
==== Front
Poult Sci
Poult Sci
Poultry Science
0032-5791
1525-3171
Elsevier

S0032-5791(24)00819-8
10.1016/j.psj.2024.104240
104240
GENETICS AND MOLECULAR BIOLOGY
Genome-wide re-sequencing reveals selection signatures for important economic traits in Taihang chickens
Zhang Ran ⁎
Wang Wenjun ⁎
Zhang Zhenhong ⁎
Wang Dehe ⁎
Ding Hong †
Liu Huage †
Zang Sumin ⁎
Zhou Rongyan rongyanzhou@126.com
⁎1
⁎ College of Animal Science and Technology, Hebei Agricultural University, Baoding, Hebei Province, 071001, P.R. China
† Hebei Institute of Animal Science and Veterinary Medicine, Baoding, Hebei Province, 071000, P.R. China
1 Corresponding author: rongyanzhou@126.com
22 8 2024
11 2024
22 8 2024
103 11 1042402 4 2024
14 8 2024
© 2024 The Authors. Published by Elsevier Inc. on behalf of Poultry Science Association Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Taihang chickens is precious genetic resource with excellent adaptability and disease resistance, as well as high-quality eggs and meat. However, the genetic mechanism underlying important economic traits remain largely unknown. To address this gap, we conducted whole-genome resequencing of 66 Taihang and 15 White Plymouth rock chicken (Baiyu). The population structure analysis revealed that Taihang chickens and Baiyu are 2 independent populations. The genomic regions with strong selection signals and some candidate genes related to economic and appearance traits were identified. Additionally, we found a continuously selected 1.2 Mb region on chromosome 2 that is closely related to disease resistance. Therefore, our findings were helpful in further understanding the genetic architecture of the Taihang chickens and provided a worthy theoretical basis and technological support to improve high-quality Taihang chickens.

Key words

Taihang chicken
population structure
selection signature
whole-genome sequencing
==== Body
pmcINTRODUTION

The domestic chicken with phenotypic diversity plays a significant role in both the national economy and biological research (Zhuang et al., 2023). With regard to their source of origin, domestic chickens are divisible into 2 distinct categories: commercial breeds and indigenous breeds. Commercial breeds have undergone significant genetic modification and optimization to fulfill specific market demands, and indigenous breeds have naturally adapted and been selectively bred over time to thrive in their respective regional environments. Therefore, native breeds have higher genetic diversity compared with commercial breeds, which reflects in their ability to adapt the local conditions and used as an important genetic resource for chicken breeding and improvement.

The advent of whole genome sequencing (WGS) technology has led to an exponential increase in the number of genetic variants found in a single experiment and has enabled the study of candidate genes and causal variations. Especially, WGS has greatly promoted the mining of high-throughput genetic variants which have been applied to identify the genetic diversity (Bello et al., 2023; Tian et al., 2023), population structure (Zhang et al., 2023; Zhi et al., 2023), candidate genes (Cho et al., 2022; Cha et al., 2023; Guo et al., 2024; Xue et al., 2023) for major economic traits, and the screening of molecular markers (Guo et al., 2022), providing a solid foundation for revealing the genetic mechanisms underlying complex phenotypes in chickens.

Taihang chicken, an indigeous breed with distinctive phenotypic trait of mottled-feathered and green shanks (Figures 1A and 1B), is a high-quality germplasm resource in Hebei Province (Yue et al., 2020), boasting advantages such as low feed consumption, tolerance to roughage, strong adaptability and high disease resistance. Currently, with the development of the economy, there is an increasing demand for high-quality animal products. As excellent local chicken breeds, Taihang chickens can provide delicious chicken products and be used as breeding materials to cultivate new synthetic lines, indicating that Taihang chicken have good market development prospects. White Plymouth rock chicken (Baiyu) is a introduced meat-type breed with white feather color (Figures 1C and 1D) (Sato et al., 2007; Wang et al., 2024). However, knowledge of the germplasm characteristics of Taihang chickens achieved by genome resequencing remains limited. To provide a deeper understanding of the germplasm resources and the basis of molecular breeding of Taihang chickens, the genetic structure and selected regions were analyzed with the Baiyu as reference populations. The candidate genes in regions with strong selective signature associated with important economic traits were identified.Figure 1 The photos of Taihang and Baiyu chicken populations. (A–C) Female chicken; (B–D) male chicken.

Figure 1

METERIALS AND METHODS

Ethics Statement

All animal experimental procedures were approved and conducted in accordance with the guidelines of the Hebei Agricultural University Animal Care and Use Committee (Permit Number: HB/2019/03, Baoding city, Hebei Province, China).

