Litter size is an important component trait of doe
reproduction. By improving it, production efficiency and economic benefits
can be significantly provided. Genetic marker-assisted selection (MAS) based
on proven molecular indicators could enhance the efficacy of goat selection,
as well as litter size trait. Many molecular markers have been identified
that they can be used to improve litter size in different goat breeds.
However, the presence and value of these markers vary among goat breeds. In
the present study, we used the reported loci on other breeds of goat as
candidate loci to detect whether these loci appear in this Nubian goat
population; then we proceed to genotype and detect surrounding loci (50 bp)
by multiplex PCR and sequencing technology. As a result, 69
mutations (59 SNPs and 10 indels) were screened out from 23 candidate genes
in Nubian goat population, 12 loci were significantly associated with
the litter size of first-parity individuals; 5 loci were significantly
associated with the litter size of second-parity individuals; 3 loci
were significantly associated with the litter size of third-parity
individuals. In addition, five loci were significantly associated with the
average litter size. The additive effect value of KITLG: g.18047318 G>A in first parity, KITLG: g.18152042G>A in third parity, KISS-1: g.1341674
C>G in first parity, and GHR: g.32134187G>A in
second parity exceed more than 0.40, and the preponderant alleles are G, C,
A and G, respectively. Further, linkage disequilibrium analysis of 21 mutation
loci shows that 3 haplotype blocks are formed, and the litter size of
combination type AACC in KISS-1 gene and AAGG in KITLG gene are significantly lower
than that of other combinations genotype in first parity (P<0.05). These findings
can provide effective candidate DNA markers for selecting superior
individuals in Nubian goat breeding.
Introduction
The goat (Capra hircus) is believed to be one of the first domesticated ungulates that
underwent domestication approximately 10 000 years ago; therefore it has been a
witness to the historical progress of human civilization
(Naderi et al., 2008). A variety of natural or artificial
factors (e.g., environmental changes, human migration and socioeconomic
influences) have shaped the phenotypic diversity of goat, which is one of
the most important agricultural animals for various products (e.g., milk,
meat and fiber) to humans (Silpa et al. 2018; Li et al. 2012; Takada et al. 1997b). The Nubian goat has good adaptability and reproductive
performance with more than two kids per litter. They are farmed on a large
scale in southwest China and also used to improve local doe.
Litter size is considered a trait with low
heritability (h2=0.10-0.15) (Liu et al., 2019), and
traditional direct selection is ineffective in goat breeding
(Cui et al., 2018). With the rapid
development of animal molecular genetics and modern molecular biotechnology,
the analysis of single gene or chromosome fragments has become the main
object of analyzing fecundity traits. Marker-assisted selection (MAS), based
on relevant genetic variants, is used with traits of low heritability
(h2), such as those associated with reproduction (Yh et al., 2020). The combined efficiency is significantly higher than that achievable
by breeding based on the phenotype alone (Yuan et al., 2019). To
facilitate MAS application to litter size in the goat industry, critical
genetic variants causing phenotypic advantage should be verified, which is
more convenient and efficient. Therefore, it is an effective way to scan
single nucleotide polymorphisms (SNPs) in genes with known reproductive
physiology functions. Multiplex PCR targeted amplification sequencing
(MAT-seq) is an accurate, sensitive, widely applicable and economical method
for loci detection and genotyping, which is a multiplex PCR amplification
fragment compatible with Illumina HiSeq/MiSeq platform sequencing
technology (Yoshihiko et al., 2018; Grandell et al., 2016). It is a
good method to detect target sites or fragments (< 50 bp) and to
genotype important candidate genes.
Based on current literature, some genes that affect the secretion of
reproductive hormones in the hypothalamic–pituitary–testicular axis,
ovulation rate, embryonic/fetal survival and endometrial receptivity are
considered to be the main candidate genes affecting litter size (Xu et al., 2018). The influence of candidate genes on reproductive traits differs
considerably because these genes could affect the physiological pathways,
metabolism and phenotypic expression differentially with different
paracrine or autocrine effects (Wang et al., 2018b). The major focus of
goat breeding for genetic improvement greatly relies on candidate genes,
which influence reproductive traits so as to ascertain the functions of
genes as causing changes in and association with phenotypic values of the
trait, molecular structure analysis, expression profile, sequence
variability and their association with phenotypes. The selection for
favorable alleles of candidate genes could help the development of high
litter size goat breeding. Therefore, to provide an improved understanding
of the genetic complexity underlying the litter size in Nubian goat, the
objectives of the present study were the following: (1) to detect 43 previously reported
mutations in the 24 genes associated with the litter size of other goat
breeds (e.g., Shaanbei White Cashmere goat, Jining Grey goat, Boer goat) and
whether they appear in this Nubian goat population; (2) to investigate the effects
of loci involved in reproduction on litter size in Nubian goat; and (3) to
explore the effects of locus–locus combinations on litter size. These
information will be useful in identifying valuable genetic markers for MAS
in Nubian goat.
