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Hitchhiking Mapping of Candidate Regions Associated with Fat Deposition in Iranian Thin and Fat Tail Sheep Breeds Suggests New Insights into Molecular Aspects of Fat Tail Selection

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Abstract

The fat tail is a phenotype that divides indigenous Iranian sheep genetic resources into two major groups. The objective of the present study is to refine the map location of candidate regions associated with fat deposition, obtained via two separate whole genome scans contrasting thin and fat tail breeds, and to determine the nature of the selection occurring in these regions using a hitchhiking approach. Zel (thin tail) and Lori-Bakhtiari (fat tail) breed samples that had previously been run on the Illumina Ovine 50 k BeadChip, were genotyped with a denser set of SNPs in the three candidate regions using a Sequenom Mass ARRAY platform. Statistical tests were then performed using different and complementary methods based on either site frequency (FST and Median homozygosity) or haplotype (iHS and XP-EHH). The results from candidate regions on chromosome 5 and X revealed clear evidence of selection with the derived haplotypes that was consistent with selection to near fixation for the haplotypes affecting fat tail size in the fat tail breed. An analysis of the candidate region on chromosome 7 indicated that selection differentiated the beneficial alleles between breeds and homozygosity has increased in the thin tail breed which also had the ancestral haplotype. These results enabled us to confirm the signature of selection in these regions and refine the critical intervals from 113 kb, 201 kb, and 2831 kb to 28 kb, 142 kb, and 1006 kb on chromosome 5, 7, and X respectively. These regions contain several genes associated with fat metabolism or developmental processes consisting of TCF7 and PPP2CA (OAR5), PTGDR and NID2 (OAR7), AR, EBP, CACNA1F, HSD17B10,SLC35A2, BMP15, WDR13, and RBM3 (OAR X), and each of which could potentially be the actual target of selection. The study of core haplotypes alleles in our regions of interest also supported the hypothesis that the first domesticated sheep were thin tailed, and that fat tail animals were developed later. Overall, our results provide a comprehensive assessment of how and where selection has affected the patterns of variation in candidate regions associated with fat deposition in thin and fat tail sheep breeds.
Citation: Moradi, M.H.;
Nejati-Javaremi, A.;
Moradi-Shahrbabak, M.; Dodds, K.G.;
Brauning, R.; McEwan, J.C.
Hitchhiking Mapping of Candidate
Regions Associated with Fat
Deposition in Iranian Thin and Fat
Tail Sheep Breeds Suggests New
Insights into Molecular Aspects of
Fat Tail Selection. Animals 2022,12,
1423. https://doi.org/10.3390/ani
12111423
Academic Editor: Maria
Luisa Dettori
Received: 3 March 2022
Accepted: 12 May 2022
Published: 31 May 2022
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4.0/).
animals
Article
Hitchhiking Mapping of Candidate Regions Associated with
Fat Deposition in Iranian Thin and Fat Tail Sheep Breeds
Suggests New Insights into Molecular Aspects of Fat
Tail Selection
Mohammad Hossein Moradi 1, * , Ardeshir Nejati-Javaremi 2, Mohammad Moradi-Shahrbabak 2,
Ken G. Dodds 3, Rudiger Brauning 3and John C. McEwan 3
1Department of Animal Science, Faculty of Agriculture, Arak University, Arak 38156-8-8349, Iran
2Department of Animal Science, University of Tehran, Karaj 31587-1-1167, Iran;
ardeshir.nejati@gmail.com (A.N.-J.); moradim@ut.ac.ir (M.M.-S.)
3Centre for Reproduction and Genomics, AgResearch, Invermay, Mosgiel 9053, New Zealand;
ken.dodds@agresearch.co.nz (K.G.D.); rudiger.brauning@agresearch.co.nz (R.B.);
john.mcewan@agresearch.co.nz (J.C.M.)
*Correspondence: hoseinmoradi@ut.ac.ir or h-moradi@araku.ac.ir; Tel.: +98-9183659133
Simple Summary:
Fatness-related traits are economically very important in sheep production and
are associated with serious diseases in humans. Using a denser set of SNP markers and a variety of
statistical approaches, our results were able to refine the regions associated with fat deposition and to
suggest new insights into molecular aspects of fat tail selection. These results may provide a strong
foundation for studying the regulation of fat deposition in sheep and do offer hope that the causal
mutations and the mode of inheritance of this trait will soon be discovered by further investigation.
Abstract:
The fat tail is a phenotype that divides indigenous Iranian sheep genetic resources into
two major groups. The objective of the present study is to refine the map location of candidate
regions associated with fat deposition, obtained via two separate whole genome scans contrasting
thin and fat tail breeds, and to determine the nature of the selection occurring in these regions
using a hitchhiking approach. Zel (thin tail) and Lori-Bakhtiari (fat tail) breed samples that had
previously been run on the Illumina Ovine 50 k BeadChip, were genotyped with a denser set of
SNPs in the three candidate regions using a Sequenom Mass ARRAY platform. Statistical tests were
then performed using different and complementary methods based on either site frequency (F
ST
and Median homozygosity) or haplotype (iHS and XP-EHH). The results from candidate regions
on chromosome 5 and X revealed clear evidence of selection with the derived haplotypes that was
consistent with selection to near fixation for the haplotypes affecting fat tail size in the fat tail breed.
An analysis of the candidate region on chromosome 7 indicated that selection differentiated the
beneficial alleles between breeds and homozygosity has increased in the thin tail breed which also
had the ancestral haplotype. These results enabled us to confirm the signature of selection in these
regions and refine the critical intervals from 113 kb, 201 kb, and 2831 kb to 28 kb, 142 kb, and
1006 kb
on chromosome 5, 7, and X respectively. These regions contain several genes associated with fat
metabolism or developmental processes consisting of TCF7 and PPP2CA (OAR5), PTGDR and NID2
(OAR7), AR,EBP,CACNA1F,HSD17B10, SLC35A2,BMP15,WDR13, and RBM3 (OAR X), and each
of which could potentially be the actual target of selection. The study of core haplotypes alleles in
our regions of interest also supported the hypothesis that the first domesticated sheep were thin
tailed, and that fat tail animals were developed later. Overall, our results provide a comprehensive
assessment of how and where selection has affected the patterns of variation in candidate regions
associated with fat deposition in thin and fat tail sheep breeds.
Keywords: genomic scan; selection signature; lipid metabolisms; candidate genes; fat tail sheep
Animals 2022,12, 1423. https://doi.org/10.3390/ani12111423 https://www.mdpi.com/journal/animals
Animals 2022,12, 1423 2 of 20
1. Introduction
Identifying the regions of the genome that have experienced substantial selective pres-
sure can provide a powerful tool into the location of functionally important polymorphisms
and can help prioritize targets for association mapping [
1
,
2
]. Fat tailed breeds comprise
approximately 25% of the world sheep population [
3
] and are grazed in a wide range of
countries. The major biological role of the fat tail is to serve as an energy store against
periodic food scarcity, such as in drought and winter [
4
]. It has also been used as a source of
fat (or ghee) for human consumption for millennia [
5
]. Historically, the climatic conditions
in these areas, as well as the nutritional and religious food requirements of the people,
encouraged sheep producers to select for higher fat tail weight [
6
]. Although fat-tailed
sheep breeds are still preferred under local pastoral, desert, or semi-desert conditions in
several societies [
7
], most of the advantages of a large fat tail have recently reduced in
importance due to the improved forage availability and decreased price for the product,
especially in genetic improvement programs [4,8].
Various genomic-based studies have been conducted to understand the genetic basis
and genomic architecture of sheep tails and to find the specific causal genomic variant(s)
contributing to sheep tail pattern making [
3
,
9
15
]. A brief overview on genomic outcomes,
including proposed potential genes generated from investigations on the sheep tail phe-
notype has been reported by Kalds et al. [
7
]. Additionally, transcriptomic analyses were
performed, providing sets of potential genes that contribute to the formation and the
biological emergence of sheep tails [
16
19
]. Taken together, these research efforts have
revealed several high-ranking candidate genes with no current consistency or solid opinion
about their variant causalities and expression nature. Therefore, to date the genetic mecha-
nism underlying this particular trait has not been fully elucidated and finding the regions
associated with fat deposition and the nature of the selection occurring in these locations
are two of the most important and challenging areas of research in the countries grazing
these breeds.
Genome scan approaches, such as quantitative trait locus (QTL) and hitchhiking
mapping, provide the opportunity for investigating the genetic regions associated with
different traits. QTL mapping studies have been applied to examine the genetic basis of
various economically important traits, e.g., [
20
,
21
]. However, QTL mapping basically relies
on detecting correlations between genetic markers and phenotypic traits in a segregating
population [
22
]. Also, many traits underlying adaptive divergence are not always easily
detectable at the phenotypic level and not well suited to QTL mapping. In this case, one
approach for discovering the potential genetic basis of different traits is to use hitchhiking
mapping [
23
,
24
]. Hitchhiking mapping is a population genetics approach use to identify
genomic regions influenced by selection [
25
,
26
]. A great advantage of this approach is that
it can be performed using molecular markers alone [
27
,
28
]. This approach is especially
suitable for the traits such as sheep tail pattern where all sheep breeds can be easily classified
in different classes (e.g., thin and fat tailed sheep groups) and then, offer ideal materials for
comparative analysis of their genetic basis.
Hitchhiking mapping starts with a genome scan using an approximately uniformly
spaced set of molecular markers followed by a fine scale analysis of the candidate re-
gions [
23
]. For example, a marker-based analysis of chromosome 1 in rats from warfarin-
resistant populations revealed a 0.5 centimorgan (cM) region that was the likely locus
for this trait [
29
]. This information was used later to identify the warfarin resistance
gene through more traditional candidate gene approaches and association mapping tech-
niques [30].
Genomic scans for finding candidate regions associated with fat deposition in thin and
fat tailed breeds have been described previously [
3
]. In brief, two independent experiments,
including Iranian and ovine HapMap genotyping data contrasting thin and fat tailed breeds
were analyzed, and using different statistics, especially F
ST
, three regions on chromosomes 5
(between 47,149–47,263 kb), 7 (between 46,642–46,843 kb) and X (between 58,621–61,
452 kb
)
were confirmed in both data sets. Interestingly, these regions have also been supported in
Animals 2022,12, 1423 3 of 20
recently published studies using two different ways consisting of different breeds of thin
and fat tailed sheep breeds through a genome-wide analysis of transcriptomic data [
17
,
31
]
and selective sweeps [32,33], and also using same the breeds but different animals [34].
Unlike F
ST
that has been used in the previous study [
3
], tests based on linkage dis-
equilibrium (LD) like iHS [
35
] and XP-EHH [
36
], are multi-marker tests. These tests are
commonly used for human SNP data, where there are now millions of SNPs available
(approximately one marker per 1 kb), and that which depend on SNP spacing and fre-
quency [
37
]. Densely spaced SNPs give greater power when using statistical tests that rely
on LD, as signals of selection are less likely to be lost [
38
]. The Illumina Ovine SNP50k
BeadChip, while providing uniform genome wide coverage, has a marker about every
56 kb [3]
. Fine mapping, where more SNPs are genotyped in an area of interest, improves
the ability to localize causal variants.
The allele frequency spectrum (FST and median homozygosity) and haplotype based
(iHS and XP-EHH) statistics used in the current study have been shown by previous power
analyses to be largely complementary [
39
]. F
ST
and median homozygosity are single-
marker tests and detect highly differentiated alleles between populations, where positive
selection in one area causes larger frequency differences compared to neutrally evolving
alleles. iHS, a measure of within breed evidence for selection, has a suitable power to detect
partial selective sweeps. However, a disadvantage of this approach is that it loses power
when the beneficial allele is close to fixation, because fixation will eliminate variation at
and near the selected site. In contrast, XP-EHH can detect selected alleles that have risen to
near fixation in one but not another population. Grossman et al. [
40
] showed that the F
ST
and XP-EHH signals peaked more narrowly around the causal variant, making them useful
for spatial localization, while iHS is better to distinguish causal variants and contributed
little to spatial resolution. These tests were relatively uncorrelated in neutral regions, and
only weakly correlated for neutral variants within selected regions [40].
In this study, three regions recognized as candidate regions associated with fat deposi-
tion in the tail were chosen for further analysis. This was achieved through fine-mapping
with additional SNPs, using Sequenom technology. The aim of this study was to confirm the
signature of selection in the candidate loci, narrow down the chromosomal region showing
the selective imprint and to determine the nature of the selection process occurred in these
regions. Our analyses focused on two site frequency (F
ST
and Median homozygosity) and
haplotype based (iHS and XP-EHH) statistics, and using this variety of selection sweep
tests we were able to obtain some novel results associated with fat deposition in these
breeds.
2. Materials and Methods
2.1. Animal Sampling and Genotyping
Two independent data sets consisting of Zel-Lori Bakhtiari and Ovine HapMap sam-
ples were previously used for a genome-wide scan analysis of selective sweeps in thin and
fat tail breeds [
3
]. Because the latter project did not formally phenotype the individuals
concerned and we did not have access to their DNA samples, in this research further analy-
sis was performed for the Zel-Lori Bakhtiari data. This data set consisted of 45 samples
each of the thin (Zel) and fat tailed (Lori Bakhtiari) breeds (37 Females and 8 Males per each
breed). The animals were collected to be as unrelated as possible. Three regions (Table 1),
identified as being under selection based on F
ST
and median homozygosity results from
the first genome scan, were chosen for further analysis.
Animals 2022,12, 1423 4 of 20
Table 1.
Regions chosen for fine mapping in this study and the number of SNPs that were previously
genotyped in these regions using the Ovine SNP50k BeadChip.
Chromosome Region (bp) Length (bp) Ovine SNP50k BeadChip SNPs
5 46,971,979–47,919,440 947,461 15
7 46,392,398–46,852,870 460,472 10
X 58,424,602–61,409,447 2,984,845 20
Once the regions of interest were defined, all known SNPs in each region were exam-
ined for suitability for genotyping. These included SNPs discovered on both the Solexa
and 454 platforms (http://www.sheephapmap.org/genseq.php, accessed on 31 May 2017).
A total of 156, 89, and 78 putative SNPs flanked by 150 bp on each side (301 bp total)
were submitted to Sequenom’s primer design software (Mass ARRAY Assay Design 3.1)
for the regions on chromosomes 5, 7, and X, respectively, and primers and probes were
designed in multiplex format. Of these SNPs, assays were successfully designed for 140
(90%), 75 (84%), and 66 (84%) of all SNPs initially selected for study, and were grouped into
6, 3, and 4 multiplex assays for the three regions respectively. The remaining SNPs failed
primer design, primarily due to high repeated content. The majority of these multiplex
assays were located in the first and second plex which contained 71, 62, and 57 SNPs for
chromosomes 5, 7, and X respectively. These two SNP assays were used for genotyping.
Selected SNPs, their locations, PCR primers, unextended primer, and its extension masses,
are represented in Supplementary materials Table S1. All SNPs were genotyped by use
of the mass-spectrometry based iPlex Mass Array platform provided by Sequenom (Se-
quenom, San Diego, CA, USA. http://www.sequenom.com/, accessed on 31 May 2017).
Experiments were conducted following the Sequenom iPLEX Assay application note [41].
2.2. Quality Control Filters
All samples with more than 30% missing data and subsequently all loci with more
than 15% missing data were excluded. These rejection thresholds were chosen by plotting
numbers of animals or loci against percent missing data and the cutoff point was determined
as the curve inflection point, where the rate of change in the number of excluded loci,
became linear with every increased percent of missing data [
42
]. For the remaining SNPs,
those with a minor allele frequency (MAF) of less than 10% over all samples, and an
outlier departure from Hardy—Weinberg equilibrium over all animals of a breed (p-value
< 0.0002), were excluded [
3
,
43
]. We combined the SNP genotyping results from both the
Ovine SNP50k BeadChip (in the regions of interest) and the Sequenom Mass ARRAY
platform for final analysis. Across the three regions, 25 SNPs were genotyped on both
Sequenom and Illumina platforms with a mean allelic concordance rate of 96% (range
92–99%). For the few SNPs with different genotypes, the Ovine SNP50k Bead Chip results
were used due to their greater accuracy.
2.3. Estimates of FST and Median Homozygosity
To determine the pattern of positive selection, the basic form of Wright’s fixation
index (F
ST
) was calculated as described by MacEachern et al. [
44
]. The value of F
ST
can
theoretically range from zero (showing no differentiation) to one (indicating complete
differentiation, i.e., populations are fixed for different alleles). For each set of five adjacent
SNPs, the average of F
ST
values was calculated and termed windowed F
ST
. This is an
approximate method of looking for regions where selection is apparent over multiple
markers, rather than one-off high values. A window of five markers was chosen as it
appeared to provide the better signal compare to other arbitrary window sizes [
3
]. The
windowed FST values were then plotted against genome location.
All analyses presented in this work were also performed using Weir and Cocker-
ham [
45
] and Hudson [
46
] methods. The results were almost identical for all F
ST
estimators
(r
99%), so that we have only presented F
ST
results based on Wright’s estimator in the
Animals 2022,12, 1423 5 of 20
present study for an easier comparison to previous reported results [
3
]. All scripts for
estimating Wright (F
ST
), Weir and Cockerham, and Hudson’s fixation indices were written
and performed in R v 4.0.2.
The selection for a new beneficial mutation increases the level of homozygosity in
the selected allele and neighboring regions due to the hitchhiking effect. Therefore, one
method of looking for the region where selection has taken place is to compare median
runs of homozygosity between breeds. To do this, the median run of homozygosity for
each SNP was calculated following Moradi et al. [
3
] in each animal. The length of a
run of homozygosity, that is the number of consecutive homozygous SNPs including the
one being considered, was calculated (this would be zero if the SNP being considered
was heterozygous). For each marker the median length, over the breed, of the run of
homozygosity was calculated and plotted against genomic position in the candidate regions
(25 SNPs on each side).
It should be noted that since the historical effective population sizes of males and
females are not the same for sexual and autosomal chromosomes [
47
], all analyses presented
in this paper for chromosome X were performed by using only females (37 animals per
each breed), although the analysis for both sexes produced similar results.
2.4. Determining of Ancestral Alleles
To calculate iHS and XP-EHH, the ancestral allele state of each SNP must be specified.
Ancestral alleles for the ovine chip SNP were obtained from international sheep hapmap
project (ISGC), and then to obtain the ancestral alleles for the additional Sequenom SNPs,
301 base pairs of sequence (1 bp of the alleles plus 150 bases either side of the SNP) were
aligned against Bos taurus (cattle), Sus scrofa (pig), Equus caballus (horse), Canis familiaris
(dog) and Homo sapiens (human) genomes using BLAST (http://blast.ncbi.nlm.nih.gov/
Blast, accessed on 31 May 2017). A cross-species megaBLAST of Sequenom
®
primers was
used to discover ancestral alleles for the remaining SNPs [
37
]. The ancestral allele was
taken as the base in the genome sequence at the resulting SNP position. For loci where only
one SNP allele was represented in the other species, that allele was determined as ancestral.
For other SNPs, as an additional tool in determining the ancestral allele, a phylogenetic
tree of the five species was used [
48
]. This provided a crude tree, which could be used in
decision making; for example, if all the animals had the same allele (C) apart from humans
(T), then the ancestral was more likely to be C, as humans are more distantly related than
the other species. In this research, we were able to determine the ancestral status of 36,
28, and 36 from the 41, 30, and 36 SNPs which passed quality control in the regions on
chromosome 5, 7, and X respectively.
2.5. Reconstruction of Haplotypes
A pair of haplotypes was reconstructed for each animal in the sample using fastPHASE
version 1.2.3 [
49
]. This software implements an Expectation—Maximization strategy for
estimating missing genotypes and for reconstructing haplotypes from unphased SNP
genotypes data of unrelated individuals [49].
2.6. Calculation of Integrated Haplotype Score (iHS) and Cross-Population EHH (XP-EHH)
iHS was calculated as in Voight et al. [
35
] and XP-EHH as in Sabeti et al. [
36
]. These
statistical tests were calculated using the rehh package [
50
] in R v4.0.2 and the candidate
genomic regions under selection were obtained.
Briefly and following Voight et al. [
35
], the iHS was computed for every SNP with
ancestral state information and MAF above 10% (Supplementary Table S2). This test is
based on the extended haplotype homozygosity (EHH) statistic [
39
], which measures the
decay of identity, as a function of distance, of haplotypes that carry a specified core allele
at one end. The integral of the observed decay of EHH with distance from the core allele
is calculated until EHH reaches 0.05. This integrated EHH (iHH) (summed over both
directions away from the core SNP) is denoted as iHH
A
or iHH
D
, depending on whether it
Animals 2022,12, 1423 6 of 20
is computed for the ancestral or derived core allele. The unstandardized iHS (uiHS) was
then calculated as ln (iHH
A
/iHH
D
). The uiHS is thus adjusted so that the final statistic
has a mean of 0 and a variance of 1, regardless of allele frequency at the core SNP. To do
this, the results for each breed were split into 18 equally sized allele frequency bins, from
which a mean and standard deviation were calculated. These were used in the following
equation [35]:
iHS =uiHS Ep[uiHS]
SDp[uiHS]
where the expectation and standard deviation are calculated using SNPs from the same bin
(p). Due to the different demographic histories of the X chromosome and the autosomes (e.g.,
due to smaller effective population size), we normalized the iHS scores of this chromosome
separately from those of the other chromosomes. Results of iHS are presented here as |iHS|
for a window of 10 SNPs and then plotted against genome location. We chose a window of
10 SNPs because of the longer extent of LD and SNP spacing in sheep compared to humans,
in which the window length used is commonly around 40 SNPs [
35
]. Large positive and
negative values of iHS indicate unusually long haplotypes carrying the ancestral and
derived allele, respectively; by taking the absolute iHS value, interesting variants will be
shown by large positive values.
XP-EHH compares haplotypes between populations to control for local variation
in recombination rates [
36
]. Briefly, XP-EHH is defined relative to a given SNP iin two
populations, A and B. In each population, the expected haplotype homozygosity (EHH) [
39
]
was integrated with respect to genetic distance in both directions from i. The log of the
ratio of these integrals, ln(I
A
/I
B
), is the abnormal XP-EHH (for more details see [
36
]). XP-
EHH must also be normalized for genome-wide differences in haplotype length between
populations, so that there is a mean of zero and unit variance. This was done by subtracting
the mean and dividing by the standard deviation of all scores. In this study, we defined
the XP-EHH test with respect to thin and fat tail breeds and an unusually positive value
suggests selection in fat tailed population and a negative value selection in thin tailed.
2.7. Core SNP Alleles and Haplotype Frequencies in Candidate Regions
After the aforementioned filtering process and reconstruction of haplotypes for can-
didate regions using PHASE 2.1 [
51
], the haplotypes were fed into SWEEP v.1.1 [
39
] to
detect core regions based on the EHH statistic in candidate regions, which is fully described
by Sabeti et al. [
39
]. PHASE was chosen here over fastPHASE as haplotype estimates are
slightly more accurate using PHASE (http://stephenslab.uchicago.edu/software.html,
accessed on 1 February 2021), although substantially slower to compute. For selection of
core regions and study of haplotype frequencies in selected area of our interested regions,
the results of F
ST
, iHS and XP-EHH tests were also used as additional information. Finally,
the pattern of haplotype blocks in the regions of interest were constructed and the decay of
LD (pairwise r2) was visualized using Haploview [52].
2.8. Study of Identified Genes in Candidate Regions
Genes located in the genomic regions significantly differentiated between sheep
breeds were acquired by the use of the data mining tool Biomart (http://asia.ensembl.
org/biomart/martview, accessed on 1 February 2021), with the reference assembly of the
O. aries genome OAR v3.1 [
53
]. The regions of interest in O. aries were also compared to
the corresponding areas in B. taurus as its genome is better annotated. Regions chosen for
fine mapping onto OARv3.1 and their orthologous coordinates in B. taurus (ARS-UCD1.2,
Bostau9) is shown in Supplementary Table S3. It should be noted that, due to the easier
comparison of coordinates obtained by the current study with the previous article [
3
], the
coordinates have been presented for different statistics in this study, based on OAR v1.0.
However, the coordinate of candidate regions associated with fat deposition reported in
Moradi et al. [
3
] and the fine mapped results in this study have been also shown based
on different OAR versions in Supplementary Table S4. Nearby genes within a flanking
Animals 2022,12, 1423 7 of 20
distance of 500 kbs from each region were acquired. This distance has been selected as
previously considered by Moradi et al. [
3
] for sheep and by Do et al. [
54
] for Holstein
dairy cattle. To determine the biological functions of each gene, Biological Process (BP) and
Molecular Functions (MF) of all identified genes in candidate regions were studied using
DAVID annotation [
55
]. Furthermore, a comprehensive literature review was conducted
to verify whether these genes have some relevance with fat deposition or developmental
process in sheep or other mammals.
3. Results
3.1. SNP Genotyping and Data Mining
A total of 32, 26, and 29 SNPs passed the filtering criteria in our regions of interest on
chromosome 5, 7 and X respectively (Table 2). Most of these SNPs that used for further
analysis were not available on the Illumina Ovine SNP50k BeadChip due to the lower
expected minor allele frequency (MAF) and reliability of the SNPs, discovered on both
the Solexa and 454 platforms (http://www.sheephapmap.org/genseq.php, accessed on
31 May 2017).
Table 2.
Summary of SNP characteristics for different regions, genotyped using the Sequenom assay,
before and after data cleaning and their combination with Ovine SNP50k BeadChip SNPs, for follow
up analysis.
Chromosome No. of SNPs Assayed
with Sequenom
No. of SNPs That Passed
Quality Control
Total Number of SNPs
Used for Final Analysis
Average Distance
between SNPs (kb)
5 71 32 41 (15 + 26) 123.69
7 62 26 30 (10 + 20) 17.77
X 57 29 36 (20 + 16) 82.91
1SNPs included Ovine SNP50k Bead Chip + non repeated Sequenom SNPs.
Table 2presents a descriptive summary of the characteristics of the SNPs used in the
final analysis. The distribution of SNPs varied among the regions, especially regarding to
those used in statistical tests (Supplementary Table S2); however, the average SNP intervals
were relatively consistent and the overall average distance between adjacent SNPs was
about 23 kb, 17 kb and 82 kb for candidate regions on chromosome 5, 7 and X respectively,
compared to about 60 kb and 115 kb for autosomes and chromosome X on the Illumina
Ovine SNP50k BeadChip respectively.
3.2. Distribution of FST and Median Homozygosity
To identify loci that have been targets of selection across thin and fat tailed breeds,
the windowed F
ST
was plotted against genomic location for candidate regions (Figure 1a).
Variants with unusually large F
ST
values are typically interpreted as being the targets of
local selective pressures due to the hitch-hiking effect [25].
The average of differentiation between Zel (thin tail) and Lori Bakhtiari (fat tail) for
the whole genome was 0.024 (SD = 0.036), while this parameter was 0.093 (SD = 0.136),
0.172 (SD = 0.184) and 0.279 (SD = 0.217) at the areas of interest on chromosome 5, 7 and X
respectively. As shown in Figure 1, in these regions we found evidence of selection across
relatively short distances with windowed F
ST
values > 0.30 on chromosomes 5 (between
47,149,
400–47
,245,841 bp), 7 (between 46,587,943–46,843,356 bp) and
values > 0.40
on chro-
mosome X (between 59,257,971–59,984,949 bp). The score of 0.30 is in the 99.9 percentile of
autosomal SNPs (n= 44,558) and a score of 0.40 is in the 99.0 percentile of chromosome X
SNPs (n= 1126).
When there is a selection for a causal mutation in one breed and not the other, the
breed under selection shows high homozygosity in a genomic interval while the other
does not [
39
]. To further test this hypothesis, median homozygosity was calculated and
plotted against genomic position for candidate regions (Figure 1b). The results indicate
Animals 2022,12, 1423 8 of 20
that homozygosity increased over the areas of interest on chromosome 5 and X for the fat
tailed and at the candidate region on chromosome 7 for the thin tailed breed. The largest
differences of median homozygosity were for chromosome X and homozygosity was
present for a longer distance as well, whereas these parameters are less on
Chromosome 5.
Animals 2022, 12, x FOR PEER REVIEW 8 of 21
Figure 1. Plots of windowed FST (a) and run of median homozygosity (b) in relation to genomic
position for thin and fat tail breeds in candidate regions: SNP positions in the genome (bp) are
shown on the X-axis, and windowed FST or median homozygosity are plotted on the Y-axis. Fat and
thin tailed breeds are shown by blue diamonds and red squares respectively on median homozy-
gosity plot.
The average of differentiation between Zel (thin tail) and Lori Bakhtiari (fat tail) for
the whole genome was 0.024 (SD = 0.036), while this parameter was 0.093 (SD = 0.136),
0.172 (SD = 0.184) and 0.279 (SD = 0.217) at the areas of interest on chromosome 5, 7 and X
respectively. As shown in Figure 1, in these regions we found evidence of selection across
relatively short distances with windowed FST values > 0.30 on chromosomes 5 (between
47,149,400–47,245,841 bp), 7 (between 46,587,943–46,843,356 bp) and values > 0.40 on chro-
mosome X (between 59,257,971–59,984,949 bp). The score of 0.30 is in the 99.9 percentile
of autosomal SNPs (n = 44,558) and a score of 0.40 is in the 99.0 percentile of chromosome
X SNPs (n = 1126).
When there is a selection for a causal mutation in one breed and not the other, the
breed under selection shows high homozygosity in a genomic interval while the other
does not [39]. To further test this hypothesis, median homozygosity was calculated and
plotted against genomic position for candidate regions (Figure 1b). The results indicate
that homozygosity increased over the areas of interest on chromosome 5 and X for the fat
tailed and at the candidate region on chromosome 7 for the thin tailed breed. The largest
differences of median homozygosity were for chromosome X and homozygosity was pre-
sent for a longer distance as well, whereas these parameters are less on Chromosome 5.
Figure 1.
Plots of windowed F
ST
(
a
) and run of median homozygosity (
b
) in relation to genomic posi-
tion for thin and fat tail breeds in candidate regions: SNP positions in the genome (bp) are shown on
the X-axis, and windowed F
ST
or median homozygosity are plotted on the Y-axis. Fat and thin tailed
breeds are shown by blue diamonds and red squares respectively on median homozygosity plot.
3.3. Calculation of Integrated Haplotype Score (iHS)
iHS detects signatures of strong selection in favor of alleles that have not yet reached
fixation [
35
]. The results revealed that regions on chromosomes 5 and X had no iHS peak
(Supplementary Figures S1 and S2). This observation may suggest that selected alleles have
already been fixed in these locations. However, analysis of the region on chromosome 7
displayed an obvious peak in both thin and fat tail breeds (Figure 2). For most locations
of the region the values are higher in the thin tail breed, however, there is also some even
stronger indication of selection in fat tail breed especially at the end of the region.
Animals 2022,12, 1423 9 of 20
Animals 2022, 12, x FOR PEER REVIEW 9 of 21
3.3. Calculation of Integrated Haplotype Score (iHS)
iHS detects signatures of strong selection in favor of alleles that have not yet reached
fixation [35]. The results revealed that regions on chromosomes 5 and X had no iHS peak
(Supplementary Figures S1 and S2). This observation may suggest that selected alleles
have already been fixed in these locations. However, analysis of the region on chromo-
some 7 displayed an obvious peak in both thin and fat tail breeds (Figure 2). For most
locations of the region the values are higher in the thin tail breed, however, there is also
some even stronger indication of selection in fat tail breed especially at the end of the
region.
Figure 2. Plot of |iHS| in relation to the genomic position (bp) for thin and fat tail breeds on chro-
mosome 7 (Upper) and our candidate region (lower): Fat and thin tail breeds are shown by blue
diamonds and red squares respectively, and |iHS| statistic averaged over 10 SNPs. The information
for calculating |iHS|on whole chromosome was obtained from Moradi et al. [3].
3.4. Cross-Population EHH (XP-EHH)
To identify selective sweeps in which the selected allele has approached or achieved
fixation in a subpopulation, but remains polymorphic in the population as a whole, the
standardized cross population extent of haplotype homozygosity scores (XP-EHH) were
plotted against genomic locations (Figure 3). The results revealed clear peaks in all of our
regions of interest, suggesting that selected alleles approached fixation or have risen to
near fixation in favor of fat tail breed on chromosome 5 and X while on chromosome 7 the
frequency of alleles have risen close to fixation in thin tail breed. These results are based
on linkage disequilibrium around given SNP and are in agreement with the results of
median homozygosity plots.
Figure 2.
Plot of |iHS| in relation to the genomic position (bp) for thin and fat tail breeds on
chromosome 7 (
Upper
) and our candidate region (
lower
): Fat and thin tail breeds are shown by blue
diamonds and red squares respectively, and |iHS| statistic averaged over 10 SNPs. The information
for calculating |iHS| on whole chromosome was obtained from Moradi et al. [3].
3.4. Cross-Population EHH (XP-EHH)
To identify selective sweeps in which the selected allele has approached or achieved
fixation in a subpopulation, but remains polymorphic in the population as a whole, the
standardized cross population extent of haplotype homozygosity scores (XP-EHH) were
plotted against genomic locations (Figure 3). The results revealed clear peaks in all of our
regions of interest, suggesting that selected alleles approached fixation or have risen to
near fixation in favor of fat tail breed on chromosome 5 and X while on chromosome 7 the
frequency of alleles have risen close to fixation in thin tail breed. These results are based on
linkage disequilibrium around given SNP and are in agreement with the results of median
homozygosity plots.
Sabeti et al. [
36
] considered a region as a candidate for selection in the human HapMap
Phase 2 dataset when the two population XP-EHH was above 4.34. This score is in the
99.9 percentile for thin versus fat tail breeds. As shown in Figure 3, we found evidence
of selection with |XP-EHH| value > 4.34 on chromosomes 5 (47,141,229–47,171,110 bp),
7 (46,604,500–46,642,359 bp) and X (59,187,456–60,264,325 bp). These regions are almost
identical to the positions with highest FST.
3.5. Study of Core Haplotypes and Their Ancestral Status in Candidate Regions
To evaluate the haplotype frequencies and the status of selected alleles (derived or
ancestral), the core SNP alleles, and their haplotype frequencies were investigated in the
selected regions (Table 3).
For chromosome 5 (Table 3), we defined a core region of 26 k where both F
ST
and
XP-EHH statistics were at their highest. There are 5 genotyped SNPs in this region. The
SNPs defined 14 core haplotypes (denoted haplotype 1 to 14) in thin tailed but only
6 core
haplotypes in fat tailed sheep. As shown in Table 3, there is a common haplotype
(haplotype 1) with a frequency of 90% in fat tailed sheep whereas its frequency for thin
tailed sheep is 15%. In contrast, the common haplotype in thin tail breed is haplotype 8 with
a frequency of 31%, while it is almost absent (2%) in the fat tail breed. The interesting result
Animals 2022,12, 1423 10 of 20
in this region is that all the SNPs in the common haplotype for the fat tail breed are derived
SNPs whereas all SNPs in the common haplotype for the thin tail breed are ancestral.
