Abstract
Background
The discovery of selection signatures has enabled the identification of
genomics regions under selective pressure, enhancing knowledge of
evolutionary genotype-phenotypes. Sex chromosomes play an important
role in species formation and evolution. Therefore, the exploration of
selection signatures on sex chromosomes has important biological
significance.
Results
In this study, we used the Cross Population Extend Haplotype
Homozygosity Test (XPEHH), F-statistics (F[ST]) and EigenGWAS to assess
selection signatures on the Z chromosome in 474 broiler chickens via
Illumina chicken 60 K SNP chips. SNP genotype data were downloaded from
publicly available resources. We identified 17 selection regions,
amongst which 1, 11 and 12 were identified by XPEHH, F[ST], and
EigenGWAS, respectively. Each end of the Z chromosome appeared to
undergo the highest levels of selection pressure. A total of 215
candidate genes were located in 17 selection regions, some of which
mediated lipogenesis, fatty acid production, fat metabolism, and fat
decomposition, including FGF10, ELOVL7, and IL6ST. Using abdominal
adipose tissue expression data of the chickens, 187 candidate genes
were expressed with 15 differentially expressed genes (DEGs) in fat vs.
lean lines identified. Amongst the DEGs, VCAN was related to fat
metabolism. GO pathway enrichment analysis and QTL annotations were
performed to fully characterize the selection mechanism(s) of chicken
abdominal fat content.
Conclusions
We have found some selection regions and candidate genes involving in
fat metabolism on the Z chromosome. These findings enhance our
understanding of sex chromosome selection signatures.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12863-021-00971-6.
Keywords: Z chromosome, SNP, Selection signature, Population genetics,
Gene expression
Background
The domestication of chickens in Asia (Gallus gallus) occurred around
5400 BC with Darwin suggesting their evolution from red jungle fowl
[[37]1, [38]2]. Chickens hold value from an evolutionary perspective as
they provide information that bridges knowledge between mammals and
other vertebrates [[39]3]. Domestic chickens have genetically adapted
to unique habitats through strong genetic and phenotypic alterations.
To date, an array of specialized commercial populations and inbred
chicken lines have subsequently been developed.
Selection has many effects on the genome. Allele frequencies and
polymorphism underlying selection are expected to change. With the
availability of high-quality draft sequences of the chicken genome,
high-density single nucleotide polymorphism (SNP) genotyping chips, and
whole-genome re-sequencing technologies, the detection of selection
signatures on the chicken genome have been reported. Rubin et al.
[[40]4] identified the TSHR gene (thyroid stimulating hormone receptor)
as a prominent selection signature in all domestic chickens. Guo et al.
[[41]5] identified 413 candidate genes in Xishuangbanna fighting
chickens that were related to aggressive behavior, including BDNF, NTS
and GNAO1. Boschiero et al. [[42]6] revealed more than 300 regions of
selection with many important genes, including AKAP6, IGFBP2 and IGF1R,
associated with fat deposition and muscle development.
Sex chromosomes play an important role in species formation and
evolution. Mcvicker et al. [[43]7] analyzed the selective forces that
shape hominid evolution and found that under natural selection, the
selection pressure of sex chromosomes (12–40%) exceeded those of the
autosome (19–26%). The selection pressure of autosomes and sex
chromosomes is different, and when considering sex-specific dosage
compensation, genes on the sex chromosomes are more directly and
efficiently selected than those on autosomes [[44]8, [45]9]. The size
of the chicken Z chromosome is approximately 83 Mb, accounting for 7.9%
of the chicken genome. The Z chromosome contains 1345 genes, and some
genes, including FGF10 (fibroblast growth factor 10), ELOVL7 (ELOVL
fatty acid elongase 7) and ACO1 (aconitase 1, soluble), regulated fat
deposition and development. Previous studies have focused on the
selection signatures of chicken autosomes, but the selection signals of
the chicken Z chromosome less well studied. Zhang et al. [[46]10] only
identified PC1/PCSK1 gene, located on the Z chromosome, related to
abdominal fat traits used selection signals and genome-wide association
analysis based on NEAUHLF (Northeast Agricultural University broiler
lines divergently selected for abdominal fat content) population. It is
therefore necessary to identify as many selection signatures as
possible on the Z chromosome in chickens.
In this study, we used the XPEHH, F[ST] and EigenGWAS methods to
identify the selection signatures associated with abdominal fat in the
Z chromosomes of NEAUHLF populations. Through the integration of gene
microarrays in the adipose tissue of NEAUHLF populations, we
investigated the expression profiles of the candidate genes on
selection regions at 7 weeks of age. Gene annotations and functional
enrichment were implemented to elucidate the significance of the
identified selection signatures to fat containing traits.
Results
Population structure
We performed principal component analysis (PCA) on 1937 SNPs on the Z
chromosome to identify individual patterns. The first principal
component (21.5% of the total variance) could separate the two lines
(Fig. [47]1a). The second principal component (6.1% of the total
variance) primarily revealed genetic differences in the fat lines,
whilst the third principal component (5.3% of the total variance)
primarily revealed differences in the lean lines (Fig. [48]1b).
Fig. 1.
[49]Fig. 1
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Population structure based on Z chromosome SNPs using principal
component analysis. The subgraph (a) and (b) represent two-dimensional
and three-dimensional PCA images
Selection signatures on the Z chromosome
Chicken Z chromosome selection signatures were identified between fat
and lean line populations. Table [51]1 summarizes the selection
signatures obtained using XPEHH, F[ST] and EigenGWAS. For the fat-lean
line pair, 1, 11 and 12 selection regions were identified using XPEHH,
F[ST] and EigenGWAS methods, respectively (Table [52]2 and Fig. [53]2).
A total of 17 candidate regions were identified by merging these
regions. The majority of the identified selection regions were present
on both ends of the Z chromosome, accounting for about 53%. Amongst
them, one candidate region (61.68–73.63 Mb) was identified by all
methods, and five candidate regions were identified by F[ST] and
EigenGWAS. There were 5 and 6 candidate regions only identified by
F[ST]and EigenGWAS method, respectively.
Table 1.
Selection signatures in the two chicken lines
Items Fat - Lean
XPEHH F[ST] EigenGWAS
Number of significant SNPs 50 98 83
Number of regions 1 11 12
Average length (Mb) 2.24 0.63 1.71
Total length (Mb) 2.24 6.88 20.53
[54]Open in a new tab
Table 2.
Selection regions on the Z chromosome and candidate genes detected in
the regions
Candidate Region (Mb) F[ST] EigenGWAS XPEHH
Region (Mb) Top snp F[ST] Major Genes Region (Mb) Top snp P-value Major
Genes Region (Mb) Top snp XP EHH Major Genes
0.55–0.75 0.55–0.75 rs14688003 0.56 WDR7
1.91–2.31 1.91–2.31 rs312273884 2.24E-07 SLC14A2
4.53–4.66 4.53–4.66 rs16704177 0.59
6.95–7.35 6.95–7.35 rs14689552 2.40E-05 KIAA1328
9.25–9.65 9.25–9.65 rs14784526 9.10E-07 ARHGEF39
11.23–11.57 11.23–11.57 rs16781096 0.57 SLC1A3,
13.95–14.35 13.95–14.35 rs16103326 7.97E-06 FGF10
16.75–19.95 19.81–19.95 rs14753903 0.52 16.75–17.42 rs16760225 7.23E-06
IL6ST
17.80–18.20 rs16099146 1.05E-06 PLK2
18.71–19.11 rs736458094 1.18E-05 ELOVL7
21.22–23.50 21.22–21.84 rs14755170 0.66 MAST4 22.90–23.50 rs313728591
7.91E-08 IQGAP2
28.08–28.19 28.08–28.19 rs16106682 0.55 KDM4C
32.13–33.63 32.13–32.53 rs16108416 2.11E-05 BNC2
33.03–33.63 rs314666735 1.66E-05 ADAMTSL1
36.03–40.49 36.03–36.25 rs16081040 0.54 ANXA1 39.07–39.87 rs16781643
3.03E-06 FRMD3
37.29–37.85 rs16768474 0.68 VPS13A
38.04–38.89 rs13817231 0.59 TLE4
39.22–39.45 rs14787078 0.54
40.33–40.49 rs14787751 0.57 NTRK2
47.72–51.04 47.72–47.58 rs15597824 0.59 FER 48.33–51.04 rs314662658
6.33E-06 EFNA5
50.89–51.02 rs14766378 0.73 FAM174A
52.48–53.77 52.50–52.89 rs16770232 0.53 SLC49A3 52.48–52.88 rs14768956
1.78E-06 PCGF3
53.37–53.77 rs14769714 2.52E-05 ATP5I
55.98–60.28 55.98–57.39 rs14751311 0.66 PCSK1
58.12–58.85 rs16758604 0.60 NR2F1
59.90–60.28 rs14748688 0.54 CETN3
61.68–73.63 71.43–71.58 rs14781326 0.55 PRR16 61.68–62.12 rs14774149
6.47E-07 65.73–67.97 rs14776247 −2.78 DNAJC25
62.78–63.58 rs14773275 3.68E-07 VCAN
63.88–64.28 rs16118182 2.81E-08 CKMT2
64.82–66.08 rs14774834 1.72E-07 SLC46A2
66.24–66.67 rs13788226 2.66E-07 DNAJC25
66.84–67.88 rs16775262 1.59E-06 ELAVL2
68.59–68.99 rs16776912 2.02E-08 MOB3B
69.41–70.09 rs14780063 2.38E-05
70.35–71.36 rs16125209 2.28E-06 ACO1
72.16–73.63 rs14781762 7.41E-09 KCNN2
78.84–82.42 78.84–79.27 rs15992604 5.72E-09 CDKN2A
79.67–80.45 rs14685714 1.75E-07 ZNF608
80.62–82.42 rs16683478 9.91E-09 PAX5
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Fig. 2.
[56]Fig. 2
[57]Open in a new tab
Selection signatures on the Z chromosome. The subgraphs (a), (b) and
(c) are the selection signatures detected between the populations using
the XPEHH, F[ST] and eigenGWAS methods, respectively
Candidate gene annotations for functional analysis
According to the chicken gene annotation data (Gallus gallus 6.0) in
the ENSEMBL database, we detected 215 candidate genes within 17
selection regions. Supplementary Table [58]1 summarizes the genes in
each selection region on the Z chromosome. A number of genes were found
to regulate lipogenesis, fatty acid production, fat metabolism, or fat
decomposition, including FGF10, ELOVL7 and IL6ST.
To reveal the biological functions of the genes within the identified
regions, gene Ontology (GO) pathway enrichment analyses were performed
using DAVID (v6.7) [[59]11]. Significant GO functional terms (P < 0.05)
are listed in Table [60]3, but these terms were not significant upon
Benjamini correction.
Table 3.
Functional enrichment analysis of the selected genes
Category GO ID Term P value
Biological Progresses GO:0043171 peptide catabolic process 0.0195
Cellular Components GO:0005892 acetylcholine-gated channel complex
0.0231
GO:0045211 postsynaptic membrane 0.0204
Molecular Function GO:0070006 Metalloaminopeptidase activity 0.0154
GO:0042166 acetylcholine binding 0.0230
GO:0004889 acetylcholine-activated cation-selective channel activity
0.0230
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A total of 1229 QTLs were found on the chicken Z chromosome in the
QTLdb database
([62]https://www.animalgenome.org/cgi-bin/QTLdb/GG/index). The
selection signatures overlapped on 132 QTLs of health, physiology,
exterior and production categories. Interestingly, 45 of the candidate
genes that overlapped with the QTL region were related to abdominal fat
weight, 22 were related to liver weight, 12 were related to food
intake, 11 to residual food intake, and 18 to food conversion ratios.
