Next Article in Journal
Continuous Physiological Monitoring of the Combined Exposure to Hypoxia and High Cognitive Load in Military Personnel
Previous Article in Journal
The Skin and Inflamm-Aging
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biology
Volume 12
Issue 11
10.3390/biology12111397
biology-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Whole Genome Resequencing Revealed the Genetic Relationship and Selected Regions among Baicheng-You, Beijing-You, and European-Origin Broilers

by
Kai Yang
1,†,
Jian Zhang
2,†,
Yuelei Zhao
1,
Yonggang Shao
3,
Manjun Zhai
3,
Huagui Liu
2 and
Lifan Zhang
1,*
1
College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
2
Institute of Animal Husbandry and Veterinary Medicine, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
3
College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China
*
Author to whom correspondence should be addressed.
†
These authors equally contributed to this work.
Biology 2023, 12(11), 1397; https://doi.org/10.3390/biology12111397
Submission received: 30 September 2023 / Revised: 30 October 2023 / Accepted: 1 November 2023 / Published: 3 November 2023
(This article belongs to the Section Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

Most Chinese local breeds have slow growth speed and high fat deposition, while European commercial chicken strains belong to fast-growing chicken breeds with low fat deposition. In general, the word “You” in China represents excessive fat deposition, and You-chicken is considered to be a typical fatten broiler. Among more than 100 local chickens in China, Baicheng-You and Beijing-You chickens are only two kinds of You-chicken breeds. However, their special genomic characteristics have not been well studied. This study analyzed the genome changes among these chicken breeds and obtained many candidate genes related to important traits of chickens, e.g., fat deposition, immune response, and melanin production. Our data provide valuable insights for understanding the formation of germplasm characteristics of these broiler breeds, which can be used to better protect and utilize the Chinese-You chickens.

Abstract

As the only two You-chicken breeds in China, Baicheng-You (BCY) and Beijing-You (BJY) chickens are famous for their good meat quality. However, so far, the molecular basis of germplasm of the two You-chicken breeds is not yet clear. The genetic relationship among BCY, BJY, and European-origin broilers (BRs) was analyzed using whole genome resequencing data to contribute to this issue. A total of 18,852,372 single nucleotide polymorphisms (SNPs) were obtained in this study. After quality control, 8,207,242 SNPs were applied to subsequent analysis. The data indicated that BJY chickens possessed distant distance with BRs (genetic differentiation coefficient (FST) = 0.1681) and BCY (FST = 0.1231), respectively, while BCY and BRs had a closer relationship (FST = 0.0946). In addition, by using FST, cross-population extended haplotype homozygosity (XP-EHH), and cross-population composite likelihood ratio (XP-CLR) methods, we found 374 selected genes between BJY and BRs chickens and 279 selected genes between BCY and BJY chickens, respectively, which contained a number of important candidates or genetic variations associated with feather growth and fat deposition of BJY chickens and potential disease resistance of BCY chickens. Our study demonstrates a genome-wide view of genetic diversity and differentiation among BCY, BJY, and BRs. These results may provide useful information on a molecular basis related to the special characteristics of these broiler breeds, thus enabling us to better understand the formation mechanism of Chinese-You chickens.

1. Introduction

In the list of chicken genetic resources in China, only two indigenous You-chicken breeds, namely Baicheng-You (BCY) and Beijing-You (BJY), are well known for their fat characteristics [1]. In general, the Chinese word “You” often means excessive fat deposition, which is considered a crucial economic trait in the broiler industry because of its correlation with meat quality and the cost of production. Among these two chicken breeds, BJY chickens originate from Beijing and have yellow feathers, tender meat, high fat deposition, and a unique appearance, such as feathered legs and polydactyly [1,2]. BCY chickens originate from Baicheng, Xinjiang province, which is nearly 4000 km away from Beijing. BCY chickens are mainly black and brown, show strong resistance to disease and cold, and get their name from the presence of a fat layer under the skin after fattening [1,3]. Currently, with the development of the economy, there is an increasing demand for high-quality animal products. As famous local chicken breeds in China, BCY and BJY chickens can provide fresh and delicious chicken products and be used as breeding materials to cultivate new synthetic lines or breeds with high meat quality, indicating that these two chicken breeds have good market development prospects. However, to date, the genetic discrepancy between the two Chinese-You chickens remains unknown.
In recent years, as a new sequencing technique that can detect all genomic information of individuals, genome resequencing has become the primary method for studying population genetics in chickens. For example, discovering signatures of selection in Iranian indigenous chicken ecotypes [4], digging the genomic adaptive changes in modern chickens [5], identifying the genes associated with tropical and frigid environments in chickens [6], skin color in Xichuan black-bone chickens [7], and the genetic basis of meat yield in chickens [8]. These data demonstrate that genome resequencing is a comprehensive and reliable method to explore selected regions and important functional genes related to economically important traits in chickens.
However, knowledge of the germplasm characteristics of these two Chinese You-chickens achieved by genome resequencing remains limited. Therefore, by using the data from two fast-growing European-origin broiler lines (BRs), including broiler line A (BRA) and broiler line B (BRB), as reference populations, the genetic structure and selected regions of BCY, BJY, and BRs were analyzed, thus providing a deeper understanding of the germplasm resources and the basis of molecular breeding of Chinese-You chickens.

2. Materials and Methods

2.1. Birds

Genome resequencing data from 103 individuals were used in the present study. In particular, we sequenced 31 BCY chickens (16 males and 15 females) from the BCY Chicken Conservation Farm (Baicheng, Xinjiang, China) and 32 BJY chickens (16 males and 16 females) from the BJY Chicken Conservation Farm, Beijing Academy of Agriculture and Forestry Sciences (Beijing, China). The BCY population was established in 2007 and maintained at a population size of more than 3000 birds. The BJY population was developed in 1972; the size of the conservation population varies between 3000 and 4000 birds in each generation. All animal handling and collection procedures were approved by the Animal Ethics Committee of the Nanjing Agricultural University (No. 20220318053). Additionally, resequencing data, including that of 20 BRA and 20 BRB chickens originating from Germany and France, respectively, were downloaded from the European Nucleotide Archive (No. PRJEB30270), which was produced in a previous study [5].

2.2. Library Construction and Sequencing

DNA was extracted from the blood of BCY and BJY chickens using the phenol-chloroform extraction method. DNA purity, integrity, and quantification were detected using a NanoDrop 2000 (ThermoFisher Scientific, San Jose, CA, USA), an agarose electrophoresis, and a Qubit Fluorometer (ThermoFisher Scientific, San Jose, CA, USA), respectively. Libraries for sequencing were set up using a DNA library construction kit (Illumina, San Diego, CA, USA) and sequenced using the Illumina NovaSeq 6000 sequencing platform (Berry Genomics, Beijing, China).

