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Gene_prioritization

Summary Table

NAME CATEGORY CITATION YEAR
DEPICT MISC Pers TH, Karjalainen JM, Chan Y, Westra HJ, ...&, Franke L. (2015) Biological interpretation of genome-wide association studies using predicted gene functions Nat. Commun., 6 () 5890. doi:10.1038/ncomms6890. PMID 25597830 2015
Open Targets MISC Koscielny G, An P, Carvalho-Silva D, Cham JA, ...&, Dunham I. (2017) Open Targets: a platform for therapeutic target identification and validation Nucleic Acids Res., 45 (D1) D985-D994. doi:10.1093/nar/gkw1055. PMID 27899665 2017
PoPs MISC Weeks EM, Ulirsch JC, Cheng NY, Trippe BL, ...&, Finucane HK. (2023) Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases Nat. Genet., 55 (8) 1267-1276. doi:10.1038/s41588-023-01443-6. PMID 37443254 2023
cS2G MISC Gazal S, Weissbrod O, Hormozdiari F, Dey KK, ...&, Price AL. (2022) Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity Nat. Genet., 54 (6) 827-836. doi:10.1038/s41588-022-01087-y. PMID 35668300 2022
Review-Lappalainen Review Lappalainen T, MacArthur DG. (2021) From variant to function in human disease genetics Science, 373 (6562) 1464-1468. doi:10.1126/science.abi8207. PMID 34554789 2021

MISC

DEPICT

  • NAME : DEPICT
  • SHORT NAME : DEPICT
  • FULL NAME : Data-driven Expression Prioritized Integration for Complex Traits
  • DESCRIPTION : an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.
  • URL : [https://github.com/perslab/depict](https://github.com/perslab/depict)
  • KEYWORDS : co-regulation of gene expression
  • TITLE : Biological interpretation of genome-wide association studies using predicted gene functions
  • DOI : 10.1038/ncomms6890
  • ABSTRACT : The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.
  • CITATION : Pers TH, Karjalainen JM, Chan Y, Westra HJ, ...&, Franke L. (2015) Biological interpretation of genome-wide association studies using predicted gene functions Nat. Commun., 6 () 5890. doi:10.1038/ncomms6890. PMID 25597830
  • JOURNAL_INFO : Nature communications ; Nat. Commun. ; 2015 ; 6 ; ; 5890
  • PUBMED_LINK : 25597830

Open Targets

  • NAME : Open Targets
  • DESCRIPTION : Open Targets is an innovative, large-scale, multi-year, public-private partnership that uses human genetics and genomics data for systematic drug target identification and prioritisation.
  • URL : https://www.opentargets.org/
  • TITLE : Open Targets: a platform for therapeutic target identification and validation
  • DOI : 10.1093/nar/gkw1055
  • ABSTRACT : We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.
  • CITATION : Koscielny G, An P, Carvalho-Silva D, Cham JA, ...&, Dunham I. (2017) Open Targets: a platform for therapeutic target identification and validation Nucleic Acids Res., 45 (D1) D985-D994. doi:10.1093/nar/gkw1055. PMID 27899665
  • JOURNAL_INFO : Nucleic acids research ; Nucleic Acids Res. ; 2017 ; 45 ; D1 ; D985-D994
  • PUBMED_LINK : 27899665

PoPs

  • NAME : PoPs
  • SHORT NAME : PoPs
  • FULL NAME : gene-level Polygenic Priority Score (PoPS)
  • DESCRIPTION : PoPS is a gene prioritization method that leverages genome-wide signal from GWAS summary statistics and incorporates data from an extensive set of public bulk and single-cell expression datasets, curated biological pathways, and predicted protein-protein interactions.
  • URL : https://github.com/FinucaneLab/pops
  • TITLE : Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases
  • DOI : 10.1038/s41588-023-01443-6
  • ABSTRACT : Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.
  • CITATION : Weeks EM, Ulirsch JC, Cheng NY, Trippe BL, ...&, Finucane HK. (2023) Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases Nat. Genet., 55 (8) 1267-1276. doi:10.1038/s41588-023-01443-6. PMID 37443254
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2023 ; 55 ; 8 ; 1267-1276
  • PUBMED_LINK : 37443254

cS2G

  • NAME : cS2G
  • SHORT NAME : cS2G
  • FULL NAME : optimal combined S2G strategy
  • DESCRIPTION : heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk
  • URL : [https://alkesgroup.broadinstitute.org/cS2G/code](https://alkesgroup.broadinstitute.org/cS2G/code)
  • TITLE : Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity
  • DOI : 10.1038/s41588-022-01087-y
  • ABSTRACT : Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.
  • CITATION : Gazal S, Weissbrod O, Hormozdiari F, Dey KK, ...&, Price AL. (2022) Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity Nat. Genet., 54 (6) 827-836. doi:10.1038/s41588-022-01087-y. PMID 35668300
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2022 ; 54 ; 6 ; 827-836
  • PUBMED_LINK : 35668300

Review

Review-Lappalainen

  • NAME : Review-Lappalainen
  • TITLE : From variant to function in human disease genetics
  • DOI : 10.1126/science.abi8207
  • ABSTRACT : Over the next decade, the primary challenge in human genetics will be to understand the biological mechanisms by which genetic variants influence phenotypes, including disease risk. Although the scale of this challenge is daunting, better methods for functional variant interpretation will have transformative consequences for disease diagnosis, risk prediction, and the development of new therapies. An array of new methods for characterizing variant impact at scale, using patient tissue samples as well as in vitro models, are already being applied to dissect variant mechanisms across a range of human cell types and environments. These approaches are also increasingly being deployed in clinical settings. We discuss the rationale, approaches, applications, and future outlook for characterizing the molecular and cellular effects of genetic variants.
  • CITATION : Lappalainen T, MacArthur DG. (2021) From variant to function in human disease genetics Science, 373 (6562) 1464-1468. doi:10.1126/science.abi8207. PMID 34554789
  • JOURNAL_INFO : Science (New York, N.Y.) ; Science ; 2021 ; 373 ; 6562 ; 1464-1468
  • PUBMED_LINK : 34554789