Expression profiles of the candidate genes in the selection regions
We extracted probe sets of the Gene Chip Chicken Genome Array to
represent all candidate genes located on the selection regions. A total
of 427 probes representing 187 of the 215 candidate genes were
identified. As shown in Fig. [63]3, 168 and 174 genes (p < 0.05) were
expressed in lean and fat chicken lines at 7 weeks, respectively
(Fig. [64]3a), whilst 15 DEGs (P < 0.05, fold change > 2) between fat
and lean lines were identified (Fig. [65]3b). Seven genes showed
significantly higher expression in the fat line (e.g. ADAMTSL1, FRMD3,
MOB3B, ARHGEF39, VCAN, CAST and MELK) and eight genes in the lean line
(e.g. HAUS6, MAST4, VPS13A, SPINK4, SLC1A3, GLRX, FBN2 and EFNA5).
Fig. 3.
[66]Fig. 3
[67]Open in a new tab
Candidate gene expression profiles in the adipose tissue of the
NEAUHLF. The subgraph (a) is the gene expression profile of two chicken
strains. The x-axis and y-axis is - log (P_value) of lean and fat line
respectively, and the threshold value was p < 0.05. The red points
indicate 155 genes that are expressed in the abdominal fat of both thin
and fat lines, while the blue points indicate 28 genes that are not
expressed in either line. The 13 green points indicate genes expressed
in the lean line but not in the fat line and vice versa for the 19
black points.. The subgraph (b) shows the differential expression of
genes in adipose tissues in fat and lean lines at the 7th week of age.
The threshold is P < 0.05, fold change > 2. The red points indicate
genes that are differentially expressed in the two chicken lines
Discussion
High-density SNPs chips permit the identification of genome-wide
selection signatures using site frequent spectrums, population
differentiation, and linkage disequilibrium, with known strengths and
weaknesses. In this study, we used three complementary statistical
approaches (F[ST], XPEHH, EigenGWAS) to explore the selection
signatures on the Z chromosome to minimize bias and false positives in
the broiler chickens. The F[ST] method is best suited for the detection
in events occurring in the more distant past [[68]12]. The F[ST] method
is a powerful tool to detect signatures based on group differentiation.
The XPEHH test compares extended haplotype homozygosity between
populations to detect selection signatures, which are segregated in
populations and represent points of ongoing selection. XPEHH is
therefore useful for the detection of entirely or approximately fixed
loci [[69]13]. The XPEHH test is an LD-based method, and LD is expected
to extend over longer distances in regions under recent selection. So,
selection regions detected by XPEHH were much wider [[70]14]. Ma et al.
[[71]15] pointed out that the F[ST] method may bring a higher false
positive rate compared with XPEHH. The EigenGWAS algorithm combines the
statistical framework of GWAS with eigenvector decomposition to
identify selection signatures in the genomes of the underlying
population. The EigenGWAS method uses multi-point information to
identify core SNPs and grid windows, and can identify potential loci
during selection, and a larger number of selection regions than F[ST]
and XPEHH [[72]16]. Due to the similarities and differences principles
between F[ST], XPEHH and EigenGWAS, there are differently selection
regions can be obtained using the different statistical approaches.
