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Unravelling selection signatures in a single dog breed suggests recent selection for morphological and behavioral traits

Strong selection has resulted in substantial morphological and behavioral diversity across modern dog breeds, which makes dogs interesting model animals to study the underlying genetic architecture of these traits. However, results from between‐breed analyses may confound selection signatures for behavior and morphological features that were coselected during breed development. In this study, we assess population genetic differences in a unique resource of dogs of the same breed but with systematic behavioral selection in only one population. We exploit these different breeding backgrounds to identify signatures of recent selection. Selection signatures within populations were found on chromosomes 4 and 19, with the strongest signals in behavior‐related genes. Regions showing strong signals of divergent selection were located on chromosomes 1, 24, and 32, and include candidate genes for both physical features and behavior. Some of the selection signatures appear to be driven by loci associated with coat color (Chr 24; ASIP) and length (Chr 32; FGF5), while others showed evidence of association with behavior. Our findings suggest that signatures of selection within dog breeds have been driven by selection for morphology and behavior. Furthermore, we demonstrate that combining selection scans with association analyses is effective for dissecting the traits under selection.


| INTRODUCTION
The development of current dog breeds can be viewed as a unique long-term selection experiment to study the process of domestication 1 as well as short-term evolutionary change as a consequence of intensive breeding. 2 While the domestication of the modern dog (Canis lupus familiaris) from wolves took place at least 15 000 years ago, 3 with some estimates considerably earlier (eg, 20 000 to 40 000 years ago 4 ), the popularity of dogs has led to ongoing strict selection according to breeding schemes and standards imposed by breed associations and national kennel clubs. The establishment of genetically and phenotypically distinctive breeds by this intense artificial selection pressure has resulted in high intraspecies variation for physical and physiological features, disease susceptibility and behavior traits, [5][6][7] which makes dogs powerful models to investigate the underlying genetic architecture and signatures of selection for various traits.
Genetic manifestation of the development of dog breeds can be seen as selection signatures, genomic regions targeted by natural or artificial selection that exhibit various characteristics, including population differentiation, extreme linkage disequilibrium (LD) and patterns of the haplotype structure (eg, long-range haplotypes) or mutations in coding region. 8 Accordingly, selection signatures between dog breeds have been reported for physical traits, domestication-related traits and some specific behaviors and have led to the identification of candidate genes, for example, IGF1 for body size, FGF5 for coat length and HAS2 for skin wrinkling, 2 AMY2B, MGAM and SGLT1 for adaptation to a starch-rich diet 9 and TRPM3 and ROBO1 for athletic success in sport-hunting. 10 In a recent whole-genome sequence study of 144 modern dog breeds, positive human-imposed selection was implicated in the fixation or high prevalence within breeds of a range of morphological characteristics (eg, ear shape, height, weight). 11 These recent studies for selection signatures in dogs have focused on between-breed or dog-wolf comparisons and while such studies have allowed detection of signatures related to notable physical features, signatures for more subtle traits like behavior characteristics may be confounded with or masked by signals for the physical features, which might complicate the interpretation of these signatures as appears to be the case for association signals. 12 In this study, we analyzed a single dog breed, the German Shepherd dog (GSD), to detect signals of selection. The breed was established in the late 19th century by crossing multiple breeds, with the initial purpose of creating a sheep-herding dog 13 and later use as a general working dog within the military or police. GSDs used in this study originated from two populations, the UK and Sweden; while the UK population represented a random sample of pet, show and working dogs, the Swedish dogs were bred within a breeding program of the Swedish Armed Forces (SAF) and only dogs that pass a behavior test can become working dogs or be used for breeding. Accordingly, in a previous study 14 we showed that there were significant differences between the two GSD populations for various behavior traits as measured in a questionnaire, for example, aggression against strangers or dogs, chasing, and playfulness. In contrast, morphological differences between populations were reduced compared to betweenbreed studies. We hypothesize that by comparing populations of the same breed but with different behavior-related selection strategies, we may be able to identify selection signatures for behavior as well as those for physical traits. Furthermore, by applying multiple statistical tests for the detection of selection signatures, we have increased the power to detect true signals of selection. Nonetheless, despite the within-breed approach, one of the main difficulties that remains is the identification of the actual trait(s) under selection. We addressed this issue by characterizing the relationship between selection signatures and statistical associations between genotype and phenotype (behavior and morphological traits) from the same populations. We suggest that this approach, combining population genetics and quantitative genetics methods, may also be applicable in other contexts.

