Evidence map›Paper›PMID 34799441›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes.

Shiyang Ma, James Dalgleish, Justin Lee, Chen Wang, Linxi Liu, Richard Gill, Joseph D Buxbaum, Wendy K Chung, Hugues Aschard, Edwin K Silverman and 3 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Shiyang MaDepartment of Biostatistics, Columbia University, New York, NY 10032.ORCID 0000-0002-4423-5798
James DalgleishDepartment of Biostatistics, Columbia University, New York, NY 10032.ORCID 0000-0002-2053-8786
Justin LeeQuantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305.
Chen WangDepartment of Biostatistics, Columbia University, New York, NY 10032.ORCID 0000-0001-9127-0054
Linxi LiuDepartment of Statistics, University of Pittsburgh, Pittsburgh, PA 15260.
Richard GillDepartment of Human Genetics, Genentech, South San Francisco, CA 94080.
Joseph D BuxbaumDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029.
Wendy K ChungDepartment of Pediatrics, Columbia University, New York, NY 10032.
Hugues AschardDepartment of Computational Biology, Institut Pasteur, 75015 Paris, France.ORCID 0000-0002-7554-6783
Edwin K SilvermanChanning Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115.ORCID 0000-0002-3641-3822
Michael H ChoChanning Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115.ORCID 0000-0002-4907-1657
Zihuai HeQuantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305.
Iuliana Ionita-LazaDepartment of Biostatistics, Columbia University, New York, NY 10032; ii2135@columbia.edu.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Statistical and computational methods for integrative analysis of Alzheimer's Disease geneticsR01AG066206 · NIA · STANFORD UNIVERSITY · PI HE, ZIHUAI · 2019 to 2023
$3.5M
Novel Statistical methods for DNA Sequencing Data, and applications to Autism.R01MH095797 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA · 2012 to 2022
$3.1M
Multi-omics approaches for gene discovery in Alzheimer's Disease.RF1AG072272 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA, WEI, YING · 2021 to 2021
$1.6M
Develop quantile analysis tools for sequencing and eQTL studiesR01HG008980 · NHGRI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WEI, YING · 2016 to 2019
$1.6M
Integrative methods for the identification of causal variants in mental disorderR01MH106910 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA, XU, BIN · 2016 to 2018
$1.2M
Multi-omics approaches for gene discovery in Alzheimer's Disease.R01AG072272 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA, WEI, YING · 2024 to 2024
$552k
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NCATS NIH HHS UL1 TR001863NHGRI NIH HHS R01 HG008980NIA NIH HHS R01 AG066206NIA NIH HHS R01 AG072272NIA NIH HHS RF1 AG072272NIMH NIH HHS R01 MH095797NIMH NIH HHS R01 MH106910
6 · The paper itself

Abstract

Gene-based tests are valuable techniques for identifying genetic factors in complex traits. Here, we propose a gene-based testing framework that incorporates data on long-range chromatin interactions, several recent technical advances for region-based tests, and leverages the knockoff framework for synthetic genotype generation for improved gene discovery. Through simulations and applications to genome-wide association studies (GWAS) and whole-genome sequencing data for multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests in the literature, identifies genes that replicate in larger studies, and can provide a more narrow focus on the possible causal genes at a locus by reducing the confounding effect of linkage disequilibrium. Furthermore, our results show that incorporating genetic variation in distal regulatory elements tends to improve power over conventional tests. Results for UK Biobank and BioBank Japan traits are also available in a publicly accessible database that allows researchers to query gene-based results in an easy fashion.

Indexed as

ChromatinGenotypeGenetic TestingGenome-Wide Association StudyHumansJapanLinkage DisequilibriumLungModels, GeneticPhenotypeQuantitative Trait LociWhole Genome SequencingChromatinfine-mappinggene-based association testsGWAS and whole-genome sequencingknockoff statisticslong-range chromatin interactions

Identifiers

PMID34799441
PMCPMC8617518

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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 OpenQuestion graph.