2.3. Genome Mapping, SNP Calling, and Filtering

After removing reads with an adaptor or N ratio > 10% and low-quality reads, we mapped the remaining clean reads to the chicken reference genome (Gallus gallus_GRCg6a) using the Burrows–Wheeler Aligner v0.7.15 [9] with the parameter “bwa mem -t 8 -M -R” and discarded the duplicate reads by PICARD 1.57 (github.com/broadinstitute/picard) with the parameter “picard MarkDuplicates”. SNPs were developed using GATK 4.0.0.0 [10] with the following parameters: “QD < 2.0”, “FS > 200.0”, “SOR > 10.0”, “MQRankSum < −12.5”, and “ReadPosRankSum < −8.0”. Filtering of high-quality SNPs was performed using PLINK v1.90 [11]. The Hardy–Weinberg equilibrium (HWE) test for autosomal SNPs was performed using PEDSTATS 0.6.12 [12].

2.4. Population Genetic Analysis

The population structure was analyzed using ADMIXTURE 1.3 [13], and four independent runs were performed with K ranging from 2 to 5 and then plotted using RStudio. Gene diversity within the population was estimated using the R package of adegenet with the “df2genind” function, while observed heterozygosity and IBS genetic distance were calculated using PLINK v1.90 with the commands as “--het” and “--cluster --matrix --noweb”, respectively. The phylogenetic tree was drawn using MEGA 7.0 [14]. The pairwise genetic differentiation coefficient (FST) values between the two populations were computed using VCFTOOLS 0.1.15 [15] with the command “--weir-fst-pop”.

2.5. Selected Region Analysis

The selected regions were determined using the FST and cross-population extended haplotype homozygosity (XP-EHH) methods as described in previous studies with slight modifications: (1) FST values of each single nucleotide polymorphism (SNP) were calculated using R 4.1.1 with the previous algorithm [16,17], while VCF files were phased using Beagle 4.1 and then used to calculate the XP-EHH values of each SNP using SELSCAN 1.3.0 [18,19]. (2) For the FST method, the Fisher’s exact test and Bonferroni correction were applied to calculate the difference in allele frequencies between BJY and BRs chickens and between BCY and BJY chickens, respectively. (3) SNPs with the 0.01% highest FST values and corrected p values < 0.01 or 0.1% highest |XP-EHH| values were regarded as extremely significant SNPs. (4) SNPs with FST values ≥ 0.4 and corrected p < 0.01 or |XP-EHH| values ≥ 1 were considered as significant SNPs. (5) Centering on the extremely significant SNPs, neighboring SNPs were used to define the boundaries of the region until more than two consecutive non-significant SNPs were encountered. When a region had more than five significant SNPs but no extremely significant SNPs, it was also regarded as a selected region.
The cross-population composite likelihood ratio (XP-CLR) method was also used to identify the selected region using the XPCLR program [20]. The parameters were set as 25 kb length for non-overlapping sliding windows, a maximum of 500 SNPs for each window, and 0.95 for linkage disequilibrium cutoff to apply for weighting. A region with the top 1% of xpclr_norm values was defined as a candidate region.
Genes from the above-selected regions were identified using the annotation of the chicken reference genome (Gallus gallus_GRCg6a.104).

3. Results

3.1. Characteristics of Sequencing Data

A total of 343 GB of data from 63 Chinese-You chickens was produced by genome resequencing. As shown in Table S1, an average of 30,686,143 clean reads was obtained after quality control. The average Q30, GC content, and cover depth were 94.48%, 42.82%, and 8.20×, respectively. Moreover, an average mapping rate of 98.54% was obtained using the chicken reference genome.

3.2. Identification of SNPs in BCY, BJY, and BRs Chickens

Combined with the sequencing data from 20 BRA and 20 BRB individuals, 18,852,372 SNPs were discovered in 103 individuals. After SNP filtering (Table S2), 8,207,242 autosomal SNPs were included in the selected region analysis. Meanwhile, a subset containing 5,158,483 autosomal SNPs with MAF ≥ 0.2 was applied for genetic diversity analysis.

3.3. Accessing the Genetic Diversity

Population structure analysis showed that BRA, BRB, BCY, and BJY chickens formed four independent populations (K = 4) (Figure 1A,B). Moreover, BRA and BRB formed one cluster in the phylogenetic tree (Figure 1D). Since BRA and BRB originated in Europe, they were merged as one population (BRs) and compared with the two Chinese You-chickens in the subsequent analysis. The results from the pairwise FST indicated a relatively large genetic differentiation between BJY and BRs (FST = 0.1681) and between BCY and BJY (FST = 0.1231), whereas there was a closer genetic distance between BCY and BRs (FST = 0.0946) (Figure 1C). Furthermore, the observed heterozygosity of BCY, BJY, and BRs was 0.3500, 0.3457, and 0.3566, respectively, whereas the gene diversity of BCY, BJY, and BRS was 0.5513, 0.5060, and 0.5351, respectively.

3.4. Selected Region Analysis between BJY and BRs Chickens

A total of 11,407 selected regions, including 1779 unique genes from the FST method, 1600 selected regions, including 428 unique genes from the XP-EHH test, and 384 selected regions, including 243 unique genes from XP-CLR analysis (Tables S3–S5), were identified between BJY and BRs chickens. Among these, 374 genes discovered by at least two of the three methods were defined as the final candidate genes (Table S6). Moreover, we identified many candidate genes associated with important economic traits such as INSR, ELOVL5, LCAT, and NOC3L (Table 1). Additionally, some candidate genes also contained SNPs with extremely high FST or XP-EHH values or were located in regions with extremely high XP-CLR scores, such as IGF-I, FXR1, NOX4, ASNS, UMAD1, GLCCI1, and ICA1 (Figure 2A–C).

3.5. Selected Region Analysis between BCY and BJY Chickens

A total of 5141 selected regions, including 926 unique genes from the FST method, 2382 selected regions, including 499 unique genes from the XP-EHH test, and 384 selected regions, including 219 unique genes from the XP-CLR analysis (Tables S7–S9), were identified between BCY and BJY chickens. A total of 279 genes were identified as final candidate genes with at least two of the three methods (Table S10). Among them, many candidate genes associated with important economic traits were identified, including ASIP, PAH, IL18, PLXNA4, and ESRRG (Table 2). Additionally, some candidate genes also contained SNPs with extremely high FST or XP-EHH values or were located in regions with extremely high XP-CLR scores, such as ELMOD1, CCDC6, SPTLC2, and OMA1 (Figure 3A–C).

3.6. Identification of Selected Genes in BJY Chickens

After the identification of 374 and 279 selected genes between BJY and BRs chickens and between BCY and BJY chickens, respectively, we observed that 104 genes overlapped (Table S11), including DNAJB6, WNT11, WNT9A, SHH, and LMBR. These genes may represent the unique genomic characteristics of BJY chickens, distinguishing them from BCY and BRs chickens.

