Genome-wide association study reveals novel variants for growth and egg traits in Dongxiang blue-shelled and White Leghorn chickens

Genome-wide association studies of egg production traits by whole genome sequencing of Laiwu Black chicken

Compared to high-yield commercial laying hens, Chinese indigenous chicken breeds have poor egg laying capacity due to the lack of intensive selection. However, as these breeds have not undergone systematic selection, it is possible that there is a greater abundance of genetic variations related to egg laying traits. In this study, we assessed 5 egg number (EN) traits at different stages of the egg-laying period: EN1 (from the first egg to 23 wk), EN2 (from 23 to 35 wk), EN3 (from 35 to 48 wk), EN4 (from the first egg to 35 wk), and EN5 (from the first egg to 48 wk). To investigate the molecular mechanisms underlying egg number traits in a Chinese local chicken breed, we conducted a genome-wide association study (GWAS) using data from whole-genome sequencing (WGS) of 399 Laiwu Black chickens. We obtained a total of 3.01 Tb of raw data with an average depth of 7.07 × per individual. A total of 86 genome-wide suggestive or significant single-nucleotide polymorphisms (SNP) contained within a set of 45 corresponding candidate genes were identified and found to be associated with stages EN1-EN5. The genes vitellogenin 2 (VTG2), lipase maturation factor 1 (LMF1), calcium voltage-gated channel auxiliary subunit alpha2delta 3 (CACNA2D3), poly(A) binding protein cytoplasmic 1 (PABPC1), programmed cell death 11 (PDCD11) and family with sequence similarity 213 member A (FAM213A) can be considered as the candidate genes associated with egg number traits, due to their reported association with animal reproduction traits. Noteworthy, results suggests that VTG2 and PDCD11 are not only involved in the regulation of EN3, but also in the regulation of EN5, implies that VTG2 and PDCD11 have a significant influence on egg production traits. Our study offers valuable genomic insights into the molecular genetic mechanisms that govern egg number traits in a Chinese indigenous egg-laying chicken breed. These findings have the potential to enhance the egg-laying performance of chickens.

Genome-wide association analysis of eggshell color of an F2 generation population reveals candidate genes in chickens

Eggshell color is an important visual characteristic that affects consumer preferences for eggs. Eggshell color, which has moderate to high heritability, can be effectively enhanced through molecular marker selection. Various studies have been conducted on eggshell color at specific time points. However, few longitudinal data are available on eggshell color. Therefore, the objective of this study was to investigate eggshell color using the Commission International de L'Eclairage L*a*b* system with multiple measurements at different ages (age at the first egg and at 32, 36, 40, 44, 48, 52, 56, 60, 66, and 72 weeks) within the same individuals from an F2 resource population produced by crossing White Leghorn and Dongxiang Blue chicken. Using an Affymetrix 600 single nucleotide polymorphism (SNP) array, we estimated the genetic parameters of the eggshell color trait, performed genome-wide association studies (GWASs), and screened for the potential candidate genes. The results showed that pink-shelled eggs displayed a significant negative correlation between L* values and both a* and b* values. Genetic heritability based on SNPs showed that the heritability of L*, a*, and b* values ranged from 0.32 to 0.82 for pink-shelled eggs, indicating a moderate to high level of genetic control. The genetic correlations at each time point were mostly above 0.5. The major-effect regions affecting the pink eggshell color were identified in the 10.3-13.0 Mb interval on Gallus gallus chromosome 20, and candidate genes were selected, including SLC35C2, PCIF1, and SLC12A5. Minor effect polygenic regions were identified on chromosomes 1, 6, 9, 12, and 15, revealing 11 candidate genes, including MTMR3 and SLC35E4. Members of the solute carrier family play an important role in influencing eggshell color. Overall, our findings provide valuable insights into the phenotypic and genetic aspects underlying the variation in eggshell color. Using GWAS analysis, we identified multiple quantitative trait loci (QTLs) for pink eggshell color, including a major QTL on chromosome 20. Genetic variants associated with eggshell color may be used in genomic breeding programs.

Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens

Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increasingly intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used an illumina 50K SNP chip to genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) and classical bioinformatics methods were integrated to fit genotypes with 10 economic traits in chickens. We evaluated the effectiveness of ML methods using Pearson correlation coefficients and the RMSE between predicted and actual phenotypic values and compared them with rrBLUP and BayesA. Our results indicated that ML algorithms exhibit significantly superior performance to rrBLUP and BayesA in predicting body weight and eggshell strength traits. Conversely, rrBLUP and BayesA demonstrated 2-58% higher predictive accuracy in predicting egg numbers. Additionally, the incorporation of suggestively significant SNPs obtained through the GWAS into the ML models resulted in an increase in the predictive accuracy of 0.1-27% across nearly all traits. These findings suggest the potential of combining classical bioinformatics methods with ML techniques to improve genomic prediction in the future.

Genome-wide investigation to assess copy number variants in the Italian local chicken population

BACKGROUND

Copy number variants (CNV) hold significant functional and evolutionary importance. Numerous ongoing CNV studies aim to elucidate the etiology of human diseases and gain insights into the population structure of livestock. High-density chips have enabled the detection of CNV with increased resolution, leading to the identification of even small CNV. This study aimed to identify CNV in local Italian chicken breeds and investigate their distribution across the genome.

RESULTS

Copy number variants were mainly distributed across the first six chromosomes and primarily associated with loss type CNV. The majority of CNV in the investigated breeds were of types 0 and 1, and the minimum length of CNV was significantly larger than that reported in previous studies. Interestingly, a high proportion of the length of chromosome 16 was covered by copy number variation regions (CNVR), with the major histocompatibility complex being the likely cause. Among the genes identified within CNVR, only those present in at least five animals across breeds (n = 95) were discussed to reduce the focus on redundant CNV. Some of these genes have been associated to functional traits in chickens. Notably, several CNVR on different chromosomes harbor genes related to muscle development, tissue-specific biological processes, heat stress resistance, and immune response. Quantitative trait loci (QTL) were also analyzed to investigate potential overlapping with the identified CNVR: 54 out of the 95 gene-containing regions overlapped with 428 QTL associated to body weight and size, carcass characteristics, egg production, egg components, fat deposition, and feed intake.

CONCLUSIONS

The genomic phenomena reported in this study that can cause changes in the distribution of CNV within the genome over time and the comparison of these differences in CNVR of the local chicken breeds could help in preserving these genetic resources.

Association analysis of production traits of Japanese quail (Coturnix japonica) using restriction-site associated DNA sequencing

This study was designed to perform an association analysis and identify SNP markers associated with production traits of Japanese quail using restriction-site-associated DNA sequencing. Weekly body weight data from 805 quail were collected from hatching to 16 weeks of age. A total number of 3990 eggs obtained from 399 female quail were used to assess egg quality traits. Egg-related traits were measured at the beginning of egg production (first stage) and at 12 weeks of age (second stage). Five eggs were analyzed at each stage. Traits, such as egg weight, egg length and short axes, eggshell strength and weight, egg equator thickness, yolk weight, diameter, and colour, albumen weight, age of first egg, total number of laid eggs, and egg production rate, were assessed. A total of 383 SNPs and 1151 associations as well as 734 SNPs and 1442 associations were identified in relation to quail production traits using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. The GLM-identified SNPs were located on chromosomes 1-13, 15, 17-20, 24, 26-28, and Z, underlying phenotypic traits, except for egg and albumen weight at the first stage and yolk yellowness at the second stage. The MLM-identified SNPs were positioned on defined chromosomes associated with phenotypic traits except for the egg long axis at the second stage of egg production. Finally, 35 speculated genes were identified as candidate genes for the targeted traits based on their nearest positions. Our findings provide a deeper understanding and allow a more precise genetic improvement of production traits of Galliformes, particularly in Japanese quail.

Genome-Wide Association Study of Egg-Laying Traits and Egg Quality in LingKun Chickens

Egg production is the most important trait of laying hens. To identify molecular markers and candidate genes associated with egg production and quality, such as body weight at first oviposition (BWF), the number of eggs produced in 500 days (EN500), egg weight (EW), egg shell thickness (EST), egg shell strength (ESS), and Haugh unit (HU), a genome-wide analysis was performed in 266 LingKun Chickens. The results showed that thirty-seven single nucleotide polymorphisms (SNPs) were associated with all traits (p < 9.47 × 10⁻⁸, Bonferroni correction). These SNPs were located in close proximity to or within the sequence of the thirteen candidate genes, such as Galanin And GMAP Prepropeptide (GAL), Centromere Protein (CENPF), Glypican 2 (GPC2), Phosphatidylethanolamine N-Methyltransferase (PEMT), Transcription Factor AP-2 Delta (TFAP2D), and Carboxypeptidase Q (CPQ) gene related to egg-laying and Solute Carrier Family 5 Member 7 (SLC5A7), Neurocalcin Delta (NCALD), Proteasome 20S Subunit Beta 2 (PSMB2), Slit Guidance Ligand 3 (SLIT3), and Tubulin Tyrosine Ligase Like 7 (TTLL7) genes related to egg quality. Interestingly, one of the genes involved in bone formation (SLIT3) was identified as a candidate gene for ESS. Our candidate genes and SNPs associated with egg-laying traits were significant for molecular breeding of egg-laying traits and egg quality in LingKun chickens.

Genome-wide association studies for egg quality traits in White Leghorn layers using low-pass sequencing and SNP chip data

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.

Transcriptomic analyses of the HPG axis-related tissues reveals potential candidate genes and regulatory pathways associated with egg production in ducks

Background

Egg production is one of the most important economic traits in the poultry industry. The hypothalamic-pituitary–gonadal (HPG) axis plays an essential role in regulating reproductive activities. However, the key genes and regulatory pathways within the HPG axis dominating egg production performance remain largely unknown in ducks.

Results

In this study, we compared the transcriptomic profiles of the HPG-related tissues between ducks with high egg production (HEP) and low egg production (LEP) to reveal candidate genes and regulatory pathways dominating egg production. We identified 543, 759, 670, and 181 differentially expressed genes (DEGs) in the hypothalamus, pituitary, ovary stroma, and F5 follicle membrane, respectively. Gene Ontology (GO) analysis revealed that DEGs from four HPG axis-related tissues were enriched in the "cellular component" category. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that the neuroactive ligand-receptor interaction pathway was significantly enriched based on DEGs commonly identified in all four HPG axis-related tissues. Gene expression profiles and Protein–Protein Interaction (PPI) network were performed to show the regulatory relationships of the DEGs identified. Five DEGs encoding secreted proteins in the hypothalamus and pituitary have interaction with DEGs encoding targeted proteins in the ovary stroma and F5 follicle membrane, implying that they were these DEGs might play similar roles in the regulation of egg production.

Conclusions

Our results revealed that neuroactive ligand-receptor interaction pathway and five key genes(VEGFC, SPARC, BMP2, THBS1, and ADAMTS15) were identified as the key signaling pathways and candidate genes within the HPG axis responsible for different egg production performance between HEP and LEP. This is the first study comparing the transcriptomic profiles of all HPG axis-related tissues in HEP and LEP using RNA-seq in ducks to the best of our knowledge. These data are helpful to enrich our understanding of the classical HPG axis regulating the egg production performance and identify candidate genes that can be used for genetic selection in ducks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08483-y.

