Author: "Andrew D. Bretherick" - Searchworks@Jio Institute Digital Library Search Results

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1. Evaluation of pragmatic oxygenation measurement as a proxy for Covid-19 severity

Author: Maaike C. Swets, Steven Kerr, James Scott-Brown, Adam B. Brown, Rishi Gupta, Jonathan E. Millar, Enti Spata, Fiona McCurrach, Andrew D. Bretherick, Annemarie Docherty, David Harrison, Kathy Rowan, Neil Young, ISARIC4C Investigators, Geert H. Groeneveld, Jake Dunning, Jonathan S. Nguyen-Van-Tam, Peter Openshaw, Peter W. Horby, Ewen Harrison, Natalie Staplin, Malcolm G. Semple, Nazir Lone, and J. Kenneth Baillie
Subjects: Science
Abstract: Abstract Choosing optimal outcome measures maximizes statistical power, accelerates discovery and improves reliability in early-phase trials. We devised and evaluated a modification to a pragmatic measure of oxygenation function, the $$S/F$$ S / F ratio. Because of the ceiling effect in oxyhaemoglobin saturation, $$S/F$$ S / F ratio ceases to reflect pulmonary oxygenation function at high $${S}_{p}{O}_{2}$$ S p O 2 values. We found that the correlation of $$S/F$$ S / F with the reference standard ( $${P}_{a}{O}_{2}$$ P a O 2 / $${F}_{I}{O}_{2}$$ F I O 2 ratio) improves substantially when excluding $${S}_{p}{O}_{2} > 0.94$$ S p O 2 > 0.94 and refer to this measure as $$S/{F}_{94}$$ S / F 94 . Using observational data from 39,765 hospitalised COVID-19 patients, we demonstrate that $$S/{F}_{94}$$ S / F 94 is predictive of mortality, and compare the sample sizes required for trials using four different outcome measures. We show that a significant difference in outcome could be detected with the smallest sample size using $$S/{F}_{94}$$ S / F 94 . We demonstrate that $$S/{F}_{94}$$ S / F 94 is an effective intermediate outcome measure in COVID-19. It is a non-invasive measurement, representative of disease severity and provides greater statistical power.
Published: 2023
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2. Typing myalgic encephalomyelitis by infection at onset: A DecodeME study [version 4; peer review: 2 approved]

Author: Andy Devereux-Cooke, Simon J. McGrath, Emma Northwood, Sian Leary, Pippa Stacey, Anna Redshaw, Jim Wilson, Claire Tripp, Isabel Lewis, Sonya Chowdhury, Sumy V. Baby, Øyvind Almelid, Hannes Becher, Tom Baker, Malgorzata Clyde, Thibaud Boutin, John Ireland, Diana Garcia, Ewan McDowall, Shona M. Kerr, Gemma L. Samms, David Perry, Jareth C. Wolfe, Veronique Vitart, Chris P. Ponting, and Andrew D. Bretherick
Subjects: Myalgic encephalomyelitis, Post-viral syndrome, Post-exertional malaise, Sex-bias, Sub-types, eng, Medicine
Abstract: Background: People with myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) experience core symptoms of post-exertional malaise, unrefreshing sleep, and cognitive impairment. Despite numbering 0.2-0.4% of the population, no laboratory test is available for their diagnosis, no effective therapy exists for their treatment, and no scientific breakthrough regarding pathogenesis has been made. It remains unknown, despite decades of small-scale studies, whether individuals experience different types of ME/CFS separated by onset-type, sex or age. Methods: DecodeME is a large population-based study of ME/CFS that recruited 17,074 participants in the first 3 months following full launch. Detailed questionnaire responses from UK-based participants who all reported being diagnosed with ME/CFS by a health professional provided an unparalleled opportunity to investigate, using logistic regression, whether ME/CFS severity or onset type is significantly associated with sex, age, illness duration, comorbid conditions or symptoms. Results: The well-established sex-bias among ME/CFS patients is evident in the initial DecodeME cohort: 83.5% of participants were females. What was not known previously was that females tend to have more comorbidities than males. Moreover, being female, being older and being over 10 years from ME/CFS onset are significantly associated with greater severity. Five different ME/CFS onset types were examined in the self-reported data: those with ME/CFS onset (i) after glandular fever (infectious mononucleosis); (ii) after COVID-19 infection; (iii) after other infections; (iv) without an infection at onset; and, (v) where the occurrence of an infection at or preceding onset is not known. Among other findings, ME/CFS onset with unknown infection status was significantly associated with active fibromyalgia. Conclusions: DecodeME participants differ in symptoms, comorbid conditions and/or illness severity when stratified by their sex-at-birth and/or infection around the time of ME/CFS onset.
Published: 2023
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3. Genome‐Wide Association Study of NAFLD Using Electronic Health Records

Author: Cameron J. Fairfield, Thomas M. Drake, Riinu Pius, Andrew D. Bretherick, Archie Campbell, David W. Clark, Jonathan A. Fallowfield, Caroline Hayward, Neil C. Henderson, Peter K. Joshi, Nicholas L. Mills, David J. Porteous, Prakash Ramachandran, Robert K. Semple, Catherine A. Shaw, Cathie L.M. Sudlow, Paul R.H.J. Timmers, James F. Wilson, Stephen J. Wigmore, Ewen M. Harrison, and Athina Spiliopoulou
Subjects: Diseases of the digestive system. Gastroenterology, RC799-869
Abstract: Genome‐wide association studies (GWAS) have identified several risk loci for nonalcoholic fatty liver disease (NAFLD). Previous studies have largely relied on small sample sizes and have assessed quantitative traits. We performed a case‐control GWAS in the UK Biobank using recorded diagnosis of NAFLD based on diagnostic codes recommended in recent consensus guidelines. We performed a GWAS of 4,761 cases of NAFLD and 373,227 healthy controls without evidence of NAFLD. Sensitivity analyses were performed excluding other co‐existing hepatic pathology, adjusting for body mass index (BMI) and adjusting for alcohol intake. A total of 9,723,654 variants were assessed by logistic regression adjusted for age, sex, genetic principal components, and genotyping batch. We performed a GWAS meta‐analysis using available summary association statistics. Six risk loci were identified (P
Published: 2022
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4. Identification of epigenome-wide DNA methylation differences between carriers of APOE ε4 and APOE ε2 alleles

Author: Rosie M. Walker, Kadi Vaher, Mairead L. Bermingham, Stewart W. Morris, Andrew D. Bretherick, Yanni Zeng, Konrad Rawlik, Carmen Amador, Archie Campbell, Chris S. Haley, Caroline Hayward, David J. Porteous, Andrew M. McIntosh, Riccardo E. Marioni, and Kathryn L. Evans
Subjects: Alzheimer’s disease, APOE, Apolipoprotein E, DNA methylation, Cholesterol, Lipids, Medicine, Genetics, QH426-470
Abstract: Abstract Background The apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late onset Alzheimer’s disease, whilst the ε2 allele confers protection. Previous studies report differential DNA methylation of APOE between ε4 and ε2 carriers, but associations with epigenome-wide methylation have not previously been characterised. Methods Using the EPIC array, we investigated epigenome-wide differences in whole blood DNA methylation patterns between Alzheimer’s disease-free APOE ε4 (n = 2469) and ε2 (n = 1118) carriers from the two largest single-cohort DNA methylation samples profiled to date. Using a discovery, replication and meta-analysis study design, methylation differences were identified using epigenome-wide association analysis and differentially methylated region (DMR) approaches. Results were explored using pathway and methylation quantitative trait loci (meQTL) analyses. Results We obtained replicated evidence for DNA methylation differences in a ~ 169 kb region, which encompasses part of APOE and several upstream genes. Meta-analytic approaches identified DNA methylation differences outside of APOE: differentially methylated positions were identified in DHCR24, LDLR and ABCG1 (2.59 × 10−100 ≤ P ≤ 2.44 × 10−8) and DMRs were identified in SREBF2 and LDLR (1.63 × 10−4 ≤ P ≤ 3.01 × 10−2). Pathway and meQTL analyses implicated lipid-related processes and high-density lipoprotein cholesterol was identified as a partial mediator of the methylation differences in ABCG1 and DHCR24. Conclusions APOE ε4 vs. ε2 carrier status is associated with epigenome-wide methylation differences in the blood. The loci identified are located in trans as well as cis to APOE and implicate genes involved in lipid homeostasis.
Published: 2021
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5. Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome

Author: Yanni Zeng, Carmen Amador, Chenhao Gao, Rosie M. Walker, Stewart W. Morris, Archie Campbell, Azra Frkatović, Rebecca A Madden, Mark J. Adams, Shuai He, Andrew D. Bretherick, Caroline Hayward, David J. Porteous, James F. Wilson, Kathryn L. Evans, Andrew M. McIntosh, Pau Navarro, and Chris S. Haley
Subjects: parent-of-origin effect, DNA methylation, interaction (modification) effect, mQTL, DNA methylation machinery genes, smoking, Medicine, Medicine (General), R5-920
Abstract: Background: parent-of-origin effects (POE) play important roles in complex disease and thus understanding their regulation and associated molecular and phenotypic variation are warranted. Previous studies mainly focused on the detection of genomic regions or phenotypes regulated by POE. Understanding whether POE may be modified by environmental or genetic exposures is important for understanding of the source of POE-associated variation, but only a few case studies addressing modifiable POE exist. Methods: in order to understand this high order of POE regulation, we screened 101 genetic and environmental factors such as ‘predicted mRNA expression levels’ of DNA methylation/imprinting machinery genes and environmental exposures. POE-mQTL-modifier interaction models were proposed to test the potential of these factors to modify POE at DNA methylation using data from Generation Scotland: The Scottish Family Health Study(N=2315). Findings: a set of vulnerable/modifiable POE-CpGs were identified (modifiable-POE-regulated CpGs, N=3). Four factors, ‘lifetime smoking status’ and ‘predicted mRNA expression levels’ of TET2, SIRT1 and KDM1A, were found to significantly modify the POE on the three CpGs in both discovery and replication datasets. We further identified plasma protein and health-related phenotypes associated with the methylation level of one of the identified CpGs. Interpretation: the modifiable POE identified here revealed an important yet indirect path through which genetic background and environmental exposures introduce their effect on DNA methylation, motivating future comprehensive evaluation of the role of these modifiers in complex diseases. Funding: NSFC (81971270),H2020-MSCA-ITN(721815), Wellcome (204979/Z/16/Z,104036/Z/14/Z), MRC (MC_UU_00007/10, MC_PC_U127592696), CSO (CZD/16/6,CZB/4/276, CZB/4/710), SFC (HR03006), EUROSPAN (LSHG-CT-2006-018947), BBSRC (BBS/E/D/30002276), SYSU, Arthritis Research UK, NHLBI, NIH.
Published: 2021
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6. Epigenome‐wide analyses identify DNA methylation signatures of dementia risk

Author: Rosie M. Walker, Mairead L. Bermingham, Kadi Vaher, Stewart W. Morris, Toni‐Kim Clarke, Andrew D. Bretherick, Yanni Zeng, Carmen Amador, Konrad Rawlik, Kalyani Pandya, Caroline Hayward, Archie Campbell, David J. Porteous, Andrew M. McIntosh, Riccardo E. Marioni, and Kathryn L. Evans
Subjects: alcohol, Alzheimer's disease, dementia, DNA methylation, genetic risk score, risk factors, Neurology. Diseases of the nervous system, RC346-429, Geriatrics, RC952-954.6
Abstract: Abstract Introduction Dementia pathogenesis begins years before clinical symptom onset, necessitating the understanding of premorbid risk mechanisms. Here we investigated potential pathogenic mechanisms by assessing DNA methylation associations with dementia risk factors in Alzheimer's disease (AD)–free participants. Methods Associations between dementia risk measures (family history, AD genetic risk score [GRS], and dementia risk scores [combining lifestyle, demographic, and genetic factors]) and whole‐blood DNA methylation were assessed in discovery and replication samples (n = ~400 to ~5000) from Generation Scotland. Results AD genetic risk and two dementia risk scores were associated with differential methylation. The GRS associated predominantly with methylation differences in cis but also identified a genomic region implicated in Parkinson disease. Loci associated with dementia risk scores were enriched for those previously associated with body mass index and alcohol consumption. Discussion Dementia risk measures show widespread association with blood‐based methylation, generating several hypotheses for assessment by future studies.
Published: 2020
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7. Typing myalgic encephalomyelitis by infection at onset: A DecodeME study

Author: Andrew D. Bretherick, Simon J. McGrath, Andy Devereux-Cooke, Sian Leary, Emma Northwood, Anna Redshaw, Pippa Stacey, Claire Tripp, Jim Wilson, Sonya Chowdhury, Isabel Lewis, Øyvind Almelid, Sumy V. Baby, Tom Baker, Hannes Becher, Thibaud Boutin, Malgorzata Clyde, Diana Garcia, John Ireland, Shona M. Kerr, Ewan McDowall, David Perry, Gemma L. Samms, Veronique Vitart, Jareth C. Wolfe, and Chris P. Ponting
Abstract: Background: People with myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) daily experience core symptoms of post-exertional malaise, unrefreshing sleep, and cognitive impairment or brain fog. Despite numbering 0.2-0.4% of the population, no laboratory test is available for their diagnosis, no effective therapy exists for their treatment, and no scientific breakthrough regarding their pathogenesis has been made. It remains unknown, despite decades of small-scale studies, whether individuals experience different types of ME/CFS separated by onset-type, sex or age. Methods: DecodeME is a large population-based study of ME/CFS that recruited 17,074 participants in the first 3 months following full launch. Their detailed questionnaire responses provided an unparalleled opportunity to investigate illness severity, onset, course and duration. Results: The well-established sex-bias among ME/CFS patients is evident in the initial DecodeME cohort: 83.5% of participants were females. What was not known previously was that females’ comorbidities and symptoms tend to be more numerous than males’. Moreover, being female, being older and being over 10 years from ME/CFS onset are significantly associated with greater severity. Five different ME/CFS onset types were examined in the self-reported data: those with ME/CFS onset (i) after glandular fever (infectious mononucleosis); (ii) after COVID-19 infection; (iii) after other infections; (iv) without an identified infectious onset; and, (v) where the occurrence of an infection at or preceding onset is not known. Conclusions: This revealed that people with a ME/CFS diagnosis are not a homogeneous group, as clear differences exist in symptomatology and comorbidity.
Published: 2023
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8. Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

