Genetic correlation and causal relationships between cardio-metabolic traits and lung function impairment

[1]  James R. Staley,et al.  A robust and efficient method for Mendelian randomization with hundreds of genetic variants , 2020, Nature Communications.

[2]  J. Kettunen,et al.  Insulin resistance and systemic metabolic changes in oral glucose tolerance test in 5340 individuals: an interventional study , 2019, BMC Medicine.

[3]  J. Thompson,et al.  Age at menopause and lung function: a Mendelian randomisation study , 2019, European Respiratory Journal.

[4]  H. Kim,et al.  Metabolic Syndrome in South Korean Patients with Chronic Obstructive Pulmonary Disease: A Focus on Gender Differences. , 2019, Asian nursing research.

[5]  L. Liang,et al.  Genetic overlap of chronic obstructive pulmonary disease and cardiovascular disease-related traits: a large-scale genome-wide cross-trait analysis , 2019, Respiratory Research.

[6]  P. Deloukas,et al.  Mendelian randomisation analyses find pulmonary factors mediate the effect of height on coronary artery disease , 2019, Communications Biology.

[7]  C. Chuang,et al.  Gender- and Age-Specific Associations Between Body Fat Composition and C-Reactive Protein with Lung Function: A Cross-Sectional Study , 2019, Scientific Reports.

[8]  Dajiang J. Liu,et al.  Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use , 2018, Nature Genetics.

[9]  Yali Lv,et al.  Association between chronic obstructive pulmonary disease and serum lipid levels: a meta-analysis , 2018, Lipids in Health and Disease.

[10]  Q. Xiao,et al.  Exploring the Genetic Correlation Between Growth and Immunity Based on Summary Statistics of Genome-Wide Association Studies , 2018, Front. Genet..

[11]  N. Sheehan,et al.  Age at puberty and risk of asthma: A Mendelian randomisation study , 2018, PLoS medicine.

[12]  P. Chu,et al.  A comparison of different definitions of metabolic syndrome for the risks of atherosclerosis and diabetes , 2018, Diabetology & Metabolic Syndrome.

[13]  G. Davey Smith,et al.  Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians , 2018, British Medical Journal.

[14]  D. Lawlor,et al.  Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption , 2018, bioRxiv.

[15]  B. Neale,et al.  Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.

[16]  T. Hansen,et al.  Estimating the causal effect of body mass index on hay fever, asthma and lung function using Mendelian randomization , 2018, Allergy.

[17]  R. Simó,et al.  Pulmonary Function and Sleep Breathing: Two New Targets for Type 2 Diabetes Care. , 2017, Endocrine reviews.

[18]  Andrew D. Johnson,et al.  Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney , 2017, Hypertension.

[19]  Fernando Pires Hartwig,et al.  Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption , 2017, bioRxiv.

[20]  Jessica M B Rees,et al.  Dissecting Causal Pathways Using Mendelian Randomization with Summarized Genetic Data: Application to Age at Menarche and Risk of Breast Cancer , 2017, Genetics.

[21]  Harvey Risch,et al.  Quantifying the Genetic Correlation between Multiple Cancer Types , 2017, Cancer Epidemiology, Biomarkers & Prevention.

[22]  M. Jarvelin,et al.  Age at menarche and lung function: a Mendelian randomization study , 2017, European Journal of Epidemiology.

[23]  Tanya M. Teslovich,et al.  An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans , 2017, Diabetes.

[24]  S. Thompson,et al.  Interpreting findings from Mendelian randomization using the MR-Egger method , 2017, European Journal of Epidemiology.

[25]  Olena O Yavorska,et al.  MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data , 2017, International journal of epidemiology.

[26]  Christian Gieger,et al.  Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets , 2017, Nature Genetics.

[27]  P. Nilsson,et al.  The temporal relationship between poor lung function and the risk of diabetes , 2016, BMC Pulmonary Medicine.

[28]  Tran Quoc Bao,et al.  Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants , 2016, The Lancet.

[29]  T. Lehtimäki,et al.  Metabolic profiling of alcohol consumption in 9778 young adults , 2016, bioRxiv.

