SynthesisNature genetics2026
Empirically determined baseline masking strategies and other considerations for gene-level burden tests.
Trang Nguyen, Ryan Koesterer, Poeya Haydarlou, Peter Dornbos, Satoshi Yoshiji, Alex Llamas, Dongkeun Jang, Patrick Smadbeck, Annie Moriondo, Quy Hoang and 6 more
Abstract readSystematic Review
In one paragraphSynthesis in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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What it found
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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 registryThe trial behind it
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3 · Its place in the literatureWho cites it
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4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
16 authors.
Ryan KoestererPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Poeya HaydarlouDepartment of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Cardiomyopathy and Arrhythmia, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands.ORCID http://orcid.org/0009-0000-3938-3377 Peter DornbosPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Alex LlamasPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Dongkeun JangPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Patrick SmadbeckPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Annie MoriondoPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Oliver RuebenackerPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Connie R BezzinaDepartment of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Cardiomyopathy and Arrhythmia, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands.ORCID http://orcid.org/0000-0002-0633-3514 Sean J JurgensDepartment of Clinical and Experimental Cardiology, Heart Center, Amsterdam Cardiovascular Sciences, Cardiomyopathy and Arrhythmia, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands.ORCID http://orcid.org/0000-0002-1605-9782 Noël P BurttPrograms in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Funding
In vivo and in vitro rare coding variant analyses to identify modulations of the adipocyte differentiation pathway that affect T2D riskR01DK125490 · NIDDK · BOSTON CHILDREN'S HOSPITAL · PI Jason Flannick · 2020 to 2026
$3.9MNIDDK NIH HHS R01 DK125490U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) U24 HG011453-01U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) UM1 DK105554-06
6 · The paper itselfAbstract
Rare-variant association studies typically perform gene-level tests in which coding variants are filtered (or 'masked') and aggregated based on functional annotation and allele frequency. Through a systematic literature review, we cataloged 664 masks used across 234 studies and found that masking strategies (that is, sets of masks) rarely repeat across studies and are rarely justified. To quantify their impact on association results, we applied all previously employed strategies to 54 traits within 189,947 UK Biobank exomes. Here we find that the number of significant associations greatly depends on the masking strategy (ranging from 58 to 2,523 associations), which is a key reason for the modest overlap (<30%) of associations between separate published analyses of this dataset. We empirically determine masking strategies with high discovery power for low-frequency and rare variant gene-level associations across numerous datasets and traits, and we use these to explore the impact of other factors on burden test results. These findings offer a baseline strategy in burden tests to increase study power and replicability, addressing one source of inconsistency in previous studies.
Indexed as
Genetic Association StudiesGenome-Wide Association StudyGene FrequencyGenetic VariationHumansPolymorphism, Single Nucleotide
Identifiers
PMID42104092
PMCPMC13263148
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