Animals 2022, 12, x FOR PEER REVIEW 10 of 21
Figure 3. Plot of XP-EHH relation to genomic position (bp) for thin and fat tailed breeds on whole
chromosome (upper) and our candidate region (lower) in different chromosomes: High positive
values suggest selection in fat tailed population and negative values selection in thin tailed popula-
tion. The genotyping information required for the presentation of the entire chromosomes were ob-
tained from Moradi et al. [3].
Sabeti et al. [36] considered a region as a candidate for selection in the human Hap-
Map Phase 2 dataset when the two population XP-EHH was above 4.34. This score is in
the 99.9 percentile for thin versus fat tail breeds. As shown in Figure 3, we found evidence
of selection with |XP-EHH| value > 4.34 on chromosomes 5 (47,141,229–47,171,110 bp), 7
(46,604,500–46,642,359 bp) and X (59,187,456–60,264,325 bp). These regions are almost
identical to the positions with highest FST.
3.5. Study of Core Haplotypes and Their Ancestral Status in Candidate Regions
To evaluate the haplotype frequencies and the status of selected alleles (derived or
ancestral), the core SNP alleles, and their haplotype frequencies were investigated in the
selected regions (Table 3).
Table 3. Core SNP alleles and haplotype frequencies in candidate regions for fat tail (Lori) and thin
tail (Zel) breeds: The haplotypes with higher frequency in fat and thin tailed breeds have been high-
lighted.
Figure 3.
Plot of XP-EHH relation to genomic position (bp) for thin and fat tailed breeds on whole
chromosome (
upper
) and our candidate region (
lower
) in different chromosomes: High positive
values suggest selection in fat tailed population and negative values selection in thin tailed population.
The genotyping information required for the presentation of the entire chromosomes were obtained
from Moradi et al. [3].
Table 3.
Core SNP alleles and haplotype frequencies in candidate regions for fat tail (Lori) and
thin tail (Zel) breeds: The haplotypes with higher frequency in fat and thin tailed breeds have
been highlighted.
Chromosome 5
Core SNP Alleles Core Haplotype Frequencies
SNP Variants C/T A/G T/C A/G A/C Fat Tail Breed Thin Tail Breed
Genomic position (bp) 47,149,354 47,149,400 47,165,900 47,171,110 47,175,489
Ancestral allele T G T G C
Haplotype 1 C A C A A 0.90 0.15
Haplotype 2 - * G - - - 0.01 0.06
Haplotype 3 - G T - - 0.01 -
Haplotype 4 - G - G C - 0.12
Haplotype 5 T G - - - - 0.13
Haplotype 6 T - T - - 0.01 -
Haplotype 7 T G T - - 0.04 0.11
Haplotype 8 T G T G C 0.02 0.31
Other Haplotypes - 0.14(6
Haplotypes)
Animals 2022,12, 1423 11 of 20
Table 3. Cont.
Chromosome 7
Core SNP Alleles Core Haplotype Frequencies
SNP Variants C/T A/C T/C A/C Fat Tail Breed Thin Tail
Breed
Genomic position (bp) 46,604,500
46,604,644
46,604,722 46,642,359
Ancestral allele C C C A
Haplotype 1 C C C C 0.06 0.80
Haplotype 2 - - - A 0.20 0.14
Haplotype 3 T A - - 0.02 0.03
Haplotype 4 T A - A 0.14 -
Haplotype 5 T A T - 0.31 0.02
Haplotype 6 T A T A 0.27 -
Chromosome X
Core SNP Alleles Core Haplotype Frequencies
SNP
Variants A/G C/T T/C T/A G/C G/A Fat Tail Breed Thin Tail
Breed
Position
(bp)
59,742,181 59,750,338 59,912,586 59,971,891
59,971,909 59,984,949
Ancestral
allele A T T T C A
Haplotype
1G C T T G G 0.89 0.12
Haplotype
2- - C - C A - 0.01
Haplotype
3- - C A C A - 0.18
Haplotype
4A T - A C A - 0.04
Haplotype
5A T C - - - 0.11 0.08
Haplotype
6A T C - C A - 0.01
Haplotype
7A T C A C A - 0.54
*The dashed line (-) in this table indicates that the desired nucleotide is similar to haplotype 1 nucleotide.
For chromosome 7 (Table 3) we defined a core region of 37 k in the selected area with
4 genotyped
SNPs, where all statistics showed strong evidence of selection. There were
only 4 core haplotypes (denoted haplotype 1 to 4) in thin tailed and 6 haplotypes in fat tailed
sheep. The common haplotypes of fat and thin tail breeds in this region were haplotype
5 and 1 with frequency of 31% (2% in thin tailed) and 80% (6% in fat tailed), respectively.
Once again, while the common haplotype in the fat tail breed had all derived alleles, the
common haplotype in the thin tail breed were almost all ancestral alleles (
3 out of 4
SNPs).
However, it is notable that in this region the haplotypes appear more polymorphic in the
fat tail breed and the frequency of the common haplotype of the thin tail breed is not as
high as for the other candidate regions.
Subsequently, this approach was performed in the candidate region on chromosome
X. We defined a core region of 242 k corresponding to 6 genotyped SNPs. The longer
length of the core region, in this case is due to the wider SNP spacing on chromosome X.
The genotyped SNPs defined seven core haplotypes in the thin tail breed and only two
haplotypes in the fat tail breed (Table 3). The common haplotype in the fat tail breed was
haplotype 1 with a frequency of 89%, whereas its frequency in thin tailed was 12%. In
this region the common haplotype in the thin tail breed (54%) was absent in the fat tail
breed. The results in this region were similar to the previous regions in that the derived
Animals 2022,12, 1423 12 of 20
alleles were more prevalent in the common haplotype of fat tailed (4 out of 6 core SNPs)
with the same result for ancestral alleles in the common haplotype of the thin tail breed.
Together, these results could suggest that ancestral alleles have been under selection in the
thin tail breed while the contrary happened for the fat tail breed where derived alleles have
undergone selection.
3.6. Study of the Identified Genes Associated with Fat Metabolisms in Candidate Regions
The regions of interest were investigated to determine if any genes related to fat
deposition could be identified in sheep or their corresponding areas of the cow genome.
The genes obtained in O. aries or by orthology with B. taurus and their functions are
presented in Table 4.
Table 4.
Fat metabolism related genes located within the candidate regions in O. aries and their
orthologous area in B. taurus.
Species Chromosome RefSeq Number Gene Name Gene Symbol Function
Ovis aries XNM_001037811 hydroxysteroid (17-beta)
dehydrogenase 10 HSD17B10 lipid metabolic process
X NM_000044 androgen receptor Ar lipid binding
X NM_173963 synaptophysin Syp lipid binding (Cholestrol
binding)
Bos taurus 5 NM_002715
protein phosphatase 2
(formerly 2A), catalytic
subunit, alpha isoform
PPP2CA
cellular lipid metabolic
process
lipid metabolic process
memberan lipid metabolic
process
sphingolipid metabolic
process
X NM_001034500
emopamil binding protein
(sterol isomerase) EBP lipid metabolic process
There is also the hypothesis that some genes selected for, in fat tail sheep breeds are
likely to be also associated with developmental defects or ectopic expression of
organs [56,57]
.
Our results revealed that these regions contain many genes, having some known biolog-
ical functions associated with the developmental process in O. aries (Table 5) and their
orthologous coordinates in B. taurus (Supplementary Table S5).
Table 5.
Developmental process or gene expression related genes, located within the candidate
regions in O. aries.
Chr. Gene Symbol Gene Name Functions *
5TCF7 Transcription factor 7 (T-cell specific,
HMG-box) regulation of gene expression
7PTGDR Prostaglandin D2 receptor (DP) developmental process
NID2 Nidogen 2 (osteonidogen) cellular macromolecule metabolic process
XAR Androgen receptor
gland development, organ development,
system development,
anatomical structure development, regulation of
gene expression
FOXP3 Forkhead box P3
organ development, system development, anatomical
structure development,
regulation of developmental process, regulation of
gene expression
Animals 2022,12, 1423 13 of 20
Table 5. Cont.