| Genomic structure of populations
Characterizing the genetic relationships between individual dogs is a valuable tool to evaluate the genetic structure of GSDs in this study.
The underlying population structure in the two GSD populations (250 dogs in total) was explored by applying a principal component analysis (PCA) and ancestry estimation on a pruned SNP data set. The PCA indicated a separation between the UK and Swedish populations based on the first two principal components (PCs), which explained 2.8% and 1.9% of the genetic variance, respectively ( Figure 1). With respect to PC1 and PC2, the UK dogs had a broader distribution than the Swedish GSDs, suggesting a stronger founder effect in the Swedish cohort. However, some of the UK GSDs clustered with the Swedish GSDs. The overall separation of the two populations is likely due to the geographical separation and thus primarily independent pedigrees but may also reflect the more recent origins of the Swedish population, with the SAF as the only breeder and the primary goal to breed good working dogs. The partial overlap between the two populations is likely due to the use of external dogs in the SAF breeding program, leading to some shared ancestry.  with neighboring genomic regions due to LD, as reviewed in Reference 16. We computed the integrated haplotype score (iHS), which is a variation of the extended haplotype homozygosity (EHH) statistic that aims to detect recent and incomplete selective sweeps within populations. 17 In total, 197 and 142 regions with extreme EHH were detected within the UK and Swedish GSD population, respectively. A list of SNPs belonging to the top 0.5% of the iHS statistic in the UK and Swedish populations is given in Table S2. The iHS statistic identified similar selection signatures in both populations, but the most extreme values differed between populations, as shown by the 10 regions with the highest iHS statistics ( Figure 3, Table 1 18 However, TMEM163 is also a functional candidate for physical features, for example, for eye width and depth 19 and hair color 20 in humans. NCKAP5 (NCK associated protein 5) was also identified as candidate gene for temperament in cattle 21 and has been associated with numerous neurological conditions in humans. [22][23][24] The iHS peak on Chr 4 in the Swedish population points to the CLINT1 (Clathrin Interactor 1) gene. This gene is reported to be among the top risk genes for the susceptibility to schizophrenia in humans 25 and markers near CLINT1 were suggestive peaks associated with barking tendency in a genome-wide association study of behavior traits in Labrador retrievers. 26 We conducted a gene list enrichment analysis with Enrichr 27,28 of the 256 and 338 genes that were located in and close to (within 40 kb of) the regions of the top 0.5% iHS in the UK and Swedish populations, respectively. No pathways were significantly enriched after accounting for multiple testing, however, Panther pathway analyses indicated nominally significant (P < .05) functional enrichment of several pathways for the UK population: "heterotrimeric G-protein signaling -Gi alpha and Gs alpha mediated" (P = .01; genes: GRK4, F I G U R E 2 Ancestry proportions of studied GSDs based on the pruned genomic data assuming three underlying ancestries (K = 3 clusters) as revealed by ADMIXTURE. Each cluster is represented by a color and the length of the specific colored segment indicates the dog's proportion of membership in that cluster F I G U R E 3 Distribution of integrated haplotype score (iHS) in the UK (upper plot) and Swedish population (lower plot). The red line indicates the threshold for the top 0.5% iHS GRK7, RGS12, ADCY2, ADRA2C, DRD2), "Alzheimer disease-presenilin" (P = .02; TRPC6, MMP7, MMP27, RBPJ, MMP20), "heterotrimeric Gprotein signaling -Gq alpha and Go alpha mediated" (P = .02; GRK4, GRK7, CACNA1A, RGS12, DRD2), "ionotropic glutamate receptor" (P = .03; CACNA1A, SLC17A8, GRIA4), and "axon guidance mediated by semaphorins" (P = .03; CRMP1, FYN). All of these functions have been shown to be relevant for behavior among other functions, for example, heterotrimeric G proteins in mood disorders, as reviewed in Reference 29, ionotropic glutamate receptors for long-term synaptic plasticity, as reviewed in References 30, 31 and semaphorins in neuronal structure, as reviewed in Reference 32. Nominally significant pathways for the Swedish population were "5-Hydroxytryptamine degradation" (P = .003; ALDH3A2, ALDH3A1), "apoptosis signaling" (P = .01; MAP2K3, CASP9, DAXX, BAK1, BIRC2, BIRC3), and "Thyrotropin-releasing hormone receptor signaling" (P = .03; PLCE1, STX3, TRHR).

