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Dubath C, Porcu E, Delacrétaz A, Grosu C, Laaboub N, Piras M, Von Gunten A, Conus P, Von Plessen K, Kutalik Z, Eap C. DNA methylation may mediate psychotropic drug-induced metabolic side effects: results from a 1-month observational study. Eur Psychiatry 2022. [PMCID: PMC9567522 DOI: 10.1192/j.eurpsy.2022.285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Introduction Metabolic side effects of psychotropic medications are a major drawback to patients’ effective treatment. Among the mechanisms underlying their development, DNA methylation may be involved. Objectives The aim of this study was to estimate DNA methylation changes occurring secondary to psychotropic treatment and evaluate associations between 1-month metabolic changes and baseline DNA methylation or 1-month DNA methylation changes, using an epigenome-wide approach. Methods Seventy-nine psychiatric patients recruited as part of PsyMetab study, who started a treatment with either an antipsychotic, a mood stabilizer or mirtazapine were selected. Epigenome-wide DNA methylation was measured using the Illumina Methylation EPIC BeadChip at baseline and after one month of treatment. Results A global methylation increase was observed after 1 month of treatment, which was more pronounced in patients whose weight remained stable (i.e., <2.5% weight increase). Epigenome-wide significant methylation changes were observed at 52 loci in the whole cohort and at one site, namely cg12209987, located in an intergenic region within an enhancer, specifically in patients who underwent important early weight gain (i.e., ≥5% weight increase) during the same period of treatment (p<5*10-8). Multivariable analysis confirmed an association between an increase in methylation at this locus and weight gain in the whole cohort (p=0.004). Epigenome-wide association analyses failed to identify any significant link between other metabolic changes (e.g. glucose or lipid levels) and methylation data. Conclusions These findings give new insight into the mechanisms of psychotropic drug-induced weight gain. With improved understanding of the metabolic side effects, the use of precision medicine with epigenetics may become possible Disclosure No significant relationships.
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Pirastu N, McDonnell C, Grzeszkowiak EJ, Mounier N, Imamura F, Merino J, Day FR, Zheng J, Taba N, Concas MP, Repetto L, Kentistou KA, Robino A, Esko T, Joshi PK, Fischer K, Ong KK, Gaunt TR, Kutalik Z, Perry JRB, Wilson JF. Using genetic variation to disentangle the complex relationship between food intake and health outcomes. PLoS Genet 2022; 18:e1010162. [PMID: 35653391 PMCID: PMC9162356 DOI: 10.1371/journal.pgen.1010162] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 03/22/2022] [Indexed: 02/02/2023] Open
Abstract
Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide evidence to strengthen causality in nutrition research. To this end, we first identified 283 genetic markers associated with dietary intake in 445,779 UK Biobank participants. We then converted these associations into direct genetic effects on food exposures by adjusting them for effects mediated via other traits. The SNPs which did not show evidence of mediation were then used for MR, assessing the association between genetically predicted food choices and other risk factors, health outcomes. We show that using all associated SNPs without omitting those which show evidence of mediation, leads to biases in downstream analyses (genetic correlations, causal inference), similar to those present in observational studies. However, MR analyses using SNPs which have only a direct effect on the exposure on food exposures provided unequivocal evidence of causal associations between specific eating patterns and obesity, blood lipid status, and several other risk factors and health outcomes.
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Auwerx C, Lepamets M, Sadler MC, Patxot M, Stojanov M, Baud D, Mägi R, Porcu E, Reymond A, Kutalik Z, Metspalu A, Milani L, Mägi R, Nelis M. The individual and global impact of copy-number variants on complex human traits. Am J Hum Genet 2022; 109:647-668. [PMID: 35240056 PMCID: PMC9069145 DOI: 10.1016/j.ajhg.2022.02.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/09/2022] [Indexed: 12/25/2022] Open
Abstract
The impact of copy-number variations (CNVs) on complex human traits remains understudied. We called CNVs in 331,522 UK Biobank participants and performed genome-wide association studies (GWASs) between the copy number of CNV-proxy probes and 57 continuous traits, revealing 131 signals spanning 47 phenotypes. Our analysis recapitulated well-known associations (e.g., 1q21 and height), revealed the pleiotropy of recurrent CNVs (e.g., 26 and 16 traits for 16p11.2-BP4-BP5 and 22q11.21, respectively), and suggested gene functionalities (e.g., MARF1 in female reproduction). Forty-eight CNV signals (38%) overlapped with single-nucleotide polymorphism (SNP)-GWASs signals for the same trait. For instance, deletion of PDZK1, which encodes a urate transporter scaffold protein, decreased serum urate levels, while deletion of RHD, which encodes the Rhesus blood group D antigen, associated with hematological traits. Other signals overlapped Mendelian disorder regions, suggesting variable expressivity and broad impact of these loci, as illustrated by signals mapping to Rotor syndrome (SLCO1B1/3), renal cysts and diabetes syndrome (HNF1B), or Charcot-Marie-Tooth (PMP22) loci. Total CNV burden negatively impacted 35 traits, leading to increased adiposity, liver/kidney damage, and decreased intelligence and physical capacity. Thirty traits remained burden associated after correcting for CNV-GWAS signals, pointing to a polygenic CNV architecture. The burden negatively correlated with socio-economic indicators, parental lifespan, and age (survivorship proxy), suggesting a contribution to decreased longevity. Together, our results showcase how studying CNVs can expand biological insights, emphasizing the critical role of this mutational class in shaping human traits and arguing in favor of a continuum between Mendelian and complex diseases.
