ArticleNature communications2020
Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Common and rare variant analyses implicate late-infancy cerebellar development and immune genes in ADHD.Journal of neurodevelopmental disorders · 2025Article
- Distinct pathway-based effects of blood pressure and body mass index on cardiovascular traits: comparison of novel Mendelian randomization approaches.Genome medicine · 2025Article
- Thyroid Stimulating Hormone Levels Are Related to Fatty Liver Indices Independently of Free Thyroxine: A Cross-Sectional Study.Journal of clinical medicine · 2025Article
- Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis.Communications biology · 2024Article
- Functional analysis ofbioRxiv : the preprint server for biology · 2024Article
- Whole transcriptome-based skin virome profiling in typical epidermodysplasia verruciformis reveals α-, β-, and γ-HPV infections.JCI insight · 2023Article
- Article
- SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease.Cell genomics · 2022Article
- Decoding disease: from genomes to networks to phenotypes.Nature reviews. Genetics · 2021Review
- The Evolving Story in the Genetic Analysis for Heart Failure.Frontiers in cardiovascular medicine · 2021Review
- Cross-species regulatory sequence activity prediction.PLoS computational biology · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Despite considerable progress on pathogenicity scores prioritizing variants for Mendelian disease, little is known about the utility of these scores for common disease. Here, we assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease and improve upon existing scores. We first apply stratified linkage disequilibrium (LD) score regression to evaluate published pathogenicity scores across 41 common diseases and complex traits (average N = 320K). Several of the resulting annotations are informative for common disease, even after conditioning on a broad set of functional annotations. We then improve upon published pathogenicity scores by developing AnnotBoost, a machine learning framework to impute and denoise pathogenicity scores using a broad set of functional annotations. AnnotBoost substantially increases the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying that Mendelian and common disease variants share similar properties. The boosted scores also produce improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores may improve fine-mapping and candidate gene discovery for common disease.
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