Schematic diagram of the experimental protocol across
times. GY: Guangxi Fusui Guangyang Agricultural and Livestock Co. Ltd.; LSJ:
Guangxi Lvshijie Agricultural investment co. Ltd.
Materials and methodsAnimals and DNA extraction
The study was approved by the ethics committee of Guangxi University
(Nanning, Guangxi, China) (approval number: 2016-Gxu205). A total of 355
Nubian doe (aged 12–36 months) were accessed from two farms (227 of
Guangxi Fusui Guangyang Agricultural and Livestock Co. Ltd. and 128 of Guangxi Lvshijie Agricultural investment co. Ltd.) in Guangxi Province, China. All doe were mated naturally and
raised under identical management conditions. Additionally, all selected
individuals were healthy and with detailed reproduction records (litter size
of first-, second- and third-parity individuals) from 2016 to 2018. The
experimental protocol across times is shown in Fig. 1. All efforts were
made to minimize any discomfort during blood collection. Approximately 5.0 mL of blood has been collected aseptically from the jugular vein
of each doe into 5 mL vacutainer tubes containing the anticoagulant,
ethylenediaminetetraacetic acid (EDTA-K2). Genomic DNA is extracted from
blood using the DNeasy Blood and QIAamp DNA extraction kit (Qiagen,
Germany). The quality of DNA is checked by a spectrophotometer taking ratio
of optical density (OD) value at 260 and 280 nm. The integrity of DNA was
detected by 1.0 % agarose gel electrophoresis. DNA samples were each
diluted to a working concentration of 30 ng/µL by TE buffer (pH 8.0)
and stored at -20 ∘C for DNA pooling and genotyping.
Genetics variants associated with the fecundity in goat.
GeneMolecular markerFounder breedsReferenceCSN1SI11 bp indelShaanbei White Cashmere goatWang et al. (2018a)CTNNB126 bp indelShaanbei White Cashmere goatZhang et al. (2018)GHR14 bp duplicated deletionShaanbei White Cashmere goatYang et al. (2017)GDF912 bp indel; Q320P and V397I; g.4093G>AShaanbei White Cashmere goat; Xinong Saanen dairy goat; Guanzhong dairy goat; Boer goatWang et al. (2017, 2019), An et al. (2013b)GNRH1g.3548A>GXinong Saanen dairy goat; Guanzhong dairy goat; Boer goatAn et al. (2013b)GNRHRg.-29T>GWest African Dwarf goatBemji et al. (2018)KDM6A16 bp and 5 bp indelShaanbei White Cashmere goatCui et al. (2018)KISS1g.2124T>A, g.2270C>T; T121AXinong Saanen dairy goat, Guanzhong dairy goat; Boer goat; Baladi goat, Zaraibi goat and Damascus goatAn et al. (2013c), El-Tarabany et al. (2017)KITLGg.13090G>T and g.13664C>AXinong Saanen dairy goat and Guanzhong dairy goatAn et al. (2015b)IGF1g.224A>G and g.227C>TMalabari goat and Attappady Black goatThomas et al. (2016)NGFA705GXinong Saanen dairy goat; Guanzhong dairy goatAn et al. (2013a)PDGFRB10 bp indelShaanbei White Cashmere goatYang et al. (2019)POU1F1c.682G>T and c.837T>CShaanbei White Cashmere goatZhu et al. (2019)PRLRg.151435C>T and g.173057T>C; g.62130C>TShaanbei White Cashmere goatAn et al. (2015a); Hou et al. (2014)MARCH17 bp, 15 bp, and 18 bp indelsShaanbei White Cashmere goatKang et al. (2019)CYMExon 8:c.T916CLaoshan dairy goatLai et al. (2016)CDH26Exon8:c.T1063C; c.G1035A; c.T1034CLaoshan dairy goatLai et al. (2016)NEDD4g.48710049Laoshan dairy goatLai et al. (2016)INHAc.C126G; c.C936TXinong Saanen dairy goat, Guanzhong dairy goat and Boer goatHou et al. (2012)FOLR1g.7884A>C;Boer goatHou et al. (2014)ATBF1g.25748G>AHainan Black goatZhang et al. (2015)SIRT3c.691C >TMalabari and Attappady Black goatSilpa et al. (2018)INHβAg.C936TXinong Saanen dairy goat, Guanzhong dairy goat and Boer goatHou et al. (2012)PGRg.74589762Nubian goatPreliminary workGene selection, primer design, DNA pool PCR amplification, polymorphism
identification
We hypothesized that molecular markers identified on goat breeds (e.g.,
Shaanbei White Cashmere goat, Jining Grey goat, Boer goat) that are
significantly associated with litter size would have the same effect on
Nubian goat. In the present study, genes with known biological functions in
follicular and ovarian development were first selected as target genes.
These genes or SNPs identified in this study and known to influence
reproductive traits in a goat breed were then selected as candidate genes or
markers. Therefore, 43 candidate loci (29 SNPs and 14 indels) on 24 genes
with known reproductive physiology functions in animals were selected as
potential candidate loci for goat litter size (Table 1). Among the 43
candidate loci (Table S1), 38 polymorphic loci (29 SNPs and 9 indels) of 23
genes were verified by Sanger sequencing of PCR products amplified from 5
DNA pools. These DNA pools comprised 30 randomly selected individual genomic
DNA samples as a polymerase chain reaction (PCR) template for amplification.
Specific primers for PCR were designed based on these candidate gene
sequences. The PCR was performed in a volume of 40 µL, containing 20 µL 2 × Taq PCR MasterMix, 1.5 µL each of forward and
reverse primers, 2 µL pool DNA (30 ng/µL), and 15 µL
double-distilled H2O. The PCR protocol was as follows: initial
denaturation for 3 min at 95 ∘C; denaturation for 15 s at
95 ∘C; annealing for 30 s at 50–60 ∘C; extension for
15–45 s at 72 ∘C; and a final extension for 5 min at 72 ∘C, with subsequent cooling to 4 ∘C. The PCR products were detected
by 2.0 % agarose gel electrophoresis. The purified PCR products were
sequenced using Sanger sequencing, and the sequences were compared using
DNASTAR and Chromas software to detect putative SNPs. The identified SNPs
were individually genotyped by the MTA-seq method (Sangon Biotech, Shanghai,
China). Three DNA samples were randomly selected from 355 samples to
genotype twice, and 2 blank samples (double-distilled water) were also
used to eliminate cross-contamination.
Multiplex PCR and sequencing to genotype and detect surrounding
loci (50 bp)
A panel which contains 38 target SNPs loci was designed (Table S2). Library
preparation is performed by two-step PCR. First round PCR reaction was set
up as follows: DNA (10 ng/µL) 2 µL; amplicon PCR forward primer mix
(10 µM) 1 µL; amplicon PCR reverse primer mix (10 µM) 1 µL; 2 × PCR Ready Mix 15 µL (total 25 µL) (Kapa HiFi Ready
Mix). The plate was sealed, and PCR was performed in a thermal instrument
(BIO-RAD, T100TM) using the following program: 1 cycle of denaturing at
98 ∘C for 5 min, first 8 cycles of denaturing at 98 ∘C
for 30 s, annealing at 50 ∘C for 30 s, elongation at 72 ∘C for 30 s, then 25 cycles of denaturing at 98 ∘C for 30 s,
annealing at 66 ∘C for 30 s, elongation at 72 ∘C for 30 s, extension at 72 ∘C for 5 min and finally held at 4 ∘C. The PCR products were checked by using electrophoresis in 1 % (w/v)
agarose gels in TBE buffer (Tris, boric acid, EDTA) stained with ethidium
bromide (EB) and visualized under UV light. Then we used AMPure XP beads to
purify the amplicon product. After that, the second-round PCR was performed.