Sample Information

The blood samples of 66 Taihang were collected in Taihang chicken genetic resources conservation population, and the blood samples of 15 Baiyu were also selected in white feather broiler breeding population. Each samples were from different families to guarantee sample representativeness. All selected Taihang individuals meet the typical characteristics of breeds with mottled-feathered and green shanks. The selected Baiyu individuals are purebred Rock broilers with white-feathered, yellow shank and excellent growth and meat performance. DNA was extracted from blood samples collected with breeders’ permission using a GenoPrep quick blood extraction kit (MolBreeding Company, Shijiazhuang city, Hebei Province, China). Samples were treated with RNase during extraction. The quality of isolated DNA was detected using Qubit 2.0 and library preparation using the GenoBaits DNA Library Prep Kit for illumina (ILM). The libraries were sequenced on the MGISEQ-2000/MGI-T7 platform developed by MGI Tech Company. Paired-end libraries with an insert size of 150 bp were constructed for individual. Raw sequence data used for variant calling.

Whole-Genome Genetic Variation Detection and Annotation

Quality control involved filtering out low-quality reads to obtain clean reads. Quality filtration involved removing (1) reads with ≧ 10% unidentified nucleotides, (2) reads with >50% bases having Phred quality scores of ≦20 and 3) reads aligned to the barcode adapter. Clean reads were then mapped to the Gallus gallus reference genome GRCg6a (http://ftp.ensembl.org/pub/release-106/fasta/gallus_gallus/dna) using BWA (version 0.7.15). The duplicated reads were then removed using the Picard package. Subsequently, we used GATK (version 4.0) to perform variants calling and SNPs were filtered with the following parameters “QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < −12.5″. SNPs exhibiting segregation distortions or sequencing errors were excluded. The VCFtools (version 0.1.15) software was used to perform filtering and quality control using the following criteria: maximum miss rate > 1 and minor allele frequency > 0.05. Finally, a total of 6,830,874 SNP was obtained for further analysis. The ANNOVAR (Wang et al., 2010) was employed to annotate genetic variants, the SNPs could be categorized into the following regions: exon, intron, intergenic, splice site, 3′ untranslated region (UTR), 5′ UTR, upstream and downstream. Functionally, they could be divided into the following types: synonymous mutation, missense mutation, gain or loss of stop codon and other functional annotations.

Phylogenetic Relationships and Population Structure

The Neighbor-joining (NJ) tree was constructed with PLINK (version 1.90) using the matrix of pairwise genetic distances and visualized with MEGA (version 5.0). Principal component analysis (PCA) was conducted using PLINK (version 1.90) through a dimensionality reduction clustering approach. Additionally, ADMIXTURE (version 1.30) was utilized to analyze the population structure. Given the assumed number of ancestral populations, estimate the proportion of variations in each individual's genome that originate from K ancestral populations. We calculated the population structure of 81 chickens from the 2 groups when K was set 2 to 4. The correlation coefficient (r2) between any 2 loci was calculated to evaluate linkage disequilibrium (LD) decay using the PopLDdecay (version 3.40).

Runs of Homozygosity and Inbreeding Coefficients Based on ROH (FROH)

Long homozygous fragments were scanned using PLINK (version 1.9). The following specific parameters were used to assess homozygosity: a sliding window of 50 SNP along the chromosome was used; each sliding window allowed a maximum of 1 heterozygote, 5 missing SNPs, a minimum length of runs of homozygosity (ROH) of 100 kb, a minimum density of 1 SNP per 50 kb and a maximum interval between consecutive SNP of 1,000 kb was permitted for each sliding window and the FROH was calculated.

Genome-Wide Selective Sweep

The Fixation Index (Fst), Tajima's D, π-ratio and cross-population extended haplotype homozygosity (XP-EHH) values were calculated using sliding windows with a window size of 100 kb and a step size of 10kb. Regions under selection were selected based on the condition that they belonged to the top 5% of windows.

Gene Functional Enrichment Analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene ontology (GO) terms were analyzed based on the candidate genes via Fst, Tajima's D, π-ratio and XP-EHH methods using KOBAS-intelligence (http://bioinfo.org/kobas) to investigate the biological enrichment of genes under selective pressure. The GO terms and KEGG pathways were considered to be significantly enriched only when the P ≤ 0.05.

Bioinformatics Analysis of Missense Mutations

We employ the RNAfold web server (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi). The SOPMA tool (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=npsa_sopma.html) was utilized to predict the secondary structure of proteins. To assess transmembrane regions, we employed the TMHMM (https://services.healthtech.dtu.dk/services/TMHMM-2.0/). For hazardous forecast analysis, we made use of the PolyPhen-2 online software (http://genetics.bwh.harvard.edu/pph2/). Finally, to evaluate the potential impact on protein secondary structure, we leveraged the Protter tool (http://wlab.ethz.ch/protter/start/).

RESULTS

Genomic Variants in the Taihang Chicken and Baiyu

A total of 81 individuals from 2 chicken breeds were genome re-sequenced with an average sequencing depth 10-fold of the genome. Approximately 13.8 million autosomal SNP were reported. Following filtering of minor allelic frequencies < 0.05 and call rates < 1, around 6.83 million autosomal SNP were eventually kept and utilized in subsequent experiments (Figure 2A). Most of the variations were annotated in introns (47.22%), exons (1.25%), intergenic region (47.04%), upstream (1.02%) and downstream (1.18%) of genes. This included 18,280 nonsynonymous SNPs and 67,930 synonymous SNPs (Figure 2B).Figure 2 Density (A) and type (B) of common SNPs between Taihang and Baiyu.

Figure 2

Population Structure

The analysis results based on PCA was performed with the results of the 3D plot show that all the chickens could be divided into 2 large clusters (Figure 3A). Then, a NJ tree was performed with the results of the PCA (Figure 3B). Structure results show that, When K = 3, Taihang have 2 ancestor components; When K = 4, each population has 2 ancestor components (Figure 3C); and when K = 2, each population is composed of a single ancestor component, resulting in the smallest Cross-Validation (CV) error value, and thereby indicating that the optimal result comprises 2 distinct groups (Figure 3D). In terms of the structure of the whole population, there was almost no gene exchange between the 2 breeds, indicating that the genetic distance between Taihang and Baiyu was relatively far.Figure 3 Genetic relationships and population structure between Taihang and Baiyu. (A) Principal component analysis (PCA) of 2 chicken populations. PCA1, PCA2 and PCA3 explained 65.6%, 17.3% and 17.1% of the observed variance, respectively. (B) Neighbor-joining (NJ) phylogenetic tree of 2 breeds. (C) Admixture analysis across 2 chicken populations. Proportions of genetic ancestry for 2 chicken populations with K = 2 to 4 (K represents the number of inferred ancestral populations. Different colors represent assumed ancestors. Each column represents an individual, where the lengths of the different colored segments indicate the proportion of that individual's genome that is accounted for by a particular ancestor, and the horizontal coordinates represent the name of the population). (D) Cross validation error rate (CV error).

Figure 3

Runs of Homozygosity and Linkage Disequilibrium Unveiled Genome-Wide Genetic Variation

In the ROH analysis, 417 ROH segments were found, with an average of 105.1 ROH per animal. From the length and number of ROH on each chromosome in the 2 populations, it is observed that ROH segments mostly range from 1 to 2Mb and are mainly distributed on the first 5 chromosome (Figures 4A and 4B). Through the calculation of FROH, it is found that the FROH value of sample Q4_2 is higher than that of other samples and the FROH value of Baiyu (0.0169) is generally higher than that of Taihang (0.0055) (Figure 4C).Figure 4 Runs of homozygosity (ROH) of Tai hang chicken and Baiyu. (A) The number and length of ROH distributed on each chromosome in Baiyu. (B) The number and length of ROH distributed on each chromosome in Taihang. (C) Total ROH length and Inbreeding coefficient (FROH) based on ROH.

Figure 4

By calculating the pairwise SNP linkage disequilibrium in the range of 1 to 300kb, it was found that the LD level gradually decreased with the increase in the physical distance between pairs of SNPs in each population and the decay was the fastest within the range of 1-50kb (Figure 5A). The LD coefficient r2 decreased from 0.6 to 0.1, and the LD decay rate of was higher than that of Taihang, which might be due to the high-intensity selection or inbreeding within the population of the Baiyu. Analysis of nucleotide diversity revealed that the polymorphism of the 2 populations was not hugely different (Figure 5B).Figure 5 The linkage disequilibrium analysis and nucleotide polymorphism analysis were conducted on the 2 populations of Taihang and Baiyu. (A) Decay of linkage disequilibrium in the 2 chicken populations. (B) Nucleotide polymorphism analysis.

