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Selection signatures in goats reveal copy number variants underlying breed-defining coat color phenotypes

  • Jan Henkel,

    Roles Investigation, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland, DermFocus, University of Bern, Bern, Switzerland

  • Rashid Saif,

    Roles Investigation, Resources, Writing – review & editing

    Affiliations Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland, Institute of Biotechnology, Gulab Devi Educational Complex, Lahore, Pakistan

  • Vidhya Jagannathan,

    Roles Data curation, Investigation, Methodology, Supervision, Writing – review & editing

    Affiliations Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland, DermFocus, University of Bern, Bern, Switzerland

  • Corinne Schmocker,

    Roles Investigation, Writing – review & editing

    Affiliation Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland

  • Flurina Zeindler,

    Roles Investigation, Writing – review & editing

    Affiliation School of Agricultural, Forest and Food Sciences, Bern University of Applied Sciences, Zollikofen, Switzerland

  • Erika Bangerter,

    Roles Resources, Writing – review & editing

    Affiliation Swiss Goat Breeding Association, Zollikofen, Switzerland

  • Ursula Herren,

    Roles Resources, Writing – review & editing

    Affiliation Swiss Goat Breeding Association, Zollikofen, Switzerland

  • Dimitris Posantzis,

    Roles Resources, Writing – review & editing

    Affiliation Attica Zoological Park, Spata, Greece

  • Zafer Bulut,

    Roles Resources, Writing – review & editing

    Affiliation Department of Biochemistry, Faculty of Veterinary Medicine, Selcuk University, Konya, Turkey

  • Philippe Ammann,

    Roles Resources, Writing – review & editing

    Affiliation ProSpecieRara, Basel, Switzerland

  • Cord Drögemüller,

    Roles Investigation, Resources, Supervision, Writing – review & editing

    Affiliations Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland, DermFocus, University of Bern, Bern, Switzerland

  • Christine Flury,

    Roles Investigation, Supervision, Writing – review & editing

    Affiliation School of Agricultural, Forest and Food Sciences, Bern University of Applied Sciences, Zollikofen, Switzerland

  • Tosso Leeb

    Roles Conceptualization, Funding acquisition, Supervision, Writing – original draft, Writing – review & editing

    Affiliations Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland, DermFocus, University of Bern, Bern, Switzerland

Selection signatures in goats reveal copy number variants underlying breed-defining coat color phenotypes

  • Jan Henkel, 
  • Rashid Saif, 
  • Vidhya Jagannathan, 
  • Corinne Schmocker, 
  • Flurina Zeindler, 
  • Erika Bangerter, 
  • Ursula Herren, 
  • Dimitris Posantzis, 
  • Zafer Bulut, 
  • Philippe Ammann


Domestication and human selection have formed diverse goat breeds with characteristic phenotypes. This process correlated with the fixation of causative genetic variants controlling breed-specific traits within regions of reduced genetic diversity, so called selection signatures or selective sweeps. Using whole genome sequencing of DNA pools (pool-seq) from 20 genetically diverse modern goat breeds and bezoars, we identified 2,239 putative selection signatures. In two Pakistani goat breeds, Pak Angora and Barbari, we found selection signatures in a region harboring KIT, a gene involved in melanoblast development, migration, and survival. The search for candidate causative variants responsible for these selective sweeps revealed two different copy number variants (CNVs) downstream of KIT that were exclusively present in white Pak Angora and white-spotted Barbari goats. Several Swiss goat breeds selected for specific coat colors showed selection signatures at the ASIP locus encoding the agouti signaling protein. Analysis of these selective sweeps revealed four different CNVs associated with the white or tan (AWt), Swiss markings (Asm), badgerface (Ab), and the newly proposed peacock (Apc) allele. RNA-seq analyses on skin samples from goats with the different CNV alleles suggest that the identified structural variants lead to an altered expression of ASIP between eumelanistic and pheomelanistic body areas. Our study yields novel insights into the genetic control of pigmentation by identifying six functionally relevant CNVs. It illustrates how structural changes of the genome have contributed to phenotypic evolution in domestic goats.

Author summary

Domestic animals have been selected for hundreds or sometimes even thousands of years for traits that were appreciated by their human owners. This process correlated with the fixation of causative genetic variants controlling breed-specific traits within regions of reduced genetic diversity, so called selection signatures or selective sweeps. We conducted a comprehensive screen for selection signatures in 20 phenotypically and genetically diverse modern goat breeds and identified a total of 2,239 putative selection signatures in our dataset. Follow-up experiments on selection signatures harboring known candidate genes for coat color revealed six different copy number variants (CNVs). Two of these CNVs were located in the 3’-flanking region of KIT and associated with a completely white coat color phenotype in Pak Angora goats and a white-spotted coat color phenotype in Barbari goats, respectively. The other four CNVs were located at the ASIP locus. They were associated with four different types of coat color patterning in seven Swiss goat breeds. Their functional effect is mediated by region-specific quantitative changes in ASIP mRNA expression. Our study illustrates how structural changes of the genome have contributed to phenotypic evolution in domestic goats.


Goat domestication started around 10,000 years ago in the fertile crescent and is believed to be one of the earliest domestication events of livestock animals [1, 2]. Bezoars, the wild ancestors of domestic goats are an extant species with a distribution in Western Asia from Turkey to Pakistan. Since domestication, goats followed the human migration [3] and played an economically important role for their owners by providing various products like milk, meat or fibers. These economical values were further increased by production-orientated breeding, which led to more than 600 diverse goat breeds at present time [46].

Artificial selection of domesticated goats not only resulted in specialized elite breeds for milk, meat or fibers, but also in breeds with unique coat color phenotypes [4, 7]. Due to their striking appearance, these goat breeds are of special value to their owners, selected for uniform coat color, and kept in closed populations. Coat color phenotypes are one of the most intensively studied traits in goats [812]. They include solid colored animals of different color, animals with symmetrical color patterns, and animals with white markings, white spotting phenotypes or completely white animals.

White markings, white spotting and completely white phenotypes typically result from a lack of melanocytes in the skin and hair follicles. This group of phenotypes is also termed leucism or piebaldism and characterized by defects in melanoblast development or migration [1317].

