Genome-wide association study on reproductive traits in Jinghai Yellow Chicken

Genome-wide re-sequencing reveals regulatory genes and variants involved in the regulation of intermittent fertilization intensity in Wenchang chickens

Intermittent fertilization intensity (IFI) is closely related to higher fertilization in chickens, but the genetic basis of IFI is not clearly understood. Here, we sampled a total of 939 Wenchang chickens with IFI. The IFI traits had negative correlation with the fertilization rate and exhibited huge phenotypic variations among individuals of the same strain. Based on SNPs derived from a subset of 499 whole genome data, a genome-wide association study with mixed linear model and further linkage disequilibrium analysis were performed to test potential associations between IFI traits and genomic variants. We identified 35 SNP variants and a 19.82 kb linkage disequilibrium block on chr8 significantly associated with IFI. This block is in the intron of LOC101750715, which shows significant homology with the human LMO4. Therefore, LOC101750715 and LMO4 may regulate IFI. The oviduct's immune regulation is crucial for fertilization. LMO4, activated by IL-6 and IL-23, promotes inflammation in epithelial cells. Thus, LOC101750715 and LMO4 may affect fertilization by regulating oviductal inflammation, impacting IFI. Our findings will provide targets for molecular-marker selection and genetic manipulation for lines of chickens with lower IFI.

Comparison of genomic prediction accuracy using different models for egg production traits in Taiwan country chicken

In local chickens targeted for niche markets, genotyping costs are relatively high due to the small population size and diverse breeding goals. The single-step genomic best linear unbiased prediction (ssGBLUP) model, which combines pedigree and genomic information, has been introduced to increase the accuracy of genomic estimated breeding value (GEBV). Therefore, this model may be more beneficial than the genomic BLUP (GBLUP) model for genomic selection in local chickens. Additionally, the single-step genome-wide association study (ssGWAS) can be used to extend the ssGBLUP model results to animals with available phenotypic information but without genotypic data. In this study, we compared the accuracy of (G)EBVs using the pedigree-based BLUP (PBLUP), GBLUP, and ssGBLUP models. Moreover, we conducted single-SNP GWAS (SNP-GWAS), GBLUP-GWAS, and ssGWAS methods to identify genes associated with egg production traits in the NCHU-G101 chicken to understand the feasibility of using genomic selection in a small population. The average prediction accuracy of (G)EBV for egg production traits using the PBLUP, GBLUP, and ssGBLUP models is 0.536, 0.531, and 0.555, respectively. In total, 22 suggestive- and 5% Bonferroni genome-wide significant-level SNPs for total egg number (EN), average laying rate (LR), average clutch length, and total clutch number are detected using 3 GWAS methods. These SNPs are mapped onto Gallus gallus chromosomes (GGA) 4, 6, 10, 18, and 25 in NCHU-G101 chicken. Furthermore, through SNP-GWAS and ssGWAS methods, we identify 2 genes on GGA4 associated with EN and LR: ENSGALG00000023172 and PPARGC1A. In conclusion, the ssGBLUP model demonstrates superior prediction accuracy, performing on average 3.41% than the PBLUP model. The implications of our gene results may guide future selection strategies for Taiwan Country chickens. Our results highlight the applicability of the ssGBLUP model for egg production traits selection in a small population, specifically NCHU-G101 chicken in Taiwan.

Genome-wide association study identified candidate genes for egg production traits in the Longyan Shan-ma duck

Egg production is an important economic trait in layer ducks and understanding the genetics basis is important for their breeding. In this study, a genome-wide association study (GWAS) for egg production traits in 303 female Longyan Shan-ma ducks was performed based on a genotyping-by-sequencing strategy. Sixty-two single nucleotide polymorphisms (SNPs) associated with egg weight traits were identified (P < 9.48 × 10-5), including 8 SNPs at 5% linkage disequilibrium (LD)-based Bonferroni-corrected genome-wide significance level (P < 4.74 × 10-6). One hundred and nineteen SNPs were associated with egg number traits (P < 9.48 × 10-5), including 13 SNPs with 5% LD-based Bonferroni-corrected genome-wide significance (P < 4.74 × 10-6). These SNPs annotated 146 target genes which contained known candidate genes for egg production traits, such as prolactin and prolactin releasing hormone receptor. This study identified that these associated genes were significantly enriched in egg production-related pathways (P < 0.05), such as the oxytocin signaling, MAPK signaling, and calcium signaling pathways. It was notable that 18 genes were differentially expressed in ovarian tissues between higher and lower egg production in Shan-ma ducks. The identified potential candidate genes and pathways provide insight into the genetic basis underlying the egg production trait of layer ducks.

