ArticleFrontiers in genetics2015
Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling.
Article in Frontiers in genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Testing an optimally weighted combination of common and/or rare variants with multiple traits.PloS one · 2018Article
- Beyond Rare-Variant Association Testing: Pinpointing Rare Causal Variants in Case-Control Sequencing Study.Scientific reports · 2016Article
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Authors and funding
2 authors.
Funding
Abstract
Identifying the small number of rare causal variants contributing to disease has been a major focus of investigation in recent years, but represents a formidable statistical challenge due to the rare frequencies with which these variants are observed. In this commentary we draw attention to a formal statistical framework, namely hierarchical modeling, to combine functional genomic annotations with sequencing data with the objective of enhancing our ability to identify rare causal variants. Using simulations we show that in all configurations studied, the hierarchical modeling approach has superior discriminatory ability compared to a recently proposed aggregate measure of deleteriousness, the Combined Annotation-Dependent Depletion (CADD) score, supporting our premise that aggregate functional genomic measures can more accurately identify causal variants when used in conjunction with sequencing data through a hierarchical modeling approach.
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Registered trials
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