We approach the problem of combining top-ranking association statistics or P-value from a new perspective which leads to a remarkably simple and powerful method. Statistical methods, such as the Rank Truncated Product (RTP), have been developed for combining top-ranking associations and this general strategy proved to be useful in applications for detecting combined effects of multiple disease components. To increase power, these methods aggregate signals across top ranking SNPs, while adjusting for their total number assessed in a study. Analytic expressions for combined top statistics or P-values tend to be unwieldy, which complicates interpretation, practical implementation, and hinders further developments. Here, we propose the Augmented Rank Truncation (ART) method that retains main characteristics of the RTP but is substantially simpler to implement. ART leads to an efficient form of the adaptive algorithm, an approach where the number of top ranking SNPs is varied to optimize power. We illustrate our methods by strengthening previously reported associations of $μ$-opioid receptor variants with sensitivity to pain.
We propose a ridge-penalized adaptive Mantel test (AdaMant) for evaluating the association of two high-dimensional sets of features. By introducing a ridge penalty, AdaMant tests the association across many metrics simultaneously. We demonstrate how ridge penalization bridges Euclidean and Mahalanobis distances and their corresponding linear models from the perspective of association measurement and testing. This result is not only theoretically interesting but also has important implications in penalized hypothesis testing, especially in high dimensional settings such as imaging genetics. App…
In genetic association studies, rare variants with extremely small allele frequency play a crucial role in complex traits, and the set-based testing methods that jointly assess the effects of groups of single nucleotide polymorphisms (SNPs) were developed to improve powers for the association tests. However, the powers of these tests are still severely limited due to the extremely small allele frequency, and precise estimations for the effect sizes of individual SNPs are substantially impossible. In this article, we provide an efficient set-based inference framework that addresses the two impo…
Robust Methods for Disease-Genotype Association in Genetic Association Studies: Calculate P-values Using Exact Conditional Enumeration instead of Asymptotic Approximations
Data-adaptive gene and pathway-based tests forrare-variant associations with survival outcomes
Medical College of Wisconsin（威斯康星医学院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；University of Leicester（莱斯特大学）；Instituto de Investigación Sanitaria de Santiago de Compostela（圣地亚哥-德孔波斯特拉卫生研究所）；Fundación Pública Galega de Medicina Xenómica（加利西亚公共基因组医学基金会）；Biomedical Network on Rare Diseases (CIBERER)（罕见病生物医学网络）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Cross Cancer Institute, University of Alberta（阿尔伯塔大学交叉癌症研究所）；University of Manchester（曼彻斯特大学）；Ghent University / Ghent University Hospital（根特大学/根特大学医院）
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 …
Mantel's test (MT) for association is conducted by testing the linear relationship of similarity of all pairs of subjects between two observational domains. Motivated by applications to neuroimaging and genetics data, and following the succes of shrinkage and kernel methods for prediction with high-dimensional data, we here introduce the adaptive Mantel test as an extension of the MT. By utilizing kernels and penalized similarity measures, the adaptive Mantel test is able to achieve higher statistical power relative to the classical MT in many settings. Furthermore, the adaptive Mantel test is…
Commonly in biomedical research, studies collect data in which an outcome measure contains informative excess zeros; for example when observing the burden of neuritic plaques in brain pathology studies, those who show none contribute to our understanding of neurodegenerative disease. The outcome may be characterized by a mixture distribution with one component being the `structural zero' and the other component being a Poisson distribution. We propose a novel variance components score test of genetic association between a set of genetic markers and a zero-inflated count outcome from a mixture …
The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel and interpretable way to summarize the relative importance of predictor variables. Methodologically, we develop the "RelATive cEntrality" (RATE) measure to prioritize candidate genetic variants that are not just marginally important, but whose associations also stem from significant covarying relationships with other variants in the data. We illustrate RATE through Bay…
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 …
