Evidence map›Paper›PMID 27980652›Full record

ArticleBMC proceedings2016

Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data.

Mohamad Saad, Alejandro Q Nato, Fiona L Grimson, Steven M Lewis, Lisa A Brown, Elizabeth M Blue, Timothy A Thornton, Elizabeth A Thompson, Ellen M Wijsman

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC proceedings, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 18% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 3 institutions in 1 country.

Mohamad SaadDepartment of Biostatistics, University of Washington, Seattle, WA USA ; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA.
Alejandro Q NatoDivision of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA.
Fiona L GrimsonDepartment of Statistics, University of Washington, Seattle, WA USA.
Steven M LewisDepartment of Statistics, University of Washington, Seattle, WA USA.
Lisa A BrownDepartment of Biostatistics, University of Washington, Seattle, WA USA.
Elizabeth M BlueDivision of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA.
Timothy A ThorntonDepartment of Biostatistics, University of Washington, Seattle, WA USA.
Elizabeth A ThompsonDepartment of Statistics, University of Washington, Seattle, WA USA.
Ellen M WijsmanDepartment of Biostatistics, University of Washington, Seattle, WA USA ; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA ; Department of Genome Sciences, University of Washington, Seattle, WA USA.
Seattle University · USUniversity of Washington · USUniversity of Washington Medical Center · US

Funding

THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI JAYADEY, SUMAN · 1985 to 2019
$57.2M
GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
Statistical Methods for X-Chromosome Genetic DataP01GM099568 · NIGMS · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2012 to 2016
$7.3M
Methods for the Genetic Epidemiology of Complex TraitsR37GM046255 · NIGMS · UNIVERSITY OF WASHINGTON · PI THOMPSON, ELIZABETH ALISON · 2008 to 2017
$4.2M
Next Generation Mendelian Genetics in Familial Alzheimer DiseaseR01AG039700 · NIA · UNIVERSITY OF WASHINGTON · PI BRKANAC, ZORAN · 2011 to 2015
$2.9M
Sequence-based Discovery of AD Risk & Protective AllelesU01AG049507 · NIA · UNIVERSITY OF WASHINGTON · PI WIJSMAN, ELLEN M · 2014 to 2017
$2.9M
THE GENETIC EPIDEMIOLOGY OF COMPLEX TRAITSR01GM046255 · NIGMS · UNIVERSITY OF WASHINGTON · PI THOMPSON, ELIZABETH ALISON · 1991 to 2007
$2.7M
Predoctoral Research Training in Statistical Genetics Administrative SupplementT32GM081062 · NIGMS · UNIVERSITY OF WASHINGTON · PI THORNTON, TIMOTHY ALVIN · 2007 to 2021
$2.4M
2/3 Sequencing Autism Spectrum Disorder Extended PedigreesR01MH094293 · NIMH · UNIVERSITY OF WASHINGTON · PI WIJSMAN, ELLEN M · 2012 to 2016
$1.1M
Identifying Alzheimer's disease genes using genomic and family dataR00AG040184 · NIA · UNIVERSITY OF WASHINGTON · PI BLUE, ELIZABETH ELOYCE · 2013 to 2016
$746k
Identifying Alzheimer's disease genes using genomic and family dataK99AG040184 · NIA · UNIVERSITY OF WASHINGTON · PI BLUE, ELIZABETH ELOYCE · 2011 to 2012
$180k
NIA NIH HHS K99 AG040184NIA NIH HHS P50 AG005136NIA NIH HHS R00 AG040184NIA NIH HHS R01 AG039700NIA NIH HHS U01 AG049507NIGMS NIH HHS P01 GM099568NIGMS NIH HHS R01 GM031575NIGMS NIH HHS R01 GM046255NIGMS NIH HHS R37 GM046255NIGMS NIH HHS T32 GM081062NIMH NIH HHS R01 MH094293
6 · The paper itself

Abstract

backgroundIn the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis.

methodsWe compared the performance of several family- and population-based imputation methods in large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the performance of a new IBD mapping approach that we propose, which combines IBD information from known pedigrees with information from unrelated individuals.

resultsDifferent combinations of the imputation methods have varied imputation accuracies. Moreover, we showed gains from the use of both known pedigrees and unrelated individuals with our IBD mapping approach over the use of known pedigrees only.

conclusionsOur results represent accuracies of different combinations of imputation methods that may be useful for data sets similar to the GAW19 pedigree data. Our IBD mapping approach, which uses both known pedigree and unrelated individuals, performed better than classical linkage analysis.

Identifiers

PMID27980652
PMCPMC5133511
OpenAlexW2550436568

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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.