Very light coat colors resembling white are also seen in animals that have a normal set of melanocytes synthesizing a very pale pheomelanin [18]. Melanocytes produce two types of pigments, the brown to black eumelanin and the red to yellow pheomelanin. The so-called pigment type switching, an intensively studied signaling process, governs whether a given melanocyte produces eumelanin or pheomelanin [19]. Eumelanin is produced, if MC1R is activated by its ligand α-melanocyte stimulating hormone (α-MSH), while pheomelanin is produced if α-MSH is absent and/or outcompeted by binding of the competitive antagonist ASIP to MC1R [2025]. Different alternative promoters of the ASIP gene enable spatially and temporally regulated ASIP expression, which results in characteristic patterns of eumelanin and pheomelanin synthesis [2528].

Domestication and artificial selection correlated with the fixation of causative genetic variants controlling breed-specific traits within regions of reduced genetic diversity, so called selection signatures or selective sweeps [2931]. A method detecting regions of low heterozygosity from sequence data of pooled individuals (pool-seq) was developed and used to identify loci under selection in chicken, pigs, rabbits and the Atlantic herring [3235]. Recently, pooled heterozygosity scores were also applied to monitor loci under selection in goats [36].

In the present study, we aimed to gain a better understanding of the genetic variants determining breed-specific coat color phenotypes in goats. We therefore performed a comprehensive screen for selection signatures in bezoars and 20 breeds of domesticated goats.


Selection signature analysis

For the present study, we selected 8–12 animals each from 20 phenotypically diverse domesticated goat breeds and their wild ancestor, the bezoar. We isolated genomic DNA from these animals and prepared equimolar DNA pools for sequencing (Table 1).

We obtained 2x150 bp paired-end sequence data corresponding to 30x genome coverage per pool, called high confidence SNVs and calculated pooled heterozygosity scores (−ZHp) in 150 kb sliding windows with 75 kb step size (S1 Fig; S1 and S2 Tables). The significance threshold was conservatively set at −ZHp ≥ 4, which identified 5,220 windows with extremely reduced heterozygosity (0.8% of all windows). Overlapping windows were further merged into 2,239 selection signatures (1.1% of total genomic length). This corresponded to 112 selection signatures per breed pool on average (median = 81; S3 Table).

To evaluate the validity of the pool-seq approach, we compared the results from pool-seq data to individual whole genome sequence data of 120 goats from five different Swiss breeds (S1 Table). We called SNVs from the individual sequence data and calculated Hp and −ZHp scores respectively (S2 Table). The pool-seq dataset and the dataset with individual sequences yielded similar results (S2 Fig).

As a validation of the significance threshold, we inspected our data for selection signatures near known causative variants for breed-defining coat color traits. The characteristic brown coat color of the Toggenburg goat is caused by a missense SNV in the TYRP1 gene, p.Gly496Asp [10]. Toggenburg goats showed the expected selection signature harboring the TYRP1 gene with a −ZHp value of 4.88 (Fig 1; S3 Table).

Fig 1. Manhattan plots showing −ZHp values from 20 diverse goat breeds.

The red horizontal line indicates the chosen significance threshold of −ZHp = 4. Each dot represents a 150 kb window. Each plot contains 29 autosomes and two unplaced scaffolds representing the X chromosome. Selection signatures co-localizing with known coat color genes are marked with arrows.

In addition to the search for reduced heterozygosity, we calculated FST values for each breed pool in a pairwise comparison with bezoars. The FST analysis identified 847 selection signatures or 0.4% of the total genomic length (S1 and S3 Figs, S4 Table).

CNVs at the KIT locus in two Pakistani goat breeds

The completely white Pak Angora and the white spotted Barbari breeds showed strong selection signatures harboring the KIT gene on chromosome 6 with −ZHp values of 7.20 and 4.56 (Fig 1; S3 Table). We searched for candidate causative variants within the signatures, but did not detect any coding variants in the KIT gene. However, visual inspection of the short read alignments revealed two different copy number variants (CNVs) downstream of the KIT gene in the Pak Angora and Barbari breeds (Fig 2).

Fig 2. CNVs at the KIT locus.

The coverage plot of bezoars (BEZ) does not show any copy number variation and represents the wildtype allele. In the Pak Angora breed (ANG), the coverage plot shows a triplication of ~100 kb downstream of the KIT gene. In the Barbari breed (BAR), the same region is duplicated. The Barbari allele shows a complex rearrangement involving the insertion of a ~23 kb genome segment originating at 89.2 Mb into the duplicated sequence at ~70.9 Mb with the simultaneous deletion of ~16 kb of KIT sequence. Please note that the coverage at ~89.2 Mb corresponds to three times the average. One genome equivalent corresponds to the wildtype sequence at ~89.2 Mb. Read-pair information indicated that the other two genome equivalents are inserted into the duplicated sequence at ~70.9 Mb (S4 Fig). The dashed red line indicates the average coverage across the whole genome of each pool-seq dataset.

Both CNVs started ~63 kb downstream of KIT and covered ~100 kb of the genome reference sequence without known coding DNA. The short read-aligments of read-pairs spanning the amplification breakpoints confirmed that the individual copies of the CNV were arranged in tandem in a head to tail orientation (S4 Fig). The Pak Angora allele consisted of a triplication of the 100 kb region. The Barbari allele represented a duplication of the same ~100 kb region with an additional 16,280 bp deletion in its central part. The read-pair information at the breakpoints indicated that the deleted part was replaced by a 22,702 bp genomic fragment from the 5’-flanking sequence of the RASSF6 gene, which is located 19 Mb further downstream on the same chromosome (S4 Fig; S5 Table). The shared breakpoints of the two different CNV alleles suggested a common origin of these alleles.

CNVs at the ASIP locus in Swiss goat breeds

Five Swiss goat breeds with different coat color patterns had a selection signature with −ZHp ≥ 4 in the region of the ASIP locus on chromosome 13 (Fig 1; S3 Table). We did not find any ASIP coding variation in the breeds with ASIP selection signatures.

Based on the segregation of coat color patterns in a large breeding experiment, the existence of up to 11 different caprine ASIP alleles has been postulated [8]. The most dominant of this allelic series, termed “white or tan” (AWt), is responsible for white coat color in goats [8]. Furthermore, it has been shown that the white coat color in Saanen goats is caused by a triplication of the ASIP gene [9].

Inspection of the short-read alignments of our own sequence data from Saanen goats at the ASIP locus confirmed the previously reported triplication and revealed the exact boundaries of the triplication. It spans 154,677 bp of the reference genome sequence and comprises the entire coding sequence of the ASIP, AHCY and ITCH genes. The individual copies are arranged in tandem in a head to tail orientation. Appenzell goats, another white goat breed, had the same CNV allele as the Saanen goats (Fig 3, S4 Fig, S5 Table).

Fig 3. CNVs at the ASIP locus.

A Coverage plots of the ASIP locus in different goat breeds reveal four different CNVs. The bezoar (BEZ) coverage plot shows uniform coverage and is characteristic for the wildtype allele (Abz). Underneath, four different mutant ASIP alleles associated with different CNVs are illustrated. The line on top of each plot schematically indicates the most likely configuration of these mutant alleles derived from the available short-read sequence information (S4 Fig). The dashed red line indicates the average coverage across the whole genome of each breed. B Schematic drawings and C representative photographs illustrating the coat color phenotypes of the studied breeds. The photo of the bezoar was obtained during summer, when the dark stripes at the collar and the belly are much less pronounced than in the winter coat. Note that some of the patterns show an exactly inverse distribution of eumelanin and pheomelanin. For example, goats with the Asm allele have white (pheomelanistic) facial stripes and legs, while goats with the Ab or Apc alleles have black (eumelanistic) facial stripes and legs.

We next investigated the coverage plots of Grisons Striped goats and Toggenburg goats. These two breeds show a characteristic color pattern, which has been postulated to be caused by an ASIP allele termed “Swiss markings” (Asm) [8]. They are fixed for an ASIP allele with 8 tandem copies of a 13,433 bp sequence from the 5’-flanking region of ASIP (Fig 3, S4 Fig, S5 Table).

The Chamois Colored goat and the St. Gallen Booted goat are characterized by a color pattern and an ASIP allele termed “badgerface” (Ab) [8]. Our pool-seq data revealed a five-fold amplification of 45,680 bp located ~61 kb downstream of the Asm amplification (Fig 3, S4 Fig, S5 Table).

The Peacock goat is a rare Swiss goat breed with a unique and striking coat color pattern that has not been investigated previously. Pool-seq data from Peacock goats indicated a selection signature at the ASIP locus. The ASIP allele in Peacock goats, which we propose to term “peacock” (Apc), has a quadruplication of the same ~45 kb region having five copies in the Ab allele. It is additionally flanked by triplicated segments of 27,996 bp and 41,807 bp on the left and right side of the quadruplicated sequence (Fig 3, S4 Fig, S5 Table). The central part of the Apc allele has exactly the same breakpoints as the Ab allele suggesting a common origin of Ab and Apc.

The goat genome reference sequence is derived from a San Clemente goat, which has a similar coat color pattern as bezoars. The genome reference therefore supposedly represents the wildtype allele at the ASIP locus, termed “bezoar” (Abz) [8]. Bezoars and all other Swiss goat breeds did not show any CNVs at the ASIP locus. In the remaining goat breeds the AWt, Asm and Ab alleles were segregating at low frequencies. These breeds are either not specifically selected for coat color (e.g. African dwarf goat, Beetal) or the effect of ASIP is phenotypically not visible due to epistatic effects of other genes that lead to white spotting phenotypes (e.g. Boer goat, Pak Angora, Barbari).

Quantitative analysis of ASIP mRNA expression

The different CNVs in putative regulatory regions of the ASIP gene prompted us to hypothesize that quantitative differences in ASIP expression may cause the different color patterns. We therefore obtained whole skin samples from five goats carrying different CNV alleles. We isolated RNA from matched pairs of eumelanistic and pheomelanistic skin and performed an RNA-seq experiment to determine the expression level of ASIP mRNA expression.

In Grisons Striped goats (Asm), Chamois Colored goats (Ab) and Peacock goats (Apc), the eumelanistic skin showed very low ASIP mRNA expression. The pheomelanistic skin regions in these three goats had at least 10-fold higher ASIP expression than the corresponding eumelanistic samples. The uniformly white (pheomelanistic) Saanen goat (AWt) had the highest ASIP mRNA expression. There was no obvious correlation between the quantitative ASIP mRNA expression and the intensity of the pheomelanistic pigmentation. The intensely red colored skin from the Chamois Colored goat had an intermediate ASIP mRNA expression compared to the pale white skin from e.g. the Saanen and Peacock goat. Visual inspection of the RNA-seq short-read alignments indicated the utilization of nine different 5’-untranslated exons in nine different transcript isoforms originating from different ASIP alleles (Fig 4; S1 File).

Fig 4. ASIP mRNA expression and identified transcripts in skin.

A Representative photographs of the five sampled goat breeds. The biopsy sites are numbered and indicated by red circles. B Trimmed mean of M (TMM) values of ASIP mRNA expression were determined from RNA-seq data for each sample. The colors of the bars correspond to the pigmentation of the skin samples. Please note that the Valais Blackneck goat (VAG) has a black base color that is independent of the ASIP gene. This goat has a white spotting phenotype and lacks melanocytes in its caudal half. The low ASIP expression in the unpigmented white skin sample of this goat underscores the difference to the pheomelanistic pale white pigmentation in other goats. C ASIP transcript isoforms in pheomelanistic skin samples from goats with different ASIP alleles. Transcript isoforms X1 and X2 correspond to the RefSeq accessions XM_018057735.1 and XM_018057736.1. CNV breakpoints of the Ab, AWt, and Apc alleles are indicated.


In the present study, we discovered 2,239 loci under selection in 20 diverse goat breeds with various phenotypes and different geographical origins. Our methodology comprised the identification of regions with low heterozygosity from pool-seq data in combination with pairwise FST to bezoars, the wild ancestor of domesticated goats. The pool-seq approach was validated by repeating the analyses with virtually identical results from individually sequenced goats in five breeds. We have to caution that reduced heterozygosity and high FST values may not only result from selection, but may also be due to random demographic changes.

The comprehensive catalogue of identified selection signatures can now be used as a starting point to identify causal genetic variants that control a wide variety of breed-defining traits.

