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4. Erratum to: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps (Nature Genetics, (2016), 48, 11, (1303-1312), 10.1038/ng.3668)

Author

Iotchkova, V., Huang, J., Morris, J. A., Jain, D., Barbieri, C., Ukovich, Walter, Min, J. L., Chen, L., Astle, W., Cocca, M., Deelen, P., Elding, H., Farmaki, A. -E., Franklin, C. S., Franberg, M., Gaunt, T. R., Hofman, A., Jiang, T., Kleber, M. E., Lachance, G., Luan, J., Malerba, G., Matchan, A., Mead, D., Memari, Y., Ntalla, I., Panoutsopoulou, K., Pazoki, R., Perry, J. R. B., Rivadeneira, F., Sabater-Lleal, M., Sennblad, B., Shin, S. -Y., Southam, L., Traglia, M., van Dijk, F., van Leeuwen, E. M., Zaza, G., Zhang, W., Amin, N., Butterworth, A., Chambers, J. C., Dedoussis, G., Dehghan, A., Franco, O. H., Franke, L., Frontini, M., Gambaro, G., Gasparini, P., Hamsten, A., Isaacs, A., Kooner, J. S., Kooperberg, C., Langenberg, C., Marz, W., Scott, R. A., Swertz, M. A., Toniolo, D., Uitterlinden, A. G., van Duijn, C. M., Watkins, H., Zeggini, E., Maurano, M. T., Timpson, N. J., Reiner, A. P., Auer, P. L., Soranzo, N., Iotchkova, V., Huang, J., Morris, J. A., Jain, D., Barbieri, C., Ukovich, Walter, Min, J. L., Chen, L., Astle, W., Cocca, M., Deelen, P., Elding, H., Farmaki, A. -E., Franklin, C. S., Franberg, M., Gaunt, T. R., Hofman, A., Jiang, T., Kleber, M. E., Lachance, G., Luan, J., Malerba, G., Matchan, A., Mead, D., Memari, Y., Ntalla, I., Panoutsopoulou, K., Pazoki, R., Perry, J. R. B., Rivadeneira, F., Sabater-Lleal, M., Sennblad, B., Shin, S. -Y., Southam, L., Traglia, M., van Dijk, F., van Leeuwen, E. M., Zaza, G., Zhang, W., Amin, N., Butterworth, A., Chambers, J. C., Dedoussis, G., Dehghan, A., Franco, O. H., Franke, L., Frontini, M., Gambaro, G., Gasparini, P., Hamsten, A., Isaacs, A., Kooner, J. S., Kooperberg, C., Langenberg, C., Marz, W., Scott, R. A., Swertz, M. A., Toniolo, D., Uitterlinden, A. G., van Duijn, C. M., Watkins, H., Zeggini, E., Maurano, M. T., Timpson, N. J., Reiner, A. P., Auer, P. L., and Soranzo, N.

Subjects
Author correction
Abstract

In the version of the article published, the surname of author Aaron Isaacs is misspelled as Issacs.

Published
2018

5. Low-frequency variation in TP53 has large effects on head circumference and intracranial volume

Author

Haworth, S, Shapland, CY, Hayward, C, Prins, BP, Felix, JF, Medina-Gomez, C, Rivadeneira, F, Wang, C, Ahluwalia, TS, Vrijheid, M, Guxens, M, Sunyer, J, Tachmazidou, I, Walter, K, Iotchkova, V, Jackson, A, Cleal, L, Huffmann, J, Min, JL, Sass, L, Timmers, PRHJ, Smith, G, Fisher, SE, Wilson, JF, Cole, TJ, Fernandez-Orth, D, Bønnelykke, K, Bisgaard, H, Pennell, CE, Jaddoe, VWV, Dedoussis, G, Timpson, N, Zeggini, E, Vitart, V, St Pourcain, B, consortium, UK10K, and Bhattacharya, S

Abstract

Cranial growth and development is a complex process which affects the closely related traits of head circumference (HC) and intracranial volume (ICV). The underlying genetic influences shaping these traits during the transition from childhood to adulthood are little understood, but might include both age-specific genetic factors and low-frequency genetic variation. Here, we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV + HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood, suggesting a previously unrecognized role of TP53 transcripts in human cranial development.

Published
2019

6. Low-frequency variation in TP53 has large effects on head circumference and intracranial volume

Author

Haworth, S., Shapland, C.Y., Hayward, C., Prins, B.P., Felix, J.F., Medina-Gomez, C., Rivadeneira, F., Wang, C., Ahluwalia, T.S., Vrijheid, M., Guxens, M., Sunyer, J., Tachmazidou, I., Walter, K., Iotchkova, V., Jackson, A., Cleal, L., Huffmann, J., Min, J.L., Sass, L., Timmers, P.R.H.J., Turki, S.A., Anderson, C.A., Anney, R., Antony, D., Artigas, M.S., Ayub, M., Bala, S., Barrett, J.C., Barroso, I., Beales, P., Bentham, J., Bhattacharya, S., Birney, E., Blackwood, D., Bobrow, M., Bochukova, E., Bolton, P.F., Bounds, R., Boustred, C., Breen, G., Calissano, M., Carss, K., Charlton, R., Chatterjee, K., Chen, L., Ciampi, A., Cirak, S., Clapham, P., Clement, G., Coates, G., Cocca, M., Collier, D.A., Cosgrove, C., Cox, T., Craddock, N., Crooks, Lucy, Curran, S., Curtis, D., Daly, A., Danecek, P., Day, I.N.M., Day-Williams, A., Dominiczak, A., Down, T., Du, Y., Dunham, I., Durbin, R., Edkins, S., Ekong, R., Ellis, P., Evans, D.M., Farooqi, I.S., Fitzpatrick, D.R., Flicek, P., Floyd, J., Foley, A.R., Franklin, C.S., Futema, M., Gallagher, L., Gaunt, T.R., Geihs, M., Geschwind, D., Greenwood, C.M.T., Griffin, H., Grozeva, D., Guo, X., Gurling, H., Hart, D., Hendricks, A.E., Holmans, P., Howie, B., Huang, J., Huang, L., Hubbard, T., Humphries, S.E., Hurles, M.E., Hysi, P., and Jackson, D.K.

Abstract

© 2019, The Author(s). Cranial growth and development is a complex process which affects the closely related traits of head circumference (HC) and intracranial volume (ICV). The underlying genetic influences shaping these traits during the transition from childhood to adulthood are little understood, but might include both age-specific genetic factors and low-frequency genetic variation. Here, we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV + HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood, suggesting a previously unrecognized role of TP53 transcripts in human cranial development.

Published
2019

7. PS998 JANUS KINASE AND CYTOKINE RECEPTOR MUTATIONS IN TRANSIENT ABNORMAL MYELOPOIESIS AND MYELOID LEUKEMIA IN CHILDREN WITH TRISOMY 21

Author

Labuhn, M., primary, Perkins, K., additional, Papaemmanuil, E., additional, Matzk, S., additional, Varghese, L., additional, Amstislavskiy, V., additional, Risch, T., additional, Garnett, C., additional, Iotchkova, V., additional, Scheer, C., additional, Yoshida, K., additional, Schwarzer, A., additional, Taub, J., additional, Crispino, J.D., additional, Weiss, M.J., additional, Ito, E., additional, Ogawa, S., additional, Reinhardt, D., additional, Yaspo, M.-L., additional, Campbell, P.J., additional, Roberts, I., additional, Constantinescu, S., additional, Vyas, P., additional, Heckl, D., additional, and Klusmann, J.-H., additional

Published
2019
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8. Erratum to: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

Author

Iotchkova, V. (Valentina), Huang, J. (Jie), Morris, J.A. (John A), Jain, D. (Deepti), Barbieri, C. (Caterina), Walter, K. (Klaudia), Min, J. (Josine), Chen, L. (Lu), Astle, W. (William), Cocca, M. (Massimilian), Deelen, P. (Patrick), Elding, H. (Heather), Farmaki, A.-E. (Aliki-Eleni), Franklin, C.S. (Christopher), Frånberg, M. (Mattias), Gaunt, T.R. (Tom), Hofman, A. (Albert), Jiang, T. (Tao), Kleber, M.E. (Marcus), Lachance, G. (Genevieve), Luan, J., Malerba, G. (Giovanni), Matchan, A. (Angela), Mead, D. (Daniel), Memari, Y. (Yasin), Ntalla, I. (Ioanna), Panoutsopoulou, K. (Kalliope), Pazoki, R. (Raha), Perry, J.R.B. (John R B), Rivadeneira Ramirez, F. (Fernando), Sabater-Lleal, M. (Maria), Sennblad, B. (Bengt), Shin, S.-Y., Southam, L. (Lorraine), Traglia, M. (Michela), Dijk, F. (Freerk) van, Leeuwen, E.M. (Elisa) van, Zaza, G. (Gianluigi), Zhang, W. (Weihua), Amin, N. (Najaf), Butterworth, A. (Adam), Chambers, J.C. (John C), Dedoussis, G.V. (George), Dehghan, A. (Abbas), Franco, O.H. (Oscar), Franke, L. (Lude), Frontini, M. (Mattia), Gambaro, G. (Giovanni), Gasparini, P. (Paolo), Hamsten, A. (Anders), Isaacs, A.J. (Aaron), Kooner, J.S. (Jaspal S.), Kooperberg, C. (Charles), Langenberg, C. (Claudia), März, W. (Winfried), Scott, R.A. (Robert), Swertz, M.A. (Morris A), Toniolo, D. (Daniela), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Watkins, H. (Hugh), Zeggini, E. (Eleftheria), Maurano, M.T. (Matthew T.), Timpson, N.J. (Nicholas), Reiner, A.P. (Alexander P), Auer, P.L. (Paul L), Soranzo, N. (Nicole), Iotchkova, V. (Valentina), Huang, J. (Jie), Morris, J.A. (John A), Jain, D. (Deepti), Barbieri, C. (Caterina), Walter, K. (Klaudia), Min, J. (Josine), Chen, L. (Lu), Astle, W. (William), Cocca, M. (Massimilian), Deelen, P. (Patrick), Elding, H. (Heather), Farmaki, A.-E. (Aliki-Eleni), Franklin, C.S. (Christopher), Frånberg, M. (Mattias), Gaunt, T.R. (Tom), Hofman, A. (Albert), Jiang, T. (Tao), Kleber, M.E. (Marcus), Lachance, G. (Genevieve), Luan, J., Malerba, G. (Giovanni), Matchan, A. (Angela), Mead, D. (Daniel), Memari, Y. (Yasin), Ntalla, I. (Ioanna), Panoutsopoulou, K. (Kalliope), Pazoki, R. (Raha), Perry, J.R.B. (John R B), Rivadeneira Ramirez, F. (Fernando), Sabater-Lleal, M. (Maria), Sennblad, B. (Bengt), Shin, S.-Y., Southam, L. (Lorraine), Traglia, M. (Michela), Dijk, F. (Freerk) van, Leeuwen, E.M. (Elisa) van, Zaza, G. (Gianluigi), Zhang, W. (Weihua), Amin, N. (Najaf), Butterworth, A. (Adam), Chambers, J.C. (John C), Dedoussis, G.V. (George), Dehghan, A. (Abbas), Franco, O.H. (Oscar), Franke, L. (Lude), Frontini, M. (Mattia), Gambaro, G. (Giovanni), Gasparini, P. (Paolo), Hamsten, A. (Anders), Isaacs, A.J. (Aaron), Kooner, J.S. (Jaspal S.), Kooperberg, C. (Charles), Langenberg, C. (Claudia), März, W. (Winfried), Scott, R.A. (Robert), Swertz, M.A. (Morris A), Toniolo, D. (Daniela), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Watkins, H. (Hugh), Zeggini, E. (Eleftheria), Maurano, M.T. (Matthew T.), Timpson, N.J. (Nicholas), Reiner, A.P. (Alexander P), Auer, P.L. (Paul L), and Soranzo, N. (Nicole)

Abstract

In the version of the article published, the surname of author Aaron Isaacs is misspelled as Issacs.

Published
2018
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9. Rare Variant Analysis of Human and Rodent Obesity Genes in Individuals with Severe Childhood Obesity