4. Discussion

In the present study, we found that BJY and BRs chickens have the least genetic relationship, whereas BCY and BRs chickens have a greater genetic relationship (Figure 1C). This is not surprising because the other chicken breeds, including Tulufan and Hetian chickens, originating from Xinjiang, and European-origin chickens, were clustered in the same clade, whereas the chicken breeds originating from Beijing belonged to a different clade [56], which is similar to our results. From a genome-wide perspective, this is the first report that BCY and BJY chickens have a distant genetic relationship compared to European-origin chickens, although they are all called “You-chicken” in China. Among these chicken breeds, the results of observed heterozygosity and gene diversity analyses indicated that BJY chickens had lower genetic diversity. Overall, our results provide insight into the genetic structure and genetic relationship between two European-origin chicken lines and two Chinese You-origin chicken breeds.
BJY chicken is a slow-growing broiler breed with excessive fat deposition, whereas BRs chickens are fast-growing broiler strains. As expected, several genes related to muscle growth and fat deposition, such as IGF-I, INSR, ELOVL5, LCAT, and NOC3L, were identified between BJY and BRs chickens (Table 1). As a key gene related to body growth and development, IGF-I can promote the growth of lean meat and feed efficiency of broilers [57], and its polymorphisms are correlated with body mass, breast muscle weight, and abdominal fat deposition in different broiler lines [21,58]. INSR, a cognate receptor of IGF-I, was found to be involved in fat deposition in Ethiopian indigenous sheep [39]. Consequently, our study provides further evidence for the function of IGF-I and INSR in the growth of broilers. ELOVL5 is a fatty acid elongase, and its mutations were found to affect subcutaneous fat thickness in cattle [59]. In chickens, the expression of the miR-10a-5p-ELOVL5 pair has been associated with abdominal adipocyte differentiation [29]. LCAT is considered a new adipokine that has higher expression levels in visceral fat tissues in broilers than in layers [36], whereas NOC3L is considered an influencing factor for promoting adipogenesis in mice [32]. These studies confirmed that ELOVL5, LCAT, and NOC3L have important functions in adipogenesis. Thus, our data may indicate a new clue for their function in chicken fat deposition. In addition, we found that some genes with extremely high FST or X-PEHH values are involved in other economic traits. For example, FXR1, ASNS, and NOX4 affect heat stress [23,24,34], and UMAD1, GLCCI1, and ICA1 affect eggshell color (Table 1 and Figure 2A,B) [25]. Modern broilers are increasingly vulnerable to heat stress because of their excessive growth rate. Mutations in FXR1, ASNS, and NOX4 might result in different responses of BJY and BRs chickens to thermal stress, but this hypothesis needs further verification. Recently, using the same BRA and BRB broiler populations, another study found that UMAD1, GLCCI1, and ICA1 are located at eggshell color-related QTLs [25]. The eggshell color of BJY chickens is pink or light pink [1], whereas that of BRs chickens is brown or light brown (https://aviandiv.fli.de/, accessed on 1 January 2022), implying that there are differences in eggshell color between BJY and BRs chickens. Our data also suggest that these genes may be good candidates for eggshell color traits. However, further investigations are needed to elucidate the mechanism by which UMAD1, GLCCI1, and ICA1 affect eggshell color in chickens.
As described above, BCY and BJY are the only two kinds of You-chickens in China, but there is a distant genetic relationship between them. BCY chickens mainly have black and brown feathers, whereas BJY chickens have yellow feathers. In this study, two important genes involved in melanin formation, ASIP and PAH, were observed in the selected regions of these two chicken breeds (Table 2). As a well-known regulator of melanin synthesis, the expression and polymorphisms of ASIP have been shown to affect feather color in chickens [54]. PAH is a phenylalanine hydroxylase that converts phenylalanine to tyrosine, a rate-limiting substrate for melanin formation. In silkworms, PAH is necessary for melanin biosynthesis [41]. However, its role in chicken remains unclear. Our data provide a new concept that explains the action of ASIP and PAH on melanin formation in chickens. Interestingly, many genes involved in adaptability to harsh environments were also identified in the selected regions. The annual average temperature and precipitation in Baicheng are 7.6 °C and 171 mm, respectively, whereas the annual average temperature and precipitation in Beijing are 11 °C and 644 mm, respectively [1], indicating that the climate in Baicheng is colder and drier than that in Beijing. As a result, BCY chickens may have stronger immunity to adapt to harsh environments. We discovered that IL18, PLXNA4, SPTLC2, and MARCH1 are involved in immune responses (Table 2). As a cytokine of the IL-1 family, IL18 has been shown to increase immune responses in multiple chicken diseases [60] and is regarded as an adaptive gene for tropical and harsh environments in chickens [61,62], which is similar to our data. Moreover, PLXNA4 and SPTLC2 are regulators of T cell-mediated immune responses and protective immunity [40,51], whereas MARCH1 is a multifunctional regulator of adaptive immunity [50]. Our results demonstrated that these genes could function in chicken immune responses. Furthermore, extremely high FST values were observed for ELMOD1 (Table 2 and Figure 3A), a gene involved in the development of hair cells [63]. Mutations in ELMOD1 caused deafness and hair cell dysfunction in mice [64]. However, to date, the role of ELMOD1 in chickens has not been reported. Overall, our research provides a first-hand reference for understanding the genomic differences between these two Chinese You chickens.
Additionally, we found that some important genes related to feather growth were selected in BJY chickens (Table S11), including SHH, WNT9A, and WNT11. As a key gene in hair follicle development in feathers, SHH is expressed during feather formation in chickens [65] and mainly affects mitosis and morphogenesis during the development of feather buds [66]. Silencing and overexpression of SHH resulted in irregular and enlarged feather buds during feather development in chickens, respectively [67,68]. WNT9A affects feather growth in birds [69], whereas WNT11 influences the size and shape of feather buds, and indels in this gene are related to the feathered-leg character of chickens [70,71]. These studies showed that SHH, WNT9A, and WNT11 may play important roles in feather growth. Compared to BCY and BRs chickens, BJY chickens have unique feather growth characteristics, including tibial feathers, toe feathers, and beards [1]. Accordingly, our data might provide new evidence for the role of SHH, WNT9A, and WNT11 in feather growth in BJY chickens. More interestingly, the three nearest neighbors to SHH, including NOM1, LMBR1, and RBM33, were identified as selection genes in BJY chickens compared to BCY and BRs chickens (Table S11). These genes belong to the long-range regulatory domain of SHH [72]. Previously, mutations in LMBR1 were associated with polydactyly in multiple chicken populations, including BJY chickens [73,74]. However, the functions of NOM1 and RBM33 in agricultural animals have not been clarified. Our results imply that these genes might be related to the unique characteristics of BJY chickens; however, confirmation of this assumption requires further study.

