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Low-pass sequencing data have been proposed as an alternative to single nucleotide polymorphism (SNP) chips in genome-wide association studies (GWAS) of several species. However, it has not been used in layer chickens yet. This study aims at comparing the GWAS results of White Leghorn chickens using low-pass sequencing data (1×) and 54 k SNP chip data. Ten commercially relevant egg quality traits including albumen height, shell strength, shell colour, egg weight and yolk weight collected from up to 1,420 White Leghorn chickens were analysed. The results showed that the genomic heritability estimates based on low-pass sequencing data were higher than those based on SNP chip data. Although two GWAS analyses showed similar overall landscape for most traits, low-pass sequencing captured some significant SNPs that were not on the SNP chip. In GWAS analysis using 54 k SNP chip data, after including more individuals (up to 5,700), additional significant SNPs not detected by low-pass sequencing data were found. In conclusion, GWAS using low-pass sequencing data showed similar results to those with SNP chip data and may require much larger sample sizes to show measurable advantages.
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Estudio de Asociación del Genoma Completo , Polimorfismo de Nucleótido Simple , Animales , Pollos/genética , Estudio de Asociación del Genoma Completo/veterinaria , Análisis de Secuencia por Matrices de Oligonucleótidos/veterinaria , FenotipoRESUMEN
BACKGROUND: As cage-free production systems become increasingly popular, behavioral traits such as nesting behavior and temperament have become more important. The objective of this study was to estimate heritabilities for frequency of perching and proportion of floor eggs and their genetic correlation in two Rhode Island Red lines. RESULTS: The percent of hens observed perching tended to increase and the proportion of eggs laid on the floor tended to decrease as the test progressed. This suggests the ability of hens to learn to use nests and perches. Under the bivariate repeatability model, estimates of heritability in the two lines were 0.22 ± 0.04 and 0.07 ± 0.05 for the percent of hens perching, and 0.52 ± 0.05 and 0.45 ± 0.05 for the percent of floor eggs. Estimates of the genetic correlation between perching and floor eggs were - 0.26 ± 0.14 and - 0.19 ± 0.27 for the two lines, suggesting that, genetically, there was some tendency for hens that better use perches to also use nests; but the phenotypic correlation was close to zero. Random regression models indicated the presence of a genetic component for learning ability. CONCLUSIONS: In conclusion, perching and tendency to lay floor eggs were shown to be a learned behavior, which stresses the importance of proper management and training of pullets and young hens. A significant genetic component was found, confirming the possibility to improve nesting behavior for cage-free systems through genetic selection.
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Pollos/genética , Modelos Genéticos , Oviposición/genética , Animales , Conducta Animal , Pollos/fisiología , Femenino , Polimorfismo Genético , Carácter Cuantitativo HeredableRESUMEN
Avian influenza (AI) is a devastating poultry disease that currently can be controlled only by liquidation of affected flocks. In spite of typically very high mortality rates, a group of survivors was identified and genotyped on a 600K single nucleotide polymorphism (SNP) chip to identify genetic differences between survivors, and age- and genetics-matched controls from unaffected flocks. In a previous analysis of this dataset, a heritable component was identified and several regions that are associated with outcome of the infection were localized but none with a large effect. For complex traits that are determined by many genes, genomic prediction models using all SNPs across the genome simultaneously are expected to optimally exploit genomic information. In this study, we evaluated the diagnostic value of genomic estimated breeding values for predicting AI infection outcome within and across two highly pathogenic avian influenza viral strains and two genetic lines of layer chickens using receiver operating curves. We show that genomic prediction based on the 600K SNP chip has the potential to predict disease outcome especially within the same strain of virus (area under receiver operating curve above 0.7), but did not predict well across genetic varieties (area under receiver operating curve of 0.43).
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Resistencia a la Enfermedad , Estudio de Asociación del Genoma Completo/veterinaria , Gripe Aviar/genética , Polimorfismo de Nucleótido Simple , Enfermedades de las Aves de Corral/virología , Animales , Cruzamiento , Pollos , Enfermedades de las Aves de Corral/genética , Pronóstico , Carácter Cuantitativo Heredable , Curva ROC , Análisis de Secuencia de ADN/veterinariaRESUMEN
BACKGROUND: Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line. METHODS: Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated. RESULTS: On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions. CONCLUSIONS: The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.
