Review in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.4field-weighted citation impact, top 20% 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
1 citing paper in PubMed, 6 citations in OpenAlex.
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
6 authors at 1 institution in 2 countries.
Kaitlyn A LagattutaCenter for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Hannah L ParkCenter for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.ORCID 0009-0000-5969-2433
Laurie RumkerCenter for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Kazuyoshi IshigakiCenter for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-2881-0657
Aparna Nathan *Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. aparna_nathan@hms.harvard.edu.ORCID 0000-0002-5975-2851
Soumya Raychaudhuri *Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. soumya@broadinstitute.org.ORCID 0000-0002-1901-8265
Broad Institute · US
Funding
Role of fibroblastic stromal cells and notch signaling in tissue inflammation in RA and SLEP01AI148102 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI Michael B. Brenner, Nir Hacohen · 2021 to 2026
$17.3M
Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Medical Scientist Training ProgramT32GM144273 · NIGMS · HARVARD MEDICAL SCHOOL · PI David Shumway Jones, Jacqueline A. Lees · 2022 to 2026
$14.7M
Discovery and Functional Impact of Common and Rare Variants in RAR01AR063759 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI Soumya Raychaudhuri · 2013 to 2026
$5.4M
Integrative analysis of high dimensional tissue molecular data to define key biological systems in autoimmune diseases (SBC)UC2AR081023 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI Soumya Raychaudhuri · 2022 to 2026
$4.8M
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
Integrative modelling of single-cell data to elucidate the genetic architecture of complex diseaseR56HG013083 · NHGRI · DANA-FARBER CANCER INST · PI GUSEV, ALEXANDER, PRICE, ALKES L · 2023 to 2023
$400k
Characterizing the Genomic Basis of Immune-Mediated Resilience Against Tuberculosis in an Admixed Peruvian PopulationF30AI157385 · NIAID · HARVARD MEDICAL SCHOOL · PI RUMKER, LAURIE ANN · 2021 to 2024
$178k
HLA risk alleles cause Type 1 Diabetes by modifying the selection of T cell receptors that recognize pancreatic beta cell antigensF30AI176724 · NIAID · HARVARD MEDICAL SCHOOL · PI Kaitlyn Anne Lagattuta · 2024 to 2026
Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?
Indexed as
Autoimmune DiseasesT-LymphocytesAutoimmunityGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideQuantitative Trait LociReceptors, Antigen, T-CellReceptors, Antigen, T-Cell
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.
The genetic basis of autoimmunity seen through the lens of T cell functional traits. · full record | OpenQuestion