PCR reaction was set up as follows: DNA (10 ng/µL) 2 µL; universal
P7 primer with barcode (10 µM) 1 µL; universal P5 primer (10 µM) 1 µL; 2 × PCR Ready Mix 15 µL (total 30 µL) (Kapa
HiFi Ready Mix). The plate was sealed and PCR was performed in a thermal
instrument (BIO-RAD, T100TM) by using the following program: 1 cycle of
denaturing at 95 ∘C for 3 min, then 5 cycles of denaturing at
94 ∘C for 30 s, annealing at 55 ∘C for 20 s, elongation
at 72 ∘C for 30 s, elongation at 72 ∘C for 30 s and a
final extension at 7 ∘C for 5 min. Then we used AMPure XP beads
to purify the amplicon product. The libraries were then quantified and
pooled. Paired-end sequencing of the library is performed on the HiSeqXTen
sequencers (Illumina, San Diego, CA).
Data quality control (QC) and SNP calling
Raw reads were filtered according to two steps: removing low-quality bases
from reads 3′ to 5′ (Q<20) by PRINSEQ-lite (v 0.20.3), and the
remaining clean data were mapped to the reference genome by BWA (version
0.7.13-r1126) with default parameters. A Perl script was written to
calculate each genotype of target locus. ANNOVAR (16 April 2018) was used to
detect genetic variants.
Association analysis
Statistical analyses were performed using SAS (V. 9.2) (SAS Institute Inc.).
The scattergrams of litter size at different parity were computed using
Microsoft Excel (v16.42). The general linear model (GLM) of the analysis of
variance (ANOVA) was used to analyze the relationships between the
different genotypes and litter size. Because these two goat farms are
national core farms, the geographical location is very close and all goats
were fed with standardized feeding method. The effect of the farm on the
number of goats' litter size is very small, which can be ignored. And
least-squares mean (LSM) was used for multiple comparisons in litter size of
goat with different loci genotypes according to the adjusted linear model:
Yijk=μ+Gi(+Pj)+Wk+εijk.
In this model, Yijk represents phenotypic value (litter size); μ
is the population mean; Pj shows the effect of the jth parity
(j= first, second, third or average); Gi represents the effect of
the ith genotypes (i= Ref/Ref, Ref/Alt, or Alt/Alt); Wk
represents the effect of the kth weather (k= cool or hot) and
εijk represents random error. P values < 0.05 were
considered to be significant. All statistically significant single
marker traits associated with LSM litter size were initially included in
further combined haplotypes analysis of the combined effects of different
genes on LSM litter size.
To estimate the mode of inheritance of the loci, the following functions
were used:
D=A1A0-1/2(A1A1+A0A0)A=1/2(A1A1-A0A0),
where D and A are the estimated values of dominant effect and additive
effect, respectively; A1A1, A1A0 and A0A0 are
the genotypic values of dominant homozygote, heterozygote and allozygote,
respectively. The genetic effects are partially recessive, incompletely
dominant, partially dominant, complete dominant and over dominant when
D<0, D=0, 0<D<|A|, D=|A| and D>|A|, respectively (Chong et al., 2018).
The substitution effect of alleles was calculated by linear regression model
SAS (V. 9.2):
Yij=μ+bXij+εij.
In this model, Yij represents phenotypic value (litter size); μ
is the population mean; b is the corresponding regression coefficient; Xij is the value of the proportion of each allele in the genotype and
εij represents random error.
Population parameter calculation
According to the results of genotype statistical analysis, genotypic and
allelic frequencies, polymorphism information content (PIC), genetic
homozygosity (Ho), genetic heterozygosity (He), effective allele number
(Ne) and the Hardy–Weinberg equilibrium (HWE) were analyzed by POPGENE software version 3.2. Distribution differences for loci genotypic frequencies in Nubian goats
were performed using the chi-square test in SPSS (Version 18.0) (IBM Corp.,
Armonk, NY, USA). Nubian goat populations with P>0.05 (chi-square test) were
considered to conform to the Hardy–Weinberg equilibrium.
Haplotype analysis
In the current study, only the significant SNPs in the same gene were used
to estimate the extent of linkage disequilibrium (LD) and haplotype blocks.
The extent of LD between significant SNP pairs and haplotype blocks was
estimated using the HaploView 4.2 program. Using the default setting of
HaploView 4.2 to run the program, the parameter r2 represents the
statistical association between the two SNPs. Generally, when r2>0.8, the two SNPs can be regarded as closely linked and can
be replaced each other. The association analysis between haplotype region
and litter size, Hap score and p value of Hap score were computed using R
software (haplo.states packages). The model of association analysis between
combined haplotypes and litter size is as follows in Eq. (1) except that
the genotype (G) is replaced by combined haplotypes.
