Search

Your search keyword '"Yanchun Bao"' showing total 59 results
Start Over Author "Yanchun Bao"
59 results on '"Yanchun Bao"'

1. Partnership status and positive DNA methylation age acceleration across the adult lifespan in the UK

Author

Wen Wang, Anna Dearman, Yanchun Bao, and Meena Kumari

Subjects
Partnership status, DNA methylation, Phenoage, DunedinPACE, Sex, Age, Public aspects of medicine, RA1-1270, Social sciences (General), H1-99
Abstract

Although a significant body of research has shown that married people are healthier and live longer, empirical research on sex differences in the link between marital status and health suggests results are mixed. Moreover, the sex disparities in marital status and health relationships vary across adulthood. The literature on partnership status and measures of ageing is largely focused on older age groups and is limited in its view of early adulthood. Data from waves 2 and 3 (2010–2012) of Understanding Society: UKHLS were used to examine the association of current partnership status with epigenetic age acceleration (AA) assessed with DNA methylation (DNAm) algorithms 'Phenoage' and ' DunedinPACE ' in 3492 participants (aged 16–97). Regression models were estimated separately for men and women, and further stratified by age groups.Divorced/separated and widowed people showed positive age acceleration compared to the married/cohabiting people (reference group). Some sex differences were apparent, especially, among the single and divorced/separated groups. Age differences were also apparent, for example in men, being single was negatively associated with DNAmAA in the youngest group, but positively in the oldest group compared to partnered counterparts.These findings illustrate the importance of partnerships on the ageing process, in particular marital change through divorce and widowhood for positive age acceleration in adults. For single groups, observations were heterogenous by age and sex.

Published
2023
Full Text
View/download PDF

2. Intelligent Medicine on the Regulation Effect of Weekend Catch-Up Sleep on the Relationship Between Hypertension and All-Cause Mortality

Author

Hui Li, Min Yang, and Yanchun Bao

Subjects
Weekend catch-up sleep, Hypertension, All-cause mortality, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract The current studies did not take into consideration the significant differences in sleep duration between weekdays and weekends, and the evidence on weekend catch-up sleep and the risk of cardiovascular/metabolic-related diseases was limited. This study was aimed to explore whether weekend catch-up sleep could modify the association between hypertension and all-cause mortality. This cohort study extracted the data of 5344 participants aged > 18 years in NHANES database. Demographic and clinical variables were included, and univariable and multivariable Proportional Hazards Model regression models were utilized to screen the potential confounding factors. The regulation role of mean weekend catch-up sleep on the association between hypertension and all-cause mortality was explored via the interactive product terms including mean weekend catch-up sleep, hypertension and mean weekend catch-up sleep × hypertension. In total, there were 3157 participants had hypertension. The mean follow-up time was 24.45 ± 1.51 months, and 5235 (98.57%) people were alive at the end of the follow-up. The mean weekend catch-up sleep was 0.71 h in alive people and 0.60 h in people with hypertension. After adjusting for age, gender, race, income, body mass index (BMI), depression, respiratory disease, and diabetes, hypertension was associated with an increased risk of all-cause mortality [hazard ratio (HR) = 2.17, 95% confidence interval (CI) 1.19–3.96]. The interaction between hypertension and mean weekend catch-up sleep was statistically significant in the adjusted model (P = 0.048). When we examined the HR for different mean weekend catch-up sleep groups, we observed that in people with mean weekend catch-up sleep ≤ 1 h, the HR of all-cause mortality associated with hypertension was 3.06 (95% CI 1.44–6.47) after adjusting for age, gender, race, income, BMI, depression, respiratory disease, and diabetes. Mean weekend catch-up sleep modified the association between hypertension and all-cause mortality, which suggested that weekend catch-up sleep might be important for people with hypertension. The findings might provide evidence in the management of sleep and hypertension for intelligent medical information system.

Published
2023
Full Text
View/download PDF

3. Social mobility across the lifecourse and DNA methylation age acceleration in adults in the UK

Author

Yanchun Bao, Tyler Gorrie-Stone, Eilis Hannon, Amanda Hughes, Alexandria Andrayas, Grant Neilson, Joe Burrage, Jonathon Mill, Leonard Schalkwyk, and Meena Kumari

Subjects
Medicine, Science
Abstract

Abstract Disadvantaged socio-economic position (SEP) is associated with greater biological age, relative to chronological age, measured by DNA methylation (positive ‘age acceleration’, AA). Social mobility has been proposed to ameliorate health inequalities. This study aimed to understand the association of social mobility with positive AA. Diagonal reference modelling and ordinary least square regression techniques were applied to explore social mobility and four measures of age acceleration (first-generation: ‘Horvath’, ‘Hannum’ and second-generation: ‘Phenoage’, DunedinPoAm) in n = 3140 participants of the UK Household Longitudinal Study. Disadvantaged SEP in early life is associated with positive AA for three (Hannum, Phenoage and DunedinPoAm) of the four measures examined while the second generation biomarkers are associated with SEP in adulthood (p

Published
2022
Full Text
View/download PDF

4. Massively Parallel CRISPR-Cas9 Knockout Screening in Sheep Granulosa Cells for FSH Response Genes

Author

Zaixia Liu, Lingli Dai, Tianhao Sun, Yongbin Liu, Yanchun Bao, Mingjuan Gu, Shaoyin Fu, Xiaolong He, Caixia Shi, Yu Wang, Lili Guo, Le Zhou, Fengying Ma, Risu Na, and Wenguang Zhang

Subjects
granulosa cells, FSH, massively parallel CRISPR-Cas9 library, dose-dependent genes, Veterinary medicine, SF600-1100, Zoology, QL1-991
Abstract

Follicle-stimulating hormone (FSH) regulates ovarian follicle development through specific gene expression programs. Granulosa cells (GCs) are somatic cells surrounding the oocytes, secreting gonadotropins to regulate ovulation and promote follicular development. By analyzing the effects of different doses of FSH on the proliferation of GCs, we found that adding 10 ng/mL of FSH, as the optimal concentration, could promote the growth of GCs. Furthermore, we have successfully constructed the first CRISPR-Cas9 knockout library targeting the genes on chromosomes 2 and 3 and the X chromosomes of the sheep massively parallel coding gene, as well as an ovarian GCs knockout cell library. For the first time, we have exposed the knockout cell library to a concentration of 10 ng/mL FSH to explore the underlying mechanisms. Through this screening, we have identified 836 positive–negative screening genes that are responsive to FSH, thereby revealing the regulatory mechanisms and screening the functionality of candidate genes. Next, RNA-Seq of control (0 ng/mL), low (10 ng/mL), and high (100 ng/mL) doses of FSH revealed 1708 differentially expressed genes, and combined with 836 genes, we obtained 129 FSH dose-dependent genes with extremely significant differences. This enables us to delve deeper into investigating and identifying the mechanisms by which FSH regulates GCs. More generally, we have discovered new regulatory factors and identified reproductivity-associated major effectors. These findings provide novel research directions for further studies on sheep reproduction.

Published
2024
Full Text
View/download PDF

5. A Bidirectional Mendelian Randomization Study to evaluate the causal role of reduced blood vitamin D levels with type 2 diabetes risk in South Asians and Europeans

Author

Cynthia A. Bejar, Shiwali Goyal, Shoaib Afzal, Massimo Mangino, Ang Zhou, Peter J. van der Most, Yanchun Bao, Vipin Gupta, Melissa C. Smart, Gagandeep K. Walia, Niek Verweij, Christine Power, Dorairaj Prabhakaran, Jai Rup Singh, Narinder K. Mehra, Gurpreet S. Wander, Sarju Ralhan, Sanjay Kinra, Meena Kumari, Martin H. de Borst, Elina Hyppönen, Tim D. Spector, Børge G. Nordestgaard, Piers R. Blackett, and Dharambir K. Sanghera

Subjects
Nutrition. Foods and food supply, TX341-641, Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Context Multiple observational studies have reported an inverse relationship between 25-hydroxyvitamin D concentrations (25(OH)D) and type 2 diabetes (T2D). However, the results of short- and long-term interventional trials concerning the relationship between 25(OH)D and T2D risk have been inconsistent. Objectives and methods To evaluate the causal role of reduced blood 25(OH)D in T2D, here we have performed a bidirectional Mendelian randomization study using 59,890 individuals (5,862 T2D cases and 54,028 controls) from European and Asian Indian ancestries. We used six known SNPs, including three T2D SNPs and three vitamin D pathway SNPs, as a genetic instrument to evaluate the causality and direction of the association between T2D and circulating 25(OH)D concentration. Results Results of the combined meta-analysis of eight participating studies showed that a composite score of three T2D SNPs would significantly increase T2D risk by an odds ratio (OR) of 1.24, p = 1.82 × 10–32; Z score 11.86, which, however, had no significant association with 25(OH)D status (Beta -0.02nmol/L ± SE 0.01nmol/L; p = 0.83; Z score -0.21). Likewise, the genetically instrumented composite score of 25(OH)D lowering alleles significantly decreased 25(OH)D concentrations (-2.1nmol/L ± SE 0.1nmol/L, p = 7.92 × 10–78; Z score -18.68) but was not associated with increased risk for T2D (OR 1.00, p = 0.12; Z score 1.54). However, using 25(OH)D synthesis SNP (DHCR7; rs12785878) as an individual genetic instrument, a per allele reduction of 25(OH)D concentration (-4.2nmol/L ± SE 0.3nmol/L) was predicted to increase T2D risk by 5%, p = 0.004; Z score 2.84. This effect, however, was not seen in other 25(OH)D SNPs (GC rs2282679, CYP2R1 rs12794714) when used as an individual instrument. Conclusion Our new data on this bidirectional Mendelian randomization study suggests that genetically instrumented T2D risk does not cause changes in 25(OH)D levels. However, genetically regulated 25(OH)D deficiency due to vitamin D synthesis gene (DHCR7) may influence the risk of T2D.

Published
2021
Full Text
View/download PDF

6. Systematic underestimation of the epigenetic clock and age acceleration in older subjects

Author

Louis Y. El Khoury, Tyler Gorrie-Stone, Melissa Smart, Amanda Hughes, Yanchun Bao, Alexandria Andrayas, Joe Burrage, Eilis Hannon, Meena Kumari, Jonathan Mill, and Leonard C. Schalkwyk

Subjects
DNA methylation, Epigenetic clock, Age acceleration, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate “age acceleration” in various tissues and environments. Results The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways. Conclusions The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate.

Published
2019
Full Text
View/download PDF

7. Associations of autozygosity with a broad range of human phenotypes

Author

David W Clark, Yukinori Okada, Kristjan H S Moore, Dan Mason, Nicola Pirastu, Ilaria Gandin, Hannele Mattsson, Catriona L K Barnes, Kuang Lin, Jing Hua Zhao, Patrick Deelen, Rebecca Rohde, Claudia Schurmann, Xiuqing Guo, Franco Giulianini, Weihua Zhang, Carolina Medina-Gomez, Robert Karlsson, Yanchun Bao, Traci M Bartz, Clemens Baumbach, Ginevra Biino, Matthew J Bixley, Marco Brumat, Jin-Fang Chai, Tanguy Corre, Diana L Cousminer, Annelot M Dekker, David A Eccles, Kristel R van Eijk, Christian Fuchsberger, He Gao, Marine Germain, Scott D Gordon, Hugoline G de Haan, Sarah E Harris, Edith Hofer, Alicia Huerta-Chagoya, Catherine Igartua, Iris E Jansen, Yucheng Jia, Tim Kacprowski, Torgny Karlsson, Marcus E Kleber, Shengchao Alfred Li, Ruifang Li-Gao, Anubha Mahajan, Koichi Matsuda, Karina Meidtner, Weihua Meng, May E Montasser, Peter J van der Most, Matthias Munz, Teresa Nutile, Teemu Palviainen, Gauri Prasad, Rashmi B Prasad, Tallapragada Divya Sri Priyanka, Federica Rizzi, Erika Salvi, Bishwa R Sapkota, Daniel Shriner, Line Skotte, Melissa C Smart, Albert Vernon Smith, Ashley van der Spek, Cassandra N Spracklen, Rona J Strawbridge, Salman M Tajuddin, Stella Trompet, Constance Turman, Niek Verweij, Clara Viberti, Lihua Wang, Helen R Warren, Robyn E Wootton, Lisa R Yanek, Jie Yao, Noha A Yousri, Wei Zhao, Adebowale A Adeyemo, Saima Afaq, Carlos Alberto Aguilar-Salinas, Masato Akiyama, Matthew L Albert, Matthew A Allison, Maris Alver, Tin Aung, Fereidoun Azizi, Amy R Bentley, Heiner Boeing, Eric Boerwinkle, Judith B Borja, Gert J de Borst, Erwin P Bottinger, Linda Broer, Harry Campbell, Stephen Chanock, Miao-Li Chee, Guanjie Chen, Yii-Der I Chen, Zhengming Chen, Yen-Feng Chiu, Massimiliano Cocca, Francis S Collins, Maria Pina Concas, Janie Corley, Giovanni Cugliari, Rob M van Dam, Anna Damulina, Maryam S Daneshpour, Felix R Day, Graciela E Delgado, Klodian Dhana, Alexander S F Doney, Marcus Dörr, Ayo P Doumatey, Nduna Dzimiri, S Sunna Ebenesersdóttir, Joshua Elliott, Paul Elliott, Ralf Ewert, Janine F Felix, Krista Fischer, Barry I Freedman, Giorgia Girotto, Anuj Goel, Martin Gögele, Mark O Goodarzi, Mariaelisa Graff, Einat Granot-Hershkovitz, Francine Grodstein, Simonetta Guarrera, Daniel F Gudbjartsson, Kamran Guity, Bjarni Gunnarsson, Yu Guo, Saskia P Hagenaars, Christopher A Haiman, Avner Halevy, Tamara B Harris, Mehdi Hedayati, David A van Heel, Makoto Hirata, Imo Höfer, Chao Agnes Hsiung, Jinyan Huang, Yi-Jen Hung, M Arfan Ikram, Anuradha Jagadeesan, Pekka Jousilahti, Yoichiro Kamatani, Masahiro Kanai, Nicola D Kerrison, Thorsten Kessler, Kay-Tee Khaw, Chiea Chuen Khor, Dominique P V de Kleijn, Woon-Puay Koh, Ivana Kolcic, Peter Kraft, Bernhard K Krämer, Zoltán Kutalik, Johanna Kuusisto, Claudia Langenberg, Lenore J Launer, Deborah A Lawlor, I-Te Lee, Wen-Jane Lee, Markus M Lerch, Liming Li, Jianjun Liu, Marie Loh, Stephanie J London, Stephanie Loomis, Yingchang Lu, Jian’an Luan, Reedik Mägi, Ani W Manichaikul, Paolo Manunta, Gísli Másson, Nana Matoba, Xue W Mei, Christa Meisinger, Thomas Meitinger, Massimo Mezzavilla, Lili Milani, Iona Y Millwood, Yukihide Momozawa, Amy Moore, Pierre-Emmanuel Morange, Hortensia Moreno-Macías, Trevor A Mori, Alanna C Morrison, Taulant Muka, Yoshinori Murakami, Alison D Murray, Renée de Mutsert, Josyf C Mychaleckyj, Mike A Nalls, Matthias Nauck, Matt J Neville, Ilja M Nolte, Ken K Ong, Lorena Orozco, Sandosh Padmanabhan, Gunnar Pálsson, James S Pankow, Cristian Pattaro, Alison Pattie, Ozren Polasek, Neil Poulter, Peter P Pramstaller, Lluis Quintana-Murci, Katri Räikkönen, Sarju Ralhan, Dabeeru C Rao, Wouter van Rheenen, Stephen S Rich, Paul M Ridker, Cornelius A Rietveld, Antonietta Robino, Frank J A van Rooij, Daniela Ruggiero, Yasaman Saba, Charumathi Sabanayagam, Maria Sabater-Lleal, Cinzia Felicita Sala, Veikko Salomaa, Kevin Sandow, Helena Schmidt, Laura J Scott, William R Scott, Bahareh Sedaghati-Khayat, Bengt Sennblad, Jessica van Setten, Peter J Sever, Wayne H-H Sheu, Yuan Shi, Smeeta Shrestha, Sharvari Rahul Shukla, Jon K Sigurdsson, Timo Tonis Sikka, Jai Rup Singh, Blair H Smith, Alena Stančáková, Alice Stanton, John M Starr, Lilja Stefansdottir, Leon Straker, Patrick Sulem, Gardar Sveinbjornsson, Morris A Swertz, Adele M Taylor, Kent D Taylor, Natalie Terzikhan, Yih-Chung Tham, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Annika Tillander, Russell P Tracy, Teresa Tusié-Luna, Ioanna Tzoulaki, Simona Vaccargiu, Jagadish Vangipurapu, Jan H Veldink, Veronique Vitart, Uwe Völker, Eero Vuoksimaa, Salma M Wakil, Melanie Waldenberger, Gurpreet S Wander, Ya Xing Wang, Nicholas J Wareham, Sarah Wild, Chittaranjan S Yajnik, Jian-Min Yuan, Lingyao Zeng, Liang Zhang, Jie Zhou, Najaf Amin, Folkert W Asselbergs, Stephan J L Bakker, Diane M Becker, Benjamin Lehne, David A Bennett, Leonard H van den Berg, Sonja I Berndt, Dwaipayan Bharadwaj, Lawrence F Bielak, Murielle Bochud, Mike Boehnke, Claude Bouchard, Jonathan P Bradfield, Jennifer A Brody, Archie Campbell, Shai Carmi, Mark J Caulfield, David Cesarini, John C Chambers, Giriraj Ratan Chandak, Ching-Yu Cheng, Marina Ciullo, Marilyn Cornelis, Daniele Cusi, George Davey Smith, Ian J Deary, Rajkumar Dorajoo, Cornelia M van Duijn, David Ellinghaus, Jeanette Erdmann, Johan G Eriksson, Evangelos Evangelou, Michele K Evans, Jessica D Faul, Bjarke Feenstra, Mary Feitosa, Sylvain Foisy, Andre Franke, Yechiel Friedlander, Paolo Gasparini, Christian Gieger, Clicerio Gonzalez, Philippe Goyette, Struan F A Grant, Lyn R Griffiths, Leif Groop, Vilmundur Gudnason, Ulf Gyllensten, Hakon Hakonarson, Anders Hamsten, Pim van der Harst, Chew-Kiat Heng, Andrew A Hicks, Hagit Hochner, Heikki Huikuri, Steven C Hunt, Vincent W V Jaddoe, Philip L De Jager, Magnus Johannesson, Åsa Johansson, Jost B Jonas, J Wouter Jukema, Juhani Junttila, Jaakko Kaprio, Sharon L. R. Kardia, Fredrik Karpe, Meena Kumari, Markku Laakso, Sander W van der Laan, Jari Lahti, Matthias Laudes, Rodney A Lea, Wolfgang Lieb, Thomas Lumley, Nicholas G Martin, Winfried März, Giuseppe Matullo, Mark I McCarthy, Sarah E Medland, Tony R Merriman, Andres Metspalu, Brian F Meyer, Karen L Mohlke, Grant W Montgomery, Dennis Mook-Kanamori, Patricia B Munroe, Kari E North, Dale R Nyholt, Jeffery R O’connell, Carole Ober, Albertine J Oldehinkel, Walter Palmas, Colin Palmer, Gerard G Pasterkamp, Etienne Patin, Craig E Pennell, Louis Perusse, Patricia A Peyser, Mario Pirastu, Tinca J. C. Polderman, David J Porteous, Danielle Posthuma, Bruce M Psaty, John D Rioux, Fernando Rivadeneira, Charles Rotimi, Jerome I Rotter, Igor Rudan, Hester M Den Ruijter, Dharambir K Sanghera, Naveed Sattar, Reinhold Schmidt, Matthias B Schulze, Heribert Schunkert, Robert A Scott, Alan R Shuldiner, Xueling Sim, Neil Small, Jennifer A Smith, Nona Sotoodehnia, E-Shyong Tai, Alexander Teumer, Nicholas J Timpson, Daniela Toniolo, David-Alexandre Tregouet, Tiinamaija Tuomi, Peter Vollenweider, Carol A Wang, David R Weir, John B Whitfield, Cisca Wijmenga, Tien-Yin Wong, John Wright, Jingyun Yang, Lei Yu, Babette S Zemel, Alan B Zonderman, Markus Perola, Patrik K. E. Magnusson, André G Uitterlinden, Jaspal S Kooner, Daniel I Chasman, Ruth J. F. Loos, Nora Franceschini, Lude Franke, Chris S Haley, Caroline Hayward, Robin G Walters, John R. B. Perry, Tōnu Esko, Agnar Helgason, Kari Stefansson, Peter K Joshi, Michiaki Kubo, and James F Wilson