Figure 5

Genome-Wide Scanning for Selection Signatures Identify Co-Selected Region

To detect genetic differentiation signals, we calculated Fst, π-ratio, XP-EHH, and Tajima's D values in 100kb windows spanning the genome for both chicken breeds. Candidate regions were identified as those exhibiting signals that fell within the top 5% of values. By analyzing the Fst values across various window areas between the Taihang and Baiyu populations, 1034 regions were found with Fst values exceeding 0.25 (Figure 6). When Fst > 0.25, it indicates a high degree of genetic differentiation between populations (Hall, 2022).Figure 6 The distribution of Fst values between Taihang and Baiyu.

Figure 6

Candidate regions were identified as those exhibiting signals that fell within the top 5% of values. A total of 4750 selected regions and 886 genes were identified based on top 5% Fst (≥ 0.1667). Notably, 2 regions stood out with particularly high Fst values of 0.635 and 0.655, located on chromosome 1 between 168,280,001 to 168,380,000 (ENOX1) and on chromosome 24 between 6,140,001 to 6,240,000 (BCO2), respectively (Figure 7A). Subsequently, Taihang as the target group and the Baiyu as the control group, a total of 2,415 regions and 899 genes were identified based on the top 5% results of Fst and π-ratio (Taihang/Baiyu) (≥1.2666) (Figures 7A and 7B, Figures 8A and 8B). Among these selected regions, 2,016 were identified in the Baiyu, primarily distributed across chromosome 1, 2, 3, 4, 5, 6 and 7 (Figure 8C). On the other hand, 399 selected regions were found in Taihang, mainly located on chromosome 1, 2, 3, 4, 5, 8 and 24 (Figure 8D). Through XPEHH (taihang-to-baiyu) and Tajima's D (taihang-to-baiyu), 1,364 candidate genes (XPEHH (taihang-to-baiyu) ≥ 1.547 or XPEHH (taihang-to-baiyu) ≤ -1.716) and 498 candidate genes (Tajima's D (taihang-to-baiyu) ≥ 2.757) were identified, respectively (Figures 7C and 7D).Figure 7 Genome-wide selection scan in Taihang and Baiyu. Using sliding window analysis (100 kb window size, 10 kb step size). (A) Selection signatures for Fst. (B) Selection signatures for π-ratio. (C) Selection signatures for XP-EHH. (D) Selection signatures for Tajima's D. Threshold (top 5%) of Fst, π-ratio and Tajima's D is marked with a horizontal black line, and XP-EHH is marked with a horizontal blue line and red line.

Figure 7

Figure 8 Selected regions based on Fst & π-ratio. (A)Taihang and Baiyu: Selected Regions Based on Fst & π-ratio. With the horizontal axis labeled as 0 as the dividing line, the top left region represents the top 5% selected signals for Taihang chickens based on the joint screening of Z(Fst) & π-ratio. On the other hand, the top right region depicts the top 5% selected signals for the Baiyu also obtained through the joint screening of Z(Fst) & π-ratio. (B) Distribution of 2415 Selection Signal Windows on chromosome in Taihang and Baiyu. (C) Distribution of 399 Selection Signal Windows on chromosome in Taihang. (D) Distribution of 2016 Selection Signal Windows on chromosome in Baiyu.

Figure 8

The 4 methods collectively identified 2,066 nonredundant candidate genes. To minimize false positives, the final list of candidate regions under positive selection was refined to include only those regions consistently identified by all 4 methods. Consequently, a refined list was compiled containing 237 overlapping genes (Figure 9).Figure 9 Venn plots of selected regional genes. Venn diagrams showing numbers of genes identified using Fst, π-ratio, Tajima's D and XP-EHH.

Figure 9

GO and KEGG Analysis

After integration, 237 common candidate genes detected in all 4 methods were further enriched in KEGG pathways and GO terms to gain a better understanding of their functions and signaling pathways (Figure 10).Figure 10 Functional enrichment analysis of 237 selected genes: GO and KEGG pathway enrichment analysis shows significant (P ≤ 0.05) terms, pathways, and associated genes in Baiyu vs. Taihang comparison. (A-C) The Sankey-Dot plot of the Top 10 GO terms. (A) Biological Process. (B) Cellular component. (C) Molecular Function. (D) The Sankey-Dot plot of the 10 significant KEGG pathways. The size of circles for each pathway represents counts of associated genes. The color of the circles indicates the P-value.