ResultsLitter size trait analysis
Litter size from three parities was recorded in this study. The average
litter size at the first to third parity was 2.130 ± 0.022; the litter
size at the first parity was 2.065 ± 0.037; the second-parity litter
size was 2.168 ± 0.040; and the third-parity litter size was 2.343 ± 0.066. Scatterplots also show that litter size tended to increase
with the parity (Fig. 2).
Scatterplots of litter size for the three parities. A dot
represents a Nubian goat.
Verification of polymorphic loci, genotyping and quality control
Among the 43 candidate loci (29 SNPs and 14 indels) of 24 genes, 38
polymorphic loci (29 SNPs and 9 indels) of 23 genes were identified by DNA
pooling and Sanger sequencing (Table S2). Then, the primers of multiplex PCR
were redesigned and sequenced. The genotypic correlation coefficients of
three pairs of technical repeats equal to one and no genotyping signals were
detected in two blank samples, indicating that the MTA-seq method is
reliable. Sixty-nine mutations (59 SNPs and 10 indels) were screened out
from 23 candidate genes by multiplex PCR and sequencing technology (Date S3).
Single loci polymorphisms associated with litter size
Twenty-one mutations (19 SNPs and 2 indels) were significantly linked with
litter size of Nubian goat population. Twelve loci were significantly
associated with the litter size of first-parity individuals, including
POU1F1: g.34236170A>G, KITLG: g.18047318G>A and
g.18152042G>A, MARCHF1: g.1885620A>G, GDF9:
g.66027701C>T, NEDD4: g.48710049G>A, PGR:
g.74589762C>T, KISS-1: g.1341600A>G and
g.1341674C>G, GHR: g.32134266T>C, CTNNB1:
c.1684-238_1684-222delACTTGGCTGTGCACAGT and
c.1684-218_1684-211delGGAGGTGCinsT; five loci (KITLG:
g.18048657G>T, MARCHF1: g.1858739G>A, NEDD4:
g.48709794G>A, PGR: g.74589762C>T, GHR:
g.32134187G>A) were significantly associated with the litter
size of second-parity individuals; three loci (KITLG: g.18152042G>A,
ATBF1: g.39535967C>T and g:39535996C>T) were
significantly associated with the litter size of third-parity individuals.
In addition, five loci were significantly associated with the average litter
size, including INHA: g.2838305G>ins-C, g:28318345C>G
and g:28318349G>C, KITLG: g.18047318G>A, GHR:
g.32134266T>C. The additive effect values of KITLG: g.18047318
G>A in first parity and KITLG: g.18152042G>A in
third parity, KISS-1: g.1341674 C>G in first parity, GHR:
g.32134187G>A in second parity exceed more than 0.40, and the
preponderant alleles are G, C, A and G, respectively. These significantly
correlated loci detailed information is provided in Table 2. Other loci
not significantly associated with litter size in any parity were excluded
for further analysis.
Significantly association loci between the 21 polymorphism
loci (19 SNPs and 2 indels) and litter size in Nubian goat.
Litter size is presented as the least squares mean ± standard
error (SE) of phenotypic values. Different superscript letters indicate
significant differences between groups (P<0.05). “–” denotes
information is not available.
Population genetic parameters
The genotypes and allele frequencies, He, Ho, Ne, PIC and Hardy–Weinberg equilibrium test
P values, as well as other genetic parameters, associated with the
significantly correlated loci were calculated to determine the genotype
distribution among Nubian goat (Table 3). The significantly correlated loci
data indicated the minimum (maximum) values of He were 0.02 (0.46), the minimum
(maximum) value of Ho was 0.52 (0.98) and the minimum (maximum) value of Ne was 1.02
(1.94). Thirteen loci were low polymorphic status (PIC < 0.25); other
SNPs were moderate (0.25 < PIC < 0.50) or high polymorphic
(PIC > 0.50) status. Five loci deviated from Hardy–Weinberg equilibrium
(P<0.05).
Population genetic parameters of the significantly
associations loci in Nubian goat.
