Content uploaded by Rayner Gonzalez-Prendes
Author content
All content in this area was uploaded by Rayner Gonzalez-Prendes on Nov 01, 2022
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
RESEARCH ARTICLE
Integrative QTL mapping and selection
signatures in Groningen White Headed cattle
inferred from whole-genome sequences
Rayner Gonzalez-PrendesID
1
*, Catarina Ginja
2
, Juha KantanenID
3
, Nasser GhanemID
4
,
Donald R. KugonzaID
5
, Mahlako L. Makgahlela
6,7
, Martien A. M. GroenenID
1
, Richard P. M.
A. Crooijmans
1
1Animal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands,
2BIOPOLIS/CIBIO/ InBIO, Research Center in Biodiversity and Genetic Resources, University of Porto,
Vairão, Portugal, 3Natural Resources Institute Finland, Jokioinen, Finland, 4Animal Production
Department, Faculty of Agriculture, Cairo University, Giza, Egypt, 5Department of Agricultural Production,
College of Agricultural and Environmental Sciences, Makerere University, Kampala, Uganda, 6Agricultural
Research Council-Animal Production Institute, Irene, South Africa, 7Department of Animal, Wildlife and
Grassland Sciences, University of the Free State, Bloemfontein, South Africa
*rayner.gonzalezprendes@wur.nl
Abstract
Here, we aimed to identify and characterize genomic regions that differ between Groningen
White Headed (GWH) breed and other cattle, and in particular to identify candidate genes
associated with coat color and/or eye-protective phenotypes. Firstly, whole genome
sequences of 170 animals from eight breeds were used to evaluate the genetic structure of
the GWH in relation to other cattle breeds by carrying out principal components and model-
based clustering analyses. Secondly, the candidate genomic regions were identified by inte-
grating the findings from: a) a genome-wide association study using GWH, other white
headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype
(Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b)
scans for specific signatures of selection in GWH cattle by comparison with four other Dutch
traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and
the commercial Holstein Friesian; and c) detection of candidate genes identified via these
approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2)
resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of
breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep
Red (558,909). By integrating the results, we identified five genomic regions under selection
on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and
BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes asso-
ciated with retinal degeneration (e.g., CWC27 and CLUAP1), ultravioletprotection (e.g.,
ERCC8), and pigmentation (e.g. PDE4D) which are probably associated with the GWH spe-
cific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmen-
tation (ACOP). Our results will assist in characterizing the molecular basis of GWH
phenotypes and the biological implications of its adaptation.
PLOS ONE
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 1 / 19
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Gonzalez-Prendes R, Ginja C, Kantanen J,
Ghanem N, Kugonza DR, Makgahlela ML, et al.
(2022) Integrative QTL mapping and selection
signatures in Groningen White Headed cattle
inferred from whole-genome sequences. PLoS
ONE 17(10): e0276309. https://doi.org/10.1371/
journal.pone.0276309
Editor: Arnar Palsson, University of Iceland,
ICELAND
Received: August 4, 2021
Accepted: October 4, 2022
Published: October 26, 2022
Copyright: ©2022 Gonzalez-Prendes et al. This is
an open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The VCF files with
variant from all breeds will be available at https://
zenodo.org/deposit/6616286. Raw reads will be
accessed on https://www.ebi.ac.uk with the
Accession number PRJEB56301 before
publication.
Funding: The research presented in this publication
was funded by the Long-term EU-Africa research
and innovation Partnership on food and nutrition
security and sustainable Agriculture (LEAP-Agri) as
Introduction
Traditional native breeds are an important source of genetic variability adapted to local envi-
ronments. They might harbor genetic variants unique to the breed due to ecosystem adapta-
tion and, e.g. provide resistance to local diseases and/or extreme climatic conditions. Detailed
analyses of the genomic structure of those native breeds can contribute to improving the
knowledge about breed formation, and identify genes and variants with a significant impact
on the adaptation processes that shaped animal phenotypes [1–4]. This information can be
used to set up optimum breeding programs for the management of livestock genomic
resources.
The skin and coat color variation in livestock breeds are important traits that impact the
adaptation of breeds to the environment [5–8]. In the past years, numerous research projects,
such as genome-wide association studies (GWAS) [9–11] and whole-genome selective sweeps
identification [3,12] have been performed to pinpoint candidate genomic regions with signifi-
cant effects on skin and coat color variation [6,9,10,13–15]. The combination of several
sources of information can improve the power of candidate gene identification by reducing
the number of QTLs and their intervals, as well as providing additional insights into the stud-
ied biological processes [16,17].
The Groningen White Headed (GWH) breed, originated from the Groningen province of
the Netherlands, is a dual-purpose cattle known for its longevity, minimal veterinary costs,
and high fertility rate [18]. The first GWH animal was registered in the herd book in 1875, and
in 1999, the breed was considered to be endangered with approximately 830 purebreed ani-
mals [19]. Recent interest in functional traits such as fertility or resistance may open up new
opportunities for the expansion of this breed [18]. GWH animals are easily distinguished by
their phenotype, that is, solid black or red coat color, white face, and colored areas around the
eyes [18,19].
In cattle, Ambilateral Circumocular Pigmentation (ACOP) can be distinguished by a white
face and colored areas around the eyes in breeds such as the GWH [19] and Fleckvieh [9]. The
presence of this phenotype can reduce the susceptibility to eye lesions [20]. It is well-known
that non-pigmented animals have a higher incidence of eye lesions than animals with eye mar-
gin pigmentation [9,21]. A plausible explanation for this is that cattle with a non-pigmented
eye margin are exposed to more ultraviolet(UV) radiation in this region [9], which would be
more intense and harmful in the tropical areas [22].
The molecular genetic background of GWH breed has not been extensively studied [23].
Therefore, the goal of this study was to gain further knowledge on the genomic basis of the
GWH breed by analyzing whole-genome resequencing data to identify and characterize geno-
mic regions that differ between GWH and other cattle breeds, and in particular to identify can-
didate genes associated with coat color and/or eye protective phenotypes. We studied the
population structure of five Dutch traditional breeds, to evaluate the genetic distinction of the
GWH, using two approaches, which are, a model-based clustering admixture analysis and a
principal component study (PCA). Additionally, we implemented an integrative approach, to
reduce the number of false positive candidate genomic regions, taking into account the find-
ings from: a) a genome-wide association study using GWH with ACOP, breeds without the
white head phenotype (Holstein Friesian, Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and
Dutch Belted) and other white headed breeds (Simmental and Hereford); b) scans for candi-
date selective sweeps in GWH cattle compared to those of four other traditional Dutch breeds
(Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted), and the transboundary Hol-
stein Friesian; c) identification of runs of homozygosity (ROH) in the GWH breed to reduce
the number of false positive candidate selective sweeps, and d) identification of functional
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 2 / 19
part of the OPTIBOV project (LEAP-Agri-326) and
co-founded by the European Union’s Horizon 2020
research and innovation program under grant
agreement No 727715. The funding bodies had no
role in the design of the study, the collection,
analysis, interpretation of data, or the writing of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.
candidate genes in the genomic regions commonly detected by GWAS, selective sweeps and
ROH hostpots.
Materials and methods
Ethics statement
This study was conducted following the animal experimentation policy of Wageningen Uni-
versity & Research. The cattle blood samples were collected by a veterinarian during yearly
routine health inspections with written informed consent by the owners. Therefore, no Ethics
Committee approval for animal care was needed for this research.
Animals. We used 170 animals from eight breeds. We first sampled 120 unrelated animals
as part of the LEAP-Agri project OPTIBOV (https://www.optibov.com/) and in collaboration
with the respective breed associations, including 5 Holstein Friesian; 21 GWH; 23 Meuse-
Rhine-Yssel; 23 Dutch Belted; 24 Dutch Friesian; and 24 Deep Red. In total, 92 cows and 28
bulls were included in this study (for more details see S1 Table). Secondly, white headed ani-
mals with no ACOP were retrieved from two more breeds (25 Simmental and 25 Hereford)
included in the 1000 Bull Genomes Project (Run9 version) [24,25]. These 50 animals with
completely white heads (lacking ACOP) were used only for the genome-wide association anal-
ysis to contrast against the GWH breed, which exhibits ACOP.
DNA sample preparation and sequencing. The GENTRA Blood kit (Qiagen N.V.) was
used for the isolation of genomic DNA from EDTA blood samples. The quantification and
quality of the obtained DNA were assessed using the Qubit fluorometer (Qiagen N.V.). DNA
was paired-end sequenced (read length of 150 base pair) as single-indexed genomic libraries
using the Illumina Novaseq6000 (Illumina Inc., USA). Finally, raw reads were preprocessed by
trimming the adapter sequences and removing the reads with 50% of low-quality nucleotides
and fewer than 36 base pairs in length with fastp v0.23.1 [26].
