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

Abstract

BackgroundThe 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® Axiom® 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.ResultsTo 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.ConclusionsWhichever 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.

Genome-wide association study for carcase traits in spent hens at 72 weeks old

“…Although we identified several candidate genes associated with carcase traits, in consideration of the quantitative trait being controlled by polygenes and many QTLs or genes has been identified related to carcase traits of broiler chicken in previous reports, it is necessary to compile identified candidate genes to provide guidance for breeding selection. Moreover, SNP markers in the current result and previous papers are helpful to design accurate and cheap SNP chip (Herry et al 2018), which provide more power and information to genomic selection strategies promoting breeding value adequate and reliable. In addition, they might be carried out as markers in increasing egg quality in layer hens, particularly for egg weight and eggshell quality, because of the same candidate genes obtained for these traits.…”

Interest of using imputation for genomic evaluation in layer chicken

“…All the results concerning imputation accuracy were presented in the study by Herry et al. (2018) , but the evolution of the mean correlations between true and imputed genotypes for the 2 different methodologies are recalled in Figure 3 .…”

“…Several low density SNP chips were previously designed in silico by selecting a subset of SNP ( Herry et al., 2018 ) from the HD SNP chip.…”

“…For each low-density SNP chip designed, imputation accuracy of the selection candidates was previously assessed as the mean correlation between true and imputed genotypes ( Herry et al., 2018 ). Correlations were calculated one SNP at a time for all the candidates, as suggested in Pearson's method.…”

“…The idea is to select a subset of markers from the HD SNP chip and to impute the genotypes at missing markers. Three main methods to select the marker panel have been developed: (1) selection of a subset of SNP chosen at regular intervals along each chromosome taking into account or not the minor allele frequency ( MAF ) of the selected SNP ( Habier et al., 2009 , Weigel et al., 2009 , Zhang et al., 2011 , Cleveland and Hickey, 2013 , Wang et al., 2013 , Herry et al., 2018 ), (2) selection of a subset of SNP having high effects on different traits of interest ( Weigel et al., 2009 , Zhang et al., 2011 ), or (3) selection of a subset of SNP based on linkage disequilibrium ( LD ) between markers ( Herry et al., 2018 ). The latter method was studied because of the particularities of the Gallus gallus genome ( International Chicken Genome Sequencing Consortium, 2004 ) and the particular structure of the avian LD ( Megens et al., 2009 , Qanbari et al., 2010 , Hérault et al., 2018 ).…”