The sex (X) chromosome undergoes more drift than autosomes, as its
effective population size (Ne) is three-quarters that of autosomes
[[73]17]. McVicker et al. [[74]7] found that X chromosome has suffered
higher selection pressure than autosomes. The NEAUHLF broiler
population came from bi-directional divergently selected for abdominal
fat content. Zhang et al. [[75]10] found that four candidate regions of
chromosome Z were identified as selection signature using long-range
heterozygosity changes or allele frequency differences methods, and the
0.73 Mb PC1/PCSK1 region of the Z chromosome was the most heavily
selected region based on genome-wide using the NEAUHLF populations. The
Z chromosome contains some genes involving fat metabolism, such as
FGF10, ELOVL7 and ACO1. However, Zhang et al. [[76]10] did not identify
selection signatures overlapped these genes. Selection signatures
determined by multiple methods are deemed more credible [[77]15,
[78]18]. So, in this study, we used more methods to independently
identify potential selection regions on the Z chromosome related on
abdominal fat development to verify and supplement the previous
findings. We detected three regions overlapped Zhang’s results using
the F[ST] method based on population differentiation (lean vs fat
lines) or the EigenGWAS method in this study. Furthermore, we
identified 14 other selection regions. These novel selection regions
will provide specific gene targets for the control of chicken fatness
traits or other traits genetically correlated with fatness. For
example, we identified 61.68–73.63 Mb regions detected by three
methods, and 69 genes that overlapped with the region, including
DNAJC25, GNG10 and AKAP2. Interestingly, DNAJC25 is a member of DNAJ
gene family identified by Liu et al. [[79]19] as highly expressed in
chicken liver tissue using transcriptome sequencing analysis. The
DNAJB6 gene, located on gga2, is a member of the DNAJ gene family and
has a similar sequence to the DNAJC25 gene. Jin et al. [[80]20]
previously found that the DNAJB6 gene was expressed in the abdominal
fat and liver tissues of the 14th generation NEAUHLF population, and
was differentially expressed between the fat line and the lean line.
Moreover, the expression level of DNAJB6 in abdominal adipose tissue
was significantly negatively correlated with abdomen fat weight and
abdomen fat percentage [[81]20].
In this study, there are 215 candidate genes overlapped 17 selection
regions on chromosome Z. Amongst the candidate genes, IL6ST, ELOVL7,
CKMT2 and FGF10 genes were also identified by Gholami et al. [[82]21]
in three commercial layer breeds and 14 non-commercial breeds. The
VCAN, ST8SIA4, FBN2, ERAP1 and CAST were also identified by Fu et al.
[[83]14], showing 10 regions of high confidence for selection on the Z
chromosome, detected in male Cornish lines (a meat type breed), and
female lines from White Rock (a dual-purpose breed). We also found that
the majority of candidate genes expressed in the adipose tissue of G8
NEAUHLF fat and lean lines, and 15 genes (including MAST4, SPINK4,
VCAN, EFNA5, ARHGEF39, etc) in the adipose tissue significantly
differed between fat vs. lean birds using microarray gene expression
data. Among them, MAST4 encodes a microtubule-associated
serine/threonine kinase; SPINK4 is a serine peptidase inhibitor; and
ARHGEF39 encodes a rho guanine nucleotide exchange factor key to Rho
mediated signal transduction.
According to known gene functions, some candidate genes were associated
with the fat content of chickens, such as the FGF10 gene. FGF10 is a
mesenchymal factor affecting epithelial cells. Matsubara et al.
[[84]22] reported that FGF10 when secreted in chicken adipose tissue
contributes to adipogenesis, and is down regulated during the early
stages of chicken adipocyte differentiation. Konishi et al. [[85]23]
showed that FGF10 stimulates proliferation in the white adipose tissue
of mice. In addition, Yamasaki et al. [[86]24] highlighted FGF10 as an
important intercellular signaling molecule during lipogenesis that is
abundantly expressed in the adipose tissue of adult rats.
Due to high species conservation, the identified genes related to human
or mice obesity traits may hold importance for adipose deposition in
chickens, such as ELOVL7, IL6ST, IQGAP2, PAX5 and CKMT2 (Table [87]4).
