ArticleAmerican journal of human genetics2022
A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.
Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer.Nature genetics · 2022Pooled it
- A multi-ancestry genetic reference for the Quebec population.Nature communications · 2026Article
- FAVOR 2.0: A reengineered functional annotation of variants online resource for interpreting genomic variation.Nucleic acids research · 2026Article
- Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact.iScience · 2025Article
- Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci.PLoS computational biology · 2025Article
- Variance-components tests for genetic association with multiple interval-censored outcomes.Statistics in medicine · 2024Article
- SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.Human molecular genetics · 2024Article
- Identification of Pancreatic Cancer Germline Risk Variants With Effects That Are Modified by Smoking.JCO precision oncology · 2024Article
- sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.Frontiers in immunology · 2024Article
- Genetic risk variants in New Yorkers of Puerto Rican and Dominican Republic heritage with Parkinson's disease.NPJ Parkinson's disease · 2023Article
- An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping.Molecular and cellular neurosciences · 2023Article
- An allelic-series rare-variant association test for candidate-gene discovery.American journal of human genetics · 2023Article
- Multitrait genome-wide analyses identify new susceptibility loci and candidate drugs to primary sclerosing cholangitis.Nature communications · 2023Article
- TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions.Bioinformatics (Oxford, England) · 2023Article
- FAVOR: functional annotation of variants online resource and annotator for variation across the human genome.Nucleic acids research · 2023Article
- Scalable approaches for functional analyses of whole-genome sequencing non-coding variants.Human molecular genetics · 2022Review
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Authors and funding
12 authors.
Funding
Abstract
Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.
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