Dataset Information

Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants.

ABSTRACT: Gene-based tests of association (e.g., variance components and burden tests) are now common practice for analyses attempting to elucidate the contribution of rare genetic variants on common disease. As sequencing datasets continue to grow in size, the number of variants within each set (e.g., gene) being tested is also continuing to grow. Pathway-based methods have been used to allow for the initial aggregation of gene-based statistical evidence and then the subsequent aggregation of evidence across the pathway. This "multi-set" approach (first gene-based test, followed by pathway-based) lacks thorough exploration in regard to evaluating genotype-phenotype associations in the age of large, sequenced datasets. In particular, we wonder whether there are statistical and biological characteristics that make the multi-set approach optimal vs. simply doing all gene-based tests? In this paper, we provide an intuitive framework for evaluating these questions and use simulated data to affirm us this intuition. A real data application is provided demonstrating how our insights manifest themselves in practice. Ultimately, we find that when initial subsets are biologically informative (e.g., tending to aggregate causal genetic variants within one or more subsets, often genes), multi-set strategies can improve statistical power, with particular gains in cases where causal variants are aggregated in subsets with less variants overall (high proportion of causal variants in the subset). However, we find that there is little advantage when the sets are non-informative (similar proportion of causal variants in the subsets). Our application to real data further demonstrates this intuition. In practice, we recommend wider use of pathway-based methods and further exploration of optimal ways of aggregating variants into subsets based on emerging biological evidence of the genetic architecture of complex disease.

SUBMITTER: Fore R

PROVIDER: S-EPMC7680887 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants.

Fore Ruby R Boehme Jaden J Li Kevin K Westra Jason J Tintle Nathan N

Frontiers in genetics 20201109

Gene-based tests of association (e.g., variance components and burden tests) are now common practice for analyses attempting to elucidate the contribution of rare genetic variants on common disease. As sequencing datasets continue to grow in size, the number of variants within each set (e.g., gene) being tested is also continuing to grow. Pathway-based methods have been used to allow for the initial aggregation of gene-based statistical evidence and then the subsequent aggregation of evidence ac ...[more]

PMID: 33240333

Similar Datasets

Project description:IntroductionImplementation science has traditionally focused on the implementation of evidence-based practices, but the field has increasingly recognized the importance of addressing de-implementation (i.e., the process of reducing low-value care). Most studies on de-implementation strategies have used a combination of strategies without addressing factors that sustain the use of LVC and there is a lack of information about which strategies are most effective and what mechanisms of change might underlie these strategies. Applied behavior analysis is an approach that could be a potential method to gain insights into the mechanisms of de-implementation strategies to reduce LVC. Three research questions are addressed in this study: What contingencies (three-term contingencies or rule-governing behavior) related to the use of LVC can be found in a local context and what strategies can be developed based on an analysis of these contingencies?; Do these strategies change targeted behaviors?; How do the participants describe the strategies' contingencies and the feasibility of the applied behavior analysis approach?Materials and methodsIn this study, we used applied behavior analysis to analyze contingencies that maintain behaviors related to a chosen LVC, the unnecessary use of x-rays for knee arthrosis within a primary care center. Based on this analysis, strategies were developed and evaluated using a single-case design and a qualitative analysis of interview data.ResultsTwo strategies were developed: a lecture and feedback meetings. The results from the single-case data were inconclusive but some of the findings may indicate a behavior change in the expected direction. Such a conclusion is supported by interview data showing that participants perceived an effect in response to both strategies.ConclusionThe findings illustrate how applied behavior analysis can be used to analyze contingencies related to the use of LVC and to design strategies for de-implementation. It also shows an effect of the targeted behaviors even though the quantitative results are inconclusive. The strategies used in this study could be further improved to target the contingencies better by structuring the feedback meetings better and including more precise feedback.

Project description:BackgroundIn a longitudinal cohort study, we investigated the interplay of harsh parenting and genetic variation across a set of functionally related dopamine genes, in association with children's externalizing behavior. This is one of the first studies to employ gene-based and gene-set approaches in tests of Gene by Environment (G × E) effects on complex behavior. This approach can offer an important alternative or complement to candidate gene and genome-wide environmental interaction (GWEI) studies in the search for genetic variation underlying individual differences in behavior.MethodsGenetic variants in 12 autosomal dopaminergic genes were available in an ethnically homogenous part of a population-based cohort. Harsh parenting was assessed with maternal (n = 1881) and paternal (n = 1710) reports at age 3. Externalizing behavior was assessed with the Child Behavior Checklist (CBCL) at age 5 (71 ± 3.7 months). We conducted gene-set analyses of the association between variation in dopaminergic genes and externalizing behavior, stratified for harsh parenting.ResultsThe association was statistically significant or approached significance for children without harsh parenting experiences, but was absent in the group with harsh parenting. Similarly, significant associations between single genes and externalizing behavior were only found in the group without harsh parenting. Effect sizes in the groups with and without harsh parenting did not differ significantly. Gene-environment interaction tests were conducted for individual genetic variants, resulting in two significant interaction effects (rs1497023 and rs4922132) after correction for multiple testing.ConclusionOur findings are suggestive of G × E interplay, with associations between dopamine genes and externalizing behavior present in children without harsh parenting, but not in children with harsh parenting experiences. Harsh parenting may overrule the role of genetic factors in externalizing behavior. Gene-based and gene-set analyses offer promising new alternatives to analyses focusing on single candidate polymorphisms when examining the interplay between genetic and environmental factors.

Project description:BackgroundThe consolidation of pathway databases, such as KEGG, Reactome and ConsensusPathDB, has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy.ResultsWe propose a method that uses set cover to reduce pathway redundancy, without merging pathways. The proposed approach considers three objectives: removal of pathway redundancy, controlling pathway size and coverage of the gene set. By applying set cover to the ConsensusPathDB dataset we were able to produce a reduced set of pathways, representing 100% of the genes in the original data set with 74% less redundancy, or 95% of the genes with 88% less redundancy. We also developed an algorithm to simplify enrichment data and applied it to a set of enriched osteoarthritis pathways, revealing that within the top ten pathways, five were redundant subsets of more enriched pathways. Applying set cover to the enrichment results removed these redundant pathways allowing more informative pathways to take their place.ConclusionOur method provides an alternative approach for handling pathway redundancy, while ensuring that the pathways are of homogeneous size and gene coverage is maximised. Pathways are not altered from their original form, allowing biological knowledge regarding the data set to be directly applicable. We demonstrate the ability of the algorithms to prioritise redundancy reduction, pathway size control or gene set coverage. The application of set cover to pathway enrichment results produces an optimised summary of the pathways that best represent the differentially regulated gene set.

Dataset Information

Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants.

Publications

Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets