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Article

Integrating Whole-Genome Resequencing and RNA Sequencing Data Reveals Selective Sweeps and Differentially Expressed Genes Related to Nervous System Changes in Luxi Gamecocks

by
Jieke Zhou
,
Ying Chang
,
Junying Li
,
Haigang Bao
* and
Changxin Wu
National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Genes 2023, 14(3), 584; https://doi.org/10.3390/genes14030584
Submission received: 2 December 2022 / Revised: 11 February 2023 / Accepted: 13 February 2023 / Published: 25 February 2023
(This article belongs to the Section Animal Genetics and Genomics)
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Abstract

:
The Luxi gamecock developed very unique morphological and behavioral features under the special artificial selection of the most famous Chinese gamecocks. There are very few research studies on the genetics and selection of the Luxi gamecock. We used six methods (Fst, Tajima’s D, hapFLK, iHS, XP-EHH, and Runs of homozygosity) to detect selective sweeps in whole-genome resequencing data of 19 Luxi gamecocks compared to other Chinese indigenous chickens. Eleven genes that were highly related to nervous system development (CDH18, SLITRK1, SLITRK6, NDST3, ATP23, LRIG3, IL1RAPL1, GADL1, C5orf22, UGT8, WISP1, and WNT9A) appeared in at least four methods and were regarded as the most significant genes under selection. Differentially expressed gene (DEG) analysis based on the RNA sequencing data of the cerebral cortex and middle brain between six Luxi gamecocks, Tibetan chickens, and white leghorns found that most differentially expressed genes were enriched in pathways with nervous system functions. Genes associated with aggressiveness-related neurotransmitters (SLC4A2, DRD1, DRD2, ADRA2A, and ADRA2B) showed differential expression rates in Luxi gamecocks as well. Combined results showed that most genes in selective sweep regions were also differentially expressed in Luxi gamecocks including the most significant genes (SLITRK6, IL1RAPL1, GADL1, WISP1, and LRIG3). This study provides more insight into molecular mechanisms of the aggressiveness of gamecocks and aims to promote further studies on animal and human aggression.

1. Introduction

Gamecocks have been selected for entertainment for at least 2000 years [1]. It is not only an ornamental chicken breed but is also capable of breeding new commercial chickens with high performance. China has several native gamecock breeds, including the Luxi gamecock, the Henan gamecock, the Tulufan gamecock, the Xishuangbanna gamecock, and the Zhangxi gamecock, among others [2]. These breeding resources mostly remain unexploited until now, and we know little about the genetics of the unique and economic traits of cock fighting. Mitochondrial DNA revealed genetic relationships among Chinese gamecocks [3,4] and suggested that gamecocks may be the earliest domesticated chicken in China. Guo et al. [5] reported some selected genes whose functions were associated with organ development, aggressive behaviors, and energy metabolism in the Xishuangbanna gamecocks compared to the Tibetan chicken and red jungle fowl via several selection signature methods. Until 2020, Luo et al. [6] used whole-genome resequencing data to reveal the genome diversity of Chinese gamecocks and the genetic relationships between gamecocks and other China native chickens and commercial lines. Several selective sweeps were found that may be related to the aggressiveness of Chinese gamecocks, such as alkylglycerol monooxygenase (AGMO), isoprenoid synthase domain-containing protein (ISPD), and carboxypeptidase Z (CPZ). Komiyama et al. [7,8,9,10,11,12] completed a series of research studies on the Japanese gamecock intending to illuminate its origin, as well as its relationships with Japanese indigenous chickens and candidate genes concerning aggressiveness. The research found that the Japanese gamecock may be the origin of other Japanese indigenous chickens [12] and that selective sweeps were related to aggressiveness, including the dopamine receptor (DRD) gene family in the Japanese gamecock [7,8]. Li et al. [13] detected 33 significant SNPs related to aggressiveness in chickens in a genome-wide association study, the most significant of which was the one located in the intron region of the sortilin-related VPS10 domain-containing receptor 2 (SORCS2) gene. However, all these studies only found selection sweeps in genomes, and the expression situations of these genes were under-represented.
Studies on animal aggressiveness already found that three main neurotransmitters affected aggressiveness: serotonin, noradrenaline, and dopamine [13,14,15,16,17,18,19,20,21]. Usually, serotonin inhibits an animal’s aggressiveness [14,15]. Animals with high levels of serotonin are tamer than those with low levels [14,15,16,17,18]. For example, the silver fox selected for low levels of defensive aggressive behavior showed higher serotonin levels than the wild breed [14]. In mice experiments, high levels of serotonin were also associated with less aggression [14,15,16,17]. In humans, low levels of serotonin were also highly associated with aggressive and violent behaviors [18]. In contrast to serotonin, noradrenaline levels showed a positive relation with aggressiveness in both human and nonhuman animals [19]. High levels of noradrenaline were also seen to make people more aggressive when faced with external stimulators [19]. On the other hand, mice were found to have less aggressive behaviors with low levels of noradrenaline [20]. Komiyama et al. [10] tested the concentrations of four monoamine neurotransmitters and six derived metabolites in the cerebral cortex, midbrain, and striatum of the Japanese gamecock and shaver brown chickens, and found that only the noradrenaline level in the midbrain of the Japanese gamecock was significantly higher. In most cases, a high level of dopamine will increase an animal’s aggressiveness [14], just like noradrenaline. In hens [21] and green anoles [22], studies found that the excitation of dopamine receptor type-1 and type-2 (DRD1 and DRD2) will impair aggressive behaviors. Li et al. [13] found that SOCRS2 genes were highly associated with chicken aggressiveness and its knock-down decreased the expression level of DRD1, DRD2, DRD3, and DRD4 genes.
The Luxi gamecock is representative of Chinese gamecocks and is mainly spread over the Shandong province of China. In this study, our main goal was to find whether entertainment fighting selection changes nervous system functions in Luxi gamecocks, especially genes related to serotonin, noradrenaline, and dopamine. We wish to narrow down the scope of candidate genes by integrating whole-genome resequencing data and RNA sequencing data. We sampled 19 Luxi gamecocks for whole-genome resequencing and collected another 120 WGS data sets from five Chinese indigenous chicken breeds [5,23,24] to detect selective sweeps in the Luxi gamecock. To minimize false positives and to obtain a comprehensive set of selective sweeps, we combined five methods that use all three pieces of genome information to obtain more accurate and reliable results (Tajima’D is based on an allele frequency spectrum, iHS and XP-EHH are based on linkage disequilibrium haplotypes, and Fst and hapFLK are based on population differentiation). Then, we extracted total RNA from the cerebral cortex and midbrain samples of six Luxi gamecocks, six Tibetan chickens, and six white leghorns to perform differential gene expression analysis and screen potential genes which may influence nervous system development and aggressiveness-related neurotransmitters. Finally, we observed overlapping genes between selective sweeps and differentially expressed genes and found that selection for entertainment fighting indeed changed the nervous system of the Luxi gamecock, including genes relating to three neurotransmitters.

