Identification of new genes and quantitative trait locis associated with growth curve parameters in F2 chicken population using genome-wide association study

Copy number variations affecting growth curve parameters in a crossbred chicken population

Copy number variations (CNVs) are key structural variations in the genome and may contribute to phenotypic differences. In this study, we used a F2 chicken population created from reciprocal crossing between fast-growing Arian broiler line and Urmia native chickens. The chickens were genotyped by 60 K SNP BeadChip, and PennCNV algorithm was used to detect genome-wide CNVs. The growth curve parameters of W0, k, L, Wf, Wi, ti and average GR were used as phenotypic data. The association between CNV and growth curve parameters was carried out using the CNVRanger R/Bioconductor package. Five CNV regions (CNVRs) were chosen for the validation experiment using qPCR. Gene enrichment analysis was done using WebGestalt. The STRING database was used to search for significant pathways. The results identified 966 CNVs and 600 CNVRs including 468 gains, 67 losses, and 65 both events on autosomal chromosomes. Validation of the CNVRs obtained from the qPCR assay were 79 % consistent with the prediction by PennCNV. A total of 43 significant CNVs were obtained for the seven growth curve parameters. The 416 genes annotated for significant CNVs. Six genes out of 416 genes were most related to growth curve parameters. These genes were LCP2, Dock2, CD80, CYFIP1, NIPA1 and NIPA2. Some of these genes in their biological process were associated with the growth, reproduction and development of cells or organs that ultimately lead to the growth of the body. The results of the study could pave the way for better understanding the molecular process of CNVs and growth curve parameters in birds.

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.

Structural variant landscapes reveal convergent signatures of evolution in sheep and goats

BACKGROUND

Sheep and goats have undergone domestication and improvement to produce similar phenotypes, which have been greatly impacted by structural variants (SVs). Here, we report a high-quality chromosome-level reference genome of Asiatic mouflon, and implement a comprehensive analysis of SVs in 897 genomes of worldwide wild and domestic populations of sheep and goats to reveal genetic signatures underlying convergent evolution.

RESULTS

We characterize the SV landscapes in terms of genetic diversity, chromosomal distribution and their links with genes, QTLs and transposable elements, and examine their impacts on regulatory elements. We identify several novel SVs and annotate corresponding genes (e.g., BMPR1B, BMPR2, RALYL, COL21A1, and LRP1B) associated with important production traits such as fertility, meat and milk production, and wool/hair fineness. We detect signatures of selection involving the parallel evolution of orthologous SV-associated genes during domestication, local environmental adaptation, and improvement. In particular, we find that fecundity traits experienced convergent selection targeting the gene BMPR1B, with the DEL00067921 deletion explaining ~10.4% of the phenotypic variation observed in goats.

CONCLUSIONS

Our results provide new insights into the convergent evolution of SVs and serve as a rich resource for the future improvement of sheep, goats, and related livestock.

A large-scale comparison of the meat quality characteristics of different chicken breeds in South China

Meat quality traits are essential for producing high-quality broilers, but the genetic improvement has been limited by the complexity of measurement methods and the numerous traits involved. To systematically understand the meat quality characteristics of different broiler breeds, this study collected data on slaughter performance, skin color, fat deposition, and meat quality traits of 434 broilers from 12 different breeds in South China. The results showed that there was no significant difference in the live weight and slaughter weight of various broiler breeds at their respective market ages. Commercial broiler breeds such as Xiaobai and Huangma chickens had higher breast muscle and leg muscle rates. The skin and abdominal fat of Huangma chickens cultivated in the consumer market in South China exhibited significantly higher levels of yellowness compared to other varieties. Concerning fat traits, we observed that Wenchang chickens exhibited a strong ability to fat deposition, while the younger breeds showed lower fat deposition. Additionally, there were significant positive correlations found among different traits, including traits related to weight, traits related to fat, and skin color of different parts. Hierarchical clustering analysis revealed that fast-growing and large broiler Xiaobai chickens formed a distinct cluster based on carcass characteristics, skin color, and meat quality traits. Principal component analysis (PCA) was used to extract multiple principal components as substitutes for complex meat quality indicators, establishing a chicken meat quality evaluation model to differentiate between different breeds of chickens. At the same time, we identified 46, 22, and 20 SNP loci and their adjacent genes that were significantly associated with muscle mass traits, fat deposition, and skin color through genome-wide association studies (GWAS). The above results are helpful for systematically understanding the differences and characteristics of meat quality traits among different breeds.

Genome-wide association study of growth curve parameters reveals novel genomic regions and candidate genes associated with metatarsal bone traits in chickens

The growth and development of chicken bones have an enormous impact on the health and production performance of chickens. However, the development pattern and genetic regulation of the chicken skeleton are poorly understood. This study aimed to evaluate metatarsal bone growth and development patterns in chickens via non-linear models, and to identify the genetic determinants of metatarsal bone traits using a genome-wide association study (GWAS) based on growth curve parameters. Data on metatarsal length (MeL) and metatarsal circumference (MeC) were obtained from 471 F2 chickens (generated by crossing broiler sires, derived from a line selected for high abdominal fat, with Baier layer dams) at 4, 6, 8, 10, and 12 weeks of age. Four non-linear models (Gompertz, Logistic, von Bertalanffy, and Brody) were used to fit the MeL and MeC growth curves. Subsequently, the estimated growth curve parameters of the mature MeL or MeC (A), time-scale parameter (b), and maturity rate (K) from the non-linear models were utilized as substitutes for the original bone data in GWAS. The Logistic and Brody models displayed the best goodness-of-fit for MeL and MeC, respectively. Single-trait and multi-trait GWASs based on the growth curve parameters of the Logistic and Brody models revealed 4 618 significant single nucleotide polymorphisms (SNPs), annotated to 332 genes, associated with metatarsal bone traits. The majority of these significant SNPs were located on Gallus gallus chromosome (GGA) 1 (167.433-176.318 Mb), GGA2 (96.791-103.543 Mb), GGA4 (65.003-83.104 Mb) and GGA6 (64.685-95.285 Mb). Notably, we identified 12 novel GWAS loci associated with chicken metatarsal bone traits, encompassing 35 candidate genes. In summary, the combination of single-trait and multi-trait GWASs based on growth curve parameters uncovered numerous genomic regions and candidate genes associated with chicken bone traits. The findings benefit an in-depth understanding of the genetic architecture underlying metatarsal growth and development in chickens.

Unravelling novel and pleiotropic genes for cannon bone circumference and bone mineral density in Yorkshire pigs

Leg weakness is a prevalent health condition in pig farms. The augmentation of cannon bone circumference and bone mineral density can effectively improve limb strength in pigs and alleviate leg weakness. This study measured forelimb cannon bone circumference (fCBC) and rear limb cannon bone circumference (rCBC) using an inelastic tapeline and rear limb metatarsal area bone mineral density (raBMD) using a dual-energy X-ray absorptiometry bone density scanner. The samples of Yorkshire castrated boars were genotyped using a 50K single-nucleotide polymorphism (SNP) array. The SNP-chip data were imputed to the level of whole-genome sequencing data (iWGS). This study used iWGS data to perform genome-wide association studies and identified novel significant SNPs associated with fCBC on SSC6, SSC12, and SSC13, rCBC on SSC12 and SSC14, and raBMD on SSC7. Based on the high phenotypic and genetic correlations between CBC and raBMD, multi-trait meta-analysis was performed to identify pleiotropic SNPs. A significant potential pleiotropic quantitative trait locus (QTL) regulating both CBC and raBMD was identified on SSC15. Bayes fine mapping was used to establish the confidence intervals for these novel QTLs with the most refined confidence interval narrowed down to 56 kb (15.11 to 15.17 Mb on SSC12 for fCBC). Furthermore, the confidence interval for the potential pleiotropic QTL on SSC15 in the meta-analysis was narrowed down to 7.45 kb (137.55 to137.56 Mb on SSC15). Based on the biological functions of genes, the following genes were identified as novel regulatory candidates for different phenotypes: DDX42, MYSM1, FTSJ3, and MECOM for fCBC; SMURF2, and STC1 for rCBC; RGMA for raBMD. Additionally, RAMP1, which was determined to be located 23.68 kb upstream of the confidence interval of the QTL on SSC15 in the meta-analysis, was identified as a potential pleiotropic candidate gene regulating both CBC and raBMD. These findings offered valuable insights for identifying pathogenic genes and elucidating the genetic mechanisms underlying CBC and BMD.

