In one paragraphArticle in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
18 authors.
Yijia Christiana LiuStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0009-0001-2078-651X Anna S E CuomoTranslational Genomics Program, Garvan Institute of Medical Research, Sydney, NSW, Australia.ORCID 0000-0001-5168-6979 Yi HuangProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-5734-650X Joaquin Perez-SchindlerProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0003-3141-8859 Bellis MinProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0009-0004-9268-969X Sanchari DattaProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-3613-5180 Nivedita NambrathProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Linfeng HuStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0009-0004-2898-9268 Kisung NamStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-7317-092X Masahiro KanaiStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0001-5165-4408 Angli XueTranslational Genomics Program, Garvan Institute of Medical Research, Sydney, NSW, Australia.ORCID 0000-0002-0285-0426 Ramnik J XavierInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-5630-5167 Mark J DalyStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-0949-8752 Daniel G MacArthurCentre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-5771-2290 Joseph E PowellTranslational Genomics Program, Garvan Institute of Medical Research, Sydney, NSW, Australia.ORCID 0000-0002-5070-4124 Melina ClaussnitzerProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0003-2450-736X Benjamin M NealeStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0003-1513-6077 Wei ZhouStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0001-7719-0859 Funding
ROLE OF DIETARY CONSTITUENTS ON GENE EXPRESSION IN INTESTINAL EPITHELIUMP30DK040561 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Elizabeth Austen Lawson, Takara Leah Stanley · 1994 to 2026
$31.6MBridging the gap between type 2 diabetes GWAS and therapeutic targetsUM1DK126185 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI CLAUSSNITZER, MELINA C, GLOYN, ANNA LOUISE · 2020 to 2024
$9.5MStatistical methods for studies of rare variantsR01MH101244 · NIMH · HARVARD MEDICAL SCHOOL · PI Benjamin Michael Neale, ALKES L PRICE · 2013 to 2026
$9.4MStatistical methods to localize disease heritability and identify biological mechanismsR37MH107649 · NIMH · BROAD INSTITUTE, INC. · PI Benjamin Michael Neale · 2019 to 2026
$7.0MComputational and Statistical Methods for Genetic Association Studies of Disease Course Over TimeR01HG014518 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI ZHOU, WEI · 2025 to 2025
$3.3MResources to interpret genetic signals and multi-tissue mechanisms for type 2 diabetes and related traitsRC2DK144819 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Melina C Claussnitzer, Anna Louise Gloyn · 2025 to 2026
$3.2MAn integrative approach to disease gene discovery combining genetic variation, gene expression, and epigenetics.R00HG012222 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI Wei Zhou · 2024 to 2026
$747kNHGRI NIH HHS R00 HG012222NHGRI NIH HHS R01 HG014518NIDDK NIH HHS P30 DK040561NIDDK NIH HHS RC2 DK144819NIDDK NIH HHS UM1 DK126185NIMH NIH HHS R01 MH101244NIMH NIH HHS R37 MH107649
6 · The paper itselfAbstract
Many disease-associated variants are thought to act through gene regulation, yet conventional eQTL mapping explains only a fraction of GWAS loci, potentially because regulatory effects vary across cellular states and environments. We present CASTIE, a scalable Poisson mixed-model framework that directly models sparse single-cell read counts and enables genome-wide testing of genotype-by-context interactions without pre-screening for static effects. Applying CASTIE to 1.2 million peripheral blood mononuclear cells from 982 OneK1K donors identified 3,155 context-dependent eQTL associations, including 2,022 eGenes without detectable static effects. These associations yielded 374 colocalizations across 94 traits, representing 270 unique loci, of which 197 were not recovered using the corresponding static eQTLs. The colocalizations linked trait associations to specific cellular contexts and genes, including
Identifiers
PMID42780159
PMCPMC13596484
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