Abstract
Reproductive traits are economically important for beef cattle
production; however, these traits are still a bottleneck in indicine
cattle since these animals typically reach puberty at older ages when
compared to taurine breeds. In addition, reproductive traits are
complex phenotypes, i.e., they are controlled by both the environment
and many small-effect genes involved in different pathways. In this
study, we conducted genome-wide association study (GWAS) and functional
analyses to identify important genes and pathways associated with
heifer rebreeding (HR) and with the number of calvings at 53 months of
age (NC53) in Nellore cows. A total of 142,878 and 244,311 phenotypes
for HR and NC53, respectively, and 2,925 animals genotyped with the
Illumina Bovine HD panel (Illumina^®, San Diego, CA, USA) were used in
GWAS applying the weighted single-step GBLUP (WssGBLUP) method. Several
genes associated with reproductive events were detected in the 20 most
important 1Mb windows for both traits. Significant pathways for HR and
NC53 were associated with lipid metabolism and immune processes,
respectively. MHC class II genes, detected on chromosome 23 (window
25-26Mb) for NC53, were significantly associated with pregnancy success
of Nellore cows. These genes have been proved previously to be
associated with reproductive traits such as mate choice in other breeds
and species. Our results suggest that genes associated with the
reproductive traits HR and NC53 may be involved in embryo development
in mammalian species. Furthermore, some genes associated with mate
choice may affect pregnancy success in Nellore cattle.
Introduction
Heifer rebreeding (HR) is the ability of a primiparous cow to rebreed
after having the first calving—is an important trait in beef cattle
production. Heifers that conceive soon after the first calving permit a
fast return on investments. HR has been considered as a selection
criterion in some breeding programs, particularly in indicine breeds
[[30]1, [31]2] as cows of these breeds usually present low rebreeding
rates [[32]3]. This trait shows a high genetic correlation with other
important reproductive traits such as age at first calving [[33]4] and
stayability [[34]5].
An alternative selection criterion for HR is the number of calving at
53 mo (NC53) [[35]6]. In addition to discriminating between cows with 2
calves and those that did not rebreed as primiparous (1 calf), NC53
also allows considering in the genetic evaluation heifers that did not
get pregnant. Compared with stayability, NC53 allows more emphasis on
rebreeding of primiparous cows, one of the main bottlenecks for
improving efficiency of beef cows in the tropics, besides allowing
anticipating the genetic evaluation of sires using information of their
progeny.
Despite the economic importance of HR and NC53, the genetic improvement
of these traits is challenging because they present low heritability
[[36]5, [37]6], are expressed relatively late in life and just in
females, which present lower selection intensity compared to males. As
a consequence, genetic evaluations for these traits using only pedigree
and phenotypic information result in expected breeding values with low
accuracy. Trying to overcome this, genome-wide association studies
(GWAS) have been performed with the hope to detect genomic regions
associated to reproductive traits, aiming to use this information as a
complementary tool to increase genetic gain.
In general, GWAS of reproductive traits have confirmed the polygenic
nature of these traits. Nevertheless, some studies have identified
important candidate genes for reproductive traits of beef cattle
[[38]7, [39]8, [40]9, [41]10, [42]11]. Conducting GWAS in independent
populations is important to reinforce the association of some genomic
regions with the trait of interest. When a genomic region is detected
as important by different studies the evidence that this region harbor
a true QTL is supposedly increased. Carrying out function analyses with
the candidate genes may also help to unravel pathways and key genes
involved in the expression of the trait of interest. In this study, we
conducted GWAS and functional analyses aiming to identify important
candidate genes and pathways associated with HR and NC53 in Bos indicus
Nellore cows.
Material and methods
Phenotypes
Phenotypic information of Nellore cows was obtained from the Aliança
Nelore database. The cows were born from 1984 to 2010 and were raised
on 188 different commercial farms located in the southeastern, western
and central regions of Brazil and in Paraguay. The feeding system
adopted by these farms basically consists of tropical pastures, mineral
salt and water ad libitum. In the dry season, the cows usually receive
mineral supplementation.
During the breeding season, which lasts about 90 days and usually
occurs in the rainy period, the heifers are either artificially
inseminated or naturally mated. Generally, the first mating of heifers
occurs at about 26 months of age, although some herds expose the
heifers earlier at around 14–18 months of age in an anticipated mating
season. In the data analyzed, 46.6% (66,643) of the heifers were
exposed early, corresponding to an early pregnancy rate of 21.4%
(14,287). Heifers that were exposed early and did not conceive had a
second chance at about 26 months of age in the regular mating season,
and all heifers that did not get pregnant during this period were
culled, including those that were exposed for the first time. During
the breeding season, the cows were either artificially inseminated or
naturally mated using single or multiple sires (4–5 sires). There were
48.08%, 47.58% and 4.35% of calves born from artificial insemination,
multiple sires and single sires, respectively.
Heifer rebreeding was defined as success (1) or failure (0), i.e.,
heifers that calved or not, respectively, since they had produced the
first calf. NC53 was defined as 0, 1 or 2 for those heifers that did
not have any calf, one calf or two calves at 53 months of age, given
they had the opportunity to reach this age and had performance records
until long-yearling age [[43]6].
During editing, heifers with age at first calving less than 21 or
greater than 40 months, age at second calving less than 32 or greater
than 53 months, and a calving interval less than 11 months were
excluded. The remaining number of phenotypes after editing was 142,878
and 244,311 for HR and NC53, respectively. For HR, 45.1% of the heifers
failed and 54.9% succeeded to rebreed. For NC53, 38.0%, 28.6% and 33.4%
had 0, 1 or 2 calves, respectively. The pedigree file for the two
traits contained 369,878 animals distributed over five generations.
Genotypes
A total of 2,925 animals were genotyped with the Illumina Bovine HD
panel (Illumina^®, San Diego, CA, USA). These animals included 2,212
Nellore heifers/cows from 12 different herds and 713 Nellore sires that
had an average of 73.6 offspring evaluated for HR and NC53. The
genotyped heifers and sires were born from 2002 to 2009 and from 1965
to 2006, respectively.
Quality control of genotypes was performed excluding single nucleotide
polymorphisms (SNPs) from non-autosomal regions, SNPs mapped to the
same position, and SNPs with a p-value for Hardy-Weinberg equilibrium <
10^−5, a GC score < 0.15, a call rate < 0.95 and a minor allele
frequency < 0.02. Samples with a call rate < 0.9 were discarded. The
remaining number of SNPs and then samples after quality control was
409,376 and 2,923, respectively.
Statistical analysis
The SNP marker effects were estimated using the weighted single-step
GBLUP (WssGBLUP) method proposed by Wang et al. [[44]12]. This method
was chosen because it permits to combine pedigree, phenotypic and
genomic information in a single step, weighting the SNP marker effects
according to the variance explained by each SNP, i.e., a higher weight
is attributed to the SNP that explains a higher proportion of the
genetic variance. This method is particularly appealing when there are
many more phenotypes than genotypes as in the present study. Phenotypic
information from non-genotyped animals allows predicting more
accurately the genetic merit of genotyped animals (a[g]). As the SNP
effect estimates from WssGBLUP are computed as a function of a[g],
better predictions of a[g] would ultimately result in better estimation
of SNP effects [[45]13]. The WssGBLUP analyses were run using the
BLUPF90 family programs [[46]14]. The method first computes the
breeding values then the SNP effects, as described below.
Predicted breeding values were obtained with the following threshold
animal model [[47]15]:
[MATH:
y=Xβ+Zaa+e, :MATH]
where y is a vector of underlying liabilities for HR and NC53, β is a
vector of fixed effects of the contemporary group, a is a vector of
random additive direct genetic effects (breeding values), X and Z[a]
are incidence matrices relating elements in β and a to y, respectively,
and e is the vector of random residuals. Contemporary groups were
defined by concatenating the information of herd, year and season of
birth, and weaning and yearling management groups of the heifers.
Contemporary groups containing fewer than five heifers and without
variability in HR or NC53, i.e., groups consisting of animals with the
same categorical response, were excluded from the data. The underlying
liabilities for HR and NC53 were defined as follows: HR = 0, if y < t1;
HR = 1, if y > t1; NC53 = 0, if y < t1; NC53 = 1, if t1 < y < t2, and
NC53 = 2, if y > t2, where t1 and t2 are the thresholds corresponding
to the discontinuity in the observed scale of HR and NC53.
