, Norwegian Centre for Mental Disorders Research (NORMENT), University of Bergen, Bergen, Norway. 113Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway. 114Department of Clinical Medicine, Laboratory Building, Haukeland University Hospital, Bergen, Norway. 115Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada. 116American School of Professional Psychology, Argosy University, Northern Virginia, Arlington, VA, USA. 117Department of Cancer Epidemiology and Prevention, M Skłodowska-Curie Cancer Center - Oncology Center, Warsaw, Poland. 118BESE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. 119Department of Psychiatry, University of Lausanne-University Hospital of Lausanne (UNIL-CHUV), Lausanne, Switzerland. 120Department of Psychiatry, University of Campania ‘Luigi Vanvitelli’, Naples, Italy. 121Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. 122Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada. 123Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany. 124Department of Psychiatry, University College Cork, Cork, Ireland. 125Eist Linn Adolescent Unit, Bessborough, Health Service Executive South, Cork, Ireland. 126Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia. 127Molecular Epidemiology Section (Department of Medical Statistics), Leiden University Medical Centre, Leiden, the Netherlands. 128Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland. 129Division of Child and Adolescent Psychiatry, Geneva University Hospital, Geneva, Switzerland. 130Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, ND, USA. 131National Center for PTSD, VA Boston Healthcare System, Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA. 132Department of Medicine, Surgery and Dentistry ‘Scuola Medica Salernitana’, University of Salerno, Salerno, Italy. 133Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. 134Department of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, Italy. 135Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA. 136Kartini Clinic, Portland, OR, USA. 137Center for Human Genome Research at the Massachusetts General Hospital, Boston, MA, USA. 138Biostatistics and Computational Biology Unit, University of Otago, Christchurch, New Zealand. 139Saint Joan de Déu Research Institute, Saint Joan de Déu Barcelona Children’s Hospital, Barcelona, Spain. 140Institute of Biomedicine (IBUB), University of Barcelona, Barcelona, Spain. 141Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain. 142Institute of Clinical Medicine, University of Oslo, Oslo, Norway. 143Department of Health Science, University of Florence, Florence, Italy. 144Department of Biometry, University of Helsinki, Helsinki, Finland. 145Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. 146Stanley Center for Psychiatric Research, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. 147Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany. 148Eating Disorders Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany. 149Department of Psychiatry, Neurobiology, Pharmacology, and Biotechnologies, University of Pisa, Pisa, Italy. 150Department of Psychiatry, Poznań University of Medical Sciences, Poznań, Poland. 151Department of Neurosciences, Padua Neuroscience Center, University of Padova, Padua, Italy. 152Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany. 153Department of Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Ontario, Canada. 154J. Craig Venter Institute (JCVI), La Jolla, CA, USA. 155Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria. 156Department of Pediatrics and Center of Applied Genomics, First Faculty of Medicine, Charles University, Prague, Czech Republic. 157Center for Eating Disorders Ursula, Rivierduinen, Leiden, the Netherlands. 158Department of Psychiatry, Leiden University Medical Centre, Leiden, the Netherlands. 159Department of Child and Adolescent Psychiatry, Poznań University of Medical Sciences, Poznań, Poland. 160IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy. 161Department of Psychiatry and Biobehavioral Science, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA. 162David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. 163Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 164Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Lodz, Poland. 165Department of Psychiatry, University of Naples SUN, Naples, Italy. 166Department of Psychiatry, University of Perugia, Perugia, Italy. 167Brain Sciences Department, Stremble Ventures, Limassol, Cyprus. 168Adolescent Health Unit, Second Department of Pediatrics, ‘P. & A. Kyriakou’ Children’s Hospital, University of Athens, Athens, Greece. 169Pediatric Intensive Care Unit, ‘P. & A. Kyriakou’ Children’s Hospital, University of Athens, Athens, Greece. 170Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, the Netherlands. 171School of Psychology, Flinders University, Adelaide, South Australia, Australia. 172Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. 173Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. 174Toronto General Hospital, Toronto, Ontario, Canada. 175Institute of Translational Genomics, Helmholtz Zentrum München, Neuherberg, Germany. 176Department of Internal Medicine VI, Psychosomatic Medicine and Psychotherapy, University Medical Hospital Tübingen, Tübingen, Germany International Headache Genetics Consortium Verneri Anttila177, Ville Artto178, Andrea Carmine Belin179, Irene de Boer180, Dorret I. Boomsma181, Sigrid Børte182, Daniel I. Chasman183, Lynn Cherkas184, Anne Francke Christensen185, Bru Cormand186, Ester Cuenca-Leon177, George Davey-Smith187, Martin Dichgans188, Cornelia van Duijn189, Tonu Esko57, Ann Louise Esserlind190, Michel Ferrari180, Rune R. Frants180, Tobias Freilinger191, Nick Furlotte192, Padhraig Gormley177, Lyn Griffiths193, Eija Hamalainen194, Thomas Folkmann Hansen6, Marjo Hiekkala195, M. Arfan Ikram189, Andres Ingason196, Marjo-Riitta Järvelin197, Risto Kajanne194, Mikko Kallela178, Jaakko Kaprio98,99, Mari Kaunisto195, Lisette J. A. Kogelman6, Christian Kubisch198, Mitja Kurki177, Tobias Kurth199, Lenore Launer200, Terho Lehtimaki201, Davor Lessel198, Lannie Ligthart181, Nadia Litterman192, Arn van den Maagdenberg180, Alfons Macaya202, Rainer Malik188, Massimo Mangino184, George McMahon187, Bertram Muller-Myhsok203, Benjamin M. Neale177, Carrie Northover192, Dale R. Nyholt193, Jes Olesen190, Aarno Palotie58,99,137, Priit Palta194, Linda Pedersen182, Nancy Pedersen1, Danielle Posthuma181, Patricia Pozo-Rosich204, Alice Pressman205, Olli Raitakari206, Markus Schürks199, Celia Sintas186, Kari Stefansson196, Hreinn Stefansson196, Stacy Steinberg196, David Strachan207, Gisela Terwindt180, Marta Vila-Pueyo202, Maija Wessman195, Bendik S. Winsvold182, Huiying Zhao193 and John Anker Zwart182 177Broad Institute of MIT and Harvard, Cambridge, MA, USA. 178Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland. 179Karolinska Institutet, Stockholm, Sweden. 180Leiden University Medical Centre, Leiden, the Netherlands. 181VU University, Amsterdam, the Netherlands. 182Oslo University Hospital and University of Oslo, Oslo, Norway. 183Harvard Medical School, Cambridge, MA, USA. 184Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK. 185Danish Headache Center, Copenhagen University Hospital, Copenhagen, Denmark. 186University of Barcelona, Barcelona, Spain. 187Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK. 188Institute for Stroke and Dementia Research, Munich, Germany. 189Erasmus University Medical Centre, Rotterdam, the Netherlands. 190Danish Headache Center, Department of Neurology, Rigshospitalet, Glostrup, Denmark. 191University of Tübingen, Tübingen, Germany. 19223&Me Inc., Mountain View, CA, USA. 193Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. 194Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. 195Folkhälsan Institute of Genetics, Helsinki, Finland. 196Decode genetics Inc., Reykjavik, Iceland. 197University of Oulu, Biocenter Oulu, Finland. 198University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 199Harvard Medical School, Boston, MA, USA. 200National Institute on Aging, Bethesda, MD, USA. 201School of Medicine, University of Tampere, Tampere, Finland. 202Vall d’Hebron Research Institute, Barcelona, Spain. 203Max Planck Institute of Psychiatry, Munich, Germany. 204Headache Research Group, Universitat Autònoma de Barcelona, Barcelona, Spain. 205Sutter Health, Sacramento, CA, USA. 206Department of Medicine, University of Turku, Turku, Finland. 207Population Health Research Institute, St George’s University of London, London, UK. 23andMe Research Team Michelle Agee208, Babak Alipanahi208, Adam Auton208, Robert Bell208, Katarzyna Bryc208, Sarah Elson208, Pierre Fontanillas208, Nicholas Furlotte208, Karl Heilbron208, David Hinds208, Karen Huber208, Aaron Kleinman208, Nadia Litterman208, Jennifer McCreight208, Matthew McIntyre208, Joanna Mountain208, Elizabeth Noblin208, Carrie Northover208, Steven Pitts208, J. Sathirapongsasuti208, Olga Sazonova208, Janie Shelton208, Suyash Shringarpure208, Chao Tian208, Joyce Tung208, Vladimir Vacic208 and Catherine Wilson208 20823andMe, Inc., Mountain View, CA, US
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- 2020
10. Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases
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An, Jiyuan, Gharahkhani, Puya, Law, Matthew H., Ong, Jue-Sheng, Han, Xikun, Olsen, Catherine M., Neale, Rachel E., Lai, John, Vaughan, Tom L., Gockel, Ines, Thieme, René, Böhmer, Anne C., Jankowski, Janusz, Fitzgerald, Rebecca C., Schumacher, Johannes, Palles, Claire, Whiteman, David C., MacGregor, Stuart, Gammon, Marilie D., Corley, Douglas A., Shaheen, Nicholas J., Bird, Nigel C., Hardie, Laura J., Murray, Liam J., Reid, Brian J., Chow, Wong-Ho, Risch, Harvey A., Ye, Weimin, Liu, Geoffrey, Romero, Yvonne, Bernstein, Leslie, Wu, Anna H., Agee, M., Alipanahi, B., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., McIntyre, M. H., Mountain, J. L., Noblin, E. S., Northover, C. A. M., Pitts, S. J., Sathirapongsasuti, J. Fah, Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tian, C., Tung, J. Y., Vacic, V., Wilson, C. H., Gharahkhani, Puya [0000-0002-4203-5952], Law, Matthew H. [0000-0002-4303-8821], Ong, Jue-Sheng [0000-0002-6062-710X], Han, Xikun [0000-0002-3823-7308], Olsen, Catherine M. [0000-0003-4483-1888], Böhmer, Anne C. [0000-0002-5716-786X], Fitzgerald, Rebecca C. [0000-0002-3434-3568], MacGregor, Stuart [0000-0001-6731-8142], and Apollo - University of Cambridge Repository
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631/67/1504/1477 ,631/208/205/2138 ,45/43 ,article ,38/39 ,631/208/68 ,692/699/1503/1476/196 ,health care economics and organizations ,humanities ,digestive system diseases - Abstract
Funder: The Swedish Esophageal Cancer Study was funded by grants (R01 CA57947-03) from the National Cancer Institute he California Tobacco Related Research Program (3RT-0122; and; 10RT-0251) Marit Peterson Fund for Melanoma Research. CIDR is supported by contract HHSN268200782096C, Gastroesophageal reflux disease (GERD) is caused by gastric acid entering the esophagus. GERD has high prevalence and is the major risk factor for Barrett’s esophagus (BE) and esophageal adenocarcinoma (EA). We conduct a large GERD GWAS meta-analysis (80,265 cases, 305,011 controls), identifying 25 independent genome-wide significant loci for GERD. Several of the implicated genes are existing or putative drug targets. Loci discovery is greatest with a broad GERD definition (including cases defined by self-report or medication data). Further, 91% of the GERD risk-increasing alleles also increase BE and/or EA risk, greatly expanding gene discovery for these traits. Our results map genes for GERD and related traits and uncover potential new drug targets for these conditions.
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- 2019
11. Overlapping genetic architecture between Parkinson disease and melanoma
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Dube, U., Ibanez, L., Budde, J. P., Benitez, B. A., Davis, A. A., Harari, O., Iles, M. M., Law, M. H., Brown, K. M., Agee, M., Alipanahi, B., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., Mccreight, J. C., Mcintyre, M. H., Mountain, J. L., Noblin, E. S., Northover, C. A. M., Pitts, S. J., Sathirapongsasuti, J. F., Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tian, C., Tung, J. Y., Vacic, V., Wilson, C. H., Bishop, D. T., Lee, J. E., Brossard, M., Martin, N. G., Moses, E. K., Song, F., Barrett, J. H., Kumar, R., Easton, D. F., Pharoah, P. D., Swerdlow, A. J., Kypreou, K. P., Taylor, J. C., Harland, M., Randerson-Moor, J., Akslen, L. A., Andresen, P. A., Avril, M. F., Azizi, E., Scarra, G. B., Debniak, T., Duffy, D. L., Elder, D. E., Fang, S., Friedman, E., Galan, P., Ghiorzo, P., Gillanders, E. M., Goldstein, A. M., Gruis, N. A., Hansson, J., Helsing, P., Hocevar, M., Hoiom, V., Ingvar, C., Kanetsky, P. A., Chen, W. V., Landi, M. T., Lang, J., Lathrop, G. M., Lubinski, J., Mackie, R. M., Mann, G. J., Molven, A., Montgomery, G. W., Novakovic, S., Olsson, H., Puig, S., Puig-Butille, J. A., Wu, W., Qureshi, A. A., Radford-Smith, G. L., van der Stoep, N., van Doorn, R., Whiteman, D. C., Craig, J. E., Schadendorf, E., Simms, L. A., Burdon, K. P., Nyholt, D. R., Pooley, K. A., Orr, N., Stratigos, A. J., Cust, A. E., Ward, S. V., Hayward, N. K., Han, J., Schulze, H. J., Dunning, A. M., Bishop, J. A., Demenais, F., Amos, C. I., Macgregor, S., and Cruchaga, C.
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0301 basic medicine ,Oncology ,medicine.medical_specialty ,Genetic correlation ,Multifactorial Inheritance ,Skin Neoplasms ,Medizin ,TWAS ,Disease ,Melanoma ,Parkinson disease ,Polygenic ,Shared genetic architecture ,Pathology and Forensic Medicine ,Correlation ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,0302 clinical medicine ,Internal medicine ,Gene expression ,Medicine ,Humans ,Gene ,business.industry ,Parkinson Disease ,medicine.disease ,Genetic architecture ,030104 developmental biology ,Case-Control Studies ,Cutaneous melanoma ,Neurology (clinical) ,business ,030217 neurology & neurosurgery ,Genome-Wide Association Study - Abstract
Epidemiologic studies have reported inconsistent results regarding an association between Parkinson disease (PD) and cutaneous melanoma (melanoma). Identifying shared genetic architecture between these diseases can support epidemiologic findings and identify common risk genes and biological pathways. Here, we apply polygenic, linkage disequilibrium-informed methods to the largest available case-control, genome-wide association study summary statistic data for melanoma and PD. We identify positive and significant genetic correlation (correlation: 0.17, 95% CI 0.10-0.24; P = 4.09 x 10(-06)) between melanoma and PD. We further demonstrate melanoma and PD-inferred gene expression to overlap across tissues (correlation: 0.14, 95% CI 0.06 to 0.22; P = 7.87 x 10(-04)) and highlight seven genes including PIEZO1, TRAPPC2L, and SOX6 as potential mediators of the genetic correlation between melanoma and PD. These findings demonstrate specific, shared genetic architecture between PD and melanoma that manifests at the level of gene expression.
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- 2019
12. Identification of common genetic risk variants for autism spectrum disorder
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Grove, J., Ripke, S., Als, T.D., Mattheisen, M., Walters, R.K., Won, H., Pallesen, J., Agerbo, E., Andreassen, O.A., Anney, R., Awashti, S., Belliveau, R., Bettella, F., Buxbaum, J.D., Bybjerg-Grauholm, J., Baekvad-Hansen, M., Cerrato, F., Chambert, K., Christensen, J.H., Churchhouse, C., Dellenvall, K., Demontis, D., Rubeis, S. de, Devlin, B., Djurovic, S., Dumont, A.L., Goldstein, J.I., Hansen, C.S., Hauberg, M.E., Hollegaard, M.V., Hope, S., Howrigan, D.P., Huang, H., Hultman, C.M., Klei, L., Maller, J., Martin, J., Martin, A.R., Moran, J.L., Nyegaard, M., Naeland, T., Palmer, D.S., Palotie, A., Pedersen, C.B., Pedersen, M.G., dPoterba, T., Poulsen, J.B., St Pourcain, B., Qvist, P., Rehnstrom, K., Reichenberg, A., Reichert, J., Robinson, E.B., Roeder, K., Roussos, P., Saemundsen, E., Sandin, S., Satterstrom, F.K., Smith, G.D., Stefansson, H., Steinberg, S., Stevens, C.R., Sullivan, P.F., Turley, P., Walters, G.B., Xu, X.Y., Stefansson, K., Geschwind, D.H., Nordentoft, M., Hougaard, D.M., Werge, T., Mors, O., Mortensen, P.B., Neale, B.M., Daly, M.J., Borglum, A.D., Wray, N.R., Trzaskowski, M., Byrne, E.M., Abdellaoui, A., Adams, M.J., Air, T.M., Andlauer, T.F.M., Bacanu, S.A., Beekman, A.T.F., Bigdeli, T.B., Binder, E.B., Blackwood, D.H.R., Bryois, J., Buttenschon, H.N., Cai, N., Castelao, E., Clarke, T.K., Coleman, J.R.I., Colodro-Conde, L., Couvy-Duchesne, B., Craddock, N., Crawford, G.E., Davies, G., Deary, I.J., Degenhardt, F., Derks, E.M., Direk, N., Dolan, C.V., Dunn, E.C., Eley, T.C., Escott-Price, V., Kiadeh, F.F.H., Finucane, H.K., Forstner, A.J., Frank, J., Gaspar, H.A., Gill, M., Goes, F.S., Gordon, S.D., Hall, L.S., Hansen, T.F., Herms, S., Hickie, I.B., Hoffmann, P., Homuth, G., Horn, C., Hottenga, J.J., Ising, M., Jansen, R., Jorgenson, E., Knowles, J.A., Kohane, I.S., Kraft, J., Kretzschmar, W.W., Krogh, J., Kutalik, Z., Li, Y., Lind, P.A., MacIntyre, D.J., MacKinnon, D.F., Maier, R.M., Maier, W., Marchini, J., Mbarek, H., McGrath, P., McGuffin, P., Medland, S.E., Mehta, D., Middeldorp, C.M., Mihailov, E., Milaneschi, Y., Milani, L., Mondimore, F.M., Montgomery, G.W., Mostafavi, S., Mullins, N., Nauck, M., Ng, B., Nivard, M.G., Nyholt, D.R., O'Reilly, P.F., Oskarsson, H., Owen, M.J., Painter, J.N., Peterson, R.E., Pettersson, E., Peyrot, W.J., Pistis, G., Posthuma, D., Quiroz, J.A., Rice, J.P., Riley, B.P., Rivera, M., Mirza, S.S., Schoevers, R., Schulte, E.C., Shen, L., Shi, J.X., Shyn, S.I., Sigurdsson, E., Sinnamon, G.C.B., Smit, J.H., Smith, D.J., Streit, F., Strohmaier, J., Tansey, K.E., Teismann, H., Teumer, A., Thompson, W., Thomson, P.A., Thorgeirsson, T.E., Traylor, M., Treutlein, J., Trubetskoy, V., Uitterlinden, A.G., Umbricht, D., Auwera, S. van der, Hemert, A.M. van, Viktorin, A., Visscher, P.M., Wang, Y.P., Webb, B.T., Weinsheimer, S.M., Wellmann, J., Willemsen, G., Witt, S.H., Wu, Y., Xi, H.S., Yang, J., Zhang, F.T., Arolt, V., Baune, B.T., Berger, K., Boomsma, D.I., Cichon, S., Dannlowski, U., Geus, E.J.C. de, DePaulo, J.R., Domenici, E., Domschke, K., Esko, T., Grabe, H.J., Hamilton, S.P., Hayward, C., Heath, A.C., Kendler, K.S., Kloiber, S., Lewis, G., Li, Q.S., Lucae, S., Madden, P.A.F., Magnusson, P.K., Martin, N.G., McIntosh, A.M., Metspalu, A., Muller-Myhsok, B., Nothen, M.M., O'Donovan, M.C., Paciga, S.A., Pedersen, N.L., Penninx, B.W.J.H., Perlis, R.H., Porteous, D.J., Potash, J.B., Preisig, M., Rietschel, M., Schaefer, C., Schulze, T.G., Smoller, J.W., Tiemeier, H., Uher, R., Volzke, H., Weissman, M.M., Lewis, C.M., Levinson, D.F., Breen, G., Agee, M., Alipanahi, B., Auton, A., Bell, R.K., Bryc, K., Elson, S.L., Fontanillas, P., Furlotte, N.A., Hromatka, B.S., Huber, K.E., Kleinman, A., Litterman, N.K., McIntyre, M.H., Mountain, J.L., Noblin, E.S., Northover, C.A.M., Pitts, S.J., Sathirapongsasuti, J.F., Sazonova, O.V., Shelton, J.F., Shringarpure, S., Tung, J.Y., Vacic, V., Wilson, C.H., Psychiat Genomics Consortium, BUPGEN, 23andMe Re, Biological Psychology, APH - Methodology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, APH - Mental Health, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, Adult Psychiatry, Psychiatry, Human genetics, Amsterdam Reproduction & Development (AR&D), VU University medical center, APH - Digital Health, Aarno Palotie / Principal Investigator, Institute for Molecular Medicine Finland, Genomics of Neurological and Neuropsychiatric Disorders, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Perceptual and Cognitive Neuroscience (PCN), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Me Research Team, Epidemiology, and Child and Adolescent Psychiatry / Psychology
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Male ,Netherlands Twin Register (NTR) ,Multifactorial Inheritance ,Autism Spectrum Disorder ,Denmark ,LD SCORE REGRESSION ,LOCI ,Genome-wide association study ,DE-NOVO ,0302 clinical medicine ,Polymorphism (computer science) ,Risk Factors ,SYNAPTIC PLASTICITY ,CELL-SURFACE ,Child ,Genetics ,0303 health sciences ,HERITABILITY ,Genetic Predisposition to Disease/genetics ,1184 Genetics, developmental biology, physiology ,Polymorphism, Single Nucleotide/genetics ,Phenotype ,3. Good health ,Schizophrenia ,Autism spectrum disorder ,Child, Preschool ,Genome-Wide Association Study/methods ,Female ,SIMONS SIMPLEX COLLECTION ,Adolescent ,Biology ,NEURITE OUTGROWTH ,Polymorphism, Single Nucleotide ,behavioral disciplines and activities ,Article ,03 medical and health sciences ,mental disorders ,medicine ,Humans ,Genetic Predisposition to Disease ,GENOME-WIDE ASSOCIATION ,SDG 2 - Zero Hunger ,Multifactorial Inheritance/genetics ,METAANALYSIS ,030304 developmental biology ,Case-control study ,Heritability ,medicine.disease ,Autism Spectrum Disorder/genetics ,Case-Control Studies ,3111 Biomedicine ,030217 neurology & neurosurgery ,Genome-Wide Association Study - Abstract
Published in final edited form as: Nat Genet. 2019 March ; 51(3): 431–444. doi:10.1038/s41588-019-0344-8., Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture, we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD., The iPSYCH project is funded by the Lundbeck Foundation (grant numbers R102-A9118 and R155-2014-1724) and the universities and university hospitals of Aarhus and Copenhagen. Genotyping of iPSYCH and PGC samples was supported by grants from the Lundbeck Foundation, the Stanley Foundation, the Simons Foundation (SFARI 311789 to MJD), and NIMH (5U01MH094432–02 to MJD). The Danish National Biobank resource was supported by the Novo Nordisk Foundation. Data handling and analysis on the GenomeDK HPC facility was supported by NIMH (1U01MH109514–01 to M O’Donovan and ADB). High-performance computer capacity for handling and statistical analysis of iPSYCH data on the GenomeDK HPC facility was provided by the Centre for Integrative Sequencing, iSEQ, Aarhus University, Denmark (grant to ADB). Drs. S De Rubeis and JD Buxbaum were supported by NIH grants MH097849 (to JDB) and MH111661 (to JDB), and by the Seaver Foundation (to SDR and JDB). Dr J Martin was supported by the Wellcome Trust (grant no: 106047). O. Andreassen received funding from Research Council of Norway (#213694, #223273, #248980, #248778), Stiftelsen KG Jebsen and South-East Norway Health Authority. We thank the research participants and employees of 23andMe for making this work possible.
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- 2019
13. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder
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Demontis, D, Walters, R, Martin, J, Mattheisen, M, Als, T, Agerbo, E, Baldursson, G, Belliveau, R, Bybjerg-Grauholm, J, Baekvad-Hansen, M, Cerrato, F, Chambert, K, Churchhouse, C, Dumont, A, Eriksson, N, Gandal, M, Goldstein, J, Grasby, K, Grove, J, Gudmundsson, O, Hansen, C, Hauberg, M, Hollegaard, M, Howrigan, D, Huang, H, Maller, J, Martin, A, Martin, N, Moran, J, Pallesen, J, Palmer, D, Pedersen, C, Pedersen, M, Poterba, T, Poulsen, J, Ripke, S, Robinson, E, Satterstrom, F, Stefansson, H, Stevens, C, Turley, P, Walters, G, Won, H, Wright, M, Andreassen, O, Asherson, P, Burton, C, Boomsma, D, Cormand, B, Dalsgaard, S, Franke, B, Gelernter, J, Geschwind, D, Hakonarson, H, Haavik, J, Kranzler, H, Kuntsi, J, Langley, K, Lesch, K, Middeldorp, C, Reif, A, Rohde, L, Roussos, P, Schachar, R, Sklar, P, Sonuga-Barke, E, Sullivan, P, Thapar, A, Tung, J, Waldman, I, Medland, S, Stefansson, K, Nordentoft, M, Hougaard, D, Werge, T, Mors, O, Mortensen, P, Daly, M, Faraone, S, Borglum, A, Neale, B, Albayrak, O, Anney, R, Arranz, M, Banaschewski, T, Bau, C, Biederman, J, Buitelaar, J, Casas, M, Charach, A, Crosbie, J, Dempfle, A, Doyle, A, Ebstein, R, Elia, J, Freitag, C, Focker, M, Gill, M, Grevet, E, Hawi, Z, Hebebrand, J, Herpertz-Dahlmann, B, Hervas, A, Hinney, A, Hohmann, S, Holmans, P, Hutz, M, Ickowitz, A, Johansson, S, Kent, L, Kittel-Schneider, S, Lambregts-Rommelse, N, Lehmkuhl, G, Loo, S, McGough, J, Meyer, J, Mick, E, Middletion, F, Miranda, A, Mota, N, Mulas, F, Mulligan, A, Nelson, F, Nguyen, T, Oades, R, O'Donovan, M, Owen, M, Palmason, H, Ramos-Quiroga, J, Renner, T, Ribases, M, Rietschel, M, Rivero, O, Romanos, J, Romanos, M, Rothenberger, A, Royers, H, Sanchez-Mora, C, Scherag, A, Schimmelmann, B, Schafer, H, Sergeant, J, Sinzig, J, Smalley, S, Steinhausen, H, Thompson, M, Todorov, A, Vasquez, A, Walitza, S, Wang, Y, Warnke, A, Williams, N, Witt, S, Yang, L, Zayats, T, Zhang-James, Y, Smith, G, Davies, G, Ehli, E, Evans, D, Fedko, I, Greven, C, Groen-Blokhuis, M, Guxens, M, Hammerschlag, A, Hartman, C, Heinrich, J, Hottenga, J, Hudziak, J, Jugessur, A, Kemp, J, Krapohl, E, Murcia, M, Myhre, R, Nolte, I, Nyholt, D, Ormel, J, Ouwens, K, Pappa, I, Pennell, C, Plomin, R, Ring, S, Standl, M, Stergiakouli, E, St Pourcain, B, Stoltenberg, C, Sunyer, J, Thiering, E, Tiemeier, H, Tiesler, C, Timpson, N, Trzaskowski, M, van der Most, P, Vilor-Tejedor, N, Wang, C, Whitehouse, A, Zhao, H, Agee, M, Alipanahi, B, Auton, A, Bell, R, Bryc, K, Elson, S, Fontanillas, P, Furlotte, N, Hinds, D, Hromatka, B, Huber, K, Kleinman, A, Litterman, N, McIntyre, M, Mountain, J, Northover, C, Pitts, S, Sathirapongsasuti, J, Sazonova, O, Shelton, J, Shringarpure, S, Tian, C, Vacic, V, Wilson, C, ADHD Working Grp Psychiat Genomics, Early Lifecourse Genetic, 23andMe Res Team, Institute for Molecular Medicine Finland, Centre of Excellence in Complex Disease Genetics, Psychiatrie & Neuropsychologie, RS: MHeNs - R3 - Neuroscience, Psychiatry, ADHD Working Group of the Psychiatric Genomics Consortium (PGC), 23andme Research Team, University of St Andrews. Cellular Medicine Division, University of St Andrews. Institute of Behavioural and Neural Sciences, University of St Andrews. School of Medicine, Child and Adolescent Psychiatry / Psychology, Erasmus MC other, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Life Course Epidemiology (LCE), Clinical Neuropsychology, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Personalized Medicine, Clinical Child and Family Studies, LEARN! - Child rearing, and APH - Methodology
- Subjects
Netherlands Twin Register (NTR) ,Male ,Trastorns per dèficit d'atenció amb hiperactivitat en els infants ,LD SCORE REGRESSION ,Medizin ,Genome-wide association study ,US CHILDREN ,Genoma humà ,Attention deficit disorder with hyperactivity in children ,Medical and Health Sciences ,Cohort Studies ,0302 clinical medicine ,2.1 Biological and endogenous factors ,POLYGENIC RISK ,Aetiology ,Child ,IDENTIFIES 11 ,SEXUAL-BEHAVIOR ,Early Lifecourse & Genetic Epidemiology (EAGLE) Consortium ,Pediatric ,0303 health sciences ,education.field_of_study ,Genome ,Genetic Predisposition to Disease/genetics ,1184 Genetics, developmental biology, physiology ,Brain ,3rd-DAS ,Single Nucleotide ,Biological Sciences ,Polymorphism, Single Nucleotide/genetics ,3. Good health ,Mental Health ,Meta-analysis ,Child, Preschool ,Genetic Loci/genetics ,Genome-Wide Association Study/methods ,Trastorns per dèficit d'atenció amb hiperactivitat en els adults ,Attention Deficit Disorder (ADD) ,Female ,Attention Deficit Disorder with Hyperactivity/genetics ,RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry ,SDG 4 - Quality Education ,Clinical psychology ,Risk ,Adolescent ,DEFICIT HYPERACTIVITY DISORDER ,Concordance ,Population ,PROVIDES INSIGHTS ,QH426 Genetics ,Biology ,Quantitative trait locus ,Brain/physiology ,Polymorphism, Single Nucleotide ,23andMe Research Team ,behavioral disciplines and activities ,Gene Expression Regulation/genetics ,Article ,150 000 MR Techniques in Brain Function ,GENETIC ARCHITECTURE ,03 medical and health sciences ,All institutes and research themes of the Radboud University Medical Center ,Clinical Research ,Behavioral and Social Science ,mental disorders ,medicine ,Genetics ,Attention deficit hyperactivity disorder ,Humans ,Genetic Predisposition to Disease ,Polymorphism ,education ,Preschool ,QH426 ,030304 developmental biology ,Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] ,ASSOCIATION METAANALYSIS ,Prevention ,Human Genome ,Case-control study ,MAJOR DEPRESSION ,medicine.disease ,Attention Deficit Hyperactivity Disorder (ADHD) ,Genetic architecture ,Brain Disorders ,ADHD Working Group of the Psychiatric Genomics Consortium ,Gene Expression Regulation ,Attention Deficit Disorder with Hyperactivity ,Genetic Loci ,RC0321 ,Attention deficit disorder with hyperactivity in adults ,3111 Biomedicine ,030217 neurology & neurosurgery ,Genome-Wide Association Study ,Developmental Biology - Abstract
Attention deficit/hyperactivity disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 individuals diagnosed with ADHD and 35,191 controls that identifies variants surpassing genome-wide significance in 12 independent loci, finding important new information about the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes and around brain-expressed regulatory marks. Analyses of three replication studies: a cohort of individuals diagnosed with ADHD, a self-reported ADHD sample and a meta-analysis of quantitative measures of ADHD symptoms in the population, support these findings while highlighting study-specific differences on genetic overlap with educational attainment. Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits. Postprint
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- 2019
14. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms
- Author
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Jones, SE, Lane, JM, Wood, AR, van Hees, VT, Tyrrell, J, Beaumont, RN, Jeffries, AR, Dashti, HS, Hillsdon, M, Ruth, KS, Tuke, MA, Yaghootkar, H, Sharp, SA, Jie, YJ, Thompson, WD, Harrison, JW, Dawes, A, Byrne, EM, Tiemeier, Henning, Allebrandt, KV, Bowden, J, Ray, DW, Freathy, RM, Murray, A, Mazzotti, DR, Gehrman, PR, Lawlor, DA, Frayling, TM, Rutter, MK, Hinds, DA, Saxena, R, Weedon, MN, Agee, M, Alipanahi, B, Auton, A, Bell, RK, Bryc, K, Elson, SL, Fontanillas, P, Furlotte, NA, Huber, KE, Kleinman, A, Litterman, NK, McCreight, JC, McIntyre, MH, Mountain, JL, Noblin, ES, Northover, CAM, Pitts, SJ, Sathirapongsasuti, JF, Sazonova, OV, Shelton, JF, Shringarpure, S, Tian, C, Tung, JY, Vacic, V, Wilson, CH, Epidemiology, and Psychiatry
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- 2019
15. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms
- Author
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Jones, S. (S.), Lane, J.M., Wood, A.R. (Andrew), van Hees, V.T., Tyrrell, A.W.R., Beaumont, RN, Jeffries, A.R., Dashti, HS, Hillsdon, M., Ruth, KS, Tuke, MA, Yaghootkar, H. (Hanieh), Sharp, S.A., Jie, Y.J., Thompson, W.D., Harrison, J.W., Dawes, A., Byrne, E.M. (Enda), Tiemeier, H.W. (Henning), Allebrandt, K.V., Bowden, J., Ray, D.W., Freathy, R.M. (Rachel), Murray, A. (Anna), Mazzotti, D.R., Gehrman, P.R. (Philip), Lawlor, D.A. (Debbie), Frayling, T.M. (Timothy), Rutter, M.K., Hinds, DA, Saxena, R. (Richa), Weedo, M.N. (Michael), Agee, M., Alipanahi, B., Auton, A., Bell, R.K., Bryc, K., Elson, S.L., Fontanillas, P. (Pierre), Furlotte, NA, Huber, K.E., Kleinman, A, Litterman, N.K., McCreight, J.C., McIntyre, M.H., Mountain, J.L., Noblin, E.S., Northover, C.A.M., Pitts, S.J. (Steven), Sathirapongsasuti, JF, Sazonova, O.V., Shelton, J.F., Shringarpure, S., Tian, C, Tung, JY, Vacic, V., Wilson, C.H., Jones, S. (S.), Lane, J.M., Wood, A.R. (Andrew), van Hees, V.T., Tyrrell, A.W.R., Beaumont, RN, Jeffries, A.R., Dashti, HS, Hillsdon, M., Ruth, KS, Tuke, MA, Yaghootkar, H. (Hanieh), Sharp, S.A., Jie, Y.J., Thompson, W.D., Harrison, J.W., Dawes, A., Byrne, E.M. (Enda), Tiemeier, H.W. (Henning), Allebrandt, K.V., Bowden, J., Ray, D.W., Freathy, R.M. (Rachel), Murray, A. (Anna), Mazzotti, D.R., Gehrman, P.R. (Philip), Lawlor, D.A. (Debbie), Frayling, T.M. (Timothy), Rutter, M.K., Hinds, DA, Saxena, R. (Richa), Weedo, M.N. (Michael), Agee, M., Alipanahi, B., Auton, A., Bell, R.K., Bryc, K., Elson, S.L., Fontanillas, P. (Pierre), Furlotte, NA, Huber, K.E., Kleinman, A, Litterman, N.K., McCreight, J.C., McIntyre, M.H., Mountain, J.L., Noblin, E.S., Northover, C.A.M., Pitts, S.J. (Steven), Sathirapongsasuti, JF, Sazonova, O.V., Shelton, J.F., Shringarpure, S., Tian, C, Tung, JY, Vacic, V., and Wilson, C.H.
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- 2019
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16. Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases
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An, J, Gharahkhani, P, Law, MH, Ong, J-S, Han, X, Olsen, CM, Neale, RE, Lai, J, Vaughan, TL, Bohmer, AC, Jankowski, J, Fitzgerald, RC, Schumacher, J, Palles, C, Whiteman, DC, MacGregor, S, Gammon, MD, Corley, DA, Shaheen, NJ, Bird, NC, Hardie, LJ, Murray, LJ, Reid, BJ, Chow, W-H, Risch, HA, Ye, W, Liu, G, Romero, Y, Bernstein, L, Wu, AH, Agee, M, Alipanahi, B, Auton, A, Bell, RK, Bryc, K, Elson, SL, Fontanillas, P, Furlotte, NA, Hinds, DA, Huber, KE, Kleinman, A, Litterman, NK, McIntyre, MH, Mountain, JL, Noblin, ES, Northover, CAM, Pitts, SJ, Sathirapongsasuti, JF, Sazonova, OV, Shelton, JF, Shringarpure, S, Tian, C, Tung, JY, Vacic, V, Wilson, CH, An, J, Gharahkhani, P, Law, MH, Ong, J-S, Han, X, Olsen, CM, Neale, RE, Lai, J, Vaughan, TL, Bohmer, AC, Jankowski, J, Fitzgerald, RC, Schumacher, J, Palles, C, Whiteman, DC, MacGregor, S, Gammon, MD, Corley, DA, Shaheen, NJ, Bird, NC, Hardie, LJ, Murray, LJ, Reid, BJ, Chow, W-H, Risch, HA, Ye, W, Liu, G, Romero, Y, Bernstein, L, Wu, AH, Agee, M, Alipanahi, B, Auton, A, Bell, RK, Bryc, K, Elson, SL, Fontanillas, P, Furlotte, NA, Hinds, DA, Huber, KE, Kleinman, A, Litterman, NK, McIntyre, MH, Mountain, JL, Noblin, ES, Northover, CAM, Pitts, SJ, Sathirapongsasuti, JF, Sazonova, OV, Shelton, JF, Shringarpure, S, Tian, C, Tung, JY, Vacic, V, and Wilson, CH
- Abstract
Gastroesophageal reflux disease (GERD) is caused by gastric acid entering the esophagus. GERD has high prevalence and is the major risk factor for Barrett's esophagus (BE) and esophageal adenocarcinoma (EA). We conduct a large GERD GWAS meta-analysis (80,265 cases, 305,011 controls), identifying 25 independent genome-wide significant loci for GERD. Several of the implicated genes are existing or putative drug targets. Loci discovery is greatest with a broad GERD definition (including cases defined by self-report or medication data). Further, 91% of the GERD risk-increasing alleles also increase BE and/or EA risk, greatly expanding gene discovery for these traits. Our results map genes for GERD and related traits and uncover potential new drug targets for these conditions.
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- 2019
17. Genome-wide association and epidemiological analyses reveal common genetic origins between uterine leiomyomata and endometriosis
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Gallagher, C. S. (C. S.), Makinen, N. (N.), Harris, H. R. (H. R.), Rahmioglu, N. (N.), Uimari, O. (O.), Cook, J. P. (J. P.), Shigesi, N. (N.), Ferreira, T. (T.), Velez-Edwards, D. R. (D. R.), Edwards, T. L. (T. L.), Mortlock, S. (S.), Ruhioglu, Z. (Z.), Day, F. (F.), Becker, C. M. (C. M.), Karhunen, V. (V.), Martikainen, H. (H.), Jarvelin, M. -. (M. -R.), Cantor, R. M. (R. M.), Ridker, P. M. (P. M.), Terry, K. L. (K. L.), Buring, J. E. (J. E.), Gordon, S. D. (S. D.), Medland, S. E. (S. E.), Montgomery, G. W. (G. W.), Nyholt, D. R. (D. R.), Hinds, D. A. (D. A.), Tung, J. Y. (J. Y.), Perry, J. R. (J. R. B.), Lind, P. A. (P. A.), Painter, J. N. (J. N.), Martin, N. G. (N. G.), Morris, A. P. (A. P.), Chasman, D. I. (D. I.), Missmer, S. A. (S. A.), Zondervan, K. T. (K. T.), Morton, C. C. (C. C.), Agee, M. (Michelle), Alipanahi, B. (Babak), Auton, A. (Adam), Bell, R. K. (Robert K.), Bryc, K. (Katarzyna), Elson, S. L. (Sarah L.), Fontanillas, P. (Pierre), Furlotte, N. A. (Nicholas A.), Huber, K. E. (Karen E.), Kleinman, A. (Aaron), Litterman, N. K. (Nadia K.), McIntyre, M. H. (Matthew H.), Mountain, J. L. (Joanna L.), Noblin, E. S. (Elizabeth S.), Northover, C. A. (Carrie A. M.), Pitts, S. J. (Steven J.), Sathirapongsasuti, J. F. (J. Fah), Sazonova, O. V. (Olga V.), Shelton, J. F. (Janie F.), Shringarpure, S. (Suyash), Tian, C. (Chao), Vacic, V. (Vladimir), Wilson, C. H. (Catherine H.), Gallagher, C. S. (C. S.), Makinen, N. (N.), Harris, H. R. (H. R.), Rahmioglu, N. (N.), Uimari, O. (O.), Cook, J. P. (J. P.), Shigesi, N. (N.), Ferreira, T. (T.), Velez-Edwards, D. R. (D. R.), Edwards, T. L. (T. L.), Mortlock, S. (S.), Ruhioglu, Z. (Z.), Day, F. (F.), Becker, C. M. (C. M.), Karhunen, V. (V.), Martikainen, H. (H.), Jarvelin, M. -. (M. -R.), Cantor, R. M. (R. M.), Ridker, P. M. (P. M.), Terry, K. L. (K. L.), Buring, J. E. (J. E.), Gordon, S. D. (S. D.), Medland, S. E. (S. E.), Montgomery, G. W. (G. W.), Nyholt, D. R. (D. R.), Hinds, D. A. (D. A.), Tung, J. Y. (J. Y.), Perry, J. R. (J. R. B.), Lind, P. A. (P. A.), Painter, J. N. (J. N.), Martin, N. G. (N. G.), Morris, A. P. (A. P.), Chasman, D. I. (D. I.), Missmer, S. A. (S. A.), Zondervan, K. T. (K. T.), Morton, C. C. (C. C.), Agee, M. (Michelle), Alipanahi, B. (Babak), Auton, A. (Adam), Bell, R. K. (Robert K.), Bryc, K. (Katarzyna), Elson, S. L. (Sarah L.), Fontanillas, P. (Pierre), Furlotte, N. A. (Nicholas A.), Huber, K. E. (Karen E.), Kleinman, A. (Aaron), Litterman, N. K. (Nadia K.), McIntyre, M. H. (Matthew H.), Mountain, J. L. (Joanna L.), Noblin, E. S. (Elizabeth S.), Northover, C. A. (Carrie A. M.), Pitts, S. J. (Steven J.), Sathirapongsasuti, J. F. (J. Fah), Sazonova, O. V. (Olga V.), Shelton, J. F. (Janie F.), Shringarpure, S. (Suyash), Tian, C. (Chao), Vacic, V. (Vladimir), and Wilson, C. H. (Catherine H.)
- Abstract
Uterine leiomyomata (UL) are the most common neoplasms of the female reproductive tract and primary cause for hysterectomy, leading to considerable morbidity and high economic burden. Here we conduct a GWAS meta-analysis in 35,474 cases and 267,505 female controls of European ancestry, identifying eight novel genome-wide significant (P < 5 × 10−8) loci, in addition to confirming 21 previously reported loci, including multiple independent signals at 10 loci. Phenotypic stratification of UL by heavy menstrual bleeding in 3409 cases and 199,171 female controls reveals genome-wide significant associations at three of the 29 UL loci: 5p15.33 (TERT), 5q35.2 (FGFR4) and 11q22.3 (ATM). Four loci identified in the meta-analysis are also associated with endometriosis risk; an epidemiological meta-analysis across 402,868 women suggests at least a doubling of risk for UL diagnosis among those with a history of endometriosis. These findings increase our understanding of genetic contribution and biology underlying UL development, and suggest overlapping genetic origins with endometriosis.
- Published
- 2019
18. An atlas of genetic influences on osteoporosis in humans and mice
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Morris, JA, Kemp, JP, Youlten, SE, Laurent, L, Logan, JG, Chai, RC, Vulpescu, NA, Forgetta, V, Kleinman, A, Mohanty, ST, Sergio, CM, Quinn, J, Nguyen-Yamamoto, L, Luco, A-L, Vijay, J, Simon, M-M, Pramatarova, A, Medina-Gomez, C, Trajanoska, K, Ghirardello, EJ, Butterfield, NC, Curry, KF, Leitch, VD, Sparkes, PC, Adoum, A-T, Mannan, NS, Komla-Ebri, DSK, Pollard, AS, Dewhurst, HF, Hassall, TAD, Beltejar, M-JG, Agee, M, Alipanahi, B, Auton, A, Bell, RK, Bryc, K, Elson, SL, Fontanillas, P, Furlotte, NA, McCreight, JC, Huber, KE, Litterman, NK, McIntyre, MH, Mountain, JL, Noblin, ES, Northover, CAM, Pitts, SJ, Sathirapongsasuti, JF, Sazonova, OV, Shelton, JF, Shringarpure, S, Tian, C, Tung, JY, Vacic, V, Wilson, CH, Adams, DJ, Vaillancourt, SM, Kaptoge, S, Baldock, P, Cooper, C, Reeve, J, Ntzani, EE, Evangelou, E, Ohlsson, C, Karasik, D, Rivadeneira, F, Kiel, DP, Tobias, JH, Gregson, CL, Harvey, NC, Grundberg, E, Goltzman, D, Lelliott, CJ, Hinds, DA, Ackert-Bicknell, CL, Hsu, Y-H, Maurano, MT, Croucher, PI, Williams, GR, Bassett, JHD, Evans, DM, Richards, JB, Wellcome Trust, and Internal Medicine
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CHROMATIN ,Male ,Bone density ,Osteoporosis ,Genome-wide association study ,Fractures, Bone ,Mice ,0302 clinical medicine ,Bone Density ,CALCANEUS ,11 Medical and Health Sciences ,Bone mineral ,Genetics & Heredity ,Mice, Knockout ,0303 health sciences ,HERITABILITY ,Middle Aged ,Phenotype ,3. Good health ,Female ,BONE-MINERAL DENSITY ,Life Sciences & Biomedicine ,Adult ,medicine.medical_specialty ,Single-nucleotide polymorphism ,Biology ,Polymorphism, Single Nucleotide ,23andMe Research Team ,Article ,03 medical and health sciences ,FRACTURES ,Internal medicine ,Genetics ,medicine ,Animals ,Humans ,Genetic Predisposition to Disease ,GENOME-WIDE ASSOCIATION ,METAANALYSIS ,HEEL ,030304 developmental biology ,Aged ,Science & Technology ,HIP ,Bone fracture ,Odds ratio ,06 Biological Sciences ,medicine.disease ,Endocrinology ,QUANTITATIVE ULTRASOUND ,030217 neurology & neurosurgery ,Genome-Wide Association Study ,Developmental Biology - Abstract
Osteoporosis is a common debilitating chronic disease diagnosed primarily using bone mineral density (BMD). We undertook a comprehensive assessment of human genetic determinants of bone density in 426,824 individuals, identifying a total of 518 genome-wide significant loci, (301 novel), explaining 20% of the total variance in BMD—as estimated by heel quantitative ultrasound (eBMD). Next, meta-analysis identified 13 bone fracture loci in ~1.2M individuals, which were also associated with BMD. We then identified target genes from cell-specific genomic landscape features, including chromatin conformation and accessible chromatin sites, that were strongly enriched for genes known to influence bone density and strength (maximum odds ratio = 58, P = 10-75). We next performed rapid throughput skeletal phenotyping of 126 knockout mice lacking eBMD Target Genes and showed that these mice had an increased frequency of abnormal skeletal phenotypes compared to 526 unselected lines (P < 0.0001). In-depth analysis of one such Target Gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This comprehensive human and murine genetic atlas provides empirical evidence testing how to link associated SNPs to causal genes, offers new insights into osteoporosis pathophysiology and highlights opportunities for drug development.
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- 2018
19. MA19.09 AI-Based Early Detection and Subtyping of Non-Small Cell Lung Cancer from Blood Samples Using Orphan Noncoding RNAs
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Karimzadeh, M., Cavazos, T.B., Wang, J., Multhaup, M., Fang, Y., Ku, J., Wang, J., Zhao, X., Wang, K., Hanna, R., Afolabi, O.I., Huang, A., Corti, D., Garcia, K., Joshi, T., Nguyen, D., Kong, Y., Arensdorf, P., Chau, K.H., Hartwig, A., Li, H., Patel, S., Goodarzi, H., Fish, L., Hormozdiari, F., and Alipanahi, B.
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- 2023
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20. Channel estimation for time-hopping pulse position modulation ultra-wideband communication systems
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Alizad, A.R., primary, Alipanahi, B., additional, Shiva, M., additional, Jamali, S.H., additional, and Nader-Esfahani, S., additional
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- 2008
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21. A Blind Channel Estimation Technique for TH-PPM UWB Systems
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Alizad, A., primary, Alipanahi, B., additional, Ghasemi, A., additional, Shiva, M., additional, Jamali, S., additional, and Nader-Esfahani, S., additional
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- 2006
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22. A New Approach for UWB Channel Estimation.
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Alizad, A.R., Alipanahi, B., Ghasemi, A., Shiva, M., Jamali, S.H., and Nader-Esfahani, S.
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- 2005
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23. A novel soft handoff algorithm for fair network resources distribution.
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AliPanahi, B. and Karzand, M.
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- 2005
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24. Genome-Wide Mutation Landscape in Autism Spectrum Disorder
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Ryan Yuen, Merico, D., Cao, H., Alipanahi, B., Thiruvahindrapuram, B., Pellecchia, G., Tong, X., Cao, D., Sun, Y., Li, M., Chen, W., Jin, X., Nalpathamkalam, T., Bookman, M., Bingham, J., Gross, S., Loy, D., Walker, S., Howe, J. L., Pletcher, M., Marshall, C. R., Szatmari, P., Glazer, D., Frey, B. J., Ring, R. H., and Scherer, S. W.
25. Genome-wide characteristics of de novo mutations in autism
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Ryan Yuen, Merico D, Cao H, Pellecchia G, Alipanahi B, Thiruvahindrapuram B, Tong X, Sun Y, Cao D, Zhang T, Wu X, Jin X, Zhou Z, Liu X, Nalpathamkalam T, Walker S, Jl, Howe, Wang Z, Jr, Macdonald, and Sw, Scherer
26. A novel soft handoff algorithm for fair network resources distribution
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AliPanahi, B., primary and Karzand, M., additional
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27. A New Approach for UWB Channel Estimation
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Alizad, A.R., primary, Alipanahi, B., additional, Ghasemi, A., additional, Shiva, M., additional, Jamali, S.H., additional, and Nader-Esfahani, S., additional
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28. Cancer treatment monitoring using cell-free DNA fragmentomes.
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van 't Erve I, Alipanahi B, Lumbard K, Skidmore ZL, Rinaldi L, Millberg LK, Carey J, Chesnick B, Cristiano S, Portwood C, Wu T, Peters E, Bolhuis K, Punt CJA, Tom J, Bach PB, Dracopoli NC, Meijer GA, Scharpf RB, Velculescu VE, Fijneman RJA, and Leal A
- Subjects
- Humans, Female, Male, Middle Aged, Mutation, Aged, Whole Genome Sequencing methods, Prognosis, Neoplasms genetics, Neoplasms therapy, Neoplasms mortality, Circulating Tumor DNA genetics, Circulating Tumor DNA blood, Biomarkers, Tumor genetics, Cell-Free Nucleic Acids genetics, Cell-Free Nucleic Acids blood, Colorectal Neoplasms genetics, Colorectal Neoplasms mortality, Lung Neoplasms genetics, Lung Neoplasms mortality
- Abstract
Circulating cell-free DNA (cfDNA) assays for monitoring individuals with cancer typically rely on prior identification of tumor-specific mutations. Here, we develop a tumor-independent and mutation-independent approach (DELFI-tumor fraction, DELFI-TF) using low-coverage whole genome sequencing to determine the cfDNA tumor fraction and validate the method in two independent cohorts of patients with colorectal or lung cancer. DELFI-TF scores strongly correlate with circulating tumor DNA levels (ctDNA) (r = 0.90, p < 0.0001, Pearson correlation) even in cases where mutations are undetectable. DELFI-TF scores prior to therapy initiation are associated with clinical response and are independent predictors of overall survival (HR = 9.84, 95% CI = 1.72-56.10, p < 0.0001). Patients with lower DELFI-TF scores during treatment have longer overall survival (62.8 vs 29.1 months, HR = 3.12, 95% CI 1.62-6.00, p < 0.001) and the approach predicts clinical outcomes more accurately than imaging. These results demonstrate the potential of using cfDNA fragmentomes to estimate tumor burden in cfDNA for treatment response monitoring and clinical outcome prediction., (© 2024. The Author(s).)
- Published
- 2024
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29. Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models.
- Author
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Cosentino J, Behsaz B, Alipanahi B, McCaw ZR, Hill D, Schwantes-An TH, Lai D, Carroll A, Hobbs BD, Cho MH, McLean CY, and Hormozdiari F
- Subjects
- Humans, Genome-Wide Association Study methods, Genetic Loci, Polymorphism, Single Nucleotide genetics, Deep Learning, Pulmonary Disease, Chronic Obstructive genetics
- Abstract
Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case-control status from high-dimensional raw spirograms and use the model's predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)
- Published
- 2023
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30. DeepNull models non-linear covariate effects to improve phenotypic prediction and association power.
- Author
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McCaw ZR, Colthurst T, Yun T, Furlotte NA, Carroll A, Alipanahi B, McLean CY, and Hormozdiari F
- Subjects
- Computer Simulation, Linear Models, Research Design, Genome-Wide Association Study methods, Phenotype
- Abstract
Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average)., (© 2022. The Author(s).)
- Published
- 2022
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31. Succinct dynamic de Bruijn graphs.
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Alipanahi B, Kuhnle A, Puglisi SJ, Salmela L, and Boucher C
- Subjects
- High-Throughput Nucleotide Sequencing, Research Design, Sequence Analysis, DNA, Algorithms, Software
- Abstract
Motivation: The de Bruijn graph is one of the fundamental data structures for analysis of high throughput sequencing data. In order to be applicable to population-scale studies, it is essential to build and store the graph in a space- and time-efficient manner. In addition, due to the ever-changing nature of population studies, it has become essential to update the graph after construction, e.g. add and remove nodes and edges. Although there has been substantial effort on making the construction and storage of the graph efficient, there is a limited amount of work in building the graph in an efficient and mutable manner. Hence, most space efficient data structures require complete reconstruction of the graph in order to add or remove edges or nodes., Results: In this article, we present DynamicBOSS, a succinct representation of the de Bruijn graph that allows for an unlimited number of additions and deletions of nodes and edges. We compare our method with other competing methods and demonstrate that DynamicBOSS is the only method that supports both addition and deletion and is applicable to very large samples (e.g. greater than 15 billion k-mers). Competing dynamic methods, e.g. FDBG cannot be constructed on large scale datasets, or cannot support both addition and deletion, e.g. BiFrost., Availability and Implementation: DynamicBOSS is publicly available at https://github.com/baharpan/dynboss., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
- Published
- 2021
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32. Buffering updates enables efficient dynamic de Bruijn graphs.
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Alanko J, Alipanahi B, Settle J, Boucher C, and Gagie T
- Abstract
Motivation: The de Bruijn graph has become a ubiquitous graph model for biological data ever since its initial introduction in the late 1990s. It has been used for a variety of purposes including genome assembly (Zerbino and Birney, 2008; Bankevich et al., 2012; Peng et al., 2012), variant detection (Alipanahi et al., 2020b; Iqbal et al., 2012), and storage of assembled genomes (Chikhi et al., 2016). For this reason, there have been over a dozen methods for building and representing the de Bruijn graph and its variants in a space and time efficient manner., Results: With the exception of a few data structures (Muggli et al., 2019; Holley and Melsted, 2020; Crawford et al.,2018), compressed and compact de Bruijn graphs do not allow for the graph to be efficiently updated, meaning that data can be added or deleted. The most recent compressed dynamic de Bruijn graph (Alipanahi et al., 2020a), relies on dynamic bit vectors which are slow in theory and practice. To address this shortcoming, we present a compressed dynamic de Bruijn graph that removes the necessity of dynamic bit vectors by buffering data that should be added or removed from the graph. We implement our method, which we refer to as BufBOSS, and compare its performance to Bifrost, DynamicBOSS, and FDBG. Our experiments demonstrate that BufBOSS achieves attractive trade-offs compared to other tools in terms of time, memory and disk, and has the best deletion performance by an order of magnitude., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)
- Published
- 2021
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33. Genomewide Association Studies of LRRK2 Modifiers of Parkinson's Disease.
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Lai D, Alipanahi B, Fontanillas P, Schwantes-An TH, Aasly J, Alcalay RN, Beecham GW, Berg D, Bressman S, Brice A, Brockman K, Clark L, Cookson M, Das S, Van Deerlin V, Follett J, Farrer MJ, Trinh J, Gasser T, Goldwurm S, Gustavsson E, Klein C, Lang AE, Langston JW, Latourelle J, Lynch T, Marder K, Marras C, Martin ER, McLean CY, Mejia-Santana H, Molho E, Myers RH, Nuytemans K, Ozelius L, Payami H, Raymond D, Rogaeva E, Rogers MP, Ross OA, Samii A, Saunders-Pullman R, Schüle B, Schulte C, Scott WK, Tanner C, Tolosa E, Tomkins JE, Vilas D, Trojanowski JQ, Uitti R, Vance JM, Visanji NP, Wszolek ZK, Zabetian CP, Mirelman A, Giladi N, Orr Urtreger A, Cannon P, Fiske B, and Foroud T
- Subjects
- Aged, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Mutation, Penetrance, Leucine-Rich Repeat Serine-Threonine Protein Kinase-2 genetics, Parkinson Disease genetics
- Abstract
Objective: The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age-at-onset of Parkinson's disease., Methods: We performed the first genomewide association study of penetrance and age-at-onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non-cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age-at-onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genomewide association study of Parkinson's disease was able to explain variability in penetrance and age-at-onset in LRRK2 mutation carriers., Results: A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; p value = 2.5E-08, beta = 1.27, SE = 0.23, risk allele: C) met genomewide significance for the penetrance model. Co-immunoprecipitation analyses of LRRK2 and CORO1C supported an interaction between these 2 proteins. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: p value = 1.1E-07; age-at-onset top variant: p value = 9.3E-07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age-at-onset., Interpretation: This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations. ANN NEUROL 2021;90:82-94., (© 2021 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)
- Published
- 2021
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34. Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.
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Alipanahi B, Hormozdiari F, Behsaz B, Cosentino J, McCaw ZR, Schorsch E, Sculley D, Dorfman EH, Foster PJ, Peng LH, Phene S, Hammel N, Carroll A, Khawaja AP, and McLean CY
- Subjects
- Datasets as Topic, Fluorescein Angiography, Genome-Wide Association Study, Glaucoma, Open-Angle diagnostic imaging, Humans, Models, Anatomic, Optic Disk diagnostic imaging, Phenotype, Risk Assessment, Machine Learning, Optic Disk anatomy & histology
- Abstract
Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10
-8 ) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR: select loci near genes involved in neuronal and synaptic biology or harboring variants are known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)- Published
- 2021
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35. Metagenome SNP calling via read-colored de Bruijn graphs.
- Author
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Alipanahi B, Muggli MD, Jundi M, Noyes NR, and Boucher C
- Subjects
- Algorithms, Metagenomics, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Metagenome, Software
- Abstract
Motivation: Metagenomics refers to the study of complex samples containing of genetic contents of multiple individual organisms and, thus, has been used to elucidate the microbiome and resistome of a complex sample. The microbiome refers to all microbial organisms in a sample, and the resistome refers to all of the antimicrobial resistance (AMR) genes in pathogenic and non-pathogenic bacteria. Single-nucleotide polymorphisms (SNPs) can be effectively used to 'fingerprint' specific organisms and genes within the microbiome and resistome and trace their movement across various samples. However, to effectively use these SNPs for this traceability, a scalable and accurate metagenomics SNP caller is needed. Moreover, such an SNP caller should not be reliant on reference genomes since 95% of microbial species is unculturable, making the determination of a reference genome extremely challenging. In this article, we address this need., Results: We present LueVari, a reference-free SNP caller based on the read-colored de Bruijn graph, an extension of the traditional de Bruijn graph that allows repeated regions longer than the k-mer length and shorter than the read length to be identified unambiguously. LueVari is able to identify SNPs in both AMR genes and chromosomal DNA from shotgun metagenomics data with reliable sensitivity (between 91% and 99%) and precision (between 71% and 99%) as the performance of competing methods varies widely. Furthermore, we show that LueVari constructs sequences containing the variation, which span up to 97.8% of genes in datasets, which can be helpful in detecting distinct AMR genes in large metagenomic datasets., Availability and Implementation: Code and datasets are publicly available at https://github.com/baharpan/cosmo/tree/LueVari., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
- Published
- 2021
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- View/download PDF
36. Author Correction: The effect of LRRK2 loss-of-function variants in humans.
- Author
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Whiffin N, Armean IM, Kleinman A, Marshall JL, Minikel EV, Goodrich JK, Quaife NM, Cole JB, Wang Q, Karczewski KJ, Cummings BB, Francioli L, Laricchia K, Guan A, Alipanahi B, Morrison P, Baptista MAS, Merchant KM, Ware JS, Havulinna AS, Iliadou B, Lee JJ, Nadkarni GN, Whiteman C, Daly M, Esko T, Hultman C, Loos RJF, Milani L, Palotie A, Pato C, Pato M, Saleheen D, Sullivan PF, Alföldi J, Cannon P, and MacArthur DG
- Published
- 2021
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- View/download PDF
37. Disease risk scores for skin cancers.
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Fontanillas P, Alipanahi B, Furlotte NA, Johnson M, Wilson CH, Pitts SJ, Gentleman R, and Auton A
- Subjects
- Adult, Aged, Aged, 80 and over, Carcinoma, Basal Cell etiology, Carcinoma, Basal Cell pathology, Carcinoma, Squamous Cell etiology, Cross-Sectional Studies, Datasets as Topic, Direct-To-Consumer Screening and Testing statistics & numerical data, Female, Follow-Up Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Incidence, Longitudinal Studies, Male, Medical History Taking, Melanoma etiology, Melanoma pathology, Middle Aged, Neoplasm Recurrence, Local etiology, Neoplasm Recurrence, Local pathology, Odds Ratio, Prospective Studies, Risk Assessment methods, Risk Factors, Skin pathology, Skin radiation effects, Skin Neoplasms etiology, Skin Neoplasms pathology, Surveys and Questionnaires statistics & numerical data, Ultraviolet Rays adverse effects, White People genetics, Carcinoma, Basal Cell epidemiology, Carcinoma, Squamous Cell epidemiology, Melanoma epidemiology, Models, Statistical, Neoplasm Recurrence, Local epidemiology, Skin Neoplasms epidemiology
- Abstract
We trained and validated risk prediction models for the three major types of skin cancer- basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma-on a cross-sectional and longitudinal dataset of 210,000 consented research participants who responded to an online survey covering personal and family history of skin cancer, skin susceptibility, and UV exposure. We developed a primary disease risk score (DRS) that combined all 32 identified genetic and non-genetic risk factors. Top percentile DRS was associated with an up to 13-fold increase (odds ratio per standard deviation increase >2.5) in the risk of developing skin cancer relative to the middle DRS percentile. To derive lifetime risk trajectories for the three skin cancers, we developed a second and age independent disease score, called DRSA. Using incident cases, we demonstrated that DRSA could be used in early detection programs for identifying high risk asymptotic individuals, and predicting when they are likely to develop skin cancer. High DRSA scores were not only associated with earlier disease diagnosis (by up to 14 years), but also with more severe and recurrent forms of skin cancer.
- Published
- 2021
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38. The effect of LRRK2 loss-of-function variants in humans.
- Author
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Whiffin N, Armean IM, Kleinman A, Marshall JL, Minikel EV, Goodrich JK, Quaife NM, Cole JB, Wang Q, Karczewski KJ, Cummings BB, Francioli L, Laricchia K, Guan A, Alipanahi B, Morrison P, Baptista MAS, Merchant KM, Ware JS, Havulinna AS, Iliadou B, Lee JJ, Nadkarni GN, Whiteman C, Daly M, Esko T, Hultman C, Loos RJF, Milani L, Palotie A, Pato C, Pato M, Saleheen D, Sullivan PF, Alföldi J, Cannon P, and MacArthur DG
- Subjects
- Adult, Aged, Aged, 80 and over, Biological Specimen Banks, Cell Line, Embryonic Stem Cells metabolism, Female, Gain of Function Mutation genetics, Heterozygote, Humans, Leucine-Rich Repeat Serine-Threonine Protein Kinase-2 antagonists & inhibitors, Leucine-Rich Repeat Serine-Threonine Protein Kinase-2 metabolism, Longevity genetics, Lymphocytes metabolism, Male, Middle Aged, Myocytes, Cardiac metabolism, Parkinson Disease drug therapy, Parkinson Disease genetics, Phenotype, Leucine-Rich Repeat Serine-Threonine Protein Kinase-2 genetics, Loss of Function Mutation genetics
- Abstract
Human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants) provide natural in vivo models of human gene inactivation and can be valuable indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes
1,2 . Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinson's disease3,4 , suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. While preclinical studies in model organisms have raised some on-target toxicity concerns5-8 , the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here, we systematically analyze pLoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)9 , 49,960 exome-sequenced individuals from the UK Biobank and over 4 million participants in the 23andMe genotyped dataset. After stringent variant curation, we identify 1,455 individuals with high-confidence pLoF variants in LRRK2. Experimental validation of three variants, combined with previous work10 , confirmed reduced protein levels in 82.5% of our cohort. We show that heterozygous pLoF variants in LRRK2 reduce LRRK2 protein levels but that these are not strongly associated with any specific phenotype or disease state. Our results demonstrate the value of large-scale genomic databases and phenotyping of human loss-of-function carriers for target validation in drug discovery.- Published
- 2020
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39. Aligning optical maps to de Bruijn graphs.
- Author
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Mukherjee K, Alipanahi B, Kahveci T, Salmela L, and Boucher C
- Subjects
- Genome, Restriction Mapping, Sequence Analysis, DNA, Algorithms, Software
- Abstract
Motivation: Optical maps are high-resolution restriction maps (Rmaps) that give a unique numeric representation to a genome. Used in concert with sequence reads, they provide a useful tool for genome assembly and for discovering structural variations and rearrangements. Although they have been a regular feature of modern genome assembly projects, optical maps have been mainly used in post-processing step and not in the genome assembly process itself. Several methods have been proposed for pairwise alignment of single molecule optical maps-called Rmaps, or for aligning optical maps to assembled reads. However, the problem of aligning an Rmap to a graph representing the sequence data of the same genome has not been studied before. Such an alignment provides a mapping between two sets of data: optical maps and sequence data which will facilitate the usage of optical maps in the sequence assembly step itself., Results: We define the problem of aligning an Rmap to a de Bruijn graph and present the first algorithm for solving this problem which is based on a seed-and-extend approach. We demonstrate that our method is capable of aligning 73% of Rmaps generated from the Escherichia coli genome to the de Bruijn graph constructed from short reads generated from the same genome. We validate the alignments and show that our method achieves an accuracy of 99.6%. We also show that our method scales to larger genomes. In particular, we show that 76% of Rmaps can be aligned to the de Bruijn graph in the case of human data., Availability and Implementation: The software for aligning optical maps to de Bruijn graph, omGraph is written in C++ and is publicly available under GNU General Public License at https://github.com/kingufl/omGraph., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
- Published
- 2019
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40. Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences.
- Author
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Lakin SM, Kuhnle A, Alipanahi B, Noyes NR, Dean C, Muggli M, Raymond R, Abdo Z, Prosperi M, Belk KE, Morley PS, and Boucher C
- Subjects
- Reproducibility of Results, Data Mining methods, Databases, Genetic, Drug Resistance, Microbial genetics, High-Throughput Nucleotide Sequencing, Machine Learning, Markov Chains, Metagenomics, Sequence Analysis, DNA
- Abstract
The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to determine the genes present in the sample. Mapping sequence reads to known genes is traditionally accomplished using alignment. Alignment methods have high specificity but are limited in their ability to detect sequences that are divergent from the reference database, which can result in a substantial false negative rate. We address this shortcoming through the creation of Meta-MARC, which enables detection of diverse resistance sequences using hierarchical, DNA-based Hidden Markov Models. We first describe Meta-MARC and then demonstrate its efficacy on simulated and functional metagenomic datasets. Meta-MARC has higher sensitivity relative to competing methods. This sensitivity allows for detection of sequences that are divergent from known antimicrobial resistance genes. This functionality is imperative to expanding existing antimicrobial gene databases., Competing Interests: Competing interestsThe authors declare no competing interests.
- Published
- 2019
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41. Building large updatable colored de Bruijn graphs via merging.
- Author
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Muggli MD, Alipanahi B, and Boucher C
- Subjects
- Genomics, Metagenome, Algorithms, Sequence Analysis, DNA, Software
- Abstract
Motivation: There exist several large genomic and metagenomic data collection efforts, including GenomeTrakr and MetaSub, which are routinely updated with new data. To analyze such datasets, memory-efficient methods to construct and store the colored de Bruijn graph were developed. Yet, a problem that has not been considered is constructing the colored de Bruijn graph in a scalable manner that allows new data to be added without reconstruction. This problem is important for large public datasets as scalability is needed but also the ability to update the construction is also needed., Results: We create a method for constructing the colored de Bruijn graph for large datasets that is based on partitioning the data into smaller datasets, building the colored de Bruijn graph using a FM-index based representation, and succinctly merging these representations to build a single graph. The last step, merging succinctly, is the algorithmic challenge which we solve in this article. We refer to the resulting method as VariMerge. This construction method also allows the graph to be updated with new data. We validate our approach and show it produces a three-fold reduction in working space when constructing a colored de Bruijn graph for 8000 strains. Lastly, we compare VariMerge to other competing methods-including Vari, Rainbowfish, Mantis, Bloom Filter Trie, the method of Almodaresi et al. and Multi-BRWT-and illustrate that VariMerge is the only method that is capable of building the colored de Bruijn graph for 16 000 strains in a manner that allows it to be updated. Competing methods either did not scale to this large of a dataset or do not allow for additions without reconstruction., Availability and Implementation: VariMerge is available at https://github.com/cosmo-team/cosmo/tree/VARI-merge under GPLv3 license., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)
- Published
- 2019
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42. The Parkinson's phenome-traits associated with Parkinson's disease in a broadly phenotyped cohort.
- Author
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Heilbron K, Noyce AJ, Fontanillas P, Alipanahi B, Nalls MA, and Cannon P
- Abstract
In order to systematically describe the Parkinson's disease phenome, we performed a series of 832 cross-sectional case-control analyses in a large database. Responses to 832 online survey-based phenotypes including diseases, medications, and environmental exposures were analyzed in 23andMe research participants. For each phenotype, survey respondents were used to construct a cohort of Parkinson's disease cases and age-matched and sex-matched controls, and an association test was performed using logistic regression. Cohorts included a median of 3899 Parkinson's disease cases and 49,808 controls, all of European ancestry. Highly correlated phenotypes were removed and the novelty of each significant association was systematically assessed (assigned to one of four categories: known, likely, unclear, or novel). Parkinson's disease diagnosis was associated with 122 phenotypes. We replicated 27 known associations and found 23 associations with a strong a priori link to a known association. We discovered 42 associations that have not previously been reported. Migraine, obsessive-compulsive disorder, and seasonal allergies were associated with Parkinson's disease and tend to occur decades before the typical age of diagnosis for Parkinson's disease. The phenotypes that currently comprise the Parkinson's disease phenome have mostly been explored in relatively small purpose-built studies. Using a single large dataset, we have successfully reproduced many of these established associations and have extended the Parkinson's disease phenome by discovering novel associations. Our work paves the way for studies of these associated phenotypes that explore shared molecular mechanisms with Parkinson's disease, infer causal relationships, and improve our ability to identify individuals at high-risk of Parkinson's disease., Competing Interests: Drs. Heilbron, Fontanillas, Alipanahi, Cannon, and members of the 23andMe Research Team are employees of and have stock, stock options, or both, in 23andMe. Dr. Nalls is supported by a consulting contract between Data Tecnica International and the National Institute on Aging (project number: Z01-AG000949-02). The remaining authors declare no competing interests.
- Published
- 2019
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43. Correspondence between cerebral glucose metabolism and BOLD reveals relative power and cost in human brain.
- Author
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Shokri-Kojori E, Tomasi D, Alipanahi B, Wiers CE, Wang GJ, and Volkow ND
- Subjects
- Adult, Biomarkers metabolism, Brain pathology, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging methods, Male, Middle Aged, Multimodal Imaging, Nerve Net physiology, Neurons metabolism, Positron-Emission Tomography, Young Adult, Brain physiology, Brain Chemistry physiology, Brain Mapping, Glucose metabolism
- Abstract
The correspondence between cerebral glucose metabolism (indexing energy utilization) and synchronous fluctuations in blood oxygenation (indexing neuronal activity) is relevant for neuronal specialization and is affected by brain disorders. Here, we define novel measures of relative power (rPWR, extent of concurrent energy utilization and activity) and relative cost (rCST, extent that energy utilization exceeds activity), derived from FDG-PET and fMRI. We show that resting-state networks have distinct energetic signatures and that brain could be classified into major bilateral segments based on rPWR and rCST. While medial-visual and default-mode networks have the highest rPWR, frontoparietal networks have the highest rCST. rPWR and rCST estimates are generalizable to other indexes of energy supply and neuronal activity, and are sensitive to neurocognitive effects of acute and chronic alcohol exposure. rPWR and rCST are informative metrics for characterizing brain pathology and alternative energy use, and may provide new multimodal biomarkers of neuropsychiatric disorders.
- Published
- 2019
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44. Does conservation account for splicing patterns?
- Author
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Wainberg M, Alipanahi B, and Frey B
- Subjects
- Animals, Area Under Curve, Brain metabolism, Exons, Gene Expression Regulation, Humans, Introns, Organ Specificity genetics, RNA Splice Sites, Regulatory Sequences, Nucleic Acid, Alternative Splicing, Evolution, Molecular, Models, Biological
- Abstract
Background: Alternative mRNA splicing is critical to proteomic diversity and tissue and species differentiation. Exclusion of cassette exons, also called exon skipping, is the most common type of alternative splicing in mammals., Results: We present a computational model that predicts absolute (though not tissue-differential) percent-spliced-in of cassette exons more accurately than previous models, despite not using any 'hand-crafted' biological features such as motif counts. We achieve nearly identical performance using only the conservation score (mammalian phastCons) of each splice junction normalized by average conservation over 100 bp of the corresponding flanking intron, demonstrating that conservation is an unexpectedly powerful indicator of alternative splicing patterns. Using this method, we provide evidence that intronic splicing regulation occurs predominantly within 100 bp of the alternative splice sites and that conserved elements in this region are, as expected, functioning as splicing regulators. We show that among conserved cassette exons, increased conservation of flanking introns is associated with reduced inclusion. We also propose a new definition of intronic splicing regulatory elements (ISREs) that is independent of conservation, and show that most ISREs do not match known binding sites or splicing factors despite being predictive of percent-spliced-in., Conclusions: These findings suggest that one mechanism for the evolutionary transition from constitutive to alternative splicing is the emergence of cis-acting splicing inhibitors. The association of our ISREs with differences in splicing suggests the existence of novel RNA-binding proteins and/or novel splicing roles for known RNA-binding proteins.
- Published
- 2016
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45. Genome-wide characteristics of de novo mutations in autism.
- Author
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Yuen RK, Merico D, Cao H, Pellecchia G, Alipanahi B, Thiruvahindrapuram B, Tong X, Sun Y, Cao D, Zhang T, Wu X, Jin X, Zhou Z, Liu X, Nalpathamkalam T, Walker S, Howe JL, Wang Z, MacDonald JR, Chan A, D'Abate L, Deneault E, Siu MT, Tammimies K, Uddin M, Zarrei M, Wang M, Li Y, Wang J, Wang J, Yang H, Bookman M, Bingham J, Gross SS, Loy D, Pletcher M, Marshall CR, Anagnostou E, Zwaigenbaum L, Weksberg R, Fernandez BA, Roberts W, Szatmari P, Glazer D, Frey BJ, Ring RH, Xu X, and Scherer SW
- Abstract
De novo mutations (DNMs) are important in Autism Spectrum Disorder (ASD), but so far analyses have mainly been on the ~1.5% of the genome encoding genes. Here, we performed whole genome sequencing (WGS) of 200 ASD parent-child trios and characterized germline and somatic DNMs. We confirmed that the majority of germline DNMs (75.6%) originated from the father, and these increased significantly with paternal age only (p=4.2×10
-10 ). However, when clustered DNMs (those within 20kb) were found in ASD, not only did they mostly originate from the mother (p=7.7×10-13 ), but they could also be found adjacent to de novo copy number variations (CNVs) where the mutation rate was significantly elevated (p=2.4×10-24 ). By comparing DNMs detected in controls, we found a significant enrichment of predicted damaging DNMs in ASD cases (p=8.0×10-9 ; OR=1.84), of which 15.6% (p=4.3×10-3 ) and 22.5% (p=7.0×10-5 ) were in the non-coding or genic non-coding, respectively. The non-coding elements most enriched for DNM were untranslated regions of genes, boundaries involved in exon-skipping and DNase I hypersensitive regions. Using microarrays and a novel outlier detection test, we also found aberrant methylation profiles in 2/185 (1.1%) of ASD cases. These same individuals carried independently identified DNMs in the ASD risk- and epigenetic- genes DNMT3A and ADNP. Our data begins to characterize different genome-wide DNMs, and highlight the contribution of non-coding variants, to the etiology of ASD.- Published
- 2016
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46. Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine.
- Author
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Stavropoulos DJ, Merico D, Jobling R, Bowdin S, Monfared N, Thiruvahindrapuram B, Nalpathamkalam T, Pellecchia G, Yuen RKC, Szego MJ, Hayeems RZ, Shaul RZ, Brudno M, Girdea M, Frey B, Alipanahi B, Ahmed S, Babul-Hirji R, Porras RB, Carter MT, Chad L, Chaudhry A, Chitayat D, Doust SJ, Cytrynbaum C, Dupuis L, Ejaz R, Fishman L, Guerin A, Hashemi B, Helal M, Hewson S, Inbar-Feigenberg M, Kannu P, Karp N, Kim R, Kronick J, Liston E, MacDonald H, Mercimek-Mahmutoglu S, Mendoza-Londono R, Nasr E, Nimmo G, Parkinson N, Quercia N, Raiman J, Roifman M, Schulze A, Shugar A, Shuman C, Sinajon P, Siriwardena K, Weksberg R, Yoon G, Carew C, Erickson R, Leach RA, Klein R, Ray PN, Meyn MS, Scherer SW, Cohn RD, and Marshall CR
- Abstract
The standard of care for first-tier clinical investigation of the etiology of congenital malformations and neurodevelopmental disorders is chromosome microarray analysis (CMA) for copy number variations (CNVs), often followed by gene(s)-specific sequencing searching for smaller insertion-deletions (indels) and single nucleotide variant (SNV) mutations. Whole genome sequencing (WGS) has the potential to capture all classes of genetic variation in one experiment; however, the diagnostic yield for mutation detection of WGS compared to CMA, and other tests, needs to be established. In a prospective study we utilized WGS and comprehensive medical annotation to assess 100 patients referred to a paediatric genetics service and compared the diagnostic yield versus standard genetic testing. WGS identified genetic variants meeting clinical diagnostic criteria in 34% of cases, representing a 4-fold increase in diagnostic rate over CMA (8%) (p-value = 1.42e-05) alone and >2-fold increase in CMA plus targeted gene sequencing (13%) (p-value = 0.0009). WGS identified all rare clinically significant CNVs that were detected by CMA. In 26 patients, WGS revealed indel and missense mutations presenting in a dominant (63%) or a recessive (37%) manner. We found four subjects with mutations in at least two genes associated with distinct genetic disorders, including two cases harboring a pathogenic CNV and SNV. When considering medically actionable secondary findings in addition to primary WGS findings, 38% of patients would benefit from genetic counseling. Clinical implementation of WGS as a primary test will provide a higher diagnostic yield than conventional genetic testing and potentially reduce the time required to reach a genetic diagnosis., Competing Interests: Competing Interests: DM RJ, NM, BT, TN, GP, RKCY, MS, RH, RZS, MB, MG, BF, BA, SA, MTC, LC, AC, CC, LD, RE, LF, AG, BH, MH, SH, MIF, PK, NK, RK, JK, EL, HM, SMM, RML, EN, GN, NP, NQ, JR, MR, AS, AS, CS, PS, KS, RW, GY, CC, SWS, RDC, and CRM declare no conflicts of interest. SB, DJS, PNR and MSM are scientific advisors for Gene42 Inc., which provides support services for the free (open source) PhenoTips software. RE and RK are employees of Complete Genomics. RAL was an employee of Complete Genomics at the time of the study and is currently employed by WuXi NextCODE Genomics.
- Published
- 2016
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47. Whole-Genome Sequencing Suggests Schizophrenia Risk Mechanisms in Humans with 22q11.2 Deletion Syndrome.
- Author
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Merico D, Zarrei M, Costain G, Ogura L, Alipanahi B, Gazzellone MJ, Butcher NJ, Thiruvahindrapuram B, Nalpathamkalam T, Chow EW, Andrade DM, Frey BJ, Marshall CR, Scherer SW, and Bassett AS
- Subjects
- Adolescent, Adult, Case-Control Studies, DiGeorge Syndrome genetics, Female, Humans, Male, Middle Aged, RNA, Long Noncoding genetics, RNA-Binding Proteins genetics, Schizophrenia epidemiology, DiGeorge Syndrome complications, Genome, Human, Schizophrenia genetics
- Abstract
Chromosome 22q11.2 microdeletions impart a high but incomplete risk for schizophrenia. Possible mechanisms include genome-wide effects of DGCR8 haploinsufficiency. In a proof-of-principle study to assess the power of this model, we used high-quality, whole-genome sequencing of nine individuals with 22q11.2 deletions and extreme phenotypes (schizophrenia, or no psychotic disorder at age >50 years). The schizophrenia group had a greater burden of rare, damaging variants impacting protein-coding neurofunctional genes, including genes involved in neuron projection (nominal P = 0.02, joint burden of three variant types). Variants in the intact 22q11.2 region were not major contributors. Restricting to genes affected by a DGCR8 mechanism tended to amplify between-group differences. Damaging variants in highly conserved long intergenic noncoding RNA genes also were enriched in the schizophrenia group (nominal P = 0.04). The findings support the 22q11.2 deletion model as a threshold-lowering first hit for schizophrenia risk. If applied to a larger and thus better-powered cohort, this appears to be a promising approach to identify genome-wide rare variants in coding and noncoding sequence that perturb gene networks relevant to idiopathic schizophrenia. Similarly designed studies exploiting genetic models may prove useful to help delineate the genetic architecture of other complex phenotypes., (Copyright © 2015 Merico et al.)
- Published
- 2015
- Full Text
- View/download PDF
48. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.
- Author
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Alipanahi B, Delong A, Weirauch MT, and Frey BJ
- Subjects
- Position-Specific Scoring Matrices, Computational Biology methods, DNA-Binding Proteins chemistry, RNA-Binding Proteins chemistry, Sequence Analysis, Protein methods, Software
- Abstract
Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.
- Published
- 2015
- Full Text
- View/download PDF
49. RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease.
- Author
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Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RK, Hua Y, Gueroussov S, Najafabadi HS, Hughes TR, Morris Q, Barash Y, Krainer AR, Jojic N, Scherer SW, Blencowe BJ, and Frey BJ
- Subjects
- Adaptor Proteins, Signal Transducing genetics, Computer Simulation, DNA genetics, Exons genetics, Genetic Code, Genetic Markers, Genetic Variation, Humans, Introns genetics, Models, Genetic, MutL Protein Homolog 1, Mutation, Missense, Nuclear Proteins genetics, Polymorphism, Single Nucleotide, Quantitative Trait Loci, RNA Splice Sites genetics, RNA-Binding Proteins genetics, Artificial Intelligence, Child Development Disorders, Pervasive genetics, Colorectal Neoplasms, Hereditary Nonpolyposis genetics, Genome-Wide Association Study methods, Molecular Sequence Annotation methods, Muscular Atrophy, Spinal genetics, RNA Splicing genetics
- Abstract
To facilitate precision medicine and whole-genome annotation, we developed a machine-learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of more than 650,000 intronic and exonic variants revealed widespread patterns of mutation-driven aberrant splicing. Intronic disease mutations that are more than 30 nucleotides from any splice site alter splicing nine times as often as common variants, and missense exonic disease mutations that have the least impact on protein function are five times as likely as others to alter splicing. We detected tens of thousands of disease-causing mutations, including those involved in cancers and spinal muscular atrophy. Examination of intronic and exonic variants found using whole-genome sequencing of individuals with autism revealed misspliced genes with neurodevelopmental phenotypes. Our approach provides evidence for causal variants and should enable new discoveries in precision medicine., (Copyright © 2015, American Association for the Advancement of Science.)
- Published
- 2015
- Full Text
- View/download PDF
50. Widespread intron retention in mammals functionally tunes transcriptomes.
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Braunschweig U, Barbosa-Morais NL, Pan Q, Nachman EN, Alipanahi B, Gonatopoulos-Pournatzis T, Frey B, Irimia M, and Blencowe BJ
- Subjects
- 3T3 Cells, Animals, Cell Differentiation genetics, Cell Line, Cell Line, Tumor, Cells, Cultured, Evolution, Molecular, HeLa Cells, Humans, K562 Cells, Mammals classification, Mice, Models, Genetic, Organ Specificity, Principal Component Analysis, RNA Polymerase II metabolism, RNA Precursors genetics, RNA Precursors metabolism, Reverse Transcriptase Polymerase Chain Reaction, Species Specificity, Vertebrates classification, Vertebrates genetics, Alternative Splicing, Introns genetics, Mammals genetics, Transcriptome genetics
- Abstract
Alternative splicing (AS) of precursor RNAs is responsible for greatly expanding the regulatory and functional capacity of eukaryotic genomes. Of the different classes of AS, intron retention (IR) is the least well understood. In plants and unicellular eukaryotes, IR is the most common form of AS, whereas in animals, it is thought to represent the least prevalent form. Using high-coverage poly(A)(+) RNA-seq data, we observe that IR is surprisingly frequent in mammals, affecting transcripts from as many as three-quarters of multiexonic genes. A highly correlated set of cis features comprising an "IR code" reliably discriminates retained from constitutively spliced introns. We show that IR acts widely to reduce the levels of transcripts that are less or not required for the physiology of the cell or tissue type in which they are detected. This "transcriptome tuning" function of IR acts through both nonsense-mediated mRNA decay and nuclear sequestration and turnover of IR transcripts. We further show that IR is linked to a cross-talk mechanism involving localized stalling of RNA polymerase II (Pol II) and reduced availability of spliceosomal components. Collectively, the results implicate a global checkpoint-type mechanism whereby reduced recruitment of splicing components coupled to Pol II pausing underlies widespread IR-mediated suppression of inappropriately expressed transcripts., (© 2014 Braunschweig et al.; Published by Cold Spring Harbor Laboratory Press.)
- Published
- 2014
- Full Text
- View/download PDF
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