We particularly focused on selection signatures harboring known coat color genes and identified two CNVs in the 3’-flanking region of KIT in two Pakistani goat breeds, the completely white Pak Angora breed and the white spotted Barbari breed. An association between white coat color and the KIT locus has been reported before in Iranian Markhoz goats, which also represents an Angora type goat [37]. The KIT gene is flanked by several hundred kilobases of non-coding genomic DNA on either side, which are required for the precise regulation of its temporal and spatial expression. The KIT protein is a receptor tyrosine kinase mediating a survival signal for several different cell types including melanoblasts and melanocytes, but also e.g. hematopoetic stem cells, mast cells, interstitial cells of Cajal and spermatogonia [38,39]. KIT is a proto-oncogene and its overexpression may have detrimental consequences such as tumor development [40,41]. Insufficient expression of functional KIT protein in melanoblasts or melanocytes will lead to apoptosis of these cells and results in white spotting phenotypes [15,4245].

Structural variants at the KIT locus cause several other breed defining coat color phenotypes in domestic animals, such as the dominant white and belt phenotypes in pigs [33,46,47], color-sided and lineback in cattle [48,49], and tobiano spotting in horses [50]. Lineback in cattle and tobiano in horses also involve structural variants in the 3’-flanking region of KIT [49,50]. All these phenotypes are characterized by striking alterations in pigmentation without any deleterious consequences on the other KIT dependent cell types, which would be expected to result in potentially serious health problems. The comparative data from other species strongly suggest that the newly detected caprine KIT CNVs in goats cause the complete lack of skin and hair pigmentation in Pak Angora and the white spotted phenotype in Barbari goats due to altered expression of KIT during fetal development of melanoblasts.

The ASIP gene codes for the agouti signaling protein, the competitive inhibitor of melanocortin 1 receptor expressed on melanocytes [20]. Variation in the quantitative amount of ASIP mRNA expression from different promoters is the central mechanism regulating the so-called pigment-type switching [19,21,28]. The regulatory elements of the ASIP gene are most likely contained in its large 5’-flanking region. Differing from the KIT locus, ASIP does not contain a very large 3’-flanking region. Spatially and temporally regulated synthesis of eumelanin and pheomelanin enables mammals to express a wide variety of coat color patterns that are essential for e.g. camouflage or mate recognition in many wild species.

ASIP variants cause a wide variety of breed defining coat color phenotypes in domestic animals. ASIP loss of function variants, typically in the coding sequence, are responsible for recessive black in e.g. dogs [51], horses [52], and rabbits [53]. Gain of function variants in ASIP, such as an ectopic overexpression lead to dominant red phenotypes [54]. There are only very few examples of non-coding regulatory variants at the ASIP locus that have been fully characterized at the molecular level. One important example is the mouse black-and tan allele (at), which is caused by a ~6 kb retroviral-like insertion in the region of the hair cycle-specific promoter [25]. In black and tan at mice, hairs are no longer banded and show a uniformly yellow or uniformly black pigmentation. Amplification of the entire ASIP gene has previously been shown to cause the white coat color in many sheep breeds [18] and the white or tan allele (AWt) in Saanen goats [9].

Our study confirmed the previous results and defined the exact breakpoints of the caprine AWt triplication that actually comprises not only the ASIP gene, but also the flanking AHCY and ITCH genes. Unexpectedly, we did not observe selection signatures at the window harboring the ASIP gene in the Saanen and Appenzell breeds. Both of these breeds are strictly selected for uniform white (pheomelanistic) coat color and have a very high frequency of the AWt allele. We think that the lack of a significant −ZHp score at ASIP in these two breeds is caused by at least three factors. For the calculation of the −ZHp score, we considered only SNVs with a maximum coverage of 50x in order to suppress artifacts caused by non-specific mapping of highly repetitive sequences. As the ASIP locus is triplicated in Saanen and Appenzell goats their pools had >50x average coverage at ASIP and almost no SNVs were called in the region. Furthermore, when we inspected the whole genome sequencing data from 24 individual Appenzell goats, we found that only 22 of them were AWt/AWt as expected. The remaining two animals had the genotype AWt/Asm. As AWt is the most dominant allele in the series, it apparently has still not reached absolute fixation and other ASIP alleles are segregating at least in the Appenzell goat breed. Finally, the three copies of the triplication had several sequence differences, which were called as variable positons with a 2:1 ratio of the alleles. This provides a biological explanation why the selection signature might be weaker than expected.

We observed significant selection signatures at the ASIP gene in five Swiss goat breeds. In these breeds, we identified three additional non-coding CNVs in the 5’-region of ASIP that are likely to cause the Swiss markings (Asm), badgerface (Ab) and peacock (Apc) alleles in goats. We have to caution that the peacock phenotype has not been reported before and may be influenced by additional genes other than ASIP.

The corresponding coat color phenotypes are very interesting as they have characteristic patterns of eumelanistic and pheomelanistic pigmentation. Domestic goats with these alleles therefore represent a valuable resource for dissecting the precise function of individual regulatory elements in future studies. The Asm and Ab alleles result in almost exactly inverted distributions of eumelanin and pheomelanin. Our RNA-seq data confirm that the different pigmentation patterns are caused by different levels of ASIP mRNA transcription at different body locations. These data also revealed that the different caprine ASIP gene alleles give rise to a higher number of non-coding 5’-exons compared to other mammals [25,26]. Additional data, such as Cage-seq and full-length Iso-seq data will be required for a comprehensive annotation of all possible transcription start sites and splice isoforms in goats. Such data are expected to become available soon with the advances of the FAANG project [55].

In conclusion, we identified 2,239 selection signatures in 20 diverse goat breeds with various coat color phenotypes. These selection signatures revealed six different functionally relevant CNVs underlying breed-defining coat color phenotypes in goats. The results should help to advance our mechanistic understanding of temporal and spatial regulation of transcription.

Materials and methods

Ethics statement

All animal experiments were performed according the local regulations. All animals in this study were examined with the consent of their owners. Sample collection was approved by the “Cantonal Committee For Animal Experiments” (Canton of Bern; permit 75/16).


For this study, 244 female animals of 20 phenotypical diverse goat breeds and their wild ancestor, the bezoar, were sampled (S1 Table). Ten of the analyzed goat breeds originate from Switzerland, eight from Pakistan and two from Africa. Swiss and African breeds were sampled in Switzerland. Pakistani breeds were sampled in Pakistan. bezoar samples were from zoo animals. For the Swiss goat breeds, we selected representative animals of the breeds and excluded any first-degree relatives. For the other goat breeds, we did not have full pedigree information and used convenience samples. Genomic DNA was isolated from EDTA blood samples.