Subjects
Science
Abstract

Inbreeding depression has been observed in many different species, but in humans a systematic analysis has been difficult so far. Here, analysing more than 1.3 million individuals, the authors show that a genomic inbreeding coefficient (FROH) is associated with disadvantageous outcomes in 32 out of 100 traits tested.

Published
2019
Full Text
View/download PDF

8. Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9

Author

Amand F. Schmidt, Michael V. Holmes, David Preiss, Daniel I. Swerdlow, Spiros Denaxas, Ghazaleh Fatemifar, Rupert Faraway, Chris Finan, Dennis Valentine, Zammy Fairhurst-Hunter, Fernando Pires Hartwig, Bernardo Lessa Horta, Elina Hypponen, Christine Power, Max Moldovan, Erik van Iperen, Kees Hovingh, Ilja Demuth, Kristina Norman, Elisabeth Steinhagen-Thiessen, Juri Demuth, Lars Bertram, Christina M. Lill, Stefan Coassin, Johann Willeit, Stefan Kiechl, Karin Willeit, Dan Mason, John Wright, Richard Morris, Goya Wanamethee, Peter Whincup, Yoav Ben-Shlomo, Stela McLachlan, Jackie F. Price, Mika Kivimaki, Catherine Welch, Adelaida Sanchez-Galvez, Pedro Marques-Vidal, Andrew Nicolaides, Andrie G. Panayiotou, N. Charlotte Onland-Moret, Yvonne T. van der Schouw, Giuseppe Matullo, Giovanni Fiorito, Simonetta Guarrera, Carlotta Sacerdote, Nicholas J. Wareham, Claudia Langenberg, Robert A. Scott, Jian’an Luan, Martin Bobak, Sofia Malyutina, Andrzej Pająk, Ruzena Kubinova, Abdonas Tamosiunas, Hynek Pikhart, Niels Grarup, Oluf Pedersen, Torben Hansen, Allan Linneberg, Tine Jess, Jackie Cooper, Steve E. Humphries, Murray Brilliant, Terrie Kitchner, Hakon Hakonarson, David S. Carrell, Catherine A. McCarty, Kirchner H. Lester, Eric B. Larson, David R. Crosslin, Mariza de Andrade, Dan M. Roden, Joshua C. Denny, Cara Carty, Stephen Hancock, John Attia, Elizabeth Holliday, Rodney Scott, Peter Schofield, Martin O’Donnell, Salim Yusuf, Michael Chong, Guillaume Pare, Pim van der Harst, M. Abdullah Said, Ruben N. Eppinga, Niek Verweij, Harold Snieder, Lifelines Cohort authors, Tim Christen, D. O. Mook-Kanamori, the ICBP Consortium, Stefan Gustafsson, Lars Lind, Erik Ingelsson, Raha Pazoki, Oscar Franco, Albert Hofman, Andre Uitterlinden, Abbas Dehghan, Alexander Teumer, Sebastian Baumeister, Marcus Dörr, Markus M. Lerch, Uwe Völker, Henry Völzke, Joey Ward, Jill P. Pell, Tom Meade, Ingrid E. Christophersen, Anke H. Maitland-van der Zee, Ekaterina V. Baranova, Robin Young, Ian Ford, Archie Campbell, Sandosh Padmanabhan, Michiel L. Bots, Diederick E. Grobbee, Philippe Froguel, Dorothée Thuillier, Ronan Roussel, Amélie Bonnefond, Bertrand Cariou, Melissa Smart, Yanchun Bao, Meena Kumari, Anubha Mahajan, Jemma C. Hopewell, Sudha Seshadri, the METASTROKE Consortium of the ISGC, Caroline Dale, Rui Providencia E. Costa, Paul M. Ridker, Daniel I. Chasman, Alex P. Reiner, Marylyn D. Ritchie, Leslie A. Lange, Alex J. Cornish, Sara E. Dobbins, Kari Hemminki, Ben Kinnersley, Marc Sanson, Karim Labreche, Matthias Simon, Melissa Bondy, Philip Law, Helen Speedy, James Allan, Ni Li, Molly Went, Niels Weinhold, Gareth Morgan, Pieter Sonneveld, Björn Nilsson, Hartmut Goldschmidt, Amit Sud, Andreas Engert, Markus Hansson, Harry Hemingway, Folkert W. Asselbergs, Riyaz S. Patel, Brendan J. Keating, Naveed Sattar, Richard Houlston, Juan P. Casas, and Aroon D. Hingorani

Subjects
Genetic association studies, Mendelian randomisation, LDL-cholesterol, Phenome-wide association scan, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9. Methods Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration. Results The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable. Conclusions Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.

Published
2019
Full Text
View/download PDF

9. Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array

Author

Georgina Mansell, Tyler J. Gorrie-Stone, Yanchun Bao, Meena Kumari, Leonard S. Schalkwyk, Jonathan Mill, and Eilis Hannon

Subjects
DNA methylation, Epigenome-wide association study (EWAS), Multiple testing, Illumina EPIC array, Power, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background There has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies. Results We quantified DNA methylation in the Understanding Society cohort (n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p-values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10− 8. Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Nevertheless, we found no evidence that this bias influences analyses by increasing the likelihood of affected sites to be false positives. Finally, we performed power calculations for EPIC based DNA methylation studies, demonstrating that existing studies with data on ~ 1000 samples are adequately powered to detect small differences at the majority of sites. Conclusion We propose that a significance threshold of P

Published
2019
Full Text
View/download PDF

10. Frequency and Distribution of Refractive Error in Adult Life: Methodology and Findings of the UK Biobank Study.

Author

Phillippa M Cumberland, Yanchun Bao, Pirro G Hysi, Paul J Foster, Christopher J Hammond, Jugnoo S Rahi, and UK Biobank Eyes & Vision Consortium

Subjects
Medicine, Science
Abstract

PURPOSE:To report the methodology and findings of a large scale investigation of burden and distribution of refractive error, from a contemporary and ethnically diverse study of health and disease in adults, in the UK. METHODS:U K Biobank, a unique contemporary resource for the study of health and disease, recruited more than half a million people aged 40-69 years. A subsample of 107,452 subjects undertook an enhanced ophthalmic examination which provided autorefraction data (a measure of refractive error). Refractive error status was categorised using the mean spherical equivalent refraction measure. Information on socio-demographic factors (age, gender, ethnicity, educational qualifications and accommodation tenure) was reported at the time of recruitment by questionnaire and face-to-face interview. RESULTS:Fifty four percent of participants aged 40-69 years had refractive error. Specifically 27% had myopia (4% high myopia), which was more common amongst younger people, those of higher socio-economic status, higher educational attainment, or of White or Chinese ethnicity. The frequency of hypermetropia increased with age (7% at 40-44 years increasing to 46% at 65-69 years), was higher in women and its severity was associated with ethnicity (moderate or high hypermetropia at least 30% less likely in non-White ethnic groups compared to White). CONCLUSIONS:Refractive error is a significant public health issue for the UK and this study provides contemporary data on adults for planning services, health economic modelling and monitoring of secular trends. Further investigation of risk factors is necessary to inform strategies for prevention. There is scope to do this through the planned longitudinal extension of the UK Biobank study.

Published
2015
Full Text
View/download PDF

17. A Bidirectional Mendelian Randomization Study to evaluate the causal role of reduced blood vitamin D levels with type 2 diabetes risk in South Asians and Europeans

Author

Meena Kumari, Vipin Gupta, Shoaib Afzal, Gurpreet Singh Wander, Jai Rup Singh, Christine Power, Martin H. de Borst, Gagandeep Kaur Walia, Massimo Mangino, Sanjay Kinra, Sarju Ralhan, Shiwali Goyal, Narinder K. Mehra, Peter J. van der Most, Ang Zhou, Dharambir K. Sanghera, Tim D. Spector, Piers R. Blackett, Dorairaj Prabhakaran, Cynthia Bejar, Melissa C. Smart, Børge G. Nordestgaard, Yanchun Bao, Niek Verweij, Elina Hyppönen, Groningen Institute for Organ Transplantation (GIOT), Groningen Kidney Center (GKC), Bejar, Cynthia A, Goyal, Shiwali, Afzal, Shoaib, Mangino, Massimo, Zhou, Ang, Hyppönen, Elina, and Sanghera, Dharambir K

Subjects
0301 basic medicine, medicine.medical_specialty, 25-HYDROXYVITAMIN D, RC620-627, endocrine system diseases, Medicine (miscellaneous), 030209 endocrinology & metabolism, Single-nucleotide polymorphism, Context (language use), Genome-wide association study, Type 2 diabetes, DETERMINANTS, Standard score, SUSCEPTIBILITY, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, Asian People, PEOPLE, Internal medicine, Mendelian randomization, Vitamin D and neurology, Humans, Medicine, TX341-641, Vitamin D, GENOME-WIDE ASSOCIATION, Nutritional diseases. Deficiency diseases, INSULIN-RESISTANCE, Nutrition and Dietetics, Nutrition. Foods and food supply, business.industry, Research, Odds ratio, Mendelian Randomization Analysis, Vitamin D Deficiency, medicine.disease, C-REACTIVE PROTEIN, CENTRAL OBESITY, PREVALENCE, 030104 developmental biology, Endocrinology, Diabetes Mellitus, Type 2, HEALTH, business
Abstract

Context Multiple observational studies have reported an inverse relationship between 25-hydroxyvitamin D concentrations (25(OH)D) and type 2 diabetes (T2D). However, the results of short- and long-term interventional trials concerning the relationship between 25(OH)D and T2D risk have been inconsistent. Objectives and methods To evaluate the causal role of reduced blood 25(OH)D in T2D, here we have performed a bidirectional Mendelian randomization study using 59,890 individuals (5,862 T2D cases and 54,028 controls) from European and Asian Indian ancestries. We used six known SNPs, including three T2D SNPs and three vitamin D pathway SNPs, as a genetic instrument to evaluate the causality and direction of the association between T2D and circulating 25(OH)D concentration. Results Results of the combined meta-analysis of eight participating studies showed that a composite score of three T2D SNPs would significantly increase T2D risk by an odds ratio (OR) of 1.24, p = 1.82 × 10–32; Z score 11.86, which, however, had no significant association with 25(OH)D status (Beta -0.02nmol/L ± SE 0.01nmol/L; p = 0.83; Z score -0.21). Likewise, the genetically instrumented composite score of 25(OH)D lowering alleles significantly decreased 25(OH)D concentrations (-2.1nmol/L ± SE 0.1nmol/L, p = 7.92 × 10–78; Z score -18.68) but was not associated with increased risk for T2D (OR 1.00, p = 0.12; Z score 1.54). However, using 25(OH)D synthesis SNP (DHCR7; rs12785878) as an individual genetic instrument, a per allele reduction of 25(OH)D concentration (-4.2nmol/L ± SE 0.3nmol/L) was predicted to increase T2D risk by 5%, p = 0.004; Z score 2.84. This effect, however, was not seen in other 25(OH)D SNPs (GC rs2282679, CYP2R1 rs12794714) when used as an individual instrument. Conclusion Our new data on this bidirectional Mendelian randomization study suggests that genetically instrumented T2D risk does not cause changes in 25(OH)D levels. However, genetically regulated 25(OH)D deficiency due to vitamin D synthesis gene (DHCR7) may influence the risk of T2D.

Published
2021
Full Text
View/download PDF

18. Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving

Author

Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao, and Bei Jiang

Subjects
FOS: Computer and information sciences, Computer Science - Computers and Society, Computer Science - Computation and Language, Computers and Society (cs.CY), General Medicine, Computation and Language (cs.CL)
Abstract

With widening deployments of natural language processing (NLP) in daily life, inherited social biases from NLP models have become more severe and problematic. Previous studies have shown that word embeddings trained on human-generated corpora have strong gender biases that can produce discriminative results in downstream tasks. Previous debiasing methods focus mainly on modeling bias and only implicitly consider semantic information while completely overlooking the complex underlying causal structure among bias and semantic components. To address these issues, we propose a novel methodology that leverages a causal inference framework to effectively remove gender bias. The proposed method allows us to construct and analyze the complex causal mechanisms facilitating gender information flow while retaining oracle semantic information within word embeddings. Our comprehensive experiments show that the proposed method achieves state-of-the-art results in gender-debiasing tasks. In addition, our methods yield better performance in word similarity evaluation and various extrinsic downstream NLP tasks., Comment: Accepted by AAAI 2022

Published
2021
Full Text
View/download PDF

19. Efficient empirical likelihood inference for recovery rate of COVID19 under double-censoring

Author

Jie Hu, Wei Liang, Hongsheng Dai, and Yanchun Bao

Subjects
Statistics and Probability, Applied Mathematics, Statistics, Probability and Uncertainty
Abstract

Doubly censored data are very common in epidemiology studies. Ignoring censorship in the analysis may lead to biased parameter estimation. In this paper, we highlight that the publicly available COVID19 data may involve high percentage of double-censoring and point out the importance of dealing with such missing information in order to achieve better forecasting results. Existing statistical methods for doubly censored data may suffer from the convergence problems of the EM algorithms or may not be good enough for small sample sizes. This paper develops a new empirical likelihood method to analyze the recovery rate of COVID19 based on a doubly censored dataset. The efficient influence function of the parameter of interest is used to define the empirical likelihood (EL) ratio. We prove that

Published
2020

20. Assessing the robustness of sisVIVE in a Mendelian randomization study to estimate the causal effect of body mass index on income using multiple SNPs from understanding society

Author

Meena Kumari, Melissa C. Smart, Paul Clarke, and Yanchun Bao

Subjects
Statistics and Probability, Epidemiology, Computer science, Consistency criterion, pleiotropic bias, Polymorphism, Single Nucleotide, 01 natural sciences, Body Mass Index, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Personal income, Lasso (statistics), MR‐Median, Robustness (computer science), Mendelian randomization, Econometrics, Humans, Computer Simulation, Genetic Predisposition to Disease, 030212 general & internal medicine, 0101 mathematics, Research Articles, instrumental variables, Models, Genetic, Instrumental variable, Mendelian Randomization Analysis, Outcome (probability), Regression, Causality, Income, MR‐Egger, Regression Analysis, sisVIVE, Research Article
Abstract

The "some invalid, some valid instrumental variable estimator" (sisVIVE) is a lasso-based method for instrumental variables (IVs) regression of outcome on an exposure. In principle, sisVIVE is robust to some of the IVs in the analysis being invalid, in the sense of being related to the outcome variable through pathways not mediated by the exposure. In this paper, we consider the application of sisVIVE to a Mendelian randomization study in which multiple genetic variants are used as IVs to estimate the causal effect of body mass index on personal income in the presence of unobserved confounding. In addition to analyzing data from the large-scale longitudinal household survey Understanding Society, we conduct a simulation study to (a) assess the performance of sisVIVE in relation to that of competing robust methods like "MR-Egger" and "MR-Median" and (b) identify scenarios under which its absolute performance is poor. We find that sisVIVE outperforms alternative robust methods, in terms of mean-square error, across a wide range of scenarios, but that its performance is poor in absolute terms when the presence of indirect pleiotropy leads to failure of the "InSIDE" condition, which is not explicitly required for identification. We argue that this is because the consistency criterion for sisVIVE does not identify the true causal effect when InSIDE fails.

Published
2018
Full Text
View/download PDF

21. Socioeconomic Position and DNA Methylation Age Acceleration Across the Life Course

Author

Meena Kumari, Eilis Hannon, Amanda Hughes, T.J. Gorrie-Stone, Leonard C. Schalkwyk, Jonathan Mill, Yanchun Bao, Melissa C. Smart, and Joe Burrage

Subjects
Adult, Male, 0301 basic medicine, Socioeconomic position, Epidemiology, Original Contributions, Population, Acceleration (differential geometry), socioeconomic factors, Biology, Social class, Epigenesis, Genetic, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Humans, Social position, Letters to the Editor, education, Aged, Aged, 80 and over, education.field_of_study, DNA methylation, Working parent, aging, Confounding, Social environment, Health Status Disparities, Middle Aged, United Kingdom, Confidence interval, 030104 developmental biology, 030220 oncology & carcinogenesis, epigenomics, 8. Economic growth, Life course approach, Female, Psychology, 030217 neurology & neurosurgery, Demography
Abstract

Accelerated DNA methylation age is linked to all-cause mortality and environmental factors, but studies of associations with socioeconomic position are limited. Researchers generally use small selected samples, and it is unclear how findings obtained with 2 commonly used methods for calculating methylation age (the Horvath method and the Hannum method) translate to general population samples including younger and older adults. Among 1,099 United Kingdom adults aged 28–98 years in 2011–2012, we assessed the relationship of Horvath and Hannum DNA methylation age acceleration with a range of social position measures: current income and employment, education, income and unemployment across a 12-year period, and childhood social class. Accounting for confounders, participants who had been less advantaged in childhood were epigenetically “older” as adults: In comparison with participants who had professional/managerial parents, Hannum age was 1.07 years higher (95% confidence interval: 0.20, 1.94) for participants with parents in semiskilled/unskilled occupations and 1.85 years higher (95% confidence interval: 0.67, 3.02) for those without a working parent at age 14 years. No other robust associations were seen. Results accord with research implicating early life circumstances as critical for DNA methylation age in adulthood. Since methylation age acceleration as measured by the Horvath and Hannum estimators appears strongly linked to chronological age, researchers examining associations with the social environment must take steps to avoid age-related confounding.

Published
2018
Full Text
View/download PDF

22. Systematic underestimation of the epigenetic clock and age acceleration in older subjects

Author

T.J. Gorrie-Stone, Meena Kumari, Eilis Hannon, Joe Burrage, Jonathan Mill, Amanda Hughes, Leonard C. Schalkwyk, Alexandria Andrayas, Yanchun Bao, Louis El Khoury, and Melissa C. Smart

Subjects
Aging, Association test, lcsh:QH426-470, Epigenetic clock, Biology, Epigenesis, Genetic, 03 medical and health sciences, Acceleration, 0302 clinical medicine, Biological Clocks, Covariate, Humans, Epigenetics, Spurious relationship, lcsh:QH301-705.5, 030304 developmental biology, 0303 health sciences, DNA methylation, Research, lcsh:Genetics, lcsh:Biology (General), Age acceleration, DNA Methylation Profile, Older people, 030217 neurology & neurosurgery, Demography
Abstract

Background The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate “age acceleration” in various tissues and environments. Results The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways. Conclusions The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate.