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Pollos/genética , Genómica/métodos , Linaje , Polimorfismo de Nucleótido Simple , Sitios de Carácter Cuantitativo , Animales , Teorema de Bayes , Cruzamiento , Huevos/normas , Femenino , Genoma , Genotipo , Modelos Animales , Modelos Genéticos , Fenotipo , Selección GenéticaRESUMEN
BACKGROUND: Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken. METHODS: In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production. RESULTS: Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line. CONCLUSIONS: The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.
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Pollos/genética , Selección Genética , Selección Artificial/genética , Animales , Pollos/fisiología , Modelos Genéticos , Linaje , Fenotipo , Sitios de Carácter CuantitativoRESUMEN
Selection of livestock for disease resistance is challenging due to the difficulty in obtaining reliable phenotypes. Antibodies are immunological molecules that provide direct and indirect defenses against infection and link the activities of both the innate and adaptive compartments of the immune system. As a result, antibodies have been used as a trait in selection for immune defense. The goal of this study was to identify genomic regions associated with natural and induced antibodies in chickens using low-pass sequencing. Enzyme-linked immunosorbent assays were used to quantify innate (natural) antibodies binding KLH, OVA, and PHA and induced (adaptive) antibodies binding IBD, IBV, NDV, and REO. We collected plasma from four White Leghorn (WL), two White Plymouth Rock (WPR), and two Rhode Island Red (RIR) lines. Samples numbers ranged between 198 and 785 per breed. GWAS was performed within breed on data pre-adjusted for Line-Hatch-Sex effects using GCTA. A threshold of p = 10-6 was used to select genes for downstream annotation and enrichment analysis with SNPEff and Panther. Significant enrichment was found for the defense/immunity protein, immunoglobulin receptor superfamily, and the antimicrobial response protein in RIR; and the immunoglobulin receptor superfamily, defense/immunity protein, and protein modifying enzyme in WL. However, none were present in WPR, but some of the selected SNP were annotated in immune pathways. This study provides new insights regarding the genetics of the antibody response in layer chickens.
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BACKGROUND: A fresh, good quality egg has a firm and gelatinous albumen that anchors the yolk and restricts growth of microbiological pathogens. As the egg ages, the gel-like structure collapses, resulting in thin and runny albumen. Occasionally thin albumen is found in a fresh egg, giving the impression of a low quality product. A mapping population consisting of 1599 F2 hens from a cross between White Rock and Rhode Island Red lines was set up, to identify loci controlling albumen quality. The phenotype for albumen quality was evaluated by albumen height and in Haugh units (HU) measured on three consecutive eggs from each F2 hen at the age of 40 weeks. For the fine-mapping analysis, albumen height and HU were used simultaneously to eliminate contribution of the egg size to the phenotype. RESULTS: Linkage analysis in a small population of seven half-sib families (668 F2) with 162 microsatellite markers spread across 27 chromosomes revealed two genome-wide significant regions with additive effects for HU on chromosomes 7 and Z. In addition, two putative genome-wide quantitative trait loci (QTL) regions were identified on chromosomes 4 and 26. The QTL effects ranged from 2 to 4% of the phenotypic variance. The genome-wide significant QTL regions on chromosomes 7 and Z were selected for fine-mapping in the full set composed of 16 half-sib families. In addition, their existence was confirmed by an association analysis in an independent commercial Hy-Line pure line. CONCLUSIONS: We identified four chicken genomic regions that affect albumen quality. Our results also suggest that genes that affect albumen quality act both directly and indirectly through several different mechanisms. For instance, the QTL regions on both fine-mapped chromosomes 7 and Z overlapped with a previously reported QTL for eggshell quality, indicating that eggshell membranes may play a role in albumen quality.
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Pollos/genética , Clara de Huevo , Ovalbúmina , Sitios de Carácter Cuantitativo , Animales , Mapeo Cromosómico , Cromosomas , Interpretación Estadística de Datos , Femenino , Ligamiento Genético , Marcadores Genéticos , Genoma , Genotipo , Repeticiones de Microsatélite , Oviposición , FenotipoRESUMEN
A genome-wide association study (GWAS) using Bayesian variable selection was performed to determine genomic regions associated with mortality due to Marek's disease virus (MDV) infection in layers. Mortality (%) under experimental disease challenge (500 plaque-forming units of a very virulent plus MDV strain) was recorded for progeny groups (average 15.5 birds; range 3 to 30) of 253 genotyped sires from four generations of a brown-egg layer line. An additional generation of 43 sires with progeny data was used to validate results. Sires were genotyped with a 42K Illumina single-nucleotide polymorphism (SNP) chip. Methods BayesB (pi = 0.995) and BayesCpi, with or without weighting residuals by the size of progeny groups were applied. The proportion of genetic variance contributed by SNPs within each 1-megabase (Mb) genomic region was quantified. Average mortality was 33% but differed significantly between generations. Genetic markers explained about 11% of phenotypic variation in mortality. Correlations between genomic estimated breeding values and percentage of progeny mortality for the validation generation (sons of individuals in training) were 0.12, 0.17, 0.02, and 0.16 for BayesB, weighted BayesB, BayesCpi, and weighted BayesCpi, respectively, when using the whole genome, and 0.03, 0.20, -0.06, and 0.14, when using only SNP from the 10, 1-Mb regions, explaining the largest proportion of genetic variance according to each method. Results suggest that regions on chromosomes 2, 3, 4, 9, 15, 18, and 21 are associated with Marek's disease resistance and can be used for selection and that accounting for the size of progeny groups has a large impact on correct localization of such genomic regions.