ELOVL7 shows altered affinity for the elongation of precursor fatty
acids and mediates the extension of saturated fatty acids of up to 24
carbon atoms [[88]26]. IL6ST is an IL-6 transducer and a potent
modulator of fat metabolism in humans, known to increase fat oxidation
and fatty acid re-esterification [[89]25]. IQGAP2 deficiency influences
hepatic free fatty acid uptake, fatty acid synthesis, and lipogenesis,
suggesting its importance in obesity [[90]27]. PAX5 is a paired box 5
gene for which Melka et al. [[91]29] performed GWAS in human
adolescents from the French-Canadian founder population, revealing the
association of its locus with total fat mass (TFM) and body mass index
(BMI) in 6.4 and 3.7% of TFM and BMI heritability estimates,
respectively. These results imply that PAX5 plays a key role in obesity
regulation. CKMT2 (creatine kinase, mitochondrial 2) is a creatine
kinase isoenzyme. Müller et al. [[92]28] showed that CKMT2 is an
effective modulator of ATP synthase coupled respiration and is
exclusively expressed in human brown adipose tissue. CKMT2 also
regulates energy metabolism.
Table 4.
Candidate genes in the selection regions and their functions
Gene symbol Location (Mb) Full name Function of association
FGF10 13.97–14.03 Fibroblast growth factor 10 Promotes fat formation
[[93]22]
IL6ST 17.02–17.06 Interleukin 6 signal transducer Increases fat
oxidation and fatty acid re-esterification [[94]25].
ELOVL7 18.87–18.91 ELOVL fatty acid elongase 7 Extension of saturated
fatty acids [[95]26]
IQGAP2 23.46–23.58 IQ motif containing GTPase activating protein 2
Influences the liver uptake of free fatty acids, fatty acid synthesis
and adipogenesis [[96]27]
CKMT2 63.95–63.98 Creatine kinase, mitochondrial 2 Regulation of energy
metabolism, expression in brown adipose tissue [[97]28]
PAX5 82.01–82.12 Paired box 5 Regulates obesity [[98]29]
[99]Open in a new tab
Conclusion
In this study, 17 selection regions were screened through the analysis
of selection signatures in the chicken Z chromosome, including 215
candidate genes, some of which are involved in lipogenesis, fatty acid
production, fat metabolism and fat decomposition, such as FGF10,
ELOVL7, IL6ST. Moreover, in the candidate region, using abdominal fat
expression data from chickens, 187 candidate genes were identified as
expressed in the fat and lean lines, with 15 genes identified as
differentially expressed. GO pathways enrichment and QTL annotations
provided additional information on the selection mechanism(s) of
chicken abdominal fat content. The culmination of these data enhances
our understanding of sex chromosome selection signatures and their role
in fat deposition in chickens.
Methods
Genotype data and population
SNP genotype data were downloaded from GEO Datasets on the NCBI website
(GEO accession: [100]GSE58551) [[101]30]. Based on Illumina chicken
60 K SNP chips, 48,035 SNPs from 28 autosomes and Z chromosomes in 475
male broilers of the 11th generation (G11) (203 lean lines and 272 fat
lines) were identified from NEAUHLF [[102]10]. We mapped the SNP loci
on the Z chromosome of all birds to the chicken reference genome
(Gallus gallus 6.0), resulting in 1973 SNP loci. We applied QC
measurements on the SNP loci on the Z chromosome of all birds using
PLINK (v1.90) software: (1) SNP loci call rates of 0.95; (2) Sample
call rates of 0.95; and (3) Minor allele frequencies (MAF) of 0.01 were
discounted. In total, 1937 SNPs and 474 birds were investigated to
detect selective sweeps in the chicken sex chromosomes. Specific
details of broiler breeding strategy have been described by Zhang et
al. [[103]10].
Principal component analysis
We performed PCA to distinguish population structures using EigenGWAS
software [[104]16] based on 1937 SNPs on the Z chromosome. The first
ten eigenvalues and their corresponding eigenvectors were then
calculated.
