Investigating asthma heterogeneity through shared and distinct genetics: Insights from genome-wide cross-trait analysis
2020; Elsevier BV; Volume: 147; Issue: 3 Linguagem: Inglês
10.1016/j.jaci.2020.07.004
ISSN1097-6825
AutoresZhaozhong Zhu, Kohei Hasegawa, Carlos A. Camargo, Liming Liang,
Tópico(s)IL-33, ST2, and ILC Pathways
ResumoAsthma is a heterogeneous respiratory disease reflecting distinct pathobiologic mechanisms. These mechanisms are based, at least partly, on different genetic factors shared by many other conditions, such as allergic diseases and obesity. Investigating the shared genetic effects enables better understanding of the mechanisms of phenotypic correlations and is less subject to confounding by environmental factors. The increasing availability of large-scale genome-wide association study (GWAS) for asthma has enabled researchers to examine the genetic contributions to the epidemiologic associations between asthma subtypes and those between coexisting diseases and/or traits and asthma. Studies have found not only shared but also distinct genetic components between asthma subtypes, indicating that the heterogeneity is related to distinct genetics. This review summarizes a recently compiled analytic approach—genome-wide cross-trait analysis—to determine shared and distinct genetic architecture. The genome-wide cross-trait analysis features in several analytic aspects: genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score, and functional analysis. In this article, we discuss in detail the scientific goals that can be achieved by these analyses, their advantages, and their limitations. We also make recommendations for future directions: (1) ethnicity-specific asthma GWASs and (2) application of cross-trait methods to multiomics data to dissect the heritability found in GWASs. Finally, these analytic approaches are also applicable to complex and heterogeneous traits beyond asthma. Asthma is a heterogeneous respiratory disease reflecting distinct pathobiologic mechanisms. These mechanisms are based, at least partly, on different genetic factors shared by many other conditions, such as allergic diseases and obesity. Investigating the shared genetic effects enables better understanding of the mechanisms of phenotypic correlations and is less subject to confounding by environmental factors. The increasing availability of large-scale genome-wide association study (GWAS) for asthma has enabled researchers to examine the genetic contributions to the epidemiologic associations between asthma subtypes and those between coexisting diseases and/or traits and asthma. Studies have found not only shared but also distinct genetic components between asthma subtypes, indicating that the heterogeneity is related to distinct genetics. This review summarizes a recently compiled analytic approach—genome-wide cross-trait analysis—to determine shared and distinct genetic architecture. The genome-wide cross-trait analysis features in several analytic aspects: genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score, and functional analysis. In this article, we discuss in detail the scientific goals that can be achieved by these analyses, their advantages, and their limitations. We also make recommendations for future directions: (1) ethnicity-specific asthma GWASs and (2) application of cross-trait methods to multiomics data to dissect the heritability found in GWASs. Finally, these analytic approaches are also applicable to complex and heterogeneous traits beyond asthma. Asthma is a common chronic respiratory disease that affects approximately 340 million individuals worldwide.1Gobal Initiative for Asthma. Global strategy for asthma management and prevention. 2018. Available at: www.ginasthma.org. 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Shi H. Liu C.L. Panganiban R.A. Chung W. et al.Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK biobank.J Allergy Clin Immunol. 2020; 145: 537-549Abstract Full Text Full Text PDF PubMed Scopus (192) Google Scholar,13Granell R. Henderson A.J. Evans D.M. Smith G.D. Ness A.R. Lewis S. et al.Effects of BMI, fat mass, and lean mass on asthma in childhood: a Mendelian randomization study.PLoS Med. 2014; 11e1001669Crossref Scopus (79) Google Scholar Emerging evidence also suggests the role of adiposopathy—"sick fat" or adipose tissue dysfunction—in the pathogenesis of complex disease conditions, including asthma.10Peters U. Dixon A.E. Forno E. Obesity and asthma.J Allergy Clin Immunol. 2018; 141: 1169-1179Abstract Full Text Full Text PDF PubMed Scopus (516) Google Scholar Adiposopathy is characterized by impaired adipogenesis, altered lipid metabolism, and adipose and/or systemic inflammation (eg, upregulated IL-6, TH1 polarization, and TH17 pathways).10Peters U. Dixon A.E. Forno E. Obesity and asthma.J Allergy Clin Immunol. 2018; 141: 1169-1179Abstract Full Text Full Text PDF PubMed Scopus (516) Google Scholar Furthermore, studies have also reported that mental health disorders, such as attention-deficit/hyperactivity disorder,14Cortese S. Sun S. Zhang J. Sharma E. Chang Z. Kuja-Halkola R. et al.Association between attention deficit hyperactivity disorder and asthma: a systematic review and meta-analysis and a swedish population-based study.Lancet Psychiatry. 2018; 5: 717-726Abstract Full Text Full Text PDF PubMed Scopus (99) Google Scholar anxiety, and major depressive disorder, are a comorbidity of asthma.15Scott K.M. Von Korff M. Ormel J. Zhang M.Y. Bruffaerts R. Alonso J. et al.Mental disorders among adults with asthma: results from the world mental health survey.Gen Hosp Psychiatry. 2007; 29: 123-133Crossref PubMed Scopus (247) Google Scholar Yet, studies have suggested that such associations are potentially bidirectional.16Chen E. Miller G.E. Stress and inflammation in exacerbations of asthma.Brain Behav Immun. 2007; 21: 993-999Crossref PubMed Scopus (288) Google Scholar,17Lavoie K.L. Cartier A. Labrecque M. Bacon S.L. Lemiere C. Malo J.L. et al.Are psychiatric disorders associated with worse asthma control and quality of life in asthma patients?.Respir Med. 2005; 99: 1249-1257Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar For example, anxiety can induce asthma symptoms,16Chen E. Miller G.E. Stress and inflammation in exacerbations of asthma.Brain Behav Immun. 2007; 21: 993-999Crossref PubMed Scopus (288) Google Scholar whereas living with an asthma condition (eg, poor asthma control and worse asthma-related quality of life) may have mental health implications.17Lavoie K.L. Cartier A. Labrecque M. Bacon S.L. Lemiere C. Malo J.L. et al.Are psychiatric disorders associated with worse asthma control and quality of life in asthma patients?.Respir Med. 2005; 99: 1249-1257Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar The exact mechanisms that underlie these mental health disorder–asthma associations remain uncertain. Understanding the exact pathobiology of asthma involves several major challenges—the identification of causal mechanisms, the effect of multiple environmental factors (eg, diet, physical activity, air pollution, and environmental microbiome), and the heterogeneity of asthma itself. Although asthma had been considered a single disease for decades, a growing body of literature has revealed that asthma comprises a range of heterogeneous subtypes differing in presentation and disease course18Borish L. Culp J.A. Asthma: a syndrome composed of heterogeneous diseases.Ann Allergy, Asthma Immunol. 2008; 101: 1-9Abstract Full Text Full Text PDF PubMed Scopus (106) Google Scholar and that the heterogeneity is based, at least partly, on different genetic factors for asthma subtypes (eg, childhood vs adult asthma, allergic vs nonallergic asthma).12Zhu Z. Guo Y. Shi H. Liu C.L. Panganiban R.A. Chung W. et al.Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK biobank.J Allergy Clin Immunol. 2020; 145: 537-549Abstract Full Text Full Text PDF PubMed Scopus (192) Google Scholar,19Ferreira M.A.R. Mathur R. Vonk J.M. Szwajda A. Brumpton B. Granell R. et al.Genetic architectures of childhood- and adult-onset asthma are partly distinct.Am J Hum Genet. 2019; 104: 665-684Abstract Full Text Full Text PDF PubMed Scopus (151) Google Scholar,20Pividori M. Schoettler N. Nicolae D.L. Ober C. Im H.K. Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies.Lancet Respir Med. 2019; 7: 509-522Abstract Full Text Full Text PDF PubMed Scopus (191) Google Scholar Accordingly, examinations of subtype-specific genetics in conjunction with shared genetic factors between coexistent diseases or traits (eg, allergic diseases and obesity) and asthma should inform research on the heterogeneity in asthma and provide insight into corresponding pathology (Fig 1).21Zhu Z. Zhu X. Liu C.L. Shi H. Shen S. Yang Y. et al.Shared genetics of asthma and mental health disorders: a large-scale genome-wide cross-trait analysis.Eur Respir J. 2019; 54Crossref PubMed Scopus (92) Google Scholar,22Zhu Z. Lee P.H. Chaffin M.D. Chung W. Loh P.R. Lu Q. et al.A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases.Nat Genet. 2018; 50: 857-864Crossref PubMed Scopus (153) Google Scholar The genetic effect of asthma is significant, with the heritability estimates ranging from 35% to 95%.23Ober C. Yao T.C. The genetics of asthma and allergic disease: a 21st century perspective.Immunol Rev. 2011; 242: 10-30Crossref PubMed Scopus (488) Google Scholar Genome-wide association studies (GWASs) have been widely applied to complex diseases for more than 2 decades, with a greatly increased sample size. However, according to Schoettler et al, in the GWAS of asthma, a larger sample size with heterogeneous subtypes is not necessarily better than a smaller sample size for homogeneous subtypes to identify the relevant genetic variants because the genetic background between asthma subtypes may be different.24Schoettler N. Rodriguez E. Weidinger S. Ober C. Advances in asthma and allergic disease genetics - is bigger always better?.J Allergy Clin Immunol. 2019; Abstract Full Text Full Text PDF Scopus (55) Google Scholar Thus, investigating the shared genetic contribution to coexistent diseases or traits (eg, allergic disease, obesity) and specific asthma subtypes (eg, allergic asthma, obesity-associated asthma phenotype) would boost the power to detect subtype-specific variants that would have been masked by a traditional single-disease GWAS (Fig 1). A comprehensive characterization of these shared genetic architectures would improve understanding of the multiple dimensions of asthma pathobiology. Traditionally, examining the phenotypic correlation or coexistence of other factors is a useful way to investigate the heterogeneity of asthma. However, this approach may have residual confounding and provide insufficient biologic insight as to which underlying mechanism(s) drive the association. A major advantage in going from phenotypic correlations to genetic correlations is improved understanding of the mechanism(s): shared genetic components can be identified at different levels from the whole genome to individual variants, providing insights into the reasons why asthma and coexistent diseases or traits are correlated. Furthermore, genetic correlations are less subject to confounding by environmental factors for several reasons. After adequate. control for population ancestry, genetic correlation would occur only if the germline genetic variant is causal or in linkage disequilibrium (LD) with the causal variant of both traits. A purely environmental confounding factor (eg, air pollution) would not lead to genetic correlation because it is not associated with any genetic variant (Fig 2, A and B). In contrast, if an environmental factor is an intermediary step between the genetic variant and the trait, it is in the causal pathway and is not considered a confounder (ie, it does not create a false genetic correlation between the 2 traits) (Fig 2, C). Population stratification is arguably the only confounding factor in GWAS, but it can be effectively controlled by using principal components from genome-wide genetic markers.25Price A.L. Zaitlen N.A. Reich D. Patterson N. New approaches to population stratification in genome-wide association studies.Nat Rev Genet. 2010; 11: 459-463Crossref PubMed Scopus (807) Google Scholar Once the genetic effect on diseases and traits has been robustly established, the genetic correlation between diseases and traits can be reliably estimated and replicated.26Ferreira M.A. Vonk J.M. Baurecht H. Marenholz I. Tian C. Hoffman J.D. et al.Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.Nat Genet. 2017; 49: 1752-1757Crossref PubMed Scopus (341) Google Scholar, 27Pickrell J.K. Berisa T. Liu J.Z. Segurel L. Tung J.Y. Hinds D.A. Detection and interpretation of shared genetic influences on 42 human traits.Nat Genet. 2016; 48: 709-717Crossref PubMed Scopus (734) Google Scholar, 28Melen E. Himes B.E. Brehm J.M. Boutaoui N. Klanderman B.J. Sylvia J.S. et al.Analyses of shared genetic factors between asthma and obesity in children.J Allergy Clin Immunol. 2010; 126: 631-637.e1-8Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar, 29Lehto K. Pedersen N.L. Almqvist C. Lu Y. Brew B.K. Asthma and affective traits in adults: a genetically informative study.Eur Respir J. 2019; 53Crossref PubMed Scopus (28) Google Scholar In the following sections, we will discuss a range of detailed analyses that can be used to compile a comprehensive investigation between asthma and other coexistent diseases or traits. With the increasing availability of large-scale genetic data for asthma, such as the GABRIEL Consortium,30Moffatt M.F. Gut I.G. Demenais F. Strachan D.P. Bouzigon E. Heath S. et al.A large-scale, consortium-based genomewide association study of asthma.N Engl J Med. 2010; 363: 1211-1221Crossref PubMed Scopus (1610) Google Scholar the Trans-National Asthma Genetic Consortium,31Demenais F. Margaritte-Jeannin P. Barnes K.C. Cookson W.O.C. Altmuller J. Ang W. et al.Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks.Nat Genet. 2018; 50: 42-53Crossref PubMed Scopus (333) Google Scholar and the UK Biobank,12Zhu Z. Guo Y. Shi H. Liu C.L. Panganiban R.A. Chung W. et al.Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK biobank.J Allergy Clin Immunol. 2020; 145: 537-549Abstract Full Text Full Text PDF PubMed Scopus (192) Google Scholar,21Zhu Z. Zhu X. Liu C.L. Shi H. Shen S. Yang Y. et al.Shared genetics of asthma and mental health disorders: a large-scale genome-wide cross-trait analysis.Eur Respir J. 2019; 54Crossref PubMed Scopus (92) Google Scholar,22Zhu Z. Lee P.H. Chaffin M.D. Chung W. Loh P.R. Lu Q. et al.A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases.Nat Genet. 2018; 50: 857-864Crossref PubMed Scopus (153) Google Scholar,26Ferreira M.A. Vonk J.M. Baurecht H. Marenholz I. Tian C. Hoffman J.D. et al.Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.Nat Genet. 2017; 49: 1752-1757Crossref PubMed Scopus (341) Google Scholar,32Shrine N. Portelli M.A. John C. Soler Artigas M. Bennett N. Hall R. et al.Moderate-to-severe asthma in individuals of european ancestry: a genome-wide association study.Lancet Respir Med. 2019; 7: 20-34Abstract Full Text Full Text PDF PubMed Scopus (169) Google Scholar as well as the advancement of genetic epidemiology and statistical genetics methods, researchers are now able to examine the genetic contribution to the epidemiologic associations between asthma subtypes and those between coexistent diseases or traits and asthma. For example, to understand the genetics of asthma heterogeneity, 2 recent studies examined the genetic overlap between asthma subtypes—childhood asthma and adult asthma—by using the UK Biobank and 23andMe data.19Ferreira M.A.R. Mathur R. Vonk J.M. Szwajda A. Brumpton B. Granell R. et al.Genetic architectures of childhood- and adult-onset asthma are partly distinct.Am J Hum Genet. 2019; 104: 665-684Abstract Full Text Full Text PDF PubMed Scopus (151) Google Scholar,20Pividori M. Schoettler N. Nicolae D.L. Ober C. Im H.K. Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies.Lancet Respir Med. 2019; 7: 509-522Abstract Full Text Full Text PDF PubMed Scopus (191) Google Scholar They both found substantially shared (eg, IL1RL1, HLA–DQA1) but also distinct (eg, ORMDL3 specific for childhood asthma) genetic components between these 2 subtypes, supporting the idea that the heterogeneity is related to distinct genetics.19Ferreira M.A.R. Mathur R. Vonk J.M. Szwajda A. Brumpton B. Granell R. et al.Genetic architectures of childhood- and adult-onset asthma are partly distinct.Am J Hum Genet. 2019; 104: 665-684Abstract Full Text Full Text PDF PubMed Scopus (151) Google Scholar,20Pividori M. Schoettler N. Nicolae D.L. Ober C. Im H.K. Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies.Lancet Respir Med. 2019; 7: 509-522Abstract Full Text Full Text PDF PubMed Scopus (191) Google Scholar These fundamental studies largely depend on single-trait analysis, and they can be further extended by our recently implemented study design called genome-wide cross-trait analysis, which is broadly applicable to asthma and many other diseases and/or traits. The design has been successfully applied to the UK Biobank and GWAS consortia data sets and has determined the shared genetic architectures between asthma and allergic diseases,22Zhu Z. Lee P.H. Chaffin M.D. Chung W. Loh P.R. Lu Q. et al.A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases.Nat Genet. 2018; 50: 857-864Crossref PubMed Scopus (153) Google Scholar obesity,12Zhu Z. Guo Y. Shi H. Liu C.L. Panganiban R.A. Chung W. et al.Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK biobank.J Allergy Clin Immunol. 2020; 145: 537-549Abstract Full Text Full Text PDF PubMed Scopus (192) Google Scholar and mental health disorders,21Zhu Z. Zhu X. Liu C.L. Shi H. Shen S. Yang Y. et al.Shared genetics of asthma and mental health disorders: a large-scale genome-wide cross-trait analysis.Eur Respir J. 2019; 54Crossref PubMed Scopus (92) Google Scholar which were reproducible in other studies.26Ferreira M.A. Vonk J.M. Baurecht H. Marenholz I. Tian C. Hoffman J.D. et al.Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.Nat Genet. 2017; 49: 1752-1757Crossref PubMed Scopus (341) Google Scholar, 27Pickrell J.K. Berisa T. Liu J.Z. Segurel L. Tung J.Y. Hinds D.A. Detection and interpretation of shared genetic influences on 42 human traits.Nat Genet. 2016; 48: 709-717Crossref PubMed Scopus (734) Google Scholar, 28Melen E. Himes B.E. Brehm J.M. Boutaoui N. Klanderman B.J. Sylvia J.S. et al.Analyses of shared genetic factors between asthma and obesity in children.J Allergy Clin Immunol. 2010; 126: 631-637.e1-8Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar, 29Lehto K. Pedersen N.L. Almqvist C. Lu Y. Brew B.K. Asthma and affective traits in adults: a genetically informative study.Eur Respir J. 2019; 53Crossref PubMed Scopus (28) Google Scholar A genome-wide cross-trait analysis features several analyses: genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score, and GWAS functional analysis. Each component is discussed in more detail in subsequent sections and depicted in Fig 3. A glossary of the cross-trait GWAS terminology may be found in Table I. A summary of genome-wide cross-trait analysis methods may be found in Table II.Table IGlossary of terms related to genome-wide cross-trait analysisTermDefinitionCross-trait meta-analysisA meta-analysis testing the null hypothesis that none of the traits being examined is associated with the genetic variant. One genetic variant is tested at a time.eQTLsGenetic variants that are associated with the gene expression levels.Genetic correlationAssuming that all genetic variants have some effect on a trait and that their effect size follows a gaussian distribution (called the infinitesimal model), the genetic correlation between 2 traits (A and B) measures the Pearson correlation between the genetic variant effect on traits A and B.GWASAn analytic method that tests the association between each genetic variant and a specific phenotype (a disease status or a quantitative trait). One genetic variant is tested at a time.HLA/MHC regionA genomic region of an approximately 3.6-Mb genome sequence located on the chromosome 6p21, which is mainly known for its pervasive pleiotropic effect and immune-related function. The extended MHC region is at 25 to 34 Mb on chromosome 6.Horizontal pleiotropyA genetic variant or gene having independent effects on multiple traits that do not have a causal effect on each other.Instrumental variablesVariables that are associated with the modifiable exposure or risk factor of interest and affect the outcome only through the exposure or risk factor.Mendelian randomizationAn analytic approach that examines the causality of an observed association of a modifiable exposure or risk factor with an outcome of interest by using ≥1 genetic instrumental variables.Polygenic risk scoreA score based on a set of disease and/or trait-associated genetic variants, commonly defined as the weighted sum of their genotypes. Weights are chosen by their association effect on the disease and/or trait, directly from GWAS or further modified on the basis of a suitable statistical model incorporating all genetic variants on the