Gorfine, M., Qu, C.,Peters, U., & Hsu, L. (2024). Unveiling challenges in Mendelian randomization for gene-environment interaction. Genetic Epidemiology, 48, 164–189. https://doi.org/10.1002/gepi.22552
Gorfine, M., Qu, C.,Peters, U., & Hsu, L. (2024). Unveiling challenges in Mendelian randomization for gene-environment interaction. Genetic Epidemiology, 48, 164–189. https://doi.org/10.1002/gepi.22552
We propose two novel one-sample Mendelian randomization (MR) approaches to causal inference from count-type health outcomes, tailored to both equidispersion and overdispersion conditions. Selecting valid single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) poses a key challenge for MR approaches, as it requires meeting the necessary IV assumptions. To bolster the proposed approaches by addressing violations of IV assumptions, we incorporate a process for removing invalid SNPs that violate the assumptions. In simulations, our proposed approaches demonstrate robustness to the violations, delivering valid estimates, and interpretable type-I errors and statistical power. This increases the practical applicability of the models. We applied the proposed approaches to evaluate the causal effect of fetal hemoglobin (HbF) on the vaso-occlusive crisis and acute chest syndrome (ACS) events in patients with sickle cell disease (SCD) and revealed the causal relation between HbF and ACS events in these patients. We also developed a user-friendly Shiny web application to facilitate researchers' exploration of causal relations.
Genome-wide association studies (GWAS) have provided large numbers of genetic markers that can be used as instrumental variables in a Mendelian Randomisation (MR) analysis to assess the causal effect of a risk factor on an outcome. An extension of MR analysis, multivariable MR, has been proposed to handle multiple risk factors. However, adjusting or stratifying the outcome on a variable that is associated with it may induce collider bias. For an outcome that represents progression of a disease, conditioning by selecting only the cases may cause a biased MR estimation of the causal effect of the risk factor of interest on the progression outcome. Recently, we developed instrument effect regression and corrected weighted least squares (CWLS) to adjust for collider bias in observational associations. In this paper, we highlight the importance of adjusting for collider bias in MR with a risk factor of interest and disease progression as the outcome. A generalised version of the instrument effect regression and CWLS adjustment is proposed based on a multivariable MR model. We highlight the assumptions required for this approach and demonstrate its utility for bias reduction. We give an illustrative application to the effect of smoking initiation and smoking cessation on Crohn's disease prognosis, finding no evidence to support a causal effect.
Transcriptome-wide association studies (TWAS) have been widely used to identify thousands of likely causal genes for diseases and complex traits using predicted expression models. However, most existing TWAS methods rely on gene expression alone and overlook other regulatory mechanisms of gene expression, including DNA methylation and splicing, that contribute to the genetic basis of these complex traits and diseases. Here we introduce a multi-omics method that integrates gene expression, DNA methylation, and splicing data to improve the identification of associated genes with our traits of interest. Through simulations and by analyzing genome-wide association study (GWAS) summary statistics for 24 complex traits, we show that our integrated method, which leverages these complementary omics biomarkers, achieves higher statistical power, and improves the accuracy of likely causal gene identification in blood tissues over individual omics methods. Finally, we apply our integrated model to a lung cancer GWAS data set, demonstrating the integrated models improved identification of prioritized genes for lung cancer risk.
(2024), The 2024 Annual Meeting of the International Genetic Epidemiology Society. Genetic Epidemiology, 48: 344-398. https://doi.org/10.1002/gepi.22598
In the originally-published article, several abstracts were inadvertently left out. They appear on the following pages.
We apologize for this error.
