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With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate causal relationships between traits. Unlike conventional MR, cis -MR focuses on a single genomic region using only cis -SNPs. For example, using cis -pQTLs for a protein as exposure for a disease opens a cost-effective path for drug target discovery. However, few methods effectively handle pleiotropy and linkage disequilibrium (LD) of cis -SNPs. Here, we propose cisMR-cML, a method based on constrained maximum likelihood, robust to IV assumption violations with strong theoretical support. We further clarify the severe but largely neglected consequences of the current practice of modeling marginal, instead of conditional genetic effects, and only using exposure-associated SNPs in cis- MR analysis. Numerical studies demonstrated our method’s superiority over other existing methods. In a drug-target analysis for coronary artery disease (CAD), including a proteome-wide application, we identified three potential drug targets, PCSK9, COLEC11 and FGFR1 for CAD. cis-Mendelian randomization can be used to infer the causal effect of a molecular trait on an outcome. Here, the authors address challenges in current cis-MR studies and present cisMR-cML, a method robust to horizontal pleiotropy and linkage disequilibrium.

Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer’s disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app ( https://zhaotongl.shinyapps.io/cMLgraph/ ) for users to explore any subset of the 17 traits of interest.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Mendelian randomization (MR) is an instrumental variable (IV) method using genetic variants such as single nucleotide polymorphisms (SNPs) as IVs to disentangle the causal relationship between an exposure and an outcome. Since any causal conclusion critically depends on the three valid IV assumptions, which will likely be violated in practice, MR methods robust to the IV assumptions are greatly needed. As such a method, Egger regression stands out as one of the most widely used due to its easy use and perceived robustness. Although Egger regression is claimed to be robust to directional pleiotropy under the instrument strength independent of direct effect (InSIDE) assumption, it is known to be dependent on the orientations/coding schemes of SNPs (i.e. which allele of an SNP is selected as the reference group). The current practice, as recommended as the default setting in some popular MR software packages, is to orientate the SNPs to be all positively associated with the exposure, which however, to our knowledge, has not been fully studied to assess its robustness and potential impact. We use both numerical examples (with both real data and simulated data) and analytical results to demonstrate the practical problem of Egger regression with respect to its heavy dependence on the SNP orientations. Under the assumption that InSIDE holds for some specific (and unknown ) coding scheme of the SNPs, we analytically show that other coding schemes would in general lead to the violation of InSIDE. Other related MR and IV regression methods may suffer from the same problem. Cautions should be taken when applying Egger regression (and related MR and IV regression methods) in practice.

Mendelian randomization (MR) has been increasingly applied for causal inference among traits, e.g. between potential risk factors and diseases, with observational data by using genetic variants as instrumental variables (IVs). Despite many successful MR applications, there are several gaps in the current literature to be filled. For example, only few (if any) MR methods can handle the violation of all IV assumptions, sample overlap in the GWAS data and/or linkage disequilibrium among IVs. And most of the MR applications only consider the total causal effect of one trait on the other. In this dissertation, we consider these important aspects to improve the robustness and effectiveness of MR. For the first project, we propose a two-step approach called Graph-MRcML, where we first apply an extended MR method to infer a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. For the second project, we take a different route to consider multivariable MR, which includes multiple exposures in the model and estimates the direct effect of each exposure on the outcome while adjusting for possible mediating effects of other exposures. We propose an efficient and robust MVMR method based on constrained maximum likelihood, called MVMR-cML. For the third project, we move from polygenic MR to cis-MR, which uses correlated cis-variants from a single genomic region, compared to independent variants across the whole genome. A major difference is the need for taking into account linkage disequilibrium among cis-variants, for which we propose a robust cisMR-cML method. We conduct theoretical investigations, extensive simulations and real data applications to showcase the advantages of the three proposed methods in this work.

SNP heritability of a trait is measured by the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms (SNPs). Linear mixed models are routinely used to estimate SNP heritability for many complex traits. The basic concept behind this approach is to model genetic contribution as a random effect, where the variance of this genetic contribution attributes to the heritability of the trait. This linear mixed model approach requires estimation of ‘relatedness’ among individuals in the sample, which is usually captured by estimating a genetic relationship matrix (GRM). Heritability is estimated by the restricted maximum likelihood (REML) or method of moments (MOM) approaches, and this estimation relies heavily on the GRM computed from the genetic data on individuals. Presence of population substructure in the data could significantly impact the GRM estimation and may introduce bias in heritability estimation. The common practice of accounting for such population substructure is to adjust for the top few principal components of the GRM as covariates in the linear mixed model. Here we propose an alternative way of estimating heritability in multi-ethnic studies. Our proposed approach is a MOM estimator derived from the Haseman-Elston regression and gives an asymptotically unbiased estimate of heritability in presence of population stratification. It introduces adjustments for the population stratification in a second-order estimating equation and allows for the total phenotypic variance vary by ethnicity. We study the performance of different MOM and REML approaches in presence of population stratification through extensive simulation studies. We estimate the heritability of height, weight and other anthropometric traits in the UK Biobank cohort to investigate the impact of subtle population substructure on SNP heritability estimation.