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.

Identify Candidate Genes Associated with the Weight and Egg Quality Traits in Wenshui Green Shell-Laying Chickens by the Copy Number Variation-Based Genome-Wide Association Study

Copy number variation (CNV), as an essential source of genetic variation, can have an impact on gene expression, genetic diversity, disease susceptibility, and species evolution in animals. To better understand the weight and egg quality traits of chickens, this paper aimed to detect CNVs in Wenshui green shell-laying chickens and conduct a copy number variation regions (CNVRs)-based genome-wide association study (GWAS) to identify variants and candidate genes associated with their weight and egg quality traits to support related breeding efforts. In our paper, we identified 11,035 CNVRs in Wenshui green shell-laying chickens, which collectively spanned a length of 13.1 Mb, representing approximately 1.4% of its autosomal genome. Out of these CNVRs, there were 10,446 loss types, 491 gain types, and 98 mixed types. Notably, two CNVRs showed significant correlations with egg quality, while four CNVRs exhibited significant associations with body weight. These significant CNVRs are located on chromosome 4. Further analysis identified potential candidate genes that influence weight and egg quality traits, including FAM184B, MED28, LAP3, ATOH8, ST3GAL5, LDB2, and SORCS2. In this paper, the CNV map of the Wenshui green shell-laying chicken genome was constructed for the first time through population genotyping. Additionally, CNVRs can be employed as molecular markers to genetically improve chickens' weight and egg quality traits.

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 Production Traits in Shuanglian Chickens Using Whole Genome Sequencing

Egg production is the most important economic trait in laying hens. To identify molecular markers and candidate genes associated with egg production traits, such as age at first egg (AFE), weight at first egg (WFE), egg weight (EW), egg number (EN), and maximum consecutive egg laying days (MCD), a genome-wide analysis by whole genome sequencing was performed in Shuanglian chickens. Through whole genome sequencing and quality control, a total of 11,006,178 SNPs were obtained for further analysis. Heritability estimates ranged from moderate to high for EW (0.897) and MCD (0.632), and from low to moderate (0.193~0.379) for AFE, WFE, and EN. The GWAS results showed 11 genome-wide significant SNPs and 23 suggestive significant SNPs were identified to be associated with EN, MCD, WFE, and EW. Linkage disequilibrium analysis revealed twenty-seven SNPs associated with EN were located in a 0.57 Mb region on GGA10, and clustered into five blocks. Through functional annotation, three candidate genes NEO1, ADPGK, and CYP11A1, were identified to be associated with EN, while the S1PR4, LDB2, and GRM8 genes was linked to MCD, WFE, and EW, respectively. These findings may help us to better understand the molecular mechanisms underlying egg production traits in chickens and contribute to genetic improvement of these traits.

Insights into genetic diversity and phenotypic variations in domestic geese through comprehensive population and pan-genome analysis

BACKGROUND

Domestic goose breeds are descended from either the Swan goose (Anser cygnoides) or the Greylag goose (Anser anser), exhibiting variations in body size, reproductive performance, egg production, feather color, and other phenotypic traits. Constructing a pan-genome facilitates a thorough identification of genetic variations, thereby deepening our comprehension of the molecular mechanisms underlying genetic diversity and phenotypic variability.

RESULTS

To comprehensively facilitate population genomic and pan-genomic analyses in geese, we embarked on the task of 659 geese whole genome resequencing data and compiling a database of 155 RNA-seq samples. By constructing the pan-genome for geese, we generated non-reference contigs totaling 612 Mb, unveiling a collection of 2,813 novel genes and pinpointing 15,567 core genes, 1,324 softcore genes, 2,734 shell genes, and 878 cloud genes in goose genomes. Furthermore, we detected an 81.97 Mb genomic region showing signs of genome selection, encompassing the TGFBR2 gene correlated with variations in body weight among geese. Genome-wide association studies utilizing single nucleotide polymorphisms (SNPs) and presence-absence variation revealed significant genomic associations with various goose meat quality, reproductive, and body composition traits. For instance, a gene encoding the SVEP1 protein was linked to carcass oblique length, and a distinct gene-CDS haplotype of the SVEP1 gene exhibited an association with carcass oblique length. Notably, the pan-genome analysis revealed enrichment of variable genes in the "hair follicle maturation" Gene Ontology term, potentially linked to the selection of feather-related traits in geese. A gene presence-absence variation analysis suggested a reduced frequency of genes associated with "regulation of heart contraction" in domesticated geese compared to their wild counterparts. Our study provided novel insights into gene expression features and functions by integrating gene expression patterns across multiple organs and tissues in geese and analyzing population variation.

