Evidence map›Paper›PMID 25380502›Full record

ArticleEpidemiology (Cambridge, Mass.)2015

Variation in predictive ability of common genetic variants by established strata: the example of breast cancer and age.

Hugues Aschard, Noah Zaitlen, Sara Lindström, Peter Kraft

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Field cancerization in breast cancer.The Journal of pathology · 2022
    Review
  4. Gene-lifestyle interactions in the genomics of human complex traits.European journal of human genetics : EJHG · 2022
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
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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

4 authors.

Hugues AschardFrom the aProgram in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA; bDepartment of Medicine, University of California San Francisco, San Francisco, CA; and cDepartment of Biostatistics, Harvard School of Public Health, Boston, MA.
Noah Zaitlen
Sara Lindström
Peter Kraft

Funding

GRADUATE TRAINING IN BIOSTATISTICST32ES007142 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Brent Andrew Coull · 1985 to 2026
$11.9M
Epidemiologic StudiesU19CA148065 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI AHSAN, HABIBUL, BRUGGE, JOAN SIEFERT · 2010 to 2014
$10.6M
Evaluating the Clinical Validity of Novel Gene-Environment Risk ProfilesR21DK084529 · NIDDK · HARVARD SCHOOL OF PUBLIC HEALTH · PI KRAFT, PETER · 2009 to 2010
$409k
NCI NIH HHS CA148065NCI NIH HHS U19 CA148065NIDDK NIH HHS R21 DK084529NIDDK NIH HHS R21-DK084529NIEHS NIH HHS T32 ES007142
6 · The paper itself

Abstract

backgroundRecent studies of breast cancer and common genetic markers have failed to identify pervasive gene-gene and gene-environment interactions. Theoretical considerations also suggest that the contribution of modest interactions to risk discrimination in the general population is likely small. However, the clinical utility of common breast cancer risk markers may nonetheless differ across strata defined by known risk factors, such as age.

methodsWe examined the age-specific per-allele odds ratios of 15 common single nucleotide polymorphisms (SNPs) found to be associated with breast cancer in 1142 breast cancer cases and 1145 controls from the Nurses' Health Study. We calculated the age-specific discriminatory ability of risk models incorporating these SNPs. We then conducted simulation studies to explore how hypothetical underlying genetic models may fit the observed results.

resultsAlthough all individual SNP-by-age interactions were modest, we found a negative interaction effect between age and a genetic risk score defined by the sum of risk alleles (P = 0.04). We also observed a decrease in discriminatory ability, as measured by the area under the curve (AUC), of the SNPs with age (P = 0.04). Simulation studies revealed models where the AUC can differ by strata defined by a risk factor without the presence of interactions; however, our study suggests that the observed differences in AUC are explained by the age-specific effect of the SNPs.

conclusionThe identification of risk factors that alter the effect of multiple genetic variants can help to explain the genetic architecture of multifactorial diseases and identify subgroups of persons who may benefit from genetic screening.

Indexed as

Gene-Environment InteractionAgedAge FactorsArea Under CurveBreast NeoplasmsCase-Control StudiesFemaleGenetic Predisposition to DiseaseHumansMiddle AgedOdds RatioPolymorphism, Single NucleotideRisk Factors

Identifiers

PMID25380502
PMCPMC5082110

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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.