genome.Vertical pleiotropy (genetic causality)A genetic variant or gene having an effect on a trait that has causal effect on another trait. Open table in a new tab Table IISummary of genome-wide cross-trait analysis methodsAnalysis methodSoftwareAdvantagesDisadvantagesExamples of application in asthma or complex traits/PMIDGenetic correlationLDSC/S-LDSCRequires only GWAS summary statistics; computationally efficient; accounts for additive confounding in single-trait heritability (such as population stratification) and confounding in genetic correlation (such as overlapping samples); can allow relatively flexible heritability architecture in MAF, LD, and functional categories (the authors LDSC recommended S-LDSC)Is sensitive to other genetic architectures not captured by the baseline LD model; requires that the reference panel LD and GWAS summary statistics be computed from the same populationChildhood asthma and adult asthma/30929738, allergic diseases and asthma/29785011, obesity and asthma/31669095, mental health disorders and asthma/31619474GCTA/GCTA-LDMSEstimates genetic correlation with high accuracy; the LD and association effect are computed from the same genotype data; accounts for different genetic architectures by MAF and LD categories (the authors of GCTA recommended GCTA–LDMS-I)Requires genotype data; computation is infeasible for an extremely large data setComplex traits/ 21167468SumHer/BLD-LDAKIs similar to LDSC but assumes a specific parametric model for MAF/LD-dependent genetic architecture and multiplicative inflation bias due to population stratification or family relatedness; can allow the same baseline LD categories as in LDSC (the authors of LDAK recommended BLD-LDAK/BLD-LDAK-alpha)Is sensitive to other genetic architecture deviated from the assumed parametric model; requires that the reference panel LD and GWAS summary statistics be computed from the same populationComplex traits/ 32203469Cross-trait meta-analysisASSETAccounts for overlapping samplesIs applicable only to binary traitsAllergic diseases and asthma/29785011, mental health disorders and asthma/31619474CPASSOCIs applicable to both binary and continuous traitsYields potential false positives due to overlapping samplesObesity and asthma/31669095MTAGAccounts for possibly unknown sample overlapRequires an assumption that all variants share the same genetic correlation across all traits (ie, no subset-specific effect is assumed)Complex traits/29292387Mendelian randomizationInverse variance–weighted approachIs applicable when the genetic variants' pleiotropic effects (genetic variant–outcome direct effect) happen to cancel outAccounts for only the designed scenario; requires independent variantsAsthma and cancer/32006205Egger regressionIs applicable when the genetic variant–exposure association is independent of the pleiotropic effect; appears to protect false positives in several simulation studiesAccounts for only the designed scenario; requires independent variants; when the outcome GWAS is low-power, its power to detect causal effect could be substantially smaller than that of other methodsAsthma and cancer/32006205Weighted median estimator<50% (counts or total weights) of the genetic variants are invalid instrumentsAccounts for only the designed scenarioAsthma and cancer/32006205Weighted mode-based estimate (weighted MBE)Is applicable when the variants satisfying the exclusion restriction assumption give a causal effect estimate that is the majority among the effect estimates from all variants in the analysis; appears to protect false positives in several simulation studiesAccounts for only the designed scenarioAsthma and cancer/32006205GSMRAccounts for LD between variants; detects and accounts for outliers that could violate the exclusion restriction assumptionRequires sufficient numbers of GWAS significant variants; requires a genetic variant–exposure association that is independent of the pleiotropic effectObesity and asthma/31669095, mental health disorders and asthma/31619474MR-PRESSODetects and accounts for outliers that could violate the exclusion restriction assumptionRequires independent and sufficient numbers of GWAS significant variants; requires a geneti
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