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Pollos , Genoma Viral , Estudio de Asociación del Genoma Completo/métodos , Herpesvirus Gallináceo 2/patogenicidad , Enfermedad de Marek/genética , Enfermedad de Marek/mortalidad , Animales , Teorema de Bayes , Cruzamiento , Femenino , Marcadores Genéticos , Estudio de Asociación del Genoma Completo/veterinaria , Genotipo , Polimorfismo de Nucleótido Simple , Enfermedades de las Aves de Corral/genética , Enfermedades de las Aves de Corral/mortalidad , Sitios de Carácter CuantitativoRESUMEN
Efficiency of production is increasingly important with the current escalation of feed costs and demands to minimize the environmental footprint. The objectives of this study were 1) to estimate heritabilities for daily feed consumption and residual feed intake and their genetic correlations with production and egg-quality traits; 2) to evaluate accuracies of estimated breeding values from pedigree- and marker-based prediction models; and 3) to localize genomic regions associated with feed efficiency in a brown egg layer line. Individual feed intake data collected over 2-wk trial periods were available for approximately 6,000 birds from 8 generations. Genetic parameters were estimated with a multitrait animal model; methods BayesB and BayesCπ were used to estimate marker effects and find genomic regions associated with feed efficiency. Using pedigree information, feed efficiency was found to be moderately heritable (h(2) = 0.46 for daily feed consumption and 0.47 for residual feed intake). Hens that consumed more feed and had greater residual feed intake (lower efficiency) had a genetic tendency to lay slightly more eggs with greater yolk weights and albumen heights. Regions on chromosomes 1, 2, 4, 7, 13, and Z were found to be associated with feed intake and efficiency. The accuracy from genomic prediction was higher and more persistent (better maintained across generations) than that from pedigree-based prediction. These results indicate that genomic selection can be used to improve feed efficiency in layers.
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Fenómenos Fisiológicos Nutricionales de los Animales , Pollos/fisiología , Huevos , Conducta Alimentaria , Linaje , Crianza de Animales Domésticos , Animales , Pollos/genética , Huevos/análisis , Femenino , Marcadores Genéticos , Componentes Genómicos , Modelos GenéticosRESUMEN
The cuticle is an invisible barrier that protects the internal egg contents from microorganisms entering through gas exchange pores. Eggs which have a good cuticle are least likely to be penetrated by microorganisms and improved cuticle cover should reduce vertical transmission of microorganisms and improve biosecurity. The aim was to carry out a genome wide association study for cuticle deposition in 3 independent populations of laying hens using tartrazine and lissamine green staining. Eggs from â¼8,000 hens represented 2 White Leghorn and 1 Rhode Island Red breed. Estimates of heritability using pedigree or genomic relationship matrices were in the 0.2 to 0.3 range. The results were breed specific. Across the populations, genomic regions on chromosomes 1, 2, 4, 5, and 8 were identified as significantly associated with cuticle deposition. No single loci had a large effect. A comparison was made with genes differentially expressed in the shell gland when cuticle deposition was manipulated, however none were obvious candidates for cuticle deposition. The results support the polygenic nature of the trait and the information will help in the future to understand the genetic variance and what might control cuticle deposition and the microbiological safety of the egg.
