Evidence map›Paper›PMID 28761973›Full record

ArticleCalcified tissue international2017

Enhanced Identification of Potential Pleiotropic Genetic Variants for Bone Mineral Density and Breast Cancer.

Cheng Peng, Hui-Ling Lou, Feng Liu, Jie Shen, Xu Lin, Chun-Ping Zeng, Ji-Rong Long, Kuan-Jui Su, Lan Zhang, Jonathan Greenbaum and 3 more

Open access · greenAbstract read
In one paragraph

Article in Calcified tissue international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

9 citing papers in PubMed, 15 citations in OpenAlex.

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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

13 authors at 8 institutions in 2 countries.

Cheng PengDepartment of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, People's Republic of China.
Hui-Ling LouDepartment of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, People's Republic of China.
Feng LiuDepartment of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, People's Republic of China.
Jie ShenDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China.
Xu LinDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China.
Chun-Ping ZengDepartment of Endocrinology and Metabolism, Affiliated Nanhai Hospital of Southern Medical University, Guangzhou, People's Republic of China.
Ji-Rong LongDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
Kuan-Jui SuDepartment of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA.
Lan ZhangDepartment of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA.
Jonathan GreenbaumDepartment of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA.
Wei-Feng DengHunan University of Medicine, Huaihua, 418000, People's Republic of China.
Yu-Mei LiSchool of Mathematics and Computational Science, Huaihua University, Huaihua, 418008, Hunan, People's Republic of China.
Hong-Wen DengDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China. hdeng2@tulane.edu.
Tulane University · USGuangzhou First People's Hospital · CNThird Affiliated Hospital of Southern Medical University · CNGuangzhou Medical University · CNHuaihua University · CNHunan University · CNSouthern Medical University · CNVanderbilt University · US

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Tony Y. Hu · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI YU-PING WANG · 2017 to 2026
$24.3M
Epigenomewide DNA Methylation Study for Osteoporosis RiskR01AR059781 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2012 to 2016
$3.1M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
Identification of Proteins Important for Male OsteoporosisR01AR057049 · NIAMS · UNIVERSITY OF MISSOURI KANSAS CITY · PI DENG, HONG-WEN · 2009 to 2013
$2.4M
Integration of brain imaging with genomic and epigenomic dataR01MH104680 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI CALHOUN, VINCE D, DENG, HONG-WEN · 2014 to 2017
$2.1M
Integration of fMRI imaging, genomics, network and biological knowledgeR01MH107354 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2015 to 2018
$1.9M
Integration of multiscale genomic data for comprehensive analysis of complex diseR01GM109068 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2014 to 2018
$1.6M
Research Training in Gene-Environment Interaction in ChinaD43TW009107 · FIC · TULANE UNIVERSITY OF LOUISIANA · PI GU, DONGFENG, HE, JIANG · 2012 to 2016
$1.1M
FIC NIH HHS D43 TW009107NIAMS NIH HHS R01 AR057049NIAMS NIH HHS R01 AR059781NIAMS NIH HHS R01 AR069055NIA NIH HHS U19 AG055373NIGMS NIH HHS P20 GM109036NIGMS NIH HHS R01 GM109068NIMH NIH HHS R01 MH104680NIMH NIH HHS R01 MH107354
6 · The paper itself

Abstract

Epidemiological and clinical evidences have shown that bone mineral density (BMD) has a close relationship with breast cancer (BC). They might potentially have a shared genetic basis. By incorporating information about these pleiotropic effects, we may be able to explore more of the traits' total heritability. We applied a recently developed conditional false discovery rate (cFDR) method to the summary statistics from two independent GWASs to identify the potential pleiotropic genetic variants for BMD and BC. By jointly analyzing two large independent GWASs of BMD and BC, we found strong pleiotropic enrichment between them and identified 102 single-nucleotide polymorphisms (SNPs) in BMD and 192 SNPs in BC with cFDR < 0.05, including 230 SNPs that might have been overlooked by the standard GWAS analysis. cFDR-significant genes were enriched in GO terms and KEGG pathways which were crucial to bone metabolism and/or BC pathology (adjP < 0.05). Some cFDR-significant genes were partially validated in the gene expressional validation assay. Strong interactions were found between proteins produced by cFDR-significant genes in the context of biological mechanism of bone metabolism and/or BC etiology. Totally, we identified 7 pleiotropic SNPs that were associated with both BMD and BC (conjunction cFDR < 0.05); CCDC170, ESR1, RANKL, CPED1, and MEOX1 might play important roles in the pleiotropy of BMD and BC. Our study highlighted the significant pleiotropy between BMD and BC and shed novel insight into trait-specific as well as the potentially shared genetic architecture for both BMD and BC.

Indexed as

Genetic PleiotropyPolymorphism, Single NucleotideBone DensityBreast NeoplasmsFemaleGenome-Wide Association StudyHumansBone mineral density (BMD)Breast cancerGenome wide association study (GWAS)Pleiotropy

Identifiers

PMID28761973
PMCPMC5796546
OpenAlexW2741656241

What OpenQuestion holds

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Registered trials

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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.