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Investigating the footprint of post-domestication dispersal on the diversity of modern European, African and Asian goats

Genetics Selection Evolution volume 56, Article number: 55 (2024) Cite this article

Abstract

Background

Goats were domesticated in the Fertile Crescent about 10,000 years before present (YBP) and subsequently spread across Eurasia and Africa. This dispersal is expected to generate a gradient of declining genetic diversity with increasing distance from the areas of early livestock management. Previous studies have reported the existence of such genetic cline in European goat populations, but they were based on a limited number of microsatellite markers. Here, we have analyzed data generated by the AdaptMap project and other studies. More specifically, we have used the geographic coordinates and estimates of the observed (Ho) and expected (He) heterozygosities of 1077 European, 1187 African and 617 Asian goats belonging to 38, 43 and 22 different breeds, respectively, to find out whether genetic diversity and distance to Ganj Dareh, a Neolithic settlement in western Iran for which evidence of an early management of domestic goats has been obtained, are significantly correlated.

Results

Principal component and ADMIXTURE analyses revealed an incomplete regional differentiation of European breeds, but two genetic clusters representing Northern Europe and the British-Irish Isles were remarkably differentiated from the remaining European populations. In African breeds, we observed five main clusters: (1) North Africa, (2) West Africa, (3) East Africa, (4) South Africa, and (5) Madagascar. Regarding Asian breeds, three well differentiated West Asian, South Asian and East Asian groups were observed. For European and Asian goats, no strong evidence of significant correlations between Ho and He and distance to Ganj Dareh was found. In contrast, in African breeds we detected a significant gradient of diversity, which decreased with distance to Ganj Dareh.

Conclusions

The detection of a genetic cline associated with distance to the Ganj Dareh in African but not in European or Asian goat breeds might reflect differences in the post-domestication dispersal process and subsequent migratory movements associated with the management of caprine populations from these three continents.

Background

Goats were domesticated 10,000 years before present (YBP) in the Fertile Crescent from distinct bezoar populations, a process that was dispersed in time and space but featured by connected human communities [1, 2]. Neolithic goats showed considerable genetic structure associated with geography, so different gene pools were established when human populations with their livestock migrated to Europe, Asia, and Africa [2]. The potential routes of the post-domestication spread of livestock across Europe [3], Asia [4, 5] and Africa [6] have been reported by several authors. Such a dispersal process may cause genetic clines characterized by a decrease in genetic diversity of livestock populations over geographical distance to the domestication area. In European goats, a gradual reduction of genetic diversity with increasing distance to the Fertile Crescent was observed [7, 8], but a limited number of microsatellite markers were used to investigate the patterns of genetic variation in these studies. Here, we have used Illumina Goat SNP50 BeadChip [9] data generated in the AdaptMap project [10] and other studies [11,12,13,14,15] to assess the existence of genetic clines associated with the post-domestication dispersal of goats in Europe, Africa and Asia.

Methods

Genotype data

We have used published Illumina Goat SNP50 BeadChip data of European, African and Asian goats generated in the Adaptmap project [10, 16]. In addition to the Adaptmap data, we have also retrieved 50 K data from 473 Swiss goats from 10 different breeds [17]. Moreover, the Old Irish Goat Society based on Mulranny (https://oldirishgoat.ie) provided 50 K data from 383 Old Irish and Old English goats. With regard to African breeds, we retrieved previously published 50 K data from Algerian (N = 48; [11]), Sudanese (N = 72; [12]), and South African (commercial and local breeds N = 114; [13]) goats. Regarding Asian breeds, we combined 50 K data from Chinese (N = 193; [14]) and Iranian (N = 235; [15]) goats. We excluded from our study crossbred populations, and we maintained the number of animals per breed in a range between 15 to 50 individuals (with the only exception of the Carpathian goat, N = 14) by using the “bite.representative.sampling” function of the BITE R package v.2 [18]. This tool preserves the variance structure of the original data set, despite reducing the sample size to a user-defined number. In total, our final data set contained genotype data from 1077 European, 1187 African and 617 Asian goats belonging to 38, 43 and 22 populations, respectively. Observed and expected heterozygosity measurements and geographic coordinates of all goat populations included in the current work are described in Tables 1, 2, 3 and Additional file 1: Figure S1. By using the PLINK v 1.9 software [19] and taking as a reference the goat ARS1 genome [20], the chromosome number, genomic position and name of each SNP were updated, resulting in the retention of 49,376 single nucleotide polymorphisms (SNPs) for European goats, 49,056 SNPs for African goats and 48,898 SNPs for Asian goats. The PLINK v 1.9 software [19] was also used to merge different data sets and filter out uninformative markers i.e. (1) SNPs with minor allele frequencies (MAF) lower than 0.05, (2) SNPs with missing call rates higher than 0.05, (3) SNPs that did not fulfil the Hardy–Weinberg expectation (P ≤ 0.001), and (4) unmapped SNPs. Moreover, individuals with missing call rates higher than 0.1 were also excluded. After these filtering steps, the African, European and Asian data sets comprised 25,990, 18,135 and 26,888 SNPs respectively. The final total data set (combined data sets of African, European, and Asian breeds) contained, after filtering, 39,030 SNPs genotyped in 2881 goats from 81 breeds.