CONCLUSION

This accomplishment originates from the discernment of a multitude of selection signals and candidate genes associated with a wide array of traits, thereby markedly enhancing our understanding of the processes underlying domestication and breeding in geese. Moreover, assembling the pan-genome for geese has yielded a comprehensive apprehension of the goose genome, establishing it as an indispensable asset poised to offer innovative viewpoints and make substantial contributions to future geese breeding initiatives.

Genomic Selection for Live Weight in the 14th Month in Alpine Merino Sheep Combining GWAS Information

Alpine Merino Sheep is a novel breed reared from Australian Merino Sheep as the father and Gansu Alpine Fine-Wool Sheep as the mother, living all year in cold and arid alpine areas with exceptional wool quality and meat performance. Body weight is an important economic trait of the Alpine Merino Sheep, but there is limited research on identifying the genes associated with live weight in the 14th month for improving the accuracy of the genomic prediction of this trait. Therefore, this study's sample comprised 1310 Alpine Merino Sheep ewes, and the Fine Wool Sheep 50K Panel was used for genome-wide association study (GWAS) analysis to identify candidate genes. Moreover, the trial population (1310 ewes) in this study was randomly divided into two groups. One group was used as the population for GWAS analysis and screened for the most significant top 5%, top 10%, top 15%, and top 20% SNPs to obtain prior marker information. The other group was used to estimate the genetic parameters based on the weight assigned by heritability combined with different prior marker information. The aim of this study was to compare the accuracy of genomic breeding value estimation when combined with prior marker information from GWAS analysis with the optimal linear unbiased prediction method for genome selection (GBLUP) for the breeding value of target traits. Finally, the accuracy was evaluated using the five-fold cross-validation method. This research provides theoretical and technical support to improve the accuracy of sheep genome selection and better guide breeding. The results demonstrated that eight candidate genes were associated with GWAS analysis, and the gene function query and literature search results suggested that FAM184B, NCAPG, MACF1, ANKRD44, DCAF16, FUK, LCORL, and SYN3 were candidate genes affecting live weight in the 14th month (WT), which regulated the growth of muscle and bone in sheep. In genome selection analysis, the heritability of GBLUP to calculate the WT was 0.335-0.374, the accuracy after five-fold cross-verification was 0.154-0.190, and after assigning different weights to the top 5%, top 10%, top 15%, and top 20% of the GWAS results in accordance with previous information to construct the G matrix, the accuracy of the WT in the GBLUP model was improved by 2.59-7.79%.

Transcriptome identification and characterization of long non-coding RNAs in the ovary of hens at four stages

Long non-coding RNAs (lncRNAs) play important roles in transcriptional and post-transcriptional regulation. LncRNAs, which are defined as non-coding RNAs more than 200 bp in length, are involved in key biological processes, such as cell proliferation and differentiation, epigenetic regulation, and gene transcriptional translation. Recent studies have shown that lncRNAs also play major regulatory roles in the reproduction of mammals. However, knowledge of the roles of lncRNAs in the chicken ovary lacking. In this study, we performed RNA-seq analyses of ovarian tissue from Hy-Line brown laying hens at four physiological stages [15, 20, 30, and 68 weeks of age (W)]. We identified 657 lncRNA transcripts that were differentially expressed during ovarian development, the number of down-regulated lncRNAs was higher than the number of up-regulated lncRNAs during development. We predicted the cis and trans target genes of the DE lncRNAs and constructed a lncRNA-mRNA interaction network, which indicated that the DE genes (DEGs) and the target genes of the DE lncRNAs are mainly involved in signaling pathways associated with ovarian development, including oocyte meiosis, calcium signaling pathways, ECM-receptor interactions, and ribosome and focal adhesion. Overall, we found that twelve lncRNAs were strongly involved in ovarian development: LNC_013443, LNC_001029, LNC_005713, LNC_016762, ENSGALT00000101857, LNC_003913, LNC_013692, LNC_012219, LNC_004140, ENSGALT00000096941, LNC_009356, and ENSGALT00000098716. In summary, our study utilized RNA-seq analysis of hen ovaries to explore key lncRNAs involved in ovarian development and function. Furthermore, the comprehensive analysis identified the target genes of these lncRNAs providing a better understanding of the mechanisms underlying ovarian development in hens and a theoretical basis for further research.

