Evidence map›Paper›PMID 26306250›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015

Leveraging Interaction between Genetic Variants and Mammographic Findings for Personalized Breast Cancer Diagnosis.

Jie Liu, Yirong Wu, Irene Ong, David Page, Peggy Peissig, Catherine McCarty, Adedayo A Onitilo, Elizabeth Burnside

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. 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
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Jie LiuUniversity of Wisconsin, Madison, WI, US.
Yirong WuUniversity of Wisconsin, Madison, WI, US.
Irene OngUniversity of Wisconsin, Madison, WI, US.
David PageUniversity of Wisconsin, Madison, WI, US.
Peggy PeissigMarshfield Clinic Research Foundation, Marshfield, WI, US.
Catherine McCartyEssentia Institute of Rural Health, Duluth, MN, US.
Adedayo A OnitiloMarshfield Clinic Research Foundation, Marshfield, WI, US ; Department of Hematology/Oncology, Marshfield Clinic Weston Center, Weston, WI, US.
Elizabeth BurnsideUniversity of Wisconsin, Madison, WI, US.

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI AMY TRENTHAM-DIETZ · 1985 to 2026
$142.6M
Institutional Clinical and Translational Science AwardUL1TR000427 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI DREZNER, MARC KENNETH · 2012 to 2016
$32.2M
TrainingU54AI117924 · NIAID · UNIVERSITY OF WISCONSIN-MADISON · PI CRAVEN, MARK W. · 2014 to 2018
$11.3M
Machine Learning for Identifying Adverse Drug EventsR01GM097618 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI CALDWELL, MICHAEL DEFOIX, HANSEN, RICHARD A. · 2011 to 2018
$3.8M
Genome-Wide Study of Cataract and Low HDL in the Personalized Medicine Research PU01HG004608 · NHGRI · MARSHFIELD CLINIC RESEARCH FOUNDATION · PI MCCARTY, CATHERINE ANNE · 2007 to 2010
$3.3M
Secure Sharing of Clinical History & Genetic Data: Empowering Predictive Pers. MeR01LM011028 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI PAGE, CHARLES DAVID · 2011 to 2014
$2.2M
Breast Cancer GWAS: Function and Environmental InteractionsR01ES017400 · NIEHS · UNIVERSITY OF WISCONSIN-MADISON · PI GOULD, MICHAEL N, NEWTON, MICHAEL A · 2009 to 2013
$2.1M
Informatics Techniques to Optimize Mammography for Aging PopulationsR01CA165229 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2012 to 2016
$1.7M
Machine Learning for Improved Mammography ScreeningR01CA127379 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2007 to 2010
$1.4M
Integrating Machine Learning and Physician Expertise for Breast Cancer DiagnosisR01LM010921 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2011 to 2014
$1.2M
NCATS NIH HHS UL1 TR000427NCI NIH HHS P30 CA014520NCI NIH HHS R01 CA127379NCI NIH HHS R01 CA165229NHGRI NIH HHS U01 HG004608NIAID NIH HHS U54 AI117924NIEHS NIH HHS R01 ES017400NIGMS NIH HHS R01 GM097618NLM NIH HHS R01 LM010921NLM NIH HHS R01 LM011028
6 · The paper itself

Abstract

Recent large-scale genome-wide association studies (GWAS) have identified a number of genetic variants associated with breast cancer which showed great potential for clinical translation, especially in breast cancer diagnosis via mammograms. However, the amount of interaction between these genetic variants and mammographic features that can be leveraged for personalized diagnosis remains unknown. Our study utilizes germline genetic variants and mammographic features that we collected in a breast cancer case-control study. By computing the conditional mutual information between the genetic variants and mammographic features given the breast cancer status, we identified six interaction pairs which elevate breast cancer risk and five interaction pairs which reduce breast cancer risk.

Identifiers

PMID26306250
PMCPMC4525263

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.