Comparison between different breeds of laying hens in terms of eggshell translucency and its distribution in various ends of the eggshell

Eggshell translucency is a ubiquitous external eggshell quality problem caused by variations of eggshell ultrastructure or shell membrane. In previous studies, researchers have widely investigated this phenomenon with nutritional, environmental, and genetic perspectives in many breeds. However, most studies referring to phenotypic measurement of shell translucency have been performed using a relatively subjective two-, three-, or four-grading methods, which made it impossible to compare distribution of shell translucency among different breeds. In this study, we aimed to explore variations of translucent eggshell spots in different breeds and their distribution in blunt, middle, and sharp ends of eggshell using a relatively objective grayscale recognition method. We selected 45 eggs from each flock of pure lines, commercial strains, and Chinese local breeds (10 flocks, aged 60 to 70 wk), and stored them in a constant environment for 5 d. Then measured eggshell translucency using grayscale recognition method. Indicators of shell translucency included sum of spot areas on the whole eggshell (SUSA), sum area of the whole eggshell (SUSHA), RSS (ratio of SUSA to SUSHA), quantity of spots (QS), average spot area in eggshell (AAES), and diameter of spots in eggshell (DS). As results, in Hy-Line Brown, Brown-Egg Dwarf Layer, and Taihang (pink-shelled) breeds, phenotypic intensity of eggshell translucency was slight; in Rhode Island Red, Jingfen-1, and Dongxiang breeds, phenotypic intensity of eggshell translucency was relatively extensive; and in Jinghong-1, Hy-Line Sonia, White Leghorn, and Taihang (blue-shelled), phenotypic intensity of eggshell translucency was at an intermediate level. In general, the larger the RSS, the larger the QS, AAES, and DS. Of 3 ends for most breeds, eggshell translucency of blunt and sharp ends was usually greater than that of middle ends, and blunt ends seemed to have the most extensive eggshell translucency. Findings from this study could provide a reference for population selection to locate genes regulating shell translucency and to explore the physical structure mechanism for eggshell translucency formation.

Identification of candidate genomic regions for chicken egg number traits based on genome-wide association study

Background

Since the domestication of chicken, various breeds have been developed for food production, entertainment, and so on. Compared to indigenous chicken breeds which generally do not show elite production performance, commercial breeds or lines are selected intensely for meat or egg production. In the present study, in order to understand the molecular mechanisms underlying the dramatic differences of egg number between commercial egg-type chickens and indigenous chickens, we performed a genome-wide association study (GWAS) in a mixed linear model.

Results

We obtained 148 single nucleotide polymorphisms (SNPs) associated with egg number traits (57 significantly, 91 suggestively). Among them, 4 SNPs overlapped with previously reported quantitative trait loci (QTL), including 2 for egg production and 2 for reproductive traits. Furthermore, we identified 32 candidate genes based on the function of the screened genes. These genes were found to be mainly involved in regulating hormones, playing a role in the formation, growth, and development of follicles, and in the development of the reproductive system. Some genes such as NELL2 (neural EGFL like 2), KITLG (KIT ligand), GHRHR (Growth hormone releasing hormone receptor), NCOA1 (Nuclear receptor coactivator 1), ITPR1 (inositol 1, 4, 5-trisphosphate receptor type 1), GAMT (guanidinoacetate N-methyltransferase), and CAMK4 (calcium/calmodulin-dependent protein kinase IV) deserve our attention and further study since they have been reported to be closely related to egg production, egg number and reproductive traits. In addition, the most significant genomic region obtained in this study was located at 48.61–48.84 Mb on GGA5. In this region, we have repeatedly identified four genes, in which YY1 (YY1 transcription factor) and WDR25 (WD repeat domain 25) have been shown to be related to oocytes and reproductive tissues, respectively, which implies that this region may be a candidate region underlying egg number traits.

Conclusion

Our study utilized the genomic information from various chicken breeds or populations differed in the average annual egg number to understand the molecular genetic mechanisms involved in egg number traits. We identified a series of SNPs, candidate genes, or genomic regions that associated with egg number, which could help us in developing the egg production trait in chickens.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07755-3.