Genomic selection pressure discovery using site-frequency spectrum and reduced local variability statistics in Pakistani Dera-Din-Panah goat

BACKGROUND

Population geneticists have long sought to comprehend various selection traces accumulated in the goat genome due to natural or human driven artificial selection through breeding practices, which led the wild animals to domestication, so understanding evolutionary process may helpful to utilize the full genetic potential of goat genome.

METHODS AND RESULTS

As a step forward to pinpoint the selection signals in Pakistani Dera-Din-Panah (DDP) goat, whole-genome pooled sequencing (n = 12) was performed, and 618,236,192 clean paired-end reads were mapped against ARS1 reference goat assembly. Five different selection signature statistics were applied using four site-frequency spectrum (SFS) methods (Tajima's D ([Formula: see text]), Fay and Wu's H ([Formula: see text]), Zeng's E ([Formula: see text]), [Formula: see text]) and one reduced local variability approach named pooled heterozygosity ([Formula: see text]). The under-selection regions were annotated with significant threshold values of [Formula: see text]≥4.7, [Formula: see text]≥6, [Formula: see text]≥2.5, Pool-HMM ≥ 12, and [Formula: see text]≥5 that resulted in accumulative 364 candidate gene hits. The highest genomic selection signals were observed on Chr. 4, 6, 10, 12, 15, 16, 18, 20, and 27 and harbor ADAMTS6, CWC27, RELN, MYCBP2, FGF14, STIM1, CFAP74, GNB1, CALML6, TMEM52, FAM149A, NADK, MMP23B, OPN3, FH, MFHAS1, KLKB1, RRM1, KMO, SPEF2, F11, KIT, KMO, ERI1, ATP8B4, and RHOG genes. Next, the validation of our captured genomic hits was also performed by more than one applied statistics which harbor meat production, immunity, and reproduction associated genes to strengthen our hypothesis of under-selection traits in this Pakistani goat breed. Furthermore, common candidate genes captured by more than one statistical method were subjected to gene ontology and KEGG pathway analysis to get insights of particular biological processes associated with this goat breed.

CONCLUSION

Current perception of genomic architecture of DDP goat provides a better understanding to improve its genetic potential and other economically important traits of medium to large body size, milk, and fiber production by updating the genomic insight driven breeding strategies to boost the livestock and agriculture-based economy of the country.

Identification of candidate genomic regions for egg yolk moisture content based on a genome-wide association study

BACKGROUND

Eggs represent important sources of protein and are widely loved by consumers. Egg yolk taste is an important index for egg selection, and the moisture content of the egg yolk affects the taste. To understand the molecular mechanism underlying egg yolk moisture content, this study determined the phenotype and heritability of egg yolk water content and conducted a genome-wide association study (GWAS) using a mixed linear model.

RESULTS

We determined the phenotype and heritability of thermogelled egg yolk water content (TWC) and found that the average TWC was 47.73%. Moreover, significant variations occurred (41.06-57.12%), and the heritability was 0.11, which indicates medium-low heritability. Through the GWAS, 48 single nucleotide polymorphisms (SNPs) related to TWC (20 significantly, 28 suggestively) were obtained, and they were mainly located on chromosomes 10 and 13. We identified 36 candidate genes based on gene function and found that they were mainly involved in regulating fat, protein, and water content and embryonic development. FGF9, PIAS1, FEM1B, NOX5, GLCE, VDAC1, IGFBP7, and THOC5 were involved in lipid formation and regulation; AP3S2, GNPDA1, HSPA4, AP1B1, CABP7, EEF1D, SYTL3, PPP2CA, SKP1, and UBE2B were involved in protein folding and hydrolysis; and CSF2, SOWAHA, GDF9, FSTL4, RAPGEF6, PAQR5, and ZMAT5 were related to embryonic development and egg production. Moreover, MICU2, ITGA11, WDR76, BLM, ANPEP, TECRL, EWSR1, and P4HA2 were related to yolk quality, while ITGA11, WDR76, BLM, and ANPEP were potentially significantly involved in egg yolk water content and thus deserve further attention and research. In addition, this study identified a 19.31-19.92 Mb genome region on GGA10, and a linkage disequilibrium analysis identified strong correlations within this region. Thus, GGA10 may represent a candidate region for TWC traits.