The covariance between a and e was assumed to be absent and their
variances were equal to
[MATH:
Hσa2 :MATH]
and
[MATH:
Iσe2 :MATH]
, respectively, where
[MATH: σa2
:MATH]
and
[MATH: σe2
:MATH]
are the additive direct and residual variances, respectively, H is the
matrix which combines pedigree and genomic information [[48]16], and I
is an identity matrix. Since the variable in the underlying
distribution is not observable, the parameterization
[MATH:
σe2=1<
/mn> :MATH]
was adopted [[49]17].
The parameter estimates of the threshold model were obtained under a
Bayesian framework using the THRGIBBS1F90 Gibbs sampling program
[[50]18]. Default prior distributions were assumed for the variance
components and for the fixed and random effects. The Gibbs sampler was
run in a single chain of 500,000 iterations, with a burn-in period of
50,000 and a thinning interval of 50 iterations, totaling 9,000
posterior samples for each parameter to be estimated. The posterior
means of the samples were used as the parameter estimates. The
convergence of Monte Carlo chains of
[MATH: σa2
:MATH]
and heritability was evaluated using the postGibbsf90 software [[51]14]
and the Geweke test of the BOA R package [[52]19].
After computing the breeding values, the solutions of SNP effects (û)
were then obtained according to VanRaden et al. [[53]20] and Stranden &
Garrick [[54]21]: û = DZ′[ZDZ′]^−1 â[g], where D is a diagonal matrix
with weights for SNP effects, Z is a matrix relating genotypes of each
locus, and â[g] is the vector of predicted breeding values of genotyped
animals. The SNP effects breeding values, the D matrix and the SNP
effects were iteratively recomputed over two iterations, as suggested
by Wang et al. [[55]12]. This number of iterations was chosen based on
the results of a simulation study [[56]13].
The diagonal elements of D (d[i]) were calculated as: d[i] =
û[i]^2p[i](1-p[i]), where û[i] is the allele substitution effect of the
i^th marker estimated from the previous iteration, and p[i] is the
allele frequency of the second allele of the i^th marker [[57]12].
Prior to recomputing û, the D matrix was normalized to enforce the
total genetic variance to be constant across iterations.
Detection of significant windows and functional groups
The proportion of variance explained by SNPs within non-overlapping
consecutive 1-Mb windows was evaluated for both traits. A total of
2,523 windows spanning all autosomes were considered, with an average
density of 162±48 SNPs per window. [58]S1 and [59]S2 Tables show the
top 20 significant windows for HR and NC53, respectively. Annotated
genes located within these windows were further inspected. The list of
genes was provided by the NCBI Map Viewer tool
([60]www.ncbi.nlm.nih.gov/mapview/) using the Bos taurus Annotation
Release 103 and Bos taurus UMD 3.1 as reference assembly.
The DAVID software [[61]22, [62]23], ClueGO program [[63]24] and
Cytoscape plug-in [[64]25] were used to group genes according to
similarity of the biological processes in which they are involved,
aiming to verify if these functional clusters were related to
reproductive events.
Results and discussion
Heritability estimates
The Geweke test [[65]26] indicated convergence of the chains ([66]Table
1). The heritability was 0.19 for HR and NC53 ([67]Table 1), in
agreement with the estimates reported in other studies on the Nellore
breed [[68]2, [69]5, [70]6]. This result indicates that, in addition to
being highly influenced by the environment, HR and NC53 are affected by
an additive genetic component, a finding justifying the execution of
GWAS.
Table 1. Estimates of marginal posterior distributions of heritability for
heifer rebreeding (HR) and number of calves at 53 months of age (NC53) of
Nelore cattle.
HR NC53
Heritability[71]^a 0.194 ± 0.013 0.185 ± 0.008
HPD 95% 0.168–0.218 0.171–0.200
z-score[72]^b -0.576 -0.989
p-value[73]^b 0.565 0.323
ESS 149.3 388.1
[74]Open in a new tab
HPD 95%, 95% highest posterior density interval (inferior—superior);
ESS, Effective Sample Size.
^aMean±s.d.
^bStatistics of Geweke Test.
Significant windows
[75]S1 and [76]S2 Tables show the BTA and position of the top 20 1-Mb
significant windows for HR and NC53, respectively, as well as the
percentage of genetic additive variance explained by each window and
the genes within in each window. All significant genes are described
below.
In [77]S1 Table, window 71-72Mb of BTA11 harbors the FOSL2 gene related
to endometrial decidualization [[78]27], RBKS associated with oocyte
maturation [[79]28], and BRE associated with steroidogenesis in the
ovary and maintenance of placental function [[80]29]. The subsequent
window of BTA11 (72-73Mb) harbors important genes previously reported
to be associated with reproductive events, such as SLC30A3 [[81]30] and
CAD [[82]31] described as estrogen receptors, CIB4 associated with male
sheep fecundity [[83]32], EMILIN1 associated with placentation and
trophoblast invasion in the uterine wall [[84]33], TCF23 which plays an
important role in endometrial decidualization [[85]34], UCN associated
with steroidogenesis and maintenance of placental function which is
expressed in mature mouse spermatozoa and rat epididymis [[86]35,
[87]36, [88]37], and IFT172, PREB and SNX17 which are associated with
neural development [[89]38, [90]39, [91]40, [92]41]. Furthermore,
window 49-50Mb of BTA17 has been reported to be associated with age at
first calving in Nellore cows [[93]13].
As shown in [94]S2 Table, window 6-7Mb of BTA3 harbors the DDR2 gene,
which has been associated with first calving and HR in Nellore cattle
[[95]10] in a study that used a subset of the present dataset. Window
39-40Mb of BTA9 harbors the AMD1 gene, which is overexpressed in bovine
oocyte and cumulus cells [[96]42]. In addition, the AMD1 protein was
located in luminal and epithelial cells of bovine endometrium [[97]43].
This window also harbors FYN associated with mouse oocyte maturation
[[98]44] and maternal-fetal immune tolerance in humans and mice
[[99]45], REV3L which contributes to genome stability during neoplastic
transformation and progression in mice—targeted disruption of this gene
results in lethality during midgestation [[100]46, [101]47], and
SLC16A10 which is differentially expressed in human placenta at
mid-gestation, regulating embryonic development and growth [[102]48].
Window 70-71Mb of BTA 17 was significant for both traits. This window
harbors CHEK2 which is down-regulated in human oocytes cryopreserved by
slow freezing, reducing oocyte development competence [[103]49], and
plays an important role in DNA damage repair in mouse oocyte meiosis
[[104]50], and XBP1, a regulator gene associated with endoplasmic
reticulum stress, influencing oocyte maturation and embryo development
[[105]51]. In pigs, XBP1 expression promoted oocyte maturation,
embryonic genome activation and early embryonic development in vitro
[[106]52]. In cattle, XBP1 is involved in corpus luteum development and
maintenance [[107]53], and is upregulated in large follicles [[108]54].
Window 18-19Mb of BTA26 harbors FRAT1 which is differentially expressed
during the secretory phases of the human endometrium [[109]55], and
PGAM1 related to spermatogenesis success in mice [[110]56] whose
expression was found to be increased in cows with postpartum infection
caused by pathogenic bacteria [[111]57].
Some genes (SLIT1, TNR, FAM181B, IFT172, PPM1G, SNX17, and PREB)
associated with neural development [[112]38, [113]39, [114]40, [115]41,
[116]58, [117]59, [118]60, [119]61, [120]62, [121]63] were also found
in the top 20 significant windows for both traits. Costa et al.
[[122]10] also detected genes related to neural development that were
associated with reproductive traits. Neurulation is an important and
one of the first events of embryogenesis [[123]64]. Failure in this
stage can result in a non-viable pregnancy, a fact that would explain
this association. Genes associated with male fertility [[124]32,
[125]36, [126]37, [127]56] were also found within the top 20 windows
(CIB4, UCN, and PGAM1), a finding that would explain the genetic
correlation between male and female reproductive traits as reported by
Irano et al. [[128]11]. Another possible explanation is that sperm
quality influences pregnancy success. Thus, these genes may be
associated with andrological parameters that also improve conception
rates [[129]65].
Functional gene grouping
The top GO terms for the five functional groups with the highest
enrichment score for HR and NC53 obtained by DAVID analyses are shown
in Tables [130]2 and [131]3, respectively. The genes associated with HR
were generally distributed in pathways related to metabolism and
molecular functions. The top GO term for HR was “intracellular
organelle lumen”. Functional group 2 (fatty acid metabolism) contained
genes that play a role in the catabolism of short- (ACADS on BTA17 at
65-66Mb) and long-chain (HADHA and HADHB on BTA11 at 73-74Mb) fatty
acids. The supply of lipophilic compounds such as fatty acids and some
vitamins across the placenta influences growth and fetal fat mass
formation [[132]66]. Moreover, fatty acids are precursors of many sex
hormones [[133]67] and may influence the estrous cycle and pregnancy
maintenance.