5-hydroxytryptamine (serotonin) is an important neurotransmitter and
plays a key role in numerous behavioral disorders and characteristics, for example, depression 33 and aggressiveness. 34

| Selection signatures between populations
Another approach to identify signatures of selection is the comparison of genetic variation (eg, allele frequencies or haplotype structure) fold-containing (BPIF) superfamily of genes are located in this region (BPIFB2, BPIFB6, BPIFB3, BPIFB4, BPIFA2, BPIFA3, BPIFA1, and BPIFB1). It was shown that these genes play a role in the innate immune system and lipoprotein metabolism, but also in the brain's response to oxidative stress (aging), relevant for neuropsychiatric diseases. 36 Table S3). The NEDD9 (Neural Precursor Cell Expressed, Developmentally Down-Regulated 9) gene has been shown to be associated to cognitive impairment in mice, 38 ADTRP is important for vascular development and function in mouse and zebrafish 39 and TMEM170B has been reported to be downregulated in TCGA human breast cancer data. 40 The region with the highest absolute score (3.8) for the Swedish population was located on Chr 12 (3.6-7.5 Mb). This region contains 59 genes; RNF8 and TBC1D22B are closest to the SNP with the most extreme score.
The ubiquitin gene RNF8 (ring finger protein 8) plays a role in the immune system and has also been linked to autism; a recent study in RNF8 knockout mice indicated a role of this gene in synapse formation and cerebellar-dependent learning abilities. 41 The function of TBC1D22B is largely unknown but it may encode a GTPase-activating protein.
As a third approach to identifying differential selection between the populations, we identified the regions showing differences in extended homozygosity. To identify these selection signatures, we calculated the between-population differences in runs of homozygosity (ΔROH Prop ), which describes the difference in the proportion of dogs with an ROH of a specified length at a given SNP.

| Target regions for divergent selection signatures between populations
In the detection of selection signatures, the application of multiple approaches is recommended to reduce the rate of false positive signals. 16 To identify target regions under differential selection in the two GSD populations, we selected regions from the 99th percentile (top 1%) of each score distribution (SNP window-based F ST , ΔROH Prop , and XP-EHH) and searched for intersecting signals between two or three of the approaches. Using this criterion, we identified 433 SNPs (Table S3) Figure 4). One Panther pathway was nominally significantly (P < .05) enriched by these 114 genes: "p53 pathway feedback loops" (P = .03; CDKN1A, RBL1).
The SNPs identified as under divergent selection by these analyses were further tested for their association with different traits (coat color, coat length, and behavior) separately for each population to identify the putative trait under selection.
A visual inspection of the Circos plot (Figure 4)