Author: Fallerini, Chiara, Picchiotti, Nicola, Baldassarri, Margherita, Zguro, Kristina, Daga, Sergio, Fava, Francesca, Benetti, Elisa, Amitrano, Sara, Bruttini, Mirella, Palmieri, Maria, Croci, Susanna, Lista, Mirjam, Beligni, Giada, Valentino, Floriana, Meloni, Ilaria, Tanfoni, Marco, Minnai, Francesca, Colombo, Francesca, Cabri, Enrico, Fratelli, Maddalena, Gabbi, Chiara, Mantovani, Stefania, Frullanti, Elisa, Gori, Marco, Crawley, Francis P, Butler-Laporte, Guillaume, Richards, Brent, Zeberg, Hugo, Lipcsey, Miklos, Hultström, Michael, Ludwig, Kerstin U, Schulte, Eva C, Pairo-Castineira, Erola, Baillie, John Kenneth, Schmidt, Axel, Frithiof, Robert, Mari, Francesca, Renieri, Alessandra, Furini, Simone Simone Furini, Francesca, Montagnani, Mario, Tumbarello, Ilaria, Rancan, Massimiliano, Fabbiani, Barbara, Rossetti, Laura, Bergantini, Miriana, D'Alessandro, Paolo, Cameli, David, Bennett, Federico, Anedda, Simona, Marcantonio, Sabino, Scolletta, Federico, Franchi, Maria Antonietta Mazzei, Susanna, Guerrini, Edoardo, Conticini, Luca, Cantarini, Bruno, Frediani, Danilo, Tacconi, Chiara Spertilli Raffaelli, Marco, Feri, Alice, Donati, Raffaele, Scala, Luca, Guidelli, Genni, Spargi, Marta, Corridi, Cesira, Nencioni, Leonardo, Croci, Gian Piero Caldarelli, Maurizio, Spagnesi, Davide, Romani, Paolo, Piacentini, Maria, Bandini, Elena, Desanctis, Silvia, Cappelli, Anna, Canaccini, Agnese, Verzuri, Valentina, Anemoli, Manola, Pisani, Agostino, Ognibene, Alessandro, Pancrazzi, Maria, Lorubbio, Massimo, Vaghi, Antonella, D 'Arminio Monforte, Federica Gaia Miraglia, Mario, U Mondelli, Massimo, Girardis, Sophie, Venturelli, Stefano, Busani, Andrea, Cossarizza, Andrea, Antinori, Alessandra, Vergori, Arianna, Emiliozzi, Stefano, Rusconi, Matteo, Siano, Arianna, Gabrieli, Agostino, Riva, Daniela, Francisci, Elisabetta, Schiaroli, Francesco, Paciosi, Andrea, Tommasi, Pier Giorgio Scotton, Francesca, Andretta, Sandro, Panese, Stefano, Baratti, Renzo, Scaggiante, Francesca, Gatti, Saverio Giuseppe Parisi, Francesco, Castelli, Eugenia, Quiros-Roldan, Melania Degli Antoni, Isabella, Zanella, Matteo Della Monica, Carmelo, Piscopo, Mario, Capasso, Roberta, Russo, Immacolata, Andolfo, Achille, Iolascon, Giuseppe, Fiorentino, Massimo, Carella, Marco, Castori, Filippo, Aucella, Pamela, Raggi, Rita, Perna, Matteo, Bassetti, Antonio Di Biagio, Maurizio, Sanguinetti, Luca, Masucci, Alessandra, Guarnaccia, Serafina, Valente, Oreste De Vivo, Gabriella, Doddato, Rossella, Tita, Annarita, Giliberti, Maria Antonietta Mencarelli, Caterina Lo Rizzo, Anna Maria Pinto, Valentina, Perticaroli, Francesca, Ariani, Miriam Lucia Carriero, Laura Di Sarno, Diana, Alaverdian, Elena, Bargagli, Marco, Mandalà, Alessia, Giorli, Lorenzo, Salerni, Patrizia, Zucchi, Pierpaolo, Parravicini, Elisabetta, Menatti, Tullio, Trotta, Ferdinando, Giannattasio, Gabriella, Coiro, Fabio, Lena, Leonardo Gianluca Lacerenza, Domenico, A Coviello, Cristina, Mussini, Enrico, Martinelli, Sandro, Mancarella, Luisa, Tavecchia, Mary Ann Belli, Lia, Crotti, Gianfranco, Parati, Maurizio, Sanarico, Francesco, Raimondi, Filippo, Biscarini, Alessandra, Stella, Marco, Rizzi, Franco, Maggiolo, Diego, Ripamonti, Claudia, Suardi, Tiziana, Bachetti, Maria Teresa La Rovere, Simona, Sarzi-Braga, Maurizio, Bussotti, Katia, Capitani, Simona, Dei, Sabrina, Ravaglia, Rosangela, Artuso, Elena, Andreucci, Giulia, Gori, Angelica, Pagliazzi, Erika, Fiorentini, Antonio, Perrella, Francesco, Bianchi, Paola, Bergomi, Emanuele, Catena, Riccardo, Colombo, Sauro, Luchi, Giovanna, Morelli, Paola, Petrocelli, Sarah, Iacopini, Sara, Modica, Silvia, Baroni, Francesco Vladimiro Segala, Francesco, Menichetti, Marco, Falcone, Giusy, Tiseo, Chiara, Barbieri, Tommaso, Matucci, Grassi, Davide, Ferri, Claudio, Marinangeli, Franco, Brancati, Francesco, Antonella, Vincenti, Valentina, Borgo, Lombardi, Stefania, Mirco, Lenzi, Massimo Antonio Di Pietro, Francesca, Vichi, Benedetta, Romanin, Letizia, Attala, Cecilia, Costa, Andrea, Gabbuti, Menè, Roberto, Umberto, Zuccon, Lucia, Vietri, Stefano, Ceri, Pietro, Pinoli, Patrizia, Casprini, Giuseppe, Merla, Gabriella Maria Squeo, Marcello, Maffezzoni, Raffaele, Bruno, Marco, Vecchia, Marta, Colaneri, Serena, Ludovisi, Yanara, Marincevic-Zuniga, Jessica, Nordlund, Tomas, Luther, Anders, Larsson, Katja Hanslin Anna Gradin, Sarah, Galien, Sara Bulow Anderberg, Jacob, Rosén, Sten, Rubertsson, Hugo, Zeberg, Robert, Frithiof, Miklós, Lipcsey, Michael, Hultström, Sara Clohisey Peter Horby, Johnny, Millar, Julian, Knight, Hugh, Montgomery, David, Maslove, Lowell, Ling, Alistair, Nichol, Charlotte, Summers, Tim, Walsh, Charles, Hinds, Malcolm, G Semple, Peter J, M Openshaw, Manu, Shankar-Hari, Antonia, Ho, Danny, Mcauley, Chris, Ponting, Kathy, Rowan, J Kenneth Baillie, Fiona, Griffiths, Wilna, Oosthuyzen, Jen, Meikle, Paul, Finernan, James, Furniss, Ellie, Mcmaster, Andy, Law, Sara, Clohisey, Trevor, Paterson, Tony, Wackett, Ruth, Armstrong, Lee, Murphy, Angie, Fawkes, Richard, Clark, Audrey, Coutts, Lorna, Donnelly, Tammy, Gilchrist, Katarzyna, Hafezi, Louise, Macgillivray, Alan, Maclean, Sarah, Mccafferty, Kirstie, Morrice, Jane, Weaver, Ceilia, Boz, Ailsa, Golightly, Mari, Ward, Hanning, Mal, Helen, Szoor-McElhinney, Adam, Brown, Ross, Hendry, Andrew, Stenhouse, Louise, Cullum, Dawn, Law, Sarah, Law, Rachel, Law, Max Head Fourman, Maaike, Swets, Nicky, Day, Filip, Taneski, Esther, Duncan, Marie, Zechner, Nicholas, Parkinson, Erola, Pairo-Castineira, Lucija, Klaric, Andrew, D Bretherick, Konrad, Rawlik, Dorota, Pasko, Susan, Walker, Nick, Parkinson, Clark, D Russell, Anne, Richmond, Elvina, Gountouna, David, Harrison, Wang, Bo, Yang, Wu, Alison, Meynert, Athanasios, Kousathanas, Loukas, Moutsianas, Zhijian, Yang, Ranran, Zhai, Chenqing, Zheng, Graeme, Grimes, Jonathan, Millar, Barbara, Shih, Jian, Yang, Xia, Shen, Chris, P Ponting, Albert, Tenesa, Andrew, Law, Veronique, Vitart, James, F Wilson, Collier, D, Wood, S, Zak, A, Borra, C, Matharu, M, May, P, Alldis, Z, Mitchelmore, O, Bowles, R, Easthorpe, A, Bibi, F, Lancoma-Malcolm, I, Gurasashvili, J, Pheby, J, Shiel, J, Bolton, M, Patel, M, Taylor, M, Zongo, O, Ebano, P, Harding, P, Astin-Chamberlain, R, Choudhury, Y, Cox, A, Kallon, D, Burton, M, Hall, R, Blowes, S, Prime, Z, Biddle, J, Prysyazhna, O, Newman, T, Tierney, C, Kassam, J, Shankar-Hari, M, Ostermann, M, Campos, S, Bociek, A, Lim, R, Grau, N, O Jones, T, Whitton, C, Marotti, M, Arbane, G, Bonner, S, Hugill, K, Reid, J, Welters, I, Waugh, V, Williams, K, Shaw, D, J Fernandez Roman, M Lopez Martinez, Johnson, E, Waite, A, Johnson, B, Hamilton, O, Mulla, S, Mcphail, M, Smith, J, K Baillie, J, Barclay, L, Hope, D, Mcculloch, C, Mcquillan, L, Clark, S, Singleton, J, Priestley, K, Rea, N, Callaghan, M, Campbell, R, Andrew, G, Marshall, L, Mckechnie, S, Hutton, P, Bashyal, A, Davidson, N, Summers, C, Polgarova, P, Stroud, K, Pathan, N, Elston, K, Agrawal, S, Battle, C, Newey, L, Rees, T, Harford, R, Brinkworth, E, Williams, M, Murphy, C, White, I, Croft, M, Bandla, N, Gellamucho, M, Tomlinson, J, Turner, H, Davies, M, Quinn, A, Hussain, I, Thompson, C, Parker, H, Bradley, R, Griffiths, R, Scriven, J, Gill, J, Puxty, A, Cathcart, S, Salutous, D, Turner, L, Duffy, K, Puxty, K, Joseph, A, Herdman-Grant, R, Simms, R, Swain, A, Naranjo, A, Crowe, R, Sollesta, K, Loveridge, A, Baptista, D, Morino, E, Davey, M, Golden, D, Jones, J, J Moreno Cuesta, Haldeos, A, Bakthavatsalam, D, Vincent, R, Elhassan, M, Xavier, K, Ganesan, A, Purohit, D, Abdelrazik, M, Morgan, J, Akeroyd, L, Bano, S, Warren, D, Bromley, M, Sellick, K, Gurr, L, Wilkinson, B, Nagarajan, V, Szedlak, P, Cupitt, J, Stoddard, E, Benham, L, Preston, S, Slawson, N, Bradshaw, Z, Brown, J, Caswell, M, Smelling, Bamford, P, Faulkner, M, Cawley, K, Jeffrey, H, London, E, Sainsbury, H, Nagra, I, Nasir, F, Dunmore, Ce, Jones, R, Abraheem, A, Al-Moasseb, M, Girach, R, Brantwood, C, Alexander, P, Bradley-Potts, J, Allen, S, Felton, T, Manna, S, Farnell-Ward, S, Leaver, S, Queiroz, J, Maccacari, E, Dawson, D, C Castro Delgado, R Pepermans Saluzzio, Ezeobu, O, Ding, L, Sicat, C, Kanu, R, Durrant, G, Texeira, J, Harrison, A, Samakomva, T, Willis, H, Hopkins, B, Thrasyvoulou, L, Jackson, M, Zaki, A, Tibke, C, Bennett, S, Woodyatt, W, Kent, A, Goodwin, E, Brandwood, C, Clark, R, Smith, L, Rooney, K, Thomson, N, Rodden, N, Hughes, E, Mcglynn, D, Clark, C, Clark, P, Abel, L, Sundaram, R, Gemmell, L, Brett, M, Hornsby, J, Macgoey, P, Price, R, Digby, B, O'Neil, P, Mcconnell, P, Henderson, P, Henderson, S, Sim, M, Kennedy-Hay, S, Mcparland, C, Rooney, L, Baxter, N, Pogson, D, Rose, S, Daly, Z, Brimfield, L, K Phull, M, Hussain, M, Pogreban, T, Rosaroso, L, E Salciute, L Grauslyte, Brealey, D, Wraith, E, Maccallum, N, Bercades, G, Hass, I, Smyth, D, Reyes, A, Martir, G, D Clement, I, Webster, K, Hays, C, Gulati, A, Hodgson, L, Margarson, M, Gomez, R, Baird, Y, Thirlwall, Y, Folkes, L, Butler, A, Meadows, E, Moore, S, Raynard, D, Fox, H, Riddles, L, King, K, Kimber, S, Hobden, G, Mccarthy, A, Cannons, V, Balagosa, I, Chadbourn, I, Gardner, A, Horner, D, Mclaughlanv, D, Charles, B, Proudfoot, N, Marsden, T, L Mc Morrow, Blackledge, B, Pendlebury, J, Harvey, A, Apetri, E, Basikolo, C, Catlow, L, Doonan, R, Knowles, K, Lee, S, Lomas, D, Lyons, C, Perez, J, Poulaka, M, Slaughter, M, Slevin, K, Thomas, V, Walker, D, Harris, J, Drummond, A, Tully, R, Dearden, J, Philbin, J, Munt, S, Rishton, C, O'Connor, G, Mulcahy, M, Dobson, E, Cuttler, J, Edward, M, Rose, A, Sloan, B, Buckley, S, Brooke, H, Smithson, E, Charlesworth, R, Sandu, R, Thirumaran, M, Wagstaff, V, J Cebrian Suarez, Kaliappan, A, Vertue, M, Nicholson, A, Riches, J, Solesbury, A, Kittridge, L, Forsey, M, Maloney, G, Cole, J, Davies, R, Hill, H, Thomas, E, Williams, A, Duffin, D, Player, B, Radhakrishnan, J, Gibson, S, Lyle, A, Mcneela, F, Patel, B, Gummadi, M, Sloane, G, Dormand, N, Salmi, S, Farzad, Z, Cristiano, D, Liyanage, K, Thwaites, V, Varghese, M, Meredith, M, Mills, G, Willson, J, Harrington, K, Lenagh, B, Cawthron, K, Masuko, S, Raithatha, A, Bauchmuller, K, Ahmad, N, Barker, J, Jackson, Y, Kibutu, F, Bird, S, Watson, G, Martin, J, Bevan, E, C Wrey Brown, Trodd, D, English, K, Bell, G, Wilcox, L, Katary, A, Gopal, S, Lake, V, Harris, N, Metherell, S, Radford, E, Moore, F, Bancroft, H, Daglish, J, Sangombe, M, Carmody, M, Rhodes, J, Bellamy, M, Garg, A, Kuravi, A, Virgilio, E, Ranga, P, Butler, J, Botfield, L, Dexter, C, Fletcher, J, Shanmugasundaram, P, Hambrook, G, Burn, I, Manso, K, Thornton, D, Tebbutt, J, Penn, R, Hulme, J, Hussain, S, Maqsood, Z, Joseph, S, Colley, J, Hayes, A, Ahmed, C, Haque, R, Clamp, S, Kumar, R, Purewal, M, Baines, B, Frise, M, Jacques, N, Coles, H, Caterson, J, S Gurung Rai, Brunton, M, Tilney, E, Keating, L, Walden, A, Antcliffe, D, Gordon, A, Templeton, M, Rojo, R, Banach, D, S Sousa Arias, Fernandez, Z, Coghlan, P, Williams, D, Jardine, C, Bewley, J, Sweet, K, Grimmer, L, Johnson, R, Garland, Z, Gumbrill, B, Phillips, C, L Ortiz-Ruiz de Gordoa, Peasgood, E, Tridente, A, K Shuker, S Greer, Lynch, C, Pothecary, C, Roche, L, Deacon, B, Turner, K, Singh, J, G Sera Howe, Paul, P, Gill, M, Wynter, I, Ratnam, V, Shelton, S, Naisbitt, J, Melville, J, Baruah, R, Morrison, S, Mcgregor, A, Parris, V, Mpelembue, M, Srikaran, S, Dennis, C, Sukha, A, Verlande, M, Holding, K, Riches, K, Downes, C, Swan, C, Rostron, A, Roy, A, Woods, L, Cornell, S, Wakinshaw, F, Creagh-Brown, B, Blackman, H, Salberg, A, Smith, E, Donlon, S, Mtuwa, S, Michalak-Glinska, N, Stone, S, Beazley, C, Pristopan, V, Nikitas, N, Lankester, L, Wells, C, S Raj, A, Fletcher, K, Khade, R, Tsinaslanidis, G, Mcmahon, M, Fowler, S, Coventry, T, Stewart, R, Wren, L, Mwaura, E, Mew, L, Scaletta, D, Williams, F, Inweregbu, K, Lancaster, N, Cunningham, M, Daniels, A, Harrison, L, Hope, S, Jones, S, Crew, A, Wray, G, Matthews, J, Crawley, R, Carter, J, Birkinshaw, I, Ingham, J, Scott, Z, Howard, K, Joy, R, Roche, S, Clark, M, Purvis, S, Morrison, A, Strachan, D, Clements, S, Black, K, Parmar, C, Altabaibeh, A, Simpson, K, Mostoles, L, Gilbert, K, L, Ma, Alvaro, A, Thomas, M, Faulkner, B, Worner, R, Hayes, K, Gendall, E, Blakemore, H, Borislavova, B, Goff, E, Vuylsteke, A, Mwaura, L, Zamikula, J, Garner, L, Mitchell, A, Mepham, S, Cagova, L, Fofano, A, Holcombe, H, Praman, K, Szakmany, T, E Heron, A, Cherian, S, Cutler, S, Roynon-Reed, A, Randell, G, Convery, K, K Stammers, D Fottrell-Gould, Hudig, L, Keshet-Price, J, Peters, M, O'Neill, L, Ray, S, Belfield, H, Mchugh, T, Jones, G, Akinkugbe, O, Tomas, A, Abaleke, E, Beech, E, Meghari, H, Yussuf, S, Bamford, A, Hairsine, B, Dooks, E, Farquhar, F, Packham, S, Bates, H, Armstrong, L, Kaye, C, Allan, A, Medhora, J, Liew, J, Botello, A, Anderson, F, Cusack, R, Golding, H, Prager, K, Williams, T, Leggett, S, Golder, K, Male, M, Jones, O, Criste, K, Marani, M, Anumakonda, V, Amin, V, Karthik, K, Kausar, R, Anastasescu, E, Reid, K, Jacqui, M, Hormis, A, Walker, R, Duncan, T, Uriel, A, Ustianowski, A, T-Michael, H, Bruce, M, Connolly, K, Smith, K, Partridge, R, Griffin, D, Mcdonald, M, Muchenje, N, Martin, D, Filipe, H, Eastgate, C, Jackson, C, Gratrix, A, Foster, L, Martinson, V, Stones, E, Caroline, Abernathy, Parkinson, P, Reed, A, Prendergast, C, Rogers, P, Woodruff, M, Shokkar, R, Kaul, S, Barron, A, Collins, C, Beavis, S, Whileman, A, Dale, K, Hawes, J, Pritchard, K, Gascoyne, R, Stevenson, L, Jha, R, Lim, L, Krishnamurthy, V, Parker, R, Turner-Bone, I, Wilding, L, Reddy, A, Whiteley, S, Wilby, E, Howcroft, C, Aspinwall, A, Charlton, S, Ogg, B, Menzies, D, Pugh, R, Allan, E, Lean, R, Davies, F, Easton, J, Qiu, X, Kumar, S, Darlington, K, Houston, G, O'Brien, P, Geary, T, Allan, J, Meikle, A, Hughes, G, Balasubramaniam, M, Latham, S, Mckenna, E, Flanagan, R, Sathe, S, Davies, E, Chablani, M, Kirkby, A, Netherton, K, Archer, S, Yates, B, Ashbrook-Raby, C, Cole, S, Casey, M, Cabrelli, L, Chapman, S, Austin, P, Hutcheon, A, Whyte, C, Almaden-Boyle, C, Pattison, N, Cruz, C, Vochin, A, Kent, H, Thomas, A, Murdoch, S, David, B, Penacerrada, M, Lubimbi, G, Bastion, V, Wulandari, R, Valentine, J, Clarke, D, Serrano-Ruiz, A, Hierons, S, Ramos, L, Demetriou, C, Mitchard, S, White, K, White, N, Pitts, S, Branney, D, Frankham, J, Watters, M, Langton, H, Prout, R, Page, V, Varghes, T, Cowton, A, Kay, A, Potts, K, Birt, M, Kent, M, Wilkinson, A, Jude, E, Turner, V, Savill, H, Mccormick, J, Coulding, M, Siddiqui, S, Mercer, O, Rehman, H, Potla, D, Capps, N, Donaldson, D, Button, H, Martin, T, Hard, K, Agasou, A, Tonks, L, Arden, T, Boyle, P, Carnahan, M, Strickley, J, Adams, C, Childs, D, Rikunenko, R, Leigh, M, Breekes, M, Wilcox, R, Bowes, A, Tiveran, H, Hurford, F, Summers, J, Carter, A, Hussain, Y, Ting, L, Javaid, A, Motherwell, N, Moore, H, Millward, H, Jose, S, Schunki, N, Noakes, A, Clulow, C, Sadera, G, Jacob, R, Jones, C, Blunt, M, Coton, Z, Curgenven, H, S Mohamed Ally, Beaumont, K, Elsaadany, M, Fernandes, K, I Ali Mohamed Ali, Rangarajan, H, Sarathy, V, Selvanayagam, S, Vedage, D, White, M, Smith, M, Truman, N, Chukkambotla, S, Keith, S, Cockerill-Taylor, J, Ryan-Smith, J, Bolton, R, Springle, P, Dykes, J, Thomas, J, Khan, M, T Hijazi, M, Massey, E, Croston, G, Reschreiter, H, Camsooksai, J, Patch, S, Jenkins, S, Humphrey, C, Wadams, B, Bhatia, N, Msiska, M, Adanini, O, Attwood, B, Parsons, P, Tatham, K, Jhanji, S, Black, E, A Dela Rosa, Howle, R, Thomas, B, Bemand, T, Raobaikady, R, Saha, R, Staines, N, Daniel, A, Finn, J, Hutter, J, Doble, P, Shovelton, C, Pawley, C, Kannan, T, Hill, M, Combes, E, Monnery, S, Joefield, T, Popescu, M, Thankachen, M, Oblak, M, Little, J, Mcivor, S, Brady, A, Whittle, H, Prady, H, Chan, R, Ahmed, A, Morris, A, Gibson, C, Gordon, E, Keenan, S, Quinn, H, Benyon, S, Marriott, S, Zitter, L, Park, L, Baines, K, Lyons, M, Holland, M, Keenan, N, Young, M, Garrioch, S, Dawson, J, Tolson, M, Scholefield, B, R, Bi, Richardson, N, Schumacher, N, Cosier, T, Millen, G, Higham, A, Turki, S, Allen, L, Crisp, N, Hazleton, T, Knight, A, Deery, J, Price, C, Turney, S, Tilbey, S, Beranova, E, Wright, D, Georg, L, Twiss, S, Wadd, S, Postlethwaite, K, Gondo, P, Masunda, B, Kayani, A, Hadebe, B, Whiteside, J, Clarke, N, Donnison, P, Trim, F, Leadbitter, I, Butcher, D, O'Sullivan, S, Purewal, B, Bell, S, Rivers', V, O'Leary, R, Birch, J, Collins, E, Anderson, S, Hammerton, K, Andrews, E, Burns, K, Edmond, I, Todd, A, Donnachie, J, Turner, P, Prentice, L, Symon, L, Runciman, N, Auld, F, Halkes, M, Mercer, P, Thornton, L, Debreceni, G, Wilkins, J, Brown, A, Crickmore, V, Subramanian, G, Marshall, R, Jennings, C, Latif, M, Bunni, L, Spivey, M, Bean, S, Burt, K, Linnett, V, Ritzema, J, Sanderson, A, Mccormick, W, Bokhari, M, Kapoor, R, Loader, D, Ayers, A, Harrison, W, North, J, Belagodu, Z, Parasomthy, R, Olufuwa, O, Gherman, A, Fuller, B, Stuart, C, Kelsall, O, Davis, C, Wild, L, Wood, H, Thrush, J, Durie, A, Austin', K, Archer, K, Anderson, P, Vigurs, C, Thorpe, C, Knights, E, Boyle, N, Price, A, Kubisz-Pudelko, A, Wood, D, Lewis, A, Board, S, Pippard, L, Perry, J, Beesley, K, Rattray, A, Lee, E, Lennon, L, Douglas, K, Bell, D, Boyle, R, Glass, L, M Nauman Akhtar, Dent, K, Potoczna, D, Pearson, S, Horsley, E, Spencer, S, Mullan, D, Skinner, D, Gaylard, J, Ortiz-Ruizdegordoa, L, Barber, R, Hewitt, C, Hilldrith, A, Shepardson, S, Wills, M, Jackson-Lawrence, K, Gupta, A, Easthope, A, Timlick, E, Gorman, C, Otaha, I, Gales, A, Coetzee, S, Raj, M, Peiu, M, Quaid, S, Watson, E, Elliott, K, Mallinson, J, Chandler, B, Turnbull, A, Finch, C, Holl, C, Cooper, J, Evans, A, Khaliq, W, Collins, A, E Treus Gude, Love, N, L van Koutrik, Hunt, J, Kaye, D, Fisher, E, Brayne, A, Tuckey, V, Jackson, P, Parkin, J, Raith, E, Tariq, A, Houlden, H, Tucci, A, Hardy, J, Moncur, E, Highgate, J, Cowley, A, Mitra, A, Stead, R, Behan, T, Burnett, C, Newton, M, Heeney, E, Pollard, R, Hatton, J, Patel, A, Kasipandian, V, Allibone, S, M Genetu, R, Otahal, I, O'Brien, L, Omar, Z, Perkins, E, Davies, K, Tetla, D, Shelley, B, Irvine, V, Williams, S, Williams, P, Goodsell, J, Tutton, R, Bough, L, Winter-Goodwin, B, Kitson, R, Pinnell, J, Wilson, A, Nortcliffe, T, Wood, T, Home, M, Holdroyd, K, Robinson, M, Shaw, R, Greig, J, Brady, M, Haigh, A, Matupe, L, Usher, M, Mellor, S, Dale, S, Gledhill, L, Shaw, L, Turner, G, Kelly, D, Anwar, B, Riley, H, Sturgeon, H, Ali, A, Thomis, L, Melia, D, Dance, A, Hanson, K, Humphreys, S, Frost, I, Gopal, V, Godden, J, Holden, A, Swann, S, Smith, T, Clapham, M, Poultney, U, Harper, R, Rice, P, Reece-Anthony, R, Gurung, B, Moultrie, S, Odam, M, Mayer, A, Bellini, A, Pickard, A, Bryant, J, Roe, N, Sowter, J, Lang, K, Taylor, J, Barry, P, Hobrok, M, Tench, H, Wolf-Roberts, R, Mcguinness, H, Loosley, R, Hawcutt, D, Rad, L, O'Malley, L, Saunderson, P, Seddon, G, Anderson, T, Rogers, N, Ruddy, J, Harkins, M, Beith, C, Mcalpine, A, Ferguson, L, Grant, P, Macfadyen, S, Mclaughlin, M, Baird, T, Rundell, S, Welsh, B, Hamill, R, Fisher, F, Gregory, J, Axel, Schmidt, Kerstin, U Ludwig, Selina, Rolker, Markus, M Nöthen, Julia, Fazaal, Verena, Keitel, Björn, Jensen, Torsten, Feldt, Lisa, Knopp, Julia, Schröder, Carlo, Maj, Fabian, Brand, Marc, M Berger, Thorsten, Brenner, Anke, Hinney, Oliver, Witzke, Robert, Bals, Christian, Herr, Nicole, Ludwig, Jörn, Walter, Jochen, Schneider, Johanna, Erber, Christoph, D Spinner, Clemens, M Wendtner, Christof, Winter, Ulrike, Protzer, Nicolas, Casadei, Stephan, Ossowski, Olaf, H Riess, Eva, C Schulte, J Brent Richards, Guillaume, Butler-Laporte, Mirosław, Kwasniewski, Urszula, Korotko, Karolina, Chwialkowska, Magdalena, Niemira, Jerzy, Jaroszewicz, Barbara, Sobala-Szczygiel, Beata, Puzanowska, Anna, Parfieniuk-Kowerda, Diana, Martonik, Anna, Moniuszko-Malinowska, Sławomir, Pancewicz, Dorota, Zarębska-Michaluk, Krzysztof, Simon, Monika, Pazgan-Simon, Iwona, Mozer-Lisewska, Maciej, Bura, Agnieszka, Adamek, Krzysztof Tomasiewicz Małgorzata Pawłowska, Anna, Piekarska, Aleksandra, Berkan-Kawinska, Andrzej, Horban, Justyna, Kowalska, Regina, Podlasin, Piotr, Wasilewski, Arsalin, Azzadin, Miroslaw, Czuczwar, Slawomir, Czaban, Paweł, Olszewski, Jacek, Bogocz, Magdalena, Ochab, Anna, Kruk, Sandra, Uszok, Agnieszka, Bielska, Anna, Szałkowska, Justyna, Raczkowska, Gabriela, Sokołowska, Joanna, Chorostowska-Wynimko, Aleksandra, Jezela-Stanek, Adriana, Roży, Urszula, Lechowicz, Urszula, Polowianiuk, Kamil, Grubczak, Aleksandra, Starosz, Andrzej, Eljaszewicz, Wiktoria, Izdebska, Adam, Krętowski, Robert, Flisiak, Marcin Moniuszko Malak Abedalthagafi Manal Alaamery, Salam, Massadeh, Mohamed, Fawzy, Hadeel, Albardis, Nora, Aljawini, Moneera, Alsuwailm, Faisal, Almalki, Serghei, Mangul, Junghyun, Jung, Hamdi, Mbarek, Chadi, Saad, Yaser, Al-Sarraj, Wadha, Al-Muftah, Radja, Badji, Asma Al Thani, Said, I Ismail, HGI, WES/WGS Working Group Within the, Consortium, GenOMICC, Study, GEN-COVID Multicenter, Renieri, Alessandra [0000-0002-0846-9220], Apollo - University of Cambridge Repository, NIHR, Fallerini, C, Picchiotti, N, Baldassarri, M, Zguro, K, Daga, S, Fava, F, Benetti, E, Amitrano, S, Bruttini, M, Palmieri, M, Croci, S, Lista, M, Beligni, G, Valentino, F, Meloni, I, Tanfoni, M, Minnai, F, Colombo, F, Cabri, E, Fratelli, M, Gabbi, C, Mantovani, S, Frullanti, E, Gori, M, Crawley, F, Butler-Laporte, G, Richards, B, Zeberg, H, Lipcsey, M, Hultström, M, Ludwig, K, Schulte, E, Pairo-Castineira, E, Baillie, J, Schmidt, A, Frithiof, R, Mari, F, Renieri, A, Furini, S, and Crotti, L
Subjects: Male, Medicin och hälsovetenskap, Linkage disequilibrium, Medizin, severity, Genome-wide association study, Disease, WES/WGS Working Group Within the HGI, Logistic regression, Severity of Illness Index, Medical and Health Sciences, Whole Exome Sequencing, Cohort Studies, 0302 clinical medicine, Lasso (statistics), GEN-COVID Multicenter Study, Germany, 80 and over, Genetics (clinical), Exome sequencing, Original Investigation, Genetics & Heredity, Aged, 80 and over, 0303 health sciences, Adult, Aged, COVID-19, Female, Humans, Italy, Middle Aged, Polymorphism, Single Nucleotide, Quebec, SARS-CoV-2, Sweden, United Kingdom, Genetic Predisposition to Disease, Phenotype, Single Nucleotide, covid-19, 1104 Complementary and Alternative Medicine, GenOMICC Consortium, Human, coding variants, Computational biology, Biology, 03 medical and health sciences, Exome Sequencing, Genetics, Polymorphism, Gene, 030304 developmental biology, 0604 Genetics, 1114 Paediatrics and Reproductive Medicine, Cohort Studie, 030217 neurology & neurosurgery, Coding (social sciences)
Abstract: The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. Supplementary Information The online version contains supplementary material available at 10.1007/s00439-021-02397-7.
Published: 2021
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9. Whole genome sequencing identifies multiple loci for critical illness caused by COVID-19