Figure 10

In GO analysis, such as nervous system development (GO:0,007,399; P = 0.0086), cell-cell adhesion mediated by cadherin (GO:0,044,331; P = 0.0114), cell growth (GO:0,016,049; P = 0.0050) and positive regulation of response to DNA damage stimulus (GO:0,016,339; P = 0.0135), which are related to bone and egg formation and growth development. In KEGG analysis, 3 significant enriched pathways were obtained, including Gap junction (gga04,540; P = 0.0061), Melanogenesis (gga04,916; P = 0.0086) and Hedgehog signaling pathway (gga04,340; P = 0.0289), which might be related to immunity and melanin formation. The above significantly enriched GO terms and KEGG pathways are involved with 34 genes: RBBP7,TUBB3, NPAS2, CDH1, SYT1, RAB11A, LLPH, RPL13, API5, SLC24A1, BRE, CDH4, CHRM3, MAD2L2, CTTN, WNT8A, IGFBP2, SOX1, TUBB6, DRAXIN, MBP, EIF4A3, VSX1, ACYP1, COL6A1, MC1R, KIF7, GRM5, EVC2, ADCY1, EVC, PLCB2, and TCF7L2. We can preliminarily infer that there are differences in certain traits between Taihang and Baiyu. After conducting a literature search, the identified 15 candidate genes related to specific traits can be classified into 3 categories (appearance, economics and immune) (Table 1).Table 1 Candidate genes associated with important functions between Taihang and Baiyu.

Table 1Character	Class	Gene	Reference	
Appearance	Feather	MC1R
GRM5	Li et al. (2021); Gu et al. (2024); Wang et al. (2022); Mastrangelo et al. (2020).	
Economics	reproductive	MAD2L2
TCF7L2
NPAS2	Pinto et al. (2007); Ding et al. (2023); Zhao et al. (2020); Du et al. (2022).	
	egg quality	CDH4
CTTN	Wan et al. (2017); Chen et al. (2024).	
	meat quality	CHRM3	Mohammadi et al. (2022).	
	Fatdeposition	COL6A1
KIF7	Zhang et al. (2020); Ling et al. (2023).	
	growth rate	IGFBP2	Hosnedlova et al. (2020); Leng et al. (2009).	
immune		EVC
EIF4A3
TUBB6	Muscatello et al. (2015); Gao et al. (2022); Gao et al. (2021).	

In addition, a strong selection scanning region of 1.2Mb length (chromosome 2: 102,300,000 to 103,500,000bp) has been verified by 4 selection scanning methods (Fst, Tajima's D, π-ratio, and XP-EHH) was continuously (Figure 11A and Table 2). The genes within these selected regions are: GREB1L, ESCO1, ABHD3, MIB1, GATA6, RBBP8, CABLES1, TMEM241, RIOK3, RMC1, NPC1, ANKRD29, LAMA3, TTC39C, OSBPL1 and IMPACT (Figure 11B). After extracting SNPs from the continuously selected 1.2Mb region, functional analysis was conducted to identify missense mutations. The genotype frequencies of these mutations were computed in 2 separate populations. Furthermore, the distribution of genotypes and allele frequency revealed large differences in these 5 genes between the 2 populations (Figure 11C).Figure 11 Selection analysis and genotype difference in Taihang and Baiyu. (A) Four selection signal methods jointly identify a 1.2Mb region on chromosome 2 that is under selection. (B) Genes contained within the selected region (2:102300000-103500000). (C) Genotype heatmap of 5 genes (ESCO1, GATA6, RBBP8, NPC1, and LAMA3) with missense mutation sites, with pink representing Baiyu and lake blue representing Taihang. Red/blue indicates homozygosity for the major allele.

Figure 11

Table 2 Four selection signal methods filter the aggregate of co-selected fragments (intervals greater than 1Mb).

Table 2Method	Chromosome	Start	End	Length	
Tajima's D	1	143.60	144.60	1.0	
1	161.60	162.60	1.0	
2	102.20	103.50	1.3	
4	70.60	72.20	1.6	
XP-EHH	2	81.90	83.10	1.2	
2	102.20	103.60	1.4	
π-ratio	1	142.23	143.49	1.3	
1	143.53	144.62	1.1	
1	161.60	162.64	1.0	
1	167.56	168.62	1.1	
2	102.14	103.58	1.4	
4	69.87	72.20	2.3	
Fst	1	141.52	143.18	1.7	
1	143.76	145.33	1.6	
1	167.58	168.76	1.2	
2	102.12	103.68	1.6	
3	15.57	16.64	1.1	

Among these 8 missense mutations, the difference of allelic frequency is the largest for 2 mutations, both of which are located in the RBBP8. The allele g. 102,845,737G on chromosome 2 displayed an abundant distribution (frequency = 0.83) in Taihang, whereas it showed an opposite pattern (frequency = 0.13) in Baiyu. The allele g. 102,846,736G on chromosome 2 displayed an abundant distribution (frequency = 0.82) in Taihang, whereas it showed an opposite pattern (frequency = 0.13) in Baiyu (Table 3). After calculating the genotype frequencies, it was found that there were absences of individuals with the GG, CC, GG, and TT genotype in Baiyu: g.102,645,868G > A, g.102,845,737G > C, g.103,060,097C > T, and g.103,064,058T > C, respectively. Additionally, there was an absence of AA genotype individuals at the g.102,645,868A > G in the Taihang (Figure 12).Table 3 Information of 8 missense mutation sites.

