Showing papers by "George Davey Smith published in 2017"
TL;DR: How established methods of meta‐regression and random effects modelling from mainstream meta‐analysis are being adapted to perform MR analyses are clarified, and the ability of two popular random effects models to provide robustness to pleiotropy under the IVW approach is investigated.
Abstract: Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent years, it has become commonplace to attempt MR analyses by synthesising summary data estimates of genetic association gleaned from large and independent study populations. This is referred to as two-sample summary data MR. Unfortunately, due to the sheer number of variants that can be easily included into summary data MR analyses, it is increasingly likely that some do not meet the IV assumptions due to pleiotropy. There is a pressing need to develop methods that can both detect and correct for pleiotropy, in order to preserve the validity of the MR approach in this context. In this paper, we aim to clarify how established methods of meta-regression and random effects modelling from mainstream meta-analysis are being adapted to perform this task. Specifically, we focus on two contrastin g approaches: the Inverse Variance Weighted (IVW) method which assumes in its simplest form that all genetic variants are valid IVs, and the method of MR-Egger regression that allows all variants to violate the IV assumptions, albeit in a specific way. We investigate the ability of two popular random effects models to provide robustness to pleiotropy under the IVW approach, and propose statistics to quantify the relative goodness-of-fit of the IVW approach over MR-Egger regression. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.
702 citations
TL;DR: A minimum set of criteria for use in triangulation in aetiological epidemiology is proposed, the key sources of bias of several approaches are summarized and how these might be integrated within a triangulated framework are described.
Abstract: Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding. This is particularly the case when the key sources of bias of some of the approaches would predict that findings would point in opposite directions if they were due to such biases. Where there are inconsistencies, understanding the key sources of bias of each approach can help to identify what further research is required to address the causal question. The aim of this paper is to illustrate how triangulation might be used to improve causal inference in aetiological epidemiology. We propose a minimum set of criteria for use in triangulation in aetiological epidemiology, summarize the key sources of bias of several approaches and describe how these might be integrated within a triangulation framework. We emphasize the importance of being explicit about the expected direction of bias within each approach, whenever this is possible, and seeking to identify approaches that would be expected to bias the true causal effect in different directions. We also note the importance, when comparing results, of taking account of differences in the duration and timing of exposures. We provide three examples to illustrate these points.
698 citations
Wellcome Trust Centre for Human Genetics1, Imperial College London2, University of Oulu3, Agency for Science, Technology and Research4, National Institutes of Health5, King's College London6, Ealing Hospital7, National University of Singapore8, University of Turin9, University Medical Center Groningen10, University of Tartu11, University of Bristol12, University College London13, University of Eastern Finland14, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico15, University of Kiel16, Leiden University Medical Center17, Dresden University of Technology18, University of Düsseldorf19, University of Surrey20, Erasmus University Rotterdam21, Max Healthcare22, Technische Universität München23, University of Naples Federico II24, Science for Life Laboratory25, Wellcome Trust Sanger Institute26, University of Ulm27, Ludwig Maximilian University of Munich28, University of Kelaniya29, Institute of Cancer Research30, King Abdulaziz University31, Queen Mary University of London32, Massachusetts Institute of Technology33, Health Protection Agency34, Churchill Hospital35, University of Oxford36, Imperial College Healthcare37
TL;DR: In this article, the authors used epigenome-wide association to show that body mass index (BMI), a key measure of adiposity, is associated with widespread changes in DNA methylation.
Abstract: Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances1,2. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation3,4,5,6, a key regulator of gene expression and molecular phenotype7. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10−7, range P = 9.2 × 10−8 to 6.0 × 10−46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10−6, range P = 5.5 × 10−6 to 6.1 × 10−35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07–2.56); P = 1.1 × 10−54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.
667 citations
International Agency for Research on Cancer1, University of Bristol2, University Hospitals Bristol NHS Foundation Trust3, Vanderbilt University Medical Center4, University of Kentucky5, University of Copenhagen6, Lund University7, Technische Universität München8, Fred Hutchinson Cancer Research Center9, Harvard University10, Dartmouth College11, University of Liverpool12, Umeå University13, National Institute of Occupational Health14, New Generation University College15, Radboud University Nijmegen16, BC Cancer Agency17, Washington State University18, University of Hawaii19, Ontario Institute for Cancer Research20, University of Southern California21, Technion – Israel Institute of Technology22, University of Salzburg23, Curie Institute24, Nofer Institute of Occupational Medicine25, University of Ostrava26, Charles University in Prague27, Nanjing Medical University28, University of Oviedo29, University of Sheffield30, University of Texas MD Anderson Cancer Center31, University of Pittsburgh32, Lunenfeld-Tanenbaum Research Institute33
TL;DR: The results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma, and the latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior.