Chr. Gene Symbol Gene Name Functions *
FGD1 FYVE, RhoGEF and PH domain
containing 1
organ development, system development, anatomical
structure development,
regulation of developmental process
BMP15 Bone morphogenetic proteins 15
organ development, system development, anatomical
structure development,
regulation of developmental process
HSD17B10 hydroxysteroid (17-beta)
dehydrogenase 10
organ development, system development, anatomical
structure development
TIMP1 TIMP metallopeptidase inhibitor 1 organ development, system development, anatomical
structure development
PFKFB1 Hydroxysteroid (17-beta)
dehydrogenase 10
organ development, system development, anatomical
structure development
SLC35A2 Solute Carrier Family 35 Member A2 organ development, system development, anatomical
structure development
ALAS2 Aminolevulinate, delta-, synthase 2 organ development, system development, anatomical
structure development
HEPH Hephaestin organ development, system development, anatomical
structure development
PCSK1N Proproteinconvertasesubtilisin/kexin
type 1 inhibitor
organ development, system development, anatomical
structure development
SHROOM4 Shroom family member 4 organ development, system development, anatomical
structure development
CACNA1F
Calcium channel, voltage-dependent, L
type, alpha 1F subunit system development, anatomical structure development
TFE3 Transcription factor binding to IGHM
enhancer 3
regulation of developmental process, regulation of
gene expression
ELK1
ELK1, member of ETS oncogene family
regulation of gene expression
KDM5C Lysine (K)-specific demethylase 5C regulation of gene expression
ZNF41, 81 Zinc finger protein 41, 81 regulation of gene expression
*
gland development: The process whose specific outcome is the progression of a gland over time, from its
formation to the mature structure. A gland is an organ specialized for secretion. organ development: Development
of a tissue or tissues that work together to perform a specific function or functions. Organs are commonly observed
as visibly distinct structures, but may also exist as loosely associated clusters of cells that work together to perform
a specific function or functions. system development: The process whose specific outcome is the progression of an
organismal system over time, from its formation to the mature structure. A system is a regularly interacting or
interdependent group of organs or tissues that work together to carry out a given biological process. anatomical
structure development: The biological process whose specific outcome is the progression of an anatomical
structure from an initial condition to its mature state. An anatomical structure is any biological entity that occupies
space and is distinguished from its surroundings. developmental process: A biological process whose specific
outcome is the progression of an integrated living unit: an anatomical structure (which may be a subcellular
structure, cell, tissue, or organ), or organism over time from an initial condition to a later condition. regulation of
developmental process: Any process that modulates the frequency, rate, or extent of development, the biological
process whose specific outcome is the progression of a multicellular organism over time from an initial condition
to a later condition. regulation of gene expression: Any process that modulates the frequency, rate, or extent of
gene expression. This includes the production of an RNA transcript as well as any processing to produce a mature
RNA product or an mRNA and the translation of that mRNA into protein.
4. Discussion
An increasing number of studies have been conducted to detect signals of recent
positive selection on a genome-wide scale in different domestic animals [5860]; however,
there are relatively few genomic regions identified that have been subject to selection for a
specific mutation underlying evolutionary shifts in a trait. In this paper, in order to fine
map and get more insight into the genomic basis of fat deposition in thin and fat tail breeds,
we have investigated three candidate regions using this approach. These candidate regions
were analyzed using a variety of statistics to clarify the signals of selection observed in
these regions. Our analyses focused on two allele frequency spectrum (F
ST
and median ho-
mozygosity) and haplotype based (|iHS| and XP-EHH) statistics. These tests were chosen
because previous power analyses suggested that these are largely complementary [39].
Animals 2022,12, 1423 14 of 20
The study of the candidate regions on chromosome 5 and X revealed obvious evidence
of the selection using an F
ST
, median homozygosity, and XP-EHH test in a relatively narrow
region; while an examination of these regions identified no particular |iHS| peak. With a
hypothesis that historically different selection pressures operated in thin and fat tail breeds
and somehow selection acted on a variant that was advantageous only in one breed, these
results suggest that selection in these regions occurred for mutations affecting fat tail size
as the beneficial mutations have risen to near fixation in fat tailed breeds. This suggestion
is also supported by the core haplotype frequencies observed in candidate regions on these
chromosomes as the common core haplotypes in fat tail breeds were near fixation (Table 3).
Analysis of the region on chromosome 7 indicated strong evidence of selection using
all selective sweep statistics. The results of F
ST
and median homozygosity suggested that
the selection differentiated the beneficial alleles between breeds and that homozygosity has
been increased in favor of thin tailed in this region. However, |iHS| and XP-EHH revealed
additional information. |iHS| provided evidence of partial selection in both breeds, while
the XP-EHH results showed that the selected alleles have approached fixation in the thin
tail breed.
As discussed earlier, the power of the |iHS| statistics to detect selective sweeps is
greatest at a moderate allele frequency (~40–60%), while XP-EHH test is more powerful
for detecting selective sweeps close to fixation (>80%) [
35
,
61
]. However, both of these
methods do have power outside their optimal ranges. Sabeti et al. [
36
] demonstrated that
the iHS statistic could detect signals over the range of 20–80% and XP-EHH do have power
between 60–100%. Overall, these results suggest that while the frequency of selected alleles
has been raised to fixation in thin tail breed, its frequency should be ~60–80% to be picked
up by both methods. Simultaneously, the favorable allele should be increased to mid-range
frequency in the fat tail breed. The results of core haplotype frequencies in this region
(Table 3) are in consistent to this point as the common haplotype in thin tail breed has a
frequency of 80%, whereas all haplotypes observed in this region appears polymorphic
in the fat tail breed. One inference is that there has been an ongoing infusion of fat tailed
haplotypes into this breed, but also selection for the thin-tailed phenotype.
To further test this hypothesis, we constructed the pattern of haplotype blocks in
this region and the decay of LD (pairwise r
2
) was visualized using Haploview [
52
]. The
effect of a selective sweep on patterns of variation is expected to decline with time (due to
recombination) and if a selective sweep is still ongoing in a subpopulation, the hitchhiking
haplotype is expected to be rather long [
39
,
57
]. Our results (Supplementary Figure S3)
revealed that although there were haplotype blocks in both breeds, they extended for
longer distances in the fat tailed compared to the thin tail breed. This suggests that as the
prevalence of selected alleles increased in thin tail breed, the LD around variants decayed
due to recombination, while the selection in the fat tail breed is younger (longer haplotype
blocks). These results confirm our observations for evidence of selection in this region with
both |iHS| and XP-EHH tests.
The earliest known depiction of a fat tail sheep is on an Uruk III stone vessel about
5000 years before present, approximately 4000 years after initial domestication [
62
]. Given
that fat tailed breeds are now prevalent in the Fertile Crescent, where sheep were originally
domesticated, while thin tailed sheep breeds are predominant in peripheral areas and
that the wild ancestor of sheep is thin tail, it has been assumed that the first domesticated
sheep were thin tailed and fat tail was developed later [
62
,
63
]. We have investigated this
hypothesis through the classification of selected alleles in the core haplotypes of our regions
of interest as ancestral or derived. Our results provide the preliminary molecular evidence
to confirm this assumption since, we observed that in almost all cases, derived alleles have
been under selection pressure in the fat tail breed, and this is consistent with the selection
of a new mutation in these breeds (Table 3).
Population demographic history can also cause similar patterns on DNA sequence
variation and could be a source of error in making inferences on genomic targets of selection.
This caveat can be avoided by screening a large number of markers (as has previously
Animals 2022,12, 1423 15 of 20
been performed in this research) spaced across whole genome [
3
], as selection will result in
regional patterns compared to the genome-wide effects of population history and demo-
graphic events [
23
,
64
]. Similarly conducting this type of fine-scale analysis at the candidate
genomic regions using a dense set of markers and multiple statistical tests, reduces the
chance that a signature of selection will be a false positive if it is detected in more than one
marker locus and statistical test [40].
The candidate regions of interest have previously been studied using OAR v1.0 in
O. aries
and their corresponding area of B. taurus and no particular candidate genes associ-
ated with fat deposition were identified [
3
]. In this study, investigation of these regions
using the newly available sheep genome OAR v3.1 [
53
], defined some genes associated
with fat metabolism in O. aries or their orthologous areas of B. taurus (Table 4). Protein
phosphatase 2
(formerly 2A), catalytic subunit, alpha isoform (PPP2CA) has a variety
of roles in different biological process such as cellular lipid metabolic, membrane lipid
metabolic and sphingolipid metabolic process, while hydroxysteroid (17-beta) dehydroge-
nase 10 (HSD17B10) and emopamil binding protein (EBP) genes play some roles in lipid
metabolic process and androgen receptor (Ar) and synaptophysin (Syp) genes get partici-
pate in lipid binding [
65
]. Interestingly, most of the genes (or their gene families) identified
here, have been recently reported as candidate genes associated with lipid metabolisms
using various molecular techniques in sheep. Yuan et al. [
66
] implemented differential
expression analysis using RNA-seq technology in longissimus dorsi muscle tissue (MUT),
perirenal adipose tissue (PAT) and tail adipose tissue (TAT) of different Chines short and fat
tailed sheep breeds and revealed that PPP1CA is highly expressed in TAT. Also, PPP1CA
was identified as plausible genes associated with the fat-tailed or fat-rumped phenotype by
comparing copy number variations (CNVs) with different tail types [
67
]. Moreover, protein
phosphatase 1, catalytic subunit, gamma isozyme (PPP1CC) have been under selection
signature in Chinese thin and fat-tailed sheep breed and reported to be associated with tail
type [
68
]. HSD17B12 has also been reported to be associated with fat tail metabolism in thin
and fat tailed sheep breeds using deep transcriptome analysis with RNA-Seq data [
69
]. It is
reported that HSD17B12 (act as elongates) are important genes for controlling the overall
balance of fatty acid composition [
69
]. Therefore, it seems the gene families and isoforms of
Protein phosphatase (PPP) and hydroxysteroid (HSD) have important roles in molecular
regulations of fat deposition in tail.
To confirm the results of our study, the exon 1 of PPP2CA gene was amplified and its
variation patterns were sequenced in an independent study on Zel and Lori-Bakhtiari sheep
breeds [
34
]. Two patterns were identified and the results of sequencing showed that in Lori-
Bakhtiari, Del/Del genotype resulted in heavier fat tail than T/T genotype (
5.20 ±0.21 kg
vs. 3.28
±
0.12 kg) (p< 0.05) while, in Zel, the effect of genotypes on carcass fat percentage
and triglyceride was significant, so that the T/T genotype had more carcass fat percentage
comparing to Del/Del genotypes (p< 0.05). Overall, it seems as the annotation of the
ovine genome becomes more complete, all genes located in the candidate regions will be
identified and promising targets can then be verified by further experimentation.
A result which is irrelevant to the inheritance of the trait, but provides an insight into
a possible mechanism of fat deposition in this organ, are the results of Gokdal et al. [
56
]
who examined the effects of docking in fat tail breeds. The carcasses of the docked group
contained more kidney, pelvic and internal fat than the intact lambs as well as a higher
percentage of subcutaneous and intramuscular fat. The weights of the different carcass
cut of the docked lambs were also heavier than those of the intact group. However, there
was little change in overall carcass composition, suggesting that the genes affecting the
fat tail phenotype are associated with the localization of fat stores to a regional depot
rather than control of the overall level of fat deposition. This observation also may provide
support to the suggestion that some genes selected for in fat tail sheep breeds in these
regions are likely to be also associated with developmental defects or ectopic expression of
organs. Our results revealed that these regions contain many genes, having some known
biological functions associated with developmental process (Table 5and Table S5). Several
Animals 2022,12, 1423 16 of 20
earlier studies provide evidence for this issue. For example, the transcription factor (TCF)
genes have been among highly differentially expressed genes in perirenal adipose tissue
(PAT) and identified as being the most likely to account for the fat-tailed phenotype of
sheep [
66
]. TCF7 is involved in the Wnt/
β
-catenin signaling pathway, and this pathway
plays a critical role in regulating sheep [
69
] and porcine [
70
] adipogenesis genes expression.
Zhu et al. [
71
] studied copy number variations (CNVs) and selection signatures on the
X chromosome of Chinese indigenous sheep with different tail types and revealed that
the regions harboring CNVs and selective sweeps in different sheep breeds overlapped
with calcium channel, voltage-dependent, L type, alpha 1F subunit (CACNA1F) gene
that could be as associated with tail type in these breeds [
43
]. In addition, HSD17B10 [
69
],
SLC35A2 [
68
], AR and TIMP1 [
31
], have been identified as candidate genes that affect fat tail
development. Moreover, it is important to note that our regions of interest overlapped with
some genes, for example BMP15,WDR13 and RBM3 that belong to the gene families that
their closely related genes consisting BMP2 [
9
,
32
,
68
,
72
,
73
], WDR92 [
17
,
68
] and RBM11 [
6
]
have recently reported to be associated with fat tail formation and adipose tissue gene
expression in sheep.
Finally, fine mapping of candidate regions using different sweep statistical tests has
enabled us to confirm the signature of selection in these chromosomal regions and better re-
fine the critical regions from 113 kb (47,149,400–47,263,230) to 28 kb (47,146,931–47,175,489)
on chromosome 5, from 201 kb (46,642,359–46,843,356) to 142 kb (consisting to shorter
intervals: 46,587,943–46,642,359 and 46,765,080–46,852,870) on chromosome 7 and from
2831 kb (58,621,412–61,452,816) to 1006 kb (59,257,971–60,264,325) on chromosome X. These
regions were refined considering all statistical test results (Supplementary Figure S4). Ac-
quiring of the genes located within the regions of interest after fine mapping revealed some
genes consisting TCF7 on chromosome 5, PTGDR and NID2 on chromosome 7 and finally
AR on chromosome X that have a multiple effect on lipid metabolisms, macromolecule
metabolic process, organ/gland development or associated with ectopic expression of
organs simultaneously.
Recently published study on the origin of European sheep as revealed by the diversity
of the Balkan breeds and by optimizing population-genetic analysis tools [
74
] using a
variety of sheep breed samples from Southwest-Asian, Mediterranean, Central-European
and North-European showed that the thin-tailed Zel sheep is found to be in the same
genetic cluster as the fat-tailed Iranian sheep, whereas the fat-tailed Italian Laticauda is
related to other breeds in central Italy. This may imply that the tail phenotype is encoded
by a limited number of genes. By combining information of the present study, previously
reported and annotated biological functional genes, we suggest PPP2CA and TCF7 (OAR5),
PTGDR and NID2 (OAR7), AR,EBP,CACNA1F,HSD17B10,SLC35A2,BMP15,WDR13 and
RBM3 (OAR X) as the most promising candidate genes for type of tail traits.
It is obvious that understanding the mechanisms that underlie fat tail inheritance in
sheep is difficult to verify solely by selective sweep profiling. While this study has now
refined three regions located on chromosome 5, 7 and X, associated with fat tail sheep
breeds, it is likely that other genomic regions may also be involved. Some other studies
have identified a region on Chromosome 15 for example, close to PDGFD as associated with
the fat tail phenotype [
12
,
13
]. Definitive studies of the actions of these regions will require
larger-scale designs in segregating populations where trait measurements are recorded.
Also, these regions may still be too large to efficiently implement technologies such as
marker assisted selection or positional cloning. More detailed and larger scale experiments
from these and other thin and fat tailed breeds may allow us to refine the location of the
causal mutations. Likewise, it does not exclude contemporaneous selection for other traits,
and any regions identified still need to be tested for a functional genetic relationship via
trait measurement in contrasting genotypes or phenotypes. Specifically, future studies
should be conducted in reciprocal F
2
crosses to provide independent and causal evidence
and verify the mode of inheritance. If these areas are shown to have a significant effect, then
further work sequencing either side of the regions can help the search for causal variants.
Animals 2022,12, 1423 17 of 20
5. Conclusions
Our results provide a comprehensive assessment of how and where selection has
affected the patterns of variation in candidate regions associated with fat deposition in thin
and fat tail sheep breeds. These results enabled us to confirm the signature of selection
in these regions, refine the critical intervals regions, and to identify the most promising
candidate genes associated with fat deposition in thin and fat tail sheep. These results may
provide a strong foundation for studying the regulation of fat deposition in sheep and do
offer hope that the causal mutations and the mode of inheritance of this trait will soon be
discovered by further experimentation.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/ani12111423/s1, Table S1: Selected SNPs for genotyping via
Sequenom iPlex Mass ARRAY platform and their locations, PCR primers, unextended primer and
its extension masses. (XLS Format), Table S2: The number of animals, distances and number of
SNPs used in this research for different statistical tests in Zel (thin tail) and Lori-Bakhtiari (fat tail)
breeds, Table S3: Regions chosen for gene mapping onto O. aries (OAR v3.1) and their orthologous
coordinates in B. taurus (ARS-UCD1.2, Bostau9), Table S4: The coordinate of candidate regions
associated with fat deposition reported in article Moradi et al. [
3
] and the fine mapped results in this
study based on different OAR versions, Table S5: Developmental process or gene expression related
genes, located in/close the orthologous area of the regions of interest in B. taurus, Supplementary
Figure S1: Plot of iHS relation to genomic position (bp) for thin and fat tailed breeds in candidate
region on Chromosome 5, Figure S2: Plot of iHS relation to genomic position (bp) for thin and fat
tailed breeds in candidate region on Chromosome X, Figure S3: A graphical representation of pairwise
r2 for the candidate region on chromosome 7 in thin and fat tail breeds calculated and visualized
using Haploview, Figure S4: Refinement of candidate regions associated with fat deposition in thin
and fat tail breeds using different sweep statistical tests: refined candidate regions has been shown by
green line and the gene located within each region of interest highlighted by pink colours. XP-EHH
and |iHS| values have had 4 and 2 subtracted respectively, so the values fit on the same scale.
Author Contributions:
M.H.M. planned and performed the analyses, and drafted the manuscript,
A.N.-J. coordinated the study and supervised the analysis, M.M.-S. coordinated the study and sample
collection, K.G.D. and R.B. provided statistical and analysis support and J.C.M. supervised the
analysis and participated in the design of the study. All authors have contributed to the editing of
the article, and approved the final manuscript. All authors have read and agreed to the published
version of the manuscript.
Funding:
This study was funded by Animal Science Research Institute of Iran, Mobarakandish
Institute and AgResearch, New Zealand, Project number: PRJ-2016/11547. The funders were not
involved in the study design, collection, analysis, interpretation of data, the writing of this article or
the decision to submit it for publication.
Institutional Review Board Statement:
Sampling was carried out by trained veterinarians within
the frame of vaccination campaigns, hence no permission from the animal research ethics committee
was necessary. Veterinarians adhered to standard procedures and relevant national guidelines to
ensure appropriate animal care.