ENSCAFG00000009954
-SNPs located in that region that have adjusted P < .1 ("Regional asso-   [52][53][54] The loss of these genes leads to growth restriction, aggression, self-injurious behaviors and mental retardation in affected individuals. The association analysis revealed a significant association between SNPs in this region and aggressive behavior toward strangers in the Swedish GSD population and PRKG2 has previously been reported as a top candidate gene for anxiety in mice. 55 However, the region on Chr 32 is in close proximity to the BMP3 gene associated with skull morphology 56  been shown to carry a derived allele in this gene previously associated with brachycephaly, 56 thus selection on skull morphology seems unlikely. However, we also found a highly significant association with coat length in both populations (Table 2, Figure S2), suggesting that this trait drives the selection signature on Chr 32 (via FGF5).

| Which traits are under selection?
One of the main difficulties in interpreting genomic selection signa- We employed several approaches to characterize the relationship between the detected selection signatures and phenotypic traits that were recorded for these populations. First, we repeated the ADMIX-TURE analysis using only genotypes from SNPs identified as selection signatures ( Figure S1) and fitted the ancestry assignment probabilities to the three individual clusters that were detected as factors in linear models for the phenotypes. We observed significant associations between UK (primarily associated with cluster 1) and Swedish (cluster

3) ancestries and some behavior traits (Stranger-directed interest,
Dog-directed fear) (Table S4). Furthermore, highly significant associations were identified between the ancestries and other dog characteristics, including the function of the dog (working, pet or show dog), coat length, and coat color ( which has previously been associated with coat length, is located in close proximity to the selection signature on Chr 32 and we detected a highly significant association with coat length for this region (BMP3, associated with skull morphology, is also located near this region, but as discussed above, our data does not support a signature of selection associated with this trait). We also detected well-described genes associated with coat color (Chr 24: ASIP, RALY). Together these results suggest that selection for morphological traits (coat length and coat color) has driven differences between the two populations in the genomic regions on Chr 24 and 32. In contrast, the region we detected on Chr 1 showed an association with Chasing in the UK population and comprises candidate genes with functions in behavior, but was not associated with morphological traits that we measured.
Moreover, some of the selection signature regions showed associations with both morphological and behavior traits, for example, the region on Chr 32 was associated with both Stranger-directed aggression and coat length in the Swedish population (Table 2). Furthermore, genes associated with physical appearance like ASIP have previously been associated with behavior traits, for example, social behavior in mice. 62 Thus, it is possible that some of the selection signatures we detected are also associated with multiple traits.

| Limitations of the study
By comparing UK and Swedish GSDs, we hypothesized that we would be able to detect selection signatures for behavior because behavior was the main selection target in the Swedish population. However, we found that the geographical origin of the dogs was confounded with other attributes, for example, coat color and length. We addressed the issue of which trait(s) were under selection by characterizing the relationship between selection signatures and associations with phenotypic attributes (behavior, coat length, coat color), recognizing that the sample size for the association analyses within populations was small and therefore these results should be interpreted with caution. In addition, measurements on other morphological traits (eg, body size and weight) were not available, but these might also be under selection and should be considered in future studies. We conclude that our study of GSD has identified selection signatures probably driven by selection for coat color and length (eg, at the ASIP and FGF5 genes) as well as other signatures that may be related to differential selection for behavior between the

| Samples and phenotypes
The GSDs used in this analysis originated from the UK and Sweden.
For the UK population, GSDs that were at least 2 years old and registered with the UK Kennel Club were recruited via email to participate in a study on behavior genetics. 14

| Genomic structure of populations
To characterize the genomic structure of the GSD populations, a PCA and a cluster analysis were performed. PLINK version 1.9 65