Abstract: Background: Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic ...
653 citations
TL;DR: In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.
Abstract: Mendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel’s First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions. In this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR. In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.
406 citations
TL;DR: Challenges in interpreting Mendelian randomization analyses are described, including those from studies using genetic variants to assess causality of multiple traits; studies describing pleiotropic variants; and those investigating variants that disrupt normal function of an exposure.
Abstract: Mendelian randomization (MR) is a burgeoning field that involves the use of genetic variants to assess causal relationships between exposures and outcomes MR studies can be straightforward; for example, genetic variants within or near the encoding locus that is associated with protein concentrations can help to assess their causal role in disease However, a more complex relationship between the genetic variants and an exposure can make findings from MR more difficult to interpret In this Review, we describe some of these challenges in interpreting MR analyses, including those from studies using genetic variants to assess causality of multiple traits (such as branched-chain amino acids and risk of diabetes mellitus); studies describing pleiotropic variants (for example, C-reactive protein and its contribution to coronary heart disease); and those investigating variants that disrupt normal function of an exposure (for example, HDL cholesterol or IL-6 and coronary heart disease) Furthermore, MR studies on variants that encode enzymes responsible for the metabolism of an exposure (such as alcohol) are discussed, in addition to those assessing the effects of variants on time-dependent exposures (extracellular superoxide dismutase), cumulative exposures (LDL cholesterol), and overlapping exposures (triglycerides and non-HDL cholesterol) We elaborate on the molecular features of each relationship, and provide explanations for the likely causal associations In doing so, we hope to contribute towards more reliable evaluations of MR findings
400 citations
TL;DR: In this article, the authors used 1000 Genomes Project-imputed genotype data in up to ∼370,000 women to identify 389 independent signals (P < 5 × 10-8) for age at menarche, a milestone in female pubertal development.
Abstract: The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project-imputed genotype data in up to ∼370,000 women, we identify 389 independent signals (P < 5 × 10-8) for age at menarche, a milestone in female pubertal development. In Icelandic data, these signals explain ∼7.4% of the population variance in age at menarche, corresponding to ∼25% of the estimated heritability. We implicate ∼250 genes via coding variation or associated expression, demonstrating significant enrichment in neural tissues. Rare variants near the imprinted genes MKRN3 and DLK1 were identified, exhibiting large effects when paternally inherited. Mendelian randomization analyses suggest causal inverse associations, independent of body mass index (BMI), between puberty timing and risks for breast and endometrial cancers in women and prostate cancer in men. In aggregate, our findings highlight the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility.
392 citations
TL;DR: It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases, as well as single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population.
Abstract: IMPORTANCE: The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation. OBJECTIVE: To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. DATA SOURCES: Genomewide association studies (GWAS) published up to January 15, 2015. STUDY SELECTION: GWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available. DATA EXTRACTION AND SYNTHESIS: Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population. MAIN OUTCOMES AND MEASURES: Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. RESULTS: Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15]). CONCLUSIONS AND RELEVANCE: It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.
376 citations
TL;DR: Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.
Abstract: Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.
327 citations
Mariaelisa Graff1, Robert A. Scott2, Anne E. Justice1, Kristin L. Young1 +346 more•Institutions (101)
TL;DR: In additional genome-wide meta-analyses adjusting for PA and interaction with PA, 11 novel adiposity loci are identified, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.
Abstract: Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.
275 citations
Cincinnati Children's Hospital Medical Center1, Statens Serum Institut2, University of Gothenburg3, Oulu University Hospital4, Roy J. and Lucille A. Carver College of Medicine5, University of Bristol6, Stanford University7, University of Cincinnati8, Yale University9, Wayne State University10, University of Oulu11, Broad Institute12, Harvard University13, University of Helsinki14, University of Iowa15, University of Copenhagen16, Norwegian Institute of Public Health17
TL;DR: It is found that variants at the EBF1, EEFSEC, AGTR2, WNT4, ADCY5, and RAP2C loci were associated with gestational duration and variants atThe EBF 1, EE FSEC, and AG TR2 loci with preterm birth.