Whole genome sequencing of pools (pool-seq)

Breed pools were prepared by pooling equimolar amounts of 12 animals per breed (ANG: 10, BEZ: 8 and STG: 10). Illumina TruSeq PCR-free genomic DNA libraries with an insert size of 350 bp were prepared. Each breed pool was sequenced on one lane of an Illumina HiSeq 3000 instrument and on average 300 million 2x150 bp paired-end reads per breed pool were collected (S1 Table).

Mapping and variant calling

Adapter sequences, reads with too many Ns and low quality bases were trimmed or ultimately discarded, if the remaining read length was < 50 bp with fastq-mcf version 1.1.2 (settings: -l 50 -S -q 20). The cleaned reads were mapped to the goat reference genome ARS1 [56] with Burrows-Wheeler Aligner (BWA-MEM) algorithm version 0.7.13 [57] using the “-M” flag to mark shorter alignments as secondary. The resulting mapping files in SAM format were converted into BAM format and coordinate sorted using SAMtools version 1.3 [58]. A local indel realignment was performed using the Genome Analysis Toolkit version 3.7 [59] with default settings. Duplicated reads were marked, using Picard Tools version 2.2.1 ( with default settings for patterned flow cell models. Single nucleotide variants were called using (i) Genome Analysis Toolkit UnifiedGenotyper version 3.7 [59] with the settings: -glm SNP, -stand_call_conf 20, -out_mode EMIT_VARIANTS_ONLY and –ploidy 16/20/24 and (ii) SAMtools mpileup [60] with the settings -q 15, -Q 20, -C 50 and -B. The variants resulting from UnifiedGenotyper were filtered for high quality variants with GATK’s VariantFiltration tool using the generic hard-filtering recommendations available from, while the mpileup files were streamed to the PoPoolation2 version 1.201 pipeline [61]. We used the scripts mpileup2sync.jar with settings --fastq-type sanger and --min-qual 20 and with the settings --min-coverage 15, --max-coverage 50 and --min-count 3. Both pipelines yielded similar numbers of SNV (S2 Table).

Sweep analysis of pool-seq data

A screen for selective sweeps was performed using the SNV file produced for each breed pool individually by the mpileup PoPoolation2 pipeline. At each identified SNV position in the files, we took the numbers of major (nMAJ) and minor (nMIN) allele counts observed in each breed and calculated pooled heterozygosity (Hp) [32] with an in-house written script. The script applies Hp = 2ΣnMAJΣnMIN/(ΣnMAJ+ΣnMIN)2 in a sliding 50% overlapping window approach. We evaluated the results with different window sizes (25 to 300 kb) and decided on 150 kb as the most appropriate size [33, 34]. The obtained Hp values for all 34,382 overlapping 150 kb windows across the whole genome were Z transformed, performing ZHp = (Hp-μHp/σHp). Windows with a −ZHp ≥ 4 were retained as selective windows and adjacent or overlapping selective windows were merged into selection signatures, individually per breed. We annotated the identified selection signatures along with NCBI’s Capra hircus Annotation Release 102 (S3 Table). In addition, to the Hp calculation, we calculated weighted population FST values for each SNV in a 150 kb sliding, 50% overlapping window approach. We applied the script of the Popoolation2 pipeline. The script was run with the settings --min-count 2 --min-coverage 4 --max-coverage 50 --window-size 150000 --step-size 75000 --suppress-noninformative and --pool-size 10:12:12:12:8:12:12:12:12:12:12:12:12:12:12:12:10:12:12:12:12. It used the previously obtained sync file of all pools combined as input and calculated weighted population pairwise Fst values using the standard equation as shown in Hartl and Clark [62]. This resulted in 210 pairwise comparisons, from which we selected the comparisons between the 20 domesticated goat breeds with the bezoar. The obtained FST values were Z transformed, performing ZFST = (FST-μFST/σFST).

Whole genome re-sequencing of individual goats and variant calling

In addition to the pool-seq experiment, we selected 120 goats from five Swiss breeds for individual whole genome re-sequencing. The 24 animals per breed included the 12 goats represented in the breed pools (S1 Table). Illumina TruSeq PCR-free DNA libraries with an insert size of 350 bp were prepared and sequenced on an Illumina NovaSeq 6000 instrument, yielding on average 240 million 2x150 bp paired-end reads per goat (S1 Table). Clean reads were produced by running fastp, version 0.12.5 [63], an ultra-fast all-in-one FASTQ preprocessor capable of trimming polyG tails, a known issue of NovaSeq reads. The cleaned reads were mapped to the ARS1 goat reference genome [56] with Burrows-Wheeler Aligner (BWA-MEM) algorithm using the “-M” flag to mark shorter alignments as secondary. The resulting SAM files were converted into BAM files and coordinate sorted using SAMtools. Duplicated reads were marked, using Picard Tools ( with default settings for patterned flow cell models. The marked BAM files were streamed to GATK’s BaseRecalibrator tool, supported with known SNV provided by the VarGoats consortium ( Subsequently, GATK’s HaplotypeCaller with the settings --emitRefConfidence GVCF and -stand_call_conf 30 was used to call genome-wide variants [59]. The variant files were merged and GATK’s GenotypeGVCFs was used to call variants in the 120 goats combined. As a next step, the called variants were filtered for high quality variants with GATK’s VariantFiltration tool (version 3.8) using the generic hard-filtering recommendations available from SnpEff [64] and NCBI’s Capra hircus Annotation Release 102 was used to annotate the variants.

Sweep analysis of individual goats

To calculate Hp scores of the individual goats, we selected biallelic, passed SNPs per breed using GATK’s SelectVariants tool (version 3.8), applying --restrictAllelesTo BIALLELIC --selectTypeToInclude SNP --sample_expressions '(APZ/BST/PFA/STG/VAG)' --maxNOCALLnumber 0 --excludeFiltered --excludeNonVariants. This yielded on average 14.9 million SNVs per combined Swiss goat breed, comprising each 24 individually sequenced animals (S2 Table). As a next step, the VCF files containing only biallelic, passed SNPs were transformed into table format using GATK’s VariantsToTable tool. This table contained only information regarding SNP position, reference allele and genotype of the 24 animals. With an in-house written Python script, we converted the table produced with GATK’s VariantsToTable to major and minor alleles and counted the number of observations. This output was then used for Hp calculation as described in sweep analysis of pool-seq data.

