Evidence map›Paper›PMID 22499699›Full record

ArticleStatistical applications in genetics and molecular biology2012

Improving pedigree-based linkage analysis by estimating coancestry among families.

Chris Glazner, Elizabeth Alison Thompson

Abstract read
In one paragraph

Article in Statistical applications in genetics and molecular biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

6 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Joint inference of identity by descent along multiple chromosomes from population samples.Journal of computational biology : a journal of computational molecular cell biology · 2014
    Article
  3. Efficient identification of equivalences in dynamic graphs and pedigree structures.Journal of computational biology : a journal of computational molecular cell biology · 2013
    Article
  4. Review
  5. Article
  6. 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

2 authors at 1 institution in 1 country.

Chris GlaznerUniversity of Washington.
Elizabeth Alison Thompson
University of Washington · US

Funding

Methods for the Genetic Epidemiology of Complex TraitsR37GM046255 · NIGMS · UNIVERSITY OF WASHINGTON · PI THOMPSON, ELIZABETH ALISON · 2008 to 2017
$4.2M
Predoctoral Research Training in Statistical Genetics Administrative SupplementT32GM081062 · NIGMS · UNIVERSITY OF WASHINGTON · PI THORNTON, TIMOTHY ALVIN · 2007 to 2021
$2.4M
NIGMS NIH HHS R37 GM046255NIGMS NIH HHS T32 GM081062
6 · The paper itself

Abstract

We present a method for improving the power of linkage analysis by detecting chromosome segments shared identical by descent (IBD) by individuals not known to be related. Existing Markov chain Monte Carlo methods sample descent patterns on pedigrees conditional on observed marker data. These patterns can be stored as IBD graphs, which express shared ancestry only, rather than specific family relationships. A model for IBD between unrelated individuals allows the estimation of coancestry between individuals in different pedigrees. IBD graphs on separate pedigrees can then be combined using these estimates. We report results from analyses of three sets of simulated marker data on two different pedigrees. We show that when families share a gene for a trait due to shared ancestry on the order of tens of generations, our method can detect a linkage signal when independent analyses of the families do not.

Indexed as

Genetic LinkageModels, GeneticPedigreeAllelesComputer SimulationFamilyGenotypeHumansLod Score

Identifiers

PMID22499699
PMCPMC3368982
OpenAlexW2071490866

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.