Evidence map›Paper›PMID 28827797›Full record

ArticlePLoS genetics2017

Composite likelihood method for inferring local pedigrees.

Amy Ko, Rasmus Nielsen

Abstract read
In one paragraph

Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  4. CORRECTING MODEL MISSPECIFICATION IN RELATIONSHIP ESTIMATES.bioRxiv : the preprint server for biology · 2024
    Article
  5. Reconstructing parent genomes using siblings and other relatives.bioRxiv : the preprint server for biology · 2024
    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

2 authors.

Amy KoDepartment of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America.ORCID http://orcid.org/0000-0001-7993-5463
Rasmus NielsenDepartment of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pedigrees contain information about the genealogical relationships among individuals and are of fundamental importance in many areas of genetic studies. However, pedigrees are often unknown and must be inferred from genetic data. Despite the importance of pedigree inference, existing methods are limited to inferring only close relationships or analyzing a small number of individuals or loci. We present a simulated annealing method for estimating pedigrees in large samples of otherwise seemingly unrelated individuals using genome-wide SNP data. The method supports complex pedigree structures such as polygamous families, multi-generational families, and pedigrees in which many of the member individuals are missing. Computational speed is greatly enhanced by the use of a composite likelihood function which approximates the full likelihood. We validate our method on simulated data and show that it can infer distant relatives more accurately than existing methods. Furthermore, we illustrate the utility of the method on a sample of Greenlandic Inuit.

Indexed as

Data Interpretation, StatisticalGenotypeModels, GeneticPedigreeComputer SimulationGenome, HumanHaplotypesHumansLikelihood FunctionsPolymorphism, Single Nucleotide

Identifiers

PMID28827797
PMCPMC5578687

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.