Rare variant association testing in the non-coding genome

Bocher, Ozvan; Génin, Emmanuelle

doi:10.1007/s00439-020-02190-y

Cited by 25 publications

(24 citation statements)

References 112 publications

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“…Even though whole genome sequencing data are now more often available on cases and controls, rare variant association tests (RVAT) usually remain restricted to the coding part of the genome. This is explained by the lack of tools to explore rare variant associations outside genes (11). Indeed, RVAT requires the definition of testing units that are easily defined through genes in coding regions and the selection in these regions of the most functionally-relevant variants.…”

Section: Discussionmentioning

confidence: 99%

“…This is for example the case in the RNF213 gene where an enrichment in rare variants located in the C-terminal region is found in Moyamoya cases (10). Defining testing units and qualifying variants is also much more challenging in the non-coding genome due to the lack of defined genomic elements and the higher difficulty to predict the functional impact of noncoding variants (11). It is yet a question of interest as several studies have shown the importance of rare non-coding variants in the development of complex diseases (12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

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Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Bocher

Ludwig

Marenne

et al. 2021

Preprint

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Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests.We propose a new strategy to perform RVAT on WGS data: “RAVA-FIRST” (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the GnomAD populations, which are referred to as “CADD regions”. (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 that is enriched for rare variants in early-onset patients and that was that was missed by standard sliding windows procedures.RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages.Author SummaryTechnological progresses have made possible whole genome sequencing at an unprecedented scale, opening up the possibility to explore the role of genetic variants of low frequency in common diseases. The challenge is now methodological and requires the development of novel methods and strategies to analyse sequencing data that are not limited to assessing the role of coding variants. With RAVA-FIRST, we propose a novel strategy to investigate the role of rare variants in the whole-genome that takes benefit from biological information. Especially, RAVA-FIRST relies on testing units that go beyond genes to gather rare variants in the association tests. In this work, we show that this new strategy presents several advantages compared to existing methods. RAVA-FIRST offers an easy and straightforward analysis of genome-wide rare variants, especially the intergenic ones which are frequently left behind, making it a promising tool to get a better understanding of the biology of complex diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Bocher

Ludwig

Marenne

et al. 2021

Preprint

Self Cite

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“…Determining the impact of rare non-coding variants on TG regulation is a major challenge in the eld of human genetics [23]. Here, we present the utility of our GRN based ENET models for weighting rare variants within kernel-based association tests to improve their power.…”

Section: Accounting For Chromatin Interactions Between Tfbs and Gene mentioning

confidence: 99%

Modeling Transcriptional Regulation Using Gene Regulatory Networks Based on Multi-Omics Data Sources

Patel

Bush²

2020

Preprint

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BackgroundTranscriptional regulation is complex, requiring multiple cis(local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. ResultsWe describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for SKAT(sequence kernel association test) analyses of gene expression. ConclusionsOur models generate refined effect estimates for individual transcription factors, allow characterization of their roles across the genome, and provide a framework for integrating multiple data types into a single model of transcriptional regulation.

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“…However, these methods have limited utility for defining analysis units in the noncoding genome 21 . For example, for gene-centric analysis, STAAR has been used with two noncoding genetic categories of regulatory regions (masks): using promoters and enhancers in GeneHancer 22 overlaid with Cap Analysis of Gene Expression (CAGE) sites 23,24 ; for non-gene-centric analysis, fixed-size sliding windows can be used to scan the genome.…”

Section: Introductionmentioning

confidence: 99%

A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

Zhou

et al. 2021

Preprint

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Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants’ (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.

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Rare variant association testing in the non-coding genome

Cited by 25 publications

References 112 publications

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Modeling Transcriptional Regulation Using Gene Regulatory Networks Based on Multi-Omics Data Sources

A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

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