CONCLUSION

The molecular genetic mechanism involved in TWC was revealed through heritability measurements and GWAS, which identified a series of SNPs, candidate genes, and candidate regions related to TWC. These results provide insights on the molecular mechanism of egg yolk moisture content and may aid in the development of new egg traits.

Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

Background

Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses.

Results

Different genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding.

Conclusions

We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00766-y.

Transcriptomics and Metabolomics Analysis of the Ovaries of High and Low Egg Production Chickens

Simple Summary

The ovarian tissues of different breeds of hens during egg production were investigated through transcriptomics and metabolomics to provide a more comprehensive understanding of the molecular mechanisms of the ovary during egg production. Four genes involved in egg production were predicted by the transcriptome, including P2RX1, INHBB, VIPR2, and FABP3, and several close metabolites associated with reproduction were identified in the metabolome, including 17α-hydroxyprogesterone, iloprost, spermidine and adenosine. Correlation analysis of specific differential genes and differential metabolites identified important gene-metabolite pairs VIPR2–Spermidine and P2RX1–Spermidine in the reproductive process.

Abstract

Egg production is a pivotal indicator for evaluating the fertility of poultry, and the ovary is an essential organ for egg production and plays an indispensable role in poultry production and reproduction. In order to investigate different aspects of egg production mechanisms in different poultry, in this study we performed a metabolomic analysis of the transcriptomic combination of the ovaries of two chicken breeds, the high-production Ninghai indigenous chickens and the low-production Wuliangshan black-boned chickens, to analyze the biosynthesis and potential key genes and metabolic pathways in the ovaries during egg production. We predicted four genes in the transcriptomic that are associated with egg production, namely P2RX1, INHBB, VIPR2, and FABP3, and identified three important pathways during egg production, “Calcium signaling pathway”, “Neuroactive ligand–receptor interaction” and “Cytokine–cytokine receptor interaction”, respectively. In the metabolomic 149 significantly differential metabolites were identified, 99 in the negative model and 50 in the positive model, of which 17α-hydroxyprogesterone, iloprost, spermidine, and adenosine are important metabolites involved in reproduction. By integrating transcriptomics and metabolomics, the correlation between specific differential genes and differential metabolites identified important gene-metabolite pairs “VIPR2-Spermidine” and “P2RX1-Spermidine” in egg production. In conclusion, these data provide a better understanding of the molecular differences between the ovaries of low- and high-production hens and provide a theoretical basis for further studies on the mechanics of poultry egg production.

Genome-Wide Association Study and Selective Sweep Analysis Reveal the Genetic Architecture of Body Weights in a Chicken F2 Resource Population

Rapid growth is one of the most important economic traits in broiler breeding programs. Identifying markers and genes for growth traits may not only benefit marker-assisted selection (MAS)/genomic selection (GS) but also provide important information for understanding the genetic architecture of growth traits in broilers. In the present study, an F₂ resource population derived from a cross between the broiler and Baier yellow chicken (a Chinese local breed) was used and body weights from 1 to 12 weeks of age [body weight (BW) 1–BW12)] were measured. A total of 519 F₂ birds were genome re-sequenced, and a combination of genome-wide association study (GWAS) and selective sweep analysis was carried out to characterize the genetic architecture affecting chicken body weight comprehensively. As a result, 1,539 SNPs with significant effects on body weights at different weeks of age were identified using a genome-wide efficient mixed-model association (GEMMA) package. These SNPs were distributed on chromosomes 1 and 4. Besides, windows under selection identified for BW1–BW12 varied from 1,581 to 2,265. A total of 42 genes were also identified with significant effects on BW1–BW12 based on both GWAS and selective sweep analysis. Among these genes, diacylglycerol kinase eta (DGKH), deleted in lymphocytic leukemia (DLEU7), forkhead box O17 (FOXO1), karyopherin subunit alpha 3 (KPNA3), calcium binding protein 39 like (CAB39L), potassium voltage-gated channel interacting protein 4 (KCNIP4), and slit guidance ligand 2 (SLIT2) were considered as important genes for broiler growth based on their basic functions. The results of this study may supply important information for understanding the genetic architecture of growth traits in broilers.

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.