5. Conclusions

In summary, this study revealed the genetic relationships among BRs, BCY, and BJY chickens. Moreover, selected regions containing a large number of functional genes related to specific breed characteristics were also discovered, such as SHH, WNT9A, and WNT11 for feather growth and ELOVL5, LCAT, and NOC3L for fat deposition in BJY chickens, IL18, PLXNA4, SPTLC2, and MARCH1 for harsh environmental adaptability in BCY chickens, as well as IGF-I for growth and UMAD1, GLCCI1, ICA1 for eggshell color in BRs chickens. These results provide valuable clues for elucidating the formation of fat deposition and potential disease resistance in the broilers, which can be used to improve the production performance of local chicken breeds while preserving their appearance features in subsequent breeding.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology12111397/s1, Table S1: Characteristics of sequencing data in Baicheng-You and Beijing-You chickens; Table S2: Quality-control for filtering SNPs. Table S3: Candidate genes between Beijing-You and European-origin broilers (BRs) identified by the FST method; Table S4: Candidate genes between Beijing-You and European-origin broilers (BRs) identified by the XP-EHH method; Table S5: Candidate genes between Beijing-You and European-origin broilers (BRs) identified by the XP-CLR method; Table S6: Overlapping genes between Beijing-You and European-origin broilers (BRs) identified using at least two methods; Table S7: Candidate genes between Baicheng-You and Beijing-You chickens identified by the FST method; Table S8: Candidate genes between Baicheng-You and Beijing-You chickens identified by the XP-EHH method; Table S9: Candidate genes between Baicheng-You and Beijing-You chickens identified by the XP-CLR method; Table S10: Overlapping genes between Baicheng-You and Beijing-You chickens identified using at least two methods; Table S11: The selected genes in Beijing-You chickens compared with Baicheng-You and European-origin broilers (BRs).

Author Contributions

Conceptualization, L.Z.; methodology, K.Y.; formal analysis, K.Y., J.Z., Y.Z. and Y.S.; supervision, M.Z. and H.L.; writing—original draft preparation, K.Y.; writing—review and editing, J.Z. and L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Beijing Indigenous Animal Genetic Resources Protection Project (202203310001), the Jiangsu Agricultural Science and Technology Innovation Fund (CX(22)3036), and the Accurate Identification Project of Livestock and Poultry Germplasm Resources (202114).

Institutional Review Board Statement

The experimental procedures in this study were approved by the Animal Ethics Committee of the Nanjing Agricultural University (Nanjing, China) (No. 20220318053).

Informed Consent Statement

Not applicable.

Data Availability Statement

Genome resequencing datasets: 31 BCY chickens and 32 BJY chickens are available from the SRA database (accession number PRJNAT792508).