Manhattan plots

The −ZHp values were plotted using the function manhattan of the qqman package [65] with R [66]. Each data point represents a 150 kb window. A red horizontal line was drawn representing the chosen significance threshold of −ZHp ≥ 4 (corresponding to 0.8% of all windows).

CNV analyses

Coverage plots for regions of interest were created by calculating the coverage of each base in a defined region of interest using Samtools depth -b. Additionally coverage stats across the whole genomes, including the average coverage were calculated using goleft covstats ( Taken both results together, we plotted the coverage using R plot type h version 3.4.1 and indicated the average coverage line. Potential CNVs were also visually evaluated by inspection of the short-read alignemnts (bam-files) in the Integrative Genome Viewer (IGV) [67].

Skin biopsies and total RNA extraction

Skin biopsies were taken from five slaughtered animals of different goat breeds (SAN, BST, GFG, PFA and VAG). Two 6 mm punch biopsies were taken from differentially pigmented body areas of each animal (S6 Table). The biopsies were immediately put in RNAlater (Qiagen) for at least 24 h and then frozen at –20°C. Prior to RNA extraction, the skin biopsies were homogenized mechanically with the TissueLyser II device from Qiagen. Total RNA was extracted from the homogenized tissue using the RNeasy Fibrous Tissue Mini Kit (Qiagen) according to the manufacturer’s instructions. RNA quality was assessed with a FragmentAnalyzer (Advanced Analytical) and the concentration was measured using a Qubit Fluorometer (ThermoFisher Scientific).

Whole transcriptome sequencing (RNA-seq)

From each sample, 1 μg of high quality total RNA (RIN >9) was used for library preparation with the Illumina TruSeq Stranded mRNA kit. The 10 libraries were pooled and sequenced on an S1 flow cell with 2x50 bp paired-end sequencing using an Illumina NovaSeq 6000 instrument. On average, 31.5 million paired-end reads per sample were collected (S6 Table). All reads that passed quality control were mapped to the ARS1 goat reference genome assembly using STAR aligner (version 2.6.0c) [68]. The read abundance was calculated using HTseq (version 0.9.1) [69] and a gff3 file obtained from NCBI’s Capra hircus Annotation Release 102. We used the EdgeR package [70] to read the HTseq count data and calculated the log fold changes using the exactTest function where the biological co-efficient of variation (BCV) was set to 0.1. Trimmed mean of M (TMM) values of ASIP mRNA expression were determined for each sample [71].

Supporting information

S1 Fig. Distributions of Hp, −ZHp, FST and −ZFST.


S2 Fig. −ZHp scores Manhattan plots of pooled sequencing and single sequencing.


S3 Fig. Manhattan plots of −ZHp scores and ZFST scores.


S1 File. FASTA sequences of nine different caprine ASIP transcripts.


S1 Table. Read statistics and accessions of pool-seq and individual WGS data.


S2 Table. Pool-seq and individual WGS SNV statistics.


S3 Table. Hp selection signatures per breed pool.


S4 Table. FST selection signatures per breed pool.


S6 Table. Descriptive statistics and accessions of RNA-seq datasets.



The authors are grateful to all goat owners and breeding organizations who donated samples and shared pedigree data and phenotype information of their animals. We thank Eva Andrist, Nathalie Besuchet Schmutz, Muriel Fragnière, and Sabrina Schenk for expert technical assistance, the Next Generation Sequencing Platform of the University of Bern for performing the high-throughput sequencing experiments, and the Interfaculty Bioinformatics Unit of the University of Bern for providing high performance computing infrastructure. Furthermore, we thank Christian Gazzarin for the photos and Sarah Stangl for the graphical illustrations of the different Swiss goat breeds.


