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

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

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

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

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

Funding GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file 32: Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.Peer reviewedPublisher PD

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

Funding Information: GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file : Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Designing for Deep Engagement

Flow state represents the quality of meaningful experience-- an effortless, depth of attention that is often undermined in our interrupt-driven, modern society. In this thesis, I present four novel interventions to promote states of deep engagement. Evaluating whether one of these interventions has a meaningful impact on flow state is difficult to do. The bulk of my work, then, focuses on the methodological challenges of flow state research. Herein I tackle three weaknesses in our ability to make strong, generalizable predictions about the causal link between environmental stimuli and flow states: (1) I discuss advancing how we represent the environment (specifically for aural stimuli) using phenomenological principles; (2) I advance the state-of-the-art in how we represent and measure flow bio-behaviorally (with the goal of integrating physiology into our judgements); and (3) I evaluate methodological weaknesses in current experimental flow work. To do this, I present experimental work on models of auditory attention, new wearables and survey instruments for flow estimation, and an experiment that compares flow as measured in lab and at home across varying task structures. This thesis contributes a suite of state-of-the-art psychophysiological and behavioral hardware tools designed to inform inference about flow in-the-wild; it also contributes two unique, open-source, naturalistic datasets collected with them. Combined with time-aware, probabilistic representations of cognition, this work sets the stage for a precise and explicit bio-behavioral definition of flow states that will improve our ability to understand its relationship to our environment. In so doing, it points to an improved approach for social psychology more generally.Ph.D

Toward intelligent, personal air quality monitoring

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages [209]-215).Air pollution is responsible for :1/8 of deaths around the world. While the importance of air quality has led to a boom in inexpensive air sensors, studies have shown that the status quo of sparse, fixed sensors cannot accurately capture personal exposure levels of nearby populations. Especially in urban landscapes, pollutant concentrations can vary over just a few seconds or a few meters. Unfortunately, the portable monitors that are capable of accurately measuring these pollutants cost thousands of dollars. That hasn't stopped a deluge of cheap, portable consumer devices from entering the market. These solutions frequently claim better accuracy, but universally fail under real-world validation. Instead of competing to build a more accurate sensor, we take the approach of trying to predict when we can trust the cheap sensor we have, based on ambient conditions and measurements. Well-designed, sub-$100 sensors have recently started to perform with high precision and accuracy. While their fundamental operation is sound, these affordable sensors cannot incorporate costly, industry standard techniques for mitigating issues like cross-sensitivity, dynamic airflow, or high humidity. Fortunately, if the core principles of the device are robust, machine learning techniques should be able to predict systematic measurement failure based on a handful of related indicators. In this thesis, we test and demonstrate the potential for logistic regression machine learning techniques to predict and classify sensor measurements as 'correct' or 'incorrect' with high reliability. These techniques are also useful for quantifying sensor precision as well as cross-seasonal prediction strength. After demonstrating the value of this approach, we implement a scalable database solution using a semantic web technology know as ChainAPI. The tools developed for this framework allow automatic learning algorithms to crawl through the database, access the most recent data, update their training model, and populate the database with the processed data for other crawling scripts to interact with. This backend has implications for air quality data storage, interaction, and exchange. Finally, we build a portable, Bluetooth enabled air quality device that connects to ChainAPI through a mobile phone app, and takes advantage of the machine learning algorithms running in its backend. This device improves the reliability of sensor data compared with similar-cost systems. The learnAir device empowers individuals to trust their personal air quality data, and provokes a dialog about sensor reliability in the citizen sensing community. Its novel database architecture promotes new ways of interacting with large, dynamic datasets, and new tools to characterize affordable sensors and devices. Finally, applied logistic regression algorithms assure the accuracy of cheap, distributed sensor data- creating a trusted way for researchers to collaborate with citizen scientists from around the world.by David B. Ramsay.S.M

The Devolutionary Jekyll and Post-devolutionary Hyde of the Two Morvern Callar

The two Morvern CallarsAlan Warner\u27s 1995 novel and Lynne Ramsay\u27s 2002 film adaptationare key contemporary Scottish texts yet represent two quite different moments in Scotland\u27s recent cultural history. Warner\u27s novel is decidedly devolutionary in its handling of Scotland and Scottishness. Although superficially faithful to its source text, Ramsay\u27s film is actually far more faithful to its depoliticizedor differently politicizedearly post-devolutionary moment. Examining the two Morvern Callars in light of Robert Stam\u27s theory of adaptation helps us understand both their complex relationship and the ideological consequences of aesthetic choices. © 2012 Taylor & Francis Group, LLC

Literature Reviews and the Hermeneutic Circle

Conducting a literature review is a vital part of any research. Library and information science (LIS) professionals often play a central role in supporting academics in their efforts to locate relevant publications and in teaching novice researchers skills associated with literature reviews. This paper examines literature review processes with the aim to contribute to better understanding of their complexity and uncertainty and to propose a new approach to literature reviews that is capable of dealing with such complexity and uncertainty