Imaging genetics aims to uncover the hidden relationship between imaging quantitative traits (QTs) and genetic markers (e.g. single nucleotide polymorphism (SNP)), and brings valuable insights into the pathogenesis of complex diseases, such as cancers and cognitive disorders (e.g. the Alzheimer's Disease). However, most linear models in imaging genetics didn't explicitly model the inner relationship among QTs, which might miss some potential efficiency gains from information borrowing across brain regions. In this work, we developed a novel Bayesian regression framework for identifying significant associations between QTs and genetic markers while explicitly modeling spatial dependency between QTs, with the main contributions as follows. Firstly, we developed a spatial-correlated multitask linear mixed-effects model (LMM) to account for dependencies between QTs. We incorporated a population-level mixed effects term into the model, taking full advantage of the dependent structure of brain imaging-derived QTs. Secondly, we implemented the model in the Bayesian framework and derived a Markov chain Monte Carlo (MCMC) algorithm to achieve the model inference. Further, we incorporated the MCMC samples with the Cauchy combination test (CCT) to examine the association between SNPs and QTs, which avoided computationally intractable multi-test issues. The simulation studies indicated improved power of our proposed model compared to classic models where inner dependencies of QTs were not modeled. We also applied the new spatial model to an imaging dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Recent evidence highlights the usefulness of DNA methylation (DNAm) biomarkers as surrogates for exposure to risk factors for non-communicable diseases in epidemiological studies and randomized trials. DNAm variability has been demonstrated to be tightly related to lifestyle behavior and exposure to environmental risk factors, ultimately providing an unbiased proxy of an individual state of health. At present, the creation of DNAm surrogates relies on univariate penalized regression models, with elastic-net regularizer being the gold standard when accomplishing the task. Nonetheless, more adva…
Inferring a graphical model or network from observational data from a large number of variables is a well studied problem in machine learning and computational statistics. In this paper we consider a version of this problem that is relevant to the analysis of multiple phenotypes collected in genetic studies. In such datasets we expect correlations between phenotypes and between individuals. We model observations as a sum of two matrix normal variates such that the joint covariance function is a sum of Kronecker products. This model, which generalizes the Graphical Lasso, assumes observations a…
Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine-mapped genetic data, there may be hundreds of genetic variants in a single gene region any of which could be used to assess this causal relationship. However, using too many genetic variants in the analysis can lead to spurious estimates and inflated Type 1 error rates. But if only a few genetic variants are used, then the majority of the data is ignored and estimates are highly sensitive to the particular choice of variants. We propose an approach based on summari…
Principal Component analysis (PCA) is a useful statistical technique that is commonly used for multivariate analysis of correlated variables. It is usually applied as a dimension reduction method: the top principal components (PCs) explaining most of total variance are tested for association with a predictor of interest, and the remaining PCs are ignored. This strategy has been widely applied in genetic epidemiology, however some of its aspects are not well appreciated in the context of single nucleotide polymorphisms (SNPs) association testing. In this study, we review the theoretical basis o…
When genetic variants in a gene cluster are associated with a disease outcome, the causal pathway from the variants to the outcome can be difficult to disentangle. For example, the chemokine receptor gene cluster contains genetic variants associated with various cytokines. Associations between variants in this cluster and stroke risk may be driven by any of these cytokines. Multivariable Mendelian randomization is an extension of standard univariable Mendelian randomization to estimate the direct effects of related exposures with shared genetic predictors. However, when genetic variants are cl…
In Genome-Wide Association Studies (GWAS) where multiple correlated traits have been measured on participants, a joint analysis strategy, whereby the traits are analyzed jointly, can improve statistical power over a single-trait analysis strategy. There are two questions of interest to be addressed when conducting a joint GWAS analysis with multiple traits. The first question examines whether a genetic loci is significantly associated with any of the traits being tested. The second question focuses on identifying the specific trait(s) that is associated with the genetic loci. Since existing me…
Modern data sets in various domains often include units that were sampled non-randomly from the population and have a latent correlation structure. Here we investigate a common form of this setting, where every unit is associated with a latent variable, all latent variables are correlated, and the probability of sampling a unit depends on its response. Such settings often arise in case-control studies, where the sampled units are correlated due to spatial proximity, family relations, or other sources of relatedness. Maximum likelihood estimation in such settings is challenging from both a comp…
Robust Mendelian Randomization Estimation using Weighted Quantile Regression
Faculté de Pharmacie, Université de Montréal（蒙特利尔大学药学院）；Centre de Recherche Azrieli du CHU Sainte-Justine（圣贾斯丁大学医院中心阿兹里埃尔研究中心）；Department of Mathematics and Statistics, McGill University（麦吉尔大学数学与统计系）；Département de médecine sociale et préventive, Université de Montréal（蒙特利尔大学社会与预防医学系）
Genome-wide Association Studies (GWASs) for complex diseases often collect data on multiple correlated endo-phenotypes. Multivariate analysis of these correlated phenotypes can improve the power to detect genetic variants. Multivariate analysis of variance (MANOVA) can perform such association analysis at a GWAS level, but the behavior of MANOVA under different trait models has not been carefully investigated. In this paper, we show that MANOVA is generally very powerful for detecting association but there are situations, such as when a genetic variant is associated with all the traits, where …
In genome-wide association studies (GWASs), there is an increasing need for detecting the associations between a genetic variant and multiple traits. In studies of complex diseases, it is common to measure several potentially correlated traits in a single GWAS. Despite the multivariate nature of the studies, single-trait-based methods remain the most widely-adopted analysis procedure, owing to their simplicity for studies with multiple traits as their outcome. However, the association between a genetic variant and a single trait sometimes can be weak, and ignoring the actual correlation among …
Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect causal relationships. We developed a method to quantify causal relationships between genetically correlated traits using GWAS summary association statistics. In particular, our method quantifies what part of the genetic component of trait 1 is also causal for trait 2 using mixed fourth moments $E(α_1^2α_1α_2)$ and $E(α_2^2α_1α_2)$ of the bivariate effect size …
In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung cancer). A pervasive question is then whether that marker has independent effects on all phenotypes. In this article, we address this question by assessing whether there is a direct genetic effect on one phenotype that is not mediated through the other phenotypes. In particular, we investigate how to identify and estimate such direct genetic effects on the basis of (matched) case-control data. We discuss conditions u…
In genome-wide association studies (GWAS), penalization is an important approach for identifying genetic markers associated with trait while mixed model is successful in accounting for a complicated dependence structure among samples. Therefore, penalized linear mixed model is a tool that combines the advantages of penalization approach and linear mixed model. In this study, a GWAS with multiple highly correlated traits is analyzed. For GWAS with multiple quantitative traits that are highly correlated, the analysis using traits marginally inevitably lose some essential information among multip…
Understanding factors that contribute to the increased likelihood of disease transmission between two individuals is important for infection control. However, analyzing measures of genetic relatedness is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured transmission dynamics, and the distinctive distributional characteristics of some of the outcomes. We develop two novel hierarchical Bayesian spatial methods for analyzing dyadic genetic relatedness data, in the form of patristic di…
Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Ou…
Background: The development of classification methods for personalized medicine is highly dependent on the identification of predictive genetic markers. In survival analysis it is often necessary to discriminate between influential and non-influential markers. Usually, the first step is to perform a univariate screening step that ranks the markers according to their associations with the outcome. It is common to perform screening using Cox scores, which quantify the associations between survival and each of the markers individually. Since Cox scores do not account for dependencies between the …
In genetic association studies, detecting phenotype-genotype association is a primary goal. We assume that the relationship between the data -phenotype, genetic markers and environmental covariates - can be modelled by a generalized linear model (GLM). The inclusion of environmental covariates makes it possible to account for important confounding factors, such as sex and population substructure. A multivariate score statistic, which under the complete null hypothesis of no phenotype-genotype association asymptotically has a multivariate normal distribution with a covariance matrix that can be…
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression frameworks that models the covariate effects parametrically, while the genetic markers are considered non-parametrically. KDC represents a class of methods that includes distance covariance (DC) and Hilbert-Schmidt Independence Criterion (HSIC), which are nonparametric tests of independence. We show the equivalence be…
