Evidence map›Paper›PMID 20701809›Full record

ArticleGenetics, selection, evolution : GSE2010

Reconstructing CNV genotypes using segregation analysis: combining pedigree information with CNV assay.

John M Henshall, Vicki A Whan, Belinda J Norris

Open access · goldAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2010. 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
0.4field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

John M HenshallCSIRO Livestock Industries, FD McMaster Laboratory Chiswick, Armidale, 2350, NSW, Australia. John.Henshall@csiro.au
Vicki A Whan
Belinda J Norris
University of Queensland · AUCommonwealth Scientific and Industrial Research Organisation · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRepeated blocks of genome sequence have been shown to be associated with genetic diversity and disease risk in humans, and with phenotypic diversity in model organisms and domestic animals. Reliable tests are desirable to determine whether individuals are carriers of copy number variants associated with disease risk in humans and livestock, or associated with economically important traits in livestock. In some cases, copy number variants affect the phenotype through a dosage effect but in other cases, allele combinations have non-additive effects. In the latter cases, it has been difficult to develop tests because assays typically return an estimate of the sum of the copy number counts on the maternally and paternally inherited chromosome segments, and this sum does not uniquely determine the allele configuration. In this study, we show that there is an old solution to this new problem: segregation analysis, which has been used for many years to infer alleles in pedigreed populations.

methodsSegregation analysis was used to estimate copy number alleles from assay data on simulated half-sib sheep populations. Copy number variation at the Agouti locus, known to be responsible for the recessive self-colour black phenotype, was used as a model for the simulation and an appropriate penetrance function was derived. The precision with which carriers and non-carriers of the undesirable single copy allele could be identified, was used to evaluate the method for various family sizes, assay strategies and assay accuracies.

resultsUsing relationship data and segregation analysis, the probabilities of carrying the copy number alleles responsible for black or white fleece were estimated with much greater precision than by analyzing assay results for animals individually. The proportion of lambs correctly identified as non-carriers of the undesirable allele increased from 7% when the lambs were analysed alone to 80% when the lambs were analysed in half-sib families.

conclusionsWhen a quantitative assay is used to estimate copy number alleles, segregation analysis of related individuals can greatly improve the precision of the estimates. Existing software for segregation analysis would require little if any change to accommodate the penetrance function for copy number assay data.

Indexed as

Genetic TechniquesPedigreeAllelesAnimalsChromosome SegregationComputer SimulationDNA Copy Number VariationsFemaleGene DosageGene FrequencyGenotypeHeterozygoteMalePenetranceSheep

Identifiers

PMID20701809
PMCPMC2928190
OpenAlexW2145964146

What OpenQuestion holds

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LicenceCC BY
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.