GeneChromosomeStartEndRefAltNo. of does Allelic frequencies Genotypic frequencies Population parameters PICχ2P valueTotalRef/RefRef/AltAlt/AltRefAltRef/RefRef/AltAlt/AltH0HENeChi squarePOU1F1NC_030808.13423617034236170AG35518214132AGAAAGGG0.590.411.700.330.390.820.710.290.510.400.09INHANC_030809.12831830528318305Gins_C355152203–Gins_C(0.29)GGG/ins_C–0.590.411.690.3356.910.000.710.430.572831834528318345CG355204151–CGCCCG–0.670.331.500.3325.900.000.790.210.570.432831834928318349GC35515216241GCGGGCCC0.550.451.820.350.050.980.660.340.430.460.12KITLGNC_030812.11804731818047318GA355267835GAGGGAAA0.770.231.290.200.260.880.870.130.750.230.011804865718048657GT35515616336GTGGGTTT0.560.441.790.340.480.790.670.330.440.460.101815204218152042GA3552441056GAGGGAAA0.720.281.380.241.970.370.840.160.690.300.02MARCHF1NC_030813.118587391858739GA3553478–GAGGGA–0.980.021.020.020.050.830.990.010.980.0218856201885620AG355291595AGAAAGGG0.820.181.210.160.990.610.900.100.820.170.01GDF9NC_030814.16602770166027701CT35518515416CTCCCTTT0.610.391.630.315.270.070.740.260.520.430.05NEDD4NC_030817.14870979448709794GA35512517060GAGGGAAA0.520.481.940.370.030.990.590.410.350.480.174871004948710049GA355297562GAGGGAAA 0.010.850.151.180.140.130.930.920.080.840.16PGRNC_030822.17458976274589762CT35523910412CTCCCTTT0.700.301.420.250.030.990.820.180.670.290.03KISS-1NC_030823.113416001341600AG35543166146AGAAAGGG0.540.461.840.350.160.920.350.650.120.470.4113416741341674CG355293260CGCCCGGG0.720.281.400.24341.080.000.830.170.830.010.17ATBF1NC_030825.13953596739535967CT355251104–CTCCCT–0.750.251.330.2210.460.000.850.150.710.293953599639535996CT355251104–CTCCCT–0.750.251.330.2210.460.000.850.150.710.29GHRNC_030827.13213418732134187GA355296572GAGGGAAA0.840.161.190.140.180.920.910.090.830.160.013213426632134266TC355304474TCTTTCCC0.860.141.170.131.930.380.920.080.860.130.01CTNNB1NC_030829.11371229713712314TACTTGGCTGTGCACAGTdel_TACTTGGCTGTGCACAGT3553496–TACTTGGCTGTGCACAGTdel_TACTTGGCTGTGCACAGTTTTACTTGGCTGTGCACAGT/del_TACTTGGCTGTGCACAGT–0.980.021.020.020.030.870.990.010.980.021371231813712325GGAGGTGCdel_GGAGGTGC3553496–GGAGGTGCdel_GGAGGTGCGGGGAGGTGC/del_GGAGGTGC–0.980.021.020.020.030.87
A, G, C and T of the gene frequency and genotype frequency column
respectively represent the adenine, guanine, cytosine and thymine; PIC,
polymorphic information content; Ho, homozygosity; He, heterozygosity; Ne,
number of effective allele; χ2 and p value denote the chi-square value
and
P value of Hardy–Weinberg equilibrium test; “–” denotes information is not available.
χ20.05=5.991, χ20.01=9.21, df = 1. P<0.05 means the difference is
significant, and P>0.05 means the difference is not significant.
Linkage disequilibrium plot of 15 significant association
loci. Intra-gene haplotypes (with blocks) are illustrated based on r2
measurements. The numbers in blocks represent r2 values. The degree of
LD is reflected by the color intensity of the block. Red boxes are tag
mutations included in association analysis. Linkage disequilibrium pattern
for the significant SNPs in KISS-1, KITLG and NEDD4 gene. Scale of red color indicates the
extend linkage disequilibrium (D′ value).
Haplotype analysis
The D′(r2) value is a factional indicator of LD and is
calculated in this study. Three haplotype blocks were identified in Nubian
goat. The D′ (r2) values are 1.00 (0.18), 1.00(0.075), 1.00(0.134) in
Block 1 (KISS-1: g.1341600 A>G and KISS-1: g.1341674 C>G), Block 2
(KITLG: g.18047318 G>A and KITLG: g.18048657 G>T) and
Block 3 (KITLG: g.18048657 G>T and KITLG: g.18152042 G>A), which are
closely linked (Fig. 3), respectively. Block 1 has three different
haplotypes: GC (64.5 %), AG (9.0 %) and AC (26.5 %).