Table 2. Gene category and pathway enrichment analysis of HR genes.
Functional groups Top Go Term (Id~Name) Ontology ES FDR
1 GO: 0070013≈ Intracellular Organelle lumen Cellular Component 1.17
5.4E1
2 Kegg: Fatty acid metabolism 1.08 2.0E1
3 Zinc finger region: RING-type 0.73 3.5E1
4 GO:0005509~Calcium ion binding Molecular Function 0.60 9.4E1
5 Microtubule 0.55 8.9E1
[134]Open in a new tab
ES, Enrichment Score; FDR, False Discovery Rate.
Table 3. Gene category and pathway enrichment analysis of NC53 genes.
Functional groups Top Go Term (Id~Name) Ontology ES FDR
1 Go:0042613~MHC class II Protein complex Cellular Component 7.81
7.3E-2
2 Kegg: Asthma 5.10 1.8E-4
3 GO: 0000166~Nucleotide binding Molecular Function 0.93 7.0E1
4 GO: 0046872~Metal ion binding Molecular Function 0.52 9.8E1
5 GO: 0006915~Apoptotic process Biological Process 0.40 1.0E2
[135]Open in a new tab
ES, Enrichment Score; FDR, False Discovery Rate.
Functional group 5 (microtubule) contained the DYNLL1, KIF3C and TRIM54
genes (BTA17 at 65-66Mb, BTA11 at 73–74 Mb and BTA11 at 72-73Mb,
respectively). The DYNLL1 and KIF3C genes encode two motor proteins
called kinesins and dyneins that regulate intracellular transport
through microtubules. These genes are associated with the mitotic
process and play important roles in embryo development and cell
migration and differentiation [[136]68, [137]69, [138]70, [139]71].
Microtubules are responsible for many intracellular movements,
including chromosome separation during cell division. When a cell
enters mitosis, the disassembly rate of microtubules increases about
ten-fold and the number of microtubules increases by five- to ten-fold
[[140]72]. Since mitosis is also essential for embryonic growth and
development [[141]73], these microtubule genes affect the success of
embryo development.
For NC53, most genes were associated with immune processes. The top GO
term for this trait was “MHC class II protein complex”, which is
directly associated with the immune system and pregnancy success
[[142]74]. This group of genes will be further discussed below.
The Cytoscape software was used to cluster the genes into functional
groups. For HR, 25 GO terms/groups were generated ([143]Fig 1) and
three of them were statistically oversignificant (p < 0.001), namely
“beta-oxidation of hexanoyl-CoA to butanoyl-CoA” (the most significant
with p = 2.77E-6), “mitochondrial fatty acid beta-oxidation of
saturated fatty acids”, and “mitochondrial fatty acid beta-oxidation”.
These pathways are associated with lipid metabolism, which was a
significant functional group in DAVID analysis. The genes grouped in
these pathways were the same as those identified by DAVID analysis
(ACADS, HADHA, and HADHB).
Fig 1. Gene network of heifer rebreeding.
[144]Fig 1
[145]Open in a new tab
Each color represents a functional group that harbored related
subgroups and genes.
Metabolism of fatty acids is directly associated with the pregnancy
success. Females with no satisfactory corporal condition that do not
receive a good nutrition supplementation after calving have higher
interval from calving to estrus, decrease conception rate and produce
lighter calves [[146]75]. The significant pathways related to lipid
metabolism might suggest that there are cows that accumulate more fat
in comparison to others. As a consequence, they have a better
rebreeding performance.
The “Olfactory signaling pathways” was a significant cluster (0.05 <
p-value < 0.01). Genes clustered in this group were: OR51D1, OR51E1,
OR52B4, OR52K1, OR52K2 (BTA15, at 51-52Mb). Two of these, OR51D1 and
OR52K1 were found to be expressed in female and male primordial germ
cells and in unfertilized human oocytes, participating in gamete
production [[147]76]. Furthermore, olfactory receptors integrate many
metabolic processes and some of them play important role in
reproduction [[148]76].
