A pathway-centric approach to rare variant association analysis

Richardson, Tom G; Timpson, Nicholas J.; Campbell, Colin; Gaunt, Tom R.

doi:10.1038/ejhg.2016.113

Cited by 14 publications

(11 citation statements)

References 52 publications

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“…Despite the significant shift towards lower p values when considering the 45 genes jointly, none of these were individually significant when accounting for multiple testing ( ) (Supplementary Data 7 ). Near identical results were obtained when classifying variants using the Combined Annotation Dependent Depletion (CADD) tool 30 instead of SIFT/PolyPhen-2 (Supplementary Data 7 ). We explored three approaches to increase the power of the burden test.…”

Section: Resultsmentioning

confidence: 52%

IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes

et al. 2018

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GWAS have identified >200 risk loci for Inflammatory Bowel Disease (IBD). The majority of disease associations are known to be driven by regulatory variants. To identify the putative causative genes that are perturbed by these variants, we generate a large transcriptome data set (nine disease-relevant cell types) and identify 23,650 cis-eQTL. We show that these are determined by ∼9720 regulatory modules, of which ∼3000 operate in multiple tissues and ∼970 on multiple genes. We identify regulatory modules that drive the disease association for 63 of the 200 risk loci, and show that these are enriched in multigenic modules. Based on these analyses, we resequence 45 of the corresponding 100 candidate genes in 6600 Crohn disease (CD) cases and 5500 controls, and show with burden tests that they include likely causative genes. Our analyses indicate that ≥10-fold larger sample sizes will be required to demonstrate the causality of individual genes using this approach.

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Section: Resultsmentioning

confidence: 52%

IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes

et al. 2018

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“…Recent research has shown that the cumulative effect of rare deleterious variation in relation to disease can in some cases be revealed by simply aggregating SNVs into genes and then pathways when running SKAT [26]. To demonstrate the added value of the network propagation step over this approach, we conducted set-based association testing for rare, deleterious SNVs with case/control status in ADNI.…”

Section: Resultsmentioning

confidence: 99%

Network propagation of rare mutations in Alzheimer’s disease reveals tissue-specific hub genes and communities

Scelsi

Napolioni

Greicius

et al. 2019

Preprint

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BackgroundState-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes.ResultsWe applied NETPAGE to sporadic, late-onset Alzheimer’s disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through gene networks. The result of network propagation is a set of smoothed gene scores used to predict disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed mutation profile acted as a robust predictor of case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we showed tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms and gene sets with known connections to AD.ConclusionsThe presented framework enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

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“…Instead of focusing on crude association studies, there are other innovative approaches that could provide additional information while studying rare variants. Some of the ways of exploring the data, include using other biological information, including gene expression (as reviewed by Verheijen and Sleegers [98]), methylation and biological pathways [99][100][101], in combination with genetic association data, to boost the statistical power of the analyses. To boost the statistical power of genetic association analyses, Ho et al proposed a novel weightadjustment approach to combine gene expression, methylation, transcriptional regulation and protein abundance information into rare variant analysis.…”

Section: Alternate Methodsmentioning

confidence: 99%

Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease

2019

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Purpose of Review It is well established that sporadic Alzheimer's disease (AD) is polygenic with common and rare genetic variation alongside environmental factors contributing to disease. Here, we review our current understanding of the genetic architecture of disease, paying specific attention to rare susceptibility variants, and explore some of the limitations in rare variant detection and analysis. Recent Findings Rare variation has been shown to robustly associate with disease. These include potentially damaging and loss of function mutations that are easily modelled in silico, in vitro and in vivo, and represent potentially druggable targets. A number of risk genes, including TREM2, SORL1 and ABCA7 show multiple independent associations suggesting that they may influence disease via multiple mechanisms. With transcriptional regulation, inflammatory response and modification of protein production suggested to be of primary importance. Summary We are at the beginning of our journey of rare variant detection in AD. Whole exome sequencing has been the predominant technology of choice. While fruitful, this has introduced a number of challenges with regard to data integration. Ultimately the future of disease-associated rare variant identification lies in whole genome sequencing projects that will allow the testing of the full range of genomic variation.

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A pathway-centric approach to rare variant association analysis

Cited by 14 publications

References 52 publications

IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes

IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes

Network propagation of rare mutations in Alzheimer’s disease reveals tissue-specific hub genes and communities

Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease

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