Short read alignment, mapping, variant detection, and filtering. The mem option from
BWA v0.7.17-r1188 [27] was used to map the cleaned reads to the bovine reference genome
(assembly version ARS-UCD1.2) [28]. Aligned reads from each animal were stored in binary
BAM files using SAM tools v0.1.19 [29]. Freebayes software [30] was used for population-
based variant calling with default parameters except for: -min-alternate-count = 3, -haplotype-
length = 0, -ploidy = 2, -min-alternate-fraction = 0.2, and -min-base-quality = 30. Variants
with a phred-scaled probability <20 and a depth of coverage by sample <5 were removed
using the Bcftools v1.9 [31] software.
Population structure assessment with principal component analysis and individual
ancestry estimation. We used PC analysis to assess the population structure of the Dutch
cattle breeds. This analysis was conducted using the variance-standardized relationship matrix
[32] with PLINK v1.9 [32]. We considered only autosomal and biallelic variants with an r
2
<
0.5 between variants within a window of 50 SNPs and with a genotyping rate >0.95. The
results from the PCA were visualized using the R package ggplot2 v3.3.5 [33].
Individual ancestry was evaluated by a model-based clustering method with the ADMIX-
TURE software v1.23 [34]. This method used the allele frequencies and the proportions of the
ancestral populations in each sample to model the probability of the observed genotypes [34].
In the model, the K-value (optimal number of clusters) was estimated as the one with the low-
est cross-validation error (CV) [34]. The ADMIXTURE algorithm was performed using values
of Kranging between 2 and 6. The analysis was performed with a total of 120 unrelated ani-
mals from Dutch breeds and included 1,354,139 autosomal variants with a r
2
<0.5 within win-
dows of 50 variants over the genome and a minor allele frequency (MAF) >0.05.
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 3 / 19
Genome-wide association study. A genome-wide association study was used to identify
and characterize genome regions that differ between GWH and other breeds to find out candi-
date genes funtionally related with pigmentation and/or the eye protective phenotypes, e.g.
ACOP. We used a mixed-model approach developed by Zhou and Stephens [35] in the
Genome-wide Efficient Mixed-Model Association v0.98.1 [35] program. The mixed-model
approach accounted for the population structure by including in the random effect the covari-
ance structure from the genomic kinship matrix. In a first step, the association analysis was
performed between GWH and non-white headed Dutch breeds (5 Holstein Friesian; 24 Dutch
Friesian; 23 Meuse-Rhine-Yssel; 23 Dutch Belted; and 24 Deep Red). A total of 14,285,317
autosomal variants with a MAF >0.05 were used to evaluate the relationship between each
variant and the GWH breed phenotypes:
y¼Wαþxdþuþε
where ywas the binary phenotype, one for the GWH individuals with ACOP and zero for
Dutch Belted, Deep Red, Meuse-Rhine-Yssel, Dutch Friesian, and Holstein Friesian; Wthe
matrix of incidence for the fixed effects; αthe intercept vector of ones; xcontains the vector
with SNP genotypes by sample; δrepresents the marker effect size; ucontains a vector with the
random genetic effects that follow a n-dimensional multivariate normal distribution with u*
MVN
n
(0,λτ
−1
K) for n individuals and being λthe ratio from two components of variance,
τ
−1
is the variance of the residual error, and Kthe kinship matrix derived from the genotypes
from each sample; ε*MVN
n
(0,τ
−1
I
n
) the vector containing the errors, with I representing
the identity matrix. The nominal p-values from the association study were corrected using the
false discovery rate (FDR) approach implemented in the R function p.adjust [36] and Benja-
mini & Hochberg [37] method. We considered those variants with a q-value (from the FDR
test) lower than 0.001 as significantly associated. Here, a QTL and the co-localization between
QTLs and significant selective sweeps were defined following the method reported by Gonza-
lez-Prendes et al. [38]. In brief, we considered only genomic regions with more than two sig-
nificantly associated variants as candidate QTL. The co-localization between QTLs or between
QTLs and selective sweeps was considered if the genomic regions overlapped by at least one
base pair.
In a second step, variants from two additional breeds (25 animals from the Simmental
breed and 25 from Hereford) with white heads and no ACOP were retrieved from the 1000
Bull Genomes Project (Run9 version) [24,25] to perform the GWAS between these and
GWH. We decided to keep the analysis with those two transboundary breeds separated from
the remaining five Dutch breeds because we used different approaches to detect variants from
whole genome resequencing data and we did not want to lose informative variants segregating
in the populations at low frequency for subsequent analyses. The Simmental and Hereford
sequence data, with a mean depth of coverage of 11.68 X (between 1.84 and 44.17) [24,25],
were merged with the data obtained from the 120 animals in our study, including 21 GWH
individuals using PLINK v1.9 [32] with default parameters. The association study was per-
formed with a total of 9,655,666 variants with a genotype call rate above 0.9, a MAF higher
than 0.05 and using the model described above.
Identification and annotation of selective sweeps
The variants identified in each sample were used to explore the presence of genomic regions
under selection in each breed with two complementary methods. First, Sweep Detector
(SweeD) v3.0 [39] software, was applied using a composite likelihood ratio test to find candi-
date selective sweeps across the genome based on Site Frequency Spectrum patterns of
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 4 / 19
variations [40]. We defined a window size of 5 kb across the genome to calculate the Site Fre-
quency Spectrum patterns, and the outlier regions falling within the top 1% of the composite
likelihood-ratio test distribution were selected as significant regions. Second, a complementary
approach based on linkage disequilibrium implemented in OmegaPlus v3.0.3 [41] was applied.
Here, the ω-statistic is calculated based on patterns of linkage disequilibrium close to a recently
fixed mutation. A high value of ω-statistic at a specific genomic location can indicate a geno-
mic region under selection. In this method, we used the same window size of 5 kb bins across
the genome and outlier regions with the highest values (top 1%) of ω-statistic were considered
significant. Finally, only candidate selective sweeps within the 1% of the highest scores
obtained by both methods were annotated using Ensembl 101 [42] database and used for sub-
sequent analyses.
Runs of homozygosity identification in the GWH breed. The detection of ROH in the
GWH breed was implemented with detectRUNS v0.9.6 [43] program. This analysis was used
as a complementary method to confirm and reduce the number of candidate genomic regions
that co-localize between the GWAS signals and selective sweeps. Genomic regions with ROH
hotspots were selected to control the number of false positive candidate selective sweeps and
GWAS signals by selecting only genomic regions that co-localize between them. A sliding win-
dow-based method was applied to detect regions with at least 15 variants in a run with 250 kb
as the minimum length and a maximum distance between consecutive variants of one Mb.
Additionally, we considered one variant per 10 kb as the lower density limit and only one miss-
ing or heterozygous variant per run. Potential ROH hotspots were identified by selecting only
genomic regions containing the most frequent (top 1%) variants in a run in the GWH popula-
tion [44–46].
Results and discussion
After the mapping of the Dutch cattle breeds and Holstein Friesian short read sequences to the
bovine reference genome (assembly ARS-UCD1.2), the depth of coverage across samples, in
average, was 8.7X ranging from 7X to 13X (S1 Table). The number of variants per breed, bial-
lelic variants and variants that are specific to each breed are shown in Table 1. The overall
number of annotated variants was 21,313,663, and the number of SNPs per animal (between 6
and 7 million, S1 Table) and per breed (between 13 and 17 million, Table 1) are within the
range of that obtained in other studies on B.taurus [47–53]. The breed with the highest num-
ber of breed-specific variants was Deep Red (558,909), whereas the Holstein Friesian showed
the lowest number (148,213). The low number of specific variants detected in Holstein Friesian
compared with the remaining breeds in this study is most likely because of the small effective
population size associated with a strong artificial selection pressure [54]. However, as the num-
ber of samples (n = 5) for Holstein Friesian is low, specific variants with low frequency may be
Table 1. Number of variants by breeds and breed-specific variants detected in 6 cattle populations.
Breeds Mean genome coverage Number of variants Number of biallelic variants Number of biallelic breed-specific variants
Holstein Friesian 9.20 13,218,695 12,376,751 148,213
Meuse-Rhine-Yssel 9.78 16,642,547 15,751,146 304,064
Groningen White Headed 8.19 15,554,352 14,675,130 368,389
Dutch Frisian 8.46 16,025,075 15,139,002 374,333
Dutch Belted 8.30 16,463,618 15,574,124 475,279
Deep Red 8.75 17,804,474 16,906,802 558,909
Mean 8.78 15,951,460 15,070,493 371,531
https://doi.org/10.1371/journal.pone.0276309.t001
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 5 / 19
underestimated and the results must be taken with caution. Functional annotation analysis
revealed that the detected variants mapped to intronic (46.18%) or intergenic (42.61%)
regions. Only, 1.1% (389,472 variants) mapped to exonic regions, of which 146,057 were mis-
sense and 212,473 were synonymous variants (S2 Table).
