Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network

By Seyoung Kim, Eric P. Xing. 2009

250 citations recorded by the source corpus.

Many complex disease syndromes, such as asthma, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. This raises a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to directly and effectively incorporate the correlation structure of multiple quantitative traits such as clinical metrics and gene expressions in association analysis. Our approach represents correlation information explicitly among the quantitative traits as a quantitative trait network (QTN) and then leverages this network to encode structured regularization functions in a multivariate regression model over the genotypes and traits. The result is that the genetic markers that jointly influence subgroups of highly correlated traits can be detected jointly with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical framework. This allows our method to borrow information across correlated phenotypes to discover the genetic markers that perturb a subset of the correlated traits synergistically. Using simulated datasets based on the HapMap consortium and an asthma dataset, we compared the performance of our method with other methods based on single-marker analysis and regression-based methods that do not use any of the relational information in the traits. We found that our method showed an increased power in detecting causal variants affecting correlated traits. Our results showed that, when correlation patterns among traits in a QTN are considered explicitly and directly during a structured multivariate genome association analysis using our proposed methods, the power of detecting true causal SNPs with possibly pleiotropic effects increased significantly without compromising performance on non-pleiotropic SNPs.

Subfields: Genetics

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Citations in the corpus

Federated Multi-Task Learning (2017, background): “The ﬁrst (e.g., [13, 4, 10, 23]) assumes that a clustered, sparse, or low-rank structure between the tasks is known a priori .”
ScholarWorks @ UTRGV ScholarWorks @ UTRGV (methodology): “In addition, Kim and Xing [13] used lasso regression considering the case that a SNP is weakly associated with highly correlated multiple traits rather than a single trait.”
Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, methodology): “To perform feature selection with a signed feature graph, GFlasso [62] extends LapLasso as”
Personalized Federated Learning: An Attentive Collaboration Approach (2020, background): “Multi-task learning aims to train personalized models for multiple related tasks in a centralized manner by either assuming the structure of the relationships between tasks to be known apriori [26, 27, 28, 29], or attempting to learn the relationships between tasks from data [30, 31, 32].”
Feature Selection for Classification: A Review (2014, methodology): “To perform feature selection with a signed feature graph, GFlasso employs a different l1 regularization over a graph [32], penalty(w,G) = λ‖w‖1 + (1− λ) ∑”
Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies (2013, background): “ir univariate counterparts [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]), but also because of a growing appreciation of the potential gains in power from multivariate association analyses [25, 26, 27, 28, 29, 30, 3, 31]. Indeed, [31] emphasizes that, compared with standard univariate ana”
Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression (2012, methodology): “• Genotype dataset: This dataset has genotypes as input variables and phenotypes or observed traits as output variables [12].”
Feature Selection (2016, background): “Since features show certain kinds of dependencies in these cases, we can model them by an undirected graph, where nodes represent features and edges among nodes show the pairwise dependencies between features (Sandler et al. 2009; Kim and Xing 2009; Yang et al. 2012).”
Heterogeneous multitask learning with joint sparsity constraints (2009, unknown): “In our previous work [7], we developed a regularized regression method, called a graph-guided fused lasso, for multitask regression problem that takes advantage of the graph structure over tasks to encourage a selection of common inputs across highly correlated traits in the graph.”