Evidence map›Paper›PMID 35668300›Full record

ArticleNature genetics2022

Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity.

Steven Gazal, Omer Weissbrod, Farhad Hormozdiari, Kushal K Dey, Joseph Nasser, Karthik A Jagadeesh, Daniel J Weiner, Huwenbo Shi, Charles P Fulco, Luke J O'Connor and 3 more

Open access · greenAbstract read
In one paragraph

Article in Nature genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
118citing papers in PubMed, 5 pooled it
32.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

118 citing papers in PubMed, 5 syntheses or guidelines pooled it, 188 citations in OpenAlex.

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  8. Shared genetic architecture between DTI-ALPS traits and neurodegenerative diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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58 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 1 country.

Steven GazalDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. gazal@usc.edu.ORCID http://orcid.org/0000-0003-4510-5730
Omer WeissbrodDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0001-9860-0626
Farhad HormozdiariDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Kushal K DeyDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0002-3520-2345
Joseph NasserBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Karthik A JagadeeshDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Daniel J WeinerBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Huwenbo ShiDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0001-9886-877X
Charles P FulcoBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Luke J O'ConnorBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-2730-9668
Bogdan PasaniucDepartments of Computational Medicine, Human Genetics, Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Jesse M EngreitzBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Alkes L PriceDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.ORCID http://orcid.org/0000-0002-2971-7975
Broad Institute · USUniversity of California, Los Angeles · US

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Statistical methods for studies of rare variantsR01MH101244 · NIMH · HARVARD MEDICAL SCHOOL · PI Benjamin Michael Neale, ALKES L PRICE · 2013 to 2026
$9.4M
Statistical methods to localize disease heritability and identify biological mechanismsR37MH107649 · NIMH · BROAD INSTITUTE, INC. · PI Benjamin Michael Neale · 2019 to 2026
$7.0M
Methods for Genome-wide Association Studies in Admixed PopulationsR01HG006399 · NHGRI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2011 to 2024
$6.3M
Joint genomic and statistical analyses of schizophrenia and bipolar to decipher genetic susceptibilityR01MH115676 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Roel A Ophoff, Bogdan Pasaniuc · 2018 to 2026
$5.9M
Predicting the impact of genetic variants, genes and pathways on human DiseaseU01HG012009 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI ALKES L PRICE, Soumya Raychaudhuri · 2021 to 2026
$4.2M
Functionally specialized components of disease heritability in ENCODE dataU01HG009379 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L, RAYCHAUDHURI, SOUMYA · 2017 to 2021
$2.5M
Network-based prediction and validation of causal schizophrenia genes and variantsR01MH109978 · NIMH · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI EGGAN, KEVIN C, KELLIS, MANOLIS · 2016 to 2020
$2.1M
From common to rare variant functional architectures of human diseasesR00HG010160 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI GAZAL, STEVEN · 2020 to 2022
$727k
New approaches for leveraging single-cell data to identify disease-critical genes and gene setsK99HG012203 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI DEY, KUSHAL KUMAR · 2022 to 2022
$109k
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHGRI NIH HHS K99 HG012203NHGRI NIH HHS R00 HG010160NHGRI NIH HHS R01 HG006399NHGRI NIH HHS T32 HG002295NHGRI NIH HHS U01 HG009379NHGRI NIH HHS U01 HG012009NIMH NIH HHS R01 MH101244NIMH NIH HHS R01 MH109978NIMH NIH HHS R01 MH115676NIMH NIH HHS R37 MH107649
6 · The paper itself

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.

Indexed as

Genome-Wide Association StudyPolymorphism, Single NucleotidePhenotype

Identifiers

PMID35668300
PMCPMC9894581
OpenAlexW4282915079

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.