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Pollos , Estudio de Asociación del Genoma Completo , Animales , Femenino , Estudio de Asociación del Genoma Completo/veterinaria , Pollos/genética , Pollos/microbiología , Óvulo , Genoma , Fenotipo , Cáscara de Huevo/microbiología , HuevosRESUMEN
BACKGROUND: Occurrence of blood and meat inclusions is an internal egg quality defect. Mass candling reveals most of the spots, but because brown eggshell hampers selection in brown chicken lines it has not been possible to eliminate the defect by selection. Estimated frequency of blood and meat inclusions in brown layers is about 18% whereas it is 0.5% in white egg layers. Several factors are known to increase the incidence of this fault: genetic background, low level of vitamin A and/or D, stress or infections, for instance. To study the genetic background of the defect, a mapping population of 1599 F2 hens from a cross of White Rock and Rhode Island Red lines was set up. RESULTS: Our histopathological analyses show that blood spots consist of mainly erythrocytes and that meat spots are accumulations of necrotic material. Linkage analysis of 27 chromosomes with 162 microsatellite markers revealed one significant quantitative trait locus (QTL) affecting blood spot and meat spot frequency. We sequenced a fragment of a candidate gene within the region, ZO-2, coding for a tight junction protein. Nine polymorphisms were detected and two of them were included in fine-mapping and association analysis. Fine-mapping defined the QTL result. To further verify the QTL, association analyses were carried out in two independent commercial breeding lines with the marker MCW241 and surrounding SNPs. Association was found mainly in a 0.8 Mb-wide chromosomal area on GGAZ. CONCLUSIONS: There was good agreement between the location of the QTL region on chromosome Z and the association results in the commercial breeds analyzed. Variations found in tight junction protein ZO-2 and microRNA gga-mir-1556 may predispose egg layers to blood and meat spot defects. This paper describes the first results of detailed QTL analyses of the blood and meat spots trait(s) in chickens.
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Pollos/genética , Mapeo Cromosómico , Huevos/normas , Sitios de Carácter Cuantitativo , Animales , Secuencia de Bases , Sangre , Cromosomas/genética , Cruzamientos Genéticos , Femenino , Ligamiento Genético , Genotipo , Carne , MicroARNs/genética , Repeticiones de Microsatélite , Datos de Secuencia Molecular , Conformación de Ácido Nucleico , Fenotipo , Polimorfismo de Nucleótido SimpleRESUMEN
BACKGROUND: Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line. METHODS: The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV. RESULTS: Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.
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Pollos/genética , Huevos , Polimorfismo de Nucleótido Simple , Animales , Cruzamiento , Pollos/fisiología , Femenino , LinajeRESUMEN
BACKGROUND: The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information. METHODS: The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability. RESULTS: Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.
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Pollos/genética , Genómica/métodos , Linaje , Animales , Cruzamiento , Femenino , Marcadores Genéticos , Genoma , Genotipo , Masculino , Sitios de Carácter Cuantitativo , Selección GenéticaRESUMEN
Avian Leukosis Virus subgroup E (ALVE) integrations are endogenous retroviral elements found in the chicken genome. The presence of ALVE has been reported to have negative impacts on multiple traits, including egg production and body weight. The recent development of rapid, inexpensive and specific ALVE detection methods has facilitated their characterization in elite commercial egg production lines across multiple generations. The presence of 20 ALVE was examined in 8 elite lines, from 3 different breeds. Seventeen of these ALVE (85%) were informative and found to be segregating in at least one of the lines. To test for an association between specific ALVE inserts and traits, a large genotype by phenotype study was undertaken. Genotypes were obtained for 500 to 1500 males per line, and the phenotypes used were sire-daughter averages. Phenotype data were analyzed by line with a linear model that included the effects of generation, ALVE genotype and their interaction. If genotype effect was significant, the number of ALVE copies was fitted as a regression to estimate additive ALVE gene substitution effect. Significant associations between the presence of specific ALVE inserts and 18 commercially relevant performance and egg quality traits, including egg production, egg weight and albumen height, were observed. When an ALVE was segregating in more than one line, these associations did not always have the same impact (negative, positive or none) in each line. It is hypothesized that the presence of ALVE in the chicken genome may influence production traits by 3 mechanisms: viral protein production may modulate the immune system and impact overall production performance (virus effect); insertional mutagenesis caused by viral integration may cause direct gene alterations or affect gene regulation (gene effect); or the integration site may be within or adjacent to a quantitative trait region which impacts a performance trait (linkage disequilibrium, marker effect).