Table 1 Observed and expected heterozygosities, FST, Fis and geographic coordinates of the European goat breeds (ordered by country of origin) analyzed in the current work (distances between sampling locations and Ganj Dareh are indicated in km)
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Table 2 Observed and expected heterozygosities, FST, Fis and geographic coordinates of the African goat breeds (ordered by country of origin) analyzed in the current work (distances between sampling locations and Ganj Dareh are indicated in km)
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Table 3 Observed and expected heterozygosities, FST, Fis and geographic coordinates of the Asian goat breeds (ordered by country of origin) analyzed in the current work (distances between sampling locations and Ganj Dareh are indicated in km)
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Population structure analysis

We assessed population structure using PLINK v. 1.9 [19] to carry out a principal component analysis (PCA) and the R software v.4.1.3. was employed for visualizing the resulting plot. Considering the large number of breeds and samples, the same software was used to plot the centroids of the principal components 1 and 2 for each breed, and such values were used to construct the PCA presented in the main and Additional Figures. Moreover, population structure was investigated with the ADMIXTURE v.1.3.0 package [21] with number of clusters (K) varying from 2 to 15. To assess the quality of the clustering process and thus infer the most likely K-value, we estimated the cross-validation error for each K-value. To visualize the results of the ADMIXTURE analysis, we used the Pophelper R package [22].

Correlating genome-wide diversity with distance to Ganj Dareh

We employed Arlequin v. 3.5.2.2 [23] to calculate observed heterozygosity (Ho), expected heterozygosity (He), the FST coefficient of differentiation, and the inbreeding coefficient Fis. The main reason for calculating both Ho and He is that they provide complementary information: while He is estimated from allele frequencies, Ho is calculated from individual genotypes directly and depends on both the magnitude of genetic diversity in the population and the amount of inbreeding [24]. Moreover, their contrast (Fis = 1−\(\frac{Ho}{He}\)) provides valuable insights about the patterns of variation, with negative and positive values indicating the existence of a deficit (e.g. due to admixture) or an excess (e.g. due to inbreeding) of homozygous genotypes, respectively.