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Pain O, Hodgson K, Trubetskoy V, Ripke S, Marshe VS, Adams MJ, Byrne EM, Campos AI, Carrillo-Roa T, Cattaneo A, Als TD, Souery D, Dernovsek MZ, Fabbri C, Hayward C, Henigsberg N, Hauser J, Kennedy JL, Lenze EJ, Lewis G, Müller DJ, Martin NG, Mulsant BH, Mors O, Perroud N, Porteous DJ, Rentería ME, Reynolds CF, Rietschel M, Uher R, Wigmore EM, Maier W, Wray NR, Aitchison KJ, Arolt V, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Li QS, Liu YL, Serretti A, Tsai SJ, Turecki G, Weinshilboum R, McIntosh AM, Lewis CM, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TF, Bacanu SA, Bækvad-Hansen M, Beekman AT, Bigdeli TB, Binder EB, Bryois J, Buttenschøn HN, Bybjerg-Grauholm J, Cai N, Castelao E, Christensen JH, Clarke TK, Coleman JR, Colodro-Conde L, Couvy-Duchesne B, Craddock N, Crawford GE, Davies G, Deary IJ, Degenhardt F, Derks EM, Direk N, Dolan CV, Dunn EC, Eley TC, Escott-Price V, Hassan Kiadeh FF, Finucane HK, Foo JC, Forstner AJ, Frank J, Gaspar HA, Gill M, Goes FS, Gordon SD, Grove J, Hall LS, Hansen CS, Hansen TF, Herms S, Hickie IB, Hoffmann P, Homuth G, Horn C, Hottenga JJ, Hougaard DM, Howard DM, Ising M, Jansen R, Jones I, Jones LA, Jorgenson E, Knowles JA, Kohane IS, Kraft J, Kretzschmar WW, Kutalik Z, Li Y, Lind PA, MacIntyre DJ, MacKinnon DF, Maier RM, Maier W, Marchini J, Mbarek H, McGrath P, McGuffin P, Medland SE, Mehta D, Middeldorp CM, Mihailov E, Milaneschi Y, Milani L, Mondimore FM, Montgomery GW, Mostafavi S, Mullins N, Nauck M, Ng B, Nivard MG, Nyholt DR, O’Reilly PF, Oskarsson H, Owen MJ, Painter JN, Pedersen CB, Pedersen MG, Peterson RE, Peyrot WJ, Pistis G, Posthuma D, Quiroz JA, Qvist P, Rice JP, Riley BP, Rivera M, Mirza SS, Schoevers R, Schulte EC, Shen L, Shi J, Shyn SI, Sigurdsson E, Sinnamon GC, Smit JH, Smith DJ, Stefansson H, Steinberg S, Streit F, Strohmaier J, Tansey KE, Teismann H, Teumer A, Thompson W, Thomson PA, Thorgeirsson TE, Traylor M, Treutlein J, Trubetskoy V, Uitterlinden AG, Umbricht D, Van der Auwera S, van Hemert AM, Viktorin A, Visscher PM, Wang Y, Webb BT, Weinsheimer SM, Wellmann J, Willemsen G, Witt SH, Wu Y, Xi HS, Yang J, Zhang F, Arolt V, Baune BT, Berger K, Boomsma DI, Cichon S, Dannlowski U, de Geus E, DePaulo JR, Domenici E, Domschke K, Esko T, Grabe HJ, Hamilton SP, Hayward C, Heath AC, Kendler KS, Kloiber S, Lewis G, Li QS, Lucae S, Madden PA, Magnusson PK, Martin NG, McIntosh AM, Metspalu A, Mors O, Mortensen PB, Müller-Myhsok B, Nordentoft M, Nöthen MM, O’Donovan MC, Paciga SA, Pedersen NL, Penninx BW, Perlis RH, Porteous DJ, Potash JB, Preisig M, Rietschel M, Schaefer C, Schulze TG, Smoller JW, Stefansson K, Tiemeier H, Uher R, Völzke H, Weissman MM, Werge T, Lewis CM, Levinson DF, Breen G, Børglum AD, Sullivan PF. Identifying the Common Genetic Basis of Antidepressant Response. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 2:115-126. [PMID: 35712048 PMCID: PMC9117153 DOI: 10.1016/j.bpsgos.2021.07.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 01/20/2023] Open
Abstract
Background Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.
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Auwerx C, Sadler MC, Reymond A, Kutalik Z. From Pharmacogenetics to Pharmaco-Omics:Milestones and Future Directions. HGG ADVANCES 2022; 3:100100. [PMID: 35373152 PMCID: PMC8971318 DOI: 10.1016/j.xhgg.2022.100100] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.