Genetic differentiation of the GWH breed
The genetic relationships between samples were evaluated using a PCA approach. As shown in
Fig 1A, the distribution of the samples is in concordance with the breed histories and in line
with previous results obtained for traditional Dutch populations [23]. While, Holstein Friesian
occupied the central position, PC1 separated the dual-purpose breeds Meuse-Rhine-Yssel and
Deep Red, which are genetically closely related [55], from all others. This is in agreement with
the history of these two breeds where Deep Red originated from the Meuse-Rhine-Yssel in the
east of the Noord-Brabant province following multiple generations of selection for coat color
[55]. The PC2 separated the GWH from other breeds, providing further support for the genetic
differentiation of this population. The model-based clustering analysis supported the PCA
results. We used the information obtained from the PCA, which showed six different clusters,
to run the model-based clustering analysis from K= 2 to K= 6, and the smallest CV error to
estimate the best number of Kancestral populations. The results (Fig 2) supported the high
differentiation of the GWH breed at K= 3 in an independent genetic cluster. The separation of
Dutch Friesian and Dutch Belted breeds occurred at K= 4, and finally the Meuse-Rhine-Yssel
and Deep Red formed two distinct clusters at K= 5, which had the smallest CV error (0.54),
reflecting their close genetic relationship [55]. In this analysis, we included the Holstein Frie-
sian breed, however, determining the extent of admixture in this breed requires further studies
of a larger sample size [56]. In the admixture analysis, populations with a low number of sam-
ples are less likely to be assigned to their own ancestral cluster and as a consequence, they are
depicted across multiple drifted groups [56].
With the separation of the GWH population from the non-white-headed breeds, we
decided to investigate if this breed, with ACOP, is also isolated from white headed breeds
Fig 1. Principal component analysis (A) 120 samples from six breeds (Holstein Friesian, Dutch Friesian, Dutch Belted, Deep Red, Meuse-Rhine-Yssel and
GWH); (B) The 70 animals from the three populations, GWH breed plus two white headed breeds (Hereford and Simmental) used for the GWAS in the second
step. Individuals from the GWH breed (red circle) are distantly positioned from all other breeds in both plots. The % symbol indicates the percentage of the
explained variance for the first and second components calculated from the eigenvalues.
https://doi.org/10.1371/journal.pone.0276309.g001
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 6 / 19
without pigmentation around the eyes, that are, Hereford and Simmental (without ACOP).
The PCA separated the breeds into three clusters based on their genomic information (Fig
1B). Animals represented in Fig 1B were used for the GWAS in the second step. The PC1,
which explains 30% of the observed variation, divided the animals with and without ACOP
and confirms the genetic differentiation of the GWH breed. The PC2, divided the Hereford
and Simmental breeds into two clear clusters indicating two separate populations in accor-
dance with previous reports [57]. This pattern, which confirms the GWH differentiation was
also obtained when the five Dutch breeds and the three commercial populations (Holstein,
Simmental, and Hereford) were combined (S1 Fig).
Genomic regions showing significant association with the GWH breed
The GWA study was used to identify and characterize genome regions that differ between
GWH and other breeds to find candidate genes possibly associated with pigmentation and/or
eye protective phenotypes e.g. ACOP, which is typical of GWH breed. Animals with ACOP
(GWH) were classified as cases and animals of the Dutch Belted, Deep Red, Meuse-Rhine-
Yssel, Dutch Friesian, and Holstein Friesian breeds were considered as controls. At the
genome-wide level (q-val<= 0.001), 137 genome hits (S3 Table and Fig 3) with more than
one significantly associated variant were detected. The associated regions were distributed
across the 29 chromosomes (Fig 3) and the regions with the most significant associations (p-
value <-4.9E-14) and with the highest number of associated variants (>100 significant
Fig 2. Population structure plot determined by the model-based clustering analysis of ADMIXTURE. Samples are
represented by stacked columns of the 2 to 5 K-proportions and the number of clusters with thelowest cross validation
error (CV = 0.54) was obtained for K= 5.
https://doi.org/10.1371/journal.pone.0276309.g002
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 7 / 19
associations) mapped to BTA4 (20.0–29.9 Mb and 116.8–118.8 Mb), BTA5 (10.1–19.7 Mb),
BTA12 (12.0–18.5 Mb), BTA15 (50.6–59.8 Mb and 60.3–67.8 Mb), BTA20 (10.2–19.9 Mb and
20.2–29.5 Mb) and BTA21 (0.4–8.8 Mb). A total of four genomic regions co-localized with
those detected by Pausch et al. [9] in Fleckvieh breed, which are, two regions located on BTA5
(10–19.7 Mb; 57.5–58.9 Mb), one on BTA13 (50.1–59.9 Mb) and one on BTA22 (30.4–32.5
Mb). The low coincidence between the studies may indicate that most associations are breed-
specific suggesting that this phenotype may have a different genetic background in these
breeds. However, multiple methodological and biological factors can influence these differ-
ences. Pausch et al. [9] used genomic information from a combination of SNP arrays (version
1 and 2 of Illumina BovineSNP 50K Bead chip1, and Illumina BovineHD Bead chip1777k),
whereas we used whole-genome sequence variants. Additionally, Pausch et al. [9] used a quan-
titative trait (a proportion of progeny with ACOP) in the GWAS study while in the current
study we used the ACOP traits as a binary phenotype. Finally, while large sample sizes are
needed for GWAS of complex traits, the sample size can be dramatically reduced for a case
and control analysis in binary phenotypes [58,59].
QTL detection in white headed cattle with and without ACOP. As there were no GWH
animals with a completely white head and without ACOP, 50 animals from Hereford and Sim-
mental breeds were selected from the 1000 Bull project [24]. These data were merged with var-
iants from our GWH to carry out a GWAS analysis using a total of 15,751,624 variants to: 1)
detect GWAS signals associated with the phenotype variation of GWH breed to find candidate
genes related with pigmentation and/or eye protection phenotypes, e.g. ACOP, by contrasting
breeds with ACOP (GWH) and without ACOP (Hereford and Simmental) and completely
unpigmented area around the eyes; and 2) to reduce the number of candidate genomic regions
by retrieving the QTLs overlapping with the GWAS (breeds without white head vs GWH). A
total of 187 genomic significant hits with at least two significant SNPs were detected (S4
Table), and 100 (53.4%) co-localized with the QTLs identified when the six breeds were
included in the analysis (S4 Table). This result may suggest that those regions specifically affect
the GWH breed and may be associated with its color phenotype. Interestingly, the QTL on
BTA5 (region, 10.1–19.7 Mb), was also identified by contrasting GWH vs breeds without
white head (BTA5, region 10.0–13.7 Mb). Pausch et al. [9] reported the same QTL earlier at
BTA5 (15.6–20.6 Mb, remapped to ARS-UCD1.2 assembly) which explained around 7.9% of
the total phenotypic variation of ACOP in the Fleckvieh breed [9].
Our GWAS analyses were limited by the fact that significantly associated genomic regions
can be observed due to the different genetic backgrounds between the breeds. This confound-
ing effect should either be eliminated through a better study design (e.g. F2 crosses with
another white face breed that does not show ACOP) [60–62] or by reducing the number of
false positives using a combined approach in a downstream analysis [17,63]. For example, the
Fig 3. Manhattan plots showing the GWAS results from contrasting GWH animals with the ACOP phenotype and those of the Dutch Belted, Deep Red,
Meuse-Rhine-Yssel, Dutch Friesian, and Holstein Friesian breeds without white head and non-ACOP phenotype. The y-axis of the plot represents the
-log
10
(P-values) from the GWAS and the x-axis shows the genomic location of each variant. The horizontal red line indicate the significant association (q-value
0.001) at the genome-wide level.
https://doi.org/10.1371/journal.pone.0276309.g003
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 8 / 19
application of complementary methods to investigate whether loci significantly associated
were recently selected in the population [16], the description of functions of the genes in can-
didate regions, and finally the experimental validation. As we did not have animals from the
GWH breed without ACOP we decided to investigate if our significantly associated genomic
regions were recently selected in our GWH population to detect positional candidate genes
functionally associated with pigmentation, eye disease, and/or UV protection.