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Virus de la Leucosis Aviar , Leucosis Aviar , Animales , Leucosis Aviar/genética , Virus de la Leucosis Aviar/genética , Pollos/genética , Genoma , Genotipo , Masculino , FenotipoRESUMEN
Egg production on a flock level can be summarized into several phases determined by biology of individual birds: rapid increase in production reflecting achieving sexual maturity, peak production related to maximum laying potential, followed by gradual decrease in the rate of lay as the birds age. In 1989 Yang et al. proposed a mathematical model (modified compartmental model) to describe this process. In this study a biphasic modified compartmental model was proposed for modeling, classifying, and predicting egg production in single cycle and molted flocks. Goodness-of-fit was high for both single cycle (average R2 = 0.99) and molted flocks (average R2 = 0.97), suggesting that the model could be used for benchmarking molted flocks. The difference in R2 between the biphasic model and the model used by Yang et al in 1989 can be used to differentiate between single cycle and molted flocks. The biphasic model was shown to predict future records well up to 8 wk in advance, but as with any regression model, caution is recommended when predicting records outside of the observed age range.
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Crianza de Animales Domésticos/métodos , Pollos/fisiología , Óvulo/fisiología , Reproducción , Animales , Femenino , Modelos Biológicos , MudaRESUMEN
BACKGROUND: The genome sequence and a high-density SNP map are now available for the chicken and can be used to identify genetic markers for use in marker-assisted selection (MAS). Effective MAS requires high linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), and sustained marker-QTL LD over generations. This study used data from a 3,000 SNP panel to assess the level and consistency of LD between single nucleotide polymorphisms (SNPs) over consecutive years in two egg-layer chicken lines, and analyzed one line by two methods (SNP-wise association and genome-wise Bayesian analysis) to identify markers associated with egg-quality and egg-production phenotypes. RESULTS: The LD between markers pairs was high at short distances (r2 > 0.2 at < 2 Mb) and remained high after one generation (correlations of 0.80 to 0.92 at < 5 Mb) in both lines. Single- and 3-SNP regression analyses using a mixed model with SNP as fixed effect resulted in 159 and 76 significant tests (P < 0.01), respectively, across 12 traits. A Bayesian analysis called BayesB, that fits all SNPs simultaneously as random effects and uses model averaging procedures, identified 33 SNPs that were included in the model >20% of the time (phi > 0.2) and an additional ten 3-SNP windows that had a sum of phi greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses. CONCLUSION: High LD correlations between markers at short distances across two generations indicate that such markers will retain high LD with linked QTL and be effective for MAS. The different association analysis methods used provided consistent results. Multiple single SNPs and 3-SNP windows were significantly associated with egg-related traits, providing genomic positions of QTL that can be useful for both MAS and to identify causal mutations.
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Cruzamiento , Pollos/genética , Genética de Población , Desequilibrio de Ligamiento , Sitios de Carácter Cuantitativo , Animales , Teorema de Bayes , Huevos , Marcadores Genéticos , Genoma , Genotipo , Modelos Genéticos , Fenotipo , Polimorfismo de Nucleótido Simple , Análisis de RegresiónRESUMEN
The ability to produce viable progeny is a complex trait, involving both male and female components. In poultry, mating ratios are usually 1 male to 6 to 12 females. Consequently, the impact of male reproductive failure is much greater than that for a female. In this study, the genetic determination of male reproductive performance, by natural mating and artificial insemination (AI), was evaluated. Semen quality was studied in 1,575 pre-selected (using a selection index of multiple egg production and quality traits) White Leghorn males of a single pure line from multiple generations. A subset of individuals with satisfactory semen quality (based on sperm count and motility) were further tested for subsequent fertility and hatchability. Genetic parameters for fertility (FER), hatch of fertile (HOF), hatch of set (HOS), sperm motility (SM), sperm count (SC), and fertility using AI (FER-AI) were estimated using single- and multi-trait animal models, with generation as fixed effect. Selected birds were genotyped using the 600K Affymetrix SNP chip. Genomic data were analyzed with the BayesB method. FER, HOS, and HOF were highly correlated, both genetically (0.82 to 0.99) and phenotypically (0.28 to 0.99), but genetic correlations with semen quality traits were not strong (0.05 to 0.43) and phenotypic correlations varied between generations (-0.13 to 0.14). Birds used for fertility and hatchability tests were pre-selected based on SM and SC, which could contribute to the lack of strong correlations between these traits (due to truncation of the distribution). Based on pedigree information, low to moderate heritabilities were estimated for reproductive traits (0.08 to 0.21). Markers explained a low proportion of phenotypic variance (0.04 to 0.15), probably due to stringent selection of genotyped individuals and the limited training set size. No genes with large effects were identified. Genomic estimated breeding values were more accurate than pedigree-based estimates but only for HOF and FERT-AI. Despite low estimates of accuracy in validation, genetic trends were positive for all analyzed traits. In conclusion, continued long-term selection can result in genetic improvement of reproductive performance of roosters.