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Darrous L, Mounier N, Kutalik Z. Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics. Nat Commun 2021; 12:7274. [PMID: 34907193 PMCID: PMC8671515 DOI: 10.1038/s41467-021-26970-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 10/28/2021] [Indexed: 01/26/2023] Open
Abstract
Mendelian Randomisation (MR) is an increasingly popular approach that estimates the causal effect of risk factors on complex human traits. While it has seen several extensions that relax its basic assumptions, most suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritabilities, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR outperforms several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results with other MR methods, LHC-MR unravels new mechanisms (how disease diagnosis might lead to improved lifestyle) and reveals new causal effects (e.g. HDL cholesterol being protective against high systolic blood pressure), hidden from standard MR methods due to a heritable confounder of opposite effect direction.
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Ponte B, Sadler MC, Olinger E, Vollenweider P, Bochud M, Padmanabhan S, Hayward C, Kutalik Z, Devuyst O. Mendelian randomization to assess causality between uromodulin, blood pressure and chronic kidney disease. Kidney Int 2021; 100:1282-1291. [PMID: 34634361 DOI: 10.1016/j.kint.2021.08.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 08/02/2021] [Accepted: 08/26/2021] [Indexed: 11/24/2022]
Abstract
UMOD variants associated with higher levels of urinary uromodulin (uUMOD) increase the risk of chronic kidney disease (CKD) and hypertension. However, uUMOD levels also reflect functional kidney tubular mass in observational studies, questioning the causal link between uromodulin production and kidney damage. We used Mendelian randomization to clarify causality between uUMOD levels, kidney function and blood pressure in individuals of European descent. The link between uUMOD and estimated glomerular filtration rate (eGFR) was first investigated in a population-based cohort of 3851 individuals. In observational data, higher uUMOD associated with higher eGFR. Conversely, when using rs12917707 (an UMOD polymorphism) as an instrumental variable in one-sample Mendelian randomization, higher uUMOD strongly associated with eGFR decline. We next applied two-sample Mendelian randomization on four genome wide association study consortia to explore causal links between uUMOD and eGFR, CKD risk (567,460 individuals) and blood pressure (757,461 individuals). Higher uUMOD levels significantly associated with lower eGFR, higher odds for eGFR decline or CKD, and higher systolic or diastolic blood pressure. Each one standard deviation (SD) increase of uUMOD decreased log-transformed eGFR by -0.15 SD (95% confidence interval -0.17 to -0.13) and increased log-odds CKD by 0.13 SD (0.12 to 0.15). One SD increase of uUMOD increased systolic blood pressure by 0.06 SD (0.03 to 0.09) and diastolic blood pressure by 0.08 SD (0.05 to 0.12). The effect of uUMOD on blood pressure was mediated by eGFR, whereas the effect on eGFR was not mediated by blood pressure. Thus, our data support that genetically driven levels of uromodulin have a direct, causal and adverse effect on kidney function outcome in the general population, not mediated by blood pressure.
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Porcu E, Sadler MC, Lepik K, Auwerx C, Wood AR, Weihs A, Sleiman MSB, Ribeiro DM, Bandinelli S, Tanaka T, Nauck M, Völker U, Delaneau O, Metspalu A, Teumer A, Frayling T, Santoni FA, Reymond A, Kutalik Z. Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome. Nat Commun 2021; 12:5647. [PMID: 34561431 PMCID: PMC8463674 DOI: 10.1038/s41467-021-25805-y] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 08/24/2021] [Indexed: 02/08/2023] Open
Abstract
Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (rBMI = 0.11, PBMI = 2.0 × 10-51 and rTG = 0.13, PTG = 1.1 × 10-68), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.
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Sulc J, Sonrel A, Mounier N, Auwerx C, Marouli E, Darrous L, Draganski B, Kilpeläinen TO, Joshi P, Loos RJF, Kutalik Z. Composite trait Mendelian randomization reveals distinct metabolic and lifestyle consequences of differences in body shape. Commun Biol 2021; 4:1064. [PMID: 34518635 PMCID: PMC8438050 DOI: 10.1038/s42003-021-02550-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/12/2021] [Indexed: 02/08/2023] Open
Abstract
Obesity is a major risk factor for a wide range of cardiometabolic diseases, however the impact of specific aspects of body morphology remains poorly understood. We combined the GWAS summary statistics of fourteen anthropometric traits from UK Biobank through principal component analysis to reveal four major independent axes: body size, adiposity, predisposition to abdominal fat deposition, and lean mass. Mendelian randomization analysis showed that although body size and adiposity both contribute to the consequences of BMI, many of their effects are distinct, such as body size increasing the risk of cardiac arrhythmia (b = 0.06, p = 4.2 ∗ 10-17) while adiposity instead increased that of ischemic heart disease (b = 0.079, p = 8.2 ∗ 10-21). The body mass-neutral component predisposing to abdominal fat deposition, likely reflecting a shift from subcutaneous to visceral fat, exhibited health effects that were weaker but specifically linked to lipotoxicity, such as ischemic heart disease (b = 0.067, p = 9.4 ∗ 10-14) and diabetes (b = 0.082, p = 5.9 ∗ 10-19). Combining their independent predicted effects significantly improved the prediction of obesity-related diseases (p < 10-10). The presented decomposition approach sheds light on the biological mechanisms underlying the heterogeneity of body morphology and its consequences on health and lifestyle.
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Porcu E, Sjaarda J, Lepik K, Carmeli C, Darrous L, Sulc J, Mounier N, Kutalik Z. Causal Inference Methods to Integrate Omics and Complex Traits. Cold Spring Harb Perspect Med 2021; 11:a040493. [PMID: 32816877 PMCID: PMC8091955 DOI: 10.1101/cshperspect.a040493] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.