Detection of a breed-specific selective sweeps in GWH
We used the whole genome resequencing data from six cattle breeds (GWH, Dutch Belted,
Deep Red, Meuse-Rhine-Yssel, Dutch Friesian, and Holstein Friesian) to find out breed-specific
selective sweeps (BSSS) in the GWH breed with two complementary methods: SweeD, which
detects selective sweeps based on the variant frequencies using a composite maximum likeli-
hood approach [39]; and OmegaPlus, that identifies patterns of linkage disequilibrium using the
ωstatistic [41]. Only significant genome regions (top 1% of the empirical distribution) in both
algorithms (SweeD [39] and OmegaPlus [41]) were selected for furher analysis. With this
approach, 257 breed-specific putative genomic regions under selection were detected (Fig 4,S5
Table). The candidate regions were distributed across the 29 autosomes (Fig 4) with sizes that
ranged from 3.4 kb to 140.4 kb and a mean of 17.8 kb. The breed with the lowest number of can-
didate regions was GWH (31), followed by Meuse-Rhine-Yssel (40), Dutch belted (41), Dutch
Friesian (46), Holstein Friesian (48), and Deep Red (51). The highly significant BSSS migth indi-
cate “divergence signals” between breeds [3]. Thus, the BSSS might be an indicator of genomic
regions affecting unique phenotypic characteristics for which the selection signal was detected
[3] and therefore can be used to validate the GWAS signals for the phenotypic variation of the
GWH breed. The regions with the most significant associations obtained by both methods were
found on BTA5 (12 Mb) and BTA20 (14–20 Mb) in GWH; BTA3 (115–118 Mb) on Dutch
Belted and BTA3 (12–13 Mb), BTA11 (93–94 Mb) and BTA22 (45–48 Mb) on Holstein Friesian
(Fig 4). When we evaluated the co-localization between the BSSS (±500 kb up-and downstream)
in GWH and QTLs detected by GWAS, eight genomic regions were also mapped with all meth-
ods (S3–S5 Tables) as follows: one on BTA4 (70.2–71.3 Mb), one on BTA5 (10.0–19.7 Mb), one
on BTA10 (26.9–29.4 Mb), one on BTA13 (60.0–61.4 Mb), one on BTA15 (55.5–59.8 Mb), two
on BTA20 (10.0–19.9, 20.0–22.7 Mb) and one on BTA25 (0.5–9.2 Mb).
We also evaluated if the candidate selective sweep co-localized with known bovine QTLs
deposited in the AnimalQTLdb [64] database. A total of 4,558 different QTLs affecting 260
traits were found within 257 candidate BSSS (S6 Table). Several of the candidate selective
sweeps highlighted loci which were mainly associated with milk quality, milk production, feed
efficiency, body weight, and several meat-related phenotypes. To be noted, these results are in
line with the economic objective established for the studied breeds; Dutch local cattle (Meuse-
Rhine-Yssel, Deep Red, and GWH) have been selected for dual-purpose characteristics includ-
ing milk production. Although our candidate selective sweeps were selected as unique in each
breed, we still can find BSSS affecting the same trait. This can be explained by the fact that in
livestock populations, including traditional cattle breeds, the selection for economically impor-
tant traits, e.g. complex traits, might happen across many loci with small effects [2,65]. The
successful identification and characterization of those BSSS that are associated with economi-
cally relevant traits can be used to: 1) improve the knowledge about the processes influencing
the genetic diversity of each breed; and 2) identify candidate genes and/or causal variants
affecting phenotypes under selection. Thus, further studies are encouraged to explore the rela-
tionship between our candidate BSSS and the impact that they may have on economically rele-
vant traits in detail as this was not an objective of the current study.
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 9 / 19
Several ROH hotspots map to QTLs and putative selective sweeps
The identification of the genomic regions in ROH in the GWH breed was implemented as a
complementary method to confirm and reduce the number of candidate genomic regions that
co-localize between the GWAS signals and BSSS. We found 4,911 ROH regions that cover on
average a total of 207.4 Mb of the genome. Of these ROH regions, around 73% (3,615) can be
classified as small (0.5–1 Mb) regions, indicating more ancient consanguinity or population
founder effects [66]. This result is common in cattle populations, where longer ROH regions
Fig 4. Genome-wide selective sweep scans using SweeD in each breed. Manhattan plots representing the composite
likelihood ratio values (y-axis) from SweeD for each marker across the genome (x-axis). The threshold of the
significant association (top 1% of the highest composite likelihood ratio values) for declaring candidate selective
sweeps is indicated by the red line. Red points indicate candidate genomic regions detected by both the SweeD and
OmegaPlus methods.
https://doi.org/10.1371/journal.pone.0276309.g004
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 10 / 19
have been found less frequently than shorter ones [67]. To reduce the number of identified
genomic regions in ROH, the ROH hotspots were defined by identifying genomic regions con-
taining the variants with the highest frequency (top 1%) in a ROH across the GWH population
(Fig 5,S7 and S8 Tables). With this approach, 57 genomic regions were detected as ROH hot-
spots. With their genomic coordinates, we were able to reveal genomic regions that co-localize
with the previously detected BSSS and GWAS signals. Five genomic regions that mapped to
BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 Mb and 20.0–22.7 Mb), and
BTA25 (0.5–9.2 Mb) were overlapped between the three methods, and thus genes on those
regions are probably under selection in the GWH breed [68,69].
Positional and functional candidate genes associated with pigmentation
and retinal diseases
We also investigated whether the function of the positional candidate genes are specifically
associated with pigmentation and/or metabolism of melanocytes. First, we focused on genes
that mapped to regions that overlapped between ROH hotspots, BSSS, and the GWAS signals
(GWH vs other Dutch breeds, and GWH vs Hereford and Simmental breeds) (Fig 5). These
regions included 141 genes (S9 Table), of which some are functional candidate genes. For
example, on BTA 5 (12–17 Mb), the transmembrane o-mannosyltransferase targeting cadher-
ins 2 (TMTC2) located at 12.2 Mb, is associated with calcium ion homeostasis [70]. Calcium
homeostasis is of major importance in melanocytes and is suggested to be regulated by mela-
nosomes [71]. The KIT Ligand (KITLG) locus (BTA 5, 18.2–18.3 Mb), which encodes a ligand
for the receptor-type-tyrosine kinase KIT and contributes to coat color in various species,
including cattle [72,73]. On BTA20 (10.9–20 Mb), the region with the most significant SNPs
contains the DEPDC1B (DEP domain-containing protein 1B) gene at position 18.5–18.6 Mb,
which is associated with the hyperproliferation of abnormal melanocyte cells [74]. This gene is
overexpressed in melanoma and encodes DEPDC1B protein that contains a DEP domain [75,
76], which plays an active role in controlling cell functions, including specific signal of retinal
photoreceptor and cell polarity [76,77].
Interestingly, there are two genes (S7 Table) in our list related with retinal diseases, for
example CWC27 (CWC27 Spliceosome Associated Cyclophilin) associated with Retinitis Pig-
mentosa [78]; on BTA25 (1.1–1.2 Mb) the function of the Clusterin Associated Protein 1
(CLUAP1) in the vertebrate eye is important for ciliogenesis and photoreceptor maintenance
[79]. Although only few cases of eye degenerative diseases with a genetic background have
been reported in cattle [80–82], recently Michot et al. [83] evidenced a group of mutations
related with eye diseases that are segregating in European cattle breeds with direct impact on
animal health e.g., the recessive frameshift mutation on RP1 gene that causes loss of vision in
cattle populations.
Fig 5. Genome-wide ROH hotspots disribution in GWH breed. The y-axis represents the percentage (%) of animals with SNPs in ROH regions and the x-
axis the genomic coordinate of each variant. The significance threshold indicating the genomicregions (ROH hotspots) containing the variants present in more
than 99% of a ROH region across the samples is indicated by the red line. Green dots represent genomic regions on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7
Mb), BTA20 (10.0–19.9 Mb and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb) that co-localize with significant SNPs commonly detected by the GWAS, selective
sweeps and ROH hotspots.
https://doi.org/10.1371/journal.pone.0276309.g005
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 11 / 19
The most significant SNPs on BTA20 mapped to genes related with UV
protection and melanocyte differentiation
The analysis of the whole genome resequencing data allowed to identify variants within candi-
date genomic regions that can help to clarify the cause of the phenotypic differences that exist
between GWH and the remaining breeds. We investigated the genomic regions on BTA20
(10.0–19.9 Mb and 20.0–22.7 Mb) because those regions contained the most significant associ-
ations at three levels (GWAS, Fig 3; BSSS, Fig 4; and ROH, Fig 5). We studied the top ten sig-
nificant SNPs in these regions to identify putatively associated genes. Nine of these SNPs
mapped to four genes (RAB3C,NDUFAF2,ZSWIM6, and PDE4D;Table 2), and 11 of them to
intergenic regions (Table 2). The linkage desequilibrium between those SNPs was high, rang-
ing from r
2
= 0.91 to one (Table 2), and one of these SNPs (rs381052637, p-value = 8.64E-22)
mapped to the 30UTR of the PDE4D gene. SNPs located in 30-UTR sequences may abolish or
create a microRNA target and consequently may lead to different activities of the gene thereby
contributing to interindividual variability [84,85].
Four of the most significant SNPs (Table 2) mapped to the Phosphodiesterase 4D (PDE4D)
gene. PDE4D is involved in the degradation of the Cyclic AMP. In humans, the skin pigment
production and its protection against the UV radiation improved with the up-regulation of
cAMP in melanocytes [86]. However, the function of PDE isoforms in pigmentation and mela-
nocyte biology has not been extensively studied. Khaled et al. [87] reported that the up-regula-
tion of PDE4D loci mediated by the MC1R-cAMP-MITF pathway led to a reduced melanocyte
pigmentation in mice [88–90]. Interestingly, genes in the MITF pathway have been linked in
many cattle breeds with coat color phenotypes [11,91,92], and also in other species [93]. As far
as we know, there is no evident relationship between the Ubiquinone Oxidoreductase Complex
Assembly Factor 2 (NDUFAF2) or Related Protein Rab-3C (RAB3C) genes with coat color or
melanogenesis. However, the RAB3C gene is part of the Rab GTPases proteins, which were
involved in cell membrane trafficking and associated with melanosomes [94]. Finally, another
interesting candidate gene that maps to 40 kb downstream of the rs381810091 SNP (p-value =
1.74E-24) is the ERCC Excision Repair 8 (ERCC8) gene, involved in protein ubiquitination and
UV response. In humans, the ERCC8 gene is associated with Ultraviolet-sensitive syndrome
[95] a genetic disorder characterized by cutaneous photosensitivity that causes differentiated
skin pigmentation and greater freckling, without an increased risk of skin tumors [95,96].
Table 2. Genomic localization of the most significant SNPs on BTA20.
GWAS results Candidate Genes
BTA Position (bp) SNP ID Localization p-value BTA Gene Start Gene End Gene Symbol
20 17,974,182 rs382263925 intronic 5.89E-23 20 17,842,584 18,052,859 ZSWIM6
18,330,187 rs381810091 intronic 1.74E-24 18,210,359 18,370,943 NDUFAF2
20,044,595 20:20044595 intronic 8.64E-22 20,014,955 20,315,593 PDE4D
20,044,910 rs380360322 intronic 8.64E-22
20,278,747 20:20278747 intronic 8.64E-22
20,314,517 rs381052637 3 prime UTR 8.64E-22
20,524,538 20:20524538 intronic 8.64E-22 20,440,181 20,735,999 RAB3C
20,542,032 20:20542032 intronic 8.64E-22
20,598,559 20:20598559 intronic 8.64E-22
1
BTA: Bos taurus chromosomes, Position (bp): position in base pair of SNP, SNP ID: Variant displaying the significant association with GWH breed, p-value: nominal
p-value.
Linkage disequilibrium based on the squared correlation (r
2
) from genotypic allele counts was higher than 0.91 between presented SNPs.
https://doi.org/10.1371/journal.pone.0276309.t002
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 12 / 19
Conclusion
The used integrative approach based on the combined use of GWAS, selective sweep and
ROH analyses identified several regions of the cattle genome (BTA4,70.2–71.3 Mb;
BTA5,10.0–19.7 Mb; BTA20,10.0–19.9 Mb, and 20.0–22.7 Mb; and BTA25,0.5–9.2 Mb) as can-
didates to explain phenotype variation in the GWH breed. Importantly, those regions con-
tained breed-specific genetic markers and candidate genes that are functionally related with
pigmentation (e.g. PDE4D), UV protection (e.g. ERCC8), or retinal degeneration (e.g. CWC27,
and CLUAP1). This finding contributes to characterizing the genetic background of the GWH
breed and provides insights to further investigate the biological pathways and causative muta-
tions influencing skin pigmentation and/or eye protective phenotypes e.g. Ambilateral Circu-
mocular Pigmentation, and the biological implications of skin pigmentation for animal
adaptation.
Supporting information
S1 Fig. Principal component analysis of the 170 cattle samples from five local Dutch
Breeds (Dutch Belted, Dutch Friesian, Meuse-Rhine-Yssel, Deep Red, and GWH) and
three commercial breeds Holstein Friesian, Hereford and Simmental. Individuals from the
GWH breed (red circle) were distantly positioned from all other breeds.
(TIF)
S1 Table. Read coverage and number of variants by animals and breeds from five local
Dutch Breeds (Dutch Belted, Dutch Friesian, Meuse-Rhine-Yssel, Deep Red, and GWH)
and the commercial breed Holstein Friesian.
(XLSX)
S2 Table. Variant effect predicted from whole-genome variant from Dutch Belted, Dutch
Friesian, Meuse-Rhine-Yssel, Deep Red, Holstein Friesian, and GWH breeds.
(XLSX)
S3 Table. Genome-wide significant QTL for GWH and five cattle breeds without white
head (Dutch Belted, Deep Red, Meuse-Rhine-Yssel, Dutch Friesian and Holstein Friesian).
(XLSX)
S4 Table. Genome-wide significant QTL for phenotypic variation of GWH from contrast-
ing Simmental, Hereford white headed breeds.
(XLSX)
S5 Table. Breed-specific selective sweep regions detected in Dutch Belted, Dutch Friesian,
Meuse-Rhine-Yssel, Deep Red, GWH, and Holstein Friesian breeds.
(XLSX)
S6 Table. Quantitative trait locus, from the Animal QTL database, in breed-specific selec-
tive sweeps detected in Dutch Belted, Dutch Friesian, Meuse-Rhine-Yssel, Deep Red, Hol-
stein Friesian and GWH breeds.
(XLSX)
S7 Table. Genomic distribution of SNPs in RHO hotspots in GWH breed.
(XLSX)
S8 Table. Genomic coordinates of RHO hotspots in GWH breed.
(XLSX)
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 13 / 19
S9 Table. Genes that mapped to genome regions on BTA4 (70.2–71.3 Mb), BTA5 (10.0–
19.7 Mb), BTA20 (10.0–19.9 Mb, and 20.0–22.7 Mb) and BTA25 (0.5–9.2 Mb).
(XLSX)
Acknowledgments
The authors are indebted to The Groningen White Headed association for its collaboration
and for providing the animal material. We gratefully acknowledge Bert Dibbits and Kimberley
Laport from Animal Breeding and Genomics (WUR) for technical laboratory support. This
work is part of the OPTIBOV project financed within the Long term European African
research and innovation Partnership on food and nutrition security and sustainable Agricul-
ture (LEAP-Agri) program (https://leap-agri.com). The authors gratefully acknowledge the
Fundac¸ão Nacional para a Ciência e a Tecnologia (FCT), Portugal, contract grant 2020.02754.
CEECIND (C.G.) and the National Research Foundation (NRF) of South Africa, Leap Agri-
326, Grant number 115577.
Author Contributions
Conceptualization: Richard P. M. A. Crooijmans.
Data curation: Rayner Gonzalez-Prendes.
Formal analysis: Rayner Gonzalez-Prendes.
Funding acquisition: Richard P. M. A. Crooijmans.
Investigation: Rayner Gonzalez-Prendes.
Methodology: Rayner Gonzalez-Prendes, Catarina Ginja, Juha Kantanen.
Resources: Juha Kantanen, Richard P. M. A. Crooijmans.
Supervision: Catarina Ginja, Martien A. M. Groenen, Richard P. M. A. Crooijmans.
Writing – original draft: Rayner Gonzalez-Prendes, Richard P. M. A. Crooijmans.