An association of CEP78, MEF2C, VPS13A and ARRDC3 genes with survivability to heat stress in an F2 chicken population

Heat stress is a serious problem in the poultry industry. An effective tool for improving heat tolerance can be genomic selection based on single nucleotide polymorphisms. This study was performed to identify genomic regions controlling survivability to heat stress in a population of F₂ chickens that accidentally experienced acute heat stress, using Illumina 60K Chicken SNP Bead Chip. After quality control in markers, 47,730 SNPs remained for genome-wide association study (GWAS). The GWAS results indicated that markers Gga_rs16111480 (p = 8.503e-08), GGaluGA354375 (p = 5.99e-07) and Gga_rs14748694 (p = 7.085e-07) located on Z chromosome showed significant association with heat stress tolerance trait. The Gga_rs16111480 marker was located inside the CEP78 gene. The marker GGaluGA354375 was located inside the LOC101752071 gene and next to the MEF2C gene. The Gga_rs14748694 marker was adjacent to LOC101752071 and MEF2C genes. Moreover, the SNP maker of Gga_rs16111480 was located on 243 kb downstream of the VPS13A gene, and the GGaluGA354375 and Gga_rs14748694 SNPs were located on 947 kb and 888 kb downstream of the ARRDC3 gene, respectively. The results of this study suggest that apart from the gene LOC101752071, which its function was unknown, each of the two MEF2C and CEP78 genes were found to be closely related to heat stress resistance in bird.

Genome-wide association studies for growth traits in broilers

BACKGROUND

The identification of markers and genes for growth traits may not only benefit for marker assist selection /genomic selection but also provide important information for understanding the genetic foundation of growth traits in broilers.

RESULTS

In the current study, we estimated the genetic parameters of eight growth traits in broilers and carried out the genome-wide association studies for these growth traits. A total of 113 QTNs discovered by multiple methods together, and some genes, including ACTA1, IGF2BP1, TAPT1, LDB2, PRKCA, TGFBR2, GLI3, SLC16A7, INHBA, BAMBI, APCDD1, GPR39, and GATA4, were identified as important candidate genes for rapid growth in broilers.

CONCLUSIONS

The results of this study will provide important information for understanding the genetic foundation of growth traits in broilers.

Association of female reproductive tract microbiota with egg production in layer chickens

BACKGROUND

The microbiota of the female reproductive tract is increasingly recognized as playing fundamental roles in animal reproduction. To explore the relative contribution of reproductive tract microbiomes to egg production in chickens, we investigated the microbiota in multiple reproductive and digestive tract sites from 128 female layer (egg-producing) chickens in comparable environments.

RESULTS

We identified substantial differences between the diversity, composition, and predicted function of site-associated microbiota. Differences in reproductive tract microbiota were more strongly associated with egg production than those in the digestive tract. We identified 4 reproductive tract microbial species, Bacteroides fragilis, Bacteroides salanitronis, Bacteroides barnesiae, and Clostridium leptum, that were related to immune function and potentially contribute to enhanced egg production.

CONCLUSIONS

These findings provide insights into the diverse microbiota characteristics of reproductive and digestive tracts and may help in designing strategies for controlling and manipulating chicken reproductive tract microbiota to improve egg production.

Hypothalamic and ovarian transcriptome profiling reveals potential candidate genes in low and high egg production of white Muscovy ducks (Cairina moschata)

In China, the low egg production rate is a major challenge to Muscovy duck farmers. Hypothalamus and ovary play essential role in egg production of birds. However, there are little or no reports from these tissues to identify potential candidate genes responsible for egg production in White Muscovy ducks. A total of 1,537 laying ducks were raised; the egg production traits which include age at first egg (days), number of eggs at 300 d, and number of eggs at 59 wk were recorded. Moreover, 4 lowest (LP) and 4 highest producing (HP) were selected at 59 wk of age, respectively. To understand the mechanism of egg laying regulation, we sequenced the hypothalamus and ovary transcriptome profiles in LP and HP using RNA-Seq. The results showed that the number of eggs at 300 d and number of eggs at 59 wk in the HP were significantly more (P < 0.001) than the LP ducks. In total, 106.98G clean bases were generated from 16 libraries with an average of 6.68G clean bases for each library. Further analysis showed 569 and 2,259 differentially expressed genes (DEGs) were identified in the hypothalamus and ovary between LP and HP, respectively. The KEGG pathway enrichment analysis revealed 114 and 139 pathways in the hypothalamus and ovary, respectively which includes Calcium signaling pathway, ECM-receptor interaction, Focal adhesion, MAPK signaling pathway, Apoptosis and Apelin signaling pathways that are involved in egg production. Based on the GO terms and KEGG pathways results, 10 potential candidate genes (P2RX1, LPAR2, ADORA1, FN1, AKT3, ADCY5, ADCY8, MAP3K8, PXN, and PTTG1) were identified to be responsible for egg production. Further, protein-protein interaction was analyzed to show the relationship between these candidate genes. Therefore, this study provides useful information on transcriptome of hypothalamus and ovary of LP and HP Muscovy ducks.

