Article in Frontiers in genetics, 2021. 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
0.3field-weighted citation impact, top 42% 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, 2 citations in OpenAlex.
Genes · 2022
Article
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
6 authors at 3 institutions in 1 country.
Manyan HuangDepartment of Epidemiology and Biostatistics, School of Public Health, Indiana University at Bloomington, Bloomington, IN, United States.
Chen LyuDepartment of Epidemiology and Biostatistics, School of Public Health, Indiana University at Bloomington, Bloomington, IN, United States.
Xin LiDepartment of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University - Purdue University Indianapolis, Indianapolis, IN, United States.
Abrar A QureshiDepartment of Dermatology, Alpert Medical School, Brown University, Providence, RI, United States.
Jiali HanDepartment of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University - Purdue University Indianapolis, Indianapolis, IN, United States.
Ming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Indiana University at Bloomington, Bloomington, IN, United States.
Indiana University Bloomington · USIndiana University – Purdue University Indianapolis · USBrown University · US
Funding
Statistical MethodsP01CA087969 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ROSNER, BERNARD A · 2000 to 2019
$77.8M
Life Course Cancer Epidemiology Cohort in WomenU01CA176726 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI ELIASSEN, A. HEATHER, WILLETT, WALTER C. · 2018 to 2025
$22.4M
Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
Cancer Epidemiology Cohort in Male Health ProfessionalsU01CA167552 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Lorelei Mucci, Walter C. Willett · 2017 to 2026
$17.0M
Premonopausal Hormone Levels and Risk of Breast CancerR01CA067262 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI HANKINSON, SUSAN E · 2002 to 2011
$11.9M
BIOCHEMICAL MARKERS IN THE NURSES'HEALTH STUDY COHORTR01CA049449 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI HANKINSON, SUSAN E · 1989 to 2008
$11.3M
Random Field Modelling of genetic and epigenetic association underlying congenital heart defects in the presence of disease heterogeneityK01HL140333 · NHLBI · TRUSTEES OF INDIANA UNIVERSITY · PI LI, MING · 2018 to 2021
$724k
Gene-by-Gene and Gene-by-Environment Interactions Underlying Congenital Heart DefectsR03HD092854 · NICHD · TRUSTEES OF INDIANA UNIVERSITY · PI LI, MING · 2018 to 2019
Cutaneous squamous cell carcinoma (cSCC) accounts for about 20% of all skin cancers, the most common type of malignancy in the United States. Genome-wide association studies (GWAS) have successfully identified multiple genetic variants associated with the risk of cSCC. Most of these studies were single-locus-based, testing genetic variants one-at-a-time. In this article, we performed gene-based association tests to evaluate the joint effect of multiple variants, especially rare variants, on the risk of cSCC by using a fast sequence kernel association test (fastSKAT). The study included 1,710 cSCC cases and 24,304 cancer-free controls from the Nurses' Health Study, the Nurses' Health Study II and the Health Professionals Follow-up Study. We used UCSC Genome Browser to define gene units as candidate loci, and further evaluated the association between all variants within each gene unit and disease outcome. Four genes
Indexed as
cutaneous squamous cell carcinomafast sequence kernel association testgeneralized genetic random fieldrare variantsregion-based association test
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.
Identifying Susceptibility Loci for Cutaneous Squamous Cell Carcinoma Using a Fast Sequence Kernel Association Test. · full record | OpenQuestion