We have chosen Ganj Dareh, in the central Zagros Mountains (Western Iran), as a location representative of the geographic coordinates of the areas of early goat management in the Fertile Crescent, since substantial archaeological and genetic evidence support the practice of goat husbandry in this region at least 10,200 YBP [1, 25]. To calculate geographic distances (in kilometers) from the sampling site of each breed to Ganj Dareh (latitude = 34.27º N and longitude = 47.47º E), we have used the latitude and longitude coordinates provided by Colli et al. [16], Stella et al.[10], and Ouchene-Khelifi et al. [11]. The sampling site lists of the South African, Algerian, Chinese and Iranian populations are available in Chokoe et al. [13], Rahmatalla et al. [12], Berihulay et al. [14], and Nazari-Ghadikolaei et al. [15], respectively, and we have searched for the corresponding coordinates in the open source databases available online (https://www.latlong.net/). For Swiss [17], Irish and British (Old Irish Goat Society, https://oldirishgoat.ie) breeds, we used centroids of country geographic coordinates to calculate distances to Ganj Dareh since the coordinates of sampling sites were not available. Geographical distances were obtained with the geosphere package [26] of the R software v.4.1.3. using the “distVincentyEllipsoid” method which considers the earth as an ellipsoid flattened at the poles, thus providing a very accurate calculation of distances [27]. We estimated pairwise FST coefficients between the Iranian Markhoz breed, which is raised in an area (Latitude = 35.32º N and Longitude = 46.98º E) close to Ganj Dareh, against all population from Europe, Africa, and Asia. Pearson correlation coefficients (r) were computed to assess if there is a linear relationship between Ho, He and FST estimates and geographical distances between breed sampling sites and Ganj Dareh by using the stats package included in the R software v.4.1.3 [28]. Linear regressions were plotted with the ggplot2 package of R software v.4.1.3. For Europe and Africa, we did two separate analyses including or excluding insular populations. The reason for not including insular populations is that they usually have reduced levels of diversity due to geographic isolation rather than to ancient post-domestication events [29]. In the case of African populations, we excluded from our analysis goats from the Boer, Savanna, and Kalahari Red breeds because there is evidence that their ancestry has an Asian component, so they are not fully representative of South African indigenous local goats [16, 30].

In addition, Ho, He and Fis values computed for each population were used to construct interpolation maps drawn using the inverse distance weighted (IDW) option implemented in the GIS software ArcGIS v. 3.2.0 (https://www.arcgis.com/index.html ESRI, Redlands, CA, United States). This deterministic method of multivariate interpolation considers a set of scattered points with known values for a variable and calculates the values of the variable for points with missing values by taking into account the weighted average of the values available at the known points. The measured values closest to the location to be predicted have more influence on the predicted value than those farther away. The sampling area of each population was used as geographic coordinates and interpolation surfaces were divided into ten equal classes. Moreover, to evaluate whether inbreeding could affect our inferences about the potential existence of gradients of diversity, we have retrieved all FROH values from goat breeds reported by Bertolini et al. [31] in the framework of the AdaptMap project (as long as their sample sizes were above 15 individuals). Then, we have calculated Pearson correlations between such coefficients and distance to Ganj Dareh.

Results and discussion

Population structure and global diversity analysis

We analyzed the population structure of the European, African and Asian goats by using ADMIXTURE (Fig. 1) and PCA (Fig. 2) tools. Regarding European breeds, we observed a partial regional differentiation, except for those from Northern Europe (Denmark, The Netherlands and Finland), Great Britain and Ireland. Strong differences in autosomal SNP as well as chromosome Y haplotype frequencies have been observed when comparing Northern and Southern European goats [16, 30] and we have detected the same trend in the PCA shown in Additional file 1 Figure S2, with the 50º latitude dividing Northern and Southern European goats. This pattern can be explained partially by the post domestication dispersal of goats across Europe through two main corridors: the Mediterranean route, which involved the maritime transportation of livestock along the Mediterranean basin until reaching the Iberian Peninsula 7300–7700 YBP, and the Danubian route, which traversed the European mainland and reached Scandinavia and the British Isles 4000 YBP [3].

Fig. 1
figure 1figure 1

ADMIXTURE analysis of African, European Asian goat breeds included in our study. Each bar represents the percentages of global ancestries from one or more of K = 2–15 genetically distinct sources for each individual. Continental subregions in Africa include the following countries: (1) Northern Africa: Morocco, Algeria, Tunisia, and Egypt, (2) Western Africa: Burkina Faso, Mali, Nigeria, Cameroon, and Canary Islands, (3) Eastern Africa: Sudan, Ethiopia, Uganda, Burundi, Kenya, Tanzania, and Malawi, (4) Southern Africa: Mozambique, Zimbabwe, and South Africa, and (5) Madagascar. Continental subregions in Europe include the following countries: (1) Northern Europe: Denmark, Finland, and The Netherlands, (2) Central Europe: Switzerland, (3) Western Europe: France, (4) Eastern Europe: Romania, (5) Southern Europe: Italy and Spain, and (6) United Kingdom and Ireland. Continental subregions in Asia include the following countries: (1) West Asia: Iran and Turkey, (2) Pakistan and (3) China. African, European and Asian breeds and subregions names are reported