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Pollos/genética , Fertilidad/genética , Animales , Cruzamiento , Femenino , Genotipo , Inseminación Artificial/veterinaria , Masculino , Linaje , Análisis de Semen/veterinaria , Recuento de Espermatozoides/veterinaria , Motilidad Espermática/genéticaRESUMEN
BACKGROUND: The frequency of recombination events varies across the genome and between individuals, which may be related to some genomic features. The objective of this study was to assess the frequency of recombination events and to identify QTL (quantitative trait loci) for recombination rate in two purebred layer chicken lines. METHODS: A total of 1200 white-egg layers (WL) were genotyped with 580 K SNPs and 5108 brown-egg layers (BL) were genotyped with 42 K SNPs (single nucleotide polymorphisms). Recombination events were identified within half-sib families and both the number of recombination events and the recombination rate was calculated within each 0.5 Mb window of the genome. The 10% of windows with the highest recombination rate on each chromosome were considered to be recombination hotspots. A BayesB model was used separately for each line to identify genomic regions associated with the genome-wide number of recombination event per meiosis. Regions that explained more than 0.8% of genetic variance of recombination rate were considered to harbor QTL. RESULTS: Heritability of recombination rate was estimated at 0.17 in WL and 0.16 in BL. On average, 11.3 and 23.2 recombination events were detected per individual across the genome in 1301 and 9292 meioses in the WL and BL, respectively. The estimated recombination rates differed significantly between the lines, which could be due to differences in inbreeding levels, and haplotype structures. Dams had about 5% to 20% higher recombination rates per meiosis than sires in both lines. Recombination rate per 0.5 Mb window had a strong negative correlation with chromosome size and a strong positive correlation with GC content and with CpG island density across the genome in both lines. Different QTL for recombination rate were identified in the two lines. There were 190 and 199 non-overlapping recombination hotspots detected in WL and BL respectively, 28 of which were common to both lines. CONCLUSIONS: Differences in the recombination rates, hotspot locations, and QTL regions associated with genome-wide recombination were observed between lines, indicating the breed-specific feature of detected recombination events and the control of recombination events is a complex polygenic trait.
RESUMEN
An outbreak of H5N2 highly pathogenic avian influenza (HPAI) in 2015, resulting in mandatory euthanization of millions of chickens, was one of the most fatal in the US history. The aim of this study was to detect genes associated with survival following natural infection with HPAI during this outbreak. Blood samples were collected from 274 individuals from 3 commercial varieties of White Leghorn. Survivors and age and genetics matched non-affected controls from each variety were included in the comparison. All individuals were genotyped on the 600k SNP array. A genome-wide association study (GWAS) with the standard frequency test in PLINK was performed within each variety, whereas logistic regression with the first 3 multidimensional scaling components as covariates was used for joined analysis of all varieties. Several SNPs located within 3 regions reached the 5% Bonferroni genome-wide threshold of significance (P < 3.87E-06). The associations were identified for 2 varieties and only within genetic variety on chromosomes 11 (variety 1), 5, and 18 (variety 3). A genome-wide scan with FST was also performed for 40, 100, and 500 kb windows to support the genome-wide association analyses. The regions with highest FST values between cases and controls were located on chromosomes 1 and Z, and overlapped a number of genes with immunological function and QTL connected to health. Only a few regions were consistent between the analyses, and were significant in the FST genome-wide scan and approaching significance in GWAS. This study confirms that resistance to HPAI is a complex, polygenic trait and that mechanisms of resistance may be population specific. Further study utilizing much larger sample sizes and/or sequence data is needed to detect genes responsible for HPAI survival.
Asunto(s)
Pollos , Resistencia a la Enfermedad/genética , Subtipo H5N2 del Virus de la Influenza A/fisiología , Gripe Aviar/genética , Polimorfismo de Nucleótido Simple , Enfermedades de las Aves de Corral/genética , Animales , Femenino , Estudio de Asociación del Genoma Completo/veterinaria , Gripe Aviar/virología , Iowa , Enfermedades de las Aves de Corral/virologíaRESUMEN
BACKGROUND: Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci (QTL) with large effects, while other traits have one or several easily detectable QTL with large effects. METHODS: Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect. A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP panel. RESULTS: Using data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1 % with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using BayesB but had no predictive power with GBLUP. CONCLUSIONS: In conclusion, In the presence of large effect QTL, BayesB did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.