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. China National Commission of Animal Genetic Resources. Animal Genetic Resources in China Poultry; China Agriculture Press: Beijing, China, 2011. [Google Scholar]
  2. Chen, J.L.; Zhao, G.P.; Zheng, M.Q.; Wen, J.; Yang, N. Estimation of genetic parameters for contents of intramuscular fat and inosine-5′-monophosphate and carcass traits in Chinese Beijing-You chickens. Poult. Sci. 2008, 87, 1098–1104. [Google Scholar] [CrossRef]
  3. Sarsenbek, A.; Wang, T.; Zhao, J.K.; Jiang, W. Comparison of carcass yields and meat quality between Baicheng-You chickens and Arbor Acres broilers. Poult. Sci. 2013, 92, 2776–2782. [Google Scholar] [CrossRef]
  4. Rostamzadeh Mahdabi, E.; Esmailizadeh, A.; Ayatollahi Mehrgardi, A.; Asadi Fozi, M. A genome-wide scan to identify signatures of selection in two Iranian indigenous chicken ecotypes. Genet. Sel. Evol. 2021, 53, 72. [Google Scholar] [CrossRef]
  5. Qanbari, S.; Rubin, C.J.; Maqbool, K.; Weigend, S.; Weigend, A.; Geibel, J.; Kerje, S.; Wurmser, C.; Peterson, A.T.; Brisbi, I.L.; et al. Genetics of adaptation in modern chicken. PLoS Genet. 2019, 15, e1007989. [Google Scholar] [CrossRef]
  6. Shi, S.; Shao, D.; Yang, L.; Liang, Q.; Han, W.; Xue, Q.; Qu, L.; Leng, L.; Li, Y.; Zhao, X.; et al. Whole genome analyses reveal novel genes associated with chicken adaptation to tropical and frigid environments. J. Adv. Res. 2023, 47, 13–25. [Google Scholar] [CrossRef]
  7. Li, D.; Sun, G.; Zhang, M.; Cao, Y.; Zhang, C.; Fu, Y.; Li, F.; Li, G.; Jiang, R.; Han, R.; et al. Breeding history and candidate genes responsible for black skin of Xichuan black-bone chicken. BMC Genom. 2020, 21, 511. [Google Scholar] [CrossRef]
  8. Tan, X.; Liu, R.; Zhao, D.; He, Z.; Li, W.; Zheng, M.; Li, Q.; Wang, Q.; Liu, D.; Feng, F.; et al. Large-scale genomic and transcriptomic analyses elucidate the genetic basis of high meat yield in chickens. J. Adv. Res. 2023. [Google Scholar] [CrossRef]
  9. Li, H.; Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 2010, 26, 589–595. [Google Scholar] [CrossRef]
  10. McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; et al. The genome analysis toolkit: A mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef]
  11. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.R.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.W.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef]
  12. Wigginton, J.E.; Abecasis, G.R. PEDSTATS: Descriptive statistics, graphics and quality assessment for gene mapping data. Bioinformatics 2005, 21, 3445–3447. [Google Scholar] [CrossRef] [PubMed]
  13. Alexander, D.H.; Novembre, J.; Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009, 19, 1655–1664. [Google Scholar] [CrossRef] [PubMed]
  14. Kumar, S.; Stecher, G.; Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 2016, 33, 1870–1874. [Google Scholar] [CrossRef] [PubMed]
  15. Danecek, P.; Auton, A.; Abecasis, G.; Albers, C.A.; Banks, E.; DePristo, M.A.; Handsaker, R.E.; Lunter, G.; Marth, G.T.; Sherry, S.T.; et al. The variant call format and VCFtools. Bioinformatics 2011, 27, 2156–2158. [Google Scholar] [CrossRef]
  16. Flori, L.; Fritz, S.; Jaffrezic, F.; Boussaha, M.; Gut, I.; Heath, S.; Foulley, J.L.; Gautier, M. The genome response to artificial selection: A case study in dairy cattle. PLoS ONE 2009, 4, e6595. [Google Scholar] [CrossRef]
  17. Nicholson, G.; Smith, A.V.; Jonsson, F.; Gustafsson, O.; Stefansson, K.; Donnelly, P. Assessing population differentiation and isolation from single-nucleotide polymorphism data. J. R. Stat. Soc. B 2002, 64, 695–715. [Google Scholar] [CrossRef]
  18. Browning, S.R.; Browning, B.L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 2007, 81, 1084–1097. [Google Scholar] [CrossRef]
  19. Szpiech, Z.A.; Hernandez, R.D. Selscan: An efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 2014, 31, 2824–2827. [Google Scholar] [CrossRef]
  20. Chen, H.; Patterson, N.; Reich, D. Population differentiation as a test for selective sweeps. Genome Res. 2010, 20, 393–402. [Google Scholar] [CrossRef]
  21. Zhou, H.; Mitchell, A.D.; McMurtry, J.P.; Ashwell, C.M.; Lamont, S.J. Insulin-like growth factor-I gene polymorphism associations with growth, body composition, skeleton integrity, and metabolic traits in chickens. Poult. Sci. 2005, 84, 212–219. [Google Scholar] [CrossRef]
  22. Zhang, H.; Du, Z.Q.; Dong, J.Q.; Wang, H.X.; Shi, H.Y.; Wang, N.; Wang, S.Z.; Li, H. Detection of genome-wide copy number variations in two chicken lines divergently selected for abdominal fat content. Bmc Genom. 2014, 15, 517. [Google Scholar] [CrossRef] [PubMed]
  23. Goel, A.; Ncho, C.M.; Choi, Y.H. Regulation of gene expression in chickens by heat stress. J. Anim. Sci. Biotechnol. 2021, 12, 11. [Google Scholar] [CrossRef] [PubMed]
  24. Hensen, S.M.; Heldens, L.; van Enckevort, C.M.; van Genesen, S.T.; Pruijn, G.J.; Lubsen, N.H. Heat shock factor 1 is inactivated by amino acid deprivation. Cell Stress Chaperones 2012, 17, 743–755. [Google Scholar] [CrossRef] [PubMed]
  25. Talebi, R.; Szmatola, T.; Meszaros, G.; Qanbari, S. Runs of homozygosity in modern chicken revealed by sequence data. G3-Genes Genom. Genet. 2020, 10, 4615–4623. [Google Scholar] [CrossRef] [PubMed]
  26. Liu, M.; Bai, J.; He, S.; Villarreal, R.; Hu, D.; Zhang, C.; Yang, X.; Liang, H.; Slaga, T.J.; Yu, Y.; et al. Grb10 promotes lipolysis and thermogenesis by phosphorylation-dependent feedback inhibition of mTORC1. Cell Metab. 2014, 19, 967–980. [Google Scholar] [CrossRef] [PubMed]
  27. Hartig, S.M.; He, B.; Long, W.; Buehrer, B.M.; Mancini, M.A. Homeostatic levels of SRC-2 and SRC-3 promote early human adipogenesis. J. Cell Biol. 2011, 192, 55–67. [Google Scholar] [CrossRef] [PubMed]
  28. Richards, M.P.; McMurtry, J.P. Expression of proglucagon and proglucagon-derived peptide hormone receptor genes in the chicken. Gen. Comp. Endocr. 2008, 156, 323–338. [Google Scholar] [CrossRef]
  29. Ma, X.; Sun, J.; Zhu, S.; Du, Z.; Li, D.; Li, W.; Li, Z.; Tian, Y.; Kang, X.; Sun, G. MiRNAs and mRNAs analysis during abdominal preadipocyte differentiation in chickens. Animals 2020, 10, 468. [Google Scholar] [CrossRef]
  30. Li, S.; Xue, T.; He, F.; Liu, Z.; Ouyang, S.; Cao, D.; Wu, J. A time-resolved proteomic analysis of transcription factors regulating adipogenesis of human adipose derived stem cells. Biochem. Biophys. Res. Commun. 2019, 511, 855–861. [Google Scholar] [CrossRef]
  31. Hedjazifar, S.; Khatib Shahidi, R.; Hammarstedt, A.; Bonnet, L.; Church, C.; Boucher, J.; Bluher, M.; Smith, U. The novel adipokine gremlin 1 antagonizes insulin action and is increased in type 2 diabetes and NAFLD/NASH. Diabetes 2020, 69, 331–341. [Google Scholar] [CrossRef]