Abstract: BackgroundDespite evidence that genetic factors contribute to the duration of gestation and the risk of preterm birth, robust associations with genetic variants have not been identified. We used large data sets that included the gestational duration to determine possible genetic associations. MethodsWe performed a genomewide association study in a discovery set of samples obtained from 43,568 women of European ancestry using gestational duration as a continuous trait and term or preterm (<37 weeks) birth as a dichotomous outcome. We used samples from three Nordic data sets (involving a total of 8643 women) to test for replication of genomic loci that had significant genomewide association (P<5.0×10−8) or an association with suggestive significance (P<1.0×10−6) in the discovery set. ResultsIn the discovery and replication data sets, four loci (EBF1, EEFSEC, AGTR2, and WNT4) were significantly associated with gestational duration. Functional analysis showed that an implicated variant in WNT4 alters the bind...
University College London1, Lancaster University2, University of Murcia3, King's College London4, Queen Mary University of London5, University of Edinburgh6, Leiden University7, University of South Australia8, European Bioinformatics Institute9, University of Bristol10, University of Essex11, University of Leicester12, University of Oxford13, University of Virginia14, University of London15
TL;DR: In this article, the authors quantify and contrast causal associations of central adiposity (waist-to-hip ratio adjusted for body mass index [WHRadjBMI]) and general adiposity with cardiometabolic disease.
Abstract: Background: The implications of different adiposity measures on cardiovascular disease etiology remain unclear. In this article, we quantify and contrast causal associations of central adiposity (waist-to-hip ratio adjusted for body mass index [WHRadjBMI]) and general adiposity (body mass index [BMI]) with cardiometabolic disease. Methods: Ninety-seven independent single-nucleotide polymorphisms for BMI and 49 single-nucleotide polymorphisms for WHRadjBMI were used to conduct Mendelian randomization analyses in 14 prospective studies supplemented with coronary heart disease (CHD) data from CARDIoGRAMplusC4D (Coronary Artery Disease Genome-wide Replication and Meta-analysis [CARDIoGRAM] plus The Coronary Artery Disease [C4D] Genetics; combined total 66 842 cases), stroke from METASTROKE (12 389 ischemic stroke cases), type 2 diabetes mellitus from DIAGRAM (Diabetes Genetics Replication and Meta-analysis; 34 840 cases), and lipids from GLGC (Global Lipids Genetic Consortium; 213 500 participants) consortia. Primary outcomes were CHD, type 2 diabetes mellitus, and major stroke subtypes; secondary analyses included 18 cardiometabolic traits. Results: Each one standard deviation (SD) higher WHRadjBMI (1 SD≈0.08 U) associated with a 48% excess risk of CHD (odds ratio [OR] for CHD, 1.48; 95% confidence interval [CI], 1.28–1.71), similar to findings for BMI (1 SD≈4.6 kg/m 2 ; OR for CHD, 1.36; 95% CI, 1.22–1.52). Only WHRadjBMI increased risk of ischemic stroke (OR, 1.32; 95% CI, 1.03–1.70). For type 2 diabetes mellitus, both measures had large effects: OR, 1.82 (95% CI, 1.38–2.42) and OR, 1.98 (95% CI, 1.41–2.78) per 1 SD higher WHRadjBMI and BMI, respectively. Both WHRadjBMI and BMI were associated with higher left ventricular hypertrophy, glycemic traits, interleukin 6, and circulating lipids. WHRadjBMI was also associated with higher carotid intima-media thickness (39%; 95% CI, 9%–77% per 1 SD). Conclusions: Both general and central adiposity have causal effects on CHD and type 2 diabetes mellitus. Central adiposity may have a stronger effect on stroke risk. Future estimates of the burden of adiposity on health should include measures of central and general adiposity.
University of Bristol1, Wayne State University2, University of Amsterdam3, Columbia University4, National Institutes of Health5, University of Adelaide6, Imperial College London7, University of Glasgow8, Leipzig University9, University of Michigan10, University of Cambridge11, Wellcome Trust Sanger Institute12, University of Milan13, Brigham and Women's Hospital14
TL;DR: Combined exposure to variants in the genes that encode the targets of CETP inhibitors and statins was associated with discordant reductions in LDL-C and apoB levels and a corresponding risk of cardiovascular events that was proportional to the attenuated reduction in apOB but significantly less than expected per unit change in LDL/C.
Abstract: Dr Oliver-Williams is supported by Homerton College, University of Cambridge. Dr Butterworth is supported by the European Research Council. Dr Danesh is supported by the Medical Research Council, British Heart Foundation, and the National Institute for Health Research. Dr Davey Smith works within the Medical Research Council Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council (MC_UU_12013/1) and the University of Bristol.
University of Bristol1, Medical Research Council2, Pompeu Fabra University3, Dartmouth College4, Erasmus University Rotterdam5, Erasmus University Medical Center6, Université de Sherbrooke7, University of California, Berkeley8, Emory University9, Norwegian Institute of Public Health10, National Institutes of Health11, University of Western Australia12, University Medical Center Groningen13, University of Paris14, North Carolina State University15, Columbia University16, University of Washington17, University of California, San Francisco18, University of Southampton19, International Agency for Research on Cancer20, Karolinska Institutet21, University of Michigan22, University of Memphis23, University of Southern Denmark24, North Carolina Central University25, Harvard University26, Kaiser Permanente27, United States Department of Health and Human Services28, Utrecht University29, University of South Carolina30, Stockholm County Council31, University of California, Davis32, Drexel University33, Duke University34, Johns Hopkins University35, Boston Children's Hospital36, Oslo University Hospital37, Southampton General Hospital38, University of Copenhagen39, Frederiksberg Hospital40
TL;DR: In this article, the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs).
Abstract: Pre-pregnancy maternal obesity is associated with adverse offspring outcomes at birth and later in life. Individual studies have shown that epigenetic modifications such as DNA methylation could contribute. Within the Pregnancy and Childhood Epigenetics (PACE) Consortium, we meta-analysed the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs). We attempted to infer causality by comparing the effects of maternal versus paternal BMI and incorporating genetic variation. In four additional cohorts (1,817 mother-child pairs), we meta-analysed the association between maternal BMI at the start of pregnancy and blood methylation in adolescents. In newborns, maternal BMI was associated with small (<0.2% per BMI unit (1 kg/m2), P < 1.06 × 10-7) methylation variation at 9,044 sites throughout the genome. Adjustment for estimated cell proportions greatly attenuated the number of significant CpGs to 104, including 86 sites common to the unadjusted model. At 72/86 sites, the direction of the association was the same in newborns and adolescents, suggesting persistence of signals. However, we found evidence for acausal intrauterine effect of maternal BMI on newborn methylation at just 8/86 sites. In conclusion, this well-powered analysis identified robust associations between maternal adiposity and variations in newborn blood DNA methylation, but these small effects may be better explained by genetic or lifestyle factors than a causal intrauterine mechanism. This highlights the need for large-scale collaborative approaches and the application of causal inference techniques in epigenetic epidemiology.
TL;DR: It is suggested that GWAS, to date, have generally not focused on phenotypes that directly relate to the progression of disease and thus speak to disease treatment, and this has yet to be realized at a level reflecting expectation.
Abstract: The past decade has been proclaimed as a hugely successful era of gene discovery through the high yields of many genome-wide association studies (GWAS). However, much of the perceived benefit of such discoveries lies in the promise that the identification of genes that influence disease would directly translate into the identification of potential therapeutic targets, but this has yet to be realized at a level reflecting expectation. One reason for this, we suggest, is that GWAS, to date, have generally not focused on phenotypes that directly relate to the progression of disease and thus speak to disease treatment.
TL;DR: A causal role of BMI and fasting insulin in pancreatic cancer etiology is suggested, and no evidence of a causal relationship was observed for type 2 diabetes, nor for dyslipidemia.
Abstract: Background
Risk factors for pancreatic cancer include a cluster of metabolic conditions such as obesity, hypertension, dyslipidemia, insulin resistance, and type 2 diabetes. Given that these risk factors are correlated, separating out causal from confounded effects is challenging. Mendelian randomization (MR), or the use of genetic instrumental variables, may facilitate the identification of the metabolic drivers of pancreatic cancer.
TL;DR: Of the factors tested, relative social deprivation best captures the aspects of the obesogenic environment responsible for the risk of obesity in genetically susceptible adults.
Abstract: Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI).
We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a 'Westernized' diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment.
We found gene-environment interactions with TDI (Pinteraction = 3 × 10 -10 ), self-reported TV watching (Pinteraction = 7 × 10 -5 ) and self-reported physical activity (Pinteraction = 5 × 10 -6 ). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely.
Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. Of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible.
TL;DR: Evidence is found that cannabis initiation increases the risk of schizophrenia, although the size of the causal estimate is small, and stronger evidence that schizophrenia risk predicts cannabis initiation, possibly as genetic instruments for schizophrenia are stronger than for cannabis initiation.
Abstract: Background: Observational associations between cannabis and
schizophrenia are well documented, but ascertaining causation is more challenging.
We used Mendelian randomization (MR), utilizing publicly available data as a method
for ascertaining causation from observational data.
Methods: We performed bi-directional two-sample MR using summary level
genomewide data from the International Cannabis Consortium (ICC) and the
Psychiatric Genomics Consortium (PGC2). Single nucleotide polymorphisms (SNPs)
associated with cannabis initiation (P < 10-5) and schizophrenia (P < 5x10-8) were
combined using an inverse-variance weight fixed-effects approach. We also used
height and education genomewide-association study data, representing negative and
positive control analyses.
Results: There was some evidence consistent with a causal effect of
cannabis initiation on risk of schizophrenia (OR 1.04 per doubling odds of cannabis
initiation, 95% CI 1.01, 1.07, P = 0.019). There was strong evidence consistent with a
causal effect of schizophrenia risk on likelihood of cannabis initiation (OR 1.10 per
doubling of the odds of schizophrenia, 95% CI 1.05, 1.14, P = 2.64 × 10-5). Findings
were as predicted for the negative control (height OR 1.00, 95% CI 0.99 to 1.01, P =
0.90) but weaker than predicted for the positive control (years in education OR 0.99,
95% CI 0.97 to 1.00, P = 0.066) analyses.
Conclusions: Our results provide some that cannabis initiation increases the
risk of schizophrenia, though the size of the causal estimate is small. We find
stronger evidence that schizophrenia risk predicts cannabis initiation, possibly as
genetic instruments for schizophrenia are stronger than for cannabis initiation.
TL;DR: A protective effect of CRP and a risk-increasing effect of sIL-6R (potentially mediated at least in part by CRP) on schizophrenia risk is suggested, and it is possible that such effects are a result of increased susceptibility to early life infection.
Abstract: Importance Positive associations between inflammatory biomarkers and risk of psychiatric disorders, including schizophrenia, have been reported in observational studies. However, conventional observational studies are prone to bias, such as reverse causation and residual confounding, thus limiting our understanding of the effect (if any) of inflammatory biomarkers on schizophrenia risk. Objective To evaluate whether inflammatory biomarkers have an effect on the risk of developing schizophrenia. Design, Setting, and Participants Two-sample mendelian randomization study using genetic variants associated with inflammatory biomarkers as instrumental variables to improve inference. Summary association results from large consortia of candidate gene or genome-wide association studies, including several epidemiologic studies with different designs, were used. Gene-inflammatory biomarker associations were estimated in pooled samples ranging from 1645 to more than 80 000 individuals, while gene-schizophrenia associations were estimated in more than 30 000 cases and more than 45 000 ancestry-matched controls. In most studies included in the consortia, participants were of European ancestry, and the prevalence of men was approximately 50%. All studies were conducted in adults, with a wide age range (18 to 80 years). Exposures Genetically elevated circulating levels of C-reactive protein (CRP), interleukin-1 receptor antagonist (IL-1Ra), and soluble interleukin-6 receptor (sIL-6R). Main Outcomes and Measures Risk of developing schizophrenia. Individuals with schizophrenia or schizoaffective disorders were included as cases. Given that many studies contributed to the analyses, different diagnostic procedures were used. Results The pooled odds ratio estimate using 18 CRP genetic instruments was 0.90 (random effects 95% CI, 0.84-0.97; P = .005) per 2-fold increment in CRP levels; consistent results were obtained using different mendelian randomization methods and a more conservative set of instruments. The odds ratio for sIL-6R was 1.06 (95% CI, 1.01-1.12; P = .02) per 2-fold increment. Estimates for IL-1Ra were inconsistent among instruments, and pooled estimates were imprecise and centered on the null. Conclusions and Relevance Under mendelian randomization assumptions, our findings suggest a protective effect of CRP and a risk-increasing effect of sIL-6R (potentially mediated at least in part by CRP) on schizophrenia risk. It is possible that such effects are a result of increased susceptibility to early life infection.
TL;DR: This large study using two-sample MR found that variants known to influence BMI had effects on PD in a manner consistent with higher BMI leading to lower risk of PD.
Abstract: BACKGROUND: Both positive and negative associations between higher body mass index (BMI) and Parkinson disease (PD) have been reported in observational studies, but it has been difficult to establish causality because of the possibility of residual confounding or reverse causation. To our knowledge, Mendelian randomisation (MR)-the use of genetic instrumental variables (IVs) to explore causal effects-has not previously been used to test the effect of BMI on PD. METHODS AND FINDINGS: Two-sample MR was undertaken using genome-wide association (GWA) study data. The associations between the genetic instruments and BMI were obtained from the GIANT consortium and consisted of the per-allele difference in mean BMI for 77 independent variants that reached genome-wide significance. The per-allele difference in log-odds of PD for each of these variants was estimated from a recent meta-analysis, which included 13,708 cases of PD and 95,282 controls. The inverse-variance weighted method was used to estimate a pooled odds ratio (OR) for the effect of a 5-kg/m2 higher BMI on PD. Evidence of directional pleiotropy averaged across all variants was sought using MR-Egger regression. Frailty simulations were used to assess whether causal associations were affected by mortality selection. A combined genetic IV expected to confer a lifetime exposure of 5-kg/m2 higher BMI was associated with a lower risk of PD (OR 0.82, 95% CI 0.69-0.98). MR-Egger regression gave similar results, suggesting that directional pleiotropy was unlikely to be biasing the result (intercept 0.002; p = 0.654). However, the apparent protective influence of higher BMI could be at least partially induced by survival bias in the PD GWA study, as demonstrated by frailty simulations. Other important limitations of this application of MR include the inability to analyse non-linear associations, to undertake subgroup analyses, and to gain mechanistic insights. CONCLUSIONS: In this large study using two-sample MR, we found that variants known to influence BMI had effects on PD in a manner consistent with higher BMI leading to lower risk of PD. The mechanism underlying this apparent protective effect warrants further study.
TL;DR: Long-term maternal use of acetaminophen during pregnancy was substantially associated with ADHD even after adjusting for indications of use, familial risk of ADHD, and other potential confounders.
Abstract: OBJECTIVES: To estimate the association between maternal use of acetaminophen during pregnancy and of paternal use before pregnancy with attention-deficit/hyperactivity disorder (ADHD) in offspring while adjusting for familial risk for ADHD and indications of acetaminophen use. METHODS: Diagnoses were obtained from the Norwegian Patient Registry for 112 973 offspring from the Norwegian Mother and Child Cohort Study, including 2246 with ADHD. We estimated hazard ratios (HRs) for an ADHD diagnosis by using Cox proportional hazard models. RESULTS: After adjusting for maternal use of acetaminophen before pregnancy, familial risk for ADHD, and indications of acetaminophen use, we observed a modest association between any prenatal maternal use of acetaminophen in 1 (HR = 1.07; 95% confidence interval [CI] 0.96–1.19), 2 (HR = 1.22; 95% CI 1.07–1.38), and 3 trimesters (HR = 1.27; 95% CI 0.99–1.63). The HR for more than 29 days of maternal acetaminophen use was 2.20 (95% CI 1.50–3.24). Use for CONCLUSIONS: Short-term maternal use of acetaminophen during pregnancy was negatively associated with ADHD in offspring. Long-term maternal use of acetaminophen during pregnancy was substantially associated with ADHD even after adjusting for indications of use, familial risk of ADHD, and other potential confounders.
TL;DR: Evidence of the effects of drinking ≤32 g/week in pregnancy is sparse and guidance could advise abstention as a precautionary principle but should explain the paucity of evidence.
Abstract: Objectives To determine the effects of low-to-moderate levels of maternal alcohol consumption in pregnancy on pregnancy and longer-term offspring outcomes. Search strategy Medline, Embase, Web of Science and Psychinfo from inception to 11 July 2016. Selection criteria Prospective observational studies, negative control and quasiexperimental studies of pregnant women estimating effects of light drinking in pregnancy (≤32 g/week) versus abstaining. Pregnancy outcomes such as birth weight and features of fetal alcohol syndrome were examined. Data collection and analysis One reviewer extracted data and another checked extracted data. Random effects meta-analyses were performed where applicable, and a narrative summary of findings was carried out otherwise. Main results 24 cohort and two quasiexperimental studies were included. With the exception of birth size and gestational age, there was insufficient data to meta-analyse or make robust conclusions. Odds of small for gestational age (SGA) and preterm birth were higher for babies whose mothers consumed up to 32 g/week versus none, but estimates for preterm birth were also compatible with no association: summary OR 1.08, 95% CI (1.02 to 1.14), I2 0%, (seven studies, all estimates were adjusted) OR 1.10, 95% CI (0.95 to 1.28), I2 60%, (nine studies, includes one unadjusted estimates), respectively. The earliest time points of exposure were used in the analysis. Conclusion Evidence of the effects of drinking ≤32 g/week in pregnancy is sparse. As there was some evidence that even light prenatal alcohol consumption is associated with being SGA and preterm delivery, guidance could advise abstention as a precautionary principle but should explain the paucity of evidence.
TL;DR: Robust evidence is provided that IR causally affects each individual BCAA and inflammation, which implies that BCAA metabolism lies on a causal pathway from adiposity and IR to type 2 diabetes.
Abstract: OBJECTIVE Insulin resistance has deleterious effects on cardiometabolic disease. We used Mendelian randomization analyses to clarify the causal relationships of insulin resistance (IR) on circulating blood-based metabolites to shed light on potential mediators of the IR to cardiometabolic disease relationship. RESEARCH DESIGN AND METHODS We used 53 single nucleotide polymorphisms associated with IR from a recent genome-wide association study (GWAS) to explore their effects on circulating lipids and metabolites. We used published summary-level data from two GWASs of European individuals; data on the exposure (IR) were obtained from meta-GWASs of 188,577 individuals, and data on the outcomes (58 metabolic measures assessed by nuclear magnetic resonance) were taken from a GWAS of 24,925 individuals. RESULTS One-SD genetically elevated IR (equivalent to 55% higher geometric mean of fasting insulin, 0.89 mmol/L higher triglycerides, and 0.46 mmol/L lower HDL cholesterol) was associated with higher concentrations of all branched-chain amino acids (BCAAs)—isoleucine (0.56 SD; 95% CI 0.43, 0.70), leucine (0.42 SD; 95% CI 0.28, 0.55), and valine (0.26 SD; 95% CI 0.12, 0.39)—as well as with higher glycoprotein acetyls (an inflammation marker) (0.47 SD; 95% CI 0.32, 0.62) ( P CONCLUSIONS We provide robust evidence that IR causally affects each individual BCAA and inflammation. Taken together with existing studies, this implies that BCAA metabolism lies on a causal pathway from adiposity and IR to type 2 diabetes.
Wellcome Trust Sanger Institute1, University of Bristol2, University of Edinburgh3, European Bioinformatics Institute4, University of Verona5, University of Trieste6, Harokopio University7, National Institutes of Health8, Erasmus University Rotterdam9, Imperial College London10, King's College London11, Queen Mary University of London12, Wellcome Trust Centre for Human Genetics13, University of Oxford14, Vita-Salute San Raffaele University15, Ealing Hospital16, University of Leicester17, University of Cambridge18, Copenhagen University Hospital19, University of Copenhagen20, University of Michigan21, Washington University in St. Louis22, Harvard University23, Broad Institute24, University of Sassari25, University College London26, University of Colorado Denver27, University of Western Australia28, Sir Charles Gairdner Hospital29, National Institute for Health Research30, Churchill Hospital31, University of Liverpool32, University of Tartu33, Imperial College Healthcare34, The Catholic University of America35, Heidelberg University36, University of Helsinki37, McGill University38, Jewish General Hospital39
TL;DR: This work applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals to report 106 genome-wide significant signals that have not been previously identified.
Abstract: Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum
TL;DR: AHRR (cg05575921) hypomethylation, a marker of smoking behaviour, provides potentially clinical relevant predictions of future smoking-related morbidity and mortality.