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.

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.

A genome-wide single nucleotide polymorphism scan reveals genetic markers associated with fertility rate in Chinese Jing Hong chicken

The function of the sperm storage tubules is directly correlated with the fertility of laying hens. However, little is known about the molecular mechanisms regulating the fertility traits in chicken. To identify genetic markers associated with reproductive traits, we calculated fertility rate at 61 to 69 wk (51 D) of Jing Hong chickens parent generation as the phenotype and the genotype were detected by the chicken 600K Affymetrix Axiom High Density single nucleotide polymorphisms (SNP)-array. The genome-wide association study using 190 Jing Hong hens showed that the 20 SNP in chromosomes 3 and 13 were significantly associated with fertility rate. To verify these results, a total of 1900 Jing Hong laying hens from 2 populations (P1 and P2) were further genotyped by polymerase chain reaction-restriction fragments length polymorphisms method. The association analysis results revealed that 12 polymorphisms (AX-75769978, AX-76582632, AX-75730546, AX-75730496, AX-75730588, AX-76530282, AX-76530329, AX-76529310, AX-75769906, AX-75755394, AX-80813697 and AX-76582809) out of 20 showed highly significant effects (P < 0.0001) on fertility rate in P1, P2 and P1+P2. Six haplotypes (TTAA, TTGG, TTAG, CTAA, CTGG, and CTAG) were inferred based on significant loci (AX-75730546 and AX-76530282) also showed significant association with fertility rate, where haplotype CTAG was shown to be markedly associated with the significantly highest (P < 0.0001) fertility rate (in P1, 86.42 ± 0.59; P2, 85.98 ± 0.59 and P1+P2, 86.16 ± 0.42) followed by other haplotypes for the irrespective of population studied. Collectively, we report for the first time that 12 SNP in the chromosomes 3 and 13 were significantly associated with fertility rate during the later stage of egg production, which could be used as the potential genetic markers that would be able to facilitate in the selection and improvement of fertility rate through chicken breeding.

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.

Transcriptome Analysis of Differentially Expressed Genes Related to the Growth and Development of the Jinghai Yellow Chicken

The growth traits are important traits in chickens. Compared to white feather broiler breeds, Chinese local broiler breeds have a slow growth rate. The main genes affecting the growth traits of local chickens in China are still unclear and need to be further explored. This experiment used fast-growth and slow-growth groups of the Jinghai Yellow chicken as the research objects. Three males and three females with similar body weights were selected from the two groups at four weeks old and eight weeks old, respectively, with a total of 24 individuals selected. After slaughter, their chest muscles were taken for transcriptome sequencing. In the differentially expressed genes screening, all of the genes obtained were screened by fold change ≥ 2 and false discovery rate (FDR) < 0.05. For four-week-old chickens, a total of 172 differentially expressed genes were screened in males, where there were 68 upregulated genes and 104 downregulated genes in the fast-growth group when compared with the slow-growth group. A total of 31 differentially expressed genes were screened in females, where there were 11 upregulated genes and 20 downregulated genes in the fast-growth group when compared with the slow-growth group. For eight-week-old chickens, a total of 37 differentially expressed genes were screened in males. The fast-growth group had 28 upregulated genes and 9 downregulated genes when compared with the slow-growth group. A total of 44 differentially expressed genes were screened in females. The fast-growth group had 13 upregulated genes and 31 downregulated genes when compared with the slow-growth group. Through gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, many genes were found to be related to cell proliferation and differentiation, muscle growth, and cell division such as SNCG, MCL1, ARNTL, PLPPR4, VAMP1, etc. Real-time PCR results were consistent with the RNA-Seq data and validated the findings. The results of this study will help to understand the regulation mechanism of the growth and development of Jinghai Yellow chicken and provide a theoretical basis for improving the growth rate of Chinese local chicken breeds.