  1. 1. Zeder MA, Hesse B. The initial domestication of goats (Capra hircus) in the Zagros mountains 10,000 years ago. Science. 2000;287: 2254–2257. pmid:10731145
  2. 2. Naderi S, Rezaei H-R, Pompanon F, Blum MG, Negrini R, Naghash H-R, et al. The goat domestication process inferred from large-scale mitochondrial DNA analysis of wild and domestic individuals. Proc Natl Acad Sci USA. 2008;105: 17659–17664. pmid:19004765
  3. 3. Colli L, Milanesi M, Talenti A, Bertolini F, Chen M, Crisa A, et al. Genome-wide SNP profiling of worldwide goat populations reveals strong partitioning of diversity and highlights post-domestication migration routes. Genet Sel Evol. 2018;50: 58. pmid:30449284
  4. 4. FAO. The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture, edited by B.D. Scherf & D. Pilling. FAO Commission on Genetic Resources for Food and Agriculture Assessments. Rome 2015. (
  5. 5. Stella A, Nicolazzi EL, Van Tassell CP, Rothschild MF, Colli L, Rosen BD, et al. AdaptMap: exploring goat diversity and adaptation. Genet Sel Evol. 2018;50: 61. pmid:30453882
  6. 6. Alberto FJ, Boyer F, Orozco-terWengel P, Streeter I, Servin B, de Villemereuil P, et al. Convergent genomic signatures of domestication in sheep and goats. Nat Comm. 2018;9: 813.
  7. 7. Burren A, Neuditschko M, Signer-Hasler H, Frischknecht M, Reber I, Menzi F, et al. Genetic diversity analyses reveal first insights into breed-specific selection signatures within Swiss goat breeds. Anim Genet. 2016;47: 727–739. pmid:27436146
  8. 8. Adalsteinsson S, Sponenberg DP, Alexieva S, Russel AJ. Inheritance of goat coat colors. J Hered. 1994;85: 267–272. pmid:7930499
  9. 9. Fontanesi L, Beretti F, Riggio V, Gomez Gonzalez E, Dall’Olio S, Davoli R, et al. Copy number variation and missense mutations of the agouti signaling protein (ASIP) gene in goat breeds with different coat colors. Cytogenet Genome Res. 2009;126:333–347. pmid:20016133
  10. 10. Becker D, Otto M, Ammann P, Keller I, Drögemüller C, Leeb T. The brown coat colour of Coppernecked goats is associated with a non-synonymous variant at the TYRP1 locus on chromosome 8. Anim Genet. 2015;46:50–54. pmid:25392961
  11. 11. Dietrich J, Menzi F, Ammann P, Drögemüller C, Leeb T. A breeding experiment confirms the dominant mode of inheritance of the brown coat colour associated with the 496Asp TYRP1 allele in goats. Anim Genet. 2015;46: 587–588. pmid:26153465
  12. 12. Menzi F, Keller I, Reber I, Beck J, Brenig B, Schutz E, et al. Genomic amplification of the caprine EDNRA locus might lead to a dose dependent loss of pigmentation. Sci Rep. 2016;6: 28438. pmid:27329507
  13. 13. Jackson IJ. Homologous pigmentation mutations in human, mouse and other model organisms. Hum Mol Genet. 1997;6: 1613–1624. pmid:9300652
  14. 14. Thomas AJ, Erickson CA. The making of a melanocyte: the specification of melanoblasts from the neural crest. Pigment Cell Melanoma Res. 2008;21: 598–610. pmid:19067969
  15. 15. Haase B, Brooks SA, Schlumbaum A, Azor PJ, Bailey E, Alaeddine F, et al. Allelic heterogeneity at the equine KIT locus in dominant white (W) horses. PLoS Genet. 2007;3: e195. pmid:17997609
  16. 16. Greenhill ER, Rocco A, Vibert L, Nikaido M, Kelsh RN. An iterative genetic and dynamical modelling approach identifies novel features of the gene regulatory network underlying melanocyte development. PLoS Genet. 2011;7: e1002265. pmid:21909283
  17. 17. Hauswirth R, Haase B, Blatter M, Brooks SA, Burger D, Drögemüller C, et al. Mutations in MITF and PAX3 cause "splashed white" and other white spotting phenotypes in horses. PLoS Genet. 2012;8: e1002653. pmid:22511888
  18. 18. Norris BJ, Whan VA. A gene duplication affecting expression of the ovine ASIP gene is responsible for white and black sheep. Genome research. 2008;18(8):1282–93. pmid:18493018
  19. 19. Barsh GS. The genetics of pigmentation: from fancy genes to complex traits. Trends Genet. 1996;12: 299–305. pmid:8783939
  20. 20. Lu D, Willard D, Patel IR, Kadwell S, Overton L, Kost T, et al. Agouti protein is an antagonist of the melanocyte-stimulating-hormone receptor. Nature. 1994; 371: 799–802. pmid:7935841
  21. 21. Millar SE, Miller MW, Stevens ME, Barsh GS. Expression and transgenic studies of the mouse agouti gene provide insight into the mechanisms by which mammalian coat color patterns are generated. Development. 1995;121: 3223–3232. pmid:7588057
  22. 22. Le Pape E, Wakamatsu K, Ito S, Wolber R, Hearing VJ. Regulation of eumelanin/pheomelanin synthesis and visible pigmentation in melanocytes by ligands of the melanocortin 1 receptor. Pigment Cell Melanoma Res. 2008;21: 477–486. pmid:18627531
  23. 23. Cieslak M, Reissmann M, Hofreiter M, Ludwig A. Colours of domestication. Biol Rev. 2011;86: 885–899. pmid:21443614
  24. 24. Graham A, Wakamatsu K, Hunt G, Ito S, Thody AJ. Agouti protein inhibits the production of eumelanin and pheomelanin in the presence and absence of alpha-melanocyte stimulating hormone. Pigment Cell Res. 1997;10: 298–303. pmid:9359625
  25. 25. Bultman SJ, Michaud EJ, Woychik RP. Molecular characterization of the mouse agouti locus. Cell. 1992;71: 1195–1204. pmid:1473152
  26. 26. Vrieling H, Duhl DM, Millar SE, Miller KA, Barsh GS. Differences in dorsal and ventral pigmentation result from regional expression of the mouse agouti gene. Proc Natl Acad Sci USA. 1994;91: 5667–5671. pmid:8202545
  27. 27. Drögemüller C, Giese A, Martins-Wess F, Wiedemann S, Andersson L, Brenig B, et al. The mutation causing the black-and-tan pigmentation phenotype of Mangalitza pigs maps to the porcine ASIP locus but does not affect its coding sequence. Mamm Genome. 2006;17: 58–66. pmid:16416091
  28. 28. Kaelin CB, Barsh GS. Genetics of pigmentation in dogs and cats. Ann Rev Anim Biosci. 2013;1: 125–156.
  29. 29. Smith JM, Haigh J. The hitch-hiking effect of a favourable gene. Genet Res. 1974;23: 23–35. pmid:4407212
  30. 30. Hermisson J, Pennings PS. Soft sweeps: molecular population genetics of adaptation from standing genetic variation. Genetics. 2005;169: 2335–2352. pmid:15716498
  31. 31. Pennings PS, Hermisson J. Soft sweeps II—molecular population genetics of adaptation from recurrent mutation or migration. Mol Biol Evol 2006;23: 1076–1084. pmid:16520336
  32. 32. Rubin CJ, Zody MC, Eriksson J, Meadows JR, Sherwood E, Webster MT, et al. Whole-genome resequencing reveals loci under selection during chicken domestication. Nature. 2010;464: 587–591. pmid:20220755