Block 2 has three different haplotypes: GT (33.1 %), GG (53.8 %) and AG
(13.1 %). Block 3 has three different haplotypes: GG
(59.2 %), AA (8.5 %) and AG (32.4 %) (Table 4).
Haplotype association analysis of three blocks with litter
size.
Hap frequency: the haplotype frequency in the population;
Hap score: statistical measurement of correlation between each haplotype and
traits. The positive or negative value of the score indicates the frequency
of the haplotype in the population with positive or negative effects on the
litter size. P value: the progressive chi-square P value is calculated by the
square of score statistics.
Association analysis of multiple markers with reproductive
traits
According to the results of linkage disequilibrium, we combined the loci
with linkage disequilibrium and analyzed the correlation with litter size
(Table 5). Two combinations (Block 1 and Block 2) were founded to be
significantly associated with litter size in the first parity (P<0.05). The litter size of combination type AACC in KISS-1 gene and combination type
AAGG in KITLG gene were obviously lower than that of other combinations.
Discussion
MAS has been widely used in the selection of important economic characters
of pigs, chickens, cattle and sheep. In the same way, there has been some
research progress in goat. But the molecular markers found in goat are
various in different breeds. Considering research progress of goat molecular
markers, being significantly associated with litter size, we hypothesized
that molecular markers identified on goat breeds (e.g., Shaanbei White
Cashmere goat, Jining Grey goat, Boer goat) would have the same effect on
Nubian goat. Therefore, we verify the polymorphisms in Nubian goat
population of reported loci significantly associated with litter size and
then perform genotyping and detection of surrounding loci (50 bp). By doing
so, we are able to provide some scientific data to enable the efficient and
rapid development of the Nubian goat industry.
In this paper, 69 mutations were screened out from 23 candidate genes in
Nubian goat by multiplex PCR and sequencing technology. However, only 21
mutations were significantly linked with litter size of Nubian goat. Interestingly, we found seven loci without mutation homozygous (INHA: g.2838305G>ins-C and
g:28318345C>G; MARCHF1: g.1858739G>A; ATBF1:
g.39535967C>T and g:39535996C>T; CTNNB1:
c.1684-238_1684-222delACTTGGCTGTGCACAGT and
c.1684-218_1684-211delGGAGGTGCinsT). It may be caused by the fact that homozygous lethal mutation and/or mutant homozygote is sterile (Nicol et al., 2009). In
this sampling group of Nubian goat, 13 loci are of low polymorphic status
(PIC < 0.25), which suggests that they have low selection potential; 8
loci are of moderate (0.25 < PIC < 0.50) or high polymorphic (PIC > 0.50) status, which suggests that they could provide more
effective genetic information. Five loci obviously deviate from
Hardy–Weinberg equilibrium (p<0.05) in Nubian goat, which is
mainly due to artificial selection and/or nonrandom mating/or genetic drift
(Wang et al., 2015).
Significantly associations between the multiple markers and
litter size in Nubian goat.
Litter size is presented as the least squares mean ± standard
error (SE) of phenotypic values. Different superscript letters indicate
significant differences between groups (P<0.05).
In these 21 polymorphic loci, they are also significantly associated with
litter size of other goat population. For example, KITLG: g.18047318
G>A is revealingly associated with litter size of Xinong Saanen
(SN), Guanzhong (GZ) and Boer goat (An et al., 2012);
GDF9: g.66027701C>T is tellingly associated with litter size of
Inner Mongolia cashmere goat (Nicol et al., 2009) as well. In
quantitative genetics, the genetic variations of major economic traits are
explained by both additive effect and dominant effect. Additive effect is
considered to be a fixed component in the breeding, which also known as
breeding value. Dominant effect refers to the deviation between the effect
value of each gene and its additive effect value, which comes from the
effect produced by the interaction between alleles. This effect is the main
part of heterosis that can be inherited but not fixed in the breeding. The genetic effect value analysis of 21 significant loci showed that the additive effect values of KITLG as g.18047318 G>A in first parity, KITLG as g.18152042G>A in third parity, KISS-1 as g.1341674 C>G in first parity, and GHR as g.32134187G>A in second parity exceed more than 0.40, and the preponderant alleles are G, C, A and G, respectively.
In haplotype structure analysis, three linkage disequilibrium blocks are
found in these 21 loci. The haplotypes are integrated into a GLM model to
explore the effect of loci–loci combinations on litter size. The litter
size of combination type AACC in KISS-1 gene and AAGG in KITLG gene are expressively
lower than that of other combinations genotype in first born (P<0.05). Our
findings indicated that homozygous individuals with the KISS-1 gene AACC and
KITLG gene AAGG genotype should not be retained in MAS breeding programs for
improving the frequency of favorable alleles in the Nubian goat.
In the functional annotation of mutation loci (Table S4), there are five
SNPs (POU1F1: g.34236170A>G; GDF9: g.66027701C>T; NEDD4:
g.48710049G>A; PGR: g.74589762C>T; KISS-1: g.1341600
A>G) in the coding region (exon) which are meaningfully related to
litter size, and three (POU1F1, NEDD4, PGR) of them are synonymous mutations. They may
change the efficiency, stability and accuracy of mRNA alternative splicing
(Kimchisarfaty et al., 2007; Duan et al., 2003) as well as protein
translation and fold (Sauna and Kimchisarfaty, 2011) to affect litter
size in doe. Another two (KISS-1 and GDF9) directly change amino acids (KISS-1: p.Ala11Thr;
GDF9: p.Ala273Val) and could change protein function, which maybe affects the
secretion of reproductive hormone (e.g., GH, PRL, TSHβ, PGR) or/and early
folliculogenesis (Hanrahan et al., 2004; Silva et al., 2011) to
affect litter size in doe. There are 16 loci located in the non-coding
region (5_prime_UTR, 3_prime_UTR, intron). Three loci (INHA: g.2838305G>ins-C, g:28318345C>G and g:28318349G>C) are located
in 5_prime_UTR. It may lead to the change of
INHA gene frame shift and/or translation activity to affect estrus start and
litter size in doe (Zi et al., 2012). Two loci are
located in 3_prime_UTR; it may affect the
binding sites of miRNA and the stability of mRNA, subcellular localization
and translation level (Stark et al., 2005), which affect litter size in
doe. Eleven loci are located in intron, which mainly involve in expression
regulation and selective shearing. They may affect the cutting of precursor
mRNA, cause exon skipping or intron inclusion, and lead to the change of
protein sequence, which causes functional difference (Wang et al., 2014)
to affect litter size in doe. This suggests that these mutations have
potential functional significance, although the underlying mechanism remains
to be elucidated in Nubian goat.
Conclusions
In conclusion, 38 previously reported mutations of 23 reproduction genes
were identified in Nubian goat, which are importantly related to litter size
of other breeds. A total of 69 mutations (59 SNPs and 10 indels) were
detected by multiplex PCR, and 21 loci were significantly correlated with
litter size in Nubian goat. The combined haplotype analysis revealed that
AACC genotype in KISS-1 gene and AAGG genotype in KTLG gene are notably lower than that
of other combination genotypes in first born. This study suggests that these
loci can be considered as effective SNP markers for genomic selection in
Nubian goat breeding. However, further research with large sample sizes is
needed to confirm the genetic effects of these mutations and molecular
mechanisms.
Data availability
Data used and analyzed during this study are available from the corresponding author upon reasonable request.
Table S1 contains primers and PCR condition applied for
pooled-DNA sequencing for the 43 candidate loci. Table S2 contains the information
of 38 polymorphic loci (29 SNPs and 9 indels) of 23 genes identified by
DNA pooling and the primers of multiplex PCR. Date S3 contains the information of 69
polymorphic loci (59 SNPs and 10 indels). Table S4 contains the annotation on the
significant mutation loci linked with litter size in Nubian goat. The supplement related to this article is available online at: https://doi.org/10.5194/aab-64-375-2021-supplement.
Author contributions
These studies were designed by SZ and QJ. SZ,
XG, YJ and YS performed all the molecular experiments. PP, HX and YW
assisted in explaining the results and preparing samples. SZ, QJ and YH
performed the writing, review and editing. All the authors read and approved
the final manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors would like to thank Guangxi Fusui Guangyang Agricultural and Livestock Co. Ltd. and Guangxi Lvshijie Agricultural investment co. Ltd. in Guangxi
Province, China, for kindly providing blood samples and the data of litter
size.
Financial support
This research has been supported by the National Modern Agricultural Industrial Technology System (nycytxgxcxtd-09-01), Science and Technology Major Project of Guangxi (Guike-AB18221067).
Review statement
This paper was edited by Steffen Maak and reviewed by two anonymous referees.
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