  32. Johmura, Y.; Watanabe, K.; Kishimoto, K.; Ueda, T.; Shimada, S.; Osada, S.; Nishizuka, M.; Imagawa, M. Fad24 causes hyperplasia in adipose tissue and improves glucose metabolism. Biol. Pharm. Bull. 2009, 32, 1656–1664. [Google Scholar] [CrossRef] [PubMed]
  33. Ghosh, M.; Niyogi, S.; Bhattacharyya, M.; Adak, M.; Nayak, D.K.; Chakrabarti, S.; Chakrabarti, P. Ubiquitin ligase COP1 controls hepatic fat metabolism by targeting ATGL for degradation. Diabetes 2016, 65, 3561–3572. [Google Scholar] [CrossRef]
  34. Hofmann, I.; Casella, M.; Schnolzer, M.; Schlechter, T.; Spring, H.; Franke, W.W. Identification of the junctional plaque protein plakophilin 3 in cytoplasmic particles containing RNA-binding proteins and the recruitment of plakophilins 1 and 3 to stress granules. Mol. Biol. Cell 2006, 17, 1388–1398. [Google Scholar] [CrossRef]
  35. Guo, L.; Chao, X.; Huang, W.; Li, Z.; Luan, K.; Ye, M.; Zhang, S.; Liu, M.; Li, H.; Luo, W.; et al. Whole transcriptome analysis reveals a potential regulatory mechanism of lncRNA-FNIP2/miR-24-3p/FNIP2 axis in chicken adipogenesis. Front. Cell Dev. Biol. 2021, 9, 653798. [Google Scholar] [CrossRef]
  36. Bornelov, S.; Seroussi, E.; Yosefi, S.; Benjamini, S.; Miyara, S.; Ruzal, M.; Grabherr, M.; Rafati, N.; Molin, A.M.; Pendavis, K.; et al. Comparative omics and feeding manipulations in chicken indicate a shift of the endocrine role of visceral fat towards reproduction. BMC Genom. 2018, 19, 295. [Google Scholar] [CrossRef]
  37. Zaganjor, E.; Yoon, H.; Spinelli, J.B.; Nunn, E.R.; Laurent, G.; Keskinidis, P.; Sivaloganathan, S.; Joshi, S.; Notarangelo, G.; Mulei, S.; et al. SIRT4 is an early regulator of branched-chain amino acid catabolism that promotes adipogenesis. Cell Rep. 2021, 36, 109345. [Google Scholar] [PubMed]
  38. Li, J.; Lee, M.; Davis, B.W.; Lamichhaney, S.; Dorshorst, B.J.; Siegel, P.B.; Andersson, L. Mutations upstream of the TBX5 and PITX1 transcription factor genes are associated with feathered legs in the domestic chicken. Mol. Biol. Evol. 2020, 37, 2477–2486. [Google Scholar]
  39. Ahbara, A.; Bahbahani, H.; Almathen, F.; Al Abri, M.; Agoub, M.O.; Abeba, A.; Kebede, A.; Musa, H.H.; Mastrangelo, S.; Pilla, F.; et al. Genome-wide variation, candidate regions and genes associated with fat deposition and tail morphology in Ethiopian indigenous sheep. Front. Genet. 2018, 9, 699. [Google Scholar] [CrossRef]
  40. Yamamoto, M.; Suzuki, K.; Okuno, T.; Ogata, T.; Takegahara, N.; Takamatsu, H.; Mizui, M.; Taniguchi, M.; Chedotal, A.; Suto, F.; et al. Plexin-A4 negatively regulates T lymphocyte responses. Int. Immunol. 2008, 20, 413–420. [Google Scholar] [CrossRef]
  41. Chen, P.; Li, L.; Wang, J.; Li, H.; Li, Y.; Lv, Y.; Lu, C. BmPAH catalyzes the initial melanin biosynthetic step in Bombyx mori. PLoS ONE 2013, 8, e71984. [Google Scholar] [CrossRef]
  42. Yun, C.Y.; Hong, S.D.; Lee, Y.H.; Lee, J.; Jung, D.E.; Kim, G.H.; Kim, S.H.; Jung, J.K.; Kim, K.H.; Lee, H.; et al. Nuclear entry of CRTC1 as druggable target of acquired pigmentary disorder. Theranostics 2019, 9, 646–660. [Google Scholar] [CrossRef]
  43. Guo, Y.; Su, A.; Tian, H.; Zhai, M.; Li, W.; Tian, Y.; Li, K.; Sun, G.; Jiang, R.; Han, R.; et al. Transcriptomic analysis of spleen revealed mechanism of dexamethasone-induced immune suppression in chicks. Genes 2020, 11, 513. [Google Scholar] [CrossRef]
  44. Cao, P.; Dai, Q.; Deng, C.; Zhao, X.; Qin, S.; Yang, J.; Ju, R.; Wang, Z.; Lu, G.; Gu, X.; et al. Genome-wide signatures of mammalian skin covering evolution. Sci. China Life Sci. 2021, 64, 1765–1780. [Google Scholar] [CrossRef]
  45. Guo, H.; Yang, K.; Deng, F.; Ye, J.; Xing, Y.; Li, Y.; Lian, X.; Yang, T. Wnt3a promotes melanin synthesis of mouse hair follicle melanocytes. Biochem. Biophys. Res. Commun. 2012, 420, 799–804. [Google Scholar] [CrossRef] [PubMed]
  46. Rawofi, L.; Edwards, M.; Krithika, S.; Le, P.; Cha, D.; Yang, Z.; Ma, Y.; Wang, J.; Su, B.; Jin, L.; et al. Genome-wide association study of pigmentary traits (skin and iris color) in individuals of east asian ancestry. PeerJ 2017, 5, e3951. [Google Scholar] [PubMed]
  47. Goel, S.; DeCristo, M.J.; Watt, A.C.; BrinJones, H.; Sceneay, J.; Li, B.B.; Khan, N.; Ubellacker, J.M.; Xie, S.; Metzger-Filho, O.; et al. CDK4/6 inhibition triggers anti-tumour immunity. Nature 2017, 548, 471–475. [Google Scholar] [CrossRef] [PubMed]
  48. Müller, S.; Perdikari, A.; Dapito, D.H.; Sun, W.; Wollscheid, B.; Balaz, M.; Wolfrum, C. ESRRG and PERM1 govern mitochondrial conversion in brite/beige adipocyte formation. Front. Endocrinol. 2020, 11, 387. [Google Scholar] [CrossRef]
  49. Rubio-Tomas, T. The SMYD family proteins in immunology: An update of their obvious and non-obvious relations with the immune system. Heliyon 2021, 7, e07387. [Google Scholar] [PubMed]
  50. Liu, H.; Mintern, J.D.; Villadangos, J.A. MARCH ligases in immunity. Curr. Opin. Immunol. 2019, 58, 38–43. [Google Scholar] [CrossRef]
  51. Wu, J.; Ma, S.; Sandhoff, R.; Ming, Y.; Hotz-Wagenblatt, A.; Timmerman, V.; Bonello-Palot, N.; Schlotter-Weigel, B.; Auer-Grumbach, M.; Seeman, P.; et al. Loss of neurological disease HSAN-I-associated gene SPTLC2 impairs CD8(+) T cell responses to infection by inhibiting T cell metabolic fitness. Immunity 2019, 50, 1218–1231.e5. [Google Scholar] [CrossRef]
  52. Quiros, P.M.; Ramsay, A.J.; Sala, D.; Fernandez-Vizarra, E.; Rodriguez, F.; Peinado, J.R.; Fernandez-Garcia, M.S.; Vega, J.A.; Enriquez, J.A.; Zorzano, A.; et al. Loss of mitochondrial protease OMA1 alters processing of the GTPase OPA1 and causes obesity and defective thermogenesis in mice. Embo J. 2012, 31, 2117–2133. [Google Scholar] [CrossRef] [PubMed]
  53. Ballanti, E.; Perricone, C.; Greco, E.; Ballanti, M.; Di Muzio, G.; Chimenti, M.S.; Perricone, R. Complement and autoimmunity. Immunol. Res. 2013, 56, 477–491. [Google Scholar] [CrossRef] [PubMed]
  54. Yang, C.W.; Ran, J.S.; Yu, C.L.; Qiu, M.H.; Zhang, Z.R.; Du, H.R.; Li, Q.Y.; Xiong, X.; Song, X.Y.; Xia, B.; et al. Polymorphism in MC1R, TYR and ASIP genes in different colored feather chickens. 3 Biotech. 2019, 9, 203. [Google Scholar] [CrossRef] [PubMed]
  55. Gracie, J.A.; Robertson, S.E.; McInnes, I.B. Interleukin-18. J. Leukoc. Biol. 2003, 73, 213–224. [Google Scholar] [CrossRef] [PubMed]
  56. Wang, M.S.; Thakur, M.; Peng, M.S.; Jiang, Y.; Frantz, L.A.F.; Li, M.; Zhang, J.J.; Wang, S.; Peters, J.; Otecko, N.O.; et al. 863 genomes reveal the origin and domestication of chicken. Cell Res. 2020, 30, 693–701. [Google Scholar] [CrossRef]
