Evidence map›Paper›PMID 25735005›Full record

ArticlePLoS computational biology2015

PRIMAL: Fast and accurate pedigree-based imputation from sequence data in a founder population.

Oren E Livne, Lide Han, Gorka Alkorta-Aranburu, William Wentworth-Sheilds, Mark Abney, Carole Ober, Dan L Nicolae

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
6.4field-weighted citation impact, top 3% 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

24 citing papers in PubMed, 41 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Oren E LivneDepartment of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America.
Lide HanDepartment of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America.
Gorka Alkorta-AranburuDepartment of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America.
William Wentworth-SheildsDepartment of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America.
Mark AbneyDepartment of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America.
Carole OberDepartment of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America.
Dan L NicolaeDepartment of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America; Departments of Medicine, and Statistics, The University of Chicago, Chicago, Illinois, United States of America.
University of Chicago · US

Funding

Re-Engineering Translational Research at the University of ChicagoUL1TR000430 · NCATS · UNIVERSITY OF CHICAGO · PI SOLWAY, JULIAN · 2012 to 2016
$20.2M
Sex-Specific Genetic Architecture of Asthma-Associated Quantitative TraitsR01HL085197 · NHLBI · UNIVERSITY OF CHICAGO · PI OBER, CAROLE · 2007 to 2015
$7.6M
Beagle: A Supercomputer for Computational Biology, Simulation, and Data AnalysisS10RR029030 · NCRR · UNIVERSITY OF CHICAGO · PI FOSTER, IAN · 2010 to 2010
$7.0M
Methods for Complex Trait Mapping in Large PedigreesR01HG002899 · NHGRI · UNIVERSITY OF CHICAGO · PI ABNEY, MARK A · 2004 to 2015
$3.5M
MOLECULAR GENETIC STUDIES OF FERTILITY IN THE HUTTERITESR01HD021244 · NICHD · UNIVERSITY OF CHICAGO · PI OBER, CAROLE · 1986 to 2011
$3.0M
NCATS NIH HHS UL1 TR000430NCRR NIH HHS S10 RR029030NHGRI NIH HHS R01 HG002899NHLBI NIH HHS R01 HL085197NICHD NIH HHS R01 HD021244NICHD NIH HHS R01 HD21244
6 · The paper itself

Abstract

Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL (PedigRee IMputation ALgorithm), a fast and accurate pedigree-based phasing and imputation algorithm for founder populations. PRIMAL incorporates both existing and original ideas, such as a novel indexing strategy of Identity-By-Descent (IBD) segments based on clique graphs. We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences. Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies. Using the IBD cliques we were also able to infer the parental origin of 83% of alleles, and genotypes of deceased recent ancestors for whom no genotype information was available. This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes, as well as parental origin effect of disease risk alleles in >1,000 individuals at minimal cost.

Indexed as

AlgorithmsFounder EffectModels, GeneticPedigreeSoftwareFemaleGenome, HumanGenomicsHumansMalePolymorphism, Single NucleotideSequence Analysis, DNASouth DakotaWhite People

Identifiers

PMID25735005
PMCPMC4348507
OpenAlexW2058662042

What OpenQuestion holds

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LicenceCC BY
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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.