Published
2019
Full Text
View/download PDF

23. Associations of autozygosity with a broad range of human phenotypes

Author

Dennis O. Mook-Kanamori, Salma M. Wakil, Lisa R. Yanek, Dominique P.V. de Kleijn, Gert J. de Borst, Alison D. Murray, Kamran Guity, Vincent W. V. Jaddoe, Mario Pirastu, Carole Ober, Giuseppe Matullo, Charles N. Rotimi, Daniela Ruggiero, Teresa Tusié-Luna, Wolfgang Lieb, Chew-Kiat Heng, John R. B. Perry, Hortensia Moreno-Macías, Jie Zhou, John M. Starr, Juhani Junttila, Lei Yu, Danielle Posthuma, Marcus Dörr, Yingchang Lu, Jonathan P. Bradfield, Einat Granot-Hershkovitz, Karina Meidtner, Wouter van Rheenen, T Esko, Maris Alver, Wen-Jane Lee, Zhengming Chen, Jennifer A. Brody, Paolo Gasparini, Yii-Der Ida Chen, Cinzia Sala, Peter P. Pramstaller, Gauri Prasad, Nana Matoba, Natalie Terzikhan, Simonetta Guarrera, Bjarke Feenstra, Peter Vollenweider, Smeeta Shrestha, Yi-Jen Hung, Lilja Stefansdottir, David R. Weir, Felix R. Day, Antonietta Robino, Liang Zhang, Lluis Quintana-Murci, Nicholas J. Timpson, Robyn E Wootton, Xue W. Mei, Dharambir K. Sanghera, Gisli Masson, Debbie A Lawlor, Thomas Meitinger, Sharon L.R. Kardia, Peter K. Joshi, Frank J. A. van Rooij, Claude Bouchard, Cassandra N. Spracklen, Ken K. Ong, Taulant Muka, Guanjie Chen, Laura J. Scott, Walter Palmas, Daniel I. Chasman, Sarah E. Medland, Krista Fischer, Blair H. Smith, Jon K. Sigurdsson, Leon Straker, Clara Viberti, Yuan Shi, Louis Pérusse, Peter J. van der Most, Timo Tõnis Sikka, Chris Haley, Kuang Lin, Leif Groop, Hester M. den Ruijter, Hakon Hakonarson, Masato Akiyama, Stephan J. L. Bakker, Sonja I. Berndt, Jeffery R. O'Connell, Cisca Wijmenga, Daniele Cusi, Lorena Orozco, Kristjan H. S. Moore, Kevin Sandow, Stephen S. Rich, Stephanie J. Loomis, George Davey Smith, Cornelia M. van Duijn, Sharvari Rahul Shukla, Agnar Helgason, Thorsten Kessler, Anuj Goel, Dan Mason, David W. Clark, James S. Pankow, Simona Vaccargiu, Uwe Völker, Tamara B. Harris, Matthew A. Allison, Clicerio Gonzalez, Sarju Ralhan, I-Te Lee, Matthias Laudes, Yen-Feng Chiu, Neil Poulter, Benjamin Lehne, John Wright, Lawrence F. Bielak, Philip L. De Jager, Reinhold Schmidt, Ya Xing Wang, Matthias Nauck, Diana L. Cousminer, Patrick Deelen, Ani Manichaikul, Stephen J. Chanock, Anders Hamsten, Barry I. Freedman, Gudmar Thorleifsson, Peter Kraft, Ozren Polasek, Jie Yao, Yoshinori Murakami, Paul M. Ridker, Anubha Mahajan, Struan F.A. Grant, Claudia Schurmann, Bjarni Gunnarsson, Catriona L. K. Barnes, Jessica van Setten, Sandosh Padmanabhan, Alena Stančáková, Markus M. Lerch, Anuradha Jagadeesan, Franco Giulianini, Daniel F. Gudbjartsson, Dwaipayan Bharadwaj, Shengchao Alfred Li, Peter S. Sever, Trevor A. Mori, Albertine J. Oldehinkel, Koichi Matsuda, Xueling Sim, Evangelos Evangelou, André G. Uitterlinden, Pekka Jousilahti, Yukihide Momozawa, Ioanna Tzoulaki, Chao A. Hsiung, Ginevra Biino, Murielle Bochud, Hannele Mattsson, Ilja M. Nolte, Sarah H. Wild, Patricia B. Munroe, Jianjun Liu, Bruce M. Psaty, Giriraj R. Chandak, Masahiro Kanai, Tony R. Merriman, Teemu Palviainen, Rodney A. Lea, Janie Corley, Nicholas J. Wareham, Alan B. Zonderman, Makoto Hirata, Matthew J. Bixley, Caroline Hayward, Nora Franceschini, Kristel R van Eijk, Etienne Patin, Daniel Shriner, Niek Verweij, Xiuqing Guo, Fredrik Karpe, Ruth J. F. Loos, Tiinamaija Tuomi, Ashley van der Spek, Patricia A. Peyser, Jessica D. Faul, Christian Fuchsberger, David Cesarini, Alex S. F. Doney, Janine F. Felix, Cornelius A. Rietveld, Jagadish Vangipurapu, Tanguy Corre, Line Skotte, Rajkumar Dorajoo, Catherine Igartua, Meena Kumari, Nona Sotoodehnia, Leonard H. van den Berg, Najaf Amin, Dale R. Nyholt, Harry Campbell, Massimiliano Cocca, Scott D. Gordon, Patrik K. E. Magnusson, John C. Chambers, Traci M. Bartz, Mike A. Nalls, Tin Aung, Nduna Dzimiri, Colin N. A. Palmer, Rob M. van Dam, Johanna Kuusisto, Russell P. Tracy, Anna Damulina, Pierre-Emmanuel Morange, Sylvain Foisy, Jing Hua Zhao, Nicholas G. Martin, Ching-Yu Cheng, Mariaelisa Graff, Rashmi B. Prasad, Alice Stanton, David-Alexandre Trégouët, Yu Guo, Helen R. Warren, Lyn R. Griffiths, Weihua Meng, Annika Tillander, Christa Meisinger, Albert V. Smith, Mark I. McCarthy, Jingyun Yang, Marine Germain, Neil Small, Linda Broer, Vilmundur Gudnason, Gunnar K. Pálsson, Michele K. Evans, Alexander Teumer, Mark J. Caulfield, Giorgia Girotto, Thomas Lumley, Tinca J. C. Polderman, Wei Zhao, Carlos A. Aguilar-Salinas, Jari Lahti, Matthew L. Albert, Yechiel Friedlander, Veikko Salomaa, Iona Y Millwood, Jan H. Veldink, Archie Campbell, Andres Metspalu, Ulf Gyllensten, Grant W. Montgomery, Veronique Vitart, Jai Rup Singh, Saima Afaq, Alan R. Shuldiner, Miao-Li Chee, Adebowale Adeyemo, Jennifer A. Smith, David A. van Heel, Jaspal S. Kooner, Daniela Toniolo, Cristian Pattaro, Jerome I. Rotter, John Whitfield, Melissa C. Smart, Kari E. North, Salman M. Tajuddin, Tallapragada Divya Sri Priyanka, Christopher A. Haiman, Diane M. Becker, Bernhard K. Krämer, Paul Elliott, Lihua Wang, He Gao, Patrick Sulem, Jinyan Huang, Chiea Chuen Khor, Ruifang Li-Gao, Åsa Johansson, Winfried März, Shai Carmi, Ilaria Gandin, Eric Boerwinkle, Gardar Sveinbjornsson, Saskia P. Hagenaars, Sander W. van der Laan, Gerard Pasterkamp, E-Shyong Tai, Hagit Hochner, Yih Chung Tham, Kent D. Taylor, Kari Stefansson, Matt J. Neville, Craig E. Pennell, Yanchun Bao, Annelot M. Dekker, Helena Schmidt, Mehdi Hedayati, Joshua Elliott, Ian J. Deary, Iris E. Jansen, Judith B. Borja, Edith Hofer, Martin Gögele, Igor Rudan, Lude Franke, Matthias Munz, Folkert W. Asselbergs, Bengt Sennblad, Imo Hofer, John D. Rioux, Pim van der Harst, Bahareh Sedaghati-khayat, Giovanni Cugliari, Morris A. Swertz, Francine Grodstein, Erwin P. Bottinger, Carol A. Wang, Andre Franke, Brian F. Meyer, Adele M. Taylor, Klodian Dhana, Jian'an Luan, Constance Turman, Robert A. Scott, May E. Montasser, Alison Pattie, Marco Brumat, Liming Li, Heiner Boeing, Karen L. Mohlke, Clemens Baumbach, Bishwa Raj Sapkota, Unnur Thorsteinsdottir, Naveed Sattar, Amy R. Bentley, Matthias B. Schulze, Ivana Kolcic, Stella Trompet, Sarah E. Harris, Ayo P. Doumatey, Charumathi Sabanayagam, David Eccles, Mary F. Feitosa, Jost B. Jonas, Massimo Mezzavilla, Mark O. Goodarzi, David Ellinghaus, Heribert Schunkert, Christian Gieger, Heikki V. Huikuri, Lingyao Zeng, Johan G. Eriksson, Woon-Puay Koh, Yucheng Jia, Gurpreet Singh Wander, James F. Wilson, Torgny Karlsson, Steven C. Hunt, Weihua Zhang, Maria Pina Concas, Zoltán Kutalik, Rebecca Rohde, Chittaranjan S. Yajnik, Yasaman Saba, Dabeeru C. Rao, Robin G. Walters, Reedik Mägi, Marie Loh, Eero Vuoksimaa, Josyf C. Mychaleckyj, Katri Räikkönen, Philippe Goyette, M. Arfan Ikram, Alicia Huerta-Chagoya, David J. Porteous, Teresa Nutile, J. Wouter Jukema, Noha A. Yousri, Yoichiro Kamatani, Maryam S. Daneshpour, Babette S. Zemel, Rona J. Strawbridge, Tien Yin Wong, Claudia Langenberg, Amy Moore, Marcus E. Kleber, Fereidoun Azizi, Avner Halevy, Erika Salvi, Francis S. Collins, Markku Laakso, Tim Kacprowski, S. Sunna Ebenesersdóttir, William R. Scott, Michael Boehnke, Jin-Fang Chai, Markus Perola, Nicola Pirastu, Wayne Huey-Herng Sheu, Robert Karlsson, Lenore J. Launer, Lili Milani, Renée de Mutsert, Fernando Rivadeneira, David A. Bennett, Nicola D. Kerrison, Paolo Manunta, Graciela E. Delgado, Magnus Johannesson, Carolina Medina-Gomez, Alanna C. Morrison, Kay-Tee Khaw, Jian-Min Yuan, Jaakko Kaprio, Melanie Waldenberger, Ralf Ewert, Hugoline G. de Haan, Andrew A. Hicks, Yukinori Okada, Maria Sabater-Lleal, Marilyn C. Cornelis, Stephanie J. London, Federica Rizzi, Jeanette Erdmann, Marina Ciullo, Michiaki Kubo, University of Edinburgh, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Osaka University Graduate School of Medicine, Laboratory for Cardiovascular Genomics and Informatics [Yokohama] (RIKEN IMS), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN)-RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), deCODE genetics [Reykjavik], Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK (BIHR), Area Science Park, Università degli studi di Trieste = University of Trieste, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, Harbor UCLA Medical Center [Torrance, Ca.], Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, Department of Electrical and Computer Engineering [Waterloo] (ECE), University of Waterloo [Waterloo], Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], Institute of Pop. Genetics, CNR, Sassari, Shardna life science Pula Cagliari, Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), Medstar Research Institute, Florida State University [Tallahassee] (FSU), University Medical Center [Utrecht], Centre for Population Health Sciences, Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Research Unit on Cardiovascular and Metabolic Diseases (ICAN), Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), California State University [Sacramento], Department of Thrombosis and Haemostasis, Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden, Medical University Graz, Department of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Frederick National Laboratory for Cancer Research (FNLCR), Wellcome Trust Centre of Human Genetics, University of Oxford, Department of Epidemiology, German Institute of Human Nutrition, University Medical Center Groningen [Groningen] (UMCG), Institute of Genetics and Biophysics, National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Department of Medicine, Surgery, and Dentistry, University of Milano, Icelandic Heart Association, Kopavogur, Iceland., Department of Epidemiology [Rotterdam], Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of Glasgow, Department of Cardiology, Leiden University Medical Center, Leiden, Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, Queen Mary University of London (QMUL), General Internal Medicine, Johns Hopkins School of Medicine, Johns Hopkins University School of Medicine [Baltimore], Institut de biologie moléculaire des plantes (IBMP), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Immunobiologie des Cellules dendritiques, Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche Translationnelle - Center for Translational Science (CRT), Institut Pasteur [Paris] (IP), Genentech, Inc., Genentech, Inc. [San Francisco], University of Tartu, Duke-NUS Medical School [Singapore], Deutsches Institut für Ernährungsforschung Potsdam-Rehbrücke (DifE), Leibniz Association, Human Genome Sequencing Center, Baylor College of Medicine, Baylor College of Medicine (BCM), Baylor University-Baylor University, University of San Carlos, Office of Population Studies Foundation, Icahn School of Medicine at Mount Sinai [New York] (MSSM), King‘s College London, Division of Cancer Epidemiology and Genetics, National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH)-National Institutes of Health [Bethesda] (NIH), University of Oxford, Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Division of Molecular & Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, Department of Internal Medicine B, University Medicine Greifswald, Greifswald, University of Chicago, University of Huddersfield, Infectious diseases division, Department of internal medicine, Washington University in Saint Louis (WUSTL), Section on Nephrology [Winston-Salem, NC, USA] (Department of Internal Medicine), Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center-Wake Forest Baptist Medical Center, Radcliffe Department of Medicine [Oxford], Harvard School of Public Health, Kunming University of Science and Technology (KMUST), Sans affiliation, University of Southern California (USC), National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Centre National de Génotypage (CNG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), MRC Centrer for Nutritional Epidemiology and Cancer Prevention and Survival, University of Cambridge [UK] (CAM), National University of Singapore (NUS), Experimental Cardiology Laboratory (ECL), Unirversity Medical Center, Department of Medical Statistics, Epidemiology and Medical Informatics, University of Zagreb, Department of Medical Genetics, Department of Medicine, University of Eastern Finland-Kuopio University Hospital, MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, Capital Normal University [Beijing], Saw Swee Hock School of Public Health, National Institute for Environmental Health Sciences Research Triangle Park, Brown University, MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, Toyota Research Institute, Helmholtz Zentrum München = German Research Center for Environmental Health, Department of Chemistry and Biochemistry [Boulder], University of Colorado [Boulder], 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), Hôpital de la Timone [CHU - APHM] (TIMONE), Metacohorts Consortium, Universiteit Leiden, Institute of Clinical Chemistry and Laboratory Medicine, University of Groningen [Groningen], Medical Research Concil Epidemiology Unit, Institute of Medical Science, Faculty of Medicine, Genetics and Pathology, Imperial College London, Génétique Evolutive Humaine - Human Evolutionary Genetics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Brigham and Women's Hospital [Boston], Erasmus University Rotterdam, Department of Chronic Disease Prevention, National Institute for Health and Welfare [Helsinki], Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System-School of public health, The University of Hong Kong (HKU)-The University of Hong Kong (HKU), Stockholm Bioinformatics Center (SBC), Stockholm University, Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, INRH, Department of Genetics, Los Angeles Biomedical Research Institute (LA BioMed), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán - National Institute of Medical Science and Nutrition Salvador Zubiran [Mexico], Western General Hospital, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Medical Research Council, Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute-University of Pittsburgh Graduate School of Public Health, Zhengzhou University of Light Industry, Department of Electrical and Electronic Engineering [Niigata Univ.], Niigata University, Genetic Epidemiology Unit, University College of London [London] (UCL), Aston Business School, Aston University [Birmingham], Division of Cancer Epidemiology and Genetics [Bethesda, MD, États-Unis], Centre Hospitalier Universitaire Vaudois (CHUV), Pennington Biomedical Research Center, University of Washington [Seattle], Guy's and St Thomas' Hospitals, Northwestern Polytechnical University [Xi'an] (NPU), Department of Social Medicine, University of Bristol [Bristol], Department of Genomics of Common Disease [London, UK], Imperial College London-Hammersmith Hospital NHS Imperial College Healthcare, Department of Internal Medicine, Institute of Clinical Molecular Biology, Kiel University, Medizinische Klinik II, Universität zu Lübeck = University of Lübeck [Lübeck], Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina, Institute for Social Research, University of Michigan System-University of Michigan System, Division of Statistical Genomics, Washington University School of Medicine, Institute for Clinical Molecular Biology, Christian-Albrechts-Universität zu Kiel (CAU), Department of Physics, RISSC-Lab-University of Naples Federico II = Università degli studi di Napoli Federico II, Lund University [Lund], Icelandic Heart Association, Heart Preventive Clinic and Research Institute, The Center for Applied Genomics, Children’s Hospital of Philadelphia (CHOP ), Génétique moléculaire de la neurotransmission et des processus neurodégénératifs (LGMNPN), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Medical Research Center Oulu, University of Oulu, University of Utah School of Medicine [Salt Lake City], The Generation R Study, Pediatrics, Epidemiology, Center for Translational and Computational Neuroimmunology [New York, NY, États-Unis] (CTCN), Department of Neurology [New York, NY, États-Unis], Columbia University Medical Center (CUMC), Columbia University [New York]-Columbia University [New York]-Columbia University Medical Center (CUMC), Columbia University [New York]-Columbia University [New York], Universität Heidelberg [Heidelberg] = Heidelberg University, Interuniversity Cardiology Institute Netherlands, School of Public Health, University of Michigan [Dearborn], Department of Epidemiology and Public Health, University of Kuopio, Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Institute of Epidemiology and Biobank PopGen, Department of Biostatistics, University of Washington, Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), Clinical Institute of Medical and Chemical Laboratory Diagnostics, Karl-Franzens-Universität Graz, Department of Genetics, Biology and Biochemistry, Università degli studi di Torino = University of Turin (UNITO), Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), QIMR Berghofer Medical Research Institute, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC)-UNC Gillings School of Global Public Health-Carolina Center for Genome Sciences, University of Illinois [Chicago] (UIC), University of Illinois System, Experimental Cardiology Laboratory, Genetic Epidemiology and Clinical Research Group, Umea University Hospital, Functional Genomics, Erasmus Medical Centre, National Human Genome Research Institute (NHGRI), School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Department of Pathological Biochemistry, Royal Infirmary, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Institute of Metabolic Science, MRC, University of Maryland School of Medicine [Baltimore, MD, USA], Centre for Molecular Epidemiology, Centre for Causal Analyses in Translational Epidemiology, University of Bristol [Bristol]-Medical Research Council, IRCCS San Raffaele Scientific Institute [Milan, Italie], U937, Génomique cardiovasculaire, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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 Hospitalier Universitaire Vaudois [Lausanne] (CHUV), University of Michigan System, HMNC Brain Health, Singapore Eye Research Institute, Partenaires INRAE, Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Department of Psychiatry and Psychotherapy, Rheinische Friedrich-Wilhelms-Universität Bonn, University of Groningen, Department of Genomics of Common Disease, Department of Microbiology, The Freeman Hospital, Department Biostatistics University of North Carolina, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, Clark, D. W., Okada, Y., Moore, K. H. S., Mason, D., Pirastu, N., Gandin, I., Mattsson, H., Barnes, C. L. K., Lin, K., Zhao, J. H., Deelen, P., Rohde, R., Schurmann, C., Guo, X., Giulianini, F., Zhang, W., Medina-Gomez, C., Karlsson, R., Bao, Y., Bartz, T. M., Baumbach, C., Biino, G., Bixley, M. J., Brumat, M., Chai, J. -F., Corre, T., Cousminer, D. L., Dekker, A. M., Eccles, D. A., van Eijk, K. R., Fuchsberger, C., Gao, H., Germain, M., Gordon, S. D., de Haan, H. G., Harris, S. E., Hofer, E., Huerta-Chagoya, A., Igartua, C., Jansen, I. E., Jia, Y., Kacprowski, T., Karlsson, T., Kleber, M. E., Li, S. A., Li-Gao, R., Mahajan, A., Matsuda, K., Meidtner, K., Meng, W., Montasser, M. E., van der Most, P. J., Munz, M., Nutile, T., Palviainen, T., Prasad, G., Prasad, R. B., Priyanka, T. D. S., Rizzi, F., Salvi, E., Sapkota, B. R., Shriner, D., Skotte, L., Smart, M. C., Smith, A. V., van der Spek, A., Spracklen, C. N., Strawbridge, R. J., Tajuddin, S. M., Trompet, S., Turman, C., Verweij, N., Viberti, C., Wang, L., Warren, H. R., Wootton, R. E., Yanek, L. R., Yao, J., Yousri, N. A., Zhao, W., Adeyemo, A. A., Afaq, S., Aguilar-Salinas, C. A., Akiyama, M., Albert, M. L., Allison, M. A., Alver, M., Aung, T., Azizi, F., Bentley, A. R., Boeing, H., Boerwinkle, E., Borja, J. B., de Borst, G. J., Bottinger, E. P., Broer, L., Campbell, H., Chanock, S., Chee, M. -L., Chen, G., Chen, Y. -D. I., Chen, Z., Chiu, Y. -F., Cocca, M., Collins, F. S., Concas, M. P., Corley, J., Cugliari, G., van Dam, R. M., Damulina, A., Daneshpour, M. S., Day, F. R., Delgado, G. E., Dhana, K., Doney, A. S. F., Dorr, M., Doumatey, A. P., Dzimiri, N., Ebenesersdottir, S. S., Elliott, J., Elliott, P., Ewert, R., Felix, J. F., Fischer, K., Freedman, B. I., Girotto, G., Goel, A., Gogele, M., Goodarzi, M. O., Graff, M., Granot-Hershkovitz, E., Grodstein, F., Guarrera, S., Gudbjartsson, D. F., Guity, K., Gunnarsson, B., Guo, Y., Hagenaars, S. P., Haiman, C. A., Halevy, A., Harris, T. B., Hedayati, M., van Heel, D. A., Hirata, M., Hofer, I., Hsiung, C. A., Huang, J., Hung, Y. -J., Ikram, M. A., Jagadeesan, A., Jousilahti, P., Kamatani, Y., Kanai, M., Kerrison, N. D., Kessler, T., Khaw, K. -T., Khor, C. C., de Kleijn, D. P. V., Koh, W. -P., Kolcic, I., Kraft, P., Kramer, B. K., Kutalik, Z., Kuusisto, J., Langenberg, C., Launer, L. J., Lawlor, D. A., Lee, I. -T., Lee, W. -J., Lerch, M. M., Li, L., Liu, J., Loh, M., London, S. J., Loomis, S., Lu, Y., Luan, J., Magi, R., Manichaikul, A. W., Manunta, P., Masson, G., Matoba, N., Mei, X. W., Meisinger, C., Meitinger, T., Mezzavilla, M., Milani, L., Millwood, I. Y., Momozawa, Y., Moore, A., Morange, P. -E., Moreno-Macias, H., Mori, T. A., Morrison, A. C., Muka, T., Murakami, Y., Murray, A. D., de Mutsert, R., Mychaleckyj, J. C., Nalls, M. A., Nauck, M., Neville, M. J., Nolte, I. M., Ong, K. K., Orozco, L., Padmanabhan, S., Palsson, G., Pankow, J. S., Pattaro, C., Pattie, A., Polasek, O., Poulter, N., Pramstaller, P. P., Quintana-Murci, L., Raikkonen, K., Ralhan, S., Rao, D. C., van Rheenen, W., Rich, S. S., Ridker, P. M., Rietveld, C. A., Robino, A., van Rooij, F. J. A., Ruggiero, D., Saba, Y., Sabanayagam, C., Sabater-Lleal, M., Sala, C. F., Salomaa, V., Sandow, K., Schmidt, H., Scott, L. J., Scott, W. R., Sedaghati-Khayat, B., Sennblad, B., van Setten, J., Sever, P. J., Sheu, W. H. -H., Shi, Y., Shrestha, S., Shukla, S. R., Sigurdsson, J. K., Sikka, T. T., Singh, J. R., Smith, B. H., Stancakova, A., Stanton, A., Starr, J. M., Stefansdottir, L., Straker, L., Sulem, P., Sveinbjornsson, G., Swertz, M. A., Taylor, A. M., Taylor, K. D., Terzikhan, N., Tham, Y. -C., Thorleifsson, G., Thorsteinsdottir, U., Tillander, A., Tracy, R. P., Tusie-Luna, T., Tzoulaki, I., Vaccargiu, S., Vangipurapu, J., Veldink, J. H., Vitart, V., Volker, U., Vuoksimaa, E., Wakil, S. M., Waldenberger, M., Wander, G. S., Wang, Y. X., Wareham, N. J., Wild, S., Yajnik, C. S., Yuan, J. -M., Zeng, L., Zhang, L., Zhou, J., Amin, N., Asselbergs, F. W., Bakker, S. J. L., Becker, D. M., Lehne, B., Bennett, D. A., van den Berg, L. H., Berndt, S. I., Bharadwaj, D., Bielak, L. F., Bochud, M., Boehnke, M., Bouchard, C., Bradfield, J. P., Brody, J. A., Campbell, A., Carmi, S., Caulfield, M. J., Cesarini, D., Chambers, J. C., Chandak, G. R., Cheng, C. -Y., Ciullo, M., Cornelis, M., Cusi, D., Smith, G. D., Deary, I. J., Dorajoo, R., van Duijn, C. M., Ellinghaus, D., Erdmann, J., Eriksson, J. G., Evangelou, E., Evans, M. K., Faul, J. D., Feenstra, B., Feitosa, M., Foisy, S., Franke, A., Friedlander, Y., Gasparini, P., Gieger, C., Gonzalez, C., Goyette, P., Grant, S. F. A., Griffiths, L. R., Groop, L., Gudnason, V., Gyllensten, U., Hakonarson, H., Hamsten, A., van der Harst, P., Heng, C. -K., Hicks, A. A., Hochner, H., Huikuri, H., Hunt, S. C., Jaddoe, V. W. V., De Jager, P. L., Johannesson, M., Johansson, A., Jonas, J. B., Jukema, J. W., Junttila, J., Kaprio, J., Kardia, S. L. R., Karpe, F., Kumari, M., Laakso, M., van der Laan, S. W., Lahti, J., Laudes, M., Lea, R. A., Lieb, W., Lumley, T., Martin, N. G., Marz, W., Matullo, G., Mccarthy, M. I., Medland, S. E., Merriman, T. R., Metspalu, A., Meyer, B. F., Mohlke, K. L., Montgomery, G. W., Mook-Kanamori, D., Munroe, P. B., North, K. E., Nyholt, D. R., O'Connell, J. R., Ober, C., Oldehinkel, A. J., Palmas, W., Palmer, C., Pasterkamp, G. G., Patin, E., Pennell, C. E., Perusse, L., Peyser, P. A., Pirastu, M., Polderman, T. J. C., Porteous, D. J., Posthuma, D., Psaty, B. M., Rioux, J. D., Rivadeneira, F., Rotimi, C., Rotter, J. I., Rudan, I., Den Ruijter, H. M., Sanghera, D. K., Sattar, N., Schmidt, R., Schulze, M. B., Schunkert, H., Scott, R. A., Shuldiner, A. R., Sim, X., Small, N., Smith, J. A., Sotoodehnia, N., Tai, E. -S., Teumer, A., Timpson, N. J., Toniolo, D., Tregouet, D. -A., Tuomi, T., Vollenweider, P., Wang, C. A., Weir, D. R., Whitfield, J. B., Wijmenga, C., Wong, T. -Y., Wright, J., Yang, J., Yu, L., Zemel, B. S., Zonderman, A. B., Perola, M., Magnusson, P. K. E., Uitterlinden, A. G., Kooner, J. S., Chasman, D. I., Loos, R. J. F., Franceschini, N., Franke, L., Haley, C. S., Hayward, C., Walters, R. G., Perry, J. R. B., Esko, T., Helgason, A., Stefansson, K., Joshi, P. K., Kubo, M., Wilson, J. F., Læknadeild (HÍ), Faculty of Medicine (UI), Félagsfræði-, mannfræði- og þjóðfræðideild (HÍ), Faculty of Sociology, Anthropology and Folkloristics (UI), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), School of Engineering and Natural Sciences (UI), Verkfræði- og náttúruvísindasvið (HÍ), Félagsvísindasvið (HÍ), School of Social Sciences (UI), Háskóli Íslands, University of Iceland, Clark, David W [0000-0002-1025-9185], Okada, Yukinori [0000-0002-0311-8472], Moore, Kristjan H S [0000-0002-9579-4362], Mason, Dan [0000-0002-0026-9216], Pirastu, Nicola [0000-0002-5363-3886], Gandin, Ilaria [0000-0003-3196-2491], Deelen, Patrick [0000-0002-5654-3966], Schurmann, Claudia [0000-0003-4158-9192], Medina-Gomez, Carolina [0000-0001-7999-5538], Karlsson, Robert [0000-0002-8949-2587], Bao, Yanchun [0000-0002-6102-5098], Biino, Ginevra [0000-0002-9936-946X], Brumat, Marco [0000-0003-3268-039X], Chai, Jin-Fang [0000-0003-3770-1137], Eccles, David A [0000-0003-4634-4995], Gordon, Scott D [0000-0001-7623-328X], Harris, Sarah E [0000-0002-4941-5106], Kacprowski, Tim [0000-0002-5393-2413], Karlsson, Torgny [0000-0001-8095-6149], Kleber, Marcus E [0000-0003-0663-7275], Mahajan, Anubha [0000-0001-5585-3420], Matsuda, Koichi [0000-0001-7292-2686], Meng, Weihua [0000-0001-5388-8494], van der Most, Peter J [0000-0001-8450-3518], Munz, Matthias [0000-0002-4728-3357], Palviainen, Teemu [0000-0002-7847-8384], Prasad, Rashmi B [0000-0002-4400-6741], Salvi, Erika [0000-0002-2724-2291], Skotte, Line [0000-0002-7398-1271], van der Spek, Ashley [0000-0001-7136-0159], Spracklen, Cassandra N [0000-0003-3590-7182], Strawbridge, Rona J [0000-0001-8506-3585], Tajuddin, Salman M [0000-0002-7919-8528], Verweij, Niek [0000-0002-4303-7685], Yanek, Lisa R [0000-0001-7117-1075], Zhao, Wei [0000-0001-7388-0692], Albert, Matthew L [0000-0001-7285-6973], Bentley, Amy R [0000-0002-0827-9101], Chanock, Stephen [0000-0002-2324-3393], Chen, Zhengming [0000-0001-6423-105X], Chiu, Yen-Feng [0000-0002-3352-4500], Cocca, Massimiliano [0000-0002-1127-7596], Collins, Francis S [0000-0002-1023-7410], Cugliari, Giovanni [0000-0002-6080-0718], Damulina, Anna [0000-0001-8241-2727], Day, Felix R [0000-0003-3789-7651], Dhana, Klodian [0000-0002-6397-7009], Dzimiri, Nduna [0000-0003-3395-5754], Elliott, Paul [0000-0002-7511-5684], Felix, Janine F [0000-0002-9801-5774], Freedman, Barry I [0000-0003-0275-5530], Girotto, Giorgia [0000-0003-4507-6589], Goel, Anuj [0000-0003-2307-4021], Goodarzi, Mark O [0000-0001-6364-5103], Gudbjartsson, Daniel F [0000-0002-5222-9857], Guity, Kamran [0000-0002-8379-9668], van Heel, David A [0000-0002-0637-2265], Hirata, Makoto [0000-0002-9994-9958], Ikram, M Arfan [0000-0003-0372-8585], Kamatani, Yoichiro [0000-0001-8748-5597], Kanai, Masahiro [0000-0001-5165-4408], Khor, Chiea Chuen [0000-0002-1128-4729], Kolcic, Ivana [0000-0001-7918-6052], Langenberg, Claudia [0000-0002-5017-7344], Lawlor, Deborah A [0000-0002-6793-2262], Liu, Jianjun [0000-0002-3255-3019], London, Stephanie J [0000-0003-4911-5290], Luan, Jian’an [0000-0003-3137-6337], Matoba, Nana [0000-0001-5329-0134], Mei, Xue W [0000-0002-6279-4884], Mezzavilla, Massimo [0000-0002-9000-4595], Milani, Lili [0000-0002-5323-3102], Mori, Trevor A [0000-0002-5264-9229], Murakami, Yoshinori [0000-0002-2826-4396], Murray, Alison D [0000-0003-4915-4847], Mychaleckyj, Josyf C [0000-0003-2595-0005], Neville, Matt J [0000-0002-6004-5433], Nolte, Ilja M [0000-0001-5047-4077], Ong, Ken K [0000-0003-4689-7530], Pálsson, Gunnar [0000-0002-8231-3961], Pankow, James S [0000-0001-7076-483X], Pattaro, Cristian [0000-0002-4119-0109], Quintana-Murci, Lluis [0000-0003-2429-6320], van Rheenen, Wouter [0000-0002-5860-1533], Rich, Stephen S [0000-0003-3872-7793], Rietveld, Cornelius A [0000-0003-4053-1861], Ruggiero, Daniela [0000-0003-3898-7827], Sabanayagam, Charumathi [0000-0002-4042-4719], Sabater-Lleal, Maria [0000-0002-0128-379X], Sala, Cinzia Felicita [0000-0003-2514-2075], Salomaa, Veikko [0000-0001-7563-5324], Scott, Laura J [0000-0002-4886-5084], Sedaghati-Khayat, Bahareh [0000-0002-7665-8648], Sennblad, Bengt [0000-0002-4360-8003], van Setten, Jessica [0000-0002-4934-7510], Smith, Blair H [0000-0002-5362-9430], Stančáková, Alena [0000-0002-1375-0252], Stanton, Alice [0000-0002-4961-165X], Straker, Leon [0000-0002-7786-4128], Sulem, Patrick [0000-0001-7123-6123], Swertz, Morris A [0000-0002-0979-3401], Taylor, Kent D [0000-0002-2756-4370], Tzoulaki, Ioanna [0000-0002-4275-9328], Veldink, Jan H [0000-0001-5572-9657], Vitart, Veronique [0000-0002-4991-3797], Völker, Uwe [0000-0002-5689-3448], Wander, Gurpreet S [0000-0002-4596-4247], Wang, Ya Xing [0000-0003-2749-7793], Wild, Sarah [0000-0001-7824-2569], Yuan, Jian-Min [0000-0002-4620-3108], Asselbergs, Folkert W [0000-0002-1692-8669], Boehnke, Mike [0000-0002-6442-7754], Bouchard, Claude [0000-0002-0048-491X], Brody, Jennifer A [0000-0001-8509-148X], Campbell, Archie [0000-0003-0198-5078], Caulfield, Mark J [0000-0001-9295-3594], Smith, George Davey [0000-0002-1407-8314], Dorajoo, Rajkumar [0000-0001-6608-2051], Ellinghaus, David [0000-0002-4332-6110], Erdmann, Jeanette [0000-0002-4486-6231], Evangelou, Evangelos [0000-0002-5488-2999], Feenstra, Bjarke [0000-0003-1478-649X], Feitosa, Mary [0000-0002-0933-2410], Franke, Andre [0000-0003-1530-5811], Grant, Struan F A [0000-0003-2025-5302], Griffiths, Lyn R [0000-0002-6774-5475], Groop, Leif [0000-0002-0187-3263], Gudnason, Vilmundur [0000-0001-5696-0084], van der Harst, Pim [0000-0002-2713-686X], Heng, Chew-Kiat [0000-0002-7309-9473], Hicks, Andrew A [0000-0001-6320-0411], Jaddoe, Vincent W V [0000-0003-2939-0041], De Jager, Philip L [0000-0002-8057-2505], Johannesson, Magnus [0000-0001-8759-6393], Johansson, Åsa [0000-0002-2915-4498], Jonas, Jost B [0000-0003-2972-5227], Jukema, J Wouter [0000-0002-3246-8359], Kaprio, Jaakko [0000-0002-3716-2455], Laakso, Markku [0000-0002-3394-7749], van der Laan, Sander W [0000-0001-6888-1404], Lahti, Jari [0000-0002-4310-5297], Martin, Nicholas G [0000-0003-4069-8020], Medland, Sarah E [0000-0003-1382-380X], Merriman, Tony R [0000-0003-0844-8726], Metspalu, Andres [0000-0002-3718-796X], Mohlke, Karen L [0000-0001-6721-153X], Montgomery, Grant W [0000-0002-4140-8139], Munroe, Patricia B [0000-0002-4176-2947], Nyholt, Dale R [0000-0001-7159-3040], Ober, Carole [0000-0003-4626-9809], Oldehinkel, Albertine J [0000-0003-3925-3913], Palmer, Colin [0000-0002-6415-6560], Perusse, Louis [0000-0001-6440-9698], Polderman, Tinca J. C. [0000-0001-5564-301X], Porteous, David J [0000-0003-1249-6106], Rioux, John D [0000-0001-7560-8326], Rivadeneira, Fernando [0000-0001-9435-9441], Rotimi, Charles [0000-0001-5759-053X], Rotter, Jerome I [0000-0001-7191-1723], Rudan, Igor [0000-0001-6993-6884], Sattar, Naveed [0000-0002-1604-2593], Sim, Xueling [0000-0002-1233-7642], Smith, Jennifer A [0000-0002-3575-5468], Teumer, Alexander [0000-0002-8309-094X], Timpson, Nicholas J [0000-0002-7141-9189], Tuomi, Tiinamaija [0000-0002-8306-6202], Wang, Carol A [0000-0002-4301-3974], Weir, David R [0000-0002-1661-2402], Whitfield, John B [0000-0002-1103-0876], Magnusson, Patrik K. E. [0000-0002-7315-7899], Uitterlinden, André G [0000-0002-7276-3387], Loos, Ruth J. F. [0000-0002-8532-5087], Franke, Lude [0000-0002-5159-8802], Haley, Chris S [0000-0002-9811-0210], Hayward, Caroline [0000-0002-9405-9550], Walters, Robin G [0000-0002-9179-0321], Joshi, Peter K [0000-0002-6361-5059], Wilson, James F [0000-0001-5751-9178], Apollo - University of Cambridge Repository, Moore, Kristjan HS [0000-0002-9579-4362], Luan, Jian'an [0000-0003-3137-6337], Grant, Struan FA [0000-0003-2025-5302], Jaddoe, Vincent WV [0000-0003-2939-0041], Polderman, Tinca JC [0000-0001-5564-301X], Magnusson, Patrik KE [0000-0002-7315-7899], Loos, Ruth JF [0000-0002-8532-5087], Neurology, Human genetics, Amsterdam Reproduction & Development (AR&D), Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Cardiovascular Centre (CVC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Stem Cell Aging Leukemia and Lymphoma (SALL), Institute for Molecular Medicine Finland, Department of Psychology and Logopedics, University Management, Developmental Psychology Research Group, Staff Services, Cognitive and Brain Aging, Research Programs Unit, Diabetes and Obesity Research Program, Johan Eriksson / Principal Investigator, Department of General Practice and Primary Health Care, Clinicum, University of Helsinki, Centre of Excellence in Complex Disease Genetics, Department of Public Health, Genetic Epidemiology, Helsinki Collegium for Advanced Studies, HUS Abdominal Center, Endokrinologian yksikkö, Bradford Teaching Hospitals NHS Foundation Trust [Bradford, UK] (BTHFT), University of Trieste, Université de Lausanne (UNIL), Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Consiglio Nazionale delle Ricerche (CNR), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Pasteur [Paris], Institut Pasteur [Paris], University of Oxford [Oxford], Medical Genetics, Dept. RSD and Public Health, IRCCS-Burlo Garofolo/University of Trieste, sans affiliation, Helmholtz-Zentrum München (HZM), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institute of Cardiovascular Science, University College London, Hammersmith Hospital NHS Imperial College Healthcare-Imperial College London, Universität zu Lübeck [Lübeck], University of Ioannina Medical School, Università degli studi di Napoli Federico II-RISSC-Lab, Universität Heidelberg [Heidelberg], University of Turin, University of California-University of California, Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), University of Helsinki-University of Helsinki, Université de Nantes (UN)-Université de Rennes 1 (UR1), Erasmus MC other, Internal Medicine, and Applied Economics