Abstract: Rationale and objectives Self-reported smoking underestimates disease risk. Smoking affects DNA methylation, in particular the cg05575921 site in the aryl hydrocarbon receptor repressor (AHRR ) gene. We tested the hypothesis that AHRR cg05575921 hypomethylation is associated with risk of smoking-related morbidity and mortality. Methods From the Copenhagen City Heart Study representing the Danish general population, we studied 9234 individuals. Using bisulphite treated leucocyte DNA, AHRR (cg05575921) methylation was measured. Rs1051730 ( CHRN3A ) genotype was used to evaluate smoking heaviness. Participants were followed for up to 22 years for exacerbations of COPD, event of lung cancer and all-cause mortality. Six-year lung cancer risk was calculated according to the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO M2012 ). Measurements and main results AHRR (cg05575921) hypomethylation was associated with former and current smoking status, high daily and cumulative smoking, short time since smoking cessation (all p values –31 ), and the smoking-related CHRN3A genotype (−0.48% per T-allele, p=0.002). The multifactorially adjusted HRs for the lowest versus highest methylation quintiles were 4.58 (95% CI 2.83 to 7.42) for COPD exacerbations, 4.87 (2.31 to 10.3) for lung cancer and 1.67 (1.48 to 1.88) for all-cause mortality. Finally, among 2576 high-risk smokers eligible for lung cancer screening by CT, observed cumulative incidences of lung cancer after 6 years for individuals in the lowest and highest methylation quintiles were 3.7% and 0.0% (p=2×10 –7 ), whereas predicted PLCO M2012 6-year risks were similar (4.3% and 4.4%, p=0.77). Conclusion AHRR (cg05575921) hypomethylation, a marker of smoking behaviour, provides potentially clinical relevant predictions of future smoking-related morbidity and mortality.
TL;DR: In this article, the third most abundant element formed by transmutation (after Re and Os) was studied under irradiation and any effects Ta might have on Re clustering in W-Re-Ta alloys.
14 Feb 2017
TL;DR: Recommendations that researchers should use when applying MR to test the effects of intrauterine exposures on postnatal offspring outcomes are provided and an illustrative example with real data is used to demonstrate how these can be applied and subsequent results appropriately interpreted.
Abstract: Mendelian randomization (MR), the use of genetic variants as instrumental variables (IVs) to test causal effects, is increasingly used in aetiological epidemiology. Few of the methodological developments in MR have considered the specific situation of using genetic IVs to test the causal effect of exposures in pregnant women on postnatal offspring outcomes. In this paper, we describe specific ways in which the IV assumptions might be violated when MR is used to test such intrauterine effects. We highlight the importance of considering the extent to which there is overlap between genetic variants in offspring that influence their outcome with genetic variants used as IVs in their mothers. Where there is overlap, and particularly if it generates a strong association of maternal genetic IVs with offspring outcome via the offspring genotype, the exclusion restriction assumption of IV analyses will be violated. We recommend a set of analyses that ought to be considered when MR is used to address research questions concerned with intrauterine effects on post-natal offspring outcomes, and provide details of how these can be undertaken and interpreted. These additional analyses include the use of genetic data from offspring and fathers, examining associations using maternal non-transmitted alleles, and using simulated data in sensitivity analyses (for which we provide code). We explore the extent to which new methods that have been developed for exploring violation of the exclusion restriction assumption in the two-sample setting (MR-Egger and median based methods) might be used when exploring intrauterine effects in one-sample MR. We provide a list of recommendations that researchers should use when applying MR to test the effects of intrauterine exposures on postnatal offspring outcomes and use an illustrative example with real data to demonstrate how our recommendations can be applied and subsequent results appropriately interpreted.
Aarhus University1, Harvard University2, University of California, Los Angeles3, University of Oslo4, Cardiff University5, Broad Institute6, Icahn School of Medicine at Mount Sinai7, Statens Serum Institut8, University of Pittsburgh9, Max Planck Society10, Wellcome Trust Sanger Institute11, Carnegie Mellon University12, University of Bristol13, deCODE genetics14, University of North Carolina at Chapel Hill15, Semel Institute for Neuroscience and Human Behavior16, University of Copenhagen17, Aarhus University Hospital18
TL;DR: It is established that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.
Abstract: Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes, in contrast to what is typically seen in other complex disorders. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.
TL;DR: Some genetic evidence suggests that HDL-cholesterol is a causal risk factor for AMD risk and that increasing HDL- cholesterol (particularly via CETP inhibition) will increase AMD risk.
TL;DR: In this article, the sequence of microstructural changes at the atomic scale in a 17-4PH steel is characterized by atom probe tomography (APT) at two different ageing temperatures, 480°C and 590°C, and the evolution in number density and fraction of CRPs and Cr-rich α′-phase was quantified and their respective contributions to the overall precipitation hardening of the material has been estimated.