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Porcu E, Gilardi F, Darrous L, Yengo L, Bararpour N, Gasser M, Marques-Vidal P, Froguel P, Waeber G, Thomas A, Kutalik Z. Triangulating evidence from longitudinal and Mendelian randomization studies of metabolomic biomarkers for type 2 diabetes. Sci Rep 2021; 11:6197. [PMID: 33737653 PMCID: PMC7973501 DOI: 10.1038/s41598-021-85684-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/03/2021] [Indexed: 02/08/2023] Open
Abstract
The number of people affected by Type 2 diabetes mellitus (T2DM) is close to half a billion and is on a sharp rise, representing a major and growing public health burden. Given its mild initial symptoms, T2DM is often diagnosed several years after its onset, leaving half of diabetic individuals undiagnosed. While several classical clinical and genetic biomarkers have been identified, improving early diagnosis by exploring other kinds of omics data remains crucial. In this study, we have combined longitudinal data from two population-based cohorts CoLaus and DESIR (comprising in total 493 incident cases vs. 1360 controls) to identify new or confirm previously implicated metabolomic biomarkers predicting T2DM incidence more than 5 years ahead of clinical diagnosis. Our longitudinal data have shown robust evidence for valine, leucine, carnitine and glutamic acid being predictive of future conversion to T2DM. We confirmed the causality of such association for leucine by 2-sample Mendelian randomisation (MR) based on independent data. Our MR approach further identified new metabolites potentially playing a causal role on T2D, including betaine, lysine and mannose. Interestingly, for valine and leucine a strong reverse causal effect was detected, indicating that the genetic predisposition to T2DM may trigger early changes of these metabolites, which appear well-before any clinical symptoms. In addition, our study revealed a reverse causal effect of metabolites such as glutamic acid and alanine. Collectively, these findings indicate that molecular traits linked to the genetic basis of T2DM may be particularly promising early biomarkers.
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Pistis G, Milaneschi Y, Vandeleur CL, Lasserre AM, Penninx BW, Lamers F, Boomsma DI, Hottenga JJ, Marques-Vidal P, Vollenweider P, Waeber G, Aubry JM, Preisig M, Kutalik Z. Obesity and atypical depression symptoms: findings from Mendelian randomization in two European cohorts. Transl Psychiatry 2021; 11:96. [PMID: 33542229 PMCID: PMC7862438 DOI: 10.1038/s41398-021-01236-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 02/08/2023] Open
Abstract
Studies considering the causal role of body mass index (BMI) for the predisposition of major depressive disorder (MDD) based on a Mendelian Randomization (MR) approach have shown contradictory results. These inconsistent findings may be attributable to the heterogeneity of MDD; in fact, several studies have documented associations between BMI and mainly the atypical subtype of MDD. Using a MR approach, we investigated the potential causal role of obesity in both the atypical subtype and its five specific symptoms assessed according to the Statistical Manual of Mental Disorders (DSM), in two large European cohorts, CoLaus|PsyCoLaus (n = 3350, 1461 cases and 1889 controls) and NESDA|NTR (n = 4139, 1182 cases and 2957 controls). We first tested general obesity measured by BMI and then the body fat distribution measured by waist-to-hip ratio (WHR). Results suggested that BMI is potentially causally related to the symptom increase in appetite, for which inverse variance weighted, simple median and weighted median MR regression estimated slopes were 0.68 (SE = 0.23, p = 0.004), 0.77 (SE = 0.37, p = 0.036), and 1.11 (SE = 0.39, p = 0.004). No causal effect of BMI or WHR was found on the risk of the atypical subtype or for any of the other atypical symptoms. Our findings show that higher obesity is likely causal for the specific symptom of increase in appetite in depressed participants and reiterate the need to study depression at the granular level of its symptoms to further elucidate potential causal relationships and gain additional insight into its biological underpinnings.
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Vuilleumier N, Antiochos P, Marques Vidal P, Virzi J, Pagano S, Satta N, Hartley O, Brandt K, Burger F, Montecucco F, Kutalik Z, Waeber G, Mach F, Vollenweider P. Antibodies against the c-terminus of apoA-1 as predictors of death in the general population but not as therapeutic targets actionable through cognate peptides immunomodulation. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.3661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Aims
Autoantibodies against apolipoprotein A-1 (anti-apoA-1 IgGs) have emerged as an independent biomarker for cardiovascular disease and mortality in humans, promote death in ApoE−/− mice, and seem to be preferentially oriented against the c-terminal part of apoA-1 (cterA1). Corresponding specific mimetic peptides were shown to reverse anti-apoA-1 IgG pro-inflammatory effects in vitro. We evaluated the association of IgG against c-terminus apoA-1 (anti-cterA1 IgGs) with all-cause mortality in the community and tested the ability of two cterA1 mimetic peptides to reverse the anti-apoA-1 IgG-induced inflammation in vitro and mortality in ApoE−/− mice.