Subjects
0301 basic medicine, 631/208/1397, Chemistry(all), Health Status, [SDV]Life Sciences [q-bio], LOCI, General Physics and Astronomy, MESH: Haplotype, MESH: Cognition, 030105 genetics & heredity, Runs of Homozygosity, Biochemistry, Consanguinity, Cognition, Inbreeding depression, 2.1 Biological and endogenous factors, Body Size, Inbreeding, Skyldleikarækt, Aetiology, Human phenotypes, lcsh:Science, MESH: Health Status, Genetics, Multidisciplinary, Inbreeding Depression, Confounding, Homozygote, RUNS, 631/208/205, 631/208/721, 3. Good health, genomic inbreeding coefficients, MESH: Risk-Taking, 631/208/730, Autozygosit, homozygosity, Erfðarannsóknir, Medical Genetics, genomic inbreeding coefficient, MESH: Homozygote, Offspring, Science, Autozygosity, Blóðsifjar, 610 Medicine & health, Biology, INBREEDING DEPRESSION, HOMOZYGOSITY, FERTILITY, QUANTIFICATION, Physics and Astronomy(all), General Biochemistry, Genetics and Molecular Biology, Article, Association, 03 medical and health sciences, Risk-Taking, 360 Social problems & social services, Journal Article, Humans, ddc:610, Allele, Alleles, Medicinsk genetik, Genetic association study, MESH: Consanguinity, MESH: Body Size, MESH: Humans, Biochemistry, Genetics and Molecular Biology(all), MESH: Alleles, Haplotype, MESH: Fertility, General Chemistry, Brain Disorders, MESH: Inbreeding Depression, 030104 developmental biology, Fertility, Haplotypes, Genetic markers, lcsh:Q, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, 3111 Biomedicine, Genetics and Molecular Biology(all)
Abstract

Publisher's version (útgefin grein)., In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding., This paper is the work of the ROHgen consortium. We thank the Sigma T2D Consortium, whose members are detailed in Supplementary Note 3. We thank the UK Biobank Resource, approved under application 19655; we acknowledge funding from the UK Medical Research Council Human Genetics Unit and MRC Doctoral Training Programme in Precision Medicine. We also thank Neil Robertson, Wellcome Trust Centre for Human Genetics, Oxford, for use of his author details management software, Authorial. Finally, we thank all the participants, researchers and funders of ROHgen cohorts. Cohort-specific acknowledgements are in Supplementary Data 2; personal acknowledgements and disclosures are in Supplementary Note 2. We thank Rachel Edwards for administrative assistance.

Published
2019
Full Text
View/download PDF

24. Body mass index, earnings and partnership: genetic instrumental variable analysis in two nationally representative UK samples

Author

Yanchun Bao, Melissa Smart, Meena Kumari, and Amanda Hughes

Subjects
education.field_of_study, Longitudinal study, Earnings, Population, Instrumental variable, Household income, education, Psychology, Body mass index, Educational attainment, Demography, Odds
Abstract

In high-income countries there is an established link between high body mass index (BMI) and low income, but the direction of this association is unclear. Recent analyses in a large UK population using genetically-instrumented BMI supported a causal influence of BMI on household income, educational attainment and job class. Since analyses were based on an age-restricted and relatively wealthy population, it is unclear whether results are generalizable, and limited income data precluded decomposition of household income effects into own-income and partnership effects. Investigation is therefore warranted in more representative UK populations where associations may differ, and where individual and partner-based mechanisms can be studied separately.Data came from two nationally-representative samples, the UK Household Longitudinal Survey (UKHLS) and the English Longitudinal Study of Ageing (ELSA). Analysis was conducted in each sample, with results then pooled by meta-analysis. We used externally-weighted polygenic scores based on the latest genome-wide association study for BMI to examine the influence of genetically-instrumented BMI on earnings, probability of employment, job class conditional on working, likelihood of partnership, and partner’s earnings.A one-unit (kg/m2) increase in genetically-instrumented BMI was associated with a roughly 9% decrease in own monthly earnings (pooled coefficient: 0.91, CI:0.86, 0.97) and lower probability of employment (OR: 0.89, CI:0.83, 0.96) or having a university degree (OR: 0.95, CI:0.90, 0.99). Employed individuals with higher genetically-instrumented BMI were less likely to have professional or managerial occupations (OR: 0.91, CI:0.86, 0.96). No associations were seen with partnership. A one-unit increase in BMI was associated with a 5% decrease in partners’ earnings, but estimates were imprecise (pooled coefficient: 0.95, CI:0.88,1.01).Results are consistent with a negative influence of body mass index on a range of labour market and educational outcomes for both men and women.Key MessagesHigher genetically-instrumented BMI was associated with lower earnings and odds of workingAlso with lower odds of holding a managerial/professional occupation or a degreeNo associations were seen with probability of cohabiting partnership

Published
2019
Full Text
View/download PDF

26. DNA methylation signature of passive smoke exposure is less pronounced than active smoking: The Understanding Society study

Author

Paige M. Hulls, Frank de Vocht, Rebecca C Richmond, Richard M. Martin, Caroline L Relton, and Yanchun Bao

Subjects
Longitudinal study, Physiology, Review Article, 010501 environmental sciences, 01 natural sciences, Biochemistry, Understanding society, Epigenesis, Genetic, 03 medical and health sciences, 0302 clinical medicine, passive smoke exposure, Humans, Medicine, Longitudinal Studies, 030212 general & internal medicine, Active smoking, EWAS, 0105 earth and related environmental sciences, General Environmental Science, DNA methylation, business.industry, Passive smoke exposure, Methylation, Peripheral blood, Passive Smoke Exposure, CpG site, Biomarker (medicine), CpG Islands, business, Genome-Wide Association Study
Abstract

Introduction The extent of the biological impact of passive smoke exposure is unclear. We sought to investigate the association between passive smoke exposure and DNA methylation, which could serve as a biomarker of health risk. Materials and methods We derived passive smoke exposure from self-reported questionnaire data among smoking and non-smoking partners of participants enrolled in the UK Household Longitudinal Study ‘Understanding Society’ (n=769). We performed an epigenome-wide association study (EWAS) of passive smoke exposure with DNA methylation in peripheral blood measured using the Illumina Infinium Methylation EPIC array. Results No CpG sites surpassed the epigenome-wide significance threshold of p, Highlights • Investigated the association between blood-based genome-wide DNA methylation and SHS exposure. • Data were drawn from smoking and non-smoking partners of participants in the Understanding Society study. • No CpG sites surpassed the epigenome-wide significance threshold in relation to partner smoking. • The DNA methylation signature associated with SHS exposure is much less pronounced than that of own smoking.

Published
2020
Full Text
View/download PDF

27. Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9

Author

Chris Finan, Yoav Ben-Shlomo, Eric B. Larson, Tine Jess, Richard W Morris, Daniel I. Chasman, Fernando Pires Hartwig, Catherine Welch, Rodney J. Scott, Helen E. Speedy, Andrzej Pajak, Raha Pazoki, André G. Uitterlinden, Torben Hansen, Marc Sanson, Hakon Hakonarson, Claudia Langenberg, Joey Ward, John Wright, Dorothée Thuillier, Ben Kinnersley, Diederick E. Grobbee, Yvonne T. van der Schouw, Pieter Sonneveld, Michiel L. Bots, Harold Snieder, Karim Labreche, Dan M. Roden, Archie Campbell, Melissa C. Smart, Christine Power, Pim van der Harst, Amélie Bonnefond, Ingrid E. Christophersen, Riyaz S. Patel, Uwe Völker, Stephen Hancock, Niels Grarup, Dennis O. Mook-Kanamori, Mariza de Andrade, Caroline Dale, N. Charlotte Onland-Moret, David R. Crosslin, Meena Kumari, Erik Ingelsson, Michael V. Holmes, Spiros Denaxas, Sudha Seshadri, Kees Hovingh, Marcus Dörr, Paul M. Ridker, Stefan Coassin, Albert Hofman, Andrew N. Nicolaides, Oluf Pedersen, Philippe Froguel, Simonetta Guarrera, Murray H. Brilliant, Sara E. Dobbins, Salim Yusuf, Kari Hemminki, Erik P A Van Iperen, Abbas Dehghan, Jill P. Pell, Alexander Teumer, Peter W. Schofield, Aroon D. Hingorani, Dan Mason, Amand F. Schmidt, Rui Bebiano Da Providencia E Costa, James M. Allan, Leslie A. Lange, Niels Weinhold, Stefan Gustafsson, Jackie F. Price, Mika Kivimäki, Hynek Pikhart, Kirchner H. Lester, Lars Lind, Philip J. Law, Cara L. Carty, David Preiss, Richard S. Houlston, Robin Young, Tom W. Meade, Martin O'Donnell, Alexander P. Reiner, Ni Li, Oscar H. Franco, Zammy Fairhurst-Hunter, Ronan Roussel, Tim Christen, Ilja Demuth, David Carrell, Catherine A. McCarty, Juan P. Casas, Johann Willeit, Peter H. Whincup, Stela McLachlan, Adelaida Sanchez-Galvez, Hartmut Goldschmidt, Guillaume Paré, Harry Hemingway, Anubha Mahajan, Elisabeth Steinhagen-Thiessen, Elizabeth G. Holliday, Giuseppe Matullo, Henry Völzke, Ian Ford, Martin Bobak, Pedro Marques-Vidal, Bertrand Cariou, Bernardo L. Horta, Melissa L. Bondy, Goya Wanamethee, Naveed Sattar, Steve E. Humphries, Marylyn D. Ritchie, Kristina Norman, Carlotta Sacerdote, Giovanni Fiorito, Sebastian E. Baumeister, Amit Sud, Dennis Valentine, Andreas Engert, Juri Demuth, Rupert Faraway, Abdonas Tamosiunas, Andrie G. Panayiotou, Terrie Kitchner, Lars Bertram, Sandosh Padmanabhan, Sofia Malyutina, Anke H. Maitland-van der Zee, Alex J. Cornish, Joshua C. Denny, Jian'an Luan, Robert A. Scott, Daniel I. Swerdlow, John Attia, Karin Willeit, Gareth J. Morgan, Michael Chong, Ruben N. Eppinga, Elina Hyppönen, Ekaterina V. Baranova, Jackie A. Cooper, Ghazaleh Fatemifar, Niek Verweij, Max Moldovan, Brendan J. Keating, M. Abdullah Said, Markus M. Lerch, Christina M. Lill, Markus Hansson, Jemma C. Hopewell, Björn Nilsson, Folkert W. Asselbergs, Ruzena Kubinova, Molly Went, Nicholas J. Wareham, Stefan Kiechl, Yanchun Bao, Allan Linneberg, Matthias Simon, Epidemiology and Data Science, Pulmonology, Paediatric Pulmonology, APH - Personalized Medicine, AII - Inflammatory diseases, AII - Cancer immunology, CCA - Cancer biology and immunology, Ear, Nose and Throat, Schmidt, Amand F, Holmes, Michael V, Preiss, David, Swerdlow, Daniel I, Hypponen, Elina, Dehghan, Abbas, Schmidt, Amand F [0000-0003-1327-0424], Apollo - University of Cambridge Repository, Lifelines Cohort, ICBP Consortium, METASTROKE Consortium of the ISGC, PharmacoTherapy, -Epidemiology and -Economics, Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Schmidt, Amand F. [0000-0003-1327-0424], Epidemiology, and Hematology

Subjects
Oncology, lcsh:Diseases of the circulatory (Cardiovascular) system, Genetic association studies, Proprotein Convertase 9/genetics, Apolipoprotein B, Anticholesteremic Agents/adverse effects, Myocardial Infarction, Blood lipids, Genome-wide association study, 030204 cardiovascular system & hematology, Coronary artery disease, Gastroenterology, Medical and Health Sciences, Stroke/epidemiology, Brain Ischemia, 0302 clinical medicine, Risk Factors, Dyslipidemias/blood, Medicine, LDL-cholesterol, Cardiac and Cardiovascular Systems, 030212 general & internal medicine, Myocardial infarction, Mendelian randomisation, 1102 Cardiorespiratory Medicine and Haematology, Randomized Controlled Trials as Topic, Kardiologi, biology, Anticholesteremic Agents, PCSK9 Inhibitors, Single Nucleotide, 16. Peace & justice, LDL/blood, 3. Good health, Stroke, Cholesterol, Treatment Outcome, Cholesterol, LDL/blood, ICBP Consortium, Phenome-wide association scan, Proprotein Convertase 9, Cardiology and Cardiovascular Medicine, Research Article, medicine.medical_specialty, Serine Proteinase Inhibitors, Down-Regulation, 610 Medicine & health, Single-nucleotide polymorphism, Placebo, Polymorphism, Single Nucleotide, Risk Assessment, 03 medical and health sciences, Internal medicine, Genetic variation, Myocardial Infarction/epidemiology, Humans, Serine Proteinase Inhibitors/adverse effects, Polymorphism, Dyslipidemias, Genetic association, Lifelines Cohort authors, METASTROKE Consortium of the ISGC, business.industry, PCSK9, Cholesterol, LDL, Odds ratio, medicine.disease, Cardiovascular System & Hematology, lcsh:RC666-701, biology.protein, Brain Ischemia/epidemiology, Clinical Medicine, business, Biomarkers, Biomarkers/blood, Genome-Wide Association Study
Abstract

BackgroundWe characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.MethodsPublished and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentrationResultsThe PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable.ConclusionsGenetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.FundingThis research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.Condensed abstractEvidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimer’s disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.

Published
2019
Full Text
View/download PDF

28. Multivariate genome-wide analyses of the well-being spectrum

Author

Eco J. C. de Geus, Bart M. L. Baselmans, Margot P van de Weijer, Yanchun Bao, Gonneke Willemsen, Meike Bartels, Hill F. Ip, Michel G. Nivard, Jouke-Jan Hottenga, Melissa C. Smart, Dorret I. Boomsma, Jenny van Dongen, Rick Jansen, Abdel Abdellaoui, Meena Kumari, Adult Psychiatry, Biological Psychology, APH - Personalized Medicine, APH - Mental Health, APH - Health Behaviors & Chronic Diseases, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Methodology, Psychiatry, and Epidemiology and Data Science

Subjects
Netherlands Twin Register (NTR), Multifactorial Inheritance, Multivariate statistics, Multivariate analysis, Quantitative Trait Loci, Gene Expression, Computational biology, Biology, Polymorphism, Single Nucleotide, Genome, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Interneurons, Genetics, Humans, Gene, 030304 developmental biology, 0303 health sciences, Genome, Human, Univariate, Brain, Computational Biology, Human genetics, Phenotype, Meta-analysis, Multivariate Analysis, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

We introduce two novel methods for multivariate genome-wide-association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (Nobs = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMAs and in an ~57% increase in the predictive power of polygenic risk scores. Supporting transcriptome- and methylome-wide analyses (TWAS and MWAS, respectively) uncovered an additional 17 and 75 independent loci, respectively. Bioinformatic analyses, based on gene expression in brain tissues and cells, showed that genes differentially expressed in the subiculum and GABAergic interneurons are enriched in their effect on the well-being spectrum.

Published
2019
Full Text
View/download PDF

29. Additional file 1: of Systematic underestimation of the epigenetic clock and age acceleration in older subjects

Author

Khoury, Louis El, Gorrie-Stone, Tyler, Smart, Melissa, Hughes, Amanda, Yanchun Bao, Andrayas, Alexandria, Burrage, Joe, Hannon, Eilis, Kumari, Meena, Mill, Jonathan, and Schalkwyk, Leonard

Abstract

Fig. S1. Describing the differences between DNA methylation ages estimated with the Horvath 2013 calculator and the agep() function. As well as Table S1. Detailing additional data sets used in this study.