Exploring the genetic architecture of feed efficiency traits in chickens

Chicken feed efficiency (FE) traits are the most important economic traits in broiler production. Several studies evaluating genetic factors affecting food consumption in chickens are available. However, most of these studies identified genomic regions containing putative quantitative trait loci for each trait separately. It is still a challenge to find common gene networks related to these traits. Therefore, here, a genome-wide association study (GWAS) was conducted to explore candidate genomic regions responsible for Feed Intake (FI), Body Weight Gain (BWG) and Feed Conversion Ratio (FCR) traits and their gene networks. A total of 1430 broilers from an experimental population was genotyped with the high density Affymetrix 600K SNP array. A total of 119 associated SNPs located in 20 chromosomes were identified, where some of them were common in more than one FE trait. In addition, novel genomic regions were prospected considering the SNPs dominance effects and sex interaction, identifying putative candidate genes only when these effects were fit in the model. Relevant candidate genes such as ATRNL1, PIK3C2A, PTPRN2, SORCS3 and gga-mir-1759 were highlighted in this study helping to elucidate the genomic architecture of feed efficiency traits. These results provide new insights on the mechanisms underlying the consumption and utilization of food in chickens.

Deciphering the mode of action and position of genetic variants impacting on egg number in broiler breeders

Background

Aim of the present study was first to identify genetic variants associated with egg number (EN) in female broilers, second to describe the mode of their gene action (additive and/or dominant) and third to provide a list with implicated candidate genes for the trait. A number of 2586 female broilers genotyped with the high density (~ 600 k) SNP array and with records on EN (mean = 132.4 eggs, SD = 29.8 eggs) were used. Data were analyzed with application of additive and dominant multi-locus mixed models.

Results

A number of 7 additive, 4 dominant and 6 additive plus dominant marker-trait significant associations were detected. A total number of 57 positional candidate genes were detected within 50 kb downstream and upstream flanking regions of the 17 significant markers. Functional enrichment analysis pinpointed two genes (BHLHE40 and CRTC1) to be involved in the ‘entrainment of circadian clock by photoperiod’ biological process. Gene prioritization analysis of the positional candidate genes identified 10 top ranked genes (GDF15, BHLHE40, JUND, GDF3, COMP, ITPR1, ELF3, ELL, CRLF1 and IFI30). Seven prioritized genes (GDF15, BHLHE40, JUND, GDF3, COMP, ELF3, CRTC1) have documented functional relevance to reproduction, while two more prioritized genes (ITPR1 and ELL) are reported to be related to egg quality in chickens.

Conclusions

Present results have shown that detailed exploration of phenotype-marker associations can disclose the mode of action of genetic variants and help in identifying causative genes associated with reproductive traits in the species.

Endocrine and genetic factors affecting egg laying performance in chickens: a review

1. Egg-laying performance reflects the overall reproductive performance of breeding hens. The genetic traits for egg-laying performance have low or medium heritability, and, depending on the period involved, usually ranges from 0.16 to 0.64. Egg-laying in chickens is regulated by a combination of environmental, endocrine and genetic factors. 2. The main endocrine factors that regulate egg-laying are gonadotropin-releasing hormone (GnRH), prolactin (PRL), follicle-stimulating hormone (FSH) and luteinising hormone (LH). 3. In the last three decades, many studies have explored this aspect at a molecular genetic level. Recent studies identified 31 reproductive hormone-based candidate genes that were significantly associated with egg-laying performance. With the development of genome-sequencing technology, 64 new candidate genes and 108 single nucleotide polymorphisms (SNPs) related to egg-laying performance have been found using genome-wide association studies (GWAS), providing novel insights into the molecular genetic mechanisms governing egg production. At the same time, microRNAs that regulate genes responsible for egg-laying in chickens were reviewed. 4. Research on endocrinological and genetic factors affecting egg-laying performance will greatly improve the reproductive performance of chickens and promote the protection, development, and utilisation of poultry. This review summarises studies on the endocrine and genetic factors of egg-laying performance in chickens from 1972 to 2019.

Identification of potential genomic regions for egg weight by a haplotype-based genome-wide association study using Bayesian methods

1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes as they are usually better at capturing multi-allelic QTL effects, compared to individual SNP genotypes in genome-wide association studies. The objectives of this study were to identify genomic regions related to egg weight traits by Bayesian methods (BayesA, BayesB, and BayesN) that fit fixed-length haplotypes using GenSel software. 2. Genotypes at 294,705 SNPs, that were common on a 600K Affymetrix chip, were phased for an egg-laying hen population of 1,063 birds. Recorded traits included first egg weight (FEW) and average egg weight at 28, 36, 56, 66, 72 and 80 weeks of age. 2. Fitting 1Mb haplotypes from BayesB resulted in the highest proportion of genetic variance explained for the egg weight traits. Based on the trait, the genetic variance explained by each marker ranged from 27% to 76%. 3. Different haplotype windows associated with egg weight traits only explained a small percentage of the genetic variance. 4. The top one 1-Mb window on GGA1 explained approximately 4.05% of total genetic variance for the FEW. Candidate genes, including PRKAR2B, HMGA2, LEMD3, GRIP1, EHBP1, MAP3K7, and MYH were identified for egg weight traits. 5. Several genomic regions, potentially associated with egg weight traits, were identified, some of which overlapped with known genes and previously reported QTL regions for egg production traits.

Genome-wide association analysis of egg production performance in chickens across the whole laying period

BACKGROUND

Egg production is the most economically-important trait in layers as it directly influences benefits of the poultry industry. To better understand the genetic architecture of egg production, we measured traits including age at first egg (AFE), weekly egg number (EN) from onset of laying eggs to 80 weeks which was divided into five stage (EN1: from onset of laying eggs to 23 weeks, EN2: from 23 to 37 weeks, EN3: from 37 to 50 weeks, EN4: from 50 to 61 weeks, EN5: from 61 to 80 weeks) based on egg production curve and total egg number across the whole laying period (Total-EN). Then we performed genome-wide association studies (GWAS) in 1078 Rhode Island Red hens using a linear mixed model.

RESULTS

Estimates of pedigree and SNP-based genetic parameter showed that AFE and EN1 exhibited high heritability (0.51 ± 0.09, 0.53 ± 0.08), while the h2 for EN in other stages varied from low (0.07 ± 0.04) to moderate (0.24 ± 0.07) magnitude. Subsequently, seven univariate GWAS for AFE and ENs were carried out independently, from which a total of 161 candidate SNPs located on GGA1, GGA2, GGA5, GGA6, GGA9 and GGA24 were identified. Thirteen SNP located on GGA6 were associated with AFE and an interesting gene PRLHR that may affect AFE through regulating oxytocin secretion in chickens. Sixteen genome-wide significant SNPs associated with EN3 were in a strong linkage disequilibrium (LD) region spanning from 117.87 Mb to 118.36 Mb on GGA1 and the most significant SNP (rs315777735) accounted for 3.57% of phenotypic variance. Genes POLA1, PDK3, PRDX4 and APOO identified by annotating sixteen genome-wide significant SNPs can be considered as candidates associated with EN3. Unfortunately, our study did not find any candidate gene for the total egg number.

CONCLUSIONS

Findings in our study could provide promising genes and SNP markers to improve egg production performance based on marker-assisted breeding selection, while further functional validation is still needed in other populations.