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Fig. 2
figure 2

Principal Component Analysis plot of European, African and Asian breeds. Principal components 1 and 2 and percentages of variance explained by them. The figure shows the centroids of principal components 1 and 2 for each breed. Samples are coloured according to their continental subregion of sampling and represented by breed acronyms. The list of complete breed names can be found in Tables 1, 2 and 3

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For African goats, we have observed five main clusters representing populations from South, West, North and East Africa plus a fifth Malagasy group (see Additional file 1: Figure S3), which was supported by the ADMIXTURE analysis (Fig. 1) and agrees with previous findings [16]. Geographic (e.g. Sahara and Kalahari deserts) and biological (e.g. Tsetse fly belt) barriers may have contributed substantially to the genetic differentiation of goat populations from West, East, North and South Africa. In the case of Malagasy goats, their genetic differentiation from continental populations is probably explained by their insular origin and the likely occurrence of a strong founder effect [29]. Finally, Palmera goats cluster with the West African breeds because they were transported to the Canary Islands by settlers of Amazigh origin 2000–2500 YBP [32].

In the case of Asian goats (see Additional file 1: Figure S4), we can observe three main clusters represented by goats from West Asia/Near East (Turkey and Iran), South Asia (Pakistan) and East Asia (China). The early diffusion of goat pastoralism in Asia has not been characterized in depth yet, but Pereira and Amorim [33] have proposed two main corridors of dispersal, i.e. (1) the central Asian steppes, traversing Afghanistan and reaching Mongolia and northern China, and (2) through the Indus Valley spreading into the Indian subcontinent and, subsequently, to Southeast Asia. Interestingly, the analysis of archaeological remains at the Djeitun site in Southern Turkmenistan dated to ca. 8500 YBP provided evidence about the important role of ovicaprids as a source of animal protein [34]. Besides, more recently, zooarchaeological and collagen peptide mass fingerprinting demonstrated the ancient husbandry of sheep and goats at the Obishir V site in Southern Kyrgyzstan 8000 YBP [34]. Moreover, evidence dating back to 4912–4761 YBP has been acquired, indicating the consumption of milk from sheep and other unidentified ruminants among Afanasievo groups in the Altai mountains [35]. These mountains serve as a natural boundary, separating the lowlands of Kazakhstan and Western Siberia from Mongolia. The entry of goats in China might have taken place through the Hexi Corridor (Gansu-Qinghai region, 5600–5000 YBP), and/or by crossing the Eurasian steppes and the Mongolian Plateau (∼5500–4500 cal YBP) [36]. This complex process of pastoralism diffusion in Asia, which is still quite unknown, might have led to the establishment of highly differentiated goat gene pools in the three regions (West, South and East Asia) under study, as shown in Additional file 1: Figure S4.

The global average values of Ho and He for European (Ho = 0.394, He = 0.393), African (Ho = 0.391, He = 0.393) and Asian (Ho = 0.381, He = 0.381) populations were quite similar and, in general, high. The average Fis coefficients, that indicate the departure of Ho from He, were − 0.0025, 0.0033 and − 0.0012 for European, African and Asian populations, respectively. With regard to FROH values (see Additional file 2: Table S1), a low average coefficient (FROH = 0.08) was estimated for European breeds, with the highest value for the northern European Landrace breed (FROH = 0.16). Similarly, for the African breeds, a low average FROH value (FROH = 0.08) was estimated, with the highest coefficients for populations from Madagascar (Sofia: FROH = 0.35; Menabe: FROH = 0.32) and the Palmera breed of the Canary Islands (FROH = 0.23). In contrast, a moderate average FROH value (FROH = 0.13) was found for Asian goats, with FROH values of 0.25 for the Kachan and Kamori breeds from Pakistan.