[30]  Nuala A Sheehan,et al.  Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome , 2015, Statistics in medicine.

[31]  J. Danesh,et al.  A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease , 2016 .

[32]  M. Daly,et al.  An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.

[33]  Ross M. Fraser,et al.  Genetic studies of body mass index yield new insights for obesity biology , 2015, Nature.

[34]  S. Thompson,et al.  Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects , 2015, American journal of epidemiology.

[35]  Tamara S. Roman,et al.  New genetic loci link adipose and insulin biology to body fat distribution , 2014, Nature.

[36]  B. Berger,et al.  Efficient Bayesian mixed model analysis increases association power in large cohorts , 2014, Nature Genetics.

[37]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[38]  Ross M. Fraser,et al.  Defining the role of common variation in the genomic and biological architecture of adult human height , 2014, Nature Genetics.

[39]  S. Burgess.  Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome , 2014, International journal of epidemiology.

[40]  Haiming Cao.  Adipocytokines in obesity and metabolic disease. , 2014, The Journal of endocrinology.

[41]  Tanya M. Teslovich,et al.  Discovery and refinement of loci associated with lipid levels , 2013, Nature Genetics.

[42]  P. Visscher,et al.  Calculating statistical power in Mendelian randomization studies. , 2013, International journal of epidemiology.

[43]  A. Butterworth,et al.  Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data , 2013, Genetic epidemiology.

[44]  Ross M. Fraser,et al.  Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits , 2013, PLoS genetics.

[45]  N. Kohno,et al.  Cross-sectional and prospective study of the association between lung function and prediabetes , 2013, BMJ Open.

[46]  A. Peters,et al.  High blood pressure, antihypertensive medication and lung function in a general adult population , 2011, Respiratory research.

[47]  P. Elliott,et al.  Meta-Analysis of Genome-Wide Association Studies in >80 000 Subjects Identifies Multiple Loci for C-Reactive Protein Levels , 2011, Circulation.

[48]  L. Guize,et al.  Lung function impairment and metabolic syndrome: the critical role of abdominal obesity. , 2009, American journal of respiratory and critical care medicine.

[49]  C. Lewis,et al.  Longitudinal association of body mass index with lung function: The CARDIA Study , 2008, Respiratory research.

[50]  J. Pankow,et al.  Cross-Sectional and Prospective Study of Lung Function in Adults With Type 2 Diabetes , 2008, Diabetes Care.

[51]  Dana Dabelea,et al.  The metabolic syndrome. , 2008, Endocrine reviews.

[52]  S. Bojesen,et al.  C-reactive protein as a predictor of prognosis in chronic obstructive pulmonary disease. , 2007, American journal of respiratory and critical care medicine.

[53]  K. Choi,et al.  Prevalence and trends of metabolic syndrome in Korea: Korean National Health and Nutrition Survey 1998–2001 , 2007, Diabetes, obesity & metabolism.

[54]  M. Zureik,et al.  Change in C-reactive protein levels and FEV1 decline: a longitudinal population-based study. , 2006, Respiratory medicine.

[55]  P. Elliott,et al.  Early growth and adult respiratory function in men and women followed from the fetal period to adulthood , 2006, Thorax.

[56]  J. Hankinson,et al.  Standardisation of spirometry , 2005, European Respiratory Journal.

[57]  Calvino Ka-Wing Cheng,et al.  Complete blood count reference interval diagrams derived from NHANES III: stratification by age, sex, and race. , 2004, Laboratory hematology : official publication of the International Society for Laboratory Hematology.

[58]  S. Ebrahim,et al.  Mendelian randomization: prospects, potentials, and limitations. , 2004, International journal of epidemiology.

[59]  P. Rantakallio,et al.  The longitudinal study of the northern Finland birth cohort of 1966. , 1988, Paediatric and perinatal epidemiology.

[60]  P. Rantakallio,et al.  Groups at risk in low birth weight infants and perinatal mortality. , 1969, Acta paediatrica Scandinavica.