Methods
Anti-cterA1 IgGs were measured on serum samples of 5220 consecutive participants included in the CoLaus study with median follow-up duration of 5.6 years. The primary study outcome was all-cause mortality. Two chemically engineered optimized cterA1 mimetic peptides were tested i) on HEK cells to modulate interleukin-8 (IL-8) and tumor necrosis-alpha (TNF-α) production, and ii) in apoE−/− mice exposed to 16 weeks of anti-apoA-1 IgG passive immunisation.
Results
Anti-cterA1 IgG independently predicted all-cause mortality, each standard deviation of anti-cterA1 IgG being associated with a 18% increase in mortality risk (Hazard Ratio:1.18, 95%confidence intervals:1.04–1.33; p=0.009). Both cterA1 mimetic peptides reduced the anti-apoA-1 IgG-induced inflammation in a dose-dependent manner in vitro, but did not rescue the anti-apoA-1 IgG-associated mortality in mice.
Conclusions
Anti-cterA1 IgG independently predict all-cause mortality in the general population. By failing to reverse the anti-apoA-1 IgG-induced mortality in mice, our data do not support the hypothesis that these autoantibodies could be actionable through cognate peptides immunomodulation.
Funding Acknowledgement
Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): This work was supported by a grant from the Leenaards Foundation (grant number 3698 to N.V.) by the Swiss National Science Foundation (grant number 310030-163335 to N.V.) and by the De Reuter Foundation (grant number 315112 to N.V.).
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Kutalik Z. Commentary on: "The contribution of tissue-specific BMI-associated gene sets to cardiometabolic disease risk: a Mendelian randomization study". Int J Epidemiol 2020; 49:1257-1258. [PMID: 32386404 DOI: 10.1093/ije/dyaa062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/24/2020] [Indexed: 11/13/2022] Open
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Mounier N, Kutalik Z. bGWAS: an R package to perform Bayesian genome wide association studies. Bioinformatics 2020; 36:4374-4376. [PMID: 32470106 PMCID: PMC7520046 DOI: 10.1093/bioinformatics/btaa549] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 05/18/2020] [Accepted: 05/25/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. AVAILABILITY AND IMPLEMENTATION bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 2020; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.2] [Citation(s) in RCA: 337] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2020] [Indexed: 01/01/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.
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de La Harpe R, Rüeger S, Kutalik Z, Ballabeni P, Suter M, Vionnet N, Laferrère B, Pralong F. Weight Loss Directly Influences Intermediate-Term Remission of Diabetes Mellitus After Bariatric Surgery: A Retrospective Case-Control Study. Obes Surg 2020; 30:1332-1338. [PMID: 31754925 PMCID: PMC10015445 DOI: 10.1007/s11695-019-04283-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
PURPOSE Roux en Y gastric bypass surgery (RYGB) is an effective therapy for patients with severe obesity. It induces both significant weight loss and rapid improvements of metabolic complications. This study was undertaken to better define the direct role of weight loss in the metabolic improvements. METHODS A retrospective, case-control study of a cohort of 649 patients with obesity who underwent RYGB, comparing higher and lower responders at 2 years after surgery (n = 100 pairs). Pairs of patients were matched for age, gender, and initial BMI. The rates of remission of diabetes, hypertension, dyslipidemia, and hyperuricemia were compared using a mixed effects logistic regression analysis. RESULTS Diabetes before surgery was present in 12/100 lower responders and 17/100 higher responders. Remission at 2 years was observed in 4/12 (33%) of lower responders, compared to 15/17 (88%) of higher responders. Thus, the odds of diabetes remission was significantly smaller in lower responders (OR = 0.067, 95% CI 0.01-0.447). A mixed model regression analysis of all the parameters for each patient showed that the odds of achieving remission of any comorbidity was significantly lower in lower responders (OR = 0.62, 95% CI = 0.39-0.97). CONCLUSION We could demonstrate that weight loss is a significant determinant of the remission of diabetes 2 years after RYGB. These data underline the importance of weight loss in the benefits of this procedure.
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Glanville KP, Coleman JR, Hanscombe KB, Euesden J, Choi SW, Purves KL, Breen G, Air TM, Andlauer TF, Baune BT, Binder EB, Blackwood DH, Boomsma DI, Buttenschøn HN, Colodro-Conde L, Dannlowski U, Direk N, Dunn EC, Forstner AJ, de Geus EJ, Grabe HJ, Hamilton SP, Jones I, Jones LA, Knowles JA, Kutalik Z, Levinson DF, Lewis G, Lind PA, Lucae S, Magnusson PK, McGuffin P, McIntosh AM, Milaneschi Y, Mors O, Mostafavi S, Müller-Myhsok B, Pedersen NL, Penninx BW, Potash JB, Preisig M, Ripke S, Shi J, Shyn SI, Smoller JW, Streit F, Sullivan PF, Tiemeier H, Uher R, Van der Auwera S, Weissman MM, O'Reilly PF, Lewis CM. Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression. Biol Psychiatry 2020; 87:419-430. [PMID: 31570195 PMCID: PMC7001040 DOI: 10.1016/j.biopsych.2019.06.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 06/26/2019] [Accepted: 06/27/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND The prevalence of depression is higher in individuals with autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Shared genetic etiology is a plausible explanation for the overlap, and in this study we tested whether genetic variation in the major histocompatibility complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression. METHODS We fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 human leukocyte antigen (HLA) alleles and 4 complement component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium Major Depressive Disorder Working Group and the UK Biobank. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants, applying both a region-wide significance threshold (3.9 × 10-6) and a candidate threshold (1.6 × 10-4). RESULTS No HLA alleles or C4 haplotypes were associated with depression at the region-wide threshold. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold for testing in HLA genes in the meta-analysis (odds ratio = 0.98, 95% confidence interval = 0.97-0.99). CONCLUSIONS We found no evidence that an increased risk for depression was conferred by HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia. These results suggest that any HLA or C4 variants associated with depression either are rare or have very modest effect sizes.