“Oocyte meiosis” is also a pathway extremely related with reproductive
events. Despite this pathway was not significant, it clustered three
genes associated with HR: FBXW11, PPP1CB, SPDYA. The PPP1CB gene is
also associated to “Olfactory” pathway. This gene plays a role in
regulation of oocytes chromatin condensation in mouse [[149]77].
For NC53, 29 GO terms/groups were generated, 19 of them were over
significant, that are presented in dark-blue in [150]Fig 2. “Asthma”
was the group that presented the higher significance level (p-value =
1.12E-9). The signalized genes from the disease pathways were the ones
from the Major Histocompatibility Complex Class II (MHC II). Shortly,
the MHC complex codifies cell surface glycoproteins that present the
peptide antigen to the T-cells [[151]78]. MHC class II molecules can
stimulate the proliferation of some inflammatory cytokines contributing
to chronic inflammation responses, as observed in some human autoimmune
diseases [[152]79,[153]80]. It is indirectly related to the
maternal-fetal tolerance and embryo development [[154]81]. This
association between MHC class II and autoimmune diseases could explain
why human diseases pathways were found by Cytoscape analyses.
Fig 2. Gene network of number of calving at 53 months of age.
[155]Fig 2
[156]Open in a new tab
Each color represents a functional group that harbored related
subgroups and genes.
MHC is composed of a group of highly polymorphic genes and its
variability is important to enable the immune system to recognize and
act against pathogens [[157]82]. The genes listed were BLA-DQB,
BOLA-DQA1, BOLA-DQA2, BOLA-DQB, BOLA-DRA, and HLA-DQA1, located on
BTA23 at 25-26Mb.
In addition to the important immune function of MHC, evidence indicates
that males and females choose MHC-dissimilar sexual partners [[158]83],
which works as a self/nonself perception to guarantee that their
offspring will be immunogenetically viable, consequently ensuring the
survival of the species. The more polymorphic the MHC, the higher the
probability of fighting efficiently different diseases. Aarnink et al.
[[159]74] observed a low survival rate in a Mauritian-origin macaque
population with a low level of polymorphism in MHC genes. Negative
natural selection acts against offspring whose parents have similar MHC
alleles. In beef cattle reproduction, the preference of some bulls for
certain cows in single/multiple sire mating might be explained by the
action of MHC genes.
The choice of the sexual partner occurs before copulation and is guided
by dissimilarity in the MHC genes of the partners that are able to
recognize MHC variability based on body odor [[160]83]. Moreover,
MHC-based mate choice continues during sperm-oocyte interaction
(chemotaxis) after copulation and is mediated by olfactory receptors
present on the gametes [[161]76]. The olfactory receptors of the
gametes “reflect” the MHC variability of the oocyte/sperm and guide
fecundation between the genetically most distant gametes.
The association of MHC genes and olfactory receptors with NC53 allows
raising some hypotheses linked to cattle production. For example,
artificial insemination does not allow mate choice, suggesting that a
dam could be inseminated with semen from a sire with similar MHC
alleles. In this case, selection for MHC variability would only be
possible during fecundation (gamete level). If MHC genes are similar,
fecundation may not occur. Thus, unsuccessful rebreeding may also be
explained by the similarity of MHC genes between sexual partners. The
detection of some olfactory receptor genes and olfactory pathways
associated with HR supports this hypothesis.
Evidently, these hypotheses need to be confirmed, but some changes in
cattle handling could be proposed until the influence of MHC has been
established. The replacement of sire semen after pregnancy failure and
the use of natural mating for part of the females might promote higher
pregnancy rates since these practices tend to better respect the
natural MHC selection process. MHC genes are not the only reason for
pregnancy failure, but the present results suggest that they may
influence pregnancy rates and therefore deserve further attention.
Conclusion
Genes that play important roles in embryo development, lipid metabolism
and sexual partner choice were associated with reproductive traits in
Nellore cattle. Further studies may confirm the biological role of
these genes behind the pregnancy success in mammals.
Supporting information
S1 Table. Genes harbored in the top20 1Mb windows for heifer
rebreeding.
(DOCX)
[162]Click here for additional data file.^ (18KB, docx)
S2 Table. Genes harbored in the top20 1Mb windows for number of calving
at 53 months of age.
(DOCX)
[163]Click here for additional data file.^ (17.2KB, docx)
Acknowledgments