Table 3Chromosome position	gene	exon region	Nucleotide changes	Amino Acid Change	SIFTa	PolyPhen-2b	Allele - frequency	
	Baiyu	Taihang	
2_102434577	ESCO1	exon 3	G>A	p.R208C	0.05	0.741	A	0.73	0.15	
G	0.27	0.85	
2_102434690	G>A	p.A170V	0.3	0.497	A	0.70	0.14	
G	0.30	0.86	
2_102645868	GATA6	exon 5	G>A	p.S299N	0.19	0.017	A	0.83	0.06	
G	0.17	0.94	
2_102845737	RBBP8	exon 10	G>C	p.M284I	0.17	0.012	C	0.87	0.17	
G	0.13	0.83	
2_102846736	exon 11	G>A	p.V412M	0.1	0.85	A	0.87	0.18	
G	0.13	0.82	
2_103060097	NPC1	exon 10	C>T	p.D560N	0.24	0.0	T	0.93	0.31	
C	0.07	0.69	
2_103064058	exon 6	T>C	p.H223R	0.55	0.0	C	0.93	0.33	
T	0.07	0.67	
2_103158715	LAMA3	exon 15	G>A	p.A600T	0.53	0.962	A	0.83	0.23	
G	0.17	0.77	
a If the score falls below or reaches 0.05, the mutation is considered harmful. As the score approaches 1, the mutation exhibits progressively less destructive characteristics.

b When the score surpasses or equals 0.50, the mutation becomes intolerable. As the score increases, the destructiveness of the mutation also intensifies.

Figure 12 Histogram of genotype frequency distribution of 8 missense mutation sites in Taihang and Baiyu.

Figure 12

After haplotype analysis, it was discovered that the 8 missense mutation sites conformed to the principle of being located within or near haplotype blocks, thus validating the reliability of these sites (Figure 13).Figure 13 Haplotype analysis results of 1Kb upstream and downstream regions of 8 missense mutation sites.

Figure 13

Prediction of Functional Consequences and Structural Impacts of Missense Mutations

After predicting the mRNA secondary structures of ESCO1, GATA6, RBBP8, NPC1, and LAMA3, the free energy of the mRNA secondary structure increased and the stability decreased after mutations occurred at 6 sites (g.102434577G > A, g.102434690G > A, g.102645868G > A, g.102845737G > C, g.103060097C > T, g.103064058T > C, g.103158715G > A). Additionally, it was found that g.102846736G > A led to a decrease in free energy and the most significant change in structure. However, despite an increase in free energy after the mutation at g.102845737G > C, no structural change was observed (Figure S1-S5).

Through the prediction of the secondary structures of ESCO1, GATA6, RBBP8, NPC1, and LAMA3 proteins, we have observed the following changes in their structural components: Following mutations at g.102434577G > A, g.102434690G > A and g.102645868G > A, there was a decline in the percentages of Alpha helix and Extended strand, while the Random coil percentage increased. The mutation at g.102845737G > C did not affect the percentages of any protein secondary structure forms. Post the mutation at g.102846736 G> A, the Alpha helix percentage increased, whereas the percentages of other structural forms decreased. The mutation at g.103060097C > T led to an increase in the percentages of Alpha helix and Random coil, accompanied by a slight decrease in the Extended strand percentage. After the mutation at g.103064058T > C, all the percentages of protein secondary structure forms showed an upward trend. Finally, the mutation at g.103158715G > A caused a decrease in the percentages of Beta turn, Extended strand, and Random coil (Table 4). NPC1 gene is a G-protein receptor with 13 transmembrane structures (Trinh et al., 2018; Figure S6). The missense mutation (g.103064058T > C) located in first transmembrane region of NPC1 does not result in any changes to the protein structure (Figure S7). After a thorough analysis utilizing both SIFT and PolyPhen-2 to assess the potential effects of SNPs on protein function. To guarantee accuracy, we deemed any sites classified as harmful by both methods as genuine harmful mutations. Following filtering process, the g.102434577G > A located on ESCO1 was identified as a harmful mutation (Table 3).Table 4 Effects of 8 SNPs on protein secondary structure.

Table 4Gene	SNP	Alpha helix	Beta turn	Extended strand	Random coil	
ESCO1	g.102434577G	28.15	3.62	14.02	54.21	
g.102434577A	27.8	0	11.45	60.75	
g.102434690G	28.15	3.62	14.02	54.21	
g.102434690A	27.8	0	11.3	60.86	
GATA6	g.102645868G	14.99	4.39	11.11	69.51	
g.102645868A	14.47	4.65	9.3	71.58	
RBBP8	g.102845737G	31.89	1.74	9.44	56.94	
g.102845737C	31.89	1.74	9.44	56.94	
g.102846736G	31.89	1.74	9.44	56.94	
g.102846736A	32.43	1.52	9.33	56.72	
NPC1	g.103060097C	36.89	4.01	19.41	36.69	
g.103060097T	39.45	4.01	19.32	37.22	
g.103064058T	36.89	4.01	19.41	36.69	
g.103064058C	39.54	4.1	19.5	36.87	
LAMA3	g.103158715G	27.56	3.64	13.7	55.11	
g.103158715A	29.7	3.1	13.49	53.7	

DISSCUSSION

Baiyu is cultivated through multi-generation selective breeding, which has excellent growth and meat production capacity. However, stress resistance is relatively poor compared with local chickens (Cortés et al., 2022; Ahmad et al., 2023). Taihang are known for their delicious meat, disease resistance, and resilience. Currently, the breeding goal for Taihang is to improve meat production performance and preserve the genetic basis for stress resistance and appearance. Therefore, we choose Baiyu as the control group for analysis. We obtained the whole genome variations to analyze the population structure and selection signatures. Firstly, we proved that Taihang and Baiyu are independent groups without any relative relationship after analyzing population structure. Then, we identified relevant candidate regions, genes and variations using a variety of methods to superimpose selection signals. In the analysis of 4 selection signature, we found a long fragment on chromosome 2 that was continuously under selection.

Based on 10x whole-genome resequencing data, the results demonstrated a clear stratification between the 2 groups of Taihang and Baiyu, indicating no signs of hybridization and provides a solid foundation for subsequent analysis of selection signals. To detect population differences between Taihang and Baiyu by analyzing population parameters. According to some researchers’ suggestions, When 0.15 < Fst < 0.25, the genetic differentiation between populations is considered large (Hall et al., 2022). Our results showed that the average Fst value was 0.182, indicating that there was a moderate genetic differentiation between the 2 populations on the whole. Notably, the selected regions with the highest Fst are located in the ENOX1 gene on chromosome 1 and the BCO2 gene on chromosome 24, respectively. ENOX1 (Ecto NOX disulfide-thiol exchanger 1) is a highly conserved NADH oxidase that helps regulate the level of nicotinamide adenine dinucleotide (NADH) in various cells, including endothelial cells (Venkateswaran et al., 2014). A study has found that the ENOX1 is significantly associated with eggshell thickness (Liu et al., 2011). The difference of egg weight between Taihang and Baiyu may be caused by the variants in ENOX1 gene. BCO2 gene encoding beta-carotene dioxygenase 2 could cleave colorful carotenoids to colorless apocarotenoids by an asymmetric cleavage reaction. The SNPs in BCO2 gene are associated with yellow beak (Enbody et al., 2021), skin (Wang et al., 2023) and shank (Jin et al., 2016) in birds. The difference of beak, skin and shank between Taihang and Baiyu may be caused by the variants in BCO2 gene.