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Karsai G, Lone M, Kutalik Z, Brenna JT, Li H, Pan D, von Eckardstein A, Hornemann T. FADS3 is a Δ14Z sphingoid base desaturase that contributes to gender differences in the human plasma sphingolipidome. J Biol Chem 2020; 295:1889-1897. [PMID: 31862735 PMCID: PMC7029104 DOI: 10.1074/jbc.ac119.011883] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/16/2019] [Indexed: 12/18/2022] Open
Abstract
Sphingolipids (SLs) are structurally diverse lipids that are defined by the presence of a long-chain base (LCB) backbone. Typically, LCBs contain a single Δ4E double bond (DB) (mostly d18:1), whereas the dienic LCB sphingadienine (d18:2) contains a second DB at the Δ14Z position. The enzyme introducing the Δ14Z DB is unknown. We analyzed the LCB plasma profile in a gender-, age-, and BMI-matched subgroup of the CoLaus cohort (n = 658). Sphingadienine levels showed a significant association with gender, being on average ∼30% higher in females. A genome-wide association study (GWAS) revealed variants in the fatty acid desaturase 3 (FADS3) gene to be significantly associated with the plasma d18:2/d18:1 ratio (p = -log 7.9). Metabolic labeling assays, FADS3 overexpression and knockdown approaches, and plasma LCB profiling in FADS3-deficient mice confirmed that FADS3 is a bona fide LCB desaturase and required for the introduction of the Δ14Z double bond. Moreover, we showed that FADS3 is required for the conversion of the atypical cytotoxic 1-deoxysphinganine (1-deoxySA, m18:0) to 1-deoxysphingosine (1-deoxySO, m18:1). HEK293 cells overexpressing FADS3 were more resistant to m18:0 toxicity than WT cells. In summary, using a combination of metabolic profiling and GWAS, we identified FADS3 to be essential for forming Δ14Z DB containing LCBs, such as d18:2 and m18:1. Our results unravel FADS3 as a Δ14Z LCB desaturase, thereby disclosing the last missing enzyme of the SL de novo synthesis pathway.
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Abstract
PURPOSE OF REVIEW Our review provides a brief summary of the most recent advances towards the identification of the genetic basis of specific aspects of obesity and the quantification of their consequences on health. We also highlight the most promising avenues to be explored in the future. RECENT FINDINGS While obesity has been demonstrated to lead to adverse cardio-metabolic consequences, the determinants of inter-individual variability remain largely unknown. The elucidation of the molecular underpinnings of this relationship is hampered by the extremely heterogeneous nature of obesity as a human trait. Recent technological advances have facilitated a more in-depth characterization of body composition at large-scale. At the pace of current data acquisition and resolution, it is realistic to improve characterization of obesity and to advise individuals based on detailed body composition combined with tissue-specific molecular signatures. Individualized predictions of health implications would enable more personalized and effective public health interventions.
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 2019; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.1] [Citation(s) in RCA: 452] [Impact Index Per Article: 90.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2019] [Indexed: 12/20/2022] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into nine sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust methods and one on other approaches), data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 18 months.
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Carmeli C, Kutalik Z, Kelly-Irving M, Delpierre C, Bochud M, Kivimaki M, Vineis P, Chadeau-Hyam M, Dermitzakis E, Stringhini S. Early life socioeconomic position and adult systemic inflammation: the role of gene regulation. Eur J Public Health 2019. [DOI: 10.1093/eurpub/ckz185.228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Adverse socioeconomic conditions in childhood affect systemic low-grade inflammation in adulthood. Studies in animals and humans suggest that socioeconomic conditions get under the skin from early life thus contributing to shape pro-inflammatory phenotypes. Although the existence of socioeconomic differences in gene regulation of the immune function have been described previously, no study has so far assessed the extent to which these differences actually explain socioeconomic variations in inflammatory markers.
Methods
First, we ran 2-sample Mendelian Randomization (MR) methods to identify putative genes whose expression drives C-reactive protein (CRP) levels in blood. We used two databases with summary statistics of associations between single nucleotide polymorphisms and gene expression (eQTLGen, N = 31,486 individuals) and CRP (CHARGE, N = 148,164 individuals) in blood. We tested 10,701 genes and retained those with FDR<0.05. Second, we used individual-participant data from a Swiss population-based study (SKIPOGH, N = 723) to estimate the proportion of the effect of paternal occupational position (SEP) on low-grade inflammation in adulthood (CRP>3mg/L) mediated by the selected genes. We estimated odds ratios and mediated proportions through counterfactual-based mediation models.