The main goal of fine-mapping is the identification of relevant genetic variants that have a causal effect on some trait of interest, such as the presence of a disease. From a statistical point of view, fine mapping can be seen as a variable selection problem. Fine-mapping methods are often challenging to apply because of the presence of linkage disequilibrium (LD), that is, regions of the genome where the variants interrogated have high correlation. Several methods have been proposed to address this issue. Here we explore the ‘Sum of Single Effects’ (SuSiE) method, applied to real data (summary statistics) from a genome-wide meta-analysis of the autoimmune liver disease primary biliary cholangitis (PBC). Fine-mapping in this data set was previously performed using the FINEMAP program; we compare these previous results with those obtained from SuSiE, which provides an arguably more convenient and principled way of generating ‘credible sets’, that is set of predictors that are correlated with the response variable. This allows us to appropriately acknowledge the uncertainty when selecting the causal effects for the trait. We focus on the results from SuSiE-RSS, which fits the SuSiE model to summary statistics, such as z-scores, along with a correlation matrix. We also compare the SuSiE results to those obtained using a more recently developed method, h2-D2, which uses the same inputs. Overall, we find the results from SuSiE-RSS and, to a lesser extent, h2-D2, to be quite concordant with those previously obtained using FINEMAP. The resulting genes and biological pathways implicated are therefore also similar to those previously obtained, providing valuable confirmation of these previously reported results. Detailed examination of the credible sets identified suggests that, although for the majority of the loci (33 out of 56) the results from SuSiE-RSS seem most plausible, there are some loci (5 out of 56 loci) where the results from h2-D2 seem more compelling. Computer simulations suggest that, overall, SuSiE-RSS generally has slightly higher power, better precision, and better ability to identify the true number of causal variants in a region than h2-D2, although there are some scenarios where the power of h2-D2 is higher. Thus, in real data analysis, the use of complementary approaches such as both SuSiE and h2-D2 is potentially warranted.
Many statistical genetics analysis methods make use of GWAS summary statistics. Best statistical practice requires evaluating these methods in realistic simulation experiments. However, simulating summary statistics by first simulating individual genotype and phenotype data is extremely computationally demanding. This high cost may force researchers to conduct overly simplistic simulations that fail to accurately measure method performance. Alternatively, summary statistics can be simulated directly from their theoretical distribution. Although this is a common need among statistical genetics researchers, no software packages exist for comprehensive GWAS summary statistic simulation. We present GWASBrewer, an open source R package for direct simulation of GWAS summary statistics. We show that statistics simulated by GWASBrewer have the same distribution as statistics generated from individual level data, and can be produced at a fraction of the computational expense. Additionally, GWASBrewer can simulate standard error estimates, something that is typically not done when sampling summary statistics directly. GWASBrewer is highly flexible, allowing the user to simulate data for multiple traits connected by causal effects and with complex distributions of effect sizes. We demonstrate example uses of GWASBrewer for evaluating Mendelian randomization, polygenic risk score, and heritability estimation methods.
Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.
Transcriptome-wide association studies (TWAS) aim to uncover genotype–phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the predicted expression for trait associations. Accurate gene expression prediction in stage 1 is crucial, as it directly impacts the power to identify associations in stage 2. Currently, the first stage of such studies is primarily conducted using linear models like elastic net regression, which fail to capture the nonlinear relationships inherent in biological systems. Deep learning methods have the potential to model such nonlinear effects, but have yet to demonstrably outperform linear methods at this task. To address this gap, we propose a new deep learning architecture to predict gene expression from genotypic variation across individuals. Our method utilizes a learnable input scaling layer in conjunction with a convolutional encoder to capture nonlinear effects and higher-order interactions without compromising on interpretability. We further augment this approach to allow for parameter sharing across multiple networks, enabling us to utilize prior information for individual variants in the form of functional annotations. Evaluations on real-world genomic data show that our method consistently outperforms elastic net regression across a large set of heritable genes. Furthermore, our model statistically significantly improved predictive performance by leveraging functional annotations, whereas elastic net regression failed to show equivalent gains when using the same information, suggesting that our method can capture nonlinear functional information beyond the capability of linear models.
Most genome-wide association studies are based on case-control designs, which provide abundant resources for secondary phenotype analyses. However, such studies suffer from biased sampling of primary phenotypes, and the traditional statistical methods can lead to seriously distorted analysis results when they are applied to secondary phenotypes without accounting for the biased sampling mechanism. To our knowledge, there are no statistical methods specifically tailored for rare variant association analysis with secondary phenotypes. In this article, we proposed two novel joint test statistics for identifying secondary-phenotype-associated rare variants based on prospective likelihood and retrospective likelihood, respectively. We also exploit the assumption of gene-environment independence in retrospective likelihood to improve the statistical power and adopt a two-step strategy to balance statistical power and robustness. Simulations and a real-data application are conducted to demonstrate the superior performance of our proposed methods.
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