2. Materials and Methods

2.1. DNA Sampling and Genome Resequencing Data

The Luxi gamecock came from the Poultry Genetic Resources and Breeding Station of China Agricultural University. In total, 19 blood samples were collected from the wing veins of 19 Luxi gamecocks (about 30 weeks old) using 1 mL injectors. The total DNA of each blood sample was extracted using a genomic DNA Kit (Cat. #DP304-03, TIANGEN Biotech (Beijing) Co., Ltd., China) according to the manufacturer’s instructions. After the DNA samples were checked and quantified, they were delivered to a commercial company (Novogene Co., Ltd., Beijing, China) for whole-genome resequencing. The datasets with paired-end reads were produced on an Illumina NovaSeq 6000 platform. The average depth of resequencing for each sample was 10×. The other 106 samples’ whole-genome sequencing data of 5 chicken breeds were downloaded from the NCBI and the ChickenSD database, as shown in Table 1.
Raw data quality was cleaned by Trimmomatic-0.39 [25] with the following commands: LEADING:3, TRAILING:3, HEADCROP:10, SLIDINGWINDOW:4:15, and MINLEN:70. The statistics of the clean data quality were assessed by FastQC (version 0.11.9) [26].

2.2. Mapping and SNP Calling

Cleaned data were mapped to the chicken reference genome GRCg6a using BWA (version 0.7.17) [27] with the mem algorithm. The picard component in GATK (version 4.1.8.1) [28] was employed to flag duplicated reads. SAM files generated by BWA were reordered by chromosome and converted to the BAM format with samtools (version 1.11) [29].
SNP calling was performed in line with a genome analysis toolkit (GATK) pipeline [28]. A set of known SNP sites of chicken was downloaded from Ensemble (http://ftp.ensembl.org/pub/release-104/variation/gvf/gallus_gallus/gallus_gallus.gvf.gz, accessed on 5 August 2021) to perform the BaseRecalibrator process in GATK and to generate the recalibrated BAM files for each individual. The joint-calling strategy of GATK was chosen to obtain more accurate results and find rare variants. Firstly, each individual generated a gvcf format mediate file via the HaplotypeCaller command of GATK. Secondly, the GenomicsDBImport command was used to create a temporary database that combined all 258 individuals’ GVCF results, divided by chromosomes. Finally, we used the GenotypeGVCFs command to obtain the final SNP calling results in a VCF file, including all samples.
After SNP calling, the results were filtered by hard criteria recommended by GATK: QD > 2.0, FS > 60.0, MQ > 40.0, MQRankSum > −12.5, ReadPosRankSum > −8.0, SOR > 3.0. The filtered SNP data were then phased and imputed by BEAGLE (version 5.2) [30] with default parameters, and only the autosome sites were retained for subsequent analysis. All identified SNPs were annotated by snpEff [31] software (latest core version accessed on 25 August 2021).
Before downstream analysis, we converted the final VCF file to a binary plink format via PLINK (version 1.9) [32]. Variants were pruned according to linkage disequilibrium (LD) to reduce potential basis by using the “–indep-pairwise 0.5” command.

2.3. Detection of Selective Sweeps

The selective sweep detection method usually uses three kinds of genome information: an allele frequency spectrum, a linkage disequilibrium haplotype, and population differentiation. To obtain the most powerful efficiency rates and reduce false-positive rates as much as possible, we chose five methods with all three kinds of information, including fixed index (Fst) [33] and hapFLK (based on population differentiation [34]), Tajima’s D (based on the allele frequency spectrum) [35], as well as the integrated haplotype score (iHS) [36] and the cross-population extended haplotype score (XP-EHH) (based on the linkage disequilibrium haplotype [37]). We also used runs of homozygosity (ROHs) [38] to detect selective sweeps, which is a more recent method for finding selective sweeps. Fst and Tajima’s D values were calculated by VCFTools (version 0.1.19) [39]. Fst was calculated in a sliding window of 40 kb size with a step of 20 kb. Tajima’s D values were calculated in a non-overlapping 10 kb sliding window. hapFLK [34] was completed with a python script provided by the author with cluster parameter k = 12. Calculations of iHS and XP-EHH were run by selscan [40] with default parameters. Only negative Tajima D, iHS, and XP-EHH values were retained for subsequent analysis. For methods except ROH, the top 1% of outliers were regarded as significant sites that may be putative sites or regions under selection. For ROH, SNPs that occurred in a ROH with high frequencies, specifically in LXFCs, were regarded as selective sites. Genes identified by at least two methods were considered as most likely putative genes under selection.

2.4. RNA Extraction and Sequencing

The heads of six chickens from each of the Luxi gamecock, Tibetan chicken, and White Leghorn were rapidly cut off to enable sampling of the cerebral cortex and midbrain in chickens that were 64–66 weeks old. The total RNA was isolated by trizol methods and then sent to Novogene Co. Ltd to sequence on the Illumina NovaSeq 6000 platform. Raw RNA data were filtered by Fastp (version 0.20.1) [41] with the following commands: -f 19, -F 19, -q 20, -c -w 6, -l 50, -n 6, and -z 6. They were then evaluated by fastQC (version 0.11.9) [26]. Hisat2 (version 2.2.1) [42] was used to build an index and map clean reads to the chicken reference genome GRCg6a, and the results were converted to BAM files by samtools (version 1.11) [29]. Data accessibility could be found in Table S1.

2.5. Gene Quantification and Differential Gene Expression Analysis

Gene expression abundance was estimated by featureCOUNTS [43] with FPKM (fragment per kilobase of transcript per million mapped reads). Differentially expressed genes (DEGs) between LXFCs and TCs or the White Leghorn (WL) chickens were detected by the DESeq2 [44] package of R. Differentially expressed genes were then detected by the threshold of an adjusted p-value (padj) below 0.05. Values of log2fold change larger than 1 or less than −1 were regarded as differentially expressed genes. Shared DEGs between LXFCs and TCs and between LXFCs and WL chickens were chosen for GO and KEGG pathway enrichment analysis. We further observed the genes that shared differential expression and were within a selective sweep region.

2.6. Gene Ontology Enrichment and Pathway Analysis

All annotated genes were submitted to g:Profiler [45] (https://biit.cs.ut.ee/gprofiler/gost, archived version Ensembl 106, Ensembl Genomes 53, built on 18 May 2022, accessed on 8 June 2022) for function and pathway enrichment analysis with the Gallus gallus organism background and a 0.05 threshold.

3. Results

3.1. Overview of the Whole-genome Sequencing Data and the RNA-Sequencing Data

After variant processing, we identified 43,259,995 SNPs and 5,488,914 indels that passed the hard-quality GATK filter of the GATK. In this study, we only kept biallelic SNPs for subsequent analysis, which excluded 2,101,476 SNP sites and kept 41,158,519. The SNPs that remained were then phased and imputed by BEAGLE with default parameters. Next, we pruned data with a linkage disequilibrium threshold of r2 = 0.5. As a result, only 24,697,629 SNPs remained.
After the quality control and mapping of raw RNA sequencing data, alignment rates ranged from 85.66% to 92.35%, with an average rate of 90.82%. Detailed alignment ratios are shown in Table S2.

3.2. Selective Sweeps Revealed Genes Are Mostly Associated with Organism Development

We used five methods (Fst [33], Tajima’D [35], hapFLK [34], his [36], and XP-EHH [37]) to detect selective sweeps in LXFCs compared to other domesticated chickens. We kept selective sweeps, at least those detected in two methods, to improve accuracy and reduce false-positive results. The top 1% of outliers were detected as selective sweeps and are shown in Figure 1. Finally, 2197 genes located in putative selective regions were found. Genes without a clear functional annotation were excluded and 363 genes remained, the list could be found in Table S3. These 363 genes were mostly associated with organism development, according to GO enrichment results, including those associated with the nervous system, which were expected to undergo selection and organ, skeleton, and embryo development, as shown in Figure 2. Eleven genes listed in Table 2 were detected by at least four methods and regarded as the most significant selective sweeps. The only gene shared by all five methods is CDH18 (cadherin 18), whose function was found to be highly related to organism development [46,47]. Moreover, at least four of the eleven genes were found to be related to nervous system function: SLITRK1 [20,48,49], SLITRK6 [50,51], IL1RAPL1 [52], and NDST3 [53,54]. Another three were reported to participate in nervous system development and function: WISP1 [55], UGT8 [56,57], and LRIG3 [58,59]. Besides these eleven genes, we also observed ADRA1D and ADRA2A, whose functions are related to noradrenaline pathways, and many SLC gene family members that encode neurotransmitter transporters [60] in the cerebrum.
Furthermore, the SNPs identified by the ROH method also found two unique outstanding peaks in the Luxi gamecock: the 5.7–6.3 Mb region of Chr3 and the 0.43–1.24 Mb region of Chr8 (Figure 1F). No functional genes were annotated in the former region, while only two functional genes were found in the latter: AMY2A (amylase, alpha 2A) and NTNG1 [61,62] (netrin G1), the latter of which is another gene associated with the nervous system.

3.3. Differentially Expressed Genes between LXFCs and TC/WL Chickens Were Significantly Associated with Nervous System in the Midbrain

Differentially expressed genes were detected according to FPKM via the DESeq2 package. Between LXFC and TC birds, 735 down-regulated genes and 442 up-regulated genes were detected in the cerebral cortex of LXFCs compared to TC birds, while 3875 down-regulated genes and 3759 up-regulated genes were detected in the midbrain of LXFCs compared to TCs. When comparing LXFC and WL chickens, 1791 down-regulated genes and 1861 up-regulated genes were detected in the cerebral cortex, while 4606 down-regulated genes and 4771 up-regulated genes were detected in the midbrain. Heat maps of the four data sets are shown in Figure 3. The shared differentially expressed genes shown in Figure 4 contained 417 down-regulated genes and 220 up-regulated genes in the cerebral cortex of LXFCs, as well as 3173 down-regulated genes and 3296 up-regulated genes in the midbrain of LXFCs.
DEGs in the cerebral cortex showed no significant GO enrichment terms. However, DEGs in the midbrain were enriched in several pathways and are shown in Figure 5. Down-regulated DEGs were mainly associated with ribosome, oxidative phosphorylation, and proteasome (Figure 5A). Up-regulated DEGs were associated with various signaling pathways (Figure 5B) related to nervous system function and development, such as the ErbB signaling pathway [63,64], the MAPK signaling pathway [65], and the mTOR signaling pathway [66,67,68].
We notably observed solute carrier family 6 member 4 (SLC6A4) which encodes the transporters of serotonin; two types of dopamine receptor genes (dopamine receptor D1 (DRD1) and dopamine receptor D2 (DRD2)) and one noradrenaline receptor gene adrenoceptor alpha 2A (ADRA2A) were up-regulated in the midbrain of LXFCs compared with TC and WL chickens. The log2fold-change values of these genes were 2.14, 0.57, 0.70, and 0.68, respectively, compared to TC chickens, and 2.17, 1.15, 1.30, and 0.64, respectively, when compared to WL chickens. Another noradrenaline receptor gene, adrenoceptor alpha 2B (ADRA2B), a shared down-regulated gene in the cerebral cortex, showed a −1.57 log2fold-change compared to TC and −0.7 compared to WL chickens. ENSGALG00000037163, a sodium-dependent serotonin transporter-like gene, was a down-regulated gene in the midbrain of LXFCs which showed a log2fold-change of −2.99 (compared to TC) and −1.52 (compared to WL chickens). All DEGs used for each comparison could be found in Table S4.

3.4. Genes under Selective Sweeps Mostly Showed Differential Expression between LXFC and TC/WL Chickens in the Midbrain

We further observed results shared between selective sweeps and DEGs. In the cerebral cortex, 19 genes were found to be shared between the two analyses of LXFC and TC chickens, with 84 genes shared between analyses of LXFC and WL chickens. In the midbrain, 187 shared genes were found between LXFC and TC birds, with 214 genes shared between LXFC and WL chickens. Seven genes of the eleven genes in Table 2 were shared genes, including SLITRK6, IMMP2L, NDST3, LRIG3, WISP1, NTNG1, and IL1RADL1. These results are shown in Figure 6
Shared DEGs in the cerebral cortex did not gain significant results in GO and KEGG enrichment. Shared DEGs in the midbrain were highly correlated with organism development, including the nervous system (Figure 6). Detailed gene list could be found in Table S5.

4. Discussion

In the present study, 19 Luxi gamecocks and 120 other samples (representing 5 breeds) were used to detect selective sweeps in the Luxi gamecock. Six Luxi gamecocks, Tibetan chickens, and white leghorns were used to conduct RNA sequencing. To gain the highest detecting power, we decided to use six methods to detect selective sweep: Tajima’s D [35], iHS [36], XP-EHH [37], Fst [33], and hapFLK [34]. Moreover, ROH information could also be used in selective sweep detection [38]. To reduce false-positive results, we only kept genes detected by at least two methods as significant genes. Eventually, 363 genes remained. GO and KEGG pathway enrichment results only showed BP terms (see Figure 2). Most BP terms were mainly associated with organism development and morphogenesis, such as the nervous system, embryo, skeleton, and organs. Among these 363 genes, 11 genes were seen as the most significant since they were detected by at least four methods, and most genes were very likely to participate in nervous system development, such as SLITRK1 [20,48,49], SLITRK6 [50,51], IL1RAPL1 [52], WISP1 [55], UGT8 [56,57] and LRIG3 [58,59]. Meanwhile, another two genes, AMY2A and NTNG1, were determined by ROH. It has also been reported that NTNG1 is associated with nervous system disease [61,62]. SLITRK1 had been found to be related to a genetic nervous disorder called Tourette’s syndrome [48,49]. SLITRK1 knock-out mice showed increased anxiety-like behaviors and abnormal noradrenergic activity, and also had higher levels of norepinephrine and metabolite 3-methoxy-4-hydroxyphenylglycol [20]. SLITRK6 regulates the outgrowth of sensory neurons in the inner ear and retina; its mutation may cause myopia and deafness [50,51]. NDST3 [53,54] and NTNG1 were both found to be highly associated with schizophrenia in humans [61,62,69]. IL1RAPL1 can affect the morphogenesis of cortical neurons in the spine and significantly change the behaviors of mice [52]. IL1RAPL1 knock-out mice had shorter fear memory, more active social behaviors, and decreased cortical neurons density in the spine [52]. WISP1 could prevent neuron injuries and could become a target in therapeutic strategies for some disorders such as traumatic injury [55]. Though the detailed function of UGT8 is unclear, it is highly expressed in the brain [56,57]. Some researchers reported connections between LRIG3 and the nervous system [59]. Moreover, among 363 significant selective genes, we overserved adrenoceptor alpha 1D (ADRA1D), adrenoceptor alpha 1A (ADRA1A), inner mitochondrial membrane peptidase subunit 2 (IMMP2L), carboxypeptidase Z (CPZ), and several numbers of the SLC gene family (solute carrier family). ADRA1D and ADRA1A encoded two kinds of adrenaline receptors. Meanwhile, noradrenaline has long been known to be related to aggressiveness in animals [70,71]. IMMP2L was another target gene of Tourette’s syndrome [72] and autism [73]. CPZ had already been reported to be related to aggressive behaviors in chickens [6,13]. Many SLC family (solute carrier family) genes (SLC35F5, SLC38A2, SLC39A6, SLC4A11, SLC4A1AP, SLC30A8, SLCO1A2, and SLC9A7) are mainly expressed in the brain to produce transporters of various neurotransmitters [60]. Various results suggest that the nervous system of the Luxi gamecock was significantly affected by entertainment fighting selection, including genes directly associated with three aggressiveness-related neurotransmitters (serotonin, noradrenaline, and dopamine).
Considering the tight relationships between selective sweeps and the nervous system, we decided to sample the cerebral cortexes and midbrains of six Luxi gamecocks, six Tibetan chickens, and six white leghorns to obtain RNA sequencing datasets. We tried to explore whether expression patterns in the nervous system of the Luxi gamecock changed compared with other chickens. Other results show that most DEGs appeared in the midbrain of LXFC chickens, rather than the cerebral cortex, where serotonin [14,15,74] and dopamine [15] are produced. GO and KEGG enrichment results of down-regulated genes in the midbrain of LXFC chickens did not show obvious connections with the nervous system. On the other hand, up-regulated genes were enriched in terms involved in nervous system development (see Figure 5). Enriched pathways were mostly associated with the nervous system, including ErbB signaling pathways [63,64], mTOR signaling pathways [66,67,68], and MAPK signaling pathways [65]. We specifically found a group of genes associated with dopamine, serotonin, and noradrenaline which showed significant differential expressions in the midbrain of LXFC birds such as SLC6A4, ENSGALG00000037163, DRD1, DRD2, ADRA2A, and ADRA2B. SLC6A4, up-regulated in the midbrain of LXFC birds, encodes serotonin transporters in the central nervous system [60], while another serotonin transporter-like gene, ENSGALG00000037163, was found to be down-regulated in the midbrain of LXFCs. DRD1 and DRD2 encode two types of dopamine receptors. Multiple animal research proved that the excitation of dopamine receptors 1 and 2 would impair aggressive behaviors [21,22,75]. There are two types of adrenaline receptors (ADRA2A was up-regulated while ADRA2B was down-regulated in the midbrain of LXFCs). Usually, there is a positive correlation between the noradrenaline level and aggressiveness [19], but a negative correlation between the adrenaline receptor alpha 2 subtype has also been reported [19].
We found that selective sweeps and differential gene expression analysis were both related to the nervous system in the brain of LXFCs. To explore further connections between selective sweep regions and DEGs, we observed the shared genes between the two analyses. Few selective sweeps showed a DEG in the cerebral cortex, while most selective sweeps were also DEGs in the midbrain of LXFCs. About 51% (187/363) of genes under selective sweeps were also DEGs in the midbrain of LXFC compared to TC chickens, while 59% (214/363) of genes under selective sweep regions were also DEGs in the midbrain of LXFC birds when compared to WL chickens. Six of the eleven most significant genes under selective sweeps included the following: SLITRK6, NDST3, IL1RAPL1, WISP1, LRIG3, and NTNG1. Most of these also influence the development and function of the nervous system. The results provide us new insight for further research on the genetics of aggressiveness in Luxi gamecocks. Future work will focus on how these genes are expressed in different breeds and at different times, as well as their relationship with the three aggressiveness-related neurotransmitters.

5. Conclusions

In this study, we first sequenced the cerebral transcriptome of the Luxi gamecock and combined it with whole-genome resequencing data. The results confirmed that the nervous system of the Luxi gamecocks changed under selection, and we located several candidate genes that indirectly or directly influence three neurotransmitters. We combined six methods to detect selective sweeps in Luxi gamecocks and found 11 significant genes that were mostly related to nervous system development. Furthermore, 417 down-regulated genes and 220 up-regulated genes were found in the cerebral cortex of Luxi gamecocks, while 3173 down-regulated genes and 3296 up-regulated genes were found in the midbrain of Luxi gamecocks. Most were enriched in pathways that were associated with nervous system functions. More than half of the genes in selective sweep regions also showed differential expression. Combined results suggest that the artificial selection for entertainment fighting significantly changed the development and function of the nervous system of Luxi gamecocks. Genes such as IL1RAPL1, SLITRK6, SLC6A4, DRD1, DRD2, ADRA1D, and ADRA2A were important candidate genes that affected the aggressiveness of Luxi gamecocks. This study provided more insight into the genetics of the aggressiveness of gamecocks and will inform further research on the genes that affect the aggressiveness of animals and humans.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes14030584/s1. Table S1: A summary of the data accessibility of WGS data used in this study, excluding LXFCs. Table S2: Alignment rates of RNA sequencing data. Table S3: A list of genes under selective sweeps that were detected by at least two methods. Table S4: Information of all significant DEGs for each comparison. Table S5: A list of genes shared by selective sweeps and DEGs.

Author Contributions

Conceptualization, H.B. and C.W.; methodology, H.B. and J.Z.; software, J.Z. and Y.C.; formal analysis, J.Z.; investigation, J.Z.; resources, J.L.; data curation, J.L. and Y.C.; writing—original draft preparation, J.Z.; writing—review and editing, H.B. and C.W.; visualization, J.Z. and Y.C.; supervision, H.B.; project administration, H.B. and C.W.; funding acquisition, H.B. and C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by National Key Research and Development Program of China (2022YFD1600902) and China Agriculture Research System (CARS-40).

Institutional Review Board Statement

All experimental procedures and animals used were ap-proved by the Ethics Review Committee for Laboratory Animal Welfare and Animal Experiment of China Agricultural University (approval code: AW60212202-1-1) (approval date: 1 December 2022).

Informed Consent Statement

Not applicable.

Data Availability Statement

The DNA sequencing data of 19 LXFC for this study can be downloaded from the National Genomics Data Center of China (GSA numbers: CRA009027; repository url: https://download.cncb.ac.cn/gsa2/CRA009027/, accessed on 8 June 2022). The RNA sequencing data for this study can be downloaded from the National Genomics Data Center of China (GSA numbers: CRA009037; repository url: https://download.cncb.ac.cn/gsa2/CRA009037/, accessed on 8 June 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhu, W.; Li, H.; Guo, J. Analysis on the Genetic Diversity and the Origin of MtDNA in Chinese Game Chicken. Acta Vet. Zootech. Sin. 2009, 40, 1560–1563. [Google Scholar] [CrossRef]
  2. China national commission of Animal Genetic Resources. Animal Genetic Resource in China: Poultry; China Agriculture Press: Beijing, China, 2011; ISBN 978-7-109-15221-2. [Google Scholar]
  3. Liu, Y.-P.; Zhu, Q.; Yao, Y.-G. Genetic Relationship of Chinese and Japanese Gamecocks Revealed by MtDNA Sequence Variation. Biochem. Genet. 2006, 44, 18–28. [Google Scholar] [CrossRef] [PubMed]
  4. Qu, L.J.; Li, X.Y.; Yang, N. Genetic Relationships among Different Breeds of Chinese Gamecocks Revealed by MtDNA Variation. Asian Australas. J. Anim. Sci. 2009, 22, 1085–1090. [Google Scholar] [CrossRef]
  5. Guo, X.; Fang, Q.; Ma, C.; Zhou, B.; Wan, Y.; Jiang, R. Whole-Genome Resequencing of Xishuangbanna Fighting Chicken to Identify Signatures of Selection. Genet. Sel. Evol. 2016, 48, 62. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Luo, W.; Luo, C.; Wang, M.; Guo, L.; Chen, X.; Li, Z.; Zheng, M.; Folaniyi, B.S.; Luo, W.; Shu, D. Genome Diversity of Chinese Indigenous Chicken and the Selective Signatures in Chinese Gamecock Chicken. Sci. Rep. 2020, 10, 1–14. [Google Scholar] [CrossRef]
  7. Komiyama, T.; Iwama, H.; Osada, N.; Nakamura, Y.; Kobayashi, H.; Tateno, Y.; Gojobori, T. Dopamine Receptor Genes and Evolutionary Differentiation in the Domestication of Fighting Cocks and Long-Crowing Chickens. PLoS ONE 2014, 9, e101778. [Google Scholar] [CrossRef] [Green Version]
  8. Komiyama, T.; Lin, M.; Ogura, A. ACGH Analysis to Estimate Genetic Variations among Domesticated Chickens. BioMed Res. Int. 2016, 2016, 1794329. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Komiyama, T.; Ikeo, K.; Gojobori, T. The Evolutionary Origin of Long-Crowing Chicken: Its Evolutionary Relationship with Fighting Cocks Disclosed by the MtDNA Sequence Analysis. Gene 2004, 333, 91–99. [Google Scholar] [CrossRef]
  10. Komiyama, T.; Yoshikawa, M.; Yokoyama, K.; Kobayashi, H. Analysis of the Source of Aggressiveness in Gamecocks. Sci. Rep. 2020, 10, 1–10. [Google Scholar] [CrossRef] [Green Version]
  11. Komiyama, T.; Ikeo, K.; Gojobori, T. Where Is the Origin of the Japanese Gamecocks? Gene 2003, 317, 195–202. [Google Scholar] [CrossRef]
  12. Komiyama, T.; Ikeo, K.; Tateno, Y.; Gojobori, T. Japanese Domesticated Chickens Have Been Derived from Shamo Traditional Fighting Cocks. Mol. Phylogenet. Evol. 2004, 33, 16–21. [Google Scholar] [CrossRef] [PubMed]
  13. Li, Z.; Zheng, M.; Abdalla, B.A.; Zhang, Z.; Xu, Z.; Ye, Q.; Xu, H.; Luo, W.; Nie, Q.; Zhang, X. Genome-Wide Association Study of Aggressive Behaviour in Chicken. Sci. Rep. 2016, 6, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Narvaes, R.; Martins de Almeida, R.M. Aggressive Behavior and Three Neurotransmitters: Dopamine, GABA, and Serotonin—A Review of the Last 10 Years. Psychol. Neurosci. 2014, 7, 601. [Google Scholar] [CrossRef]
  15. Seo, D.; Patrick, C.J.; Kennealy, P.J. Role of Serotonin and Dopamine System Interactions in the Neurobiology of Impulsive Aggression and Its Comorbidity with Other Clinical Disorders. Aggress. Violent Behav. 2008, 13, 383–395. [Google Scholar] [CrossRef] [Green Version]
  16. Cases, O.; Seif, I.; Grimsby, J.; Gaspar, P.; Chen, K.; Pournin, S.; Muller, U.; Aguet, M.; Babinet, C.; Shih, J.C.; et al. Aggressive Behavior and Altered Amounts of Brain Serotonin and Norepinephrine in Mice Lacking MAOA. Science 1995, 268, 1763–1766. [Google Scholar] [CrossRef] [Green Version]
  17. Olivier, B. Serotonin: A Never-Ending Story. Eur. J. Pharmacol. 2015, 753, 2–18. [Google Scholar] [CrossRef] [Green Version]
  18. Trifu, S.C.; Tudor, A.; Radulescu, I. Aggressive Behavior in Psychiatric Patients in Relation to Hormonal Imbalance. Exp. Ther. Med. 2020, 20, 3483–3487. [Google Scholar] [CrossRef]
  19. Haden, S.C.; Scarpa, A. The Noradrenergic System and Its Involvement in Aggressive Behaviors. Aggress. Violent Behav. 2007, 12, 1–15. [Google Scholar] [CrossRef]
  20. Katayama, K.; Yamada, K.; Ornthanalai, V.G.; Inoue, T.; Ota, M.; Murphy, N.P.; Aruga, J. Slitrk1-Deficient Mice Display Elevated Anxiety-like Behavior and Noradrenergic Abnormalities. Mol. Psychiatry 2010, 15, 177–184. [Google Scholar] [CrossRef]
  21. Dennis, R.; Cheng, H.W. The Dopaminergic System and Aggression in Laying Hens. Poult. Sci. 2011, 90, 2440–2448. [Google Scholar] [CrossRef]
  22. Smith, A.N.; Kabelik, D. The Effects of Dopamine Receptor 1 and 2 Agonists and Antagonists on Sexual and Aggressive Behaviors in Male Green Anoles. PLoS ONE 2017, 12, e0172041. [Google Scholar] [CrossRef]
  23. 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. 863 Genomes Reveal the Origin and Domestication of Chicken. Cell Res. 2020, 30, 693–701. [Google Scholar] [CrossRef]
  24. Zhang, Q.; Gou, W.; Wang, X.; Zhang, Y.; Ma, J.; Zhang, H.; Zhang, Y.; Zhang, H. Genome Resequencing Identifies Unique Adaptations of Tibetan Chickens to Hypoxia and High-Dose Ultraviolet Radiation in High-Altitude Environments. Genome Biol. Evol. 2016, 8, 765–776. [Google Scholar] [CrossRef]
  25. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [Green Version]
  26. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. 2010. Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 8 June 2022).
  27. Li, H.; Durbin, R. Fast and Accurate Long-Read Alignment with Burrows–Wheeler Transform. Bioinformatics 2010, 26, 589–595. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Poplin, R.; Ruano-Rubio, V.; DePristo, M.A.; Fennell, T.J.; Carneiro, M.O.; Van der Auwera, G.A.; Kling, D.E.; Gauthier, L.D.; Levy-Moonshine, A.; Roazen, D.; et al. Scaling Accurate Genetic Variant Discovery to Tens of Thousands of Samples. BioRxiv 2018, 201178. [Google Scholar] [CrossRef] [Green Version]
  29. Danecek, P.; Bonfield, J.K.; Liddle, J.; Marshall, J.; Ohan, V.; Pollard, M.O.; Whitwham, A.; Keane, T.; McCarthy, S.A.; Davies, R.M.; et al. Twelve Years of SAMtools and BCFtools. GigaScience 2021, 10, giab008. [Google Scholar] [CrossRef]
  30. Ayres, D.L.; Darling, A.; Zwickl, D.J.; Beerli, P.; Holder, M.T.; Lewis, P.O.; Huelsenbeck, J.P.; Ronquist, F.; Swofford, D.L.; Cummings, M.P.; et al. BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics. Syst. Biol. 2012, 61, 170–173. [Google Scholar] [CrossRef] [PubMed]
  31. Cingolani, P.; Platts, A.; Coon, M.; Nguyen, T.; Wang, L.; Land, S.J.; Lu, X.; Ruden, D.M. A Program for Annotating and Predicting the Effects of Single Nucleotide Polymorphisms, SnpEff: SNPs in the Genome of Drosophila Melanogaster Strain W1118; Iso-2; Iso-3. Fly 2012, 6, 80–92. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Chang, C.C.; Chow, C.C.; Tellier, L.C.; Vattikuti, S.; Purcell, S.M.; Lee, J.J. Second-Generation PLINK: Rising to the Challenge of Larger and Richer Datasets. Gigascience 2015, 4, s13742-015. [Google Scholar] [CrossRef]
  33. Lewontin, R.C.; Krakauer, J. Distribution of Gene Frequency as a Test of the Theory of the Selective Neutrality of Polymorphisms. Genetics 1973, 74, 175–195. [Google Scholar] [CrossRef]
  34. Fariello, M.I.; Boitard, S.; Naya, H.; SanCristobal, M.; Servin, B. Detecting Signatures of Selection through Haplotype Differentiation among Hierarchically Structured Populations. Genetics 2013, 193, 929–941. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Tajima, F. Statistical Method for Testing the Neutral Mutation Hypothesis by DNA Polymorphism. Genetics 1989, 123, 585–595. [Google Scholar] [CrossRef] [PubMed]
  36. Voight, B.F.; Kudaravalli, S.; Wen, X.; Pritchard, J.K. A Map of Recent Positive Selection in the Human Genome. PLoS Biol. 2006, 4, e72. [Google Scholar] [CrossRef] [Green Version]
  37. Sabeti, P.C.; Varilly, P.; Fry, B.; Lohmueller, J.; Hostetter, E.; Cotsapas, C.; Xie, X.; Byrne, E.H.; McCarroll, S.A.; Gaudet, R.; et al. Genome-Wide Detection and Characterization of Positive Selection in Human Populations. Nature 2007, 449, 913–918. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Szmatoła, T.; Gurgul, A.; Jasielczuk, I.; Ząbek, T.; Ropka-Molik, K.; Litwińczuk, Z.; Bugno-Poniewierska, M. A Comprehensive Analysis of Runs of Homozygosity of Eleven Cattle Breeds Representing Different Production Types. Animals 2019, 9, 1024. [Google Scholar] [CrossRef] [Green Version]
  39. 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]
  40. 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] [PubMed] [Green Version]
  41. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. Fastp: An Ultra-Fast All-in-One FASTQ Preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef]
  42. Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A Fast Spliced Aligner with Low Memory Requirements. Nat. Methods 2015, 12, 357–360. [Google Scholar] [CrossRef] [Green Version]
  43. Liao, Y.; Smyth, G.K.; Shi, W. FeatureCounts: An Efficient General Purpose Program for Assigning Sequence Reads to Genomic Features. Bioinformatics 2014, 30, 923–930. [Google Scholar] [CrossRef] [Green Version]
  44. Love, M.I.; Huber, W.; Anders, S. Moderated Estimation of Fold Change and Dispersion for RNA-sequencing Data with DESeq2. Genome Biol. 2014, 15, 1–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Raudvere, U.; Kolberg, L.; Kuzmin, I.; Arak, T.; Adler, P.; Peterson, H.; Vilo, J. G: Profiler: A Web Server for Functional Enrichment Analysis and Conversions of Gene Lists (2019 Update). Nucleic Acids Res. 2019, 47, W191–W198. [Google Scholar] [CrossRef] [Green Version]
  46. Yagi, T.; Takeichi, M. Cadherin Superfamily Genes: Functions, Genomic Organization, and Neurologic Diversity. Genes Dev. 2000, 14, 1169–1180. [Google Scholar] [CrossRef] [PubMed]
  47. Junghof, J.; Kogure, Y.; Yu, T.; Verdugo-Sivianes, E.M.; Narita, M.; Lucena-Cacace, A.; Yoshida, Y. CDH18 Is a Fetal Epicardial Biomarker Regulating Differentiation towards Vascular Smooth Muscle Cells. npj Regen. Med. 2022, 7, 14. [Google Scholar] [CrossRef] [PubMed]
  48. Grados, M.; Walkup, J. A New Gene for Tourette’s Syndrome: A Window into Causal Mechanisms? Trends Genet. 2006, 22, 291–293. [Google Scholar] [CrossRef]
  49. Miranda, D.M.; Wigg, K.; Kabia, E.M.; Feng, Y.; Sandor, P.; Barr, C.L. Association of SLITRK1 to Gilles de La Tourette Syndrome. Am. J. Med. Genet. 2009, 150B, 483–486. [Google Scholar] [CrossRef]
  50. Tekin, M.; Chioza, B.A.; Matsumoto, Y.; Diaz-Horta, O.; Cross, H.E.; Duman, D.; Kokotas, H.; Moore-Barton, H.L.; Sakoori, K.; Ota, M.; et al. SLITRK6 Mutations Cause Myopia and Deafness in Humans and Mice. J. Clin. Investig. 2013, 123, 2094–2102. [Google Scholar] [CrossRef] [Green Version]
  51. Morlet, T.; Rabinowitz, M.R.; Looney, L.R.; Riegner, T.; Greenwood, L.A.; Sherman, E.A.; Achilly, N.; Zhu, A.; Yoo, E.; O’Reilly, R.C.; et al. A Homozygous SLITRK6 Nonsense Mutation Is Associated with Progressive Auditory Neuropathy in Humans: SLITRK6 Is Associated With Human Auditory Neuropathy. Laryngoscope 2014, 124, E95–E103. [Google Scholar] [CrossRef] [Green Version]
  52. Yasumura, M.; Yoshida, T.; Yamazaki, M.; Abe, M.; Natsume, R.; Kanno, K.; Uemura, T.; Takao, K.; Sakimura, K.; Kikusui, T.; et al. IL1RAPL1 Knockout Mice Show Spine Density Decrease, Learning Deficiency, Hyperactivity and Reduced Anxiety-like Behaviours. Sci. Rep. 2015, 4, 6613. [Google Scholar] [CrossRef] [Green Version]
  53. Lencz, T.; Guha, S.; Liu, C.; Rosenfeld, J.; Mukherjee, S.; DeRosse, P.; John, M.; Cheng, L.; Zhang, C.; Badner, J.A.; et al. Genome-Wide Association Study Implicates NDST3 in Schizophrenia and Bipolar Disorder. Nat. Commun. 2013, 4, 2739. [Google Scholar] [CrossRef] [Green Version]
  54. Zhang, C.; Lu, W.; Wang, Z.; Ni, J.; Zhang, J.; Tang, W.; Fang, Y. A Comprehensive Analysis of NDST3 for Schizophrenia and Bipolar Disorder in Han Chinese. Transl. Psychiatry 2016, 6, e701. [Google Scholar] [CrossRef] [Green Version]
  55. Maiese, K. WISP1: Clinical Insights for a Proliferative and Restorative Member of the CCN Family. Curr. Neurovascular Res. 2014, 11, 378–389. [Google Scholar] [CrossRef] [Green Version]
  56. Park, H.; Lee, S.; Kim, H.-J.; Ju, Y.S.; Shin, J.-Y.; Hong, D.; von Grotthuss, M.; Lee, D.-S.; Park, C.; Kim, J.H.; et al. Comprehensive Genomic Analyses Associate UGT8 Variants with Musical Ability in a Mongolian Population. J. Med. Genet. 2012, 49, 747–752. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Zaccariotto, E.; Cachón-González, M.B.; Wang, B.; Lim, S.; Hirth, B.; Park, H.; Fezoui, M.; Sardi, S.P.; Mason, P.; Barker, R.H.; et al. A Novel Brain-Penetrant Oral UGT8 Inhibitor Decreases in Vivo Galactosphingolipid Biosynthesis in Murine Krabbe Disease. Biomed. Pharmacother. 2022, 149, 112808. [Google Scholar] [CrossRef] [PubMed]
  58. Zhao, H.; Tanegashima, K.; Ro, H.; Dawid, I.B. Lrig3 Regulates Neural Crest Formation in Xenopus by Modulating Fgf and Wnt Signaling Pathways. Development 2008, 135, 1283–1293. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Abraira, V.E.; del Rio, T.; Tucker, A.F.; Slonimsky, J.; Keirnes, H.L.; Goodrich, L.V. Cross-Repressive Interactions between Lrig3 and Netrin 1 Shape the Architecture of the Inner Ear. Development 2008, 135, 4091–4099. [Google Scholar] [CrossRef] [Green Version]
  60. Ayka, A.; Şehirli, A.Ö. The Role of the SLC Transporters Protein in the Neurodegenerative Disorders. Clin. Psychopharmacol. Neurosci. 2020, 18, 174–187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Wilcox, J.A.; Quadri, S. Replication of NTNG1 Association in Schizophrenia. Psychiatr. Genet. 2014, 24, 266–268. [Google Scholar] [CrossRef]
  62. Zhu, Y.; Yang, H.; Bi, Y.; Zhang, Y.; Zhen, C.; Xie, S.; Qin, H.; He, J.; Liu, L.; Liu, Y. Positive Association between NTNG1 and Schizophrenia in Chinese Han Population. J. Genet. 2011, 90, 499–502. [Google Scholar] [CrossRef]
  63. Mei, L.; Nave, K.-A. Neuregulin-ERBB Signaling in the Nervous System and Neuropsychiatric Diseases. Neuron 2014, 83, 27–49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Ledonne, A.; Mercuri, N.B. On the Modulatory Roles of Neuregulins/ErbB Signaling on Synaptic Plasticity. IJMS 2019, 21, 275. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Kim, E.K.; Choi, E.-J. Pathological Roles of MAPK Signaling Pathways in Human Diseases. Biochim. Biophys. Acta (BBA) Mol. Basis Dis. 2010, 1802, 396–405. [Google Scholar] [CrossRef] [Green Version]
  66. Child, N.D.; Benarroch, E.E. MTOR: Its Role in the Nervous System and Involvement in Neurologic Disease. Neurology 2014, 83, 1562–1572. [Google Scholar] [CrossRef]
  67. Maiese, K.; Chong, Z.Z.; Shang, Y.C.; Wang, S. MTOR: On Target for Novel Therapeutic Strategies in the Nervous System. Trends Mol. Med. 2013, 19, 51–60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Lipton, J.O.; Sahin, M. The Neurology of MTOR. Neuron 2014, 84, 275–291. [Google Scholar] [CrossRef] [Green Version]
  69. Eastwood, S.L.; Harrison, P.J. Decreased MRNA Expression of Netrin-G1 and Netrin-G2 in the Temporal Lobe in Schizophrenia and Bipolar Disorder. Neuropsychopharmacology 2008, 33, 933–945. [Google Scholar] [CrossRef] [Green Version]
  70. Chichinadze, K.; Chichinadze, N.; Lazarashvili, A. Hormonal and Neurochemical Mechanisms of Aggression and a New Classification of Aggressive Behavior. Aggress. Violent Behav. 2011, 16, 461–471. [Google Scholar] [CrossRef]
  71. Umukoro, S.; Aladeokin, A.C.; Eduviere, A.T. Aggressive Behavior: A Comprehensive Review of Its Neurochemical Mechanisms and Management. Aggress. Violent Behav. 2013, 18, 195–203. [Google Scholar] [CrossRef]
  72. Bertelsen, B.; Melchior, L.; Jensen, L.R.; Groth, C.; Glenthøj, B.; Rizzo, R.; Debes, N.M.; Skov, L.; Brøndum-Nielsen, K.; Paschou, P.; et al. Intragenic Deletions Affecting Two Alternative Transcripts of the IMMP2L Gene in Patients with Tourette Syndrome. Eur. J. Hum. Genet. 2014, 22, 1283–1289. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. IMGSAC; Maestrini, E.; Pagnamenta, A.T.; Lamb, J.A.; Bacchelli, E.; Sykes, N.H.; Sousa, I.; Toma, C.; Barnby, G.; Butler, H.; et al. High-Density SNP Association Study and Copy Number Variation Analysis of the AUTS1 and AUTS5 Loci Implicate the IMMP2L–DOCK4 Gene Region in Autism Susceptibility. Mol. Psychiatry 2010, 15, 954–968. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Adell, A.; Celada, P.; Abellán, M.T.; Artigas, F. Origin and Functional Role of the Extracellular Serotonin in the Midbrain Raphe Nuclei. Brain Res. Rev. 2002, 39, 154–180. [Google Scholar] [CrossRef] [PubMed]
  75. Våge, J.; Wade, C.; Biagi, T.; Fatjó, J.; Amat, M.; Lindblad-Toh, K.; Lingaas, F. Association of Dopamine-and Serotonin-Related Genes with Canine Aggression. Genes Brain Behav. 2010, 9, 372–378. [Google Scholar] [CrossRef] [PubMed]
Genes 14 00584 g001 550
Figure 1. Manhattan plots generated by six selective sweep detection methods. (A) Fst values. (B) Tajima’s D values. (C) experienced distribution p values of hapFLK. (D) experienced distribution p values of iHS. (E) experienced distribution p values of XP-EHH. (F) SNP occurrence percentage on Chr8. DCs refer to domesticated chickens except LXFCs.
Figure 1. Manhattan plots generated by six selective sweep detection methods. (A) Fst values. (B) Tajima’s D values. (C) experienced distribution p values of hapFLK. (D) experienced distribution p values of iHS. (E) experienced distribution p values of XP-EHH. (F) SNP occurrence percentage on Chr8. DCs refer to domesticated chickens except LXFCs.
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Figure 2. GO and KEGG pathway enrichment results of selected genes detected by at least two methods.
Figure 2. GO and KEGG pathway enrichment results of selected genes detected by at least two methods.
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Figure 3. Heatmaps of genes differentially expressed between LXFC and TC/WL chickens in the cerebral cortex and midbrain. (A) DEGs between the cerebral cortex of LXFC and TC birds. (B) DEGs between the midbrains of LXFC and TC birds. (C) DEGs between the cerebral cortex of LXFC and WL chickens. (D) DEGs between the midbrains of LXFC and WL birds.
Figure 3. Heatmaps of genes differentially expressed between LXFC and TC/WL chickens in the cerebral cortex and midbrain. (A) DEGs between the cerebral cortex of LXFC and TC birds. (B) DEGs between the midbrains of LXFC and TC birds. (C) DEGs between the cerebral cortex of LXFC and WL chickens. (D) DEGs between the midbrains of LXFC and WL birds.
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Figure 4. Venn diagrams of shared DEGs between the cerebral cortex and midbrain of LXFC-TC chickens and LXFC-WL chickens. (A) Shared down-regulated DEGs in the cerebral cortex between LXFC-TC and LXFC-WL chickens. (B) Shared up-regulated DEGs in the cerebral cortex between LXFC-TC and LXFC-WL chickens. (C) Shared down-regulated DEGs in the midbrain between LXFC-TC and LXFC-WL chickens. (D) Shared up-regulated DEGs in the midbrain between LXFC-TC and LXFC-WL chickens.
Figure 4. Venn diagrams of shared DEGs between the cerebral cortex and midbrain of LXFC-TC chickens and LXFC-WL chickens. (A) Shared down-regulated DEGs in the cerebral cortex between LXFC-TC and LXFC-WL chickens. (B) Shared up-regulated DEGs in the cerebral cortex between LXFC-TC and LXFC-WL chickens. (C) Shared down-regulated DEGs in the midbrain between LXFC-TC and LXFC-WL chickens. (D) Shared up-regulated DEGs in the midbrain between LXFC-TC and LXFC-WL chickens.
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Figure 5. GO and KEGG enrichment results of midbrain DEGs shared between LXFC-TC and LXFC-WL chickens. (A) shared down-regulated DEGs (B) shared up-regulated DEGs.
Figure 5. GO and KEGG enrichment results of midbrain DEGs shared between LXFC-TC and LXFC-WL chickens. (A) shared down-regulated DEGs (B) shared up-regulated DEGs.
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Figure 6. GO enrichment of genes shared between selective sweeps and DEGs in the midbrain. (A) LXFC-TC chickens. (B) LXFC-WL chickens. (C) Chord diagram of overlapping genes between LXFC-TC birds. (D) Chord diagram of overlapping genes between LXFC-WL chickens. AOD refers to animal organ development; MOD refers to multicellular organism development; MOP refers to multicellular organismal process; BD refers to bone development; SSD refers to skeletal system development; SD refers to system development; ASD refers to anatomical structure development; DP refers to developmental process; AOD refers to animal organ development; NSD refers to nervous system development.
Figure 6. GO enrichment of genes shared between selective sweeps and DEGs in the midbrain. (A) LXFC-TC chickens. (B) LXFC-WL chickens. (C) Chord diagram of overlapping genes between LXFC-TC birds. (D) Chord diagram of overlapping genes between LXFC-WL chickens. AOD refers to animal organ development; MOD refers to multicellular organism development; MOP refers to multicellular organismal process; BD refers to bone development; SSD refers to skeletal system development; SD refers to system development; ASD refers to anatomical structure development; DP refers to developmental process; AOD refers to animal organ development; NSD refers to nervous system development.
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Table
Table 1. Populations used for whole genome resequencing.
Table 1. Populations used for whole genome resequencing.
PopulationAbbreviationNumbers
Luxi gamecockLXFC19
Shandong native chickenSDNC30
Xishuangbanna gamecockYNFC16
Yunnan native chickenYNNC22
Tibetan chickenTC18
Rhode Island redRDH20
Table
Table 2. Selective sweeps detected by four methods.
Table 2. Selective sweeps detected by four methods.
GeneFull NameFstTajima’s DhapFLKiHSXP-EHH
CDH18cadherin 18√√√√√
SLITRK1SLIT and NTRK-like family member 1√-√√√
SLITRK6SLIT and NTRK-like family member 6√√√√-
NDST3N-deacetylase and N-sulfotransferase 3-√√√√
ATP23ATP23 metallopeptidase and ATP synthase assembly factor homolog√-√√√
LRIG3leucine-rich repeats and immunoglobulin-like domains 3√-√√√
IL1RAPL1glutamate decarboxylase-like 1√√√-√
GADL1interleukin 1 receptor accessory protein-like 1-√√√√
C5orf22chromosome 2 open reading frame, human C5orf22√-√√√
UGT8UDP glycosyltransferase 8√-√√√
WISP1WNT1-inducible signaling pathway protein 1√-√√√
WNT9AWnt family member 9A-√√√√
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Zhou, J.; Chang, Y.; Li, J.; Bao, H.; Wu, C. Integrating Whole-Genome Resequencing and RNA Sequencing Data Reveals Selective Sweeps and Differentially Expressed Genes Related to Nervous System Changes in Luxi Gamecocks. Genes 2023, 14, 584. https://doi.org/10.3390/genes14030584

AMA Style

Zhou J, Chang Y, Li J, Bao H, Wu C. Integrating Whole-Genome Resequencing and RNA Sequencing Data Reveals Selective Sweeps and Differentially Expressed Genes Related to Nervous System Changes in Luxi Gamecocks. Genes. 2023; 14(3):584. https://doi.org/10.3390/genes14030584

Chicago/Turabian Style

Zhou, Jieke, Ying Chang, Junying Li, Haigang Bao, and Changxin Wu. 2023. "Integrating Whole-Genome Resequencing and RNA Sequencing Data Reveals Selective Sweeps and Differentially Expressed Genes Related to Nervous System Changes in Luxi Gamecocks" Genes 14, no. 3: 584. https://doi.org/10.3390/genes14030584

APA Style

Zhou, J., Chang, Y., Li, J., Bao, H., & Wu, C. (2023). Integrating Whole-Genome Resequencing and RNA Sequencing Data Reveals Selective Sweeps and Differentially Expressed Genes Related to Nervous System Changes in Luxi Gamecocks. Genes, 14(3), 584. https://doi.org/10.3390/genes14030584

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