| Between populations
To detect divergent signatures of selection between populations, three different approaches were used: the fixation index (F ST ), XP-EHH, and differences between runs of homozygosity (ROH).
First, the F ST analysis was performed using the script described in Talenti et al. 75 The F ST between UK and Swedish dogs was calculated for each SNP according to the formula reported by Karlsson et al,76 which is a comparison of the allele frequencies between populations: and f UK 2 are frequencies in the UK population for the two alleles and f S 1 and f S 2 are allele frequencies in the Swedish population. Next, the mean F ST was calculated in 1 Mb sliding windows (windowbased F ST ) with an overlap between windows of 500 kb, resulting in each SNP being located in exactly one or two windows. To derive a SNP-based value (to select the top 1% for calculating the intersection with other methods as described below), we averaged the window-based F ST for the one or two windows in which the SNP was found.
Second, the XP-EHH statistic 77 was calculated to compare the EHH between populations, that is, whether alleles are homozygous in one population and polymorphic in the other population. The XP-EHH statistic was calculated for the UK and Swedish populations using phased haplotypes generated by Beagle version 4.1 73 in hapbin, 74 as described above.
For the third approach, ROH were computed in PLINK version 1.9. 65 We ran the analysis with the default settings of a ROH length of 1000 kb and a window size of 65 SNPs, as described above. 70 For every SNP, a homozygosity score (ROH Prop ) was calculated by dividing the number of dogs with a ROH at a specific SNP by the total number of dogs, such that ROH Prop ranges from 0 to 1, as described in Bertolini et al. 78 The absolute difference between ROH Prop between populations (ΔROH Prop ) was used as statistic to determine which ROH are highly represented in one population but underrepresented in the other population. Therefore, for every SNP, ΔROH Prop values were calculated to identify ROH that are present in the majority of dogs in one population but not in the other. First, the squared correlation (r 2 ) between all pairs of SNPs within 10 Mb was calculated in PLINK version 1.9. 65 The average r 2 was then calculated for bins of increasing distance between SNPs to identify the distance around SNPs at which average r 2 drops below 0.5. The longest bin for which average r 2 ≥ 0.5 was 200 kb.

| Gene identification and Gene ontology analysis
To characterize functional relevance of regions showing evidence of selection, the top SNPs or regions (if multiple SNPs were found within 200 kb) were annotated for genes based on the CanFam3.1 genome assembly, 80 using BEDtools 2.27 software. 81 SNPs were annotated considering a flanking region of ± 40 kb, chosen based on the average between-marker distance of the array (20 kb), which was doubled to account for nonevenly spaced SNPs and SNPs lost through qualitycontrol filtering. The genes detected for these selection signatures were then submitted to Enrichr 27,28 to perform gene set enrichment analyses.
Enrichr is an integrative web-based application that compares submitted gene lists to various gene-set libraries; the standard Fisher exact test option was used to calculate P-values for this study.

| Characterizing trait(s) under selection
We employed two approaches to gain insights into the trait(s) under selection, as detected as genomic selection signatures: (I) we modeled behavior traits and other dog characteristics as a function of the dog's ancestry based on selection signature regions and (II) we analyzed the association within each population between these traits and SNP markers in these regions. For both approaches, we compiled a genotype data set of SNPs within the regions showing evidence of selection; this included SNPs belonging to the top 0.5% of the iHS distribution in UK and Swedish populations and SNPs belonging to the top 1% of F ST , XP-EHH, and ΔROH Prop distributions that overlapped between at least two methods.
For (I), we repeated the ADMIXTURE analysis as described above, but only used genotypes of SNPs from putatively selected regions to estimate the ancestry. Then, a linear regression was performed, as described in Reference 82, to model the relationship between the traits and ancestry assignment probabilities.
For (II), we analyzed the association between the traits and SNP markers within the regions showing evidence of selection, separately for each population. Behavior traits were adjusted based on other fixed effects as defined in the previous study 67 and treated as quantitative traits, while coat color ("saddle tan," "sable," "black," "other") and coat length ("long," "short") were treated as categorical traits and not corrected for environmental factors. The association analysis was performed using GEMMA, 83 fitting the genomic relationship matrix (based on 108 817 genome-wide SNPs) as a random effect to account for population stratification. To correct for multiple testing, P-values were adjusted using the false discovery rate (FDR).

ACKNOWLEDGMENTS
The authors want to thank all owners of German Shepherd dogs participating in this study for their time and effort to answer the question-

CONFLICT OF INTEREST
The authors declare no conflict of interest.