Genome-wide association studies (GWAS) have been widely used to examine the association between single nucleotide polymorphisms (SNPs) and complex traits, where both the sample size n and the number of SNPs p can be very large. Recently, cross-trait polygenic risk score (PRS) method has gained extremely popular for assessing genetic correlation of complex traits based on GWAS summary statistics (e.g., SNP effect size). However, empirical evidence has shown a common bias phenomenon that even highly significant cross-trait PRS can only account for a very small amount of genetic variance (R^2 oft…
Gene-based testing is a commonly employed strategy in many genetic association studies. Gene-trait associations can be complex due to underlying population heterogeneity, gene-environment interactions, and various other reasons. Existing gene-based tests, such as Burden and Sequence Kernel Association Tests (SKAT), are based on detecting differences in a single summary statistic, such as the mean or the variance, and may miss or underestimate higher-order associations that could be scientifically interesting. In this paper, we propose a new family of gene-level association tests which integrat…
A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data
University of Arkansas for Medical Sciences（阿肯色大学医学科学中心）；University of Michigan（密歇根大学）；Michigan State University（密歇根州立大学）；Case Western Reserve University（凯斯西储大学）
Correlation remains to be one of the most widely used statistical tools for assessing the strength of relationships between data series. This paper presents a novel compositional correlation method for detecting linear and nonlinear relationships by considering the averages of all parts of all possible compositions of the data series instead of considering the averages of the whole series. The approach enables cumulative contribution of all local associations to the resulting correlation value. The method is applied on two different datasets: a set of four simple nonlinear polynomial functions…
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…
In large scale genetic association studies, a primary aim is to test for association between genetic variants and a disease outcome. The variants of interest are often rare, and appear with low frequency among subjects. In this situation, statistical tests based on standard asymptotic results do not adequately control the Type I error rate, especially if the case:control ratio is unbalanced. In this paper, we propose the use of permutation and approximate unconditional tests for testing association with rare variants. We use novel analytical calculations to efficiently approximate the true Typ…
A Weighted U Statistic for Genetic Association Analyses of Sequencing Data
Michigan State University（密歇根州立大学）；University of North Texas Health Science Center（北德克萨斯大学健康科学中心）；University of Arkansas for Medical Sciences（阿肯色大学医学科学大学）；University of Michigan（密歇根大学）；Mayo Clinic（梅奥诊所）
We consider in this paper detection of signal regions associated with disease outcomes in whole genome association studies. Gene- or region-based methods have become increasingly popular in whole genome association analysis as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in a pre-specified region, e.g., a gene. In view of massive intergenic regions in whole genome sequencing (WGS) studies, we propose a computationally efficient quadratic scan (Q-SCAN) statistic based method to de…
We study variance estimation and associated confidence intervals for parameters characterizing genetic effects from genome-wide association studies (GWAS) misspecified mixed model analysis. Previous studies have shown that, in spite of the model misspecification, certain quantities of genetic interests are estimable, and consistent estimators of these quantities can be obtained using the restricted maximum likelihood (REML) method under a misspecified linear mixed model. However, the asymptotic variance of such a REML estimator is complicated and not ready to be implemented for practical use. …
Bayesian Genome- and Epigenome-wide Association Studies with Gene Level Dependence
高通量遗传和表观遗传数据经常被筛选与观察到的表型的关联。例如，人们可能希望测试数十万个遗传变异或DNA甲基化位点与疾病状态的关联。这些基因组变量可以自然地根据它们编码的基因以及其他标准进行分组。然而，在这种应用中的标准实践是独立筛选，具有对多重性的普遍校正。我们提出了一种贝叶斯方法，其中一个给定的基因组变量的关联的先验概率取决于它的基因，和基因特异性的概率建模非参数。这种分层模型允许适当的基因和全基因组多重性调整，并可以被纳入到各种贝叶斯关联筛选方法中，计算复杂性的增加可以忽略不计。我们描述了一个应用程序，以筛选低级别胶质瘤和多形性胶质母细胞瘤肿瘤样本之间的DNA甲基化差异的癌症基因组图谱。软件可通过https://github.com/lockEF/BayesianScreening上的BayesianScreening for R软件包获得。
Gene expression and phenotype association can be affected by potential unmeasured confounders from multiple sources, leading to biased estimates of the associations. Since genetic variants largely explain gene expression variations, they can be used as instruments in studying the association between gene expressions and phenotype in the framework of high dimensional instrumental variable (IV) regression. However, because the dimensions of both genetic variants and gene expressions are often larger than the sample size, statistical inferences such as hypothesis testing for such high dimensional…
Functional Analysis of Variance for Association Studies
Michigan State University（密歇根州立大学）；National Institute of Environmental Health Sciences（国家环境健康科学研究所）；National Institutes of Health（美国国立卫生研究院）；Montana State University（蒙大拿州立大学）