Gout is one of the most common types of inflammatory arthritis, caused by the deposition of monosodium urate crystals in and around the joints. Previous genome-wide association studies (GWASs) have identified many genetic loci associated with raised serum urate concentrations. However, hyperuricemia alone is not sufficient for the development of gout arthritis. Here we conduct a multistage GWAS in Han Chinese using 4,275 male gout patients and 6,272 normal male controls (1,255 cases and 1,848 controls were genome-wide genotyped), with an additional 1,644 hyperuricemic controls. We discover three new risk loci, 17q23.2 (rs11653176, P =1.36 × 10 −13 , BCAS3 ), 9p24.2 (rs12236871, P =1.48 × 10 −10 , RFX3 ) and 11p15.5 (rs179785, P =1.28 × 10 −8 , KCNQ1 ), which contain inflammatory candidate genes. Our results suggest that these loci are most likely related to the progression from hyperuricemia to inflammatory gout, which will provide new insights into the pathogenesis of gout arthritis. Raised serum urate levels are a risk factor for gout, a common form of inflammatory arthritis. Here Li et al. conduct a multistage genome-wide association study in a Han Chinese population and identify three novel loci likely associated with the progression from hyperuricemia to gout.

ObjectivesThe deposition of monosodium urate (MSU) crystals within synovial joints and tissues is the initiating factor for gout arthritis. Thus, MSU crystals are a vital tool for studying gout’s molecular mechanism in animal and cellular models. This study mainly compared the excellence and worseness of MSU crystals prepared by different processes and the degree of inflammation induced by MSU crystals.MethodsMSU crystals were prepared using neutralization, alkali titration, and acid titration methods. The crystals’ shape, length, quality, and uniformity were observed by polarized light microscopy and calculated by the software Image J. The foot pad and air pouch models were used to assess the different degrees of inflammation induced by the MSU crystals prepared by the three different methods at different time points. Paw swelling was evaluated by caliper. In air pouch lavage fluid, inflammatory cell recruitment was measured by hemocytometer, and the level of IL-1β, TNF-α, and IL-18 by ELISA. Inflammatory cell infiltration was assayed by immunohistochemistry of air pouch synovial slices.ResultsFor the preparation of MSU crystals with the same uric acid, the quantity acquired by the alkalization method was highest, followed by neutralization, with the acid titration method being the lowest. The crystals prepared by neutralization were the longest. The swelling index of the foot pad induced by MSU crystals prepared by acid titration was significantly lower than that of the other methods at 24 h. The inflammatory cell recruitment and level of IL-1β, TNF-α, and IL-18 in air pouch lavage fluid were lowest in animals with crystals prepared by acid titration. IL-1β secretion induced by MSU crystals prepared by acid titration was significantly lower than that of the other two groups, but there was no significant difference in IL-18 secretion between the three groups in THP-1 macrophages and BMDMs.ConclusionsAll three methods can successfully prepare MSU crystals, but the levels of inflammation induced by the crystals prepared by the three methods were not identical. The degree of inflammation induced by MSU crystals prepared by neutralization and alkalization is greater than by acid titration, but the quantity of MSU crystals obtained by the alkalization method is higher and less time-consuming. Apparently, the window of inflammation triggered by acid titration preparation is shorter compared to other forms of crystal preparation. Overall, MSU crystals prepared by the alkaline method should be recommended for studying the molecular mechanisms of gout in animal and cellular models.

Class B1 G protein-coupled receptors (GPCRs) are important regulators of many physiological functions such as glucose homeostasis, which is mainly mediated by three peptide hormones, i.e., glucagon-like peptide-1 (GLP-1), glucagon (GCG), and glucose-dependent insulinotropic polypeptide (GIP). They trigger a cascade of signaling events leading to the formation of an active agonist–receptor–G protein complex. However, intracellular signal transducers can also activate the receptor independent of extracellular stimuli, suggesting an intrinsic role of G proteins in this process. Here, we report cryo-electron microscopy structures of the human GLP-1 receptor (GLP-1R), GCG receptor (GCGR), and GIP receptor (GIPR) in complex with G s proteins without the presence of cognate ligands. These ligand-free complexes share a similar intracellular architecture to those bound by endogenous peptides, in which, the G s protein alone directly opens the intracellular binding cavity and rewires the extracellular orthosteric pocket to stabilize the receptor in a state unseen before. While the peptide-binding site is partially occupied by the inward folded transmembrane helix 6 (TM6)–extracellular loop 3 (ECL3) juncture of GIPR or a segment of GCGR ECL2, the extracellular portion of GLP-1R adopts a conformation close to the active state. Our findings offer valuable insights into the distinct activation mechanisms of these three important receptors. It is possible that in the absence of a ligand, the intracellular half of transmembrane domain is mobilized with the help of G s protein, which in turn rearranges the extracellular half to form a transitional conformation, facilitating the entry of the peptide N-terminus.