Detection of selection signatures
Extended haplotype homozygosity (EHH) scores measure the probability
that two randomly selected chromosomes carry a tested core haplotype
that is homozygous at all SNPs [[105]31]. XPEHH scores can detect
selective sweeps in which a selected allele has achieved fixation in
one population but remains polymorphic in another [[106]32]. F[ST] can
identify genomic regions with strongly differing or differentially
fixed variants in alleles frequency between different populations,
which is the conventional measure of population genetic
differentiation. F[ST] is defined as follows:
[MATH: FST=MSP−MSGMSP+nc−1MSG :MATH]
where MSP is the mean square error within the populations, MSG
represents the mean square error between the two populations, and n[c]
represents the average sample size of the entire population after
correction [[107]33, [108]34]. The EigenGWAS algorithm combines the
statistical framework of genome-wide association studies with
eigenvector decomposition to identify selection signatures on the
underlying genome [[109]16]. The EigenGWAS method uses the single
marker regression model for association tests. However, its phenotype
is different from the phenotype of typical GWASs and it is an
individual-level eigenvector derived from genotype data. The model can
be described as the following equation:
[MATH: yki=μ+
bixij+ei
:MATH]
where y[ki] is the k th eigenvector value of individual j; x[ij] is the
value of the j th SNP for individual j; b[i] is the regression
coefficient for the i th SNP. EigenGWAS can be used as a method to find
the selection signatures among the population or across a gradient of
ancestry. In this study, XPEHH, F[ST], and EigenGWAS were used to
detect selective footprints on the chicken Z chromosome. We used
SHAPEIT (v2.12) software to generate haplotype data based on the SNPs
data. We used the LD package of R (v3.6.1) to compute the XPEHH values.
The threshold of the XPEHH at a significance level of 0.05 was ±2.
Using VCFtools [[110]35], the average F[ST] value of all SNPs in each
sliding window (window size: 100 Kb, step size: 10 Kb) was calculated.
We determined the threshold for the outlier F[ST] sliding windows
average based on the following formula:
[MATH:
Threshold=Q3
+1.5×Q3−Q1 :MATH]
Among them, Q[1] is the lower quartile (first quartile); Q[3] is the
upper quartile (third quartile). In this study, the F[ST] value of the
sliding window greater than threshold (0.51) is defined as a selection
signature. For EigenGWAS, EMMAX software [[111]36] was used for
single-marker regression. Threshold P-values of
0.05/1937 = 2.58 × 10^−5 were used to confirm statistically significant
differences. In this study, the candidate regions were determined
within 1 Mb of each other regions identified by different methods.
To reveal the biological functions of the selection signatures,
additional analyses were performed. We identified candidate genes
within the selection regions using chicken gene annotation data from
the Ensembl database. We then used the online software DAVID (v6.7)
[[112]11] to perform GO analysis based on the candidate genes obtained.
Thirdly, selection regions were mapped onto QTL obtained from the
chicken QTL database
([113]https://www.animalgenome.org/cgi-bin/QTLdb/GG/index).
Gene expression profiles
Gene expressions in the abdominal adipose tissue of seven-week-old
NEAUHLF broilers were evaluated in chicken genome arrays. According to
Wang et al. [[114]37], the raw data set has been standardized using
Affymetrix Microarray Suite 4.0 software and uploaded to the GEO
database (GEO accession number: [115]GSE8010). We downloaded the
[116]GSE8010 data set for subsequent gene differential expression
analysis [[117]37]. The ten birds were selected based on the percentage
of abdominal fat (AFP) at 7-weeks for the G 8 of NEAUHLF broilers: the
5 chickens with the highest AFP in fat line and the 5 chickens with the
lowest AFP in lean line. A one-way ANOVA was used to statistically
compare the DEGs between fat and lean line chickens.
Supplementary Information
[118]12863_2021_971_MOESM1_ESM.xlsx^ (22.5KB, xlsx)
Additional file 1: Supplementary Table 1.
Acknowledgements