Evidence map›Paper›PMID 22989048›Full record

ArticleAnnals of human genetics2012

Shared genomic segment analysis: the power to find rare disease variants.

Stacey Knight, Ryan P Abo, Haley J Abel, Deborah W Neklason, Therese M Tuohy, Randall W Burt, Alun Thomas, Nicola J Camp

Abstract read
In one paragraph

Article in Annals of human genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    Article
  7. Review
  8. Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs.IEEE transactions on visualization and computer graphics · 2019
    Article
  9. Reparameterization of PAM50 Expression Identifies Novel Breast Tumor Dimensions and Leads to Discovery of a Genome-Wide Significant Breast Cancer Locus atCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2018
    Article
  10. Article
  11. Article
  12. Article
  13. 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

8 authors.

Stacey KnightDivision of Genetic Epidemiology, University of Utah School of Medicine, Salt Lake City, UT 84108, USA. stacey.knight@hsc.utah.edu
Ryan P Abo
Haley J Abel
Deborah W Neklason
Therese M Tuohy
Randall W Burt
Alun Thomas
Nicola J Camp

Funding

TCF/LEF function during intestinal maintenance and colon tumorigenesisP01CA073992 · NCI · UNIVERSITY OF UTAH · PI NEKLASON, DEBORAH WOOD · 1998 to 2014
$29.7M
UNIVERSITY OF UTAH MEDICAL INFORMATICS TRAININGT15LM007124 · NLM · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Karen Louise Eilbeck · 1997 to 2026
$22.0M
GENETIC EPIDEMIOLOGY OF CHRONIC LYMPHOCYTIC LEUKEMIAR01CA134674 · NCI · UNIVERSITY OF UTAH · PI CAMP, NICOLA J. · 2010 to 2016
$3.8M
INHERITANCE OF DISCRETE COLORECTAL ADENOMATOUS POLYPSR01CA040641 · NCI · UNIVERSITY OF UTAH · PI BURT, RANDALL WALTER · 1985 to 2007
$2.5M
Rare Variant Associations With Severe Obesity in Utah PedigreesR01DK093151 · NIDDK · UNIVERSITY OF UTAH · PI HASSTEDT, SANDRA J · 2011 to 2014
$2.0M
Rare disease susceptibility alleles in children with Crohn diseaseR01DK091374 · NIDDK · UNIVERSITY OF UTAH · PI GUTHERY, STEPHEN L · 2011 to 2014
$1.3M
Graphical models for linkage disequilibrium in genetic mappingR01GM081417 · NIGMS · UNIVERSITY OF UTAH · PI THOMAS, ALUN · 2007 to 2010
$1.1M
Shared Genomic Segment Analysis for Localizing Multiple Myeloma GenesR21CA152336 · NCI · UNIVERSITY OF UTAH · PI CAMP, NICOLA J. · 2010 to 2011
$353k
NCI NIH HHS N01 PC035141NCI NIH HHS P01 CA073992NCI NIH HHS R01 CA040641NCI NIH HHS R01 CA134674NCI NIH HHS R21 CA152336NIDDK NIH HHS R01 DK091374NIDDK NIH HHS R01 DK093151NIGMS NIH HHS R01 GM081417NLM NIH HHS T15 LM007124
6 · The paper itself

Abstract

Shared genomic segment (SGS) analysis uses dense single nucleotide polymorphism genotyping in high-risk (HR) pedigrees to identify regions of sharing between cases. Here, we illustrate the power of SGS to identify dominant rare risk variants. Using simulated pedigrees, we consider 12 disease models based on disease prevalence, minor allele frequency and penetrance to represent disease loci that explain 0.2-99.8% of total disease risk. Pedigrees were required to contain ≥ 15 meioses between all cases and to be HR based on significant excess of disease (P < 0.001 or P < 0.00001). Across these scenarios, the power for a single pedigree ranged widely. Nonetheless, fewer than 10 pedigrees were sufficient for excellent power in the majority of models. Power increased with the risk attributable to the disease locus, penetrance and the excess of disease in the pedigree. Sharing allowing for one sporadic case was uniformly more powerful than sharing using all cases. Furthermore, an SGS analysis using a large attenuated familial adenomatous polyposis pedigree identified a 1.96 Mb region containing the known causal APC gene with genome-wide significance. SGS is a powerful method for detecting rare variants and a valuable complement to genome-wide association studies and linkage analysis.

Indexed as

GenomicsPolymorphism, Single NucleotideAdenomatous Polyposis ColiComputer SimulationGenes, APCGenes, DominantGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansModels, GeneticPedigreeRare Diseases

Identifiers

PMID22989048
PMCPMC3879794

What OpenQuestion holds

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Registered trials

None linked

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.