Author

Hendricks, A.E. Bochukova, E.G. Marenne, G. Keogh, J.M. Atanassova, N. Bounds, R. Wheeler, E. Mistry, V. Henning, E. Körner, A. Muddyman, D. McCarthy, S. Hinney, A. Hebebrand, J. Scott, R.A. Langenberg, C. Wareham, N.J. Surendran, P. Howson, J.M. Butterworth, A.S. Danesh, J. Nordestgaard, Bø.G. Nielsen, S.F. Afzal, S. Papadia, S. Ashford, S. Garg, S. Millhauser, G.L. Palomino, R.I. Kwasniewska, A. Tachmazidou, I. O'Rahilly, S. Zeggini, E. Barroso, I. Farooqi, I.S. Benzeval, M. Burton, J. Buck, N. Jäckle, A. Kumari, M. Laurie, H. Lynn, P. Pudney, S. Rabe, B. Wolke, D. Overvad, K. Tjønneland, A. Clavel-Chapelon, F. Kaaks, R. Boeing, H. Trichopoulou, A. Ferrari, P. Palli, D. Krogha, V. Panico, S. Tuminoa, R. Matullo, G. Boer, J. Van Der Schouw, Y. Weiderpass, E. Quiros, J.R. Sánchez, M.-J. Navarro, C. Moreno-Iribas, C. Arriola, L. Melander, O. Wennberg, P. Key, T.J. Riboli, E. Turki, S.A. Anderson, C.A. Anney, R. Antony, D. Soler Artigas, M. Ayub, M. Bala, S. Barrett, J.C. Beales, P. Bentham, J. Bhattacharyaa, S. Birney, E. Blackwooda, D. Bobrow, M. Bolton, P.F. Boustred, C. Breen, G. Calissanoa, M. Carss, K. Charlton, R. Chatterjee, K. Chen, L. Ciampia, A. Cirak, S. Clapham, P. Clement, G. Coates, G. Coccaa, M. Collier, D.A. Cosgrove, C. Coxa, T. Craddock, N. Crooks, L. Curran, S. Curtis, D. Daly, A. Danecek, P. Day, I.N.M. Day-Williams, A. Dominiczak, A. Down, T. Du, Y. Dunham, I. Durbin, R. Edkins, S. Ekong, R. Ellis, P. Evansa, D.M. Fitzpatrick, D.R. Flicek, P. Floyd, J. Foley, A.R. Franklin, C.S. Futema, M. Gallagher, L. Gaunt, T.R. Geihs, M. Geschwind, D. Greenwood, C.M.T. Griffin, H. Grozeva, D. Guo, X. Guo, X. Gurling, H. Hart, D. Holmans, P. Howie, B. Huang, J. Huang, L. Hubbard, T. Humphries, S.E. Hurles, M.E. Hysi, P. Iotchkova, V. Jackson, D.K. Jamshidi, Y. Joyce, C. Karczewski, K.J. Kaye, J. Keane, T. Kemp, J.P. Kennedy, K. Kent, A. Khawaja, F. Van Kogelenberg, M. Kolb-Kokocinski, A. Lachance, G. Langford, C. Lawson, D. Lee, I. Lek, M. Li, R. Li, Y. Liang, J. Lin, H. Liu, R. Lönnqvist, J. Lopes, L.R. Lopes, M. MacArthur, D.G. Mangino, M. Marchini, J. Maslen, J. Mathieson, I. McGuffin, P. McIntosh, A.M. McKechanie, A.G. McQuillin, A. Memari, Y. Metrustry, S. Migone, N. Min, J.L. Mitchison, H.M. Moayyeri, A. Morris, A. Morris, J. Muntoni, F. Northstone, K. O'Donovan, M.C. Onoufriadis, A. Oualkacha, K. Owen, M.J. Palotie, A. Panoutsopoulou, K. Parker, V. Parr, J.R. Paternoster, L. Paunio, T. Payne, F. Payne, S.J. Perry, J.R.B. Pietilainen, O. Plagnol, V. Pollitt, R.C. Porteous, D.J. Povey, S. Quail, M.A. Quaye, L. Raymond, F.L. Rehnström, K. Richards, J.B. Ridout, C.K. Ring, S. Ritchie, G.R.S. Roberts, N. Robinson, R.L. Savage, D.B. Scambler, P. Schiffels, S. Schmidts, M. Schoenmakers, N. Scott, R.H. Semple, R.K. Serra, E. Sharp, S.I. Shaw, A. Shihab, H.A. Shin, S.-Y. Skuse, D. Small, K.S. Smee, C. Smith, B.H. Davey Smith, G. Soranzo, N. Southam, L. Spasic-Boskovic, O. Spector, T.D. St Clair, D. St Pourcain, B. Stalker, J. Stevens, E. Sun, J. Surdulescu, G. Suvisaari, J. Syrris, P. Taylor, R. Tian, J. Timpson, N.J. Tobin, M.D. Valdes, A.M. Vandersteen, A.M. Vijayarangakannan, P. Visscher, P.M. Wain, L.V. Walter, K. Walters, J.T.R. Wang, G. Wang, J. Wang, Y. Ward, K. Whyte, T. Williams, H.J. Williamson, K.A. Wilson, C. Wilson, S.G. Wong, K. Xu, C. Yang, J. Zhang, F. Zhang, P. Zheng, H.-F.

Abstract

Obesity is a genetically heterogeneous disorder. Using targeted and whole-exome sequencing, we studied 32 human and 87 rodent obesity genes in 2,548 severely obese children and 1,117 controls. We identified 52 variants contributing to obesity in 2% of cases including multiple novel variants in GNAS, which were sometimes found with accelerated growth rather than short stature as described previously. Nominally significant associations were found for rare functional variants in BBS1, BBS9, GNAS, MKKS, CLOCK and ANGPTL6. The p.S284X variant in ANGPTL6 drives the association signal (rs201622589, MAF∼0.1%, odds ratio = 10.13, p-value = 0.042) and results in complete loss of secretion in cells. Further analysis including additional case-control studies and population controls (N = 260,642) did not support association of this variant with obesity (odds ratio = 2.34, p-value = 2.59 × 10-3), highlighting the challenges of testing rare variant associations and the need for very large sample sizes. Further validation in cohorts with severe obesity and engineering the variants in model organisms will be needed to explore whether human variants in ANGPTL6 and other genes that lead to obesity when deleted in mice, do contribute to obesity. Such studies may yield druggable targets for weight loss therapies. © 2017 The Author(s).

Published
2017

10. Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Author

Tachmazidou, I, Suveges, D, Min, JL, Ritchie, GRS, Steinberg, J, Walter, K, Iotchkova, V, Schwartzentruber, J, Huang, J, Memari, Y, McCarthy, S, Crawford, AA, Bombieri, C, Cocca, M, Farmaki, AE, Gaunt, TR, Jousilahti, P, Kooijman, Marjolein, Lehne, B, Malerba, G, Mannisto, S, Matchan, A, Medina Gomez, Maria, Metrustry, SJ, Nag, A, Ntalla, I, Paternoster, L, Rayner, NW, Sala, C, Scott, WR, Shihab, HA, Southam, L, St Pourcain, B, Traglia, M, Trajanoska, Katerina, Zaza, G, Zhang, WH, Artigas, MS, Bansal, N, Benn, M, Chen, ZS, Danecek, P, Lin, WY, Locke, A, Luan, JA, Manning, AK, Mulas, A, Sidore, C, Tybjaerg-Hansen, A, Varbo, A, Zoledziewska, M, Finan, C, Hatzikotoulas, K, Hendricks, AE, Kemp, JP, Moayyeri, A, Panoutsopoulou, K, Szpak, M, Wilson, SG, Boehnke, M, Cucca, F, Di Angelantonio, E, Langenberg, C, Lindgren, C, McCarthy, MI, Morris, AP, Nordestgaard, BG, Scott, RA, Tobin, MD, Wareham, NJ, Burton, P, Chambers, JC, Smith, GD, Dedoussis, G, Felix, Janine, Franco Duran, OH, Gambaro, G, Gasparini, P, Hammond, CJ, Hofman, Bert, Jaddoe, Vincent, Kleber, M, Kooner, JS, Perola, M, Relton, C, Ring, SM, Rivadeneira, Fernando, Salomaa, V, Spector, TD, Stegle, O, Toniolo, D, Uitterlinden, André, Barroso, I, Greenwood, CMT, Perry, JRB, Walker, BR, Butterworth, AS, Xue, YL, Durbin, R, Small, KS, Soranzo, N, Timpson, NJ, Zeggini, E, Tachmazidou, I, Suveges, D, Min, JL, Ritchie, GRS, Steinberg, J, Walter, K, Iotchkova, V, Schwartzentruber, J, Huang, J, Memari, Y, McCarthy, S, Crawford, AA, Bombieri, C, Cocca, M, Farmaki, AE, Gaunt, TR, Jousilahti, P, Kooijman, Marjolein, Lehne, B, Malerba, G, Mannisto, S, Matchan, A, Medina Gomez, Maria, Metrustry, SJ, Nag, A, Ntalla, I, Paternoster, L, Rayner, NW, Sala, C, Scott, WR, Shihab, HA, Southam, L, St Pourcain, B, Traglia, M, Trajanoska, Katerina, Zaza, G, Zhang, WH, Artigas, MS, Bansal, N, Benn, M, Chen, ZS, Danecek, P, Lin, WY, Locke, A, Luan, JA, Manning, AK, Mulas, A, Sidore, C, Tybjaerg-Hansen, A, Varbo, A, Zoledziewska, M, Finan, C, Hatzikotoulas, K, Hendricks, AE, Kemp, JP, Moayyeri, A, Panoutsopoulou, K, Szpak, M, Wilson, SG, Boehnke, M, Cucca, F, Di Angelantonio, E, Langenberg, C, Lindgren, C, McCarthy, MI, Morris, AP, Nordestgaard, BG, Scott, RA, Tobin, MD, Wareham, NJ, Burton, P, Chambers, JC, Smith, GD, Dedoussis, G, Felix, Janine, Franco Duran, OH, Gambaro, G, Gasparini, P, Hammond, CJ, Hofman, Bert, Jaddoe, Vincent, Kleber, M, Kooner, JS, Perola, M, Relton, C, Ring, SM, Rivadeneira, Fernando, Salomaa, V, Spector, TD, Stegle, O, Toniolo, D, Uitterlinden, André, Barroso, I, Greenwood, CMT, Perry, JRB, Walker, BR, Butterworth, AS, Xue, YL, Durbin, R, Small, KS, Soranzo, N, Timpson, NJ, and Zeggini, E

Published
2017

11. Bayesian methods for multivariate phenotype analysis in genome-wide association studies

Author

Iotchkova, V and Marchini, J

Subjects
Statistics (see also social sciences), Computationally-intensive statistics, Applications and algorithms, Mathematical genetics and bioinformatics (statistics)
Abstract

Most genome-wide association studies search for genetic variants associated to a single trait of interest, despite the main interest usually being the understanding of a complex genotype-phenotype network. Furthermore, many studies collect data on multiple phenotypes, each measuring a different aspect of the biological system under consideration, therefore it can often make sense to jointly analyze the phenotypes. However this is rarely the case and there is a lack of well developed methods for multiple phenotype analysis. Here we propose novel approaches for genome-wide association analysis, which scan the genome one SNP at a time for association with multivariate traits. The first half of this thesis focuses on an analytic model averaging approach which bi-partitions traits into associated and unassociated, fits all such models and measures evidence of association using a Bayes factor. The discrete nature of the model allows very fine control of prior beliefs about which sets of traits are more likely to be jointly associated. Using simulated data we show that this method can have much greater power than simpler approaches that do not explicitly model residual correlation between traits. On real data of six hematological parameters in 3 population cohorts (KORA, UKNBS and TwinsUK) from the HaemGen consortium, this model allows us to uncover an association at the RCL locus that was not identified in the original analysis but has been validated in a much larger study. In the second half of the thesis we propose and explore the properties of models that use priors encouraging sparse solutions, in the sense that genetic effects of phenotypes are shrunk towards zero when there is little evidence of association. To do this we explore and use spike and slab (SAS) priors. All methods combine both hypothesis testing, via calculation of a Bayes factor, and model selection, which occurs implicitly via the sparsity priors. We have successfully implemented a Variational Bayesian approach to fit this model, which provides a tractable approximation to the posterior distribution, and allows us to approximate the very high-dimensional integral required for the Bayes factor calculation. This approach has a number of desirable properties. It can handle missing phenotype data, which is a real feature of most studies. It allows for both correlation due to relatedness between subjects or population structure and residual phenotype correlation. It can be viewed as a sparse Bayesian multivariate generalization of the mixed model approaches that have become popular recently in the GWAS literature. In addition, the method is computationally fast and can be applied to millions of SNPs for a large number of phenotypes. Furthermore we apply our method to 15 glycans from 3 isolated population cohorts (ORCADES, KORCULA and VIS), where we uncover association at a known locus, not identified in the original study but discovered later in a larger one. We conclude by discussing future directions.