Results
We identified 426 genes driving CRP levels (robust to unmeasured confounding thanks to MR). They jointly mediated the effect of low father’s occupation on inflammation in adulthood for a proportion up to about 70% [95% confidence intervals (CI) 11-90%] and with an odds ratio of 1.53 [95% CI 1.05-2.15] compared to individuals with high paternal SEP. Analysis of the effects of life-course SEP trajectories (upward, downward and stable low from paternal to adult SEP) on gene regulation revealed trajectory dependent effects.
Conclusions
We suggest that adverse childhood SEP affects systemic inflammation in adulthood through a long-lasting effect on gene regulation.
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Clark DW, Okada Y, Moore KHS, Mason D, Pirastu N, Gandin I, Mattsson H, Barnes CLK, Lin K, Zhao JH, Deelen P, Rohde R, Schurmann C, Guo X, Giulianini F, Zhang W, Medina-Gomez C, Karlsson R, Bao Y, Bartz TM, Baumbach C, Biino G, Bixley MJ, Brumat M, Chai JF, Corre T, Cousminer DL, Dekker AM, Eccles DA, van Eijk KR, Fuchsberger C, Gao H, Germain M, Gordon SD, de Haan HG, Harris SE, Hofer E, Huerta-Chagoya A, Igartua C, Jansen IE, Jia Y, Kacprowski T, Karlsson T, Kleber ME, Li SA, Li-Gao R, Mahajan A, Matsuda K, Meidtner K, Meng W, Montasser ME, van der Most PJ, Munz M, Nutile T, Palviainen T, Prasad G, Prasad RB, Priyanka TDS, Rizzi F, Salvi E, Sapkota BR, Shriner D, Skotte L, Smart MC, Smith AV, van der Spek A, Spracklen CN, Strawbridge RJ, Tajuddin SM, Trompet S, Turman C, Verweij N, Viberti C, Wang L, Warren HR, Wootton RE, Yanek LR, Yao J, Yousri NA, Zhao W, Adeyemo AA, Afaq S, Aguilar-Salinas CA, Akiyama M, Albert ML, Allison MA, Alver M, Aung T, Azizi F, Bentley AR, Boeing H, Boerwinkle E, Borja JB, de Borst GJ, Bottinger EP, Broer L, Campbell H, Chanock S, Chee ML, Chen G, Chen YDI, Chen Z, Chiu YF, Cocca M, Collins FS, Concas MP, Corley J, Cugliari G, van Dam RM, Damulina A, Daneshpour MS, Day FR, Delgado GE, Dhana K, Doney ASF, Dörr M, Doumatey AP, Dzimiri N, Ebenesersdóttir SS, Elliott J, Elliott P, Ewert R, Felix JF, Fischer K, Freedman BI, Girotto G, Goel A, Gögele M, Goodarzi MO, Graff M, Granot-Hershkovitz E, Grodstein F, Guarrera S, Gudbjartsson DF, Guity K, Gunnarsson B, Guo Y, Hagenaars SP, Haiman CA, Halevy A, Harris TB, Hedayati M, van Heel DA, Hirata M, Höfer I, Hsiung CA, Huang J, Hung YJ, Ikram MA, Jagadeesan A, Jousilahti P, Kamatani Y, Kanai M, Kerrison ND, Kessler T, Khaw KT, Khor CC, de Kleijn DPV, Koh WP, Kolcic I, Kraft P, Krämer BK, Kutalik Z, Kuusisto J, Langenberg C, Launer LJ, Lawlor DA, Lee IT, Lee WJ, Lerch MM, Li L, Liu J, Loh M, London SJ, Loomis S, Lu Y, Luan J, Mägi R, Manichaikul AW, Manunta P, Másson G, Matoba N, Mei XW, Meisinger C, Meitinger T, Mezzavilla M, Milani L, Millwood IY, Momozawa Y, Moore A, Morange PE, Moreno-Macías H, Mori TA, Morrison AC, Muka T, Murakami Y, Murray AD, de Mutsert R, Mychaleckyj JC, Nalls MA, Nauck M, Neville MJ, Nolte IM, Ong KK, Orozco L, Padmanabhan S, Pálsson G, Pankow JS, Pattaro C, Pattie A, Polasek O, Poulter N, Pramstaller PP, Quintana-Murci L, Räikkönen K, Ralhan S, Rao DC, van Rheenen W, Rich SS, Ridker PM, Rietveld CA, Robino A, van Rooij FJA, Ruggiero D, Saba Y, Sabanayagam C, Sabater-Lleal M, Sala CF, Salomaa V, Sandow K, Schmidt H, Scott LJ, Scott WR, Sedaghati-Khayat B, Sennblad B, van Setten J, Sever PJ, Sheu WHH, Shi Y, Shrestha S, Shukla SR, Sigurdsson JK, Sikka TT, Singh JR, Smith BH, Stančáková A, Stanton A, Starr JM, Stefansdottir L, Straker L, Sulem P, Sveinbjornsson G, Swertz MA, Taylor AM, Taylor KD, Terzikhan N, Tham YC, Thorleifsson G, Thorsteinsdottir U, Tillander A, Tracy RP, Tusié-Luna T, Tzoulaki I, Vaccargiu S, Vangipurapu J, Veldink JH, Vitart V, Völker U, Vuoksimaa E, Wakil SM, Waldenberger M, Wander GS, Wang YX, Wareham NJ, Wild S, Yajnik CS, Yuan JM, Zeng L, Zhang L, Zhou J, Amin N, Asselbergs FW, Bakker SJL, Becker DM, Lehne B, Bennett DA, van den Berg LH, Berndt SI, Bharadwaj D, Bielak LF, Bochud M, Boehnke M, Bouchard C, Bradfield JP, Brody JA, Campbell A, Carmi S, Caulfield MJ, Cesarini D, Chambers JC, Chandak GR, Cheng CY, Ciullo M, Cornelis M, Cusi D, Smith GD, Deary IJ, Dorajoo R, van Duijn CM, Ellinghaus D, Erdmann J, Eriksson JG, Evangelou E, Evans MK, Faul JD, Feenstra B, Feitosa M, Foisy S, Franke A, Friedlander Y, Gasparini P, Gieger C, Gonzalez C, Goyette P, Grant SFA, Griffiths LR, Groop L, Gudnason V, Gyllensten U, Hakonarson H, Hamsten A, van der Harst P, Heng CK, Hicks AA, Hochner H, Huikuri H, Hunt SC, Jaddoe VWV, De Jager PL, Johannesson M, Johansson Å, Jonas JB, Jukema JW, Junttila J, Kaprio J, Kardia SLR, Karpe F, Kumari M, Laakso M, van der Laan SW, Lahti J, Laudes M, Lea RA, Lieb W, Lumley T, Martin NG, März W, Matullo G, McCarthy MI, Medland SE, Merriman TR, Metspalu A, Meyer BF, Mohlke KL, Montgomery GW, Mook-Kanamori D, Munroe PB, North KE, Nyholt DR, O'connell JR, Ober C, Oldehinkel AJ, Palmas W, Palmer C, Pasterkamp GG, Patin E, Pennell CE, Perusse L, Peyser PA, Pirastu M, Polderman TJC, Porteous DJ, Posthuma D, Psaty BM, Rioux JD, Rivadeneira F, Rotimi C, Rotter JI, Rudan I, Den Ruijter HM, Sanghera DK, Sattar N, Schmidt R, Schulze MB, Schunkert H, Scott RA, Shuldiner AR, Sim X, Small N, Smith JA, Sotoodehnia N, Tai ES, Teumer A, Timpson NJ, Toniolo D, Tregouet DA, Tuomi T, Vollenweider P, Wang CA, Weir DR, Whitfield JB, Wijmenga C, Wong TY, Wright J, Yang J, Yu L, Zemel BS, Zonderman AB, Perola M, Magnusson PKE, Uitterlinden AG, Kooner JS, Chasman DI, Loos RJF, Franceschini N, Franke L, Haley CS, Hayward C, Walters RG, Perry JRB, Esko T, Helgason A, Stefansson K, Joshi PK, Kubo M, Wilson JF. Associations of autozygosity with a broad range of human phenotypes. Nat Commun 2019; 10:4957. [PMID: 31673082 PMCID: PMC6823371 DOI: 10.1038/s41467-019-12283-6] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 08/30/2019] [Indexed: 11/20/2022] Open
Abstract
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.
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Dahlqwist E, Kutalik Z, Sjölander A. Using instrumental variables to estimate the attributable fraction. Stat Methods Med Res 2019; 29:2063-2073. [PMID: 31640504 DOI: 10.1177/0962280219879175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In order to design efficient interventions aimed to improve public health, policy makers need to be provided with reliable information of the health burden of different risk factors. For this purpose, we are interested in the proportion of cases that could be prevented had some harmful exposure been eliminated from the population, i.e. the attributable fraction. The attributable fraction is a causal measure; thus, to estimate the attributable fraction from observational data, we have to make appropriate adjustment for confounding. However, some confounders may be unobserved, or even unknown to the investigator. A possible solution to this problem is to use instrumental variable analysis. In this work, we present how the attributable fraction can be estimated with instrumental variable methods based on the two-stage estimator or the G-estimator. One situation when the problem of unmeasuredconfounding may be particularly severe is when assessing the effect of low educational qualifications on coronary heart disease. By using Mendelian randomization, a special case of instrumental variable analysis, it has been claimed that low educational qualifications is a causal risk factor for coronary heart disease. We use Mendelian randomization to estimate the causal risk ratio and causal odds ratio of low educational qualifications as a risk factor for coronary heart disease with data from the UK Biobank. We compare the two-stage and G-estimator as well as the attributable fraction based on the two estimators. The plausibility of drawing causal conclusion in this analysis is thoroughly discussed and alternative genetic instrumental variables are tested.
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Choobdar S, Ahsen ME, Crawford J, Tomasoni M, Fang T, Lamparter D, Lin J, Hescott B, Hu X, Mercer J, Natoli T, Narayan R, Subramanian A, Zhang JD, Stolovitzky G, Kutalik Z, Lage K, Slonim DK, Saez-Rodriguez J, Cowen LJ, Bergmann S, Marbach D. Assessment of network module identification across complex diseases. Nat Methods 2019; 16:843-852. [PMID: 31471613 PMCID: PMC6719725 DOI: 10.1038/s41592-019-0509-5] [Citation(s) in RCA: 140] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 07/10/2019] [Indexed: 12/11/2022]
Abstract
Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.
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