Informed Consent Statement: Not applicable.
Data Availability Statement:
All data generated or analyzed during this study are included in this
manuscript and its supplementary information files. In addition, more detail data are available from
the corresponding author on reasonable request.
Acknowledgments:
The authors gratefully acknowledge the International Sheep Genomics Consor-
tium for access to the Ovine HapMap genotypes and the Animal Breeding Center of Iran (ABCI) for
access to the records and animals of the Iranian breeds. Thanks to the staff of the University of Tehran,
Animal Science Research Institute of Iran and AgResearch, especially Tim Manley, Rayna Anderson
and Kathryn McRae who helped and supported this research. The authors also acknowledge the
financial contributions of Animal Science Research Institute of Iran, Mobarakandish Institute and
AgResearch, New Zealand. We thank Pardis Sabeti and Benjamin Voight for their invaluable advises
during this research.
Animals 2022,12, 1423 18 of 20
Conflicts of Interest: The authors declare no conflict of interest.
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... Additional file 2: Table S1. Additional genomic investigations on sheep tails [159,[218][219][220][221][222][223][224][225][226]. Table S2. ...
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Background After domestication, the evolution of phenotypically-varied sheep breeds has generated rich biodiversity. This wide phenotypic variation arises as a result of hidden genomic changes that range from a single nucleotide to several thousands of nucleotides. Thus, it is of interest and significance to reveal and understand the genomic changes underlying the phenotypic variation of sheep breeds in order to drive selection towards economically important traits. Review Various traits contribute to the emergence of variation in sheep phenotypic characteristics, including coat color, horns, tail, wool, ears, udder, vertebrae, among others. The genes that determine most of these phenotypic traits have been investigated, which has generated knowledge regarding the genetic determinism of several agriculturally-relevant traits in sheep. In this review, we discuss the genomic knowledge that has emerged in the past few decades regarding the phenotypic traits in sheep, and our ultimate aim is to encourage its practical application in sheep breeding. In addition, in order to expand the current understanding of the sheep genome, we shed light on research gaps that require further investigation. Conclusions Although significant research efforts have been conducted in the past few decades, several aspects of the sheep genome remain unexplored. For the full utilization of the current knowledge of the sheep genome, a wide practical application is still required in order to boost sheep productive performance and contribute to the generation of improved sheep breeds. The accumulated knowledge on the sheep genome will help advance and strengthen sheep breeding programs to face future challenges in the sector, such as climate change, global human population growth, and the increasing demand for products of animal origin.
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Fat deposition in sheep tails is as a result of a complicated mechanism. Mongolian sheep (MG) and Small Tail Han sheep (STH) are two fat-tailed Chinese indigenous sheep breeds while DairyMeade and East Friesian (DS) are two thin-tailed dairy sheep breeds recently introduced to China. In this study, population genomics analysis was applied to identify candidate genes associated with sheep tails based on an in-depth whole-genome sequencing of MG, STH and DS. The selective signature analysis demonstrated that GLIS1 , LOC101117953 , PDGFD and T were in the significant divergent regions between DS and STH–MG. A nonsynonymous point mutation (g.27807636G>T) was found within GLIS1 in STH–MG and resulted in a Pro to Thr substitution. As a pro-adipogenic factor, GLIS1 may play critical roles in the mesodermal cell differentiation during fetal development affecting fat deposition in sheep tails. This study gives a new insight into the genetic basis of species-specific traits of sheep tails.
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Fat tail in sheep presents a valuable energy reserve that has historically facilitated adaptation to harsh environments. However, in modern intensive and semi-intensive sheep industry systems, breeds with leaner tails are more desirable. In the present study, RNA sequencing (RNA-Seq) was applied to determine the transcriptome profiles of tail fat tissues in two Chinese sheep breeds, fat-rumped Altay sheep and thin-tailed Xinjiang fine wool (XFW) sheep, with extreme fat tail phenotype difference. Then the differentially expressed genes (DEGs) and their sequence variations were further analyzed. In total, 21,527 genes were detected, among which 3,965 displayed significant expression variations in tail fat tissues of the two sheep breeds (P < 0.05), including 707 upregulated and 3,258 downregulated genes. Gene Ontology (GO) analysis disclosed that 198 DEGs were related to fat metabolism. In Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the majority of DEGs were significantly enriched in “adipocytokine signaling,” “PPAR signaling,” and “metabolic pathways” (P < 0.05); moreover, some genes were involved in multiple pathways. Among the 198 DEGs, 22 genes were markedly up- or downregulated in tail fat tissue of Altay sheep, indicating that these genes might be closely related to the fat tail trait of this breed. A total of 41,724 and 42,193 SNPs were detected in the transcriptomic data of tail fat tissues obtained from Altay and XFW sheep, respectively. The distribution of seven SNPs in the coding regions of the 22 candidate genes was further investigated in populations of three sheep breeds with distinct tail phenotypes. In particular, the g.18167532T/C (Oar_v3.1) mutation of the ATP-binding cassette transporter A1 (ABCA1) gene and g.57036072G/T (Oar_v3.1) mutation of the solute carrier family 27 member 2 (SLC27A2) gene showed significantly different distributions and were closely associated with tail phenotype (P < 0.05). The present study provides transcriptomic evidence explaining the differences in fat- and thin-tailed sheep breeds and reveals numerous DEGs and SNPs associated with tail phenotype. Our data provide a valuable theoretical basis for selection of lean-tailed sheep breeds.
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Background: The thin-tailed sheep breeds from Europe and the fat-tailed sheep breeds from China exhibit distinct phenotypic differences in fat deposition and meat production traits. However, the molecular mechanisms underlying gene expression related to these phenotypic differences are not well understood. Allele-specific expression (ASE) refers to the significant imbalance of expression levels of two parental alleles. Characterization of such events in F1 hybrid offspring generated from these two groups of sheep breeds can minimize the external factors influencing gene expression and reveal the variants with a cis -regulatory effect on gene expression. The aim of the present study was to investigate the genetic factors that influence different fat-deposition and meat production traits between thin- and fat-tailed sheep. Results: Fifteen F1 hybrids were generated from crosses between Texel and Kazakh sheep as the representative phenotypes of thin- and fat-tailed breeds, respectively. Totally, 33 whole genomes from F1 individuals and their parents were sequenced with an average depth of ~17.21× coverage per sample. ASE analysis results from 70 RNA-seq samples of adipose and skeleton muscle tissues showed 128 ASE candidate genes were related to the function of fat deposition and meat production traits. A genome-wide scan of selective sweeps was also conducted between these two groups of sheep breeds in an effort to identify genomic regions related to fat deposition and meat production, respectively. We detected signatures of selection in ASE genes associated with fat deposition (e.g., PDGFD ) and meat production traits (e.g., LRCC2 ). Further analysis suggested that PDGFD and LRCC2 genes were speculated to be causative genes for fat deposition and meat production traits in sheep, respectively. Furthermore, AMPK signaling pathway was significantly enriched in ASE genes related to fatty acid biosynthesis in both adipose and skeleton muscle tissues, while PPAR signaling pathway was significantly enriched in ASE genes related to lipid metabolism in adipose tissue. Conclusions: Our finding illustrates that the expression of identified ASE genes could potentially lead to the differences in traits of fat deposition and meat production between thin- and fat-tailed sheep. Keywords: allele-specific expression, phenotypic difference, thin- and fat-tailed sheep, whole-genome sequencing, transcriptome
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Different sheep breeds have evolved after initial domestication, generating various tail phenotypic patterns. The phenotypic diversity of sheep tail patterns offers ideal materials for comparative analysis of its genetic basis. Evolutionary biologists, animal geneticists, breeders, and producers have been curious to clearly understand the underlying genetics behind phenotypic differences in sheep tails. Understanding the causal gene(s) and mutation(s) underlying these differences will help probe an evolutionary riddle, improve animal production performance, promote animal welfare, and provide lessons that help comprehend human diseases related to fat deposition (i.e., obesity). Historically, fat tails have served as an adaptive response to aridification and climate change. However, the fat tail is currently associated with compromised mating and animal locomotion, fat distribution in the animal body, increased raising costs, reduced consumer preference, and other animal welfare issues such as tail docking. The developing genomic approaches provide unprecedented opportunities to determine causal variants underlying phenotypic differences among populations. In the last decade, researchers have performed several genomic investigations to assess the genomic causality underlying phenotypic variations in sheep tails. Various genes have been suggested with the prominence of several potentially significant causatives, including the BMP2 and PDGFD genes associated with the fat tail phenotype and the TBXT gene linked with the caudal vertebrae number and tail length. Although the potential genes related to sheep tail characteristics have been revealed, the causal variant(s) and mutation(s) of these high‐ranking candidate genes are still elusive and need further investigation. The review discusses the potential genes, sheds light on a knowledge gap, and provides possible investigative approaches that could help determine the specific genomic causatives of sheep tail patterns. Besides, characterizing and revealing the genetic determinism of sheep tails will help solve issues compromising sheep breeding and welfare in the future.
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