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.

A pangenome graph reference of 30 chicken genomes allows genotyping of large and complex structural variants

BACKGROUND

The red junglefowl, the wild outgroup of domestic chickens, has historically served as a reference for genomic studies of domestic chickens. These studies have provided insight into the etiology of traits of commercial importance. However, the use of a single reference genome does not capture diversity present among modern breeds, many of which have accumulated molecular changes due to drift and selection. While reference-based resequencing is well-suited to cataloging simple variants such as single-nucleotide changes and short insertions and deletions, it is mostly inadequate to discover more complex structural variation in the genome.

METHODS

We present a pangenome for the domestic chicken consisting of thirty assemblies of chickens from different breeds and research lines.

RESULTS

We demonstrate how this pangenome can be used to catalog structural variants present in modern breeds and untangle complex nested variation. We show that alignment of short reads from 100 diverse wild and domestic chickens to this pangenome reduces reference bias by 38%, which affects downstream genotyping results. This approach also allows for the accurate genotyping of a large and complex pair of structural variants at the K feathering locus using short reads, which would not be possible using a linear reference.

CONCLUSIONS

We expect that this new paradigm of genomic reference will allow better pinpointing of exact mutations responsible for specific phenotypes, which will in turn be necessary for breeding chickens that meet new sustainability criteria and are resilient to quickly evolving pathogen threats.

Identification of copy number variation in Tibetan sheep using whole genome resequencing reveals evidence of genomic selection

BACKGROUND

Copy number variation (CNV) is an important source of structural variation in the mammalian genome. CNV assays present a new method to explore the genomic diversity of environmental adaptations in animals and plants and genes associated with complex traits. In this study, the genome-wide CNV distribution characteristics of 20 Tibetan sheep from two breeds (10 Oula sheep and 10 Panou sheep) were analysed using whole-genome resequencing to investigate the variation in the genomic structure of Tibetan sheep during breeding.

RESULTS

CNVs were detected using CNVnator, and the overlapping regions of CNVs between individual sheep were combined. Among them, a total of 60,429 CNV events were detected between the indigenous sheep breed (Oula) and the synthetic sheep breed (Panou). After merging the overlapping CNVs, 4927 CNV regions (CNVRs) were finally obtained. Of these, 4559 CNVRs were shared by two breeds, and there were 368 differential CNVRs. Deletion events have a higher percentage of occurrences than duplication events. Functional enrichment analysis showed that the shared CNVRs were significantly enriched in 163 GO terms and 62 KEGG pathways, which were mainly associated with organ development, neural regulation, immune regulation, digestion and metabolism. In addition, 140 QTLs overlapped with some of the CNVRs at more than 1 kb, such as average daily gain QTL, body weight QTL, and total lambs born QTL. Many of the CNV-overlapping genes such as PPP3CA, SSTR1 and FASN, overlap with the average daily weight gain and carcass weight QTL regions. Moreover, VST analysis showed that XIRP2, ABCB1, CA1, ASPA and EEF2 differed significantly between the synthetic breed and local sheep breed. The duplication of the ABCB1 gene may be closely related to adaptation to the plateau environment in Panou sheep, which deserves further study. Additionally, cluster analysis, based on all individuals, showed that the CNV clustering could be divided into two origins, indicating that some Tibetan sheep CNVs are likely to arise independently in different populations and contribute to population differences.

CONCLUSIONS

Collectively, we demonstrated the genome-wide distribution characteristics of CNVs in Panou sheep by whole genome resequencing. The results provides a valuable genetic variation resource and help to understand the genetic characteristics of Tibetan sheep. This study also provides useful information for the improvement and breeding of Tibetan sheep in the future.