Transcriptome analysis of ovary in relatively greater and lesser egg producing Jinghai Yellow Chicken

Egg production is determined by the function of ovary and is regulated by the hypothalamic-pituitary-ovary axis. The mechanism by which the ovary regulates egg production, however, is still poorly understood. The purpose of this study is to compare the transcriptome difference in ovary of relatively greater and lesser egg producing chickens, and to screen candidate genes related to egg production. A RNA sequencing was performed to analyze and compare the mRNA in ovarian tissues of relatively greater and lesser egg producing chickens. A total of 4 431 new genes expressed in the chicken ovary were mined. There were 305 differentially expressed genes (DEGs) identified between the relatively greater and lesser egg producing hens. Gene ontology analysis identified five candidate genes related to egg production, including ZP2, WNT4, AMH, IGF1, and CYP17A1 genes. Tissue expression profiles indicated these five candidate genes were highly expressed in chicken ovarian tissues, indicating a potential role in regulating chicken ovarian function and egg production. The KEGG analysis indicated the neuroactive ligand-receptor interaction pathway might have an important function in regulation of egg production. In addition, four known pathways related to reproduction were detected, including the calcium signaling, wnt signaling pathway, focal adhesion, and cytokine-cytokine receptor interaction pathways. Results of the present study indicate gene expression differences in the ovarian tissues of relatively greater and lesser egg producing chickens, and identified five important candidate genes related to egg production, which provided a theoretical basis for improving egg production of Jinghai Yellow Chickens.

Bidirectional Selection for Body Weight on Standing Genetic Variation in a Chicken Model

Experimental populations of model organisms provide valuable opportunities to unravel the genomic impact of selection in a controlled system. The Virginia body weight chicken lines represent a unique resource to investigate signatures of selection in a system where long-term, single-trait, bidirectional selection has been carried out for more than 60 generations. At 55 generations of divergent selection, earlier analyses of pooled genome resequencing data from these lines revealed that 14.2% of the genome showed extreme differentiation between the selected lines, contained within 395 genomic regions. Here, we report more detailed analyses of these data exploring the regions displaying within- and between-line genomic signatures of the bidirectional selection applied in these lines. Despite the strict selection regime for opposite extremes in body weight, this did not result in opposite genomic signatures between the lines. The lines often displayed a duality of the sweep signatures, where an extended region of homozygosity in one line, in contrast to mosaic pattern of heterozygosity in the other line. These haplotype mosaics consisted of short, distinct haploblocks of variable between-line divergence, likely the results of a complex demographic history involving bottlenecks, introgressions and moderate inbreeding. We demonstrate this using the example of complex haplotype mosaicism in the growth1 QTL. These mosaics represent the standing genetic variation available at the onset of selection in the founder population. Selection on standing genetic variation can thus result in different signatures depending on the intensity and direction of selection.

A genome-wide study to identify genes responsible for oviduct development in chickens

Molecular genetic tools provide a method for improving the breeding selection of chickens (Gallus gallus). Although some studies have identified genes affecting egg quality, little is known about the genes responsible for oviduct development. To address this issue, here we used a genome-wide association (GWA) study to detect genes or genomic regions that are related to oviduct development in a chicken F₂ resource population by employing high-density 600 K single-nucleotide polymorphism (SNP) arrays. For oviduct length and weight, which exhibited moderate heritability estimates of 0.35 and 0.39, respectively, chromosome 1 (GGA1) explained 9.45% of the genetic variance, while GGA4 to GGA8 and GGA11 explained over 1% of the variance. Independent univariate genome-wide screens for oviduct length and weight detected 69 significant SNPs on GGA1 and 49 suggestive SNPs on GGA1, GGA4, and GGA8. One hundred and fourteen suggestive SNPs were associated with oviduct length, while 73 SNPs were associated with oviduct weight. The significant genomic regions affecting oviduct weight ranged from 167.79–174.29 Mb on GGA1, 73.16–75.70 Mb on GGA4, and 4.88–4.92 Mb on GGA8. The genes CKAP2, CCKAR, NCAPG, IGFBP3, and GORAB were shown to have potential roles in oviduct development. These genes are involved in cell survival, appetite, and growth control. Our results represent the first GWA analysis of genes controlling oviduct weight and length. The identification of genomic loci and potential candidate genes affecting oviduct development greatly increase our understanding of the genetic basis underlying oviduct development, which could have an impact on the selection of egg quality.

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.