Author: Clark D Russell, Erola Pairo-Castineira, Malcolm G Semple, James F. Wilson, Jian Yang, andMe Investigators, Seán Keating, Yang Wu, Tala Zainy, Greg Elgar, Angie Fawkes, Lucija Klaric, Julian C. Knight, Paul Elliott, Richard H Scott, Charlotte Summers, David M. Maslove, Daniel F. McAuley, Marie Zechner, Sally Donovan, J Kenneth Baillie, Athanasios Kousathanas, Daniel Rhodes, Nicholas J. Parkinson, Veronique Vitart, Susan P. Walker, Peter Horby, Fiona Griffiths, Bo Wang, Peter J. M. Openshaw, Linda Todd, Andy Law, Sara Clohisey, Tom Fowler, Augusto Rendon, Alex Stuckey, Christopher A Odhams, Andrew D. Bretherick, Hugh Montgomery, Katherine S. Elliott, Shahla Salehi, Jonathan E Millar, Loukas Moutsianas, Charles J. Hinds, Kathy Rowan, Mark J. Caulfield, Fiona Maleady-Crowe, Timothy S. Walsh, Prabhu Arumugam, Georgia Chan, Lee Murphy, Afshan Siddiq, GenoMICC Investigators, Tomas Malinauskas, Konrad Rawlik, Kirstie Morrice, Chris P. Ponting, Alistair Nichol, Wilna Oosthuyzen, Manu Shankar-Hari, and Peter Goddard
Subjects: Whole genome sequencing, Genetics, education.field_of_study, Immune system, Antigen, Genetic variation, Population, Disease, Biology, Lung injury, education, Gene
Abstract: Critical illness in COVID-19 is caused by inflammatory lung injury, mediated by the host immune system. We and others have shown that host genetic variation influences the development of illness requiring critical care1or hospitalisation2;3;4following SARS-Co-V2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study recruits critically-ill cases and compares their genomes with population controls in order to find underlying disease mechanisms.Here, we use whole genome sequencing and statistical fine mapping in 7,491 critically-ill cases compared with 48,400 population controls to discover and replicate 22 independent variants that significantly predispose to life-threatening COVID-19. We identify 15 new independent associations with critical COVID-19, including variants within genes involved in interferon signalling (IL10RB, PLSCR1), leucocyte differentiation (BCL11A), and blood type antigen secretor status (FUT2). Using transcriptome-wide association and colocalisation to infer the effect of gene expression on disease severity, we find evidence implicating expression of multiple genes, including reduced expression of a membrane flippase (ATP11A), and increased mucin expression (MUC1), in critical disease.We show that comparison between critically-ill cases and population controls is highly efficient for genetic association analysis and enables detection of therapeutically-relevant mechanisms of disease. Therapeutic predictions arising from these findings require testing in clinical trials.
Published: 2021
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10. Genome-wide Analysis Identifies Novel Gallstone-susceptibility Loci Including Genes Regulating Gastrointestinal Motility

Author: Jonathan A. Fallowfield, Archie Campbell, Thomas M Drake, David J. Porteous, Paul R. H. J. Timmers, Ewen M Harrison, Neil C. Henderson, James F. Wilson, Stephen J. Wigmore, Caroline Hayward, Cameron J Fairfield, Nicholas L. Mills, Andrii Iakovliev, Peter K. Joshi, David W. Clark, Robert K. Semple, Athina Spiliopoulou, Prakash Ramachandran, Cathie Sudlow, Riinu Pius, Andrew D. Bretherick, and Catherine A Shaw
Subjects: Genetics, Gastrointestinal tract, Hepatology, Cholesterol, Genome-wide association study, Gallstones, Disease, Biology, medicine.disease, Polymorphism, Single Nucleotide, White People, chemistry.chemical_compound, chemistry, Polygene, medicine, Humans, Population study, Genetic Predisposition to Disease, Gastrointestinal Motility, Gene, Genome-Wide Association Study, Genetic association
Abstract: ObjectiveGenome-wide association studies (GWAS) have identified several risk loci for gallstone disease. As with most polygenic traits, it is likely many genetic determinants are undiscovered. The aim of this study was to identify novel genetic variants that represent new targets for gallstone research and treatment.DesignWe performed a GWAS of 28,627 gallstone cases and 348,373 controls in the UK Biobank and a GWA meta-analysis (43,639 cases and 506,798 controls) with the FinnGen cohort. We assessed pathway enrichment using gene-based then gene-set analysis and tissue expression of implicated genes in Genotype-Tissue Expression project data. We constructed a polygenic risk score (PRS) and evaluated phenotypic traits associated with the score.ResultsSeventy-five risk loci were identified (P−8) of which forty-six were novel. Pathway enrichment revealed associations with lipid homeostasis, glucuronidation, phospholipid metabolism and gastrointestinal motility. ANO1 and TMEM147, both in novel loci, are strongly expressed in the gallbladder and gastrointestinal tract. Both regulate gastrointestinal motility. The gallstone risk allele rs7599-A leads to suppression of hepatic TMEM147 expression suggesting the protein protects against gallstone formation. Individuals in the highest decile of the PRS demonstrated a 6-fold increased risk of gallstones compared to the lowest risk category. The PRS was strongly associated with increased body mass index, serum liver enzyme and C-reactive protein concentrations and decreased lipoprotein cholesterol concentrations.ConclusionThis GWAS demonstrates the polygenic nature of gallstone risk and identifies 46 novel susceptibility loci. For the first time, we implicate genes influencing gastrointestinal motility in the pathogenesis of gallstones.Summary BoxWhat is already known on this subject?Genome-wide association studies (GWAS) have identified 29 genetic variants within independent loci which increase the risk of gallstone disease.Most of these variants lie within or near to genes that regulate lipid or bile acid metabolism. Gallstones are known to have a significant genetic component with 25-50% of gallstone disease due to genetic risk factors.Much of this risk is not accounted for by the known gallstone-susceptibility loci.What are the new findings?We performed a GWAS in the UK Biobank (28,627 gallstone cases, 348,373 controls) and a GWA meta-analysis (43,639 cases and 506,798 controls) with the FinnGen cohort.We identified a total of 75 gallstone-susceptibility loci with 46 of these being new and the remaining 29 being those already identified.We annotated the variants based on their position within or near to genes and assessed pathway enrichment through gene-set analysis.We identify two novel gallstone-susceptibility loci in which the lead variants lie within genes governing gastrointestinal motility which highly expressed in gallbladder (ANO1 and TMEM147).We demonstrate further loci involved in primary cilia function.We report significant association of a polygenic risk score with gallstones using independent subsets of the study population.How might it impact on clinical practice in the foreseeable future?The genes and pathways identified represent novel targets for development of medication targeting primary or secondary prevention of gallstones. This may be of particular benefit to those unable to undergo cholecystectomy.The individual variants or polygenic risk score identified in this GWAS could form the basis for identification of individuals with high risk of gallstones to support screening or treatment of gallstone disease. This screening may be of particular benefit in populations with elevated risk of gallstones such as haemolytic disease or bariatric surgery.
Published: 2021
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11. Genome-Wide Association Study of NAFLD Using Electronic Health Records

Author: Catherine A Shaw, Robert K. Semple, Neil C. Henderson, Andrew D. Bretherick, Peter K. Joshi, Cathie Sudlow, Riinu Pius, Thomas M Drake, David J. Porteous, David W. Clark, Jonathan A. Fallowfield, Stephen J. Wigmore, James F. Wilson, Paul R. H. J. Timmers, Caroline Hayward, Athina Spiliopoulou, Prakash Ramachandran, Cameron J Fairfield, Ewen M Harrison, Archie Campbell, and Nicholas L. Mills
Subjects: Oncology, Apolipoprotein E, Male, medicine.medical_specialty, Mutation, Missense, Genome-wide association study, RC799-869, Protein Serine-Threonine Kinases, Apolipoproteins E, Non-alcoholic Fatty Liver Disease, Internal medicine, Nonalcoholic fatty liver disease, Medicine, Electronic Health Records, Humans, Genetic Predisposition to Disease, Genetic association, Adaptor Proteins, Signal Transducing, Hepatology, business.industry, Intracellular Signaling Peptides and Proteins, nutritional and metabolic diseases, Membrane Proteins, Odds ratio, Original Articles, Diseases of the digestive system. Gastroenterology, Middle Aged, medicine.disease, Mitochondrial amidoxime reducing component 1, Peptide Fragments, Codon, Nonsense, Case-Control Studies, Phospholipases A2, Calcium-Independent, Female, Original Article, business, Body mass index, Acyltransferases, TM6SF2, Genome-Wide Association Study
Abstract: Genome-wide association studies (GWAS) have identified several risk loci for nonalcoholic fatty liver disease (NAFLD). Previous studies have largely relied on small sample sizes and have assessed quantitative traits. We performed a case-control GWAS in the UK Biobank using recorded diagnosis of NAFLD based on diagnostic codes recommended in recent consensus guidelines. We performed a GWAS of 4,761 cases of NAFLD and 373,227 healthy controls without evidence of NAFLD. Sensitivity analyses were performed excluding other co-existing hepatic pathology, adjusting for body mass index (BMI) and adjusting for alcohol intake. A total of 9,723,654 variants were assessed by logistic regression adjusted for age, sex, genetic principal components, and genotyping batch. We performed a GWAS meta-analysis using available summary association statistics. Six risk loci were identified (P < 5*10-8 ) (apolipoprotein E [APOE], patatin-like phospholipase domain containing 3 [PNPLA3, transmembrane 6 superfamily member 2 [TM6SF2], glucokinase regulator [GCKR], mitochondrial amidoxime reducing component 1 [MARC1], and tribbles pseudokinase 1 [TRIB1]). All loci retained significance in sensitivity analyses without co-existent hepatic pathology and after adjustment for BMI. PNPLA3 and TM6SF2 remained significant after adjustment for alcohol (alcohol intake was known in only 158,388 individuals), with others demonstrating consistent direction and magnitude of effect. All six loci were significant on meta-analysis. Rs429358 (P = 2.17*10-11 ) is a missense variant within the APOE gene determining ϵ4 versus ϵ2/ϵ3 alleles. The ϵ4 allele of APOE offered protection against NAFLD (odds ratio for heterozygotes 0.84 [95% confidence interval 0.78-0.90] and homozygotes 0.64 [0.50-0.79]). Conclusion: This GWAS replicates six known NAFLD-susceptibility loci and confirms that the ϵ4 allele of APOE is associated with protection against NAFLD. The results are consistent with published GWAS using histological and radiological measures of NAFLD, confirming that NAFLD identified through diagnostic codes from consensus guidelines is a valid alternative to more invasive and costly approaches.
Published: 2021

12. Genome-Wide Association Study of Non-Alcoholic Fatty Liver Disease Identifies Association with Apolipoprotein E

Author: Stephen J. Wigmore, Paul R. H. J. Timmers, Ewen M Harrison, Nicholas L. Mills, Andrew D. Bretherick, Athina Spiliopoulou, Catherine Sudlow, Robert K. Semple, Riinu Pius, Thomas M Drake, David J. Porteous, Prakash Ramachandran, James F. Wilson, Catherine A Shaw, Cameron J Fairfield, Campbell A, Neil C. Henderson, Jonathan A. Fallowfield, Caroline Hayward, Peter K. Joshi, and David W. Clark
Subjects: Oncology, Apolipoprotein E, medicine.medical_specialty, business.industry, Fatty liver, Genome-wide association study, Odds ratio, medicine.disease, Internal medicine, medicine, Allele, business, Genotyping, TM6SF2, Genetic association
Abstract: Background & AimsGenome-wide association studies (GWAS) have identified several risk loci for non-alcoholic fatty liver disease (NAFLD). Previous studies have largely relied on small sample sizes and have assessed quantitative traits. We performed a case-control GWAS in the UK Biobank using recorded diagnosis of NAFLD based on diagnostic codes recommended in recent consensus guidelines.Approach & ResultsWe performed a GWAS of 4,761 cases of NAFLD and 373,227 healthy controls without evidence of NAFLD. Sensitivity analyses were performed excluding other co-existing hepatic pathology, adjusting for BMI and adjusting for alcohol intake. 9,723,654 variants were assessed by logistic regression adjusted for age, sex, genetic principal components and genotyping batch. We performed a GWA meta-analysis using available summary association statistics from two previously published case-control GWAS of NAFLD. Six risk loci were identified (PConclusionsThis GWAS demonstrates that the ∈4 allele of APOE is strongly associated with protection against NAFLD.
Published: 2021
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13. Assessing the role of genome-wide DNA methylation between smoking and risk of lung cancer using repeated measurements: the HUNT Study

Author: Thomas Battram, Vidar Beisvag, Yue Chen, Caroline L Relton, Sissel Gyrid Freim Wahl, Josine L. Min, Yi-Qian Sun, Lin Jiang, Andrew D. Bretherick, Arnar Flatberg, Frank Skorpen, Therese Haugdahl Nøst, Florence Guida, Mattias Johansson, Torkjel M. Sandanger, Yanni Zeng, Arnulf Langhammer, Rebecca C Richmond, Rosie M. Walker, Matthew Suderman, and Xiao-Mei Mai
Subjects: 0301 basic medicine, Oncology, medicine.medical_specialty, Lung Neoplasms, Epidemiology, Modifiable Risk Factors for Cancer, Genome, Epigenesis, Genetic, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Internal medicine, Mendelian randomization, Medicine, Humans, AcademicSubjects/MED00860, VDP::Medisinske Fag: 700, Lung cancer, Genetic association, EWAS, business.industry, Smoking, General Medicine, DNA, DNA Methylation, medicine.disease, Phenotype, VDP::Medical disciplines: 700, 030104 developmental biology, chemistry, 030220 oncology & carcinogenesis, Case-Control Studies, DNA methylation, CpG Islands, business, DNA hypomethylation, Genome-Wide Association Study, Causal inference
Abstract: Background It is unclear if smoking-related DNA methylation represents a causal pathway between smoking and risk of lung cancer. We sought to identify novel smoking-related DNA methylation sites in blood, with repeated measurements, and to appraise the putative role of DNA methylation in the pathway between smoking and lung cancer development. Methods We derived a nested case-control study from the Trøndelag Health Study (HUNT), including 140 incident patients who developed lung cancer during 2009–13 and 140 controls. We profiled 850 K DNA methylation sites (Illumina Infinium EPIC array) in DNA extracted from blood that was collected in HUNT2 (1995–97) and HUNT3 (2006–08) for the same individuals. Epigenome-wide association studies (EWAS) were performed for a detailed smoking phenotype and for lung cancer. Two-step Mendelian randomization (MR) analyses were performed to assess the potential causal effect of smoking on DNA methylation as well as of DNA methylation (13 sites as putative mediators) on risk of lung cancer. Results The EWAS for smoking in HUNT2 identified associations at 76 DNA methylation sites (P Conclusions DNA methylation modifications in blood did not seem to represent a causal pathway linking smoking and the lung cancer risk.
Published: 2021

14. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

Author: Giuseppe Matullo, John R. B. Perry, Meena Kumari, Gemma C Sharp, Tomáš Paus, E. Giralt-Steinhauer, Marta E. Alarcón-Riquelme, Koen F. Dekkers, F. Gagnon, Simonetta Guarrera, Cilla Söderhäll, Rosie M. Walker, Therese Tillin, M. Smarts, Juan E. Castillo-Fernandez, John M. Starr, Jean Shin, Dan Mason, T Esko, Christopher Shaw, Hannah R Elliott, Manon Bernard, David L. Corcoran, Yvonne Awaloff, Ahmad Al Khleifat, Bert Brunekreef, Clara Viberti, John Wright, Gibran Hemani, Kathryn L. Evans, Camilla Schmidt Morgen, Jouke J. Hottenga, Susan M. Ring, Terrie E. Moffitt, Silva Kasela, C. Hale, Idil Yet, Katri Räikkönen, René Luijk, Vanessa Schmoll, Kimberley Burrows, Annelot M. Dekker, D. VanHeemst, Jordana T. Bell, Jordi Jimenez-Conde, Carlotta Sacerdote, Salvatore Panico, Lili Milani, Nabila Kazmi, Torben Hansen, Aleksey Shatunov, J L Min, Richa Gupta, Henning Tiemeier, Grant W. Montgomery, Vincent W. V. Jaddoe, E.J.C. de Geus, Fernando Rivadeneira, Debbie A Lawlor, Carol A. Wang, Toni-Kim Clarke, Susanne Lucae, Nicholas J. Wareham, Jordi Sunyer, Felix R. Day, C. Soriano-Tarraga, Christoph Bock, Juan R. González, D. Aissi, J.B. van Meurs, Ian J. Deary, Ken K. Ong, Louise Arseneault, Eilis Hannon, Bastiaan T. Heijmans, Philip E. Melton, Ashok Kumar, Pierre-Emmanuel Morange, Zdenka Pausova, T.D. Spector, Nicholas G. Martin, J. Mill, Francesc Català-Moll, Alun D. Hughes, Leonard C. Schalkwyk, Giovanni Cugliari, Carlos Ruiz-Arenas, Elena Carnero-Montoro, Marine Germain, Yanni Zeng, Andrew M. McIntosh, Riccardo E. Marioni, Wilfried Karmaus, Ikram, Gonneke Willemsen, Miina Ollikainen, Karen M Ho, Craig E. Pennell, F.I. Rezwan, Darina Czamara, Ramona A. J. Zwamborn, Dorret I. Boomsma, Wendy L. McArdle, J. M. van Dongen, Guillermo Barturen, Matthew Suderman, Richie Poulton, Daniel Lawson, A. Metspalu, David-Alexandre Trégouët, Marian Beekman, Andrew D. Bretherick, Johanna Klughammer, Hongmei Zhang, M.H. van IJzendoorn, Nish Chaturvedi, Jari Lahti, Karen Sugden, Jan H. Veldink, Mariona Bustamante, Avshalom Caspi, Pooja R. Mandaviya, Judith M. Vonk, Tom R. Gaunt, Cheng-Jian Xu, John W. Holloway, Tian Lin, Tom G. Richardson, Caroline L Relton, Naomi R. Wray, Allan F. McRae, George Davey Smith, Erik Melén, Valentina Iotchkova, Ellen A. Nohr, Jaakko Kaprio, Göran Pershagen, Elisabeth B. Binder, A. al Chalabi, T.J. Gorrie-Stone, K. van Eijk, Gerard H. Koppelman, M. Lerro, Alexia Cardona, Sailalitha Bollepalli, P.E. Slagboom, Thorkild I. A. Sørensen, André G. Uitterlinden, Jaume Roquer, Peter M. Visscher, Janine F. Felix, Martin Kerick, Gail Davies, Rae-Chi Huang, Alfredo Iacoangeli, Alison D. Murray, Helsinki Institute of Life Science HiLIFE, Institute for Molecular Medicine Finland, Epigenetics of Complex Diseases and Traits, Department of Public Health, Department of Psychology and Logopedics, Biological Psychology, APH - Mental Health, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, APH - Methodology, Groningen Research Institute for Asthma and COPD (GRIAC), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre recherche en CardioVasculaire et Nutrition = Center for CardioVascular and Nutrition research (C2VN), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Epidemiology, Internal Medicine, Pediatrics, Child and Adolescent Psychiatry / Psychology, and Clinical Child and Family Studies
Subjects: Multifactorial Inheritance, ADN, Quantitative Trait Loci, Genome-wide association study, VARIANTS, Biology, Quantitative trait locus, Polymorphism, Single Nucleotide, LINKS, Epigenesis, Genetic, 03 medical and health sciences, 0302 clinical medicine, Quantitative Trait, Heritable, Genetic variation, Genetics, WIDE ASSOCIATION, GWAS, Humans, Genetic Predisposition to Disease, Genotip, METAANALYSIS, EQTL, 030304 developmental biology, Epigenesis, SNP ANALYSIS, 0303 health sciences, COMPLEX, dNaM, Chromosome Mapping, DNA, DNA Methylation, Phenotype, Genetic architecture, MODEL, Fenotip, Gene Expression Regulation, DNA methylation, MENDELIAN RANDOMIZATION, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, 3111 Biomedicine, Metilació, Transcriptome, Genètica, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs. 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.
Published: 2020
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15. Genetic mechanisms of critical illness in Covid-19

Author: Lee Murphy, Chris P. Ponting, Xia Shen, Veronique Vitart, Seán Keating, Charles J. Hinds, Kathy Rowan, James F. Wilson, Mark J. Caulfield, Caroline Hayward, Alistair Nichol, Wilna Oosthuyzen, Hugh Montgomery, David M. Maslove, Manu Shankar-Hari, Barbara Shih, Konrad Rawlik, Julian C. Knight, J Kenneth Baillie, Nicola Wrobel, Graeme R. Grimes, Chris Haley, Peter J. M. Openshaw, Loukas Moutsianas, Charlotte Summers, Angie Fawkes, David J. Porteous, Susan P. Walker, Peter Horby, Fiona Griffiths, Clark D Russell, Sara Clohisey, Marie Zechner, Anne Richmond, Timothy S. Walsh, Alison M. Meynert, Chenqing Zheng, Albert Tenesa, Bo Wang, Andy Law, Daniel F. McAuley, Andrew D. Bretherick, Max Head Fourman, Elvina Gountouna, James J Furniss, Lucija Klaric, Erola Pairo-Castineira, Malcolm G Semple, Paul Klenerman, Nicholas J. Parkinson, Lance Turtle, Richard H Scott, Dorota Pasko, Zhijian Yang, Antonia Ho, Lowell Ling, and Ranran Zhai
Subjects: business.industry, Intensive care, Genetic variation, medicine, Population study, Locus (genetics), Genome-wide association study, Disease, Bioinformatics, medicine.disease, business, Gene, Comorbidity
Abstract: The subset of patients who develop critical illness in Covid-19 have extensive inflammation affecting the lungs1 and are strikingly different from other patients: immunosuppressive therapy benefits critically-ill patients, but may harm some non-critical cases.2 Since susceptibility to life-threatening infections and immune-mediated diseases are both strongly heritable traits, we reasoned that host genetic variation may identify mechanistic targets for therapeutic development in Covid-19.3GenOMICC (Genetics Of Mortality In Critical Care, genomicc.org) is a global collaborative study to understand the genetic basis of critical illness. Here we report the results of a genome-wide association study (GWAS) in 2244 critically-ill Covid-19 patients from 208 UK intensive care units (ICUs), representing >95% of all ICU beds. Ancestry-matched controls were drawn from the UK Biobank population study and results were confirmed in GWAS comparisons with two other population control groups: the 100,000 genomes project and Generation Scotland.We identify and replicate three novel genome-wide significant associations, at chr19p13.3 (rs2109069, p = 3.98 × 10−12), within the gene encoding dipeptidyl peptidase 9 (DPP9), at chr12q24.13 (rs10735079, p =1.65 × 10−8) in a gene cluster encoding antiviral restriction enzyme activators (OAS1, OAS2, OAS3), and at chr21q22.1 (rs2236757, p = 4.99 × 10−8) in the interferon receptor gene IFNAR2. Consistent with our focus on extreme disease in younger patients with less comorbidity, we detect a stronger signal at the known 3p21.31 locus than previous studies (rs73064425, p = 4.77 × 10−30).We identify potential targets for repurposing of licensed medications. Using Mendelian randomisation we found evidence in support of a causal link from low expression of IFNAR2, and high expression of TYK2, to life-threatening disease. Transcriptome-wide association in lung tissue revealed that high expression of the monocyte/macrophage chemotactic receptor CCR2 is associated with severe Covid-19.Our results identify robust genetic signals relating to key host antiviral defence mechanisms, and mediators of inflammatory organ damage in Covid-19. Both mechanisms may be amenable to targeted treatment with existing drugs. Large-scale randomised clinical trials will be essential before any change to clinical practice.
Published: 2020
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16. Genomic and phenomic insights from an atlas of genetic effects on DNA methylation

Author: Carol A. Wang, Simonetta Guarrera, Avshalom Caspi, Eilis Hannon, Marta E. Alarcón-Riquelme, P. Eline Slagboom, Pooja R. Mandaviya, Koen F. Dekkers, Cheng-Jian Xu, Jari Lahti, Juan E. Castillo-Fernandez, Dan Mason, Dylan Aïssi, Jan H. Veldink, Mariona Bustamante, Melissa C. Smart, Guillermo Barturen, Zdenka Pausova, Jenny van Dongen, Jordi Jimenez-Conde, Craig E. Pennell, Gibran Hemani, Tom R. Gaunt, Camilla Schmidt Morgen, Ken K. Ong, Toni-Kim Clarke, Alexia Cardona, Susanne Lucae, René Luijk, T.J. Gorrie-Stone, Phillip E. Melton, Thorkild I. A. Sørensen, André G. Uitterlinden, Jordana T. Bell, Jouke-Jan Hottenga, Meena Kumari, Francesc Català-Moll, Jonathan Mill, Tõnu Esko, Sailalitha Bollepalli, Dorret I. Boomsma, Torben Hansen, Hongmei Zhang, Yanni Zeng, John Wright, Wilfried Karmaus, Martin Kerick, Carolina Soriano-Tárraga, Jaakko Kaprio, Miina Ollikainen, Eco J. C. de Geus, Jordi Sunyer, Therese Tillin, Juan R. González, Yvonne Awaloff, Faisal I. Rezwan, Karen Sugden, Nicholas G. Martin, Karen M Ho, Andres Metspalu, Marine Germain, Kristel R. van Eijk, Ramona A. J. Zwamborn, George Davey Smith, Judith M. Vonk, Tian Lin, Henning Tiemeier, Grant W. Montgomery, Naomi R. Wray, Rae-Chi Huang, Alfredo Iacoangeli, Wendy L. McArdle, Jean Shin, Michael Lerro, Darina Czamara, Valentina Iotchkova, David-Alexandre Trégouët, Johanna Klughammer, Elena Carnero-Montoro, Pierre-Emmanuel Morange, Andrew M. McIntosh, Gemma C Sharp, Alun D. Hughes, Carlos Ruiz-Arenas, John M. Starr, Riccardo E. Marioni, Peter M. Visscher, Nabila Kazmi, Ian J. Deary, Kathryn L. Evans, Terrie E. Moffitt, Janine F. Felix, Tomáš Paus, Ashok Kumar, Jaume Roquer, Christopher Shaw, Hannah R Elliott, Susan M. Ring, Nish Chaturvedi, Giovanni Cugliari, Ahmad Al Khleifat, Joyce B. J. van Meurs, Kimberley Burrows, Bert Brunekreef, Debbie A Lawlor, Clara Viberti, Louise Arseneault, Silva Kasela, Cilla Söderhäll, Idil Yet, Manon Bernard, Christoph Bock, Vincent W. V. Jaddoe, Felix R. Day, Diana van Heemst, Alison D. Murray, Nicholas J. Wareham, Giuseppe Matullo, John R. B. Perry, Gerard H. Koppelman, M. Arfan Ikram, Ammar Al Chalabi, Gonneke Willemsen, Richie Poulton, Daniel Lawson, Andrew D. Bretherick, Vanessa Schmoll, Carlotta Sacerdote, Annelot M. Dekker, Lili Milani, Fernando Rivadeneira, Erik Melén, John W. Holloway, Gareth E. Davies, Tom G. Richardson, Caroline L Relton, Josine L. Min, Göran Pershagen, Elisabeth B. Binder, Marian Beekman, Chris Haley, Richa Gupta, Bastiaan T. Heijmans, Ellen A. Nohr, Allan F. McRae, Matthew Suderman, Rosie M. Walker, David L. Corcoran, Katri Räikkönen, Marinus H. van IJzendoorn, Eva Giralt-Steinhauer, Leonard C. Schalkwyk, Aleksey Shatunov, and Tim D. Spector
Subjects: Genetics, Regulation of gene expression, 0303 health sciences, Natural selection, dNaM, Biology, Genetic architecture, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, chemistry, Genetic variation, DNA methylation, Trait, 030217 neurology & neurosurgery, DNA, 030304 developmental biology
Abstract: Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. Here we describe results of DNA methylation-quantitative trait loci (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 mQTL of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We reveal that the genetic architecture of DNAm levels is highly polygenic and DNAm exhibits signatures of negative and positive natural selection. Using shared genetic control between distal DNAm sites we construct networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic factors are associated with both blood DNAm levels and complex diseases but in most cases these associations do not reflect causal relationships from DNAm to trait or vice versa indicating a more complex genotype-phenotype map than has previously been hypothesised.
Published: 2020
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17. Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits

Author: Igor Rudan, Albert Tenesa, Caroline Hayward, James F. Wilson, Thibaud Boutin, J Kenneth Baillie, Harry Campbell, Carmen Amador, Yanni Zeng, Andrew D. Bretherick, Oriol Canela-Xandri, Pau Navarro, Veronique Vitart, Alan F. Wright, Chris P. Ponting, Konrad Rawlik, Peter K. Joshi, Chris Haley, and David W. Clark
Subjects: Male, Cancer Research, Proteome, Pulmonology, Genome-wide association study, Disease, QH426-470, Vascular Medicine, 0302 clinical medicine, Mathematical and Statistical Techniques, Pleiotropy, Drug Discovery, Medicine and Health Sciences, Coronary Heart Disease, Receptors, Immunologic, Lymphotoxin-alpha, Genetics (clinical), Genetics, 0303 health sciences, Statistics, Genomics, Phenotype, Cardiovascular Diseases, Physical Sciences, Female, Research Article, Drug Research and Development, Fibroblast Growth Factor 5, Quantitative Trait Loci, Cardiology, Single-nucleotide polymorphism, Quantitative trait locus, Biology, Fatty Acid-Binding Proteins, Research and Analysis Methods, Instrumental Variable Analysis, 03 medical and health sciences, Mendelian randomization, Mental Health and Psychiatry, Genome-Wide Association Studies, Humans, Allele, Statistical Methods, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Genetic Association Studies, Alleles, 030304 developmental biology, Pharmacology, Biology and Life Sciences, Computational Biology, Human Genetics, Mendelian Randomization Analysis, Genome Analysis, Antigens, Differentiation, Receptors, Interleukin-6, Asthma, Lipoprotein Lipase, Genetic Loci, Genetics of Disease, Schizophrenia, 030217 neurology & neurosurgery, Mathematics
Abstract: To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk., Author summary The targets of most medications prescribed today are proteins. For many common diseases our understanding of the underlying causes is often incomplete, and our ability to predict whether new drugs will be effective is remarkably poor. Attempts to use genetics to identify drug targets have an important limitation: standard study designs link disease risk to DNA but do not explain how the genotype leads to disease. In our study, we made robust statistical links between DNA variants and blood levels of 249 proteins, in two separate groups of Europeans. We then used this information to predict protein levels in large genetic studies. In many cases, this second step gives us evidence that high or low levels of a given protein play a role in causing a given disease. Among dozens of high-confidence links, we found new evidence for a causal role of a protein called SHPS1 in schizophrenia, and of another protein (FABP2) in heart disease. Our method takes advantage of information from large numbers of existing genetic studies to prioritize specific proteins as drug targets.
Published: 2020
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18. Epigenome-wide analyses identify DNA methylation signatures of dementia risk

Author: Mairead L. Bermingham, Toni-Kim Clarke, David J. Porteous, Kalyani Pandya, Stewart W. Morris, Riccardo E. Marioni, Kathryn L. Evans, Yanni Zeng, Konrad Rawlik, Archie Campbell, Andrew M. McIntosh, Andrew D. Bretherick, Rosie M. Walker, Caroline Hayward, Kadi Vaher, and Carmen Amador
Subjects: Oncology, medicine.medical_specialty, Disease, lcsh:Geriatrics, genetic risk score, lcsh:RC346-429, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, mental disorders, Genetics, risk factors, Medicine, Dementia, Genetic risk, Family history, lcsh:Neurology. Diseases of the nervous system, 030304 developmental biology, 2. Zero hunger, 0303 health sciences, DNA methylation, alcohol, business.industry, Epigenome, Methylation, Alzheimer's disease, medicine.disease, 3. Good health, lcsh:RC952-954.6, Psychiatry and Mental health, Differential Methylation, Neurology (clinical), business, Body mass index, 030217 neurology & neurosurgery, Research Article, dementia
Abstract: INTRODUCTIONDementia pathogenesis begins years before clinical symptom onset, necessitating the understanding of premorbid risk mechanisms. Here, we investigated potential pathogenic mechanisms by assessing DNA methylation associations with dementia risk factors in Alzheimer’s disease (AD)-free participants.METHODSAssociations between dementia risk measures (family history, genetic risk score (GRS), and dementia risk scores (combining lifestyle, demographic and genetic factors) and whole-blood DNA methylation were assessed in discovery and replication samples (n=∼400 – ∼5,000) from Generation Scotland.RESULTSAD genetic risk and two risk scores were associated with differential methylation. The GRS predominantly associated with methylation differences in cis but also identified a genomic region implicated in Parkinson’s disease. Loci associated with the risk scores were enriched for those previously associated with body mass index and alcohol consumption.DISCUSSIONDementia risk measures show widespread association with blood-based methylation, which indicates differences in the processes affected by genetic and demographic/lifestyle risk factors.
Published: 2020
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19. Genomic evaluation of circulating proteins for drug target characterisation and precision medicine

Author: Tõnu Esko, Lars Wallentin, Andrew D. Bretherick, Adam S. Butterworth, Jesper R. Gådin, Anders Hamsten, Anders Mälarstig, Gunnar Engström, Johan Ärnlöv, Andres Ingason, Céline Fernandez, Jian Yang, Peter M. Nilsson, George Davey-Smith, Weidong Zhang, Agneta Siegbahn, Lasse Folkersen, Thibaud Boutin, Michael V. Holmes, Lars Lind, Julie Lee, Erik Ingelsson, James F. Wilson, Olle Melander, Eric B. Fauman, Lude Franke, Amira Quazi, John Danesh, Ozren Polasek, Niclas Ericsson, Sarah E. Bergen, Mika Ala-Korpela, Andrew J. Schork, Marketa Sjögren, Seung Hoan Choi, Jan Nilsson, Åsa K Hedman, Cecilia M. Lindgren, Bram P. Prins, Daria V. Zhernakova, Yang Wu, Karl Michaëlsson, James E. Peters, Andrew P. Morris, Marju Orho-Melander, Stefan Enroth, Reedik Mägi, Stefan Gustafsson, Mikael Landén, Martin Magnusson, Qin Wang, Steven A. Lubitz, Johan Sundström, Federico De Masi, Jingyuan Fu, Rasmus Wernersson, Thomas Werge, Praveen Surendran, Tom Palmer, Paul W. Franks, Caroline Hayward, Daniel Hvidberg Hansen, Ulf Gyllensten, Vilmantas Giedraitis, Ljubica Perisic Matic, Urmo Võsa, Åsa Johansson, Alexandra Zhernakova, Bianca E. Suur, Karen Page, Gustav Smith, Erik Pålsson, Ting Qi, Harm-Jan Westra, Michael W. Nagle, Erin Macdonald-Dunlop, Annique Claringbould, Peter K. Joshi, Sölve Elmståhl, Anettne Kalnapenkis, Yan Chen, Chris Haley, and Jeremy D. Gale
Subjects: Drug, 0303 health sciences, media_common.quotation_subject, Drug target, Disease, Computational biology, 030204 cardiovascular system & hematology, Biology, Quantitative trait locus, Precision medicine, 3. Good health, 03 medical and health sciences, Human health, 0302 clinical medicine, Mendelian randomization, Gene, 030304 developmental biology, media_common
Abstract: Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. By mapping and replicating protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, we identified 467 pQTLs for 85 proteins. The pQTLs were used in combination with other sources of information to evaluate known drug targets, and suggest new target candidates or repositioning opportunities, underpinned by a) causality assessment using Mendelian randomization, b) pathway mapping usingtrans-pQTL gene assignments, and c) protein-centric polygenic risk scores enabling matching of plausible target mechanisms to sub-groups of individuals enabling precision medicine.
Published: 2020
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20. Letter to the Editor: Response to Luke Fletcher and Philip Peyton regarding 'Predictive validity of a novel non-invasive estimation of effective shunt fraction in critically ill patients'

Author: Emma M Chang, Andrew D. Bretherick, Gordon B. Drummond, and J Kenneth Baillie
Subjects: Predictive validity, Estimation, medicine.medical_specialty, Letter to the editor, Critically ill, business.industry, Non invasive, MEDLINE, lcsh:Medical emergencies. Critical care. Intensive care. First aid, lcsh:RC86-88.9, Critical Care and Intensive Care Medicine, medicine, Shunt fraction, Intensive care medicine, business, Letter to the Editor
Published: 2020
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21. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

Author: Tom Palmer, Lars Lind, George Davey Smith, Ting Qi, Michael W. Nagle, Paul W. Franks, Bram P. Prins, Julie Lee, Jingyuan Fu, Niclas Eriksson, Peter K. Joshi, Chris Haley, Ljubica Perisic Matic, Jeremy D. Gale, Mika Ala-Korpela, Michael V. Holmes, Urmo Võsa, Adam S. Butterworth, Eric B. Fauman, Anette Kalnapenkis, Reedik Mägi, Åsa Johansson, James F. Wilson, Mikael Landén, Gunnar Engström, Johan Ärnlöv, Anders Hamsten, Ozren Polasek, Andres Ingason, Andrew J. Schork, Agneta Siegbahn, Lasse Folkersen, Qin Wang, Andrew P. Morris, Johan Sundström, Daria V. Zhernakova, Olle Melander, Erik Ingelsson, Federico De Masi, Lude Franke, James E. Peters, Alexandra Zhernakova, Seung Hoan Choi, Rasmus Wernersson, Thibaud Boutin, Karl Michaëlsson, Stefan Gustafsson, Bianca E. Suur, Karen Page, Yang Wu, Caroline Hayward, Marketa Sjögren, Cecilia M. Lindgren, Stefan Enroth, Tõnu Esko, Amira Quazi, John Danesh, Anders Mälarstig, Daniel Hvidberg Hansen, Åsa K Hedman, Jan Nilsson, Ulf Gyllensten, Vilmantas Giedraitis, J. Gustav Smith, Martin Magnusson, Marju Orho-Melander, Steven A. Lubitz, Erin Macdonald-Dunlop, Thomas Werge, Praveen Surendran, Yan Chen, Céline Fernandez, Weidong Zhang, Lars Wallentin, Andrew D. Bretherick, Jian Yang, Peter M. Nilsson, Jesper R. Gådin, Annique Claringbould, Sölve Elmståhl, Sarah E Bergen, Harm-Jan Westra, Erik Pålsson, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Translational Immunology Groningen (TRIGR), Center for Liver, Digestive and Metabolic Diseases (CLDM), Stem Cell Aging Leukemia and Lymphoma (SALL), United Kingdom Research and Innovation, Folkersen, Lasse [0000-0003-0708-9530], Hansen, Daniel Hvidberg [0000-0003-3285-605X], Wu, Yang [0000-0002-0128-7280], Eriksson, Niclas [0000-0002-2152-4343], Bretherick, Andrew D [0000-0001-9258-3140], Enroth, Stefan [0000-0002-5056-9137], Lee, Julie [0000-0001-6090-6718], Ala-Korpela, Mika [0000-0001-5905-1206], Claringbould, Annique [0000-0002-9201-6557], Davey Smith, George [0000-0002-1407-8314], Fauman, Eric [0000-0002-9739-0249], Fernandez, Celine [0000-0003-1290-4982], Franke, Lude [0000-0002-5159-8802], Franks, Paul W [0000-0002-0520-7604], Giedraitis, Vilmantas [0000-0003-3423-2021], Haley, Chris [0000-0002-9811-0210], Johansson, Åsa [0000-0002-2915-4498], Lubitz, Steven [0000-0002-9599-4866], Palmer, Tom [0000-0003-4655-4511], Macdonald-Dunlop, Erin [0000-0001-6569-6086], Magnusson, Martin [0000-0003-1710-5936], Michaelsson, Karl [0000-0003-2815-1217], Nagle, Michael W [0000-0002-4677-7582], Nilsson, Peter M [0000-0002-5652-8459], Nilsson, Jan [0000-0002-9752-7479], Prins, Bram [0000-0001-5774-034X], Sundström, Johan [0000-0003-2247-8454], Werge, Thomas [0000-0003-1829-0766], Westra, Harm-Jan [0000-0001-7038-567X], Fu, Jingyuan [0000-0001-5578-1236], Esko, Tõnu [0000-0003-1982-6569], Hayward, Caroline [0000-0002-9405-9550], Landen, Mikael [0000-0002-4496-6451], Butterworth, Adam S [0000-0002-6915-9015], Holmes, Michael V [0000-0001-6617-0879], Ingelsson, Erik [0000-0003-2256-6972], Mälarstig, Anders [0000-0003-2608-1358], Apollo - University of Cambridge Repository, and 30387078 - Magnusson, P. Martin
Subjects: Proteomics, Proteome, Endocrinology, Diabetes and Metabolism, Asthma, ATP Binding Cassette Transporter 1, Cardiovascular System, Chromosome Mapping, Drug Delivery Systems, Gene Knockdown Techniques, Genome-Wide Association Study, Genomics, Humans, Inflammatory Bowel Diseases, Interleukin-1 Receptor-Like 1 Protein, Intracellular Signaling Peptides and Proteins, Linkage Disequilibrium, Mendelian Randomization Analysis, Protein-Serine-Threonine Kinases, Quantitative Trait Loci, Receptors, CCR2, Receptors, CCR5, Genome-wide association study, 030204 cardiovascular system & hematology, Chemokine receptor, 0302 clinical medicine, RECEPTOR ANTAGONIST, GWAS, Cardiac and Cardiovascular Systems, 0303 health sciences, Molecular medicine, 3. Good health, Medical genetics, Medical Genetics, Life Sciences & Biomedicine, medicine.medical_specialty, Computational biology, Biology, Protein Serine-Threonine Kinases, Article, 03 medical and health sciences, Endocrinology & Metabolism, Physiology (medical), Mendelian randomization, Internal Medicine, medicine, 030304 developmental biology, Science & Technology, Cell Biology, GENE, ANTIBODY
Abstract: Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.
Published: 2020
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22. Identification of epigenome-wide DNA methylation differences between carriers of APOE ε4 and APOE ε2

Author: Riccardo E. Marioni, Mairead L. Bermingham, Chris Haley, Archie Campbell, Stewart W. Morris, Kathryn L. Evans, David J. Porteous, Carmen Amador, Konrad Rawlik, Yanni Zeng, Andrew M. McIntosh, Caroline Hayward, Andrew D. Bretherick, Rosie M. Walker, and Kadi Vaher
Subjects: Genetics, Apolipoprotein E, 0303 health sciences, Methylation, Epigenome, Quantitative trait locus, Biology, 03 medical and health sciences, 0302 clinical medicine, DNA methylation, LDL receptor, Allele, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: BACKGROUNDThe Apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late onset Alzheimer’s disease, while the ε2 allele confers protection. Previous studies report differential DNA methylation of APOE between ε4 and ε2 carriers, but associations with epigenome-wide methylation have not previously been characterised.METHODSUsing the EPIC array, we investigated epigenome-wide differences in whole blood DNA methylation patterns between Alzheimer’s disease-free APOE ε4 (n=2469) and ε2 (n=1118) carriers from the two largest single-cohort DNA methylation samples profiled to date. Using a discovery, replication and meta-analysis study design, methylation differences were identified using epigenome-wide association analysis and differentially methylated region (DMR) approaches. Results were explored using pathway and methylation quantitative trait loci (meQTL) analyses.RESULTSWe obtained replicated evidence for DNA methylation differences in a ~169kb region, which encompasses part of APOE and several upstream genes. Meta-analytic approaches identified DNA methylation differences outside of APOE: differentially methylated positions were identified in DHCR24, LDLR and ABCG1 (2.59 x 10−100≤P≤2.44 x 10−8) and DMRs were identified in SREBF2 and LDLR (1.63 x 10−4≤P≤3.01 x 10−2). Pathway and meQTL analyses implicated lipid-related processes and high density lipoprotein cholesterol was identified as a partial mediator of the methylation differences in ABCG1 and DHCR24.CONCLUSIONSAPOE ε4 vs. ε2 carrier status is associated with epigenome-wide methylation differences in the blood. The loci identified are located in trans as well as cis to APOE and implicate genes involved in lipid homeostasis.
Published: 2019
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23. Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer

Author: Matthew Suderman, C. Penfold, Andy R Ness, Steve J Thomas, Andrew D. Bretherick, George Davey Smith, Rebecca C Richmond, Rosie M. Walker, Tom Dudding, Nabila Kazmi, Michael Pawlita, Tom R. Gaunt, Ryan Langdon, Caroline L Relton, Susan M. Ring, Hannah R Elliott, Chris Haley, Kate Ingarfield, Karen M Ho, Yanni Zeng, and Tim Waterboer
Subjects: Epigenomics, Male, 0301 basic medicine, Oncology, Muscle Proteins, Disease, Epigenesis, Genetic, Cohort Studies, Epigenome, 0302 clinical medicine, Risk Factors, Medicine, Genetics (clinical), Epigenetic biomarkers, 0303 health sciences, Smoking, HPV infection, Methylation, Middle Aged, Prognosis, 3. Good health, Survival Rate, Oropharyngeal Neoplasms, CpG site, 030220 oncology & carcinogenesis, DNA methylation, Female, ICEP, Bristol Population Health Science Institute, Adult, medicine.medical_specialty, Alcohol Drinking, Protein Serine-Threonine Kinases, 03 medical and health sciences, Internal medicine, Mendelian randomization, Genetics, Humans, Molecular Biology, Survival rate, 030304 developmental biology, Genetic association, business.industry, Research, Papillomavirus Infections, Case-control study, Cancer, Proteins, Oncogene Proteins, Viral, DNA Methylation, Mendelian Randomization Analysis, medicine.disease, Repressor Proteins, 030104 developmental biology, Case-Control Studies, CpG Islands, business, Biomarkers, Developmental Biology
Abstract: Background Smoking status, alcohol consumption and HPV infection (acquired through sexual activity) are the predominant risk factors for oropharyngeal cancer and are thought to alter the prognosis of the disease. Here, we conducted single-site and differentially methylated region (DMR) epigenome-wide association studies (EWAS) of these factors, in addition to ∼ 3-year survival, using Illumina Methylation EPIC DNA methylation profiles from whole blood in 409 individuals as part of the Head and Neck 5000 (HN5000) study. Overlapping sites between each factor and survival were then assessed using two-step Mendelian randomization to assess whether methylation at these positions causally affected survival. Results Using the MethylationEPIC array in an OPC dataset, we found novel CpG associations with smoking, alcohol consumption and ~ 3-year survival. We found no CpG associations below our multiple testing threshold associated with HPV16 E6 serological response (used as a proxy for HPV infection). CpG site associations below our multiple-testing threshold (PBonferroni < 0.05) for both a prognostic factor and survival were observed at four gene regions: SPEG (smoking), GFI1 (smoking), PPT2 (smoking) and KHDC3L (alcohol consumption). Evidence for a causal effect of DNA methylation on survival was only observed in the SPEG gene region (HR per SD increase in methylation score 1.28, 95% CI 1.14 to 1.43, P 2.12 × 10−05). Conclusions Part of the effect of smoking on survival in those with oropharyngeal cancer may be mediated by methylation at the SPEG gene locus. Replication in data from independent datasets and data from HN5000 with longer follow-up times is needed to confirm these findings.
Published: 2019
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24. Proteome-by-phenome Mendelian Randomisation detects 38 proteins with causal roles in human diseases and traits

Author: Veronique Vitart, J Kenneth Baillie, Andrew D. Bretherick, Caroline Hayward, Albert Tenesa, James F. Wilson, Carmen Amador, Harry Campbell, Thibaud Boutin, Igor Rudan, Peter K. Joshi, Chris Haley, Yanni Zeng, Alan F. Wright, David W. Clark, Oriol Canela-Xandri, Chris P. Ponting, Konrad Rawlik, and Pau Navarro
Subjects: symbols.namesake, Drug development, Phosphatase, Proteome, Mendelian inheritance, symbols, Identification (biology), Computational biology, Phenome, Biology, Phenotype, Blood proteins
Abstract: Target identification remains a crucial challenge in drug development. To enable unbiased detection of proteins and pathways that have a causal role in disease pathogenesis or progression, we propose proteome-by-phenome Mendelian Randomisation (P2MR). We first detected genetic variants associated with plasma concentration of 249 proteins. We then used 64 replicated variants in two-sample Mendelian Randomisation to quantify evidence of a causal role for each protein across 846 phenotypes: this yielded 509 robust protein-outcome links. P2MR provides substantial promise for drug target prioritisation. We provide confirmatory evidence for a causal role for the proteins encoded at multiple cardiovascular disease risk loci (FGF5, IL6R, LPL, LTA), and discovered that intestinal fatty acid binding protein (FABP2) contributes to disease pathogenesis. Additionally, we find and replicate evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia. Our results provide specific prediction of the effects of changes of plasma protein concentration on complex phenotypes in humans.
Published: 2019
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25. Correction to: Predictive validity of a novel non-invasive estimation of effective shunt fraction in critically ill patients

Author: Gordon B. Drummond, Andrew D. Bretherick, J Kenneth Baillie, and Emma M Chang
Subjects: Predictive validity, Estimation, medicine.medical_specialty, business.industry, Critically ill, Non invasive, MEDLINE, lcsh:Medical emergencies. Critical care. Intensive care. First aid, lcsh:RC86-88.9, Critical Care and Intensive Care Medicine, Text mining, medicine, Shunt fraction, business, Intensive care medicine
Abstract: An amendment to this paper has been published and can be accessed via the original article.
Published: 2020
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26. Author response: Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

Author: Uwe Völker, Ninon Mounier, J. M. van Dongen, Martina Müller-Nurasyid, Peter K. Joshi, David W. Clark, Jaanika Kronberg-Guzman, Holger Kirsten, Viktorija Kukushkina, Xia Shen, Patrick Deelen, Mawusse Agbessi, Alexis Battle, Susan M. Ring, Marie-Julie Favé, Anke Tönjes, Tõnu Esko, Z. Kutalik, Alexander Teumer, Biao Zeng, Michael Stumvoll, Katharina Schramm, Natalia Pervjakova, Greg Gibson, Hanieh Yaghootkar, Yungil Kim, Annique Claringbould, Patrick F. Sullivan, Habibul Ahsan, Lin Tong, Markus Scholz, Philip Awadalla, Mark W. Christiansen, Brenda W.J.H. Penninx, Ashis Saha, Urmo Võsa, Urko M. Marigorta, D.I. Boomsma, Peter Kovacs, Isabel Alves, Gibran Hemani, Knut Krohn, Jarno Kettunen, Krista Fischer, Anand Kumar Andiappan, James F. Wilson, Holger Prokisch, Timothy M. Frayling, Joost Verlouw, Anette Kalnapenkis, Markus Perola, Andrew D. Bretherick, Jian Yang, Peter M. Visscher, Markus Loeffler, Joseph E. Powell, Lude Franke, Marc Jan Bonder, Zoltán Kutalik, Silva Kasela, Kristi Läll, Sina A. Gharib, Paul R. H. J. Timmers, Lili Milani, Olli T. Raitakari, T Esko, Zheng Ning, Ilkka Seppälä, Andres Metspalu, Bernett Lee, Michel G. Nivard, Matthias Nauck, Brandon L. Pierce, Joachim Thiery, J.B. van Meurs, Sina Rüeger, Olaf Rötzschke, Samuli Ripatti, Mika Kähönen, Bruce M. Psaty, Rick Jansen, Futao Zhang, Terho Lehtimäki, and Xiao Feng
Subjects: Evolutionary biology, Genomics, Biology
Published: 2018
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27. Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

Author: Carsten O. Daub, Timo Lassmann, Andrew D. Bretherick, Sara Clohisey, David A. Hume, Vladimir B. Bajic, J K Baillie, Ulf Schaefer, Eli A. Stahl, Michael Rehli, Naoto Kondo, Lucile Neyton, Yoshihide Hayashizaki, Alan Gray, Peter Heutink, Piero Carninci, Damian J. Mole, Jun Kawai, Marina Lizio, Geoffrey J. Faulkner, Christine A. Wells, Jeffrey C. Barrett, Harukazu Suzuki, James Bentley Brown, Hideya Kawaji, Chris Haley, Robin Andersson, Albert Tenesa, Masayoshi Itoh, Alistair R. R. Forrest, Tom C. Freeman, Jack Satsangi, Nir Hacohen, Albin Sandelin, and Bergmann, Sven
Subjects: 0301 basic medicine, genetics [Transcriptome], Gene Expression, Crohn's Disease, Genome-wide association study, Mathematical Sciences, Cell Signaling, Crohn Disease, Databases, Genetic, Medicine and Health Sciences, 2.1 Biological and endogenous factors, genetics [Genetic Predisposition to Disease], Biology (General), Aetiology, Promoter Regions, Genetic, Regulation of gene expression, Genetics, Ecology, methods [Genomics], Transcriptional Control, Genomics, Biological Sciences, Colitis, Phenotype, 3. Good health, Computational Theory and Mathematics, Modeling and Simulation, Physical Sciences, Genomic Signal Processing, Research Article, Signal Transduction, Biotechnology, genetics [Crohn Disease], QH301-705.5, Permutation, Bioinformatics, Immunology, Single-nucleotide polymorphism, Gastroenterology and Hepatology, Biology, Autoimmune Diseases, Promoter Regions, 03 medical and health sciences, Cellular and Molecular Neuroscience, Databases, Genetic, Information and Computing Sciences, Journal Article, Genome-Wide Association Studies, Genetic predisposition, Ulcerative Colitis, Humans, Gene Regulation, Genetic Predisposition to Disease, ddc:610, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Discrete Mathematics, Gene Expression Profiling, Human Genome, Inflammatory Bowel Disease, Biology and Life Sciences, Computational Biology, Human Genetics, Cell Biology, Genome Analysis, IIBDGC Consortium, Cap analysis gene expression, Gene expression profiling, 030104 developmental biology, Combinatorics, genetics [Promoter Regions, Genetic], Genetics of Disease, Clinical Immunology, Clinical Medicine, Digestive Diseases, Transcriptome, Mathematics
Abstract: Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits., Author summary We discover that genetic variants associated with specific diseases have more in common with each other than we have previously seen. Specifically, variants associated with the same disease tend to be in parts of the genome that are turned on or off in similar complex patterns across many different cell types. We discover that genetic variants associated with specific diseases are found within regulatory elements that share patterns of expression. Specifically, variants associated with the same disease tend to be in parts of the genome that are turned on or off together in similar complex patterns across many different cell types. Knowing this helps us to find new variants associated with some diseases, and to better understand the genetic causes of other diseases. Furthermore, we discover that the genetic causes of inflammatory bowel disease fall into two distinct patterns, indicating that two aetiologically-distinct endotypes of this condition exist. Unlike other methods to learn about disease mechanisms from genetic information, our approach does not require any knowledge or assumptions about the genes themselves–it depends only on the patterns in which parts of the genome are activated in different cell types.
Published: 2018
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28. Publisher Correction: Parent of origin genetic effects on methylation in humans are common and influence complex trait variation

Author: Rosie M. Walker, Stewart W. Morris, Kathryn L. Evans, Caroline Hayward, Charley Xia, Ian J. Deary, James F. Wilson, Albert Tenesa, Yanni Zeng, Andrew D. Bretherick, Konrad Rawlik, Riccardo E. Marioni, Chris Haley, Carmen Amador, Thibaud Boutin, David W. Clark, Reka Nagy, David J. Porteous, Duncan Sproul, Andrew M. McIntosh, Pau Navarro, Oriol Canela-Xandri, and Archie Campbell
Subjects: Multidisciplinary, Science, General Physics and Astronomy, General Chemistry, Methylation, Biology, General Biochemistry, Genetics and Molecular Biology, Variation (linguistics), Evolutionary biology, Trait, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, lcsh:Q, lcsh:Science, Genomic imprinting
Abstract: In the original version of this Article, the legend in the upper panel of Figure 2 incorrectly read ‘paternal imprinting’ and should have read ‘maternal imprinting’. This has been corrected in both the PDF and HTML versions of the Article.
Published: 2019
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29. Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

Author: Albert Tenesa, Carsten O. Daub, J K Baillie, Michael Rehli, Piero Carninci, Robin Andersson, Vladimir B. Bajic, Ulf Schaefer, Nir Hacohen, Albin Sandelin, Eli A. Stahl, Yoshihide Hayashizaki, Alan Gray, Naoto Kondo, David A. Hume, Jack Satsangi, Jun Kawai, Christine A. Wells, Masayoshi Itoh, Jeffrey C. Barrett, James Bentley Brown, Hideya Kawaji, Geoffrey J. Faulkner, Alistair R. R. Forrest, Tom C. Freeman, Timo Lassmann, Marina Lizio, Sara Clohisey, Andrew D. Bretherick, Peter Heutink, Harukazu Suzuki, and Chris Haley
Subjects: Genetics, 0303 health sciences, Promoter, Genomics, Biology, Cap analysis gene expression, 03 medical and health sciences, 0302 clinical medicine, Regulatory sequence, Genetic predisposition, Transcriptional regulation, Enhancer, 030217 neurology & neurosurgery, 030304 developmental biology, Common disease-common variant
Abstract: Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share patterns of transcriptional regulation. Accordingly, shared transcriptional regulation (coexpression) may help prioritise loci associated with a given trait, and help to identify the biological processes underlying it. Using cap analysis of gene expression (CAGE) profiles of promoter and enhancer-derived RNAs across 1824 human samples, we have quantified coexpression of RNAs originating from trait-associated regulatory regions using a novel analytical method (network density analysis; NDA). For most traits studied, sequence variants in regulatory regions were linked to tightly coexpressed networks that are likely to share important functional characteristics. These networks implicate particular cell types and tissues in disease pathogenesis; for example, variants associated with ulcerative colitis are linked to expression in gut tissue, whereas Crohn’s disease variants are restricted to immune cells. We show that this coexpression signal provides additional independent information for fine mapping likely causative variants. This approach identifies additional genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. This approach enables a deeper biological understanding of the causal basis of complex traits.ONE SENTENCE SUMMARYWe discover that variants associated with a specific disease share expression profiles across tissues and cell types, enabling fine mapping and identification of new disease-associated variants, illuminating key cell types involved in disease pathogenesis.
Published: 2016
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30. Acute liver failure in Scotland between 1992 and 2009; incidence, aetiology and outcome

Author: Caroline M. Bates, D G N Craig, John P. Iredale, Kenneth J. Simpson, Andrew D. Bretherick, K. Martin, J Davidson, and G Masterton
Subjects: Adult, Male, Pediatrics, medicine.medical_specialty, acetaminophen overdose, Adolescent, medicine.medical_treatment, Population, Liver transplantation, Young Adult, Risk Factors, medicine, Humans, Prospective Studies, Prospective cohort study, education, Child, Acetaminophen, Aged, education.field_of_study, business.industry, Incidence (epidemiology), Incidence, digestive, oral, and skin physiology, General Medicine, Odds ratio, Analgesics, Non-Narcotic, Liver Failure, Acute, Middle Aged, Prognosis, Surgery, Liver Transplantation, Transplantation, Treatment Outcome, Scotland, Child, Preschool, Etiology, Female, business
Abstract: Summary Aim: To describe incidence, aetiology and outcome data for Scotland since the inception of the Scottish Liver Transplant Unit (SLTU) in 1992. Background: Acute liver failure (ALF) is a rare but frequently fatal condition. Few studies have adequate patient numbers to draw convincing conclusions over demographic features, aetiology and outcome. Design: Statistical analysis of prospectively collected data on aetiology, demographic, clinical and outcome of all admissions, including those with ALF, to the SLTU. Methods: Incidence data presented for admissions and ALF. Descriptive frequencies for aetiology, clinical, demographic and outcome data presented; including split analysis for paracetamol and nonparacetamol aetiologies. Univariate and multivariate analysis of admission factors predictive of outcome is described. Results: Nine hundred and forty-nine patients were admitted to the SLTU between 1992 and 2009. Five hundred and twenty-four patients had ALF. The annual incidence of ALF in the Scottish population is 0.62 per 100 000 and paracetamol overdose (POD) was the largest causative factor; responsible for 0.43 cases of ALF per 100 000 population per year. The odds ratio (OR) of transplantation or death was 0.47 in the POD group compared to other aetiologies; yet of not being a transplant candidate having met the Kings College Hospital poor prognostic criteria OR was 4.9. Of admissions listed for transplant 76.0% were transplanted. Of those listed and not transplanted mortality was approaching 100% and 76.1% of those transplanted survived to discharge. Conclusions: This large, prospective, single centre study with a defined geographical area and well-recorded population provides accurate data regarding ALF between 1992 and 2009.
Published: 2011

31. Evidence for α-helices in the gas phase: a case study using Melittin from honey bee venom

Author: Perdita E. Barran, Hannah Florance, Jason M. D. Kalapothakis, Andrew D. Bretherick, Bryan J. McCullough, and Andrew P. Stopford
Subjects: Models, Molecular, Molecular Sequence Data, Peptide, Mass spectrometry, Biochemistry, Melittin, Mass Spectrometry, Protein Structure, Secondary, Analytical Chemistry, chemistry.chemical_compound, Molecular dynamics, Protein structure, Electrochemistry, Environmental Chemistry, Animals, Amino Acid Sequence, Lipid bilayer, Spectroscopy, chemistry.chemical_classification, Chemistry, Honey, Bees, Melitten, Crystallography, Hydrogen–deuterium exchange, Alpha helix
Abstract: Gas phase methodologies are increasingly used to study the structure of proteins and peptides. A challenge to the mass spectrometrist is to preserve the structure of the system of interest intact and unaltered from solution into the gas phase. Small peptides are very flexible and can present a number of conformations in solution. In this work we examine Melittin a 26 amino acid peptide that forms the active component of honey bee venom. Melittin is haemolytic and has been shown to form an α-helical tetrameric structure by X-ray crystallography [M. Gribskov et al., The RCSB Protein Data Bank, 1990] and to be helical in high concentrations of methanol. Here we use ion mobility mass spectrometry, molecular dynamics and gas-phase HDX to probe its structure in the gas phase and specifically interrogate whether the helical form can be preserved. All low energy calculated structures possess some helicity. In our experiments we examine the peptide following nano-ESI from solutions with varying methanol content. Ion mobility gives collision cross sections (CCS) that compare well with values found from molecular modelling and from other reported structures, but with inconclusive results regarding the effect of solvent. There is only a slight increase in CCS with charge, showing minimal coloumbically driven unfolding. HDX supports preservation of some helical content into the gas phase and again shows little difference in the exchange rates of species sprayed from different solvents. The [M + 3H](3+) species has two exchanging populations both of which exhibit faster exchange rates than observed for the [M + 2H](2+) species. One interpretation for these results is that the time spent being analysed is sufficient for this peptide to form a helix in the 'ultimate' hydrophobic environment of a vacuum.
Published: 2011

32. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

Author: Paul RHJ Timmers, Ninon Mounier, Kristi Lall, Krista Fischer, Zheng Ning, Xiao Feng, Andrew D Bretherick, David W Clark, eQTLGen Consortium, Xia Shen, Tõnu Esko, Zoltán Kutalik, James F Wilson, and Peter K Joshi
Subjects: genomics, longevity, lifespan, complex trait, Medicine, Science, Biology (General), QH301-705.5
Abstract: We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer – but not other cancers – explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
Published: 2019
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32 results on '"Andrew D. Bretherick"'

1. Evaluation of pragmatic oxygenation measurement as a proxy for Covid-19 severity

2. Typing myalgic encephalomyelitis by infection at onset: A DecodeME study [version 4; peer review: 2 approved]

3. Genome‐Wide Association Study of NAFLD Using Electronic Health Records

4. Identification of epigenome-wide DNA methylation differences between carriers of APOE ε4 and APOE ε2 alleles

5. Lifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome

6. Epigenome‐wide analyses identify DNA methylation signatures of dementia risk

7. Typing myalgic encephalomyelitis by infection at onset: A DecodeME study

8. Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

9. Whole genome sequencing identifies multiple loci for critical illness caused by COVID-19

10. Genome-wide Analysis Identifies Novel Gallstone-susceptibility Loci Including Genes Regulating Gastrointestinal Motility

11. Genome-Wide Association Study of NAFLD Using Electronic Health Records

12. Genome-Wide Association Study of Non-Alcoholic Fatty Liver Disease Identifies Association with Apolipoprotein E

13. Assessing the role of genome-wide DNA methylation between smoking and risk of lung cancer using repeated measurements: the HUNT Study

14. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

15. Genetic mechanisms of critical illness in Covid-19

16. Genomic and phenomic insights from an atlas of genetic effects on DNA methylation

17. Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits

18. Epigenome-wide analyses identify DNA methylation signatures of dementia risk

19. Genomic evaluation of circulating proteins for drug target characterisation and precision medicine

20. Letter to the Editor: Response to Luke Fletcher and Philip Peyton regarding 'Predictive validity of a novel non-invasive estimation of effective shunt fraction in critically ill patients'

21. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

22. Identification of epigenome-wide DNA methylation differences between carriers of APOE ε4 and APOE ε2

23. Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer

24. Proteome-by-phenome Mendelian Randomisation detects 38 proteins with causal roles in human diseases and traits

25. Correction to: Predictive validity of a novel non-invasive estimation of effective shunt fraction in critically ill patients

26. Author response: Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

27. Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

28. Publisher Correction: Parent of origin genetic effects on methylation in humans are common and influence complex trait variation

29. Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

30. Acute liver failure in Scotland between 1992 and 2009; incidence, aetiology and outcome

31. Evidence for α-helices in the gas phase: a case study using Melittin from honey bee venom

32. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

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