Writing – review & editing: Rayner Gonzalez-Prendes, Catarina Ginja, Juha Kantanen, Nas-
ser Ghanem, Donald R. Kugonza, Mahlako L. Makgahlela, Martien A. M. Groenen, Richard
P. M. A. Crooijmans.
References
1. Ramey HR, Decker JE, McKay SD, Rolf MM, Schnabel RD, Taylor JF. Detection of selective sweeps in
cattle using genome-wide SNP data. BMC Genomics. 2013; 14: 382. https://doi.org/10.1186/1471-
2164-14-382 PMID: 23758707
2. Ghoreishifar SM, Eriksson S, Johansson AM, Khansefid M, Moghaddaszadeh-Ahrabi S, Parna N, et al.
Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five
Swedish cattle breeds. Genet Sel Evol. 2020; 52. https://doi.org/10.1186/s12711-020-00571-5 PMID:
32887549
3. Gutie
´rrez-Gil B, Arranz JJ, Wiener P. An interpretive review of selective sweep studies in Bos taurus
cattle populations: Identification of unique and shared selection signals across breeds. Frontiers in
Genetics. Frontiers Media S.A.; 2015. p. 167. https://doi.org/10.3389/fgene.2015.00167 PMID:
26029239
4. Qanbari S, Gianola D, Hayes B, Schenkel F, Miller S, Moore S, et al. Application of site and haplotype-
frequency based approaches for detecting selection signatures in cattle. BMC Genomics. 2011; 12:
318. https://doi.org/10.1186/1471-2164-12-318 PMID: 21679429
5. Jablonski NG, Chaplin G. The evolution of human skin coloration. J Hum Evol. 2000; 39: 57–106.
https://doi.org/10.1006/jhev.2000.0403 PMID: 10896812
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 14 / 19
6. Finch VA, Western D. Cattle colors in pastoral herds: natural selection or social preference? Ecology.
1977; 58: 1384–1392. https://doi.org/10.2307/1935090
7. Flori L, Moazami-Goudarzi K, Alary V, Araba A, Boujenane I, Boushaba N, et al. A genomic map of cli-
mate adaptation in Mediterranean cattle breeds. Mol Ecol. 2019; 28: 1009–1029. https://doi.org/10.
1111/mec.15004 PMID: 30593690
8. Cieslak M, Reissmann M, Hofreiter M, Ludwig A. Colours of domestication. Biol Rev Camb Philos Soc;
2011. pp. 885–899. https://doi.org/10.1111/j.1469-185X.2011.00177.x PMID: 21443614
9. Pausch H, Wang X, Jung S, Krogmeier D, Edel C, Emmerling R, et al. Identification of QTL for UV-pro-
tective eye area pigmentation in cattle by progeny phenotyping and genome-wide association analysis.
Moore S, editor. PLoS One. 2012; 7: e36346. https://doi.org/10.1371/journal.pone.0036346 PMID:
22567150
10. Senczuk G, Guerra L, Mastrangelo S, Campobasso C, Zoubeyda K, Imane M, et al. Fifteen shades of
grey: Combined analysis of genome-wide snp data in steppe and mediterranean grey cattle sheds new
light on the molecular basis of coat color. Genes (Basel). 2020; 11: 1–16. https://doi.org/10.3390/
genes11080932 PMID: 32823527
11. Jivanji S, Worth G, Lopdell TJ, Yeates A, Couldrey C, Reynolds E, et al. Genome-wide association anal-
ysis reveals QTL and candidate mutations involved in white spotting in cattle. Genet Sel Evol. 2019; 51:
1–18. https://doi.org/10.1186/s12711-019-0506-2 PMID: 31703548
12. Mei C, Wang H, Liao Q, Wang L, Cheng G, Wang H, et al. Genetic architecture and selection of Chinese
cattle revealed by whole genome resequencing. Mol Biol Evol. 2018; 35: 688–699. https://doi.org/10.
1093/molbev/msx322 PMID: 29294071
13. Andersson L. Genetic dissection of phenotypic diversity in farm animals. Nat Rev Genet; 2001. pp.
130–138. https://doi.org/10.1038/35052563 PMID: 11253052
14. Rees JL. Genetics of hair and skin color. Annu Rev Genet. 2003; 37: 67–90. https://doi.org/10.1146/
annurev.genet.37.110801.143233 PMID: 14616056
15. Wang C, Li H, Guo Y, Huang J, Sun Y, Min J, et al. Donkey genomes provide new insights into domesti-
cation and selection for coat color. Nat Commun. 2020; 11: 1–15. https://doi.org/10.1038/s41467-020-
19813-7 PMID: 33293529
16. Igoshin A V., Yurchenko AA, Belonogova NM, Petrovsky D V., Aitnazarov RB, Soloshenko VA, et al.
Genome-wide association study and scan for signatures of selection point to candidate genes for body
temperature maintenance under the cold stress in Siberian cattle populations. BMC Genet. 2019;20.
https://doi.org/10.1186/s12863-019-0725-0 PMID: 30885142
17. Niu Q, Zhang T, Xu L, Wang T, Wang Z, Zhu B, et al. Integration of selection signatures and multi-trait
GWAS reveals polygenic genetic architecture of carcass traits in beef cattle. Genomics. 2021; 113:
3325–3336. https://doi.org/10.1016/j.ygeno.2021.07.025 PMID: 34314829
18. CGN, 2009. Groningen White Headed breed assessment. EU GENRES 870/04 project EURECA. Cen-
tre for Genetic Resources, the Netherlands (CGN) of Wageningen UR. Available: https://edepot.wur.nl/
5600
19. Blaarkopnet -. [cited 10 Dec 2020]. Available: https://zeldzamerassen.nl/blaarkopnet/
20. Tsujita H, Plummer CE. Bovine ocular squamous cell carcinoma. Vet Clin North Am Food Anim Pract;
2010. pp. 511–529. https://doi.org/10.1016/j.cvfa.2010.08.003 PMID: 21056799
21. Frisch JE. The relative incidence and effect of bovine infectious keratoconjunctivitis in Bos indicus and
Bos taurus cattle. Anim Prod. 1975; 21: 265–274. https://doi.org/10.1017/S0003356100030737
22. Ward JK, Nielson MK. Pinkeye (bovine infectious keratoconjunctivitis) in beef cattle. J Anim Sci. 1979;
49: 361–366. https://doi.org/10.2527/jas1979.492361x PMID: 574506
23. Hulsegge I, Schoon M, Windig J, Neuteboom M, Hiemstra SJ, Schurink A. Development of a genetic
tool for determining breed purity of cattle. Livest Sci. 2019; 223: 60–67. https://doi.org/10.1016/J.
LIVSCI.2019.03.002
24. Hayes BJ, Daetwyler HD. 1000 Bull genomes project to map simple and complex genetic traits in cattle:
applications and outcomes. Annual Review of Animal Biosciences. 2019. pp. 89–102. https://doi.org/
10.1146/annurev-animal-020518-115024 PMID: 30508490
25. Daetwyler HD, Capitan A, Pausch H, Stothard P, Van Binsbergen R, Brøndum RF, et al. Whole-
genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat
Genet. 2014; 46: 858–865. https://doi.org/10.1038/ng.3034 PMID: 25017103
26. Chen S, Zhou Y, Chen Y, Gu J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics.
2018; 34: i884–i890. https://doi.org/10.1093/bioinformatics/bty560 PMID: 30423086
27. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics.
2010; 26: 589–595. https://doi.org/10.1093/bioinformatics/btp698 PMID: 20080505
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 15 / 19
28. Modernizing the bovine reference genome assembly | WCGALP Archive. [cited 3 May 2022]. Available:
http://www.wcgalp.org/proceedings/2018/modernizing-bovine-reference-genome-assembly
29. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format
and SAMtools. Bioinformatics. 2009; 25: 2078–2079. https://doi.org/10.1093/bioinformatics/btp352
PMID: 19505943
30. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. 2012 [cited 11
Jan 2021]. Available: http://arxiv.org/abs/1207.3907
31. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population
genetical parameter estimation from sequencing data. Bioinformatics. 2011; 27: 2987–2993. https://doi.
org/10.1093/bioinformatics/btr509 PMID: 21903627
32. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: A tool set for
whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81: 559–
575. https://doi.org/10.1086/519795 PMID: 17701901
33. Villanueva RAM, Chen ZJ. ggplot2: Elegant graphics for data analysis (2nd ed.). Meas Interdiscip Res
Perspect. 2019; 17: 160–167. https://doi.org/10.1080/15366367.2019.1565254
34. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals.
Genome Res. 2009; 19: 1655–1664. https://doi.org/10.1101/gr.094052.109 PMID: 19648217
35. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet.
2012; 44: 821–824. https://doi.org/10.1038/ng.2310 PMID: 22706312
36. The R Foundation. R: The R Project for Statistical Computing. 2018 [cited 16 Nov 2021]. Available:
https://www.r-project.org/
37. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to mul-
tiple testing. J R Stat Soc Ser B. 1995; 57: 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.
x
38. Gonza
´lez-Prendes R, Quintanilla R, Ma
´rmol-Sa
´nchez E, Pena RN, Ballester M, Cardoso TF, et al.
Comparing the mRNA expression profile and the genetic determinism of intramuscular fat traits in the
porcine gluteus medius and longissimus dorsi muscles. BMC Genomics. 2019;20. https://doi.org/10.
1186/s12864-019-5557-9 PMID: 30832586
39. Pavlidis P, Z
ˇivkovićD, Stamatakis A, Alachiotis N. SweeD: Likelihood-based detection of selective
sweeps in thousands of genomes. Mol Biol Evol. 2013; 30: 2224–2234. https://doi.org/10.1093/molbev/
mst112 PMID: 23777627
40. Nielsen R, Williamson S, Kim Y, Hubisz MJ, Clark AG, Bustamante C. Genomic scans for selective
sweeps using SNP data. Genome Res. 2005; 15: 1566–1575. https://doi.org/10.1101/gr.4252305
PMID: 16251466
41. Alachiotis N, Stamatakis A, Pavlidis P. OmegaPlus: A scalable tool for rapid detection of selective
sweeps in whole-genome datasets. Bioinformatics. 2012; 28: 2274–2275. https://doi.org/10.1093/
bioinformatics/bts419 PMID: 22760304
42. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids
Res. 2020; 48: D682–D688. https://doi.org/10.1093/nar/gkz966 PMID: 31691826
43. Biscarini F, Cozzi P, Gaspa G, Marra G. detectRUNS: an R package to detect runs of homozygosity
and heterozygosity in diploid genomes. [cited 21 Oct 2021]. Available: https://cran.r-project.org/web/
packages/detectRUNS/vignettes/detectRUNS.vignette.html
44. Noce A, Qanbari S, Gonza
´lez-Prendes R, Brenmoehl J, Luigi-Sierra MG, Theerkorn M, et al. Genetic
diversity of bubalus bubalis in germany and global relations of its genetic background. Front Genet.
2021; 11. https://doi.org/10.3389/fgene.2020.610353 PMID: 33552127
45. Mastrangelo S, Sardina MT, Tolone M, Di Gerlando R, Sutera AM, Fontanesi L, et al. Genome-wide
identification of runs of homozygosity islands and associated genes in local dairy cattle breeds. Animal.
2018; 12: 2480–2488. https://doi.org/10.1017/S1751731118000629 PMID: 29576040
46. Xu L, Zhao G, Yang L, Zhu B, Chen Y, Zhang L, et al. Genomic patterns of homozygosity in chinese
local cattle. Sci Reports 2019 91. 2019; 9: 1–11. https://doi.org/10.1038/s41598-019-53274-3 PMID:
31740716
47. Gibbs RA, Taylor JF, Van Tassell CP, Barendse W, Eversole KA, Gill CA, et al. Genome-wide survey of
SNP variation uncovers the genetic structure of cattle breeds. Science (80). 2009; 324: 528–532.
https://doi.org/10.1126/science.1167936 PMID: 19390050
48. Ramı´rez-Ayala LC, Rocha D, Ramos-Onsins SE, Leno-Colorado J, Charles M, Bouchez O, et al.
Whole-genome sequencing reveals insights into the adaptation of French Charolais cattle to Cuban
tropical conditions. Genet Sel Evol. 2021; 53: 1–11. https://doi.org/10.1186/s12711-020-00597-9
PMID: 33397281
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 16 / 19
49. Xia X, Zhang S, Zhang H, Zhang Z, Chen N, Li Z, et al. Assessing genomic diversity andsignatures of
selection in Jiaxian Red cattle using whole-genome sequencing data. BMC Genomics. 2021; 22.
https://doi.org/10.1186/s12864-020-07340-0 PMID: 33421990
50. Chen N, Cai Y, Chen Q, Li R, Wang K, Huang Y, et al. Whole-genome resequencing reveals world-wide
ancestry and adaptive introgression events of domesticated cattle in East Asia. Nat Commun. 2018; 9:
1–13. https://doi.org/10.1038/s41467-018-04737-0 PMID: 29904051
51. Kõks S, Reimann E, Lilleoja R, La
¨ttekivi F, Salumets A, Reemann P, et al. Sequencing and annotated
analysis of full genome of Holstein breed bull. Mamm Genome. 2014; 25: 363–373. https://doi.org/10.
1007/s00335-014-9511-5 PMID: 24770584
52. Stafuzza NB, Zerlotini A, Lobo FP, Yamagishi MEB, Chud TCS, Caetano AR, et al. Single nucleotide
variants and InDels identified from whole-genome re-sequencing of Guzerat, Gyr, Girolando and Hol-
stein cattle breeds. PLoS One. 2017; 12. https://doi.org/10.1371/journal.pone.0173954 PMID:
28323836
53. Das A, Panitz F, Gregersen VR, Bendixen C, Holm LE. Deep sequencing of Danish Holstein dairy cattle
for variant detection and insight into potential loss-of-function variants in protein coding genes. BMC
Genomics. 2015; 16: 1043. https://doi.org/10.1186/s12864-015-2249-y PMID: 26645365
54. Doekes HP, Veerkamp RF, Bijma P, Hiemstra SJ, Windig JJ. Trends in genome-wide and region-spe-
cific genetic diversity in the Dutch-Flemish Holstein-Friesian breeding program from 1986 to 2015.
Genet Sel Evol. 2018; 50: 15. https://doi.org/10.1186/s12711-018-0385-y PMID: 29642838
55. CGN, 2009. Deep Red Cattle breed assessment. EU GENRES 870/04 project EURECA. Centre for
Genetic Resources, the Netherlands (CGN) of Wageningen UR. Available: https://edepot.wur.nl/5599.
56. Lawson DJ, van Dorp L, Falush D. A tutorial on how not to over-interpret STRUCTURE and ADMIX-
TURE bar plots. Nat Commun. 2018; 9: 1–11. https://doi.org/10.1038/s41467-018-05257-7 PMID:
30108219
57. Kelleher MM, Berry DP, Kearney JF, McParland S, Buckley F, Purfield DC. Inference of population
structure of purebred dairy and beef cattle using high-density genotype data. Animal. 2017; 11: 15–23.
https://doi.org/10.1017/S1751731116001099 PMID: 27330040
58. Charlier C, Coppieters W, Rollin F, Desmecht D, Agerholm JS, Cambisano N, et al. Highly effective
SNP-based association mapping and management of recessive defects in livestock. Nat Genet. 2008;
40: 449–454. https://doi.org/10.1038/ng.96 PMID: 18344998
59. Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breed-
ing programmes. Nature Reviews Genetics. 2009. pp. 381–391. https://doi.org/10.1038/nrg2575 PMID:
19448663
60. Vilhja
´lmsson BJ, Nordborg M. The nature of confounding in genome-wide association studies. Nat Rev
Genet. 2013; 14: 1–2. https://doi.org/10.1038/nrg3382 PMID: 23165185
61. Flint J, Eskin E. Genome-wide association studies in mice. Nat Rev Genet. 2012; 13: 807–817. https://
doi.org/10.1038/nrg3335 PMID: 23044826
62. Mkize N, Maiwashe A, Dzama K, Dube B, Mapholi N. Suitability of gwas as a tool to discover snps asso-
ciated with tick resistance in cattle: A review. Pathogens. 2021; 10: 1604. https://doi.org/10.3390/
pathogens10121604 PMID: 34959558
63. Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R. A practical guide to environmental asso-
ciation analysis in landscape genomics. Mol Ecol. 2015; 24: 4348–4370. https://doi.org/10.1111/mec.
13322 PMID: 26184487
64. Hu ZL, Fritz ER, Reecy JM. AnimalQTLdb: A livestock QTL database tool set for positional QTL infor-
mation mining and beyond. Nucleic Acids Res. 2007;35. https://doi.org/10.1093/nar/gkl946 PMID:
17135205
65. Kemper KE, Saxton SJ, Bolormaa S, Hayes BJ, Goddard ME. Selection for complex traits leaves little
or no classic signatures of selection. BMC Genomics. 2014; 15. https://doi.org/10.1186/1471-2164-15-
246 PMID: 24678841
66. Howrigan DP, Simonson MA, Keller MC. Detecting autozygosity through runs of homozygosity: A com-
parison of three autozygosity detection algorithms. BMC Genomics. 2011; 12: 1–15. https://doi.org/10.
1186/1471-2164-12-460 PMID: 21943305
67. Marras G, Gaspa G, Sorbolini S, Dimauro C, Ajmone-Marsan P, Valentini A, et al. Analysis of runs of
homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy. Anim Genet.
2015; 46: 110–121. https://doi.org/10.1111/age.12259 PMID: 25530322
68. Pryce JE, Haile-Mariam M, Goddard ME, Hayes BJ. Identification of genomic regions associated with
inbreeding depression in Holstein and Jersey dairy cattle. Genet Sel Evol. 2014; 46. https://doi.org/10.
1186/s12711-014-0071-7 PMID: 25407532
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 17 / 19
69. Kim ES, Sonstegard TS, Van Tassell CP, Wiggans G, Rothschild MF. The relationship between runs of
homozygosity and inbreeding in Jersey cattle under selection. PLoS One. 2015; 10. https://doi.org/10.
1371/journal.pone.0129967 PMID: 26154171
70. Sunryd JC, Cheon B, Graham JB, Giorda KM, Fissore RA, Hebert DN. TMTC1 and TMTC2 are novel
endoplasmic reticulum tetratricopeptide repeat-containing adapter proteins involved in calcium homeo-
stasis. J Biol Chem. 2014; 289: 16085–16099. https://doi.org/10.1074/jbc.M114.554071 PMID:
24764305
71. Bush WD, Simon JD. Quantification of Ca2+ binding to melanin supports the hypothesis that melano-
somes serve a functional role in regulating calcium homeostasis. Pigment Cell Res. 2007; 20: 134–139.
https://doi.org/10.1111/j.1600-0749.2007.00362.x PMID: 17371440
72. Weich K, Affolter V, York D, Rebhun R, Grahn R, Kallenberg A, et al. Pigment intensity in dogs is associ-
ated with a copy number variant upstream of KITLG. Genes (Basel). 2020; 11. https://doi.org/10.3390/
genes11010075 PMID: 31936656
73. Talenti A, Bertolini F, Williams J, Moaeen-Ud-Din M, Frattini S, Coizet B, et al. Genomic analysis sug-
gests KITLG is responsible for a roan pattern in two pakistani goat breeds. J Hered. 2018; 109: 315–
319. https://doi.org/10.1093/jhered/esx093 PMID: 29099936
74. Xu Y, Sun W, Zheng B, Liu X, Luo Z, Kong Y, et al. DEPDC1B knockdown inhibits the development of
malignant melanoma through suppressing cell proliferation and inducing cell apoptosis. Exp Cell Res.
2019; 379: 48–54. https://doi.org/10.1016/j.yexcr.2019.03.021 PMID: 30880030
75. Ballon DR, Flanary PL, Gladue DP, Konopka JB, Dohlman HG, Thorner J. DEP-Domain-mediated reg-
ulation of GPCR signaling responses. Cell. 2006; 126: 1079–1093. https://doi.org/10.1016/j.cell.2006.
07.030 PMID: 16990133
76. Sokol S. A role for WNTS in morphogenesis and tissue polarity. Nature Cell Biology. 2000. pp. E124–
E125. https://doi.org/10.1038/35017136 PMID: 10878822
77. Consonni S V., Gloerich M, Spanjaard E, Bos JL. cAMP regulates DEP domain-mediated binding of the
guanine nucleotide exchange factor Epac1 to phosphatidic acid at the plasma membrane. Proc Natl
Acad Sci U S A. 2012; 109: 3814–3819. https://doi.org/10.1073/pnas.1117599109 PMID: 22343288
78. Xu M, Xie Y (Angela), Abouzeid H, Gordon CT, Fiorentino A, Sun Z, et al. Mutations in the spliceosome
component CWC27 cause retinal degeneration with or without additional developmental anomalies. Am
J Hum Genet. 2017; 100: 592–604. https://doi.org/10.1016/j.ajhg.2017.02.008 PMID: 28285769
79. Lee C, Wallingford JB, Gross JM. CLUAP1 is essential for ciliogenesis and photoreceptor maintenance
in the vertebrate eye. Investig Ophthalmol Vis Sci. 2014; 55: 4585–4592. https://doi.org/10.1167/iovs.
14-14888 PMID: 24970261
80. Murgiano L, Jagannathan V, Calderoni V, Joechler M, Gentile A, Dro
¨gemu¨ller C. Looking the cow in the
eye: Deletion in the NID1 gene is associated with recessive inherited cataract in romagnola cattle. PLoS
One. 2014;9. https://doi.org/10.1371/journal.pone.0110628 PMID: 25347398
81. Bradley R, Terlecki S, Clegg FG. The pathology of a retinal degeneration in Friesian cows. J Comp
Pathol. 1982; 92: 69–83. https://doi.org/10.1016/0021-9975(82)90043-3 PMID: 7068955
82. Stehman SM, William DVM, Rebhun C, Ronald DVM, Riis C. Progressive retinal atrophy in related cat-
tle. Bov Pract. 1987. https://doi.org/10.21423/bovine-vol0no22p195-197
83. Michot P, Chahory S, Marete A, Grohs C, Dagios D, Donzel E, et al. A reverse genetic approach identi-
fies an ancestral frameshift mutation in RP1 causing recessive progressive retinal degeneration in Euro-
pean cattle breeds. Genet Sel Evol. 2016; 48: 56. https://doi.org/10.1186/s12711-016-0232-y PMID:
27510606
84. Hughes TA. Regulation of gene expression by alternative untranslated regions. Trends in Genetics.
Elsevier. 2006. pp. 119–122. https://doi.org/10.1016/j.tig.2006.01.001
85. Saunders MA, Liang H, Li WH. Human polymorphism at microRNAs and microRNA target sites. Proc
Natl Acad Sci U S A. 2007; 104: 3300–3305. https://doi.org/10.1073/pnas.0611347104 PMID:
17360642
86. D’Orazio JA, Nobuhisa T, Cui R, Arya M, Spry M, Wakamatsu K, et al. Topical drug rescue strategy and
skin protection based on the role of Mc1r in UV-induced tanning. Nature. 2006; 443: 340–344. https://
doi.org/10.1038/nature05098 PMID: 16988713
87. Khaled M, Levy C, Fisher DE. Control of melanocyte differentiation by a MITF-PDE4D3 homeostatic cir-
cuit. Genes Dev. 2010; 24: 2276–2281. https://doi.org/10.1101/gad.1937710 PMID: 20952536
88. Bang J, Zippin JH. Cyclic adenosine monophosphate (cAMP) signaling in melanocyte pigmentation and
melanomagenesis. Pigm Cell Melanoma R. 2021. pp. 28–43. https://doi.org/10.1111/pcmr.12920
PMID: 32777162
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 18 / 19
89. Delyon J, Servy A, Laugier F, Andre
´J, Ortonne N, Battistella M, et al. PDE4D promotes FAK-mediated
cell invasion in BRAF-mutated melanoma. Oncogene. 2017; 36: 3252–3262. https://doi.org/10.1038/
onc.2016.469 PMID: 28092671
90. Bogunovic D, O’Neill DW, Belitskaya-Levy I, Vacic V, Yu Y Lo, Adams S, et al. Immune profile and
mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival.
Proc Natl Acad Sci U S A. 2009; 106: 20429–20434. https://doi.org/10.1073/pnas.0905139106 PMID:
19915147
91. Liu L, Harris B, Keehan M, Zhang Y. Genome scan for the degree of white spotting in dairy cattle. Anim
Genet. 2009; 40: 975–977. https://doi.org/10.1111/j.1365-2052.2009.01936.x PMID: 19531114
92. Fontanesi L, Scotti E, Russo V. Haplotype variability in the bovine MITF gene and association with pie-
baldism in Holstein and Simmental cattle breeds. Anim Genet. 2012; 43: 250–256. https://doi.org/10.
1111/j.1365-2052.2011.02242.x PMID: 22486495
93. Baxter LL, Hou L, Loftus SK, Pavan WJ. Spotlight on spotted mice: A review of white spotting mouse
mutants and associated human pigmentation disorders. Pigment Cell Res. 2004; 17: 215–224. https://
doi.org/10.1111/j.1600-0749.2004.00147.x PMID: 15140066
94. Fukuda M. Rab GTPases: Key players in melanosome biogenesis, transport, and transfer. Pigment
Cell Melanoma Res. 2021; 34: 222–235. https://doi.org/10.1111/pcmr.12931 PMID: 32997883
95. Nardo T, Oneda R, Spivak G, Vaz B, Mortier L, Thomas P, et al. A UV-sensitive syndrome patient with a
specific CSA mutation reveals separable roles for CSA in response to UV and oxidative DNA damage.
Proc Natl Acad Sci U S A. 2009; 106: 6209–6214. https://doi.org/10.1073/pnas.0902113106 PMID:
19329487
96. Laugel V, Dalloz C, Durand M, Sauvanaud F, Kristensen U, Vincent MC, et al. Mutation update for the
CSB/ERCC6 and CSA/ERCC8 genes involved in Cockayne syndrome. Hum Mutat. 2010; 31: 113–126.
https://doi.org/10.1002/humu.21154 PMID: 19894250
PLOS ONE
QTL mapping and selection signatures in Groningen White Headed cattle
PLOS ONE | https://doi.org/10.1371/journal.pone.0276309 October 26, 2022 19 / 19
