In one paragraphArticle in Genes, 2022. 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, top 94% of its field
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, 0 citations in OpenAlex.
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
6 authors at 2 institutions in 2 countries.
Meng-Kai LiDepartment of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0001-7845-8212 Yu-Xin YuanDepartment of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0002-5497-7787 Bin ZhuDepartment of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0001-9712-4023 Kai-Wen WangDepartment of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0002-6127-2697 Wing Kam FungDepartment of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China.ORCID 0000-0002-2508-4015 Ji-Yuan ZhouDepartment of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China.ORCID 0000-0003-0866-4402 Southern Medical University · CNUniversity of Hong Kong · HK
Funding
Substance Abuse & Behavioral Disinhibition: Integrating Genes & EnvironmentU01DA024417 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2007 to 2010
$10.8MTWIN STUDY OF FEMALE ALCOHOLISMS AND OTHER DISORDERSR01AA009367 · NIAAA · UNIVERSITY OF MINNESOTA TWIN CITIES · PI MCGUE, MATTHEW K. · 1992 to 2013
$9.0MTwin Family Study of Vulnerability to Substance AbuseR37DA005147 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2009 to 2018
$6.4MA Longitudinal Study of Adopted Youth and Their FamiliesR01MH066140 · NIMH · UNIVERSITY OF MINNESOTA TWIN CITIES · PI MCGUE, MATTHEW K. · 2003 to 2013
$5.8MTWIN STUDY OF ADHD, CD, AND SUBSTANCE ABUSER01DA013240 · NIDA · UNIVERSITY OF MINNESOTA TWIN CITIES · PI IACONO, WILLIAM G. · 2000 to 2010
$5.0MSIBLING MODELS OF ADOLESCENT ALCOHOL USE AND ABUSER01AA011886 · NIAAA · UNIVERSITY OF MINNESOTA TWIN CITIES · PI MCGUE, MATTHEW K. · 1998 to 2008
$2.5MNIAAA NIH HHS R01 AA009367NIAAA NIH HHS R01 AA011886NIDA NIH HHS R01 DA013240NIDA NIH HHS R37 DA005147NIDA NIH HHS U01 DA024417NIMH NIH HHS R01 MH066140
6 · The paper itselfAbstract
Skewed X chromosome inactivation (XCI-S) has been reported to be associated with some X-linked diseases, and currently several methods have been proposed to estimate the degree of the XCI-S (denoted as γ) for a single locus. However, no method has been available to estimate γ for genes. Therefore, in this paper, we first propose the point estimate and the penalized point estimate of γ for genes, and then derive its confidence intervals based on the Fieller’s and penalized Fieller’s methods, respectively. Further, we consider the constraint condition of γ∈[0, 2] and propose the Bayesian methods to obtain the point estimates and the credible intervals of γ, where a truncated normal prior and a uniform prior are respectively used (denoted as GBN and GBU). The simulation results show that the Bayesian methods can avoid the extreme point estimates (0 or 2), the empty sets, the noninformative intervals ([0, 2]) and the discontinuous intervals to occur. GBN performs best in both the point estimation and the interval estimation. Finally, we apply the proposed methods to the Minnesota Center for Twin and Family Research data for their practical use. In summary, in practical applications, we recommend using GBN to estimate γ of genes.
Indexed as
Genes, X-LinkedX Chromosome InactivationBayes TheoremComputer SimulationGenetic TechniquesBayesian methodFieller’s methodMinnesota Center for Twin and Family Research datapenalized Fieller’s methodskewed X chromosome inactivation
Identifiers
PMID35627212
PMCPMC9140558
OpenAlexW4229075356
What OpenQuestion holds
Textmetadata
LicenceCC BY
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