Published
2016

12. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel

Author

Huang, J, Howie, B, Mccarthy, S, Memari, Y, Walter, K, Min, Jl, Danecek, P, Malerba, Giovanni, Trabetti, Elisabetta, Zheng, Hf, Gambaro, G, Richards, Jb, Durbin, R, Timpson, Nj, Marchini, J, Soranzo, N, Al Turki, S, Amuzu, A, Anderson, Ca, Anney, R, Antony, D, Artigas, Ms, Ayub, M, Bala, S, Barrett, Jc, Barroso, I, Beales, P, Benn, M, Bentham, J, Bhattacharya, S, Birney, E, Blackwood, D, Bobrow, M, Bochukova, E, Bolton, Pf, Bounds, R, Boustred, C, Breen, G, Calissano, M, Carss, K, Casas, Jp, Chambers, Jc, Charlton, R, Chatterjee, K, Chen, L, Ciampi, A, Cirak, S, Clapham, P, Clement, G, Coates, G, Cocca, M, Collier, Da, Cosgrove, C, Cox, T, Craddock, N, Crooks, L, Curran, S, Curtis, D, Daly, A, Day, In, Day Williams, A, Dedoussis, G, Down, T, Du, Y, van Duijn, Cm, Dunham, I, Edkins, S, Ekong, R, Ellis, P, Evans, Dm, Farooqi, Is, Fitzpatrick, Dr, Flicek, P, Floyd, J, Foley, Ar, Franklin, Cs, Futema, M, Gallagher, L, Gasparini, P, Gaunt, Tr, Geihs, M, Geschwind, D, Greenwood, C, Griffin, H, Grozeva, D, Guo, X, Gurling, H, Hart, D, Hendricks, Ae, Holmans, P, Huang, L, Hubbard, T, Humphries, Se, Hurles, Me, Hysi, P, Iotchkova, V, Isaacs, A, Jackson, Dk, Jamshidi, Y, Johnson, J, Joyce, C, Karczewski, Kj, Kaye, J, Keane, T, Kemp, Jp, Kennedy, K, Kent, A, Keogh, J, Khawaja, F, Kleber, Me, van Kogelenberg, M, Kolb Kokocinski, A, Kooner, Js, Lachance, G, Langenberg, C, Langford, C, Lawson, D, Lee, I, van Leeuwen, Em, Lek, M, Li, R, Li, Y, Liang, J, Lin, H, Liu, R, Lönnqvist, J, Lopes, Lr, Lopes, M, Luan, J, Macarthur, Dg, Mangino, M, Marenne, G, März, W, Maslen, J, Matchan, A, Mathieson, I, Mcguffin, P, Mcintosh, Am, Mckechanie, Ag, Mcquillin, A, Metrustry, S, Migone, N, Mitchison, Hm, Moayyeri, A, Morris, J, Morris, R, Muddyman, D, Muntoni, F, Nordestgaard, Bg, Northstone, K, O'Donovan, Mc, O'Rahilly, S, Onoufriadis, A, Oualkacha, K, Owen, Mj, Palotie, A, Panoutsopoulou, K, Parker, V, Parr, Jr, Paternoster, L, Paunio, T, Payne, F, Payne, Sj, Perry, Jr, Pietilainen, O, Plagnol, V, Pollitt, Rc, Povey, S, Quail, Ma, Quaye, L, Raymond, L, Rehnström, K, Ridout, Ck, Ring, S, Ritchie, Gr, Roberts, N, Robinson, Rl, Savage, Db, Scambler, P, Schiffels, S, Schmidts, M, Schoenmakers, N, Scott, Rh, Scott, Ra, Semple, Rk, Serra, E, Sharp, Si, Shaw, A, Shihab, Ha, Shin, Sy, Skuse, D, Small, Ks, Smee, C, Smith, Gd, Southam, L, Spasic Boskovic, O, Spector, Td, St Clair, D, St Pourcain, B, Stalker, J, Stevens, E, Sun, J, Surdulescu, G, Suvisaari, J, Syrris, P, Tachmazidou, I, Taylor, R, Tian, J, Tobin, Md, Toniolo, D, Traglia, M, Tybjaerg Hansen, A, Valdes, Am, Vandersteen, Am, Varbo, A, Vijayarangakannan, P, Visscher, Pm, Wain, Lv, Walters, Jt, Wang, G, Wang, J, Wang, Y, Ward, K, Wheeler, E, Whincup, P, Whyte, T, Williams, Hj, Williamson, Ka, Wilson, C, Wilson, Sg, Wong, K, Xu, C, Yang, J, Zaza, Gianluigi, Zeggini, E, Zhang, F, Zhang, P, Zhang, W., Clinicum, Department of Psychiatry, Jie, Huang, Bryan, Howie, Shane, Mccarthy, Yasin, Memari, Klaudia, Walter, Josine L., Min, Petr, Danecek, Giovanni, Malerba, Elisabetta, Trabetti, Hou Feng, Zheng, Saeed Al, Turki, Antoinette, Amuzu, Carl A., Anderson, Richard, Anney, Dinu, Antony, María Soler, Artiga, Muhammad, Ayub, Senduran, Bala, Jeffrey C., Barrett, Inês, Barroso, Phil, Beale, Marianne, Benn, Jamie, Bentham, Shoumo, Bhattacharya, Ewan, Birney, Douglas, Blackwood, Martin, Bobrow, Elena, Bochukova, Patrick F., Bolton, Rebecca, Bound, Chris, Boustred, Gerome, Breen, Mattia, Calissano, Keren, Car, Juan Pablo, Casa, John C., Chamber, Ruth, Charlton, Krishna, Chatterjee, Lu, Chen, Antonio, Ciampi, Sebahattin, Cirak, Peter, Clapham, Gail, Clement, Guy, Coate, Cocca, Massimiliano, David A., Collier, Catherine, Cosgrove, Tony, Cox, Nick, Craddock, Lucy, Crook, Sarah, Curran, David, Curti, Allan, Daly, Ian N. M., Day, Aaron Day, William, George, Dedoussi, Thomas, Down, Yuanping, Du, Cornelia M., van Duijn, Ian, Dunham, Sarah, Edkin, Rosemary, Ekong, Peter, Elli, David M., Evan, I., Sadaf Farooqi, David R., Fitzpatrick, Paul, Flicek, James, Floyd, A., Reghan Foley, Christopher S., Franklin, Marta, Futema, Louise, Gallagher, Gasparini, Paolo, Tom R., Gaunt, Matthias, Geih, Daniel, Geschwind, Celia, Greenwood, Heather, Griffin, Detelina, Grozeva, Xiaosen, Guo, Xueqin, Guo, Hugh, Gurling, Deborah, Hart, Audrey E., Hendrick, Peter, Holman, Liren, Huang, Tim, Hubbard, Steve E., Humphrie, Matthew E., Hurle, Pirro, Hysi, Valentina, Iotchkova, Aaron, Isaac, David K., Jackson, Yalda, Jamshidi, Jon, Johnson, Chris, Joyce, Konrad J., Karczewski, Jane, Kaye, Thomas, Keane, John P., Kemp, Karen, Kennedy, Alastair, Kent, Julia, Keogh, Farrah, Khawaja, Marcus E., Kleber, Margriet van, Kogelenberg, Anja Kolb, Kokocinski, Jaspal S., Kooner, Genevieve, Lachance, Claudia, Langenberg, Cordelia, Langford, Daniel, Lawson, Irene, Lee, Elisabeth M., van Leeuwen, Monkol, Lek, Rui, Li, Yingrui, Li, Jieqin, Liang, Hong, Lin, Ryan, Liu, Jouko, Lönnqvist, Luis R., Lope, Margarida, Lope, Jian'An, Luan, Daniel G., Macarthur, Massimo, Mangino, Gaëlle, Marenne, Winfried, März, John, Maslen, Angela, Matchan, Iain, Mathieson, Peter, Mcguffin, Andrew M., Mcintosh, Andrew G., Mckechanie, Andrew, Mcquillin, Sarah, Metrustry, Nicola, Migone, Hannah M., Mitchison, Alireza, Moayyeri, James, Morri, Richard, Morri, Dawn, Muddyman, Francesco, Muntoni, Børge G., Nordestgaard, Kate, Northstone, Michael C., O'Donovan, Stephen, O'Rahilly, Alexandros, Onoufriadi, Karim, Oualkacha, Michael J., Owen, Aarno, Palotie, Kalliope, Panoutsopoulou, Victoria, Parker, Jeremy R., Parr, Lavinia, Paternoster, Tiina, Paunio, Felicity, Payne, Stewart J., Payne, John R. B., Perry, Olli, Pietilainen, Vincent, Plagnol, Rebecca C., Pollitt, Sue, Povey, Michael A., Quail, Lydia, Quaye, Lucy, Raymond, Karola, Rehnström, Cheryl K., Ridout, Susan, Ring, Graham R. S., Ritchie, Nicola, Robert, Rachel L., Robinson, David B., Savage, Peter, Scambler, Stephan, Schiffel, Miriam, Schmidt, Nadia, Schoenmaker, Richard H., Scott, Robert A., Scott, Robert K., Semple, Eva, Serra, Sally I., Sharp, Adam, Shaw, Hashem A., Shihab, So Youn, Shin, David, Skuse, Kerrin S., Small, Carol, Smee, George Davey, Smith, Lorraine, Southam, Olivera Spasic, Boskovic, Timothy D., Spector, David St, Clair, Beate St, Pourcain, Jim, Stalker, Elizabeth, Steven, Jianping, Sun, Gabriela, Surdulescu, Jaana, Suvisaari, Petros, Syrri, Ioanna, Tachmazidou, Rohan, Taylor, Jing, Tian, Martin D., Tobin, Daniela, Toniolo, Michela, Traglia, Anne Tybjaerg, Hansen, Ana M., Valde, Anthony M., Vandersteen, Anette, Varbo, Parthiban, Vijayarangakannan, Peter M., Visscher, Louise V., Wain, James T. R., Walter, Guangbiao, Wang, Jun, Wang, Yu, Wang, Kirsten, Ward, Eleanor, Wheeler, Peter, Whincup, Tamieka, Whyte, Hywel J., William, Kathleen A., Williamson, Crispian, Wilson, Scott G., Wilson, Kim, Wong, Changjiang, Xu, Jian, Yang, Gianluigi, Zaza, Eleftheria, Zeggini, Feng, Zhang, Pingbo, Zhang, Weihua, Zhang, Giovanni, Gambaro, J., Brent Richard, Richard, Durbin, Nicholas J., Timpson, Jonathan, Marchini, and Nicole, Soranzo

Subjects
Computer science, General Physics and Astronomy, Genome-wide association study, 0302 clinical medicine, Gene Frequency, Haplotype, Genetics,Biological sciences, Settore MED/14 - NEFROLOGIA, Aged, 80 and over, Genetics, 0303 health sciences, education.field_of_study, Multidisciplinary, TWINSUK, Middle Aged, single-nucleotide polymorphism, Whole-genome sequencing, WGS imputation panel, single-nucleotide polymorphism, Biological sciences, Italy, MAP, Adult, Adolescent, Genotype, WGS imputation panel, Population, Single-nucleotide polymorphism, FORMAT, Computational biology, GENOTYPE IMPUTATION, Polymorphism, Single Nucleotide, Article, White People, General Biochemistry, Genetics and Molecular Biology, Young Adult, 03 medical and health sciences, Humans, GENOME-WIDE ASSOCIATION, 1000 Genomes Project, education, Allele frequency, Alleles, Aged, 030304 developmental biology, Whole-genome sequencing, Models, Statistical, Models, Genetic, Genome, Human, Genetic Variation, General Chemistry, United Kingdom, Minor allele frequency, Renal disorders Radboud Institute for Molecular Life Sciences [Radboudumc 11], Haplotypes, 3111 Biomedicine, 030217 neurology & neurosurgery, Imputation (genetics)
Abstract

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants., Imputation uses genotype information from SNP arrays to infer the genotypes of missing markers. Here, the authors show that an imputation reference panel derived from whole-genome sequencing of 3,781 samples from the UK10K project improves the imputation accuracy and coverage of low frequency variants compared to existing methods.

Published
2015

13. Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Author

Chen, L., Ge, B., Casale, F.P., Vasquez, L., Kwan, T., Garrido-Martin, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., Datta, A., Richardson, D., Burden, F., Mead, D., Mann, A.L., Fernandez, J.M., Rowlston, S., Wilder, S.P., Farrow, S., Shao, X., Lambourne, J.J., Redensek, A., Albers, C.A., Amstislavskiy, V., Ashford, S., Berentsen, K., Bomba, L., Bourque, G., Bujold, D., Busche, S., Caron, M., Chen, S.H., Cheung, W., Delaneau, O., Dermitzakis, E.T., Elding, H., Colgiu, I., Bagger, F.O., Flicek, P., Habibi, E., Iotchkova, V., Janssen-Megens, E., Kim, B., Lehrach, H., Lowy, E., Mandoli, A., Matarese, F., Maurano, M.T., Morris, J.A., Pancaldi, V., Pourfarzad, F., Rehnstrom, K., Rendon, A., Risch, T., Sharifi, N., Simon, M.M., Sultan, M., Valencia, A., Walter, K., Wang, S.Y., Frontini, M., Antonarakis, S.E., Clarke, L., Yaspo, M.L., Beck, S., Guigo, R., Rico, D., Martens, J.H., Ouwehand, W.H., Kuijpers, T.W., Paul, D.S., Stunnenberg, H.G., Stegle, O., Downes, K., Pastinen, T., Soranzo, N., Chen, L., Ge, B., Casale, F.P., Vasquez, L., Kwan, T., Garrido-Martin, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., Datta, A., Richardson, D., Burden, F., Mead, D., Mann, A.L., Fernandez, J.M., Rowlston, S., Wilder, S.P., Farrow, S., Shao, X., Lambourne, J.J., Redensek, A., Albers, C.A., Amstislavskiy, V., Ashford, S., Berentsen, K., Bomba, L., Bourque, G., Bujold, D., Busche, S., Caron, M., Chen, S.H., Cheung, W., Delaneau, O., Dermitzakis, E.T., Elding, H., Colgiu, I., Bagger, F.O., Flicek, P., Habibi, E., Iotchkova, V., Janssen-Megens, E., Kim, B., Lehrach, H., Lowy, E., Mandoli, A., Matarese, F., Maurano, M.T., Morris, J.A., Pancaldi, V., Pourfarzad, F., Rehnstrom, K., Rendon, A., Risch, T., Sharifi, N., Simon, M.M., Sultan, M., Valencia, A., Walter, K., Wang, S.Y., Frontini, M., Antonarakis, S.E., Clarke, L., Yaspo, M.L., Beck, S., Guigo, R., Rico, D., Martens, J.H., Ouwehand, W.H., Kuijpers, T.W., Paul, D.S., Stunnenberg, H.G., Stegle, O., Downes, K., Pastinen, T., and Soranzo, N.

Abstract

Contains fulltext : 167824.pdf (Publisher’s version ) (Open Access)

Published
2016

14. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease

Author

Astle, W.J., Elding, H., Jiang, T., Allen, D., Ruklisa, D., Mann, A.L., Mead, D., Bouman, H., Riveros-Mckay, F., Kostadima, M.A., Lambourne, J.J., Sivapalaratnam, S., Downes, K., Kundu, K., Bomba, L., Berentsen, K., Bradley, J.R., Daugherty, L.C., Delaneau, O., Freson, K., Garner, S.F., Grassi, L., Guerrero, J., Haimel, M., Janssen-Megens, E.M., Kaan, A., Kamat, M., Kim, B., Mandoli, A., Marchini, J., Martens, J.H.A., Meacham, S., Megy, K., O'Connell, J., Petersen, R., Sharifi, N., Sheard, S.M., Staley, J.R., Tuna, S., Ent, M. van der, Walter, K., Wang, S., Wheeler, E., Wilder, S.P., Iotchkova, V., Moore, C., Sambrook, J., Stunnenberg, H.G., Di Angelantonio, E., Kaptoge, S., Kuijpers, T.W., Carrillo-de-Santa-Pau, E., Juan, D., Rico, D., Valencia, A., Chen, L, Ge, B., Vasquez, L., Kwan, T., Garrido-Martin, D., Watt, S., Yang, Y., Guigo, R., Beck, S., Paul, D.S., Pastinen, T., Bujold, D., Bourque, G., Frontini, M., Danesh, J., Roberts, D.J., Ouwehand, W.H., Butterworth, A.S., Soranzo, N., Astle, W.J., Elding, H., Jiang, T., Allen, D., Ruklisa, D., Mann, A.L., Mead, D., Bouman, H., Riveros-Mckay, F., Kostadima, M.A., Lambourne, J.J., Sivapalaratnam, S., Downes, K., Kundu, K., Bomba, L., Berentsen, K., Bradley, J.R., Daugherty, L.C., Delaneau, O., Freson, K., Garner, S.F., Grassi, L., Guerrero, J., Haimel, M., Janssen-Megens, E.M., Kaan, A., Kamat, M., Kim, B., Mandoli, A., Marchini, J., Martens, J.H.A., Meacham, S., Megy, K., O'Connell, J., Petersen, R., Sharifi, N., Sheard, S.M., Staley, J.R., Tuna, S., Ent, M. van der, Walter, K., Wang, S., Wheeler, E., Wilder, S.P., Iotchkova, V., Moore, C., Sambrook, J., Stunnenberg, H.G., Di Angelantonio, E., Kaptoge, S., Kuijpers, T.W., Carrillo-de-Santa-Pau, E., Juan, D., Rico, D., Valencia, A., Chen, L, Ge, B., Vasquez, L., Kwan, T., Garrido-Martin, D., Watt, S., Yang, Y., Guigo, R., Beck, S., Paul, D.S., Pastinen, T., Bujold, D., Bourque, G., Frontini, M., Danesh, J., Roberts, D.J., Ouwehand, W.H., Butterworth, A.S., and Soranzo, N.