Diversity of European goat populations is not correlated with distance to Ganj Dareh

We investigated whether Ho and He values of African, European, and Asian populations show significant correlations (r) with distance from their sampling location to Ganj Dareh. When analyzing goat populations from Europe (Fig. 3a), we obtained negative and significant correlations (Ho: r =  − 0.47, P = 0.002, Fig. 3a; He: r = − 0.40, P = 0.01, Fig. 3a) for both heterozygosity values. However, these two correlations became non-significant (Ho: r = − 0.22, P = 0.24, Fig. 3a; He: r = − 0.22, P = 0.22, Fig. 3a) when British and Irish populations were removed from the European data set. Indeed, the majority of European breeds displayed moderate to high heterozygosity values (Fig. 3a), with the exception of the populations from United Kingdom (Ho = 0.29; He = 0.32) and Ireland (Ho = 0.35; He = 0.37). Even the Spanish Bermeya and Malagueña breeds, which are located very far apart from Ganj Dareh, displayed high heterozygosities (Ho = 0.41; He = 0.40 in Bermeya and Ho = 0.42; He = 0.42 in Malagueña). On the other hand, correlations between FST values and distance to Ganj Dareh were positive and significant when insular populations were included in the analysis (r = 0.37, P = 0.02), but became non-significant (r = 0.28, P = 0.12) when such populations were removed from the analysis (see Additional file 1: Figure S5a). Moreover, correlations between FROH values and distance to Ganj Dareh with (r = 0.13, P-value = 0.52) or without (r = 0.06, P-value = 0.79) islands were non-significant (see Additional file 1: Figure S6a), and the interpolation map (see Additional file 1: Figure S7) and list (see Table 1) of Fis values evidenced that they are, in general, weak and negative.

Fig. 3
figure 3

Graphs depicting the relationships between observed and expected heterozygosities of European, African and Asian goat populations and distance between their sampling locations and Ganj Dareh. Graphs depicting the relationships (expressed as Pearson correlations and their P-values) between observed heterozygosity and expected heterozygosity and distance from Ganj Dareh (early Neolithic settlement in the Zagros Mountains representative of the geographic coordinates of the areas of early goat management in the Fertile Crescent) to sampling locations of a European breeds, including and not including insular populations, b African breeds, including and not including insular populations, c Asian populations. In all plots, country of origin is indicated with specific colours. Breed acronyms are listed in Tables 1, 2 and 3

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Such results do not fully match those of Cañón et al. [7], who described a decrease in caprine genetic diversity from the south-east to the north-west of Europe. This could be due to the limited number of microsatellite markers used by Cañón et al. [7], but also to the fact that Cañón et al. [7] had a much broader collection of Eastern European goat breeds than us. The significant gradient that we observe when British and Irish populations are included in the analysis might be due to their strong demographic recession [37], which is reflected by their high levels of homozygosity [29]. However, we cannot rule out the possibility that the low diversity of British and Irish cattle is partly explained by one or more founder effects associated with the arrival of livestock to the United Kingdom and Ireland 5800–6000 YBP, as suggested for British cattle [38].

The lack of a significant gradient of diversity in European goat breeds could be due to post-domestication migratory movements associated with trading and herding. Throughout the millennia, the Mediterranean Sea has facilitated the exchange of goods and livestock via a dense network of commercial maritime routes connecting distant port cities within and outside Europe. Indeed, Cardoso et al. [29] reported that goats from Mediterranean islands have lower levels of homozygosity than those from remote islands as Iceland, La Palma or Madagascar. In addition, the Great European Plain, which is one of the largest continuous expanses of plain on the Earth's surface, may have facilitated the exchange of goats and other livestock amongst distant locations within Europe. This interpretation is supported by the mostly negative Fis values shown in the corresponding interpolation map (see Additional file 1 Figure S7), which are compatible with a slight excess of heterozygosity. Even more, in recent times the widespread use of improved breeds (e.g., Saanen, Toggenburg and Alpine), and artificial insemination might also have contributed to increasing gene flow between distant European populations. Besides, there is evidence that these highly productive cosmopolitan breeds have introgressed many local breeds in Europe [30].

Detection of a significant gradient of diversity associated to distance to Ganj Dareh in African goats

In contrast with European goats, significant negative correlations between the diversity of African caprine populations and distances to Ganj Dareh have been observed in the data sets with (Madagascar and La Palma) and without islands (Fig. 3b). Indeed, we obtained correlation coefficients of − 0.46 (Ho, P = 0.0044) and − 0.49 (He, P = 0.0023) in the data set with no islands (Fig. 3b) and correlation coefficients of − 0.51 (Ho, P = 0.00079) and − 0.53 (He, P = 0.00043) in the data set with islands (Fig. 3b). Consistently, the magnitude of FST coefficients was highly correlated with distance from the African sampling sites to Ganj Dareh for both data sets with (r = 0.57, P = 0.00011) and without (r = 0.62, P = 0.000045) islands (see Additional file 1: Figure S5b). The correlation between FROH and distance to Ganj Dareh was not significant (r = 0.23, P = 0.21) when insular populations were excluded from the analysis, while it became significant (r = 0.38, P = 0.025) when Malagasy goats were taken into consideration (see Additional file 1: Figure S6b). This result could be anticipated because Malagasy goats have high FROH coefficients, probably because of the occurrence of a strong founder effect [29]. Based on these results and the interpolation map (see Additional file 1: Figure S7) and list (See Table 2) displaying Fis values, which are mostly close to zero and negative (except in North Africa), we conclude that the decrease of diversity associated to distance to Ganj Dareh observed in African breeds is not caused by a parallel augment of inbreeding.

We have observed that the Egyptian, Algerian, and Sudanese populations, which are closest to the Fertile Crescent, show the highest heterozygosity values (see Table 2). When proceeding southwards and particularly south-eastwards, diversity decreases, as evidenced in goat breeds from Mozambique (Ho = 0.33; He = 0.34) and Malawi (Ho = 0.35; He = 0.37), and particularly in the island of Madagascar (Ho = 0.31; He = 0.33). With regard to indigenous South African breeds, their diversity is high (Ho = 0.39; He = 0.42), probably because many of these breeds have been introgressed by Boer goats. The Boer breed has a mixed Asian and African ancestry [30], and there is evidence that Anglo-Nubian bucks contributed to its foundation [4].

The dispersal of livestock by land is expected to take place through a series of founder effects, thus generating gradients of decreasing diversity and increasing genetic differentiation as the ones observed in our work. In contrast, when domestic animals are transported by sea it is more likely to observe a leap-frog pattern of diffusion that does not necessarily result in genetic clines of differentiation or diversity. In consequence, the detection of a gradient of diversity (Ho and He) and genetic differentiation (FST) associated with distance to Ganj Dareh in African goats is consistent with an overland rather than maritime post-domestication dispersal of goats throughout the African continent, with the only exception of the North African shoreline where maritime diffusion throughout the Mediterranean Sea was important [39] as attested by remains of impressed pottery, crop plants and sheep, goats, and cattle remains found in archaeological sites in Lybia, Algeria and Morocco [40]. The predominant overland spread of domesticates in Africa (when compared to Europe) might be explained by the fact that the surfaces of Europe and Africa are about 10 million km2 and 30 million km2, respectively, while their coastal lines are 30,000 km (Africa) and 143,000 km (Europe) long [41, 42]. Besides, in Africa there is a relative scarcity of natural harbors and long navigable river systems, the latter due to the ruggedness of the terrain, with rapids and waterfalls as well as shallow river points, strong seasonal fluctuations in water flow, siltation, and sedimentation in lower reaches [41]. This means that the inner parts of the African continent are less easily accessible by navigation than European inland, making transportation of livestock difficult.

The early entry of goats in Africa probably took place in North Africa through the Sinai Peninsula as well as through the Mediterranean Sea [6], coinciding with the opening of a grassland niche in the Sahara that was gradually occupied by pastoral communities [6]. The increasing aridity of the Sahara around 4500 YBP and the consequent southward retreat of the Tsetse fly belt favored the migration of herders towards the Sahel. However, the entry of livestock into West and East Africa took place not before than 3500 YBP or even later [40], possibly because of a lack of immunity to endemic diseases. Goat and sheep remains dating back to 2400 YBP and 2100 YBP have been found at the sites of Salumano (Zambia) and Bamba (Zimbabwe), proving that the arrival of small ruminants to Southern Africa is quite recent [6]. This might have involved migrations through and along the coastal areas of the Congo Basin or facilitated by the opening of Tsetse corridors along the highland of the Rift Valley [40, 43]

As shown in Fig. 4, goats from Central and East Africa are less diverse than their Northern counterparts, possibly because the Sahara Desert, which covers 9.1 million km2, constitutes a formidable geographical barrier to the southwards spread of pastoral communities and their livestock [44]. Moreover, Central Africa overlaps with the Tsetse fly belt, which covers a geographic area of 10 million km2, between latitudes 14° N and 20° S, representing about one third of the African continent. Trypanosomiasis is a protozoan disease which causes anemia, fever, and weight loss and sometimes can be fatal, representing a heavy economic burden to African countries in which this infection is endemic [45]. Susceptibility to this parasite may have limited the diffusion and exchange of caprine stocks in Tsetse fly infested areas. Interestingly, Traorè and coworkers showed that the presence of the Tsetse fly influences the genetic variability of goats from Burkina-Faso, and they demonstrated that trypanosomiasis might have acted as a landscape boundary both for the spread of trypanosensitive goats and for strong selection pressure on trypanotolerant goats in infested areas [46].

Fig. 4
figure 4

Interpolation maps showing the geographic distribution of observed and expected heterozygosities in African, European and Asian breeds. Interpolation maps showing the distribution of genetic diversity in African, European and Asian breeds. a Observed heterozygosity, Ho. b Expected heterozygosity, He. Blue points represent sampling localities in a and b, respectively. In Europe, a reduction of diversity is evident in goats from the United Kingdom and Ireland, while in Africa low diversity coincides with the Tsetse fly belt (a geographic area comprised between latitudes 14° N and 20° S) and Madagascar. In Asia, low variation is detected in Pakistan and Southern China

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We have detected a high variability of several South African indigenous breeds even though this region remained considerably isolated from Asia and Europe [47]. We excluded from the gradient analysis South African commercial goats (Boer, Kalahari Red and Savanna) because it is well known that Boer goats have a mixed African and Asian ancestry [16, 30], and that Kalahari and Savanna goats have a strong Boer component. We kept in our analysis indigenous communal populations sampled in the main goat-producing provinces of South Africa (Limpopo, Freestate, Gauteng, Northwest), which happened to have high levels of heterozygosity. This could be due to the fact that these South African populations have been also introgressed to some extent by Boer goats as well as by goats of European origin. Indeed, the establishment, in South Africa, of British and Dutch farmers, during the seventeenth-nineteenth centuries, promoted the development or importation of highly productive breeds to improve the local stocks [4].

Absence of a gradient of caprine diversity in Asia

In the case of the Asian goat breed data set (which does not include insular breeds), we obtained correlation coefficients of − 0.32 (Ho, P = 0.15; Fig. 3c) and − 0.26 (He, P = 0.24, Fig. 3c) when contrasting heterozygosity values against distance to Ganj Dareh, while the correlation between such distance and FST values (r = 0.24, P = 0.30) was also non-significant (see Additional file 1: Figure S5c). Moreover, when we investigated the correlation between FROH and distance to Ganj Dareh (see Additional file 1: Figure S6c), we obtained a significant and positive value (r = 0.60, P = 0.02). This latter analysis only encompassed AdaptMap populations from Turkey and Pakistan, so the number of observations is relatively limited. However, the inspection of Additional file 1: Figure S6c makes evident that goat breeds from Pakistan display a range of FROH values considerably broader than those observed in European or African continental populations. The interpolation map (see Additional file 1: Figure S7) and list (Table 3) showing Fis values also evidenced that in Asian goats such coefficients are slightly positive, a potential indication about the existence of inbreeding. Kumar et al. [48] examined the diversity of seven indigenous Pakistani goat populations and found that five of them (Bari, Black Tapri, Bugitoori, Kamori and Pateri) displayed FROH values close to or above 0.10, with the Bugitoori breed being particularly inbred (FROH = 0.34). Information about the history and demography of the Pakistani breeds investigated in our study is very scarce, so it is difficult to disentangle why several of them have such high inbreeding coefficients. One potential reason would be the occurrence of series of floods (about 1 million of domestic animals were killed in 2022 floods), prolonged and extreme periods of drought, and severe heat waves which have caused significant losses of livestock resources in several places in Pakistan, including Punjab which is the most important agricultural area of the country [49]. We hypothesize that such abrupt demographic reductions might have led to increases in inbreeding levels of goat populations from the affected areas, although we cannot rule out other alternate explanations.

Conclusions

A genetic cline associated with distance to Ganj Dareh has been observed in African goats but not in their European and Asian counterparts. Regarding Asian goats, we have just sampled goat