Genome-wide association studies targeting the yield of extraembryonic fluid and production traits in Russian White chickens

Background

The Russian White is a gene pool breed, registered in 1953 after crossing White Leghorns with local populations and, for 50 years, selected for cold tolerance and high egg production (EL). The breed has great potential in meeting demands of local food producers, commercial farmers and biotechnology sector of specific pathogen-free (SPF) eggs, the former valuing the breed for its egg weight (EW), EL, age at first egg (AFE), body weight (BW), and the latter for its yield of extraembryonic fluid (YEF) in 12.5-day embryos, ratio of extraembryonic fluid to egg weight, and embryo mass. Moreover, its cold tolerance has been presumably associated with day-old chick down colour (DOCDC) – white rather than yellow, the genetic basis of these traits being however poorly understood.

Results

We undertook genome-wide association studies (GWASs) for eight performance traits using single nucleotide polymorphism (SNP) genotyping of 146 birds and an Illumina 60KBeadChip. Several suggestive associations (p < 5.16*10^− 5) were found for YEF, AFE, BW and EW. Moreover, on chromosome 2, an association with the white DOCDC was found where there is an linkage disequilibrium block of SNPs including genes that are responsible not for colour, but for immune resistance.

Conclusions

The obtained GWAS data can be used to explore the genetics of immunity and carry out selection for increasing YEF for SPF eggs production.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5605-5) contains supplementary material, which is available to authorized users.

Genome-Wide Association Analysis of Age-Dependent Egg Weights in Chickens

Egg weight (EW) is an economically-important trait and displays a consecutive increase with the hen's age. Because extremely large eggs cause a range of problems in the poultry industry, we performed a genome-wide association study (GWAS) in order to identify genomic variations that are associated with EW. We utilized the Affymetrix 600 K high density SNP array in a population of 1,078 hens at seven time points from day at first egg to 80 weeks age (EW80). Results reveal that a 90 Kb genomic region (169.42 Mb ~ 169.51 Mb) in GGA1 is significantly associated with EW36 and is also potentially associated with egg weight at 28, 56, and 66 week of age. The leading SNP could account for 3.66% of the phenotypic variation, while two promising genes (DLEU7 and MIR15A) can be mapped to this narrow significant region and may affect EW in a pleiotropic manner. In addition, one gene (CECR2 on GGA1) and two genes (MEIS1 and SPRED2 on GGA3), which involved in the processes of embryogenesis and organogenesis, were also considered to be candidates related to first egg weight (FEW) and EW56, respectively. Findings in our study could provide worthy theoretical basis to generate eggs of ideal size based on marker assisted breeding selection.

Genetic Mapping of Quantitative Trait Loci for Egg Production and Egg Quality Traits in Chickens: a Review

Chickens display a wide spectrum of phenotypic variations in quantitative traits such as egg-related traits. Quantitative trait locus (QTL) analysis is a statistical method used to understand the relationship between phenotypic (trait measurements) and genotypic data (molecular markers). We have performed QTL analyses for egg-related traits using an original resource population based on the Japanese Large Game (Oh-Shamo) and the White Leghorn breeds of chickens. In this article, we summarize the results of our extensive QTL analyses for 11 and 66 traits for egg production and egg quality, respectively. We reveal that at least 30 QTL regions on 17 different chromosomes affect phenotypic variation in egg-related traits. Each locus had an age-specific effect on traits, and a variety in effects was also apparent, such as additive, dominance, and epistatic-interaction effects. Although genome-wide association study (GWAS) is suitable for gene-level resolution mapping of GWAS loci with additive effects, QTL mapping studies enable us to comprehensively understand genetic control, such as chromosomal regions, genetic contribution to phenotypic variance, mode of inheritance, and age-specificity of both common and rare alleles. QTL analyses also describe the relationship between genotypes and phenotypes in experimental populations. Accumulation of QTL information, including GWAS loci, is also useful for studies of population genomics approached without phenotypic data in order to validate the identified genomic signatures of positive selection. The combination of QTL studies and next-generation sequencing techniques with uncharacterized genetic resources will enhance current understanding of the relationship between genotypes and phenotypes in livestock animals.