After analyzing Fst and π-ratio, it was discovered that Baiyu had larger selection regions compared to Taihang. The selected regions in Baiyu were larger than those in Taihang based on the analysis of Fst and π-ratio to screen for commonly selected regions, suggested the Baiyu have undergone adaptive evolution through years of breeding, more actively selecting gene variations related to specific traits. In contrast, Taihang may be more weak artificial selection. But relying solely on Fst or π-ratio may not be sufficient to accurately determine the existence and nature of selection pressure. Typically, it is necessary to combine other statistical methods to comprehensively assess the impact of selection pressure on the genome. A strongly selected region of up to 1.2Mb was discovered on chromosome 2 using the above 4 methods for detecting selection signals. Our findings implicate chromosome 2 as an important target for further research. A total of 16 genes were identified within the 1.2Mb region, including GREB1L, ESCO1, ABHD3, MIB1, GATA6, RBBP8, CABLES1, TMEM241, RIOK3, RMC1, NPC1, ANKRD29, LAMA3, TTC39C, OSBPL1 and IMPACT. With the exception of GREB1L, which is related to tibial quality traits (Lu et al., 2024), the majority of the genes mentioned above primarily play a role in cholesterol transport and metabolism, as well as virus replication. CABLES1 is not only a candidate gene for yellow feather traits but also involved in adipose tissue metabolism (Huang et al., 2020; Hetty et al., 2023). ABHD3 has associated with abdominal fat deposition (Linke et al., 2020). OSBPL1 and TMEM241 are involved in cholesterol transport and accumulation, respectively (Zhao et al., 2020; Zhao et al., 2023). GATA6 is a zinc-finger transcription factor belonging to the GATA family. A report has stated that adenovirus-mediated lncRNA can regulate the miR-2188-3p/GATA6 axis, leading to increased hepatic lipid synthesis and intramuscular fat (IMF) deposition (Guo et al., 2023) and is regarded as a proviral host factor for SARS-CoV-2 (Israeli et al., 2022). MIBI is involved in the replication of adenoviruses and influenza viruses (Li et al., 2023; Bauer et al., 2021; Sarbanes et al., 2021), while RIOK3 promotes the synergistic replication of MDV and REV and is also involved in the replication of RVFV (Bisom et al., 2023; Du et al., 2022). RBBP8 and LAMA3 are associated with the replication of HPV and prion viruses, respectively (Kim et al., 2022; Pinatti et al., 2020; Bruyere et al., 2023). NPC1 is a multi-pass transmembrane protein situated on lysosomes and serving as a crucial mediator in the transportation of cholesterol (Luo et al., 2020; Qian et al., 2020). It is worth noting that NPC1 is an indispensable host factor in the process of entry, infection, and pathogenesis of various viruses, including SARS-CoV, ASFV, EBOV (Amini-Bavil-Olyaee et al., 2013; Gong et al., 2016). Meantime, blocking NPC1 limits the entry and replication of the virus by disrupting cholesterol homeostasis (Ahmad et al., 2023). Therefore, cholesterol homeostasis plays an important role in virus infection. Given the diversity of viruses involved with these genes, we speculate that they may also be associated with other immunosuppressive viruses that affect poultry, including avian leukosis viruses (ALV). Various viral infectious diseases have caused tremendous losses to the poultry industry. With the advancements in genomics technology, an increasing number of antiviral candidate genes and genetic markers in chickens are being identified and screened. For instance, 2 SNPs associated with the host's resistance to MD have been identified, specifically located on the SMOC1 and PTPN3 genes, respectively (Li et al., 2013) and more than 10 loci associated with the antibody levels of IBVD on chromosome 1, 3, 5, 8, and 9 (Luo et al., 2014). In conclusion, the region spanning 1.2Mb on chromosome 2 with strong selection is highly associated with the cholesterol-mediated viral replication process, considering that cholesterol plays a crucial role as a lipid in the replication mechanism of nearly all viruses.

The 8 missense mutations were analyzed by bioinformatics, it was found that there were changes in the mRNA structure and obvious impacts on the protein secondary structure. Regardless of before or after the mutations, the protein structures were mainly composed of random coils and α-helices, while the proportions of β-turns and extended chains were relatively small. Relevant studies have shown that the expression level and translation efficiency of proteins are significantly positively correlated with the stability of mRNA secondary structure (Zhang et al., 2023). It is speculated that the variants may affect the translation efficiency of the proteins. However, further verification is needed to determine whether these 5 genes (ESCO1, GATA6, RBBP8, NPC1 and LAMA3) can regulate the immune performance of Taihang chickens. These results provide further theoretical support for the breeding of Taihang chickens with strong disease resistance.

CONCLUSION

This study comprehensively overviews of the characteristics of Taihang for the first time by utilizing multiple selection signal methods through WGS data. The numerous candidate genes potentially responsible for immune response, feather color, growth and reproductive traits were identified. Additionally, a continuously selected region associated with disease resistance was also discovered. Overall, our study holds significant importance in understanding the genetic background of Taihang and it provides a foundation for studying the genomic characteristics of other important local chicken breeds.

DISCLOSURES

The authors declare that they have no conflict of interest.

Appendix Supplementary materials

Image, application 1

ACKNOWLEDGMENTS

This study was supported by the Hebei Provincial Key Research Development Program (22326319D ), earmarked fund for Hebei Agriculture Research System (HBCT2024260204 ) and Hebei Provincial Chicken Modern Breeding Science and Technology Innovation Team (21326303D ).

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2024.104240.
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