Abstract

Contains fulltext : 163344.pdf (Publisher’s version ) (Open Access)

Published
2016

15. eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data

Author

Breeze, C.E., Paul, D.S., van Dongen, J., Butcher, L.M., Ambrose, J.C., Barrett, J.E., Lowe, R., Rakyan, V.K., Iotchkova, V., Frontini, M., Downes, K., Ouwehand, W.H., Laperle, J., Jacques, P.E., Bourque, G., Bergmann, A.K., Siebert, R., Vellenga, E., Saeed, S., Matarese, F., Martens, J.H.A., Stunnenberg, H., Teschendorff, A.E., Herrero, J., Birney, E., Dunham, I., Beck, S., Breeze, C.E., Paul, D.S., van Dongen, J., Butcher, L.M., Ambrose, J.C., Barrett, J.E., Lowe, R., Rakyan, V.K., Iotchkova, V., Frontini, M., Downes, K., Ouwehand, W.H., Laperle, J., Jacques, P.E., Bourque, G., Bergmann, A.K., Siebert, R., Vellenga, E., Saeed, S., Matarese, F., Martens, J.H.A., Stunnenberg, H., Teschendorff, A.E., Herrero, J., Birney, E., Dunham, I., and Beck, S.

Abstract

Contains fulltext : 161675.pdf (publisher's version ) (Open Access)

Published
2016

16. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

Author

Iotchkova, V. (Valentina), Huang, J. (Jian), Morris, J.A. (John A), Jain, D. (Deepti), Barbieri, C. (Caterina), Walter, K. (Klaudia), Min, J. (Josine), Chen, L. (Lu), Astle, W. (William), Cocca, M. (Massimiliano), Deelen, P. (Patrick), Elding, H. (Heather), Farmaki, A.-E. (Aliki-Eleni), Franklin, C.S. (Christopher), Frånberg, M. (Mattias), Gaunt, T.R. (Tom), Hofman, A. (Albert), Jiang, T. (Tao), Kleber, M.E. (Marcus), Lachance, G. (Genevieve), Luan, J. (Jian'An), Malerba, G. (Giovanni), Matchan, A. (Angela), Mead, D. (Daniel), Memari, Y. (Yasin), Ntalla, I. (Ioanna), Panoutsopoulou, K. (Kalliope), Pazoki, R. (Raha), Perry, J.R.B. (John), Rivadeneira Ramirez, F. (Fernando), Sabater-Lleal, M. (Maria), Sennblad, B. (Bengt), Shin, S.-Y., Southam, L. (Lorraine), Traglia, M. (Michela), Dijk, F. (Freerk) van, Leeuwen, E.M. (Elisa) van, Zaza, G. (Gianluigi), Zhang, W. (Weihua), Amin, N. (Najaf), Butterworth, A.S. (Adam), Chambers, J.C. (John), Dedoussis, G.V. (George), Dehghan, A. (Abbas), Franco, O.H. (Oscar), Franke, L. (Lude), Frontini, M. (Mattia), Gambaro, G. (Giovanni), Gasparini, P. (Paolo), Hamsten, A. (Anders), Issacs, A. (Aaron), Kooner, J.S. (Jaspal S.), Kooperberg, C. (Charles), Langenberg, C. (Claudia), März, W. (Winfried), Scott, R.A. (Robert), Swertz, M.A. (Morris A), Toniolo, D. (Daniela), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Watkins, H. (Hugh), Zeggini, E. (Eleftheria), Maurano, M.T. (Matthew T.), Timpson, N.J. (Nicholas), Reiner, A. (Alexander), Auer, P. (Paul), Soranzo, N. (Nicole), Iotchkova, V. (Valentina), Huang, J. (Jian), Morris, J.A. (John A), Jain, D. (Deepti), Barbieri, C. (Caterina), Walter, K. (Klaudia), Min, J. (Josine), Chen, L. (Lu), Astle, W. (William), Cocca, M. (Massimiliano), Deelen, P. (Patrick), Elding, H. (Heather), Farmaki, A.-E. (Aliki-Eleni), Franklin, C.S. (Christopher), Frånberg, M. (Mattias), Gaunt, T.R. (Tom), Hofman, A. (Albert), Jiang, T. (Tao), Kleber, M.E. (Marcus), Lachance, G. (Genevieve), Luan, J. (Jian'An), Malerba, G. (Giovanni), Matchan, A. (Angela), Mead, D. (Daniel), Memari, Y. (Yasin), Ntalla, I. (Ioanna), Panoutsopoulou, K. (Kalliope), Pazoki, R. (Raha), Perry, J.R.B. (John), Rivadeneira Ramirez, F. (Fernando), Sabater-Lleal, M. (Maria), Sennblad, B. (Bengt), Shin, S.-Y., Southam, L. (Lorraine), Traglia, M. (Michela), Dijk, F. (Freerk) van, Leeuwen, E.M. (Elisa) van, Zaza, G. (Gianluigi), Zhang, W. (Weihua), Amin, N. (Najaf), Butterworth, A.S. (Adam), Chambers, J.C. (John), Dedoussis, G.V. (George), Dehghan, A. (Abbas), Franco, O.H. (Oscar), Franke, L. (Lude), Frontini, M. (Mattia), Gambaro, G. (Giovanni), Gasparini, P. (Paolo), Hamsten, A. (Anders), Issacs, A. (Aaron), Kooner, J.S. (Jaspal S.), Kooperberg, C. (Charles), Langenberg, C. (Claudia), März, W. (Winfried), Scott, R.A. (Robert), Swertz, M.A. (Morris A), Toniolo, D. (Daniela), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Watkins, H. (Hugh), Zeggini, E. (Eleftheria), Maurano, M.T. (Matthew T.), Timpson, N.J. (Nicholas), Reiner, A. (Alexander), Auer, P. (Paul), and Soranzo, N. (Nicole)

Abstract

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

Published
2016
Full Text
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17. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel

Author

Huang, J. (Jie), Howie, B. (Bryan), McCarthy, S. (Shane), Memari, Y. (Yasin), Walter, K. (Klaudia), Min, J.L. (Josine L.), Danecek, P. (Petr), Malerba, G. (Giovanni), Trabetti, E. (Elisabetta), Zheng, H.-F. (Hou-Feng), Gambaro, G. (Giovanni), Richards, J.B. (Brent), Durbin, R. (Richard), Timpson, N.J. (Nicholas), Marchini, J. (Jonathan), Soranzo, N. (Nicole), Al Turki, S.H. (Saeed), Amuzu, A. (Antoinette), Anderson, C. (Carl), Anney, R. (Richard), Antony, D. (Dinu), Artigas, M.S., Ayub, M. (Muhammad), Bala, S. (Senduran), Barrett, J.C. (Jeffrey), Barroso, I.E. (Inês), Beales, P.L. (Philip), Benn, M. (Marianne), Bentham, J. (Jamie), Bhattacharya, S. (Shoumo), Birney, E. (Ewan), Blackwood, D.H.R. (Douglas), Bobrow, M. (Martin), Bochukova, E. (Elena), Bolton, P.F. (Patrick F.), Bounds, R. (Rebecca), Boustred, C. (Chris), Breen, G. (Gerome), Calissano, M. (Mattia), Carss, K. (Keren), Casas, J.P. (Juan Pablo), Chambers, J.C. (John C.), Charlton, R. (Ruth), Chatterjee, K. (Krishna), Chen, L. (Lu), Ciampi, A. (Antonio), Cirak, S. (Sebahattin), Clapham, P. (Peter), Clement, G. (Gail), Coates, G. (Guy), Cocca, M. (Massimiliano), Collier, D.A. (David), Cosgrove, C. (Catherine), Cox, T. (Tony), Craddock, N.J. (Nick), Crooks, L. (Lucy), Curran, S. (Sarah), Curtis, D. (David), Daly, A. (Allan), Day, I.N.M. (Ian N.M.), Day-Williams, A.G. (Aaron), Dedoussis, G.V. (George), Down, T. (Thomas), Du, Y. (Yuanping), Duijn, C.M. (Cornelia) van, Dunham, I. (Ian), Edkins, T. (Ted), Ekong, R. (Rosemary), Ellis, P. (Peter), Evans, D.M. (David), Farooqi, I.S. (I. Sadaf), Fitzpatrick, D.R. (David R.), Flicek, P. (Paul), Floyd, J. (James), Foley, A.R. (A. Reghan), Franklin, C.S. (Christopher S.), Futema, M. (Marta), Gallagher, L. (Louise), Gasparini, P. (Paolo), Gaunt, T.R. (Tom), Geihs, M. (Matthias), Geschwind, D. (Daniel), Greenwood, C.M.T. (Celia), Griffin, H. (Heather), Grozeva, D. (Detelina), Guo, X. (Xiaosen), Guo, X. (Xueqin), Gurling, H. (Hugh), Hart, D. (Deborah), Hendricks, A.E. (Audrey E.), Holmans, P.A. (Peter A.), Huang, L. (Liren), Hubbard, T. (Tim), Humphries, S.E. (Steve E.), Hurles, M.E. (Matthew), Hysi, P.G. (Pirro), Iotchkova, V. (Valentina), Isaacs, A. (Aaron), Jackson, D.K. (David K.), Jamshidi, Y. (Yalda), Johnson, J. (Jon), Joyce, C. (Chris), Karczewski, K.J. (Konrad), Kaye, J. (Jane), Keane, T. (Thomas), Kemp, J.P. (John), Kennedy, K. (Karen), Kent, A. (Alastair), Keogh, J. (Julia), Khawaja, F. (Farrah), Kleber, M.E. (Marcus), Van Kogelenberg, M. (Margriet), Kolb-Kokocinski, A. (Anja), Kooner, J.S. (Jaspal S.), Lachance, G. (Genevieve), Langenberg, C. (Claudia), Langford, C. (Cordelia), Lawson, D. (Daniel), Lee, I. (Irene), Leeuwen, E.M. (Elisa) van, Lek, M. (Monkol), Li, R. (Rui), Li, Y. (Yingrui), Liang, J. (Jieqin), Lin, H. (Hong), Liu, R. (Ryan), Lönnqvist, J. (Jouko), Lopes, L.R. (Luis R.), Lopes, M.C. (Margarida), Luan, J., MacArthur, D.G. (Daniel G.), Mangino, M. (Massimo), Marenne, G. (Gaëlle), März, W. (Winfried), Maslen, J. (John), Matchan, A. (Angela), Mathieson, I. (Iain), McGuffin, P. (Peter), McIntosh, A.M. (Andrew), McKechanie, A.G. (Andrew G.), McQuillin, A. (Andrew), Metrustry, S. (Sarah), Migone, N. (Nicola), Mitchison, H.M. (Hannah M.), Moayyeri, A. (Alireza), Morris, J. (James), Morris, R.W. (Richard), Muddyman, D. (Dawn), Muntoni, F., Nordestgaard, B.G. (Børge G.), Northstone, K. (Kate), O'donovan, M.C. (Michael), O'Rahilly, S. (Stephen), Onoufriadis, A. (Alexandros), Oualkacha, K. (Karim), Owen, M.J. (Michael J.), Palotie, A. (Aarno), Panoutsopoulou, K. (Kalliope), Parker, V. (Victoria), Parr, J.R. (Jeremy R.), Paternoster, L. (Lavinia), Paunio, T. (Tiina), Payne, F. (Felicity), Payne, S.J. (Stewart J.), Perry, J.R.B. (John), Pietiläinen, O.P.H. (Olli), Plagnol, V. (Vincent), Pollitt, R.C. (Rebecca C.), Povey, S. (Sue), Quail, M.A. (Michael A.), Quaye, L. (Lydia), Raymond, L. (Lucy), Rehnström, K. (Karola), Ridout, C.K. (Cheryl K.), Ring, S.M. (Susan), Ritchie, G.R.S. (Graham R.S.), Roberts, N. (Nicola), Robinson, R.L. (Rachel L.), Savage, D.B. (David), Scambler, P.J. (Peter), Schiffels, S. (Stephan), Schmidts, M. (Miriam), Schoenmakers, N. (Nadia), Scott, R.H. (Richard H.), Scott, R.A. (Robert), Semple, R.K. (Robert K.), Serra, E. (Eva), Sharp, S.I. (Sally I.), Shaw, A.C. (Adam C.), Shihab, H.A. (Hashem A.), Shin, S.-Y. (So-Youn), Skuse, D. (David), Small, K.S. (Kerrin), Smee, C. (Carol), Smith, A.V. (Davey), Southam, L. (Lorraine), Spasic-Boskovic, O. (Olivera), Spector, T.D. (Timothy), St. Clair, D. (David), St Pourcain, B. (Beate), Stalker, J. (Jim), Stevens, E. (Elizabeth), Sun, J. (Jianping), Surdulescu, G. (Gabriela), Suvisaari, J. (Jaana), Syrris, P. (Petros), Tachmazidou, I. (Ioanna), Taylor, R. (Rohan), Tian, J. (Jing), Tobin, M.D. (Martin), Toniolo, D. (Daniela), Traglia, M. (Michela), Tybjaerg-Hansen, A. (Anne), Valdes, A.M., Vandersteen, A.M. (Anthony M.), Varbo, A. (Anette), Vijayarangakannan, P. (Parthiban), Visscher, P.M. (Peter), Wain, L.V. (Louise), Walters, J.T. (James), Wang, G. (Guangbiao), Wang, J. (Jun), Wang, Y. (Yu), Ward, K. (Kirsten), Wheeler, E. (Eleanor), Whincup, P.H. (Peter), Whyte, T. (Tamieka), Williams, H.J. (Hywel J.), Williamson, K.A. (Kathleen), Wilson, C. (Crispian), Wilson, S.G. (Scott), Wong, K. (Kim), Xu, C. (Changjiang), Yang, J. (Jian), Zaza, G. (Gianluigi), Zeggini, E. (Eleftheria), Zhang, F. (Feng), Zhang, P. (Pingbo), Zhang, W. (Weihua), Huang, J. (Jie), Howie, B. (Bryan), McCarthy, S. (Shane), Memari, Y. (Yasin), Walter, K. (Klaudia), Min, J.L. (Josine L.), Danecek, P. (Petr), Malerba, G. (Giovanni), Trabetti, E. (Elisabetta), Zheng, H.-F. (Hou-Feng), Gambaro, G. (Giovanni), Richards, J.B. (Brent), Durbin, R. (Richard), Timpson, N.J. (Nicholas), Marchini, J. (Jonathan), Soranzo, N. (Nicole), Al Turki, S.H. (Saeed), Amuzu, A. (Antoinette), Anderson, C. (Carl), Anney, R. (Richard), Antony, D. (Dinu), Artigas, M.S., Ayub, M. (Muhammad), Bala, S. (Senduran), Barrett, J.C. (Jeffrey), Barroso, I.E. (Inês), Beales, P.L. (Philip), Benn, M. (Marianne), Bentham, J. (Jamie), Bhattacharya, S. (Shoumo), Birney, E. (Ewan), Blackwood, D.H.R. (Douglas), Bobrow, M. (Martin), Bochukova, E. (Elena), Bolton, P.F. (Patrick F.), Bounds, R. (Rebecca), Boustred, C. (Chris), Breen, G. (Gerome), Calissano, M. (Mattia), Carss, K. (Keren), Casas, J.P. (Juan Pablo), Chambers, J.C. (John C.), Charlton, R. (Ruth), Chatterjee, K. (Krishna), Chen, L. (Lu), Ciampi, A. (Antonio), Cirak, S. (Sebahattin), Clapham, P. (Peter), Clement, G. (Gail), Coates, G. (Guy), Cocca, M. (Massimiliano), Collier, D.A. (David), Cosgrove, C. (Catherine), Cox, T. (Tony), Craddock, N.J. (Nick), Crooks, L. (Lucy), Curran, S. (Sarah), Curtis, D. (David), Daly, A. (Allan), Day, I.N.M. (Ian N.M.), Day-Williams, A.G. (Aaron), Dedoussis, G.V. (George), Down, T. (Thomas), Du, Y. (Yuanping), Duijn, C.M. (Cornelia) van, Dunham, I. (Ian), Edkins, T. (Ted), Ekong, R. (Rosemary), Ellis, P. (Peter), Evans, D.M. (David), Farooqi, I.S. (I. Sadaf), Fitzpatrick, D.R. (David R.), Flicek, P. (Paul), Floyd, J. (James), Foley, A.R. (A. Reghan), Franklin, C.S. (Christopher S.), Futema, M. (Marta), Gallagher, L. (Louise), Gasparini, P. (Paolo), Gaunt, T.R. (Tom), Geihs, M. (Matthias), Geschwind, D. (Daniel), Greenwood, C.M.T. (Celia), Griffin, H. (Heather), Grozeva, D. (Detelina), Guo, X. (Xiaosen), Guo, X. (Xueqin), Gurling, H. (Hugh), Hart, D. (Deborah), Hendricks, A.E. (Audrey E.), Holmans, P.A. (Peter A.), Huang, L. (Liren), Hubbard, T. (Tim), Humphries, S.E. (Steve E.), Hurles, M.E. (Matthew), Hysi, P.G. (Pirro), Iotchkova, V. (Valentina), Isaacs, A. (Aaron), Jackson, D.K. (David K.), Jamshidi, Y. (Yalda), Johnson, J. (Jon), Joyce, C. (Chris), Karczewski, K.J. (Konrad), Kaye, J. (Jane), Keane, T. (Thomas), Kemp, J.P. (John), Kennedy, K. (Karen), Kent, A. (Alastair), Keogh, J. (Julia), Khawaja, F. (Farrah), Kleber, M.E. (Marcus), Van Kogelenberg, M. (Margriet), Kolb-Kokocinski, A. (Anja), Kooner, J.S. (Jaspal S.), Lachance, G. (Genevieve), Langenberg, C. (Claudia), Langford, C. (Cordelia), Lawson, D. (Daniel), Lee, I. (Irene), Leeuwen, E.M. (Elisa) van, Lek, M. (Monkol), Li, R. (Rui), Li, Y. (Yingrui), Liang, J. (Jieqin), Lin, H. (Hong), Liu, R. (Ryan), Lönnqvist, J. (Jouko), Lopes, L.R. (Luis R.), Lopes, M.C. (Margarida), Luan, J., MacArthur, D.G. (Daniel G.), Mangino, M. (Massimo), Marenne, G. (Gaëlle), März, W. (Winfried), Maslen, J. (John), Matchan, A. (Angela), Mathieson, I. (Iain), McGuffin, P. (Peter), McIntosh, A.M. (Andrew), McKechanie, A.G. (Andrew G.), McQuillin, A. (Andrew), Metrustry, S. (Sarah), Migone, N. (Nicola), Mitchison, H.M. (Hannah M.), Moayyeri, A. (Alireza), Morris, J. (James), Morris, R.W. (Richard), Muddyman, D. (Dawn), Muntoni, F., Nordestgaard, B.G. (Børge G.), Northstone, K. (Kate), O'donovan, M.C. (Michael), O'Rahilly, S. (Stephen), Onoufriadis, A. (Alexandros), Oualkacha, K. (Karim), Owen, M.J. (Michael J.), Palotie, A. (Aarno), Panoutsopoulou, K. (Kalliope), Parker, V. (Victoria), Parr, J.R. (Jeremy R.), Paternoster, L. (Lavinia), Paunio, T. (Tiina), Payne, F. (Felicity), Payne, S.J. (Stewart J.), Perry, J.R.B. (John), Pietiläinen, O.P.H. (Olli), Plagnol, V. (Vincent), Pollitt, R.C. (Rebecca C.), Povey, S. (Sue), Quail, M.A. (Michael A.), Quaye, L. (Lydia), Raymond, L. (Lucy), Rehnström, K. (Karola), Ridout, C.K. (Cheryl K.), Ring, S.M. (Susan), Ritchie, G.R.S. (Graham R.S.), Roberts, N. (Nicola), Robinson, R.L. (Rachel L.), Savage, D.B. (David), Scambler, P.J. (Peter), Schiffels, S. (Stephan), Schmidts, M. (Miriam), Schoenmakers, N. (Nadia), Scott, R.H. (Richard H.), Scott, R.A. (Robert), Semple, R.K. (Robert K.), Serra, E. (Eva), Sharp, S.I. (Sally I.), Shaw, A.C. (Adam C.), Shihab, H.A. (Hashem A.), Shin, S.-Y. (So-Youn), Skuse, D. (David), Small, K.S. (Kerrin), Smee, C. (Carol), Smith, A.V. (Davey), Southam, L. (Lorraine), Spasic-Boskovic, O. (Olivera), Spector, T.D. (Timothy), St. Clair, D. (David), St Pourcain, B. (Beate), Stalker, J. (Jim), Stevens, E. (Elizabeth), Sun, J. (Jianping), Surdulescu, G. (Gabriela), Suvisaari, J. (Jaana), Syrris, P. (Petros), Tachmazidou, I. (Ioanna), Taylor, R. (Rohan), Tian, J. (Jing), Tobin, M.D. (Martin), Toniolo, D. (Daniela), Traglia, M. (Michela), Tybjaerg-Hansen, A. (Anne), Valdes, A.M., Vandersteen, A.M. (Anthony M.), Varbo, A. (Anette), Vijayarangakannan, P. (Parthiban), Visscher, P.M. (Peter), Wain, L.V. (Louise), Walters, J.T. (James), Wang, G. (Guangbiao), Wang, J. (Jun), Wang, Y. (Yu), Ward, K. (Kirsten), Wheeler, E. (Eleanor), Whincup, P.H. (Peter), Whyte, T. (Tamieka), Williams, H.J. (Hywel J.), Williamson, K.A. (Kathleen), Wilson, C. (Crispian), Wilson, S.G. (Scott), Wong, K. (Kim), Xu, C. (Changjiang), Yang, J. (Jian), Zaza, G. (Gianluigi), Zeggini, E. (Eleftheria), Zhang, F. (Feng), Zhang, P. (Pingbo), and Zhang, W. (Weihua)

Abstract

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this

Published
2015
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18. The UK10K project identifies rare variants in health and disease

Author

Walter, K, Min, JL, Huang, J, Crooks, L, Memari, Y, McCarthy, S, Perry, JRB, Xu, C, Futema, M, Lawson, D, Iotchkova, V, Schiffels, S, Hendricks, AE, Danecek, P, Li, R, Floyd, J, Wain, LV, Barroso, I, Humphries, SE, Hurles, ME, Zeggini, E, Barrett, JC, Plagnol, V, Richards, JB, Greenwood, CMT, Timpson, NJ, Durbin, R, Soranzo, N, Bala, S, Clapham, P, Coates, G, Cox, T, Daly, A, Du, Y, Edkins, S, Ellis, P, Flicek, P, Guo, X, Huang, L, Jackson, DK, Joyce, C, Keane, T, Kolb-Kokocinski, A, Langford, C, Li, Y, Liang, J, Lin, H, Liu, R, Maslen, J, Muddyman, D, Quail, MA, Stalker, J, Sun, J, Tian, J, Wang, G, Wang, J, Wang, Y, Wong, K, Zhang, P, Birney, E, Boustred, C, Chen, L, Clement, G, Cocca, M, Smith, GD, Day, INM, Day-Williams, A, Down, T, Dunham, I, Evans, DM, Gaunt, TR, Geihs, M, Hart, D, Howie, B, Hubbard, T, Hysi, P, Jamshidi, Y, Karczewski, KJ, Kemp, JP, Lachance, G, Lek, M, Lopes, M, MacArthur, DG, Marchini, J, Mangino, M, Mathieson, I, Metrustry, S, Moayyeri, A, Northstone, K, Panoutsopoulou, K, Paternoster, L, Quaye, L, Ring, S, Ritchie, GRS, Shihab, HA, Shin, S-Y, Small, KS, Artigas, MS, Southam, L, Spector, TD, St Pourcain, B, Surdulescu, G, Tachmazidou, I, Tobin, MD, Valdes, AM, Visscher, PM, Ward, K, Wilson, SG, Yang, J, Zhang, F, Zheng, H-F, Anney, R, Ayub, M, Blackwood, D, Bolton, PF, Breen, G, Collier, DA, Craddock, N, Curran, S, Curtis, D, Gallagher, L, Geschwind, D, Gurling, H, Holmans, P, Lee, I, Lonnqvist, J, McGuffin, P, McIntosh, AM, McKechanie, AG, McQuillin, A, Morris, J, O'Donovan, MC, Owen, MJ, Palotie, A, Parr, JR, Paunio, T, Pietilainen, O, Rehnstrom, K, Sharp, SI, Skuse, D, St Clair, D, Suvisaari, J, Walters, JTR, Williams, HJ, Bochukova, E, Bounds, R, Dominiczak, A, Farooqi, IS, Keogh, J, Marenne, GL, Morris, A, O'Rahilly, S, Porteous, DJ, Smith, BH, Wheeler, E, Al Turki, S, Anderson, CA, Antony, D, Beales, P, Bentham, J, Bhattacharya, S, Calissano, M, Carss, K, Chatterjee, K, Cirak, S, Cosgrove, C, Fitzpatrick, DR, Foley, AR, Franklin, CS, Grozeva, D, Mitchison, HM, Muntoni, F, Onoufriadis, A, Parker, V, Payne, F, Raymond, FL, Roberts, N, Savage, DB, Scambler, P, Schmidts, M, Schoenmakers, N, Semple, RK, Serra, E, Spasic-Boskovic, O, Stevens, E, van Kogelenberg, M, Vijayarangakannan, P, Williamson, KA, Wilson, C, Whyte, T, Ciampi, A, Oualkacha, K, Bobrow, M, Griffin, H, Kaye, J, Kennedy, K, Kent, A, Smee, C, Charlton, R, Ekong, R, Khawaja, F, Lopes, LR, Migone, N, Payne, SJ, Pollitt, RC, Povey, S, Ridout, CK, Robinson, RL, Scott, RH, Shaw, A, Syrris, P, Taylor, R, Vandersteen, AM, Amuzu, A, Casas, JP, Chambers, JC, Dedoussis, G, Gambaro, G, Gasparini, P, Isaacs, A, Johnson, J, Kleber, ME, Kooner, JS, Langenberg, C, Luan, J, Malerba, G, Maerz, W, Matchan, A, Morris, R, Nordestgaard, BG, Benn, M, Scott, RA, Toniolo, D, Traglia, M, Tybjaerg-Hansen, A, van Duijn, CM, van Leeuwen, EM, Varbo, A, Whincup, P, Zaza, G, Zhang, W, Walter, K, Min, JL, Huang, J, Crooks, L, Memari, Y, McCarthy, S, Perry, JRB, Xu, C, Futema, M, Lawson, D, Iotchkova, V, Schiffels, S, Hendricks, AE, Danecek, P, Li, R, Floyd, J, Wain, LV, Barroso, I, Humphries, SE, Hurles, ME, Zeggini, E, Barrett, JC, Plagnol, V, Richards, JB, Greenwood, CMT, Timpson, NJ, Durbin, R, Soranzo, N, Bala, S, Clapham, P, Coates, G, Cox, T, Daly, A, Du, Y, Edkins, S, Ellis, P, Flicek, P, Guo, X, Huang, L, Jackson, DK, Joyce, C, Keane, T, Kolb-Kokocinski, A, Langford, C, Li, Y, Liang, J, Lin, H, Liu, R, Maslen, J, Muddyman, D, Quail, MA, Stalker, J, Sun, J, Tian, J, Wang, G, Wang, J, Wang, Y, Wong, K, Zhang, P, Birney, E, Boustred, C, Chen, L, Clement, G, Cocca, M, Smith, GD, Day, INM, Day-Williams, A, Down, T, Dunham, I, Evans, DM, Gaunt, TR, Geihs, M, Hart, D, Howie, B, Hubbard, T, Hysi, P, Jamshidi, Y, Karczewski, KJ, Kemp, JP, Lachance, G, Lek, M, Lopes, M, MacArthur, DG, Marchini, J, Mangino, M, Mathieson, I, Metrustry, S, Moayyeri, A, Northstone, K, Panoutsopoulou, K, Paternoster, L, Quaye, L, Ring, S, Ritchie, GRS, Shihab, HA, Shin, S-Y, Small, KS, Artigas, MS, Southam, L, Spector, TD, St Pourcain, B, Surdulescu, G, Tachmazidou, I, Tobin, MD, Valdes, AM, Visscher, PM, Ward, K, Wilson, SG, Yang, J, Zhang, F, Zheng, H-F, Anney, R, Ayub, M, Blackwood, D, Bolton, PF, Breen, G, Collier, DA, Craddock, N, Curran, S, Curtis, D, Gallagher, L, Geschwind, D, Gurling, H, Holmans, P, Lee, I, Lonnqvist, J, McGuffin, P, McIntosh, AM, McKechanie, AG, McQuillin, A, Morris, J, O'Donovan, MC, Owen, MJ, Palotie, A, Parr, JR, Paunio, T, Pietilainen, O, Rehnstrom, K, Sharp, SI, Skuse, D, St Clair, D, Suvisaari, J, Walters, JTR, Williams, HJ, Bochukova, E, Bounds, R, Dominiczak, A, Farooqi, IS, Keogh, J, Marenne, GL, Morris, A, O'Rahilly, S, Porteous, DJ, Smith, BH, Wheeler, E, Al Turki, S, Anderson, CA, Antony, D, Beales, P, Bentham, J, Bhattacharya, S, Calissano, M, Carss, K, Chatterjee, K, Cirak, S, Cosgrove, C, Fitzpatrick, DR, Foley, AR, Franklin, CS, Grozeva, D, Mitchison, HM, Muntoni, F, Onoufriadis, A, Parker, V, Payne, F, Raymond, FL, Roberts, N, Savage, DB, Scambler, P, Schmidts, M, Schoenmakers, N, Semple, RK, Serra, E, Spasic-Boskovic, O, Stevens, E, van Kogelenberg, M, Vijayarangakannan, P, Williamson, KA, Wilson, C, Whyte, T, Ciampi, A, Oualkacha, K, Bobrow, M, Griffin, H, Kaye, J, Kennedy, K, Kent, A, Smee, C, Charlton, R, Ekong, R, Khawaja, F, Lopes, LR, Migone, N, Payne, SJ, Pollitt, RC, Povey, S, Ridout, CK, Robinson, RL, Scott, RH, Shaw, A, Syrris, P, Taylor, R, Vandersteen, AM, Amuzu, A, Casas, JP, Chambers, JC, Dedoussis, G, Gambaro, G, Gasparini, P, Isaacs, A, Johnson, J, Kleber, ME, Kooner, JS, Langenberg, C, Luan, J, Malerba, G, Maerz, W, Matchan, A, Morris, R, Nordestgaard, BG, Benn, M, Scott, RA, Toniolo, D, Traglia, M, Tybjaerg-Hansen, A, van Duijn, CM, van Leeuwen, EM, Varbo, A, Whincup, P, Zaza, G, and Zhang, W

Abstract

The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.

Published
2015

19. Network inference in matrix-variate Gaussian models with non-independent noise

Author

Dahl, A, Hore, V, Iotchkova, V, and Marchini, J

Subjects
Methodology (stat.ME), FOS: Computer and information sciences, Statistics - Methodology
Abstract

Inferring a graphical model or network from observational data from a large number of variables is a well studied problem in machine learning and computational statistics. In this paper we consider a version of this problem that is relevant to the analysis of multiple phenotypes collected in genetic studies. In such datasets we expect correlations between phenotypes and between individuals. We model observations as a sum of two matrix normal variates such that the joint covariance function is a sum of Kronecker products. This model, which generalizes the Graphical Lasso, assumes observations are correlated due to known genetic relationships and corrupted with non-independent noise. We have developed a computationally efficient EM algorithm to fit this model. On simulated datasets we illustrate substantially improved performance in network reconstruction by allowing for a general noise distribution.

Published
2013
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21. A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans.

Author

Timpson, NJ, Walter, K, Min, JL, Tachmazidou, I, Malerba, G, Shin, S-Y, Chen, L, Futema, M, Southam, L, Iotchkova, V, Cocca, M, Huang, J, Memari, Y, McCarthy, S, Danecek, P, Muddyman, D, Mangino, M, Menni, C, Perry, JRB, Ring, SM, Gaye, A, Dedoussis, G, Farmaki, A-E, Burton, P, Talmud, PJ, Gambaro, G, Spector, TD, Smith, GD, Durbin, R, Richards, JB, Humphries, SE, Zeggini, E, Soranzo, N, UK1OK Consortium Members, Timpson, NJ, Walter, K, Min, JL, Tachmazidou, I, Malerba, G, Shin, S-Y, Chen, L, Futema, M, Southam, L, Iotchkova, V, Cocca, M, Huang, J, Memari, Y, McCarthy, S, Danecek, P, Muddyman, D, Mangino, M, Menni, C, Perry, JRB, Ring, SM, Gaye, A, Dedoussis, G, Farmaki, A-E, Burton, P, Talmud, PJ, Gambaro, G, Spector, TD, Smith, GD, Durbin, R, Richards, JB, Humphries, SE, Zeggini, E, Soranzo, N, and UK1OK Consortium Members

Abstract

The analysis of rich catalogues of genetic variation from population-based sequencing provides an opportunity to screen for functional effects. Here we report a rare variant in APOC3 (rs138326449-A, minor allele frequency ~0.25% (UK)) associated with plasma triglyceride (TG) levels (-1.43 s.d. (s.e.=0.27 per minor allele (P-value=8.0 × 10(-8))) discovered in 3,202 individuals with low read-depth, whole-genome sequence. We replicate this in 12,831 participants from five additional samples of Northern and Southern European origin (-1.0 s.d. (s.e.=0.173), P-value=7.32 × 10(-9)). This is consistent with an effect between 0.5 and 1.5 mmol l(-1) dependent on population. We show that a single predicted splice donor variant is responsible for association signals and is independent of known common variants. Analyses suggest an independent relationship between rs138326449 and high-density lipoprotein (HDL) levels. This represents one of the first examples of a rare, large effect variant identified from whole-genome sequencing at a population scale.

Published
2014

22. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

Author

Iotchkova, V, Huang, J, Morris, JA, Jain, D, Barbieri, C, Walter, K, Min, JL, Chen, L, Astle, W, Cocca, M, Deelen, P, Elding, H, Farmaki, A-E, Franklin, CS, Franberg, M, Gaunt, TR, Hofman, A, Jiang, T, Kleber, ME, Lachance, G, Luan, J, Malerba, G, Matchan, A, Mead, D, Memari, Y, Ntalla, I, Panoutsopoulou, K, Pazoki, R, Perry, JRB, Rivadeneira, F, Sabater-Lleal, M, Sennblad, B, Shin, S-Y, Southam, L, Traglia, M, Van Dijk, F, Van Leeuwen, EM, Zaza, G, Zhang, W, UK10K Consortium, Amin, N, Butterworth, A, Chambers, JC, Dedoussis, G, Dehghan, A, Franco, OH, Franke, L, Frontini, M, Gambaro, G, Gasparini, P, Hamsten, A, Issacs, A, Kooner, JS, Kooperberg, C, Langenberg, C, Marz, W, Scott, RA, Swertz, MA, Toniolo, D, Uitterlinden, AG, Van Duijn, CM, Watkins, H, Zeggini, E, Maurano, MT, Timpson, NJ, Reiner, AP, Auer, PL, and Soranzo, N

Subjects
Male, Heart Diseases, Genetic Loci, Genome, Human, Quantitative Trait Loci, Genetic Variation, Humans, Female, Genetic Predisposition to Disease, Sequence Analysis, DNA, Hematologic Diseases, 3. Good health, Genome-Wide Association Study
Abstract

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

23. Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Author

Chen, L, Ge, B, Casale, FP, Vasquez, L, Kwan, T, Garrido-Martín, D, Watt, S, Yan, Y, Kundu, K, Ecker, S, Datta, A, Richardson, D, Burden, F, Mead, D, Mann, AL, Fernandez, JM, Rowlston, S, Wilder, SP, Farrow, S, Shao, X, Lambourne, JJ, Redensek, A, Albers, CA, Amstislavskiy, V, Ashford, S, Berentsen, K, Bomba, L, Bourque, G, Bujold, D, Busche, S, Caron, M, Chen, S-H, Cheung, W, Delaneau, O, Dermitzakis, ET, Elding, H, Colgiu, I, Bagger, FO, Flicek, P, Habibi, E, Iotchkova, V, Janssen-Megens, E, Kim, B, Lehrach, H, Lowy, E, Mandoli, A, Matarese, F, Maurano, MT, Morris, JA, Pancaldi, V, Pourfarzad, F, Rehnstrom, K, Rendon, A, Risch, T, Sharifi, N, Simon, M-M, Sultan, M, Valencia, A, Walter, K, Wang, S-Y, Frontini, M, Antonarakis, SE, Clarke, L, Yaspo, M-L, Beck, S, Guigo, R, Rico, D, Martens, JHA, Ouwehand, WH, Kuijpers, TW, Paul, DS, Stunnenberg, HG, Stegle, O, Downes, K, Pastinen, T, and Soranzo, N

Subjects
transription, DNA methylation, QTL, monocyte, t-cell, neutrophil, histone modification, immune, 3. Good health, EWAS, allele specific
Abstract

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14$^{+}$ monocytes, CD16$^{+}$ neutrophils, and naive CD4$^{+}$ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of $\textit{cis}$-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

24. MultiMeta: An R package for meta-analyzing multi-phenotype genome-wide association studies

Author

Valentina Iotchkova, Nicole Soranzo, Paolo Gasparini, Dragana Vuckovic, Vuckovic, Dragana, Gasparini, Paolo, Soranzo, N., and Iotchkova, V.

Subjects
Statistics and Probability, Multivariate analysis, Genetic Loci, Genome-Wide Association Study, Humans, Multivariate Analysis, Phenotype, Meta-Analysis as Topic, Software, Biochemistry, Molecular Biology, Computational Theory and Mathematics, Computer Science Applications1707 Computer Vision and Pattern Recognition, Computational Mathematics, Computer science, Locus (genetics), Genome-wide association study, computer.software_genre, Computational Theory and Mathematic, Multivariate Analysi, Applications Notes, Computer Science Applications, R package, Computational Mathematic, Data mining, computer, Human
Abstract

Summary: As new methods for multivariate analysis of genome wide association studies become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance-based method for meta-analysis, generalized to an n-dimensional setting. Availability and implementation: The R package MultiMeta can be downloaded from CRAN. Contact: dragana.vuckovic@burlo.trieste.it; vi1@sanger.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2015

25. Neutrophil lymphocyte ratio as an indicator for disease progression in Idiopathic Pulmonary Fibrosis.

Author

Achaiah A, Rathnapala A, Pereira A, Bothwell H, Dwivedi K, Barker R, Iotchkova V, Benamore R, Hoyles RK, and Ho LP

Subjects
Disease Progression, Humans, Lymphocytes, Neutrophils, Retrospective Studies, Tomography, X-Ray Computed, Idiopathic Pulmonary Fibrosis
Abstract

Rationale: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease. Patients present at different stages and disease course is varied. Blood monocytes have been linked to all-cause mortality, and neutrophils to progression to IPF in patients with the indeterminate for usual interstitial pneumonia CT pattern., Objective: To determine association between blood monocytes, neutrophils and lymphocytes levels (and their derived indexes), with lung function decline and mortality in IPF., Methods: We performed a retrospective analysis of an IPF cohort (n=128) who had their first clinical visit at the Oxford Interstitial Lung Disease Service between 2013 and 2017. Association between blood monocytes, neutrophils, lymphocytes and derived indexes (within 4 months of visit) and decline in forced vital capacity (FVC) and all-cause mortality were assessed using Cox proportional hazard regression analysis. Kaplan-Meier analysis was used to assess time-to-event for 10% FVC decline and mortality for patients dichotomised to high and low leucocyte counts., Results: Median length of follow-up was 31.0 months (IQR 16.2-42.4); 41.4% demonstrated FVC decline >10% per year and 43.8% died. In multivariate models (incorporating age, gender and initial FVC%), raised neutrophils, lymphopaenia and neutrophil:lymphocyte ratio were associated with FVC decline (p≤0.01); while both monocytes and neutrophil levels (and their derived indexes) were associated with all-cause mortality (p≤0.01). Kaplan-Meier analysis also showed association between neutrophils and its derived indexes but not monocyte, with FVC decline., Conclusion: Blood neutrophil and lymphopaenia are more sensitive than monocytes as prognostic indicators of disease progression in those with established IPF., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Published
2022
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26. Monocyte and neutrophil levels are potentially linked to progression to IPF for patients with indeterminate UIP CT pattern.

Author

Achaiah A, Rathnapala A, Pereira A, Bothwell H, Dwivedi K, Barker R, Benamore R, Hoyles RK, Iotchkova V, and Ho LP

Subjects
Humans, Monocytes, Neutrophils, Prognosis, Retrospective Studies, Tomography, X-Ray Computed, Idiopathic Pulmonary Fibrosis diagnostic imaging
Abstract

Rationale: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease with poor prognosis. Identifying patients early may allow intervention which could limit progression. The 'indeterminate for usual interstitial pneumonia' (iUIP) CT pattern, defined in the 2018 IPF guidelines, could be a precursor to IPF but there is limited data on how patients with iUIP progress over time., Objective: To evaluate the radiological progression of iUIP and explore factors linked to progression to IPF., Methods: We performed a retrospective analysis of a lung fibrosis clinic cohort (n=230) seen between 2013 and 2017. Cases with iUIP were identified; first ever CTs for each patient found and categorised as 'non-progressor' or 'progressors' (the latter defined as increase in extent of disease or to 'definite' or 'probable' UIP CT pattern) during their follow-up. Lung function trends, haematological data and patient demographics were examined to explore disease evolution and potential contribution to progression., Results: 48 cases with iUIP CT pattern were identified. Of these, 32 had follow-up CT scans, of which 23 demonstrated progression. 17 patients in this cohort were diagnosed with IPF over a mean (SD) period of 3.9 (±1.9) years. Monocyte (HR: 23, 95% CI: 1.6 to 340, p=0.03) and neutrophil levels (HR: 1.8, 95% CI: 1.3 to 2.3, p<0.001), obtained around the time of initial CT, were associated with progression to IPF using Cox proportional hazard modelling., Conclusion: 53% of our evaluable patients with iUIP progressed to IPF over a mean of 4 years. Monocyte and neutrophil levels at initial CT were significantly associated with progression in disease. These data provide a single-centre analysis of the evolution of patients with iUIP CT pattern, and first signal for potential factors associated with progression to IPF., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2021
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27. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.

Author

Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, Bretherick AD, Richardson TG, Klughammer J, Iotchkova V, Sharp G, Al Khleifat A, Shatunov A, Iacoangeli A, McArdle WL, Ho KM, Kumar A, Söderhäll C, Soriano-Tárraga C, Giralt-Steinhauer E, Kazmi N, Mason D, McRae AF, Corcoran DL, Sugden K, Kasela S, Cardona A, Day FR, Cugliari G, Viberti C, Guarrera S, Lerro M, Gupta R, Bollepalli S, Mandaviya P, Zeng Y, Clarke TK, Walker RM, Schmoll V, Czamara D, Ruiz-Arenas C, Rezwan FI, Marioni RE, Lin T, Awaloff Y, Germain M, Aïssi D, Zwamborn R, van Eijk K, Dekker A, van Dongen J, Hottenga JJ, Willemsen G, Xu CJ, Barturen G, Català-Moll F, Kerick M, Wang C, Melton P, Elliott HR, Shin J, Bernard M, Yet I, Smart M, Gorrie-Stone T, Shaw C, Al Chalabi A, Ring SM, Pershagen G, Melén E, Jiménez-Conde J, Roquer J, Lawlor DA, Wright J, Martin NG, Montgomery GW, Moffitt TE, Poulton R, Esko T, Milani L, Metspalu A, Perry JRB, Ong KK, Wareham NJ, Matullo G, Sacerdote C, Panico S, Caspi A, Arseneault L, Gagnon F, Ollikainen M, Kaprio J, Felix JF, Rivadeneira F, Tiemeier H, van IJzendoorn MH, Uitterlinden AG, Jaddoe VWV, Haley C, McIntosh AM, Evans KL, Murray A, Räikkönen K, Lahti J, Nohr EA, Sørensen TIA, Hansen T, Morgen CS, Binder EB, Lucae S, Gonzalez JR, Bustamante M, Sunyer J, Holloway JW, Karmaus W, Zhang H, Deary IJ, Wray NR, Starr JM, Beekman M, van Heemst D, Slagboom PE, Morange PE, Trégouët DA, Veldink JH, Davies GE, de Geus EJC, Boomsma DI, Vonk JM, Brunekreef B, Koppelman GH, Alarcón-Riquelme ME, Huang RC, Pennell CE, van Meurs J, Ikram MA, Hughes AD, Tillin T, Chaturvedi N, Pausova Z, Paus T, Spector TD, Kumari M, Schalkwyk LC, Visscher PM, Davey Smith G, Bock C, Gaunt TR, Bell JT, Heijmans BT, Mill J, and Relton CL

Subjects
Chromosome Mapping, Epigenesis, Genetic genetics, Genome-Wide Association Study, Humans, Multifactorial Inheritance genetics, Polymorphism, Single Nucleotide genetics, Quantitative Trait, Heritable, Transcriptome genetics, DNA metabolism, DNA Methylation genetics, Gene Expression Regulation genetics, Genetic Predisposition to Disease genetics, Quantitative Trait Loci genetics
Abstract

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Published
2021
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28. Genetic perturbation of PU.1 binding and chromatin looping at neutrophil enhancers associates with autoimmune disease.

Author

Watt S, Vasquez L, Walter K, Mann AL, Kundu K, Chen L, Sims Y, Ecker S, Burden F, Farrow S, Farr B, Iotchkova V, Elding H, Mead D, Tardaguila M, Ponstingl H, Richardson D, Datta A, Flicek P, Clarke L, Downes K, Pastinen T, Fraser P, Frontini M, Javierre BM, Spivakov M, and Soranzo N

Subjects
Adult, Aged, Autoimmune Diseases immunology, Chromatin metabolism, Chromatin Immunoprecipitation Sequencing, Female, Humans, Male, Middle Aged, Neutrophils metabolism, Promoter Regions, Genetic genetics, Quantitative Trait Loci genetics, Quantitative Trait Loci immunology, Young Adult, Autoimmune Diseases genetics, Enhancer Elements, Genetic genetics, Gene Expression Regulation immunology, Neutrophils immunology, Proto-Oncogene Proteins metabolism, Trans-Activators metabolism
Abstract

Neutrophils play fundamental roles in innate immune response, shape adaptive immunity, and are a potentially causal cell type underpinning genetic associations with immune system traits and diseases. Here, we profile the binding of myeloid master regulator PU.1 in primary neutrophils across nearly a hundred volunteers. We show that variants associated with differential PU.1 binding underlie genetically-driven differences in cell count and susceptibility to autoimmune and inflammatory diseases. We integrate these results with other multi-individual genomic readouts, revealing coordinated effects of PU.1 binding variants on the local chromatin state, enhancer-promoter contacts and downstream gene expression, and providing a functional interpretation for 27 genes underlying immune traits. Collectively, these results demonstrate the functional role of PU.1 and its target enhancers in neutrophil transcriptional control and immune disease susceptibility.

Published
2021
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29. Multi-Modal Characterization of Monocytes in Idiopathic Pulmonary Fibrosis Reveals a Primed Type I Interferon Immune Phenotype.

Author

Fraser E, Denney L, Antanaviciute A, Blirando K, Vuppusetty C, Zheng Y, Repapi E, Iotchkova V, Taylor S, Ashley N, St Noble V, Benamore R, Hoyles R, Clelland C, Rastrick JMD, Hardman CS, Alham NK, Rigby RE, Simmons A, Rehwinkel J, and Ho LP

Subjects
Case-Control Studies, Cells, Cultured, Chemokine CCL2 blood, Flow Cytometry, Gene Expression Profiling, Humans, Idiopathic Pulmonary Fibrosis genetics, Idiopathic Pulmonary Fibrosis metabolism, Idiopathic Pulmonary Fibrosis pathology, Immunophenotyping, Interferon Type I genetics, Interleukin-6 blood, Lung metabolism, Lung pathology, Macrophage Colony-Stimulating Factor blood, Macrophages immunology, Macrophages metabolism, Monocytes metabolism, Phenotype, Receptors, IgG genetics, Receptors, IgG metabolism, Single-Cell Analysis, Idiopathic Pulmonary Fibrosis immunology, Interferon Type I metabolism, Lung immunology, Monocytes immunology
Abstract

Idiopathic pulmonary fibrosis (IPF) is the most severe form of chronic lung fibrosis. Circulating monocytes have been implicated in immune pathology in IPF but their phenotype is unknown. In this work, we determined the immune phenotype of monocytes in IPF using multi-colour flow cytometry, RNA sequencing and corresponding serum factors, and mapped the main findings to amount of lung fibrosis and single cell transcriptomic landscape of myeloid cells in IPF lungs. We show that monocytes from IPF patients displayed increased expression of CD64 (FcγR1) which correlated with amount of lung fibrosis, and an amplified type I IFN response ex vivo . These were accompanied by markedly raised CSF-1 levels, IL-6, and CCL-2 in serum of IPF patients. Interrogation of single cell transcriptomic data from human IPF lungs revealed increased proportion of CD64 hi monocytes and "transitional macrophages" with higher expression of CCL-2 and type I IFN genes. Our study shows that monocytes in IPF patients are phenotypically distinct from age-matched controls, with a primed type I IFN pathway that may contribute to driving chronic inflammation and fibrosis. These findings strengthen the potential role of monocytes in the pathogenesis of IPF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Fraser, Denney, Antanaviciute, Blirando, Vuppusetty, Zheng, Repapi, Iotchkova, Taylor, Ashley, St Noble, Benamore, Hoyles, Clelland, Rastrick, Hardman, Alham, Rigby, Simmons, Rehwinkel and Ho.)

Published
2021
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30. Incidence of symptomatic, image-confirmed venous thromboembolism following hospitalization for COVID-19 with 90-day follow-up.

Author

Salisbury R, Iotchkova V, Jaafar S, Morton J, Sangha G, Shah A, Untiveros P, Curry N, and Shapiro S

Subjects
Aged, Aged, 80 and over, Biomarkers, Blood Coagulation Tests, COVID-19 virology, Diagnostic Imaging methods, Disease Management, Disease Susceptibility, England epidemiology, Female, Follow-Up Studies, Hospitalization, Humans, Incidence, Male, Middle Aged, Retrospective Studies, SARS-CoV-2, Symptom Assessment, Time Factors, Venous Thromboembolism diagnosis, Venous Thromboembolism prevention & control, COVID-19 complications, COVID-19 epidemiology, Venous Thromboembolism epidemiology, Venous Thromboembolism etiology
Abstract

Although COVID-19 has been reported to be associated with high rates of venous thromboembolism (VTE), the risk of VTE and bleeding after hospitalization for COVID-19 remains unclear, and the optimal hospital VTE prevention strategy is not known. We collected retrospective observational data on thrombosis and bleeding in 303 consecutive adult patients admitted to the hospital for at least 24 hours for COVID-19. Patients presenting with VTE on admission were excluded. Data were collected until 90 days after admission or known death by using medical records and an established national VTE network. Maximal level of care was ward based in 78% of patients, with 22% requiring higher dependency care (12% noninvasive ventilation, 10% invasive ventilation). Almost all patients (97.0%) received standard thromboprophylaxis or were already receiving therapeutic anticoagulation (17.5%). Symptomatic image-confirmed VTE occurred in 5.9% of patients during index hospitalization, and in 7.2% at 90 days after admission (23.9% in patients requiring higher dependency care); half the events were isolated segmental or subsegmental defects on lung imaging. Bleeding occurred in 13 patients (4.3%) during index hospitalization (1.3% had major bleeding). The majority of bleeds occurred in patients on the general ward, and 6 patients were receiving treatment-dose anticoagulation, highlighting the need for caution in intensifying standard thromboprophylaxis strategies. Of 152 patients discharged from the hospital without an indication for anticoagulation, 97% did not receive thromboprophylaxis after discharge, and 3% received 7 days of treatment with low molecular weight heparin after discharge. The rate of symptomatic VTE in this group at 42 days after discharge was 2.6%, highlighting the need for large prospective randomized controlled trials of extended thromboprophylaxis after discharge in COVID-19., (© 2020 by The American Society of Hematology.)

Published
2020
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31. Mechanisms of Progression of Myeloid Preleukemia to Transformed Myeloid Leukemia in Children with Down Syndrome.

Author

Labuhn M, Perkins K, Matzk S, Varghese L, Garnett C, Papaemmanuil E, Metzner M, Kennedy A, Amstislavskiy V, Risch T, Bhayadia R, Samulowski D, Hernandez DC, Stoilova B, Iotchkova V, Oppermann U, Scheer C, Yoshida K, Schwarzer A, Taub JW, Crispino JD, Weiss MJ, Hayashi Y, Taga T, Ito E, Ogawa S, Reinhardt D, Yaspo ML, Campbell PJ, Roberts I, Constantinescu SN, Vyas P, Heckl D, and Klusmann JH

Published
2019
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32. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals.

Author

Iotchkova V, Ritchie GRS, Geihs M, Morganella S, Min JL, Walter K, Timpson NJ, Dunham I, Birney E, and Soranzo N

Subjects
Genome-Wide Association Study methods, Genomics methods, Humans, Molecular Sequence Annotation methods, Phenotype, Polymorphism, Single Nucleotide genetics, Quantitative Trait Loci genetics, Regulatory Sequences, Nucleic Acid genetics, Software, Disease genetics, Genome genetics
Abstract

Loci discovered by genome-wide association studies predominantly map outside protein-coding genes. The interpretation of the functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking by which to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages genome-wide association studies' findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding not offered by current methods. We further assess enrichment of genome-wide association studies for 19 traits within Encyclopedia of DNA Elements- and Roadmap-derived regulatory regions. We characterize unique enrichment patterns for traits and annotations driving novel biological insights. The method is implemented in standalone software and an R package, to facilitate its application by the research community.

Published
2019
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33. Low-frequency variation in TP53 has large effects on head circumference and intracranial volume.

Author

Haworth S, Shapland CY, Hayward C, Prins BP, Felix JF, Medina-Gomez C, Rivadeneira F, Wang C, Ahluwalia TS, Vrijheid M, Guxens M, Sunyer J, Tachmazidou I, Walter K, Iotchkova V, Jackson A, Cleal L, Huffmann J, Min JL, Sass L, Timmers PRHJ, Davey Smith G, Fisher SE, Wilson JF, Cole TJ, Fernandez-Orth D, Bønnelykke K, Bisgaard H, Pennell CE, Jaddoe VWV, Dedoussis G, Timpson N, Zeggini E, Vitart V, and St Pourcain B

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Cephalometry, Child, Female, Gene Expression Regulation, Developmental, Gene Frequency, Genome, Human, Humans, Male, Middle Aged, Skull anatomy & histology, White People, Alleles, Genetic Loci, Genetic Variation, RNA, Messenger genetics, Skull metabolism, Tumor Suppressor Protein p53 genetics
Abstract

Cranial growth and development is a complex process which affects the closely related traits of head circumference (HC) and intracranial volume (ICV). The underlying genetic influences shaping these traits during the transition from childhood to adulthood are little understood, but might include both age-specific genetic factors and low-frequency genetic variation. Here, we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV + HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood, suggesting a previously unrecognized role of TP53 transcripts in human cranial development.

Published
2019
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34. Author Correction: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

Author

Iotchkova V, Huang J, Morris JA, Jain D, Barbieri C, Walter K, Min JL, Chen L, Astle W, Cocca M, Deelen P, Elding H, Farmaki AE, Franklin CS, Franberg M, Gaunt TR, Hofman A, Jiang T, Kleber ME, Lachance G, Luan J, Malerba G, Matchan A, Mead D, Memari Y, Ntalla I, Panoutsopoulou K, Pazoki R, Perry JRB, Rivadeneira F, Sabater-Lleal M, Sennblad B, Shin SY, Southam L, Traglia M, van Dijk F, van Leeuwen EM, Zaza G, Zhang W, Amin N, Butterworth A, Chambers JC, Dedoussis G, Dehghan A, Franco OH, Franke L, Frontini M, Gambaro G, Gasparini P, Hamsten A, Isaacs A, Kooner JS, Kooperberg C, Langenberg C, Marz W, Scott RA, Swertz MA, Toniolo D, Uitterlinden AG, van Duijn CM, Watkins H, Zeggini E, Maurano MT, Timpson NJ, Reiner AP, Auer PL, and Soranzo N

Abstract

In the version of the article published, the surname of author Aaron Isaacs is misspelled as Issacs.

Published
2018
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35. Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits.

Author

Tachmazidou I, Süveges D, Min JL, Ritchie GRS, Steinberg J, Walter K, Iotchkova V, Schwartzentruber J, Huang J, Memari Y, McCarthy S, Crawford AA, Bombieri C, Cocca M, Farmaki AE, Gaunt TR, Jousilahti P, Kooijman MN, Lehne B, Malerba G, Männistö S, Matchan A, Medina-Gomez C, Metrustry SJ, Nag A, Ntalla I, Paternoster L, Rayner NW, Sala C, Scott WR, Shihab HA, Southam L, St Pourcain B, Traglia M, Trajanoska K, Zaza G, Zhang W, Artigas MS, Bansal N, Benn M, Chen Z, Danecek P, Lin WY, Locke A, Luan J, Manning AK, Mulas A, Sidore C, Tybjaerg-Hansen A, Varbo A, Zoledziewska M, Finan C, Hatzikotoulas K, Hendricks AE, Kemp JP, Moayyeri A, Panoutsopoulou K, Szpak M, Wilson SG, Boehnke M, Cucca F, Di Angelantonio E, Langenberg C, Lindgren C, McCarthy MI, Morris AP, Nordestgaard BG, Scott RA, Tobin MD, Wareham NJ, Burton P, Chambers JC, Smith GD, Dedoussis G, Felix JF, Franco OH, Gambaro G, Gasparini P, Hammond CJ, Hofman A, Jaddoe VWV, Kleber M, Kooner JS, Perola M, Relton C, Ring SM, Rivadeneira F, Salomaa V, Spector TD, Stegle O, Toniolo D, Uitterlinden AG, Barroso I, Greenwood CMT, Perry JRB, Walker BR, Butterworth AS, Xue Y, Durbin R, Small KS, Soranzo N, Timpson NJ, and Zeggini E

Subjects
Body Height genetics, Cohort Studies, DNA Methylation genetics, Databases, Genetic, Female, Genetic Variation, Humans, Lipodystrophy genetics, Male, Meta-Analysis as Topic, Obesity genetics, Physical Chromosome Mapping, Sex Characteristics, Syndrome, United Kingdom, Anthropometry, Genome, Human, Genome-Wide Association Study, Quantitative Trait Loci genetics, Sequence Analysis, DNA methods
Abstract

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2017
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36. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.

Author

Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, Mead D, Bouman H, Riveros-Mckay F, Kostadima MA, Lambourne JJ, Sivapalaratnam S, Downes K, Kundu K, Bomba L, Berentsen K, Bradley JR, Daugherty LC, Delaneau O, Freson K, Garner SF, Grassi L, Guerrero J, Haimel M, Janssen-Megens EM, Kaan A, Kamat M, Kim B, Mandoli A, Marchini J, Martens JHA, Meacham S, Megy K, O'Connell J, Petersen R, Sharifi N, Sheard SM, Staley JR, Tuna S, van der Ent M, Walter K, Wang SY, Wheeler E, Wilder SP, Iotchkova V, Moore C, Sambrook J, Stunnenberg HG, Di Angelantonio E, Kaptoge S, Kuijpers TW, Carrillo-de-Santa-Pau E, Juan D, Rico D, Valencia A, Chen L, Ge B, Vasquez L, Kwan T, Garrido-Martín D, Watt S, Yang Y, Guigo R, Beck S, Paul DS, Pastinen T, Bujold D, Bourque G, Frontini M, Danesh J, Roberts DJ, Ouwehand WH, Butterworth AS, and Soranzo N

Subjects
Alleles, Cell Differentiation, Genetic Predisposition to Disease, Hematopoietic Stem Cells pathology, Humans, Immune System Diseases pathology, Polymorphism, Single Nucleotide, Quantitative Trait Loci, White People genetics, Genetic Variation, Genome-Wide Association Study, Hematopoietic Stem Cells metabolism, Immune System Diseases genetics
Abstract

Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Published
2016
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37. Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells.

Author

Chen L, Ge B, Casale FP, Vasquez L, Kwan T, Garrido-Martín D, Watt S, Yan Y, Kundu K, Ecker S, Datta A, Richardson D, Burden F, Mead D, Mann AL, Fernandez JM, Rowlston S, Wilder SP, Farrow S, Shao X, Lambourne JJ, Redensek A, Albers CA, Amstislavskiy V, Ashford S, Berentsen K, Bomba L, Bourque G, Bujold D, Busche S, Caron M, Chen SH, Cheung W, Delaneau O, Dermitzakis ET, Elding H, Colgiu I, Bagger FO, Flicek P, Habibi E, Iotchkova V, Janssen-Megens E, Kim B, Lehrach H, Lowy E, Mandoli A, Matarese F, Maurano MT, Morris JA, Pancaldi V, Pourfarzad F, Rehnstrom K, Rendon A, Risch T, Sharifi N, Simon MM, Sultan M, Valencia A, Walter K, Wang SY, Frontini M, Antonarakis SE, Clarke L, Yaspo ML, Beck S, Guigo R, Rico D, Martens JHA, Ouwehand WH, Kuijpers TW, Paul DS, Stunnenberg HG, Stegle O, Downes K, Pastinen T, and Soranzo N

Subjects
Adult, Aged, Alternative Splicing, Female, Genetic Predisposition to Disease, Hematopoietic Stem Cells metabolism, Histone Code, Humans, Male, Middle Aged, Quantitative Trait Loci, Young Adult, Epigenomics, Immune System Diseases genetics, Monocytes metabolism, Neutrophils metabolism, T-Lymphocytes metabolism, Transcription, Genetic
Abstract

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14 + monocytes, CD16 + neutrophils, and naive CD4 + T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2016
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38. eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.

Author

Breeze CE, Paul DS, van Dongen J, Butcher LM, Ambrose JC, Barrett JE, Lowe R, Rakyan VK, Iotchkova V, Frontini M, Downes K, Ouwehand WH, Laperle J, Jacques PÉ, Bourque G, Bergmann AK, Siebert R, Vellenga E, Saeed S, Matarese F, Martens JHA, Stunnenberg HG, Teschendorff AE, Herrero J, Birney E, Dunham I, and Beck S

Subjects
DNA Methylation genetics, Genome-Wide Association Study, Humans, Karyotyping, Multiple Sclerosis genetics, Organ Specificity genetics, Stem Cells metabolism, Epigenomics, Signal Transduction genetics, Software, Statistics as Topic
Abstract

Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology., (Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2016
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39. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

Author

Iotchkova V, Huang J, Morris JA, Jain D, Barbieri C, Walter K, Min JL, Chen L, Astle W, Cocca M, Deelen P, Elding H, Farmaki AE, Franklin CS, Franberg M, Gaunt TR, Hofman A, Jiang T, Kleber ME, Lachance G, Luan J, Malerba G, Matchan A, Mead D, Memari Y, Ntalla I, Panoutsopoulou K, Pazoki R, Perry JRB, Rivadeneira F, Sabater-Lleal M, Sennblad B, Shin SY, Southam L, Traglia M, van Dijk F, van Leeuwen EM, Zaza G, Zhang W, Amin N, Butterworth A, Chambers JC, Dedoussis G, Dehghan A, Franco OH, Franke L, Frontini M, Gambaro G, Gasparini P, Hamsten A, Issacs A, Kooner JS, Kooperberg C, Langenberg C, Marz W, Scott RA, Swertz MA, Toniolo D, Uitterlinden AG, van Duijn CM, Watkins H, Zeggini E, Maurano MT, Timpson NJ, Reiner AP, Auer PL, and Soranzo N

Subjects
Female, Genetic Predisposition to Disease, Genetic Variation, Humans, Male, Quantitative Trait Loci, Sequence Analysis, DNA, Genetic Loci, Genome, Human, Genome-Wide Association Study, Heart Diseases genetics, Hematologic Diseases genetics
Abstract

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

Published
2016
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40. A multiple-phenotype imputation method for genetic studies.

Author

Dahl A, Iotchkova V, Baud A, Johansson Å, Gyllensten U, Soranzo N, Mott R, Kranis A, and Marchini J

Subjects
Algorithms, Animals, Animals, Outbred Strains, Bayes Theorem, Blood Platelets physiology, Chickens, Female, Humans, Male, Models, Genetic, Phenotype, Polymorphism, Single Nucleotide, Rats, T-Lymphocytes physiology, Triticum genetics, Genome-Wide Association Study methods
Abstract

Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.

Published
2016
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41. Significant impact of miRNA-target gene networks on genetics of human complex traits.

Author

Okada Y, Muramatsu T, Suita N, Kanai M, Kawakami E, Iotchkova V, Soranzo N, Inazawa J, and Tanaka T

Subjects
Adult, Body Height genetics, Down-Regulation, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Kidney physiology, Molecular Targeted Therapy, Multifactorial Inheritance genetics, Polymorphism, Single Nucleotide, Protein-Arginine Deiminase Type 2, Protein-Arginine Deiminases, Risk, Arthritis, Rheumatoid genetics, Hydrolases genetics, MicroRNAs genetics
Abstract

The impact of microRNA (miRNA) on the genetics of human complex traits, especially in the context of miRNA-target gene networks, has not been fully assessed. Here, we developed a novel analytical method, MIGWAS, to comprehensively evaluate enrichment of genome-wide association study (GWAS) signals in miRNA-target gene networks. We applied the method to the GWAS results of the 18 human complex traits from >1.75 million subjects, and identified significant enrichment in rheumatoid arthritis (RA), kidney function, and adult height (P < 0.05/18 = 0.0028, most significant enrichment in RA with P = 1.7 × 10(-4)). Interestingly, these results were consistent with current literature-based knowledge of the traits on miRNA obtained through the NCBI PubMed database search (adjusted P = 0.024). Our method provided a list of miRNA and target gene pairs with excess genetic association signals, part of which included drug target genes. We identified a miRNA (miR-4728-5p) that downregulates PADI2, a novel RA risk gene considered as a promising therapeutic target (rs761426, adjusted P = 2.3 × 10(-9)). Our study indicated the significant impact of miRNA-target gene networks on the genetics of human complex traits, and provided resources which should contribute to drug discovery and nucleic acid medicine.

Published
2016
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42. The UK10K project identifies rare variants in health and disease.

Author

Walter K, Min JL, Huang J, Crooks L, Memari Y, McCarthy S, Perry JR, Xu C, Futema M, Lawson D, Iotchkova V, Schiffels S, Hendricks AE, Danecek P, Li R, Floyd J, Wain LV, Barroso I, Humphries SE, Hurles ME, Zeggini E, Barrett JC, Plagnol V, Richards JB, Greenwood CM, Timpson NJ, Durbin R, and Soranzo N

Subjects
Adiponectin blood, Alleles, Cohort Studies, Exome genetics, Female, Genetic Predisposition to Disease genetics, Genetics, Medical, Genetics, Population, Genome-Wide Association Study, Genomics, Humans, Lipid Metabolism genetics, Male, Molecular Sequence Annotation, Receptors, LDL genetics, Reference Standards, Sequence Analysis, DNA, Triglycerides blood, United Kingdom, Disease genetics, Genetic Variation genetics, Genome, Human genetics, Health
Abstract

The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.

Published
2015
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43. Erratum: A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans.

Author

Timpson NJ, Walter K, Min JL, Tachmazidou I, Malerba G, Shin SY, Chen L, Futema M, Southam L, Iotchkova V, Cocca M, Huang J, Memari Y, McCarthy S, Danecek P, Muddyman D, Mangino M, Menni C, Perry JR, Ring SM, Gaye A, Dedoussis G, Farmaki AE, Burton P, Talmud PJ, Gambaro G, Spector TD, Smith GD, Durbin R, Richards JB, Humphries SE, Zeggini E, and Soranzo N

Published
2015
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44. A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans.

Author

Timpson NJ, Walter K, Min JL, Tachmazidou I, Malerba G, Shin SY, Chen L, Futema M, Southam L, Iotchkova V, Cocca M, Huang J, Memari Y, McCarthy S, Danecek P, Muddyman D, Mangino M, Menni C, Perry JR, Ring SM, Gaye A, Dedoussis G, Farmaki AE, Burton P, Talmud PJ, Gambaro G, Spector TD, Smith GD, Durbin R, Richards JB, Humphries SE, Zeggini E, and Soranzo N

Subjects
Alternative Splicing, Child, Female, Gene Frequency, Humans, Lipoproteins, HDL blood, Male, Middle Aged, Polymorphism, Genetic, Twins genetics, White People, Alleles, Apolipoprotein C-III genetics, Lipoproteins, VLDL blood, Triglycerides blood
Abstract

The analysis of rich catalogues of genetic variation from population-based sequencing provides an opportunity to screen for functional effects. Here we report a rare variant in APOC3 (rs138326449-A, minor allele frequency ~0.25% (UK)) associated with plasma triglyceride (TG) levels (-1.43 s.d. (s.e.=0.27 per minor allele (P-value=8.0 × 10(-8))) discovered in 3,202 individuals with low read-depth, whole-genome sequence. We replicate this in 12,831 participants from five additional samples of Northern and Southern European origin (-1.0 s.d. (s.e.=0.173), P-value=7.32 × 10(-9)). This is consistent with an effect between 0.5 and 1.5 mmol l(-1) dependent on population. We show that a single predicted splice donor variant is responsible for association signals and is independent of known common variants. Analyses suggest an independent relationship between rs138326449 and high-density lipoprotein (HDL) levels. This represents one of the first examples of a rare, large effect variant identified from whole-genome sequencing at a population scale.

Published
2014
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