  33. 33. Rubin CJ, Megens HJ, Martinez Barrio A, Maqbool K, Sayyab S, Schwochow D, et al. Strong signatures of selection in the domestic pig genome. Proc Natl Acad Sci USA. 2012;109: 19529–19536. pmid:23151514
  34. 34. Carneiro M, Rubin CJ, Di Palma F, Albert FW, Alfoldi J, Martinez Barrio A, et al. Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication. Science. 2014;345: 1074–1079. pmid:25170157
  35. 35. Martinez Barrio A, Lamichhaney S, Fan G, Rafati N, Pettersson M, Zhang H, et al. The genetic basis for ecological adaptation of the Atlantic herring revealed by genome sequencing. eLife. 2016;5: e12081. pmid:27138043
  36. 36. Wang X, Liu J, Zhou G, Guo J, Yan H, Niu Y, et al. Whole-genome sequencing of eight goat populations for the detection of selection signatures underlying production and adaptive traits. Sci Rep. 2016;6: 38932. pmid:27941843
  37. 37. Nazari-Ghadikolaei A, Mehrabani-Yeganeh H, Miarei-Aashtiani SR, Staiger EA, Rashidi A, Huson HJ. Genome-wide association studies identify candidate genes for coat color and mohair traits in the Iranian Markhoz goat. Front Genet. 2018;9: 105. pmid:29670642
  38. 38. Chabot B, Stephenson DA, Chapman VM, Besmer P, Bernstein A. The proto-oncogene c-kit encoding a transmembrane tyrosine kinase receptor maps to the mouse W locus. Nature. 1988;335: 88–89. pmid:2457811
  39. 39. Roskoski R Jr. Signaling by Kit protein-tyrosine kinase–the stem cell factor receptor. Biochem Biophys Res Commun. 2005;337: 1–13. pmid:16129412
  40. 40. Furitsu T, Tsujimura T, Tono T, Ikeda H, Kitayama H, Koshimizu U, et al. Identification of mutations in the coding sequence of the proto-oncogene c-kit in a human mast cell leukemia cell line causing ligand-independent activation of c-kit product. J Clin Invest. 1993;92: 1736–1744. pmid:7691885
  41. 41. Nagata H, Worobec AS, Semere T, Metcalfe DD. Elevated expression of the proto-oncogene c-kit in patients with mastocytosis. Leukemia. 1998;12: 175–181. pmid:9519779
  42. 42. Geissler EN, Ryan MA, Housman DE. The dominant-white spotting (W) locus of the mouse encodes the c-kit proto-oncogene. Cell. 1988;55: 185–192. pmid:2458842
  43. 43. Giebel LB, Spritz RA. Mutation of the KIT (mast/stem cell growth factor receptor) protooncogene in human piebaldism. Proc Nat Acad Sci USA. 1991;88: 8696–8699. pmid:1717985
  44. 44. Fleischman RA, Saltman DL, Stastny V, Zneimer S. Deletion of the c-kit protooncogene in the human developmental defect piebald trait. Proc Natl Acad Sci USA. 1991;88: 10885–10889. pmid:1720553
  45. 45. Haase B, Brooks SA, Tozaki T, Burger D, Poncet PA, Rieder S, et al. Seven novel KIT mutations in horses with white coat colour phenotypes. Anim Genet. 2009;40: 623–629. pmid:19456317
  46. 46. Marklund S, Kijas J, Rodriguez-Martinez H, Rönnstrand L, Funa K, Moller M, et al. Molecular basis for the dominant white phenotype in the domestic pig. Genome Res. 1998;8: 826–833. pmid:9724328
  47. 47. Giuffra E, Evans G, Törnsten A, Wales R, Day A, Looft H, Plastow G, Andersson L. The Belt mutation in pigs is an allele at the Dominant white (I/KIT) locus. Mamm Genome. 1999;10: 1132–1136. pmid:10594235
  48. 48. Durkin K, Coppieters W, Drögemüller C, Ahariz N, Cambisano N, Druet T, et al. Serial translocation by means of circular intermediates underlies colour sidedness in cattle. Nature. 2012;482: 81–84. pmid:22297974
  49. 49. Küttel L, Letko A, Häfliger IM, Signer-Hasler H, Joller S, Hirsbrunner G, et al. A complex structural variant at the KIT locus in cattle with the Pinzgauer spotting pattern. Anim Genet. 2019; pmid:31294880
  50. 50. Brooks SA, Lear TL, Adelson DL, Bailey E. A chromosome inversion near the KIT gene and the Tobiano spotting pattern in horses. Cytogenetic and genome research. 2007;119: 225–230. pmid:18253033
  51. 51. Kerns JA, Newton J, Berryere TG, Rubin EM, Cheng JF, Schmutz SM, et al. Characterization of the dog Agouti gene and a nonagoutimutation in German Shepherd Dogs. Mamm Genome. 2004;15: 798–808. pmid:15520882
  52. 52. Rieder S, Taourit S, Mariat D, Langlois B, Guerin G. Mutations in the agouti (ASIP), the extension (MC1R), and the brown (TYRP1) loci and their association to coat color phenotypes in horses (Equus caballus). Mamm Genome. 2001;12: 450–455. pmid:11353392
  53. 53. Fontanesi L, Forestier L, Allain D, Scotti E, Beretti F, Deretz-Picoulet S, et al. Characterization of the rabbit agouti signaling protein (ASIP) gene: transcripts and phylogenetic analyses and identification of the causative mutation of the nonagouti black coat colour. Genomics. 2010;95: 166–175. pmid:20004240
  54. 54. Berryere TG, Kerns JA, Barsh GS, Schmutz SM. Association of an Agouti allele with fawn or sable coat color in domestic dogs. Mamm Genome. 2005;16: 262–272. pmid:15965787
  55. 55. Giuffra E, Tuggle CK; FAANG Consortium. Functional Annotation of Animal Genomes (FAANG): Current Achievements and Roadmap. Annu Rev Anim Biosci. 2019;7: 65–88. pmid:30427726
  56. 56. Bickhart DM, Rosen BD, Koren S, Sayre BL, Hastie AR, Chan S, et al. Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome. Nature Genet. 2017;49: 643–650. pmid:28263316
  57. 57. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25: 1754–1760. pmid:19451168
  58. 58. 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. pmid:19505943
  59. 59. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20: 1297–1303. pmid:20644199
  60. 60. 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. pmid:21903627
  61. 61. Kofler R, Pandey RV, Schlötterer C. PoPoolation2: identifying differentiation between populations using sequencing of pooled DNA samples (Pool-Seq). Bioinformatics. 2011;27: 3435–3456. pmid:22025480
  62. 62. Hartl DL, Clark AG. Principles of population genetics: Sinauer associates Sunderland, MA; 1997.
  63. 63. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34: i884–i90. pmid:30423086
  64. 64. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6: 80–92. pmid:22728672
  65. 65. Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. bioRxiv. 2014:005165.
  66. 66. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria 2018.
  67. 67. Thorvaldsdottir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinf. 2013;14: 178–192.
  68. 68. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29: 15–21. pmid:23104886
  69. 69. Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31: 166–169. pmid:25260700
  70. 70. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26: 139–140. pmid:19910308
  71. 71. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11: R25. pmid:20196867