  57. Tomas, F.M.; Pym, R.A.; McMurtry, J.P.; Francis, G.L. Insulin-like growth factor (IGF)-I but not IGF-II promotes lean growth and feed efficiency in broiler chickens. Gen. Comp. Endocr. 1998, 110, 262–275. [Google Scholar] [CrossRef]
  58. Bian, L.H.; Wang, S.Z.; Wang, Q.G.; Zhang, S.; Wang, Y.X.; Li, H. Variation at the insulin-like growth factor 1 gene and its association with body weight traits in the chicken. J. Anim. Breed. Genet. 2008, 125, 265–270. [Google Scholar] [CrossRef]
  59. Matsumoto, H.; Shimizu, Y.; Tanaka, A.; Nogi, T.; Tabuchi, I.; Oyama, K.; Taniguchi, M.; Mannen, H.; Sasazaki, S. The SNP in the promoter region of the bovine ELOVL5 gene influences economic traits including subcutaneous fat thickness. Mol. Biol. Rep. 2013, 40, 3231–3237. [Google Scholar] [CrossRef]
  60. Wang, C.; Li, X.; Zhang, C.; Wu, T.; Li, Y.; Cheng, X. A eukaryotic expression plasmid carrying chicken interleukin-18 enhances the response to newcastle disease virus vaccine. Clin. Vaccine Immunol. 2015, 22, 56–64. [Google Scholar] [CrossRef]
  61. Gu, J.; Liang, Q.; Liu, C.; Li, S. Genomic analyses reveal adaptation to hot arid and harsh environments in native chickens of China. Front. Genet. 2020, 11, 582355. [Google Scholar] [CrossRef]
  62. Tian, S.L.; Zhou, X.M.; Phuntsok, T.; Zhao, N.; Zhang, D.J.; Ning, C.Y.; Li, D.Y.; Zhao, H.B. Genomic analyses reveal genetic adaptations to tropical climates in chickens. iScience 2020, 23, 101644. [Google Scholar] [CrossRef] [PubMed]
  63. Krey, J.F.; Dumont, R.A.; Wilmarth, P.A.; David, L.L.; Johnson, K.R.; Barr-Gillespie, P.G. ELMOD1 stimulates ARF6-GTP hydrolysis to stabilize apical structures in developing vestibular hair cells. J. Neurosci. 2018, 38, 843–857. [Google Scholar] [CrossRef]
  64. Johnson, K.R.; Longo-Guess, C.M.; Gagnon, L.H. Mutations of the mouse ELMO domain containing 1 gene (Elmod1) link small GTPase signaling to actin cytoskeleton dynamics in hair cell stereocilia. PLoS ONE 2012, 7, e36074. [Google Scholar] [CrossRef]
  65. Nohno, T.; Kawakami, Y.; Ohuchi, H.; Fujiwara, A.; Yoshioka, H.; Noji, S. Involvement of the sonic-hedgehog gene in chick feather formation. Biochem. Biophys. Res. Commun. 1995, 206, 33–39. [Google Scholar] [CrossRef] [PubMed]
  66. McKinnell, I.W.; Turmaine, M.; Patel, K. Sonic hedgehog functions by localizing the region of proliferation in early developing feather buds. Dev. Biol. 2004, 272, 76–88. [Google Scholar] [CrossRef]
  67. Abu El-Magd, M.; Sayed-Ahmed, A.; Awad, A.; Shukry, M. Regulation of chick early B-cell factor-1 gene expression in feather development. Acta Histochem. 2014, 116, 577–582. [Google Scholar] [CrossRef] [PubMed]
  68. TingBerreth, S.A.; Chuong, C.M. Sonic hedgehog in feather morphogenesis: Induction of mesenchymal condensation and association with cell death. Dev. Dyn. 1996, 207, 157–170. [Google Scholar] [CrossRef]
  69. Schielzeth, H.; Forstmeier, W.; Kempenaers, B.; Ellegren, H. QTL linkage mapping of wing length in zebra finch using genome-wide single nucleotide polymorphisms markers. Mol. Ecol. 2012, 21, 329–339. [Google Scholar] [CrossRef]
  70. Chang, C.H.; Jiang, T.X.; Lin, C.M.; Burrus, L.W.; Chuong, C.M.; Widelitz, R. Distinct Wnt members regulate the hierarchical morphogenesis of skin regions (spinal tract) and individual feathers. Mech. Dev. 2004, 121, 157–171. [Google Scholar] [CrossRef]
  71. Yang, S.; Shi, Z.; Ou, X.; Liu, G. Whole-genome resequencing reveals genetic indels of feathered-leg traits in domestic chickens. J. Genet. 2019, 98, 47. [Google Scholar] [CrossRef]
  72. Anderson, E.; Devenney, P.S.; Hill, R.E.; Lettice, L.A. Mapping the Shh long-range regulatory domain. Development 2014, 141, 3934–3943. [Google Scholar] [CrossRef] [PubMed]
  73. He, C.; Chen, Y.; Yang, K.; Zhai, Z.; Zhao, W.; Liu, S.; Ding, J.; Dai, R.; Yang, L.; Xu, K.; et al. Genetic pattern and gene localization of polydactyly in Beijing fatty chicken. PLoS ONE 2017, 12, e0176113. [Google Scholar] [CrossRef] [PubMed]
  74. Huang, Y.Q.; Deng, X.M.; Du, Z.Q.; Qiu, X.; Du, X.; Chen, W.; Morisson, M.; Leroux, S.; Ponce de Leon, F.A.; Da, Y.; et al. Single nucleotide polymorphisms in the chicken Lmbr1 gene are associated with chicken polydactyly. Gene 2006, 374, 10–18. [Google Scholar] [CrossRef] [PubMed]
Biology 12 01397 g001
Figure 1. Population structure analysis. (A,B) Population structure of European-origin broiler lines (BRs), Baicheng-You (BCY), and Beijing-You (BJY) chickens revealed by ADMIXTURE analysis. (C) Multidimensional scaling plots among BCY, BJY, and BRs chickens using the PLINK v1.90 software. (D) Phylogenetic tree among BCY, BJY, and BRs chickens.
Figure 1. Population structure analysis. (A,B) Population structure of European-origin broiler lines (BRs), Baicheng-You (BCY), and Beijing-You (BJY) chickens revealed by ADMIXTURE analysis. (C) Multidimensional scaling plots among BCY, BJY, and BRs chickens using the PLINK v1.90 software. (D) Phylogenetic tree among BCY, BJY, and BRs chickens.
Biology 12 01397 g001
Biology 12 01397 g002
Figure 2. Selected region analysis between Beijing-You (BJY) and European-origin broiler lines (BRs). Manhattan plot of genetic differentiation coefficient (FST) values (A), cross-population extended haplotype homozygosity (XP-EHH) values (B), and cross-population composite likelihood ratio (XP-CLR) scores (xpclr_norm) (C) of BJY chickens compared to BRs chickens. The genes marked by the arrows contained SNPs with extremely high FST or |XP-EHH| values or were located in the regions with extremely high XP-CLR scores. The threshold of extremely high FST, |XP-EHH|, and xpclr_norm score was greater than 0.9, 2, and 20, respectively.
Figure 2. Selected region analysis between Beijing-You (BJY) and European-origin broiler lines (BRs). Manhattan plot of genetic differentiation coefficient (FST) values (A), cross-population extended haplotype homozygosity (XP-EHH) values (B), and cross-population composite likelihood ratio (XP-CLR) scores (xpclr_norm) (C) of BJY chickens compared to BRs chickens. The genes marked by the arrows contained SNPs with extremely high FST or |XP-EHH| values or were located in the regions with extremely high XP-CLR scores. The threshold of extremely high FST, |XP-EHH|, and xpclr_norm score was greater than 0.9, 2, and 20, respectively.
Biology 12 01397 g002
Biology 12 01397 g003
Figure 3. Selected region analysis between Baicheng-You (BJY) and Beijing-You (BCY) chickens. Manhattan plot of genetic differentiation coefficient (FST) values (A), cross-population extended haplotype homozygosity (XP-EHH) values (B), and cross-population composite likelihood ratio (XP-CLR) scores (xpclr_norm) (C) of BJY compared to BCY chickens. The genes marked by the arrows contained SNPs with extremely high FST or |XP-EHH| values or were located in the regions with extremely high XP-CLR scores. The threshold of extremely high FST, |XP-EHH|, and xpclr_norm score was greater than 0.9, 2, and 20, respectively.
Figure 3. Selected region analysis between Baicheng-You (BJY) and Beijing-You (BCY) chickens. Manhattan plot of genetic differentiation coefficient (FST) values (A), cross-population extended haplotype homozygosity (XP-EHH) values (B), and cross-population composite likelihood ratio (XP-CLR) scores (xpclr_norm) (C) of BJY compared to BCY chickens. The genes marked by the arrows contained SNPs with extremely high FST or |XP-EHH| values or were located in the regions with extremely high XP-CLR scores. The threshold of extremely high FST, |XP-EHH|, and xpclr_norm score was greater than 0.9, 2, and 20, respectively.
Biology 12 01397 g003
Table 1. Candidate genes associated with important functions between Beijing-You and European-origin broiler lines.
Table 1. Candidate genes associated with important functions between Beijing-You and European-origin broiler lines.
ChrPositionGeneMethodsFunction
15528109755330373IGF-IFST, XP-CLRFat content in chickens [21]
16583377466173568SOX5FST, XP-EHHAbdominal fat deposition in chickens [22]
1189025780189161699NOX4FST, XP-EHHHeat stress in chickens [23]
22466243024676154ASNSFST, XP-EHHHeat shock in mice [24]
22479412724862531UMAD1FST, XP-EHHEggshell color in chickens [25]
22489174224943662GLCCI1FST, XP-EHHEggshell color in chickens [25]
22495410725024569ICA1FST, XP-EHHEggshell color in chickens [25]
28085253280991947GRB10FST, XP-EHHLipolysis and thermogenesis in mice [26]
2116504849116596399NCOA2FST, XP-EHHPreadipocyte differentiation in humans [27]
32941043829492850GLP1RFST, XP-EHHChicken abdominal fat [28]
38824659188283573ELOVL5FST, XP-EHHAdipocyte differentiation in chickens [29]
48284312182879154MXD4FST, XP-EHHNovel regulators of adipogenesis in humans [30]
53077667130777369GREM1FST, XP-EHHNovel adipokines in humans [31]
62105845521075606NOC3LFST, XP-EHHHyperplasia in adipose tissue in mice [32]
872506157378011COP1FST, XP-EHHFat metabolism in mice [33]
91724702617278252FXR1FST, XP-EHHEnvironmental stress in humans [34]
91744193117531426PEX5LFST, XP-EHHChicken adipogenesis in liver [35]
1110349301041693LCATXP-EHH, XP-CLRNew adipokines in chickens [36]
1596701769678598SIRT4FST, XP-CLRAdipose pathology in mice [37]
151259820112638563TBX5FST, XP-CLRFeathered legs in chickens [38]
2842184374253117INSRFST, XP-EHHFat deposition in sheep [39]
Note: IGF-I: insulin like growth factor 1; SOX5: SRY-box 5; NOX4: NADPH oxidase 4; ASNS: asparagine synthetase; UMAD1: UBAP1-MVB12-associated (UMA) domain containing 1; GLCCI1: glucocorticoid induced 1; ICA1: islet cell autoantigen 1; GRB10: growth factor receptor bound protein 10; NCOA2: nuclear receptor coactivator 2; GLP1R: glucagon like peptide 1 receptor; ELOVL5: ELOVL fatty acid elongase 5; MXD4: MAX dimerization protein 4; GREM1: gremlin 1, DAN family BMP antagonist; NOC3L: NOC3 like DNA replication regulator; COP1: COP1 E3 ubiquitin ligase; FXR1: FMR1 autosomal homolog 1; PEX5L: peroxisomal biogenesis factor 5 like; LCAT: lecithin-cholesterol acyltransferase; SIRT4: sirtuin 4; TBX5: T-box 5; INSR: insulin receptor.
Table 2. Candidate genes associated with important functions between Baicheng-You and Beijing-You chickens.
Table 2. Candidate genes associated with important functions between Baicheng-You and Beijing-You chickens.
ChrPositionGeneMethodsFunction
128871483334587PLXNA4FST, XP-EHHImmune responses in mice [40]
15507882555114899PAHFST, XP-EHHMelanin biosynthetic in bombyx mori [41]
17747787777514773KPNA1FST, XP-EHHMelanogenic process in guinea pigs [42]
1141624410141643190TNFSF13BFST, XP-CLRImmune suppression in chickens [43]
1195484990195565945UVRAGFST, XP-EHHSkin innate immunity in mammals [44]
225519032634749WNT3AFST, XP-EHHMelanin synthesis in mice [45]
22117233321382361ZNF804BFST, XP-EHHSkin and iris color in humans [46]
22270346122836675CDK6FST, XP-EHHImmunotherapies in humans [47]
32006834320429542ESRRGFST, XP-EHHMitochondrial thermogenesis in mice [48]
33404859034412338SMYD3FST, XP-EHHImmune system in humans [49]
42343722323636944MARCH1FST, XP-EHHImmunomodulation in humans [50]
53947385539531939SPTLC2XP-EHH, XP-CLRProtective immunity in humans [51]
82665013026666710OMA1FST, XP-EHH, XP-CLRMitochondrial thermogenesis in mice [52]
1626096352624278C4FST, XP-EHHAutoimmunity in humans [53]
2015672191596889ASIPXP-EHH, XP-CLRChicken plumage color [54]
2461694866173722IL18XP-EHH, XP-CLRImmune regulation in humans [55]
Note: PLXNA4: plexin A4; PAH: phenylalanine hydroxylase; KPNA1: karyopherin subunit alpha 1; TNFSF13B: tumor necrosis factor superfamily member 13b; UVRAG: UV radiation resistance-associated; WNT3A: Wnt family member 3A; ZNF804B: zinc finger protein 804B; CDK6: cyclin-dependent kinase 6; ESRRG: estrogen-related receptor gamma; SMYD3: SET and MYND domain containing 3; MARCH1: membrane-associated ring-CH-type finger 1; SPTLC2: serine palmitoyltransferase long chain base subunit 2; OMA1: OMA1 zinc metallopeptidase; C4: complement 4; ASIP: agouti signaling protein; IL18: interleukin 18.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yang, K.; Zhang, J.; Zhao, Y.; Shao, Y.; Zhai, M.; Liu, H.; Zhang, L. Whole Genome Resequencing Revealed the Genetic Relationship and Selected Regions among Baicheng-You, Beijing-You, and European-Origin Broilers. Biology 2023, 12, 1397. https://doi.org/10.3390/biology12111397

AMA Style

Yang K, Zhang J, Zhao Y, Shao Y, Zhai M, Liu H, Zhang L. Whole Genome Resequencing Revealed the Genetic Relationship and Selected Regions among Baicheng-You, Beijing-You, and European-Origin Broilers. Biology. 2023; 12(11):1397. https://doi.org/10.3390/biology12111397

Chicago/Turabian Style

Yang, Kai, Jian Zhang, Yuelei Zhao, Yonggang Shao, Manjun Zhai, Huagui Liu, and Lifan Zhang. 2023. "Whole Genome Resequencing Revealed the Genetic Relationship and Selected Regions among Baicheng-You, Beijing-You, and European-Origin Broilers" Biology 12, no. 11: 1397. https://doi.org/10.3390/biology12111397

APA Style

Yang, K., Zhang, J., Zhao, Y., Shao, Y., Zhai, M., Liu, H., & Zhang, L. (2023). Whole Genome Resequencing Revealed the Genetic Relationship and Selected Regions among Baicheng-You, Beijing-You, and European-Origin Broilers. Biology, 12(11), 1397. https://doi.org/10.3390/biology12111397

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop