Abstract
Background: In pigs, a hair whorl refers to hairs that form a ring of
growth around the direction of the hair follicle at the dorsal hip. In
China, a hair whorl is considered a negative trait that affects
marketing, and no studies have been conducted to demonstrate whether
hair whorl affects pig performance and provide an explanation for its
genetic basis. Methods: Performance-measured traits and
slaughter-measured traits of hair whorl and non-hair whorl pigs were
differentially analyzed, followed by genome-wide association analysis
(GWAS) and copy number variation (CNV) methods to investigate the
genetic basis of hair whorl in pigs. Results: Differential analysis of
2625 pigs (171 hair whorl and 2454 non-hair whorl) for performance
measures showed that hair whorl and non-hair whorl pigs differed
significantly (p < 0.05) in traits such as live births, total litter
size, and healthy litter size (p < 0.05), while differential analysis
of carcass and meat quality traits showed a significant difference only
in the 45 min pH (p = 0.0265). GWAS identified 4 SNP loci significantly
associated with the hair whorl trait, 2 of which reached
genome-significant levels, and 23 candidate genes were obtained by
annotation with the Ensembl database. KEGG and GO enrichment analyses
showed that these genes were mainly enriched in the ErbB signaling,
endothelial apoptosis regulation, and cell proliferation pathways. In
addition, CNV analysis identified 652 differential genes between hair
whorl and non-hair whorl pigs, which were mainly involved in the signal
transduction, transcription factor activity, and nuclear and
cytoplasmic-related pathways. Conclusions: The candidate genes and copy
number variation differences identified in this study provide a new
theoretical basis for pig breeding efforts.
Keywords: pig, GWAS, CNV, hair whorl, hair follicle, candidate gene
1. Introduction
Pigs are one of the most important agricultural animals in the world
and their meat traits and production performance are some of the main
factors of concern for farmers and consumers [[48]1]. In addition to
this, specific traits of pigs, such as hair whorl, lameness, and
umbilical hernia, are also of great concern to farmers [[49]2,[50]3].
In China, the hair whorl trait is regarded as a negative trait because
hair whorl pigs are considered unlucky in most parts of China, largely
stemming from traditional culture and folk customs. Although the pig
usually symbolizes hard work and good fortune, the appearance of a hair
whorl does not match people’s perceptions, with such a pig viewed as
inauspicious. As a result, when pig enterprises sell breeding and
commercial pigs with hair whorl features, pig vendors and farmers often
refuse to buy them on their behalf, seriously affecting the trade and
further weakening farmers’ incentives to breed such pigs. This not only
affects the marketing of commercial pigs but also leads to the
elimination of breeding pigs with hair whorls. Nearly 7% of our
selected population had hair whorls, a proportion that significantly
affects pig marketing. Nevertheless, there is a lack of scientific
evidence to confirm whether the hair whorl trait actually affects pig
performance or to provide a definitive explanation of its genetic
basis.
Porcine hair whorl is due to the way skin and hair follicles form
during embryonic development [[51]4]. During the early embryonic
development of the pig, skin and hair follicles begin to form on the
back, accompanied by the skin curving outward to form so-called skin
folds. As the embryo develops, the skin folds gradually disappear but
leave behind structures that affect hair growth. When the hair follicle
forms an angle with the skin surface and begins to grow upward, if the
direction of hair follicle growth is different from the angle of the
skin surface, a hair spiral is formed. Therefore, a hair whorl on the
pig’s back is caused by the formation of skin folds and the
inconsistent angle of hair follicles during embryonic development. Hair
growth in pigs is an extremely complex physiological process influenced
by many factors, such as the environment, nutrition, metabolic level,
and gene regulation, but gene regulation is the determining factor for
hair growth and development. Studies have shown that this genetic trait
is controlled through polygenic inheritance, with multiple genes acting
together to influence the direction of hair follicle growth [[52]5],
and some studies have shown that the growth of adipose tissue may be
influenced by hair follicle growth [[53]6]. A study of hair whorl in
horses deduced that the hair whorl trait was associated with a more
docile and calmer disposition [[54]7]. These authors performed GWAS
using the horse as a research model and were the first to identify a
genomic region associated with the hair whorl trait in the horse in
which genes were present that had neural and behavioral functions
[[55]8]. In summary, hair whorl, as an obvious genetic trait, reflects
a complex embryonic developmental process, and through genetic analyses
we can reveal the biological mechanisms behind it, thus contributing to
a comprehensive understanding of hair whorl formation. Secondly, the
polygenic genetic character of hair whorl means that, by identifying
related genes, we can not only improve breeding efficiency but also
develop selective breeding strategies for specific traits.
Genome-wide association studies are one of the most important tools for
studying complex traits in recent years [[56]9]. GWAS can identify
genes and variant loci associated with complex traits by detecting
associations between a large number of single nucleotide polymorphism
(SNP) loci and phenotypes. In the field of swine research, GWAS has
been widely used to study growth traits [[57]10,[58]11,[59]12],
reproductive traits [[60]13,[61]14,[62]15], meat quality
[[63]1,[64]16,[65]17,[66]18], disease resistance
[[67]19,[68]20,[69]21], and other traits, and a number of important
results have been achieved. Copy number variation (CNV) refers to
duplication and deletion variations in genomic sequences of 50 bp–5 Mb
in length. In a population, the result of integration of overlapping
regions of CNV among different individuals is called the CNV region
(CNVR). CNV is an important source of genetic variation and can
identify genetic variation among varieties [[70]22]. CNV mainly affects
gene expression and function through a variety of mechanisms. CNV is
considered to be an important source of genetic variation in addition
to SNPs, another important contributor to genetic variation [[71]23].
Currently, some CNVRs affecting economically important traits in pigs
have been identified. For example, Zheng et al. compared CNVs in
Meishan and Duroc pigs to identify a CNVR that is only present in
Meishan pigs and overlaps with reproduction-related genes [[72]24].
The aim of this study was to investigate the genetic basis of pig hair
whorl using GWAS and CNV and explore whether there are performance
differences between hair whorl and non-hair whorl hogs to provide a
more scientific and precise genetic improvement strategy for pig
breeding.
2. Materials and Methods
2.1. Study Population and Phenotypic Determination
The population used in this study was from a breeding farm in southwest
China. A total of 2625 Canadian Duroc, Canadian Large White, and
Canadian Long White pigs (667 boars and 1958 sows) born between 2020
and 2022 were used. Of these, 171 pigs were recorded as hair whorl, and
2454 pigs were recorded as non-hair whorl. In this study, the hair
whorl trait was manually determined, and the criterion for the
determination was to check whether the hairs on the back and rump of
the pigs formed a hair whorl ([73]Figure 1).
Figure 1.
[74]Figure 1
[75]Open in a new tab
Example of hair whorl and non-hair whorl pigs: (a) hair whorl pig (The
red circled part shows the hair whorl of the pig); (b) non-hair whorl
pig.
2.2. Performance Measurement and Slaughter Measurement
Performance measurement data of this population were collected,
including live litter size, total litter size, healthy litter size,
birth weight, left lactation size, right lactation size, corrected 100
kg day age, corrected 100 kg backfat, FCR (feed-to-meat ratio), etc.
The t-test in GraphPad software was used to analyze whether or not
there was a significant difference between inverted rotation and
non-inverted rotation pigs in terms of these traits.
In this study, 6 Canadian hair whorl castrated and 6 Canadian non-hair
whorl castrated Large White pig boars from a breeding farm in Southwest
China were selected for slaughter performance measurement. All test
pigs were around 65 kg and were weaned from food and prohibited from
drinking for 24 h prior to slaughter. Pigs were slaughtered in a
standard slaughtering session. After slaughter, the psoas and
longissimus dorsi of the meat were taken directly from the waist
position, and the meat quality characteristics were measured,
respectively. Specific traits were determined, including carcass
quality, meat production performance, and meat quality traits. All of
the determination methods were in accordance with the technical
specifications of the NY/T821-2004 pig muscle quality determination.
Economic traits such as body weight and size, carcass traits, meat
production performance, visceral organ rate, meat quality, and other
economic traits were systematically tested, comprehensively evaluated,
and comparatively analyzed in 6 hair whorl and 6 non-hair whorl
castrated boars.
2.3. Genotyping and Quality Control
DNA was extracted from the ear tissues of 2625 pigs using a standard
phenol/ chloroform method and then quantitatively diluted to 50 ng/μL.
All DNA samples were genotyped using a KPS Porcine breeding chip 50 K
SNP microarray from Beijing Compass Biotechnology Co., Ltd., Beijing,
China, comprising a total of 57,466 SNPs, and quality control was
performed using PLINKv1.90 [[76]25] software. Quality control for SNPs
involved removing those with a detection rate < 0.9, a minor allele
frequency (MAF) < 0.05, and a Hardy–Weinberg equilibrium (HWE) p-value
< 1 × 10^−6 [[77]26]. This resulted in the exclusion of 406 SNPs for
detection rate, 4870 for MAF, and 22,305 for HWE. Autosomal SNPs (1–18)
were extracted, leaving 22,267 SNPs from 2625 pigs for further
analysis.
The Bonferroni correction method was used to determine the genomic
significance threshold and chromosomal group significance threshold. A
total of 22,267 valid SNPs were used in this study, and the genomic
highly significant threshold was 0.01/N (N = the number of times the
statistical test was performed, i.e., the number of SNPs); the genomic
significance threshold was 0.05/N; and the chromosomal group
significance threshold was 1/N. The calculated post-genomic highly
significant level threshold was 4.5 × 10^−7; the genomic significant
level p-value was 2.25 × 10^−6; and the chromosomal significant level
threshold was 4.49 × 10^−5.
2.4. Genome-Wide Association Studies
This study used a Q + K model in the GMAT software package [[78]27] to
perform GWAS analysis on 2625 pigs based on the KPS Porcine breeding
chip 50 K SNP microarray. The model is expressed as follows:
[MATH: y=μ+W
α+xβ+Zu+
e :MATH]
(1)
where y is the vector of phenotypic values; μ is the population mean; α
is the vector of fixed effects; W is the fixed-effects design matrix; x
is the vector of SNPs; β is the SNP effect value: e is the randomized
residual; u is the individual effect; Z is the design matrix; and
[MATH:
u~N(0,Kσa2),e~N(0,Iσe2) :MATH]
.
In this study, the Bonferroni correction method was used to adjust the
significance level of the p-value of the GWAS results, and the “CMplot”
in R software was used to plot the Manhattan plot and the
Quantile–Quantile plot to represent the distribution of GWAS results
[[79]28,[80]29].
2.5. Linkage Disequilibrium Analysis
We used PLINK software to perform linkage disequilibrium analysis of 5
significant SNPs found after genome-wide association analysis. The
linkage disequilibrium (LD) values of the tagged SNPs with other SNPs
were calculated in the range of 500 kb, where PLINK used D and r^2 as
the criteria for the linkage disequilibrium analysis. The D value was
calculated using the following formula:
D = P(AB) − P(A) × P(B) (2)
where P(AB) represents the actual observed frequency of AB, and P(A) ×
P(B) represents the expected value of the frequency of AB.
r^2 is a measure of the degree of joint genetic variation between two
loci, calculated as follows:
r² = D²/(P(A)P(a)P(B)P(b)) (3)
where D² refers to the squared value of D; P(A) and P(a) represent the
frequencies of the A allele and a allele at the first locus,
respectively; and P(B) and P(b) represent the frequencies of the B
allele and b allele at the second locus, respectively.
LDdecay maps were plotted using the Plot_OnePop.plcommand in PopLDdecay
[[81]30]. The bin1 parameter in this command indicates the distance
between adjacent SNP sites, and the bin2 parameter is used to adjust
the smoothness of the LD decay curve. There were 22,267 SNPS in our
dataset. We set bin1 to 50 and bin2 to 1000.
LDBlockShow [[82]31] was used to perform block mapping of significant
SNPs, and 500 kb upstream and downstream of significant SNPs was
selected for analysis.
2.6. Analysis of Copy Number Variation
The LRR-BAF values of pigs were processed into SNP number, chromosome
number, SNP position, individual LRR value, and individual BAF value
using Python. Copy number variation detection was performed on hair
whorl and non-hair whorl pigs using PennCNV (v1.0.7) software [[83]32],
and the detected CNVs were filtered under the following conditions: the
length of the CNVs was greater than 10 kb, and the smallest number of
SNPs contained was three.
PennCNV uses the Hidden Markov model (HMM) with the following equation:
[MATH: Pr1,…,rM,b1,…,
bM=<
mrow>∑Z1…∑ZM∏i=1MPriziPbiziPz1∏i=2MPzizi
−1
:MATH]
(4)
where ri, bi, and zi represent the logR ratio, B allele frequency, and
copy number variant status at SNP i, respectively.
According to the copy number variants detected by PennCNV, the
chromosome number, start site, and end site information of each copy
number variant record were extracted, and then the coordinates of the
copy number variants were merged by taking the concatenation set using
the merge command of Bedtools software, and the CNVR was obtained for
subsequent analysis.
2.7. Identification of Candidate Genes
When making the candidate gene identification, we used the R software
(version 4.2.1) package “Biomart” [[84]33] in the Ensembl database
[[85]34,[86]35]. The candidate genes were screened for each of the
significant SNPs and copy number variant segments.
2.8. Tional Enrichment Analysis
To further annotate functional genes near significant SNPs, we screened
genes 500 kb upstream and downstream from each significant SNPs. Next,
we use the DAVID annotation tool for these functional genes to carry
out detailed comments [[87]36,[88]37,[89]38], including Gene Ontology
(GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
enrichment analysis functions, and other biological database
integration. In the presentation of the enrichment analysis results, we
used the visualization function to present the enrichment of each
pathway more clearly.
3. Results
3.1. Performance Measure
A total of 2625 pigs were recorded in this study for various
performance measures. The descriptive statistics recorded are located
in [90]Table 1, and the results of the test of variance of the
performance measures are presented in [91]Table 2, which showed that
there were significant differences between hair whorl and non-hair
whorl pigs in the number of live piglets in the same litter, total
number of piglets in the same litter, number of healthy piglets in the
same litter, the corrected 100 kg body weight of age, and FCR
(feed-to-meat ratio). After genome-wide association analysis of the
differential traits, the detected significant SNPs did not overlap with
the hair whorl-associated significant SNPs.
Table 1.
Descriptive statistics of performance measurement data.
Trait Individuals Min Mean Max SD
Number of Live litter size 2096 1 12.27 21 3.57
Number of Total litter size 2097 1 12.88 21 3.74
Number of healthy litters 2096 1 10.93 18 3.09
Birth weight 2067 0.6 1.25 2.2 0.25
Number of left teats 2096 5 7.04 9 0.61
Number of right teats 2096 5 6.91 9 0.58
Corrected 100 kg weight of age 1851 129.89 159.79 232.79 18.33
Corrected 100 kg backfat 1851 5.11 12.15 24.39 2.81
Corrected FCR 659 1.41 1.85 3.17 0.30
[92]Open in a new tab
Table 2.
Analysis of performance measurement variability results.
Trait Num of Hair Whorl Pigs Mean Num of Non-Hair Whorl Pigs Mean p
Value
Number of Live litter size 128 13.23 1968 12.21 0.0018
Number of Total litter size 128 13.94 1969 12.81 0.0009
Number of healthy litters 128 11.69 1968 10.88 0.0043
Corrected 100 kg weight of age 128 164.49 1723 159.44 0.0026
Corrected FCR 53 1.73 606 1.86 0.0015
Birth weight 128 1.22 1939 1.25 0.1429
Number of left teats 128 7.02 1968 7.05 0.6032
Number of right teats 128 6.90 1968 6.91 0.7724
Corrected 100 kg backfat 128 11.99 1723 12.16 0.5146
[93]Open in a new tab
The results of the slaughter measurements indicated that the 12 pigs in
this assay had good overall carcass traits and meat quality traits. The
slaughter rates of hair whorl and non-hair whorl pigs were relatively
similar for meat production traits, but in terms of lean meat output,
hair whorl pigs were slightly lower than non-hair whorl pigs. The
t-tests using GraphPad software showed that there was no significant
difference in leanness between hair whorl and non-hair whorl pigs (p =
0.1252); no significant difference in corrected 100 kg backfat
thickness (p = 0.51); and no significant difference in corrected 100 kg
longissimus dorsi of the meat thickness (p = 0.21). In terms of meat
quality traits, there was a significant difference between the pH of
the longissimus dorsi at 45 min after slaughter (p = 0.0265)
([94]Figure 2), while the rest of the traits were not significantly
different.
Figure 2.
Figure 2
[95]Open in a new tab
pH difference between hair whorl and non-hair whorl pigs.
3.2. The Significant SNPs for Hair Whorl Trait
After SNP quality control, genome-wide association analysis was
performed on 22,267 SNPs in 18 autosomal pairs. A total of 5 SNPs
reaching the significance level were considered to be significantly
associated with the hair whorl trait, among which CNC10170898
(chr17:44143739) and CNC10170895 (chr17:44164745) located on chromosome
17 reached the genomic significance level, and the rest that reached
the genomic significance level were: CNC10010257 (chr1:9778686),
CNCB10011881 (chr17:44126874), and CNC10023023 (chr2:141779308), the
GWAS results are displayed [96]Figure 3 and [97]Figure 4.
Figure 3.
[98]Figure 3
[99]Open in a new tab
Manhattan Plot of GWAS results of the hair whorl trait.
Figure 4.
[100]Figure 4
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QQ plot of GWAS results for the hair whorl trait.
3.3. Linkage Disequilibrium Results
The analysis results showed that the quality controlled SNPs in our
sample complied with the Hardy–Weinberg equilibrium (HWE) law, and most
SNPS had a significant LD relationship. In addition, we also found that
SNP CNC10170898 and CNC10170895 had a highly tight LD relationship (r^2
= 0976), and these 2 SNPS may contribute significantly to the hair
whorl trait.
The LD attenuation map showed that the LD values between the sites
decreased with increasing distance between them, and the image showed a
relatively smooth trend ([102]Figure 5). This indicated that the LD
relationship between these sites was relatively weak in the study
sample, and there was no dramatic LD change point. This relatively
smooth trend usually enabled us to determine the LD situation among SNP
sites more accurately, which provided valuable data support for the
study of the genome structure of the samples and further association
analysis.
Figure 5.
[103]Figure 5
[104]Open in a new tab
LD attenuation diagram.
We used the LDBlockShow package to map 500 kb upstream and downstream
of salient sites on chromosomes 1, 2, and 17 ([105]Figure 6). The
results showed that the two salient sites on chromosome 17 formed
blocks, and the significant SNP sites on chromosome 2 also formed
blocks. However, the significant sites located on chromosome 1 failed
to form blocks, which may be due to the presence of false positives at
this site. Therefore, we deleted this SNP loci.
Figure 6.
[106]Figure 6
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Significant SNP linkages: (a) chr17 43. 68 mb−44.21 mb haplotype block
diagram; (b) chr2 141.467 MB−142.234 mb haplotype block diagram; (c)
chr1 9.47 Mb−10.141 mb haplotype block diagram. The square shaped
points in the figure are the most significant snp loci of this GWAS.
3.4. Results of Copy Number Variation Analysis
Copy number variation detection of hair whorl and non-hair whorl pigs
using PennCNV showed that 1527 CNV records were detected in hair whorl
pigs, of which 1279 were CNVs of the loss type and 248 were CNVs of the
gain type. A total of 20,249 CNV records were detected in non-hair
whorl pigs, with 17,787 CNVs of the loss type and 2462 CNVs of the gain
type ([108]Table 3).
Table 3.
Results of copy number variation detection of PennCNV.
Type Hair Whorl Non-Hair Whorl
Losses 1279 (83.76%) 17,787 (87.84%)
Gains 248 (16.24%) 2462 (12.16%)
Total 1527 20,249
[109]Open in a new tab
3.5. Candidate Gene and Functional Annotation
3.5.1. GWAS Results
Candidate gene screening for significant SNPs was performed using the
pig gene sequence in the Ensembl database with the “Biomart” package in
R (version 4.2.1) software for each significant SNP. CNC10170898
(chr17: 44143739), CNC10170895 (chr17: 44164745), and CNCB10011881
(chr17:44126874) all fell on CHD6 on chr17; CNC10023023
(chr2:141779308) fell on NRG2 on chr2 ([110]Table 4).
Table 4.
Candidate genes corresponding to significant SNPs.
SNP SSC Position p Value Alleles Candidate Gene Start End
CNC10170898 17 44143739 1.05 × 10^−6 G/A CHD6 44048098 44265431
CNC10170895 17 44164745 1.32 × 10^−6 T/C CHD6 44048098 44265431
CNCB10011881 17 44126874 2.60 × 10^−5 C/T CHD6 44048098 44265431
CNC10023023 2 141779308 3.05 × 10^−5 T/G NRG2 141662369 141824727
[111]Open in a new tab
Subsequently, 500 kb upstream and downstream of 4 significant SNPs were
identified as candidate genes, and 23 candidate genes were obtained,
and pathway enrichment analysis of the above candidate genes was
carried out using DAVID ([112]Table 5). The KEGG pathway enrichment
results showed that there were three pathways, and the GO pathway
enrichment results showed that there were eight pathways, of which
there were five pathways with p < 0.05.
Table 5.
Significant gene ontology (GO) terms and Kyoto Encyclopedia of Genes
and Genomes (KEGG) pathways associated with the hair whorl trait in
pigs.
Term Count p-Value Genes
GO:2000353 positive regulation of endothelial cell apoptotic process 2
0.011625061 ECSCR, PLCG1
GO:0005654 nucleoplasm 7 0.022952206 PROB1, ZHX3, STING1, SPATA24,
CHD6, ECSCR, CXXC5
GO:0008284 positive regulation of cell population proliferation 3
0.023643003 PURA, MZB1, HBEGF
GO:0098978 glutamatergic synapse 3 0.031915019 PURA, PSD2, PLCG1
GO:0007173 epidermal growth factor receptor signaling pathway 2
0.034496111 PLCG1, HBEGF
GO:0032587 ruffle membrane 2 0.063070664 PSD2, PLCG1
GO:0140297 DNA-binding transcription factor binding 2 0.075270787 PURA,
CXXC5
GO:0098794 postsynapse 2 0.077077652 PURA, PSD2
ssc04012 ErbB signaling pathway 3 0.001177119 NRG2, PLCG1, HBEGF
ssc05171 Coronavirus disease—COVID-19 3 0.010096157 STING1, PLCG1,
HBEGF
ssc01521 EGFR tyrosine kinase inhibitor resistance 2 0.04920922 NRG2,
PLCG1
[113]Open in a new tab
SNP molecular markers significantly associated with the pig hair whorl
trait were screened by genome-wide association analysis, 4 SNP loci
significantly associated with the pig hair whorl trait were screened,
the number of individuals of different genotypes and the number of
corresponding genotypes of hair whorl were further counted at each SNP
locus, and the hair whorl rate of different genotypes (number of
individuals with hair whorl trait/number of individuals of the
genotype) was calculated. As shown in [114]Table 6, for the four SNP
loci 17:CNC10170898, 17:CNC10170895, 17:CNCB10011881, and
2:CNC10023023, the frequency of pigs with the hair whorl trait was
higher than that of individuals with other genotypes when the genotypes
were AA, CC, TT, and GG, respectively ([115]Table 6).
Table 6.
Genotype frequency and hair whorl rate distribution of candidate SNP
loci in pigs.
SNP Number Genotype Frequency (Hair Whorl Rate)
0/0 0/1 1/1
Chr17:CNC10170898 2625 GG:0.8690 (0.04910) AG:0.1265 (0.1626)
AA:0.004571 (0.4167)
Chr17:CNC10170895 2625 TT:0.8659 (0.04927) CT:0.1295 (0.1588)
CC:0.004571 (0.4167)
Chr17:CNCB10011881 2625 CC:0.8270 (0.05157) TC:0.1665 (0.1236)
TT:0.006095 (0.3125)
Chr2:CNC10023023 2625 TT:0.8827 (0.04704) GT:0.1070 (0.1922) GG:0.01029
(0.2963)
[116]Open in a new tab
3.5.2. CNV Results
Candidate gene screening for copy number variation in hair whorl and
non-hair whorl pigs was performed using pig gene sequences in the
Ensembl database with the “Biomart” package in R (version 4.2.1)
software. There were 582 candidate genes for hair whorl pigs and 1228
candidate genes for non-hair whorl pigs. The screening resulted in 652
differential genes.
The differential genes obtained were analyzed by pathway enrichment
using DAVID. The KEGG pathway enrichment results showed 8 pathways, but
the number of pathways with p < 0.05 was 0. The GO pathway enrichment
results showed 92 pathways, of which 53 pathways had p < 0.05,
including 28 that belonged to Biological Process (BP) pathways
([117]Figure 7), 11 that belonged to Molecular Function (MF) pathways
([118]Figure 8), and 14 that belonged to Cellular Component (CC)
pathways ([119]Figure 9). The results of the enrichment analysis were
visualized.
Figure 7.
[120]Figure 7
[121]Open in a new tab
GO−BP pathway enrichment results.
Figure 8.
[122]Figure 8
[123]Open in a new tab
GO−MF pathway enrichment results.
Figure 9.
[124]Figure 9
[125]Open in a new tab
GO−CC pathway enrichment results.
4. Discussion
A number of localization factors have led to the unpopularity of hair
whorl pigs for selection and retention; however, no studies have shown
that hair whorl pigs perform poorly in terms of production performance.
In our study, the total litter size, live litter size, healthy litter
size, corrected 100 kg day of age, and corrected FCR were significantly
different (p < 0.05) between hair whorl and non-hair whorl pigs, and
our genome-wide association analyses of these differing performance
measurement traits revealed that the significant SNPs detected did not
overlap with the hair whorl trait-related significant SNPs. This
suggests that the hair whorl trait may be related to different
mechanisms of inheritance than these traits, independent of each other,
or the complexity of the samples and phenotypes resulted in
insignificant genetic associations. It may also be related to biases in
statistical analysis methods or sample selection. This may require
further exploration of potential genetic links between traits in
conjunction with functional genomics. In this study, we identified 4
SNP loci significantly associated with the hair whorl trait, and a
total of 23 candidate genes were identified within 500 kb upstream and
downstream of them, with a total of eight pathways enriched (p < 0.05).
Positive regulation of the endothelial apoptotic process (GO:2000353)
refers to the enhancement or promotion of programmed endothelial cell
death through various biochemical and molecular mechanisms. Several
studies have shown that apoptosis plays an important role in the
regulation of hair follicle and vascular degeneration [[126]39]. The
ErbB signaling pathway (KEGG:ssc04012) refers to multiple processes
that dimerize or heterodimerize members of ErbB through binding to a
variety of signaling molecules and promote autophosphorylation and
downstream signaling cascades. Epidermal growth factor receptor
(GO:0007173) is a prototypical member of the ErbB family of receptor
tyrosine kinases, activated by ligand-dependent homo- or
heterodimerization [[127]40]. Histological studies have shown that EGFR
is widely distributed in epidermal derivatives of the epidermis and
skin appendages and is particularly restricted to the ORS of sebaceous
glands and anagen hair bulbs [[128]41,[129]42]. Many studies have shown
that mutant or transgenic mice associated with EGFR or its ligands have
altered hair follicle development [[130]43], and Klufa et al. have
shown that mice with constitutive deletion of EGFR in the epidermis
develop severe cutaneous inflammation, and that the hair follicle is
the epidermal structure where the inflammation begins [[131]44].
Glutamatergic synapses (GO:0098978) are synapses that use glutamate
(Glu) as the primary neurotransmitter. Robert et al. identified a
glutamatergic regulatory system with the presence of synaptic-like
vesicles in the lanceolate terminals of the hair follicles in mice and
rats [[132]45].
The results of the copy number variation analysis and GO pathway
enrichment of differential candidate genes in hair whorl versus
non-hair whorl pigs showed the enrichment of genes related to signal
transduction, transcription factor activity, the nucleus, and cytoplasm
in copy number variation. These results provide clues for a deeper
understanding of the mechanisms by which copy number variation affects
porcine the hair whorl trait. Further studies could focus on exploring
the specific functions of these enriched pathways and subcellular
localization-related genes and their relationships with the porcine
hair whorl trait, thus deepening our understanding of the regulation of
this trait.
5. Conclusions
In this study, genome-wide association analysis and copy number
variation analysis were performed on the basis of performance
measurements, which showed significant differences between hair whorl
pigs and non-hair whorl pigs in the number of live piglets in the same
litter, total number of piglets in the same litter, number of healthy
piglets in the same litter, corrected 100 kg body weight of age, and
FCR (feed-to-meat ratio). On this basis, slaughter measurements were
carried out to test whether carcass and meat quality differences
existed between hair whorl and non-hair whorl pigs, and the results of
the analysis of variance after the slaughter results showed that hair
whorl and non-hair whorl pigs performed well in terms of carcass and
meat quality, and only slight differences existed. After screening,
genome-wide association analysis detected a total of 4 significant SNPs
on SSC2 and SSC17. For these 4 SNP loci, with genotypes AA, CC, TT, and
GG, the frequency of pigs with the hair whorl trait was higher than
that of individuals without the hair whorl trait.
Based on the identification of upstream and downstream 500 kb candidate
genes, 23 possible genes were screened as candidate genes for the hair
whorl trait in pigs, and 8 significant signaling pathways were enriched
(p < 0.05), which provided a genetic basis for the subsequent
enhancement of breeding through genomic breeding technology.
Copy number variation results identified 652 differential genes between
hair whorl and non-hair whorl pigs, which were mainly enriched in
signal transduction, transcription factor activity, and nuclear and
cytoplasmic-related pathways.
Author Contributions
Conceptualization, W.J., X.Y., L.S. and L.Z. (Liangyu Zhu);
methodology, Y.Y.; software, W.J., C.L., Y.D., Y.W., L.N. and Y.Z.;
validation, Y.L.; formal analysis, W.J., M.G. and L.Z. (Liangyu Zhu);
investigation, Y.Y.; resources, L.S. and M.G.; data curation, X.Y.;
writing—original draft preparation, W.J., X.Y. and M.G.; writing—review
and editing, L.S. and L.Z. (Li Zhu); visualization, W.J. and M.G.;
supervision, M.G.; project administration, L.Z. (Li Zhu); funding
acquisition, L.S. and L.Z. (Li Zhu). All authors have read and agreed
to the published version of the manuscript.
Institutional Review Board Statement
All animal experimental procedures were formally approved by the Animal
Ethics and Welfare Committee of Sichuan Agricultural University,
Chengdu, China (approval number 2021302137).
Informed Consent Statement
Not applicable.
Data Availability Statement
The original contributions presented in the study are included in the
article, further inquiries can be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This work was supported by National Key Research and Development
Program of China (2021YFD1200801); the China Agriculture Research
System (CARS-35); the Sichuan Science and Technology Program
(2021ZDZX0008, 2021YFYZ0030, 2021YFYZ0007); and the Sichuan Pig
Innovation Team of National Modern Agricultural Industry Technology
System (sccxtd-2024-08-09).
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References