Published
2019
Full Text
View/download PDF

30. Additional file 2: of Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array

Author

Mansell, Georgina, Gorrie-Stone, Tyler, Yanchun Bao, Kumari, Meena, Schalkwyk, Leonard, Mill, Jonathan, and Hannon, Eilis

Abstract

Table S1. Estimating the multiple testing correction significance threshold for sub-samples of EPIC array DNA methylation sites. Table S2. Summary of results from tests of assumptions of linear regression separated by mean DNA methylation level. The number and percentage of DNA methylation sites significant for each test (P

Published
2019
Full Text
View/download PDF

31. Additional file 1: of Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array

Author

Mansell, Georgina, Gorrie-Stone, Tyler, Yanchun Bao, Kumari, Meena, Schalkwyk, Leonard, Mill, Jonathan, and Hannon, Eilis

Abstract

Figure S1. Distribution of minimum p-values from 1000 null EWAS simulations. The red line at 9.42 × 10–8 is the 5th percentile point and represents the 5% FWER. Figure S2. The four assumptions of linear regression (described in the left boxes), were tested using R package gvlma with four statistical tests (described in the right boxes) with arrows matching the assumptions to the relevant test. Gvlma calculates a p-value for each test, where the null hypothesis is that the assumption(s) hold true, and the alternative hypothesis is that they do not (i.e. a significant p-value indicates that the assumption(s) are violated). Figure S3. Quantile-Quantile plots of the 5 tests of the assumptions of linear regression. Plotted are the observed (x-axis) against expected (y-axis) –log10(p-value) from the (a) global (b) skewness (c) kurtosis (d) link function and (e) heteroskedasticity tests performed in the R gvlma package for all DNA methylation sites. Under the null distribution, of no significant associations, all points would be expected to lie on the red line at y = x. The observed data show a dramatic inflation of p-values smaller than expected by chance in all 5 plots indicating that many DNA methylation sites fail these statistical tests for the assumptions of linear regression. Figure S4. Scatterplots of –log10(p-value) against mean DNA methylation level from the (a) global (b) skewness (c) kurtosis (d) link function and (e) heteroskedasticity tests performed in the R gvlma package for all DNA methylation sites. Each point represents a single site, and the color of the point represents the density of points plotted (low density in grey to high density in yellow). Figure S5. Scatterplots of DNA methylation standard deviation against –log10(p-values) from the (a) global (b) skewness (c) kurtosis (d) link function and (e) heteroskedasticity tests performed in the R gvlma package. Each point represents a single DNA methylation site, and the color of the points represents the density of points plotted (low density in grey to high density in yellow). Figure S6. Boxplots of –log10(p-value) for each of the 5 tests: (a) global (b) skewness (c) kurtosis (d) link function and (e) heteroscedasticity separated by site variability status. DNA methylation sites were defined as variable if the range of their middle 80% of values, calculated as the 90th percentile (P90) minus the 10th percentile (P10) was greater than 5%. Each boxplot is colored by their mean –log10(p-value), from light yellow (lowest –log10(p-value)) to red (highest –log10(p-value). Sites with a p-value of 0 (i.e. p

Published
2019
Full Text
View/download PDF

32. Additional file 2: of Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9

Author

Schmidt, Amand, Holmes, Michael, Preiss, David, Swerdlow, Daniel, Denaxas, Spiros, Ghazaleh Fatemifar, Faraway, Rupert, Finan, Chris, Valentine, Dennis, Zammy Fairhurst-Hunter, Hartwig, Fernando, Horta, Bernardo, Hypponen, Elina, Power, Christine, Moldovan, Max, Iperen, Erik, Hovingh, Kees, Demuth, Ilja, Norman, Kristina, Steinhagen-Thiessen, Elisabeth, Demuth, Juri, Bertram, Lars, Lill, Christina, Coassin, Stefan, Willeit, Johann, Kiechl, Stefan, Willeit, Karin, Mason, Dan, Wright, John, Morris, Richard, Goya Wanamethee, Whincup, Peter, Ben-Shlomo, Yoav, McLachlan, Stela, Price, Jackie, Kivimaki, Mika, Welch, Catherine, Sanchez-Galvez, Adelaida, Marques-Vidal, Pedro, Nicolaides, Andrew, Andrie Panayiotou, N. Onland-Moret, Schouw, Yvonne, Matullo, Giuseppe, Fiorito, Giovanni, Guarrera, Simonetta, Sacerdote, Carlotta, Wareham, Nicholas, Langenberg, Claudia, Scott, Robert, Jian’An Luan, Bobak, Martin, Malyutina, Sofia, K, Andrzej PajÄ, Ruzena Kubinova, Abdonas Tamosiunas, Pikhart, Hynek, Grarup, Niels, Pedersen, Oluf, Hansen, Torben, Linneberg, Allan, Jess, Tine, Cooper, Jackie, Humphries, Steve, Brilliant, Murray, Kitchner, Terrie, Hakon Hakonarson, Carrell, David, McCarty, Catherine, Kirchner Lester, Larson, Eric, Crosslin, David, Mariza Andrade, Roden, Dan, Denny, Joshua, Carty, Cara, Hancock, Stephen, Attia, John, Holliday, Elizabeth, Scott, Rodney, Schofield, Peter, O’Donnell, Martin, Yusuf, Salim, Chong, Michael, Pare, Guillaume, Harst, Pim, M. Said, Eppinga, Ruben, Verweij, Niek, Snieder, Harold, Christen, Tim, D. Mook-Kanamori, Gustafsson, Stefan, Lind, Lars, Ingelsson, Erik, Raha Pazoki, Franco, Oscar, Hofman, Albert, Uitterlinden, Andre, Dehghan, Abbas, Teumer, Alexander, Baumeister, Sebastian, DĂśrr, Marcus, Lerch, Markus, VĂślker, Uwe, VĂślzke, Henry, Ward, Joey, Pell, Jill, Meade, Tom, Christophersen, Ingrid, Zee, Anke Maitland-Van Der, Baranova, Ekaterina, Young, Robin, Ford, Ian, Campbell, Archie, Sandosh Padmanabhan, Bots, Michiel, Grobbee, Diederick, Froguel, Philippe, DorothĂŠe Thuillier, Roussel, Ronan, AmĂŠlie Bonnefond, Cariou, Bertrand, Smart, Melissa, Yanchun Bao, Kumari, Meena, Anubha Mahajan, Hopewell, Jemma, Seshadri, Sudha, Dale, Caroline, Costa, Rui, Ridker, Paul, Chasman, Daniel, Reiner, Alex, Ritchie, Marylyn, Lange, Leslie, Cornish, Alex, Dobbins, Sara, Hemminki, Kari, Kinnersley, Ben, Sanson, Marc, Labreche, Karim, Simon, Matthias, Bondy, Melissa, Law, Philip, Speedy, Helen, Allan, James, Li, Ni, Went, Molly, Weinhold, Niels, Morgan, Gareth, Sonneveld, Pieter, BjĂśrn Nilsson, Goldschmidt, Hartmut, Sud, Amit, Engert, Andreas, Hansson, Markus, Hemingway, Harry, Asselbergs, Folkert, Riyaz Patel, Keating, Brendan, Sattar, Naveed, Houlston, Richard, Casas, Juan, and Aroon Hingorani

Abstract

Supplemental figures and study acknowledgments. (PDF 154 kb)

Published
2019
Full Text
View/download PDF

33. Properties of the epigenetic clock and age acceleration

Author

Melissa C. Smart, Yanchun Bao, Louis El Khoury, Joe Burrage, Eilis Hannon, Meena Kumari, Andrayas Ad, Amanda Hughes, J. Mill, Leonard C. Schalkwyk, and T.J. Gorrie-Stone

Subjects
0303 health sciences, Genomics, Disease, Biology, 03 medical and health sciences, 0302 clinical medicine, CpG site, Ageing, DNA methylation, Covariate, Population study, Epigenetics, 030217 neurology & neurosurgery, 030304 developmental biology, Demography
Abstract

BackgroundThe methylation status of numerous CpG sites in the human genome varies with age. The Horvath epigenetic clock used a wide variety of published DNA methylation data to produce an age prediction that has been widely used to, predict age in unknown samples, and draw conclusions about speed of ageing in various tissues, environments, and diseases. Despite its utility, there are a number of assumptions in the model that require examination. We explore the characteristics of the model in whole blood and multiple brain regions from older people, who are not well represented in the original training data, and in blood from a cross-sectional population study.ResultsWe find that the model systematically underestimates age in tissues from older people. A decrease in slope of the predicted ages were observed at approximately 60 years, indicating that some loci in the model may change differently with age, and that age acceleration measures will themselves be age-dependent. This is seen most strongly in the cerebellum but is also present in other examined tissues, and is consistently observed in multiple datasets. An apparent association of Alzheimer’s disease with age acceleration disappears when age is used as a covariate. Association tests in the literature use a variety of methods for calculating age acceleration and often do not use age as a covariate. This is a potential cause of misleading findings.ConclusionsAssociations of phenotypes with age acceleration should be evaluated cautiously, and chronological age should be included as a covariate in all analyses.

Published
2018
Full Text
View/download PDF

34. Assessing potential shared genetic aetiology between body mass index and sleep duration in 142,209 individuals

Author

Victoria, Garfield, Ghazaleh, Fatemifar, Caroline, Dale, Melissa, Smart, Yanchun, Bao, Clare H, Llewellyn, Andrew, Steptoe, Delilah, Zabaneh, and Meena, Kumari

Subjects
Adult, Male, Multifactorial Inheritance, Time Factors, body mass index, Middle Aged, genetic correlation, Phenotype, polygenic risk score, Humans, sleep duration, Female, Obesity, Self Report, Sleep, Genetic Association Studies, Research Articles, Aged, Research Article
Abstract

Observational studies find an association between increased body mass index (BMI) and short self‐reported sleep duration in adults. However, the underlying biological mechanisms that underpin these associations are unclear. Recent findings from the UK Biobank suggest a weak genetic correlation between BMI and self‐reported sleep duration. However, the potential shared genetic aetiology between these traits has not been examined using a comprehensive approach. To investigate this, we created a polygenic risk score (PRS) of BMI and examined its association with self‐reported sleep duration in a combination of individual participant data and summary‐level data, with a total sample size of 142,209 individuals. Although we observed a nonsignificant genetic correlation between BMI and sleep duration, using LD score regression (r g = −0.067 [SE = 0.039], P = 0.092) we found that a PRS of BMI is associated with a decrease in sleep duration (unstandardized coefficient = −1.75 min [SE = 0.67], P = 6.13 × 10−7), but explained only 0.02% of the variance in sleep duration. Our findings suggest that BMI and self‐reported sleep duration possess a small amount of shared genetic aetiology and other mechanisms must underpin these associations.

Published
2018

35. Leveraging DNA methylation quantitative trait loci to characterize the relationship between methylomic variation, gene expression and complex traits

Author

Amanda Hughes, T.J. Gorrie-Stone, Meena Kumari, Leonard C. Schalkwyk, Joe Burrage, Jonathan Mill, Eilis Hannon, Yanchun Bao, and Melissa C. Smart

Subjects
Genetics, 0303 health sciences, dNaM, Biology, Quantitative trait locus, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Mendelian randomization, DNA methylation, Genetic variation, Epigenetics, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Characterizing the complex relationship between genetic, epigenetic and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. In this study, we describe the most comprehensive analysis of common genetic variation on DNA methylation (DNAm) to date, using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (P < 6.52x10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified using previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits, using Summary data–based Mendelian Randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression, identifying 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database (http://www.epigenomicslab.com/online-data-resources/) as a resource to the research community.

Published
2018
Full Text
View/download PDF

36. Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences1

Author

Edward Kong, Pascal Timshel, Danielle Posthuma, Tõnu Esko, Maël Lebreton, Kathleen Mullan Harris, Philipp Koellinger, Murray B. Stein, Abraham A. Palmer, Urs Fischbacher, Robert J. Ursano, Erdogan Taskesen, Peter Eibich, Paul R. H. J. Timmers, Anke R. Hammerschlag, Ann H. Caplin, Jian Yang, Sandra Sanchez-Roige, Peter K. Joshi, Matthias Sutter, Pierre Fontanillas, Chia-Yen Chen, Albert Hofman, Patrick Turley, David A. Hinds, Futao Zhang, David Cesarini, Christina M. Lill, Laura Buzdugan, Ville Karhunen, Abdel Abdellaoui, S. Fleur W. Meddens, Henning Tiemeier, Christian L. Zund, Gert G. Wagner, Richard Karlsson Linnér, Lars Bertram, David W. Clark, Roy Thurik, André G. Uitterlinden, Maciej Trzaskowski, Mohammad Arfan Ikram, David Laibson, Cornelius A. Rietveld, Arcadi Navarro, Ernst Fehr, Yang Wu, Matthew B. McQueen, Ronald C. Kessler, Magnus Johannesson, Patrick J. F. Groenen, Gregor Hasler, James F. Wilson, Daniel Schunk, Stephen P. Tino, Pietro Biroli, Mark Alan Fontana, Juan R. González, Meena Kumari, Gerardus A. Meddens, Jonathan P. Beauchamp, Michelle N. Meyer, Jason D. Boardman, James J. Lee, Carlos Morcillo-Suarez, Aaron Kleinman, Minna Männikkö, Andrew Conlin, Adam Auton, Tune H. Pers, Michel G. Nivard, Ronald de Vlaming, Jon White, Robert Karlsson, Daniel J. Benjamin, Maarten Kooyman, Jacob Gratten, Aysu Okbay, Remy Z. Levin, Melissa C. Smart, Harriet de Wit, Conor C. Dolan, Frank J. A. van Rooij, Patrik K. E. Magnusson, Sonia Jain, Rauli Svento, Klaus M. Schmidt, Dorret I. Boomsma, Gerard Muntané, James MacKillop, Robbee Wedow, and Yanchun Bao

Subjects
Genetics, 0303 health sciences, Gabaergic neurotransmission, Single-nucleotide polymorphism, Genome-wide association study, Biology, Phenotype, 03 medical and health sciences, 0302 clinical medicine, SNP, Gene, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract

Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated (to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Published
2018
Full Text
View/download PDF

37. Linear transformation models for censored data under truncation

Author

Marialuisa Restaino, Huan Wang, Yanchun Bao, and Hongsheng Dai

Subjects
040101 forestry, Statistics and Probability, Censoring, Applied Mathematics, Bivariate survival function, Estimator, Linear transformation model, Truncation, Survival analysis, 04 agricultural and veterinary sciences, Bivariate analysis, Estimating equations, 01 natural sciences, Censoring (statistics), Regression, Large sample, Linear map, 010104 statistics & probability, Survival data, Statistics, 0401 agriculture, forestry, and fisheries, 0101 mathematics, Statistics, Probability and Uncertainty, Mathematics
Abstract

In many observational cohort studies, a pair of correlated event times are usually observed for each individual. This paper develops a new approach for the semiparametric linear transformation model to handle the bivariate survival data under both truncation and censoring. By incorporating truncation, the potential referral bias in practice is taken into account. A class of generalised estimating equations are proposed to obtain unbiased estimates of the regression parameters. Large sample properties of the proposed estimator are provided. Simulation studies under different scenarios and analyses of real-world datasets are conducted to assess the performance of the proposed estimator.

Published
2018

38. Genome-wide analysis of health-related biomarkers in the UK Household Longitudinal Study reveals novel associations

Author

Meena Kumari, John R. B. Perry, Jian'an Luan, Robert A. Scott, Delilah Zabaneh, Michaela Benzeval, Eleftheria Zeggini, Claudia Langenberg, Nicholas J. Wareham, Karoline Kuchenbaecker, Melissa C. Smart, Bram P. Prins, Ghazaleh Fatemifar, Yanchun Bao, Luan, Jian'an [0000-0003-3137-6337], Wareham, Nicholas [0000-0003-1422-2993], Perry, John [0000-0001-6483-3771], Langenberg, Claudia [0000-0002-5017-7344], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Genetic Markers, Longitudinal study, Health Status, lcsh:Medicine, Genome-wide association study, Article, 03 medical and health sciences, Liver disease, medicine, Longitudinal Studies, 1000 Genomes Project, lcsh:Science, Genetics, Family Health, Family Characteristics, Multidisciplinary, biology, Haplotype, lcsh:R, medicine.disease, Genetic architecture, United Kingdom, 3. Good health, 030104 developmental biology, Reproductive Health, Alanine transaminase, biology.protein, Biomarker (medicine), lcsh:Q, Biomarkers, Genome-Wide Association Study
Abstract

Serum biomarker levels are associated with the risk of complex diseases. Here, we aimed to gain insights into the genetic architecture of biomarker traits which can reflect health status. We performed genome-wide association analyses for twenty serum biomarkers involved in organ function and reproductive health. 9,961 individuals from the UK Household Longitudinal Study were genotyped using the Illumina HumanCoreExome array and variants imputed to the 1000 Genomes Project and UK10K haplotypes. We establish a polygenic heritability for all biomarkers, confirm associations of fifty-four established loci, and identify five novel, replicating associations at genome-wide significance. A low-frequency variant, rs28929474, (beta = 0.04, P = 2 × 10−10) was associated with levels of alanine transaminase, an indicator of liver damage. The variant is located in the gene encoding serine protease inhibitor, low levels of which are associated with alpha-1 antitrypsin deficiency which leads to liver disease. We identified novel associations (rs78900934, beta = 0.05, P = 6 × 10−12; rs2911280, beta = 0.09, P = 6 × 10−10) for dihydroepiandrosterone sulphate, a precursor to major sex-hormones, and for glycated haemoglobin (rs12819124, beta = −0.03, P = 4 × 10−9; rs761772, beta = 0.05, P = 5 × 10−9). rs12819124 is nominally associated with risk of type 2 diabetes. Our study offers insights into the genetic architecture of well-known and less well-studied biomarkers.

Published
2017
Full Text
View/download PDF

39. Predicting gene expression from genome wide protein binding profiles

Author

Xiaohui Liu, Mohsina Mahmuda Ferdous, Paul A. Wilson, Yanchun Bao, and Veronica Vinciotti

Subjects
0301 basic medicine, Regulation of gene expression, Markov random field, Microarray analysis techniques, Cognitive Neuroscience, Intron, Computational biology, Biology, Bioinformatics, Genome, DNA sequencing, Computer Science Applications, ChIP-seq, 03 medical and health sciences, Exon, 030104 developmental biology, Artificial Intelligence, Gene expression, Machine learning, Epigenetics, Chromatin immunoprecipitation
Abstract

High-throughput technologies such as chromatin immunoprecipitation (IP) followed by next generation sequencing (ChIP-seq) in combination with gene expression studies have enabled researchers to investigate relationships between the distribution of chromosome-associated proteins and the regulation of gene transcription on a genome-wide scale. Several attempts at integrative analyses have identified direct relationships between the two processes. However, a comprehensive understanding of the regulatory events remains elusive. This is in part due to the scarcity of robust analytical methods for the detection of binding regions from ChIP-seq data. In this paper, we have applied a recently proposed Markov random field model for the detection of enriched binding regions under different biological conditions and time points. The method accounts for spatial dependencies and IP efficiencies, which can vary significantly between different experiments. We further defined the enriched chromosomal binding regions as distinct genomic features, such as promoter, exon, intron, and distal intergenic, and then investigated how predictive each of these features are of gene expression activity using machine learning techniques, including neural networks, decision trees and random forest. The analysis of a ChIP-seq time-series dataset comprising six protein markers and associated microarray data, obtained from the same biological samples, shows promising results and identified biologically plausible relationships between the protein profiles and gene regulation.

Published
2017

40. Multivariate Genome-wide and integrated transcriptome and epigenome-wide analyses of the Well-being spectrum

Author

Meike Bartels, Michel G. Nivard, Gonneke Willemsen, Abdel Abdellaoui, Yanchun Bao, Melissa C. Smart, van Dongen J, Dorret I. Boomsma, de Geus E, Jouke-Jan Hottenga, van de Weijer Mp, Rick Jansen, Meena Kumari, Hill F. Ip, and Bart M. L. Baselmans

Subjects
Genetics, 0303 health sciences, Single-nucleotide polymorphism, Epigenome, Biology, Genome, Phenotype, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Gene expression, DNA methylation, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Phenotypes related to well-being (life satisfaction, positive affect, neuroticism, and depressive symptoms), are genetically highly correlated (| rg | > .75). Multivariate genome-wide analyses (Nobs = 958,149) of these traits, collectively referred to as the well-being spectrum, reveals 63 significant independent signals, of which 29 were not previously identified. Transcriptome and epigenome analyses implicate variation in gene expression at 8 additional loci and CpG methylation at 6 additional loci in the etiology of well-being. We leverage an anatomically comprehensive survey of gene expression in the brain to annotate our findings, showing that SNPs within genes excessively expressed in the cortex and part of the hippocampal formation are enriched in their effect on well-being.

Published
2017
Full Text
View/download PDF

41. THE AUTHORS REPLY

Author

Amanda Hughes, Yanchun Bao, Melissa Smart, Tyler Gorrie-Stone, Eilis Hannon, Jonathan Mill, Joe Burrage, Leo Schalkwyk, and Meena Kumari

Subjects
Epidemiology
Published
2018
Full Text
View/download PDF

42. Modelling Survival Events with Longitudinal Covariates Measured with Error

Author

Jianxin Pan, Yanchun Bao, and Hongsheng Dai

Subjects
Statistics and Probability, Mixed model, Proportional hazards model, Statistics, Covariate, Econometrics, Estimator, Fixed effects model, Random effects model, Generalized linear mixed model, Parametric statistics, Mathematics
Abstract

In survival analysis, time-dependent covariates are usually present as longitudinal data collected periodically and measured with error. The longitudinal data can be assumed to follow a linear mixed effect model and Cox regression models may be used for modelling of survival events. The hazard rate of survival times depends on the underlying time-dependent covariate measured with error, which may be described by random effects. Most existing methods proposed for such models assume a parametric distribution assumption on the random effects and specify a normally distributed error term for the linear mixed effect model. These assumptions may not be always valid in practice. In this article, we propose a new likelihood method for Cox regression models with error-contaminated time-dependent covariates. The proposed method does not require any parametric distribution assumption on random effects and random errors. Asymptotic properties for parameter estimators are provided. Simulation results show that under c...

Published
2013
Full Text
View/download PDF

43. Gene-environment interactions between education and body mass: Evidence from the UK and Finland

Author

Vikesh Amin, Niina Pitkänen, Melissa C. Smart, Yanchun Bao, Petri Böckerman, Olli T. Raitakari, Jaakko Pehkonen, Terho Lehtimäki, Meena Kumari, and Jutta Viinikainen

Subjects
0301 basic medicine, Adult, Male, ta520, obesity, Health (social science), Overweight, Biology, Body Mass Index, 03 medical and health sciences, 0302 clinical medicine, Lower body, History and Philosophy of Science, koulutustaso, Negatively associated, medicine, Humans, Mass index, Genetic Predisposition to Disease, ta516, 030212 general & internal medicine, Longitudinal Studies, Gene–environment interaction, Gene, Finland, Aged, 2. Zero hunger, Aged, 80 and over, education, ta511, nutritional and metabolic diseases, ylipaino, gene-environment interactions, ta3142, Middle Aged, medicine.disease, Obesity, United Kingdom, 030104 developmental biology, koulutus, Educational Status, lihavuus, Female, Gene-Environment Interaction, medicine.symptom, geneettiset tekijät, Body mass index, Demography
Abstract

More education is associated with a lower body mass index (BMI) and likelihood of being overweight. However, since a large proportion of the variation in body mass is due to genetic makeup, it has been hypothesized that education may moderate the genetic risk. We estimate main associations between (i) education, (ii) genetic risk, and (iii) interactions between education and genetic risk on BMI and the probability of being overweight in the UK and Finland. The estimates show that education is negatively associated with BMI and overweightness, and genetic risk is positively associated. However, the interactions between education and genetic risk are small and statistically insignificant. peerReviewed

Published
2017
Full Text
View/download PDF

44. Maximum likelihood estimate for the dispersion parameter of the negative binomial distribution

Author

Yanchun Bao, Hongsheng Dai, and Mingtang Bao

Subjects
Statistics and Probability, Beta negative binomial distribution, Statistics::Theory, Negative binomial distribution, Negative multinomial distribution, Statistics::Computation, Binomial distribution, Beta-binomial distribution, Statistics, Statistics::Methodology, Multinomial distribution, Statistics, Probability and Uncertainty, Binomial proportion confidence interval, Likelihood function, Mathematics
Abstract

This paper shows that the maximum likelihood estimate (MLE) for the dispersion parameter of the negative binomial distribution is unique under a certain condition. A fixed-point iteration algorithm is proposed and it guarantees to converge to the MLE, when the score function has a unique root.

Published
2013
Full Text
View/download PDF

45. A joint modelling approach for clustered recurrent events and death events

Author

Yanchun Bao, Sung-Kiang Chuang, Hongsheng Dai, and Tao Wang

Subjects
Statistics and Probability, medicine.medical_specialty, business.industry, medicine.medical_treatment, Within person, Implant complications, Disease cluster, Surgery, Censoring (clinical trials), Statistics, Research studies, medicine, Implant, Statistics, Probability and Uncertainty, Complication, Dental implant, business
Abstract

In dental implant research studies, events such as implant complications including pain or infection may be observed recurrently before failure events, i.e. the death of implants. It is natural to assume that recurrent events and failure events are correlated to each other, since they happen on the same implant (subject) and complication times have strong effects on the implant survival time. On the other hand, each patient may have more than one implant. Therefore these recurrent events or failure events are clustered since implant complication times or failure times within the same patient (cluster) are likely to be correlated. The overall implant survival times and recurrent complication times are both interesting to us. In this paper, a joint modelling approach is proposed for modelling complication events and dental implant survival times simultaneously. The proposed method uses a frailty process to model the correlation within cluster and the correlation within subjects. We use Bayesian methods to o...

Published
2012
Full Text
View/download PDF

46. The predictive ability of blood pressure in elderly trial patients

Author

Roseanne McNamee, Matthew J. Carr, Jianxin Pan, Yanchun Bao, and Kennedy Cruickshank

Subjects
Male, medicine.medical_specialty, Pediatrics, Physiology, medicine.medical_treatment, Blood Pressure, law.invention, Randomized controlled trial, Predictive Value of Tests, law, Internal medicine, Internal Medicine, Humans, Medicine, Stroke, Antihypertensive Agents, Aged, Clinical Trials as Topic, business.industry, Multilevel model, medicine.disease, Confidence interval, Blood pressure, Predictive value of tests, Relative risk, Hypertension, Cardiology, Female, Diuretic, Cardiology and Cardiovascular Medicine, business
Abstract

OBJECTIVES:To assess the impact of the blood pressure (BP) profile on cardiovascular risk in the Medical Research Council (UK) elderly trial; investigate whether the effects of hypertensive drugs in reducing event rates are solely a product of systolic pressure reduction.METHODS:Using longitudinal BP data from 4396 hypertensive patients, the general trend over time was estimated using a first-stage multilevel model. We then investigated how BP acted alongside other BP-related covariates in a second-stage 'time-to-event' statistical model, assessing risk for stroke events and coronary heart disease (CHD). Differences in outcome prediction between diuretic, ?-blocker and placebo treatment arms were investigated.RESULTS:The ?-blocker arm experienced comparatively poor control of current SBP, episodic peaks and variability in BP levels. After adjusting for the mean level, variability in SBP over time was significant: risk ratio was 1.15 [95% confidence interval (CI): 1.01-1.31] across all patients for stroke events. The risk ratio for current SBP was 1.36 (95% CI: 1.16-1.58). Current DBP and variability in DBP also predicted stroke independently: risk ratios was 1.43 and 1.18, respectively. The risk factors exhibited weaker associations with CHD risk; only the highest measured value and variability in SBP showed a statistically significant association: risk ratios were 1.26 and 1.16, respectively.CONCLUSION:Individual risk characterization could be augmented with additional prognostic information, besides current SBP, including current diastolic pressure, temporal variability over and above general trends and historical measurements.

Published
2012
Full Text
View/download PDF

47. An inverse probability weighted estimator for the bivariate distribution function under right censoring

Author

Hongsheng Dai and Yanchun Bao

Subjects
Statistics and Probability, Delta method, Minimum-variance unbiased estimator, Efficient estimator, Bias of an estimator, Joint probability distribution, Consistent estimator, Statistics, Estimator, Computer Science::Symbolic Computation, Statistics, Probability and Uncertainty, Invariant estimator, Mathematics
Abstract

An inverse probability weighted estimator is proposed for the joint distribution function of bivariate random vectors under right censoring. The new estimator is based on the idea of transformation of bivariate survival functions and bivariate random vectors to univariate survival functions and univariate random variables. The estimator converges weakly to a zero-mean Gaussian process with an easily estimated covariance function. Numerical studies show that the new estimator is more efficient than some existing inverse probability weighted estimators.

Published
2009
Full Text
View/download PDF

48. The Koul–Susarla–Van Ryzin and weighted least squares estimates for censored linear regression model: A comparative study

Author

Shuyuan He, Chang-Lin Mei, and Yanchun Bao

Subjects
Statistics and Probability, Applied Mathematics, Linear model, Model fitting, Asymptotic distribution, Estimator, Sample (statistics), Regression analysis, Computational Mathematics, Computational Theory and Mathematics, Consistency (statistics), Linear regression, Statistics, Mathematics
Abstract

The Koul-Susarla-Van Ryzin (KSV) and weighted least squares (WLS) methods are simple to use techniques for handling linear regression models with censored data. They do not require any iterations and standard computer routines can be employed for model fitting. Emphasis has been given to the consistency and asymptotic normality for both estimators, but the finite sample performance of the WLS estimator has not been thoroughly investigated. The finite sample performance of these two estimators is compared using an extensive simulation study as well as an analysis of the Stanford heart transplant data. The results demonstrate that the WLS approach performs much better than the KSV method and is reliable for use with censored data.

Published
2007
Full Text
View/download PDF

49. Joint modeling of ChIP-seq data via a Markov random field model

Author

Yanchun Bao, Ernst Wit, Peter A C 't Hoen, Veronica Vinciotti, and Stochastic Studies and Statistics

Subjects
Statistics and Probability, False discovery rate, FOS: Computer and information sciences, Chromatin Immunoprecipitation, Joint analysis, 01 natural sciences, Methodology (stat.ME), 010104 statistics & probability, 03 medical and health sciences, BAYESIAN-ANALYSIS, 0101 mathematics, Statistics - Methodology, 030304 developmental biology, 0303 health sciences, Markov random field, Models, Statistical, Markov chain, Markov random field model, Contrast (statistics), General Medicine, Sequence Analysis, DNA, ChIP-sequencing, Chip, Markov Chains, GENOME, Mixture distributions, Key (cryptography), Statistics, Probability and Uncertainty, Joint (audio engineering), Algorithm, Protein Binding, Statistical Distributions
Abstract

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies.

Published
2014

50. Modelling survival events with longitudinal data measured with error

Author

Hongsheng Dai, Jianxin Pan and Yanchun Bao

Subjects
Longitudinal measurements, Partial likelihood, Linear mixed model, Random effects, Proportional hazard model
Abstract

In survival analysis, time-dependent covariates are usually present as longitudinal data collected periodically and measured with error. The longitudinal data can be assumed to follow a linear mixed effect model and Cox regression models may be used for modelling of survival events. The hazard rate of survival times depends on the underlying time-dependent covariate measured with error, which may be described by random effects. Most existing methods proposed for such models assume a parametric distribution assumption on the random effects and specify a normally distributed error term for the linear mixed effect model. These assumptions may not be always valid in practice. In this paper we propose a new likelihood method for Cox regression models with error-contaminated time-dependent covariates. The proposed method does not require any parametric distribution assumption on random effects and random errors. Asymptotic properties for parameter estimators are provided. Simulation results show that under certain situations the proposed methods are more efficient than the existing methods.

Published
2013

Catalog

Books, media, physical & digital resources
See catalog results