Early Growth and Development and Nonlinear Model Fitting Analysis of Ashidan Yak

Understanding animal growth plays an important role in improving animal genetics and breeding. In order to explore the early growth and development law of Ashidan yak, the body weight (BW), wither height (WH), body oblique length (BL) and chest girth (CG) of 260 female Ashidan yaks were measured. These individuals grew under grazing conditions, and growth traits were measured at 6, 12, 18 and 30 months of age. Then the absolute growth and relative growth of Ashidan yak were calculated, and five nonlinear models (Logistic model, Gompertz model, Brody model, von Bertalanffy model and Richards model) were used to fit the growth curve of Ashidan yak. The fitting effect of the model was evaluated according to MSE, AIC and BIC. The results showed that the growth rate of Ashidan yak was the fastest from 12 to 18 months old, and the growth was slow or even stagnant from 6 to 12 months old. The AIC and BIC values of the Richards model were the lowest among the five models, with an AIC value of 4543.98 and a BIC value of 4563.19. The Richards model estimated body weight at 155.642 kg. In summary, the growth rate of female Ashidan yak changes with the seasons, growing faster in warm seasons and slower in cold seasons. Richards model is the best model to describe the growth curve of female Ashidan yak in five nonlinear models.

Whole genome sequencing revealed genetic diversity, population structure, and selective signature of Panou Tibetan sheep

BACKGROUND

The detection of selective traits in different populations can not only reveal current mechanisms of artificial selection for breeding, but also provide new insights into phenotypic variation in new varieties and the search for genes associated with important traits. Panou sheep is a cultivated breed of Tibetan sheep in China with stable genetic performance, consistent appearance and fast growth and development after decades of artificial selection and cultivation. Due to long-term adaptation to the high altitude, cold and hypoxic environment in the plateau area, they may have formed a unique gene pool that is different from other Tibetan sheep breeds. To explore the genetic resources of Panou sheep, we used next-generation sequencing technology for the first time to investigate the genome-wide population structure, genetic diversity, and candidate signatures of positive selection in Panou sheep.

RESULTS

Comparative genomic analysis with the closely related species Oula sheep (a native breed of Tibetan sheep in China) was used to screen the population selection signal of Panou sheep. Principal component analysis and neighbor joining tree showed that Panou sheep and Oula sheep had differences in population differentiation. Furthermore, analyses of population structure, they came from the same ancestor, and when K = 2, the two populations could be distinguished. Panou sheep exhibit genetic diversity comparable to Oula sheep, as shown by observed heterozygosity, expected heterozygosity and runs of homozygosity. Genome-wide scanning using the Fst and π ratio methods revealed a list of potentially selected related genes in Panou sheep compared to Oula sheep, including histone deacetylase 9 (HDAC9), protein tyrosine kinase 2 (PTK2), microphthalmia-related transcription factor (MITF), vesicular amine transporter 1 (VAT1), trichohyalin-like 1 (TCHHL1), amine oxidase, copper containing 3 (AOC3), interferon-inducible protein 35 (IFI35).

CONCLUSIONS

The results suggest that traits related to growth and development and plateau adaptation may be selection targets for the domestication and breeding improvement of Tibetan sheep. This study provides the fundamental footprints for Panou sheep breeding and management.

Investigation of egg production curve in ostrich using nonlinear functions

In most countries, ostrich farming is considered a developing branch of the efficient poultry industry. The profitability of ostrich farm requires specific consideration of productions features such as the female fertility, egg production, hatchability, and growth performance. Hence, this study aimed to fit nonlinear functions to describe the ostrich egg production pattern to achieve the most appropriate and recommendable mathematical function for future studies. For this purpose, 14,507 daily records of 184 female ostriches in 5 production seasons (periods) during 2016 to 2021 were used. Five nonlinear functions including Incomplete gamma (Wood function), Corrected gamma (McNally), nonlinear Logistic (Yang), Logistic (Nelder), and Lokhorst were fitted for modeling the egg production curve in ostrich. The goodness of fit criteria's including Mean Square Error (MSE), Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and selection of the best function. The results indicated that the Wood and the McNally functions with a slight difference in all fitting criteria were the best-fitted functions and the Yang function with the highest values of MSE, LRT, AIC, BIC, were the most inappropriate function to describe the ostrich egg production curve. The McNally and the Wood can be recommended as appropriate functions to describe egg production during 5 production seasons in the studied ostrich flock.