Design of low density SNP chips for genotype imputation in layer chicken

Autor: Sophie Allais, Pascale Le Roy, Amandine Varenne, David Picard Druet, Thierry Burlot, Florian Herry, Frédéric Hérault
Přispěvatelé: Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Novogen, ANR-10-GENOM_BTV-015 UtOpIGe, AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
0301 basic medicine
Linkage disequilibrium
laying hen
fréquence allélique
Gene Frequency
Statistics
déséquilibre de liaison
Genetics (clinical)
Oligonucleotide Array Sequence Analysis
Genetics
Animal biology
education.field_of_study
degré de parenté
[SDV.BA]Life Sciences [q-bio]/Animal biology
évaluation génomique
MAF
SNP genotyping
Genetic gain
allele frequency
Research Article
Degree of kinship
polymorphisme nucléotidique simple (SNP)
lcsh:QH426-470
Genotype
caractérisation génomique
Population
Layer chickens
Biology
Polymorphism
Single Nucleotide
Chromosomes
Imputation accuracy
03 medical and health sciences
Biologie animale
Animals
SNP
education
Genotyping
précision de sélection
SNP density
poule pondeuse
Minor allele frequency
lcsh:Genetics
030104 developmental biology
génotypage
Low density chip
Chickens
Imputation (genetics)
Zdroj: BMC Genetics (19), . (2018)
BMC Genetics
BMC Genetics, BioMed Central, 2018, 19, ⟨10.1186/s12863-018-0695-7⟩
BMC Genetics, Vol 19, Iss 1, Pp 1-14 (2018)
ISSN: 1471-2156
Popis: Background[br/] The main goal of selection is to achieve genetic gain for a population by choosing the best breeders among a set of selection candidates. Since 2013, the use of a high density genotyping chip (600K Affymetrix (R) Axiom (R) HD genotyping array) for chicken has enabled the implementation of genomic selection in layer and broiler breeding, but the genotyping costs remain high for a routine use on a large number of selection candidates. It has thus been deemed interesting to develop a low density genotyping chip that would induce lower costs. In this perspective, various simulation studies have been conducted to find the best way to select a set of SNPs for low density genotyping of two laying hen lines.[br/] Results[br/] To design low density SNP chips, two methodologies, based on equidistance (EQ) or on linkage disequilibrium (LD) were compared. Imputation accuracy was assessed as the mean correlation between true and imputed genotypes. The results showed correlations more sensitive to false imputation of SNPs having low Minor Allele Frequency (MAF) when the EQ methodology was used. An increase in imputation accuracy was obtained when SNP density was increased, either through an increase in the number of selected windows on a chromosome or through the rise of the LD threshold. Moreover, the results varied depending on the type of chromosome (macro or micro-chromosome). The LD methodology enabled to optimize the number of SNPs, by reducing the SNP density on macro-chromosomes and by increasing it on micro-chromosomes. Imputation accuracy also increased when the size of the reference population was increased. Conversely, imputation accuracy decreased when the degree of kinship between reference and candidate populations was reduced. Finally, adding selection candidates' dams in the reference population, in addition to their sire, enabled to get better imputation results.[br/] Conclusions[br/] Whichever the SNP chip, the methodology, and the scenario studied, highly accurate imputations were obtained, with mean correlations higher than 0.83. The key point to achieve good imputation results is to take into account chicken lines' LD when designing a low density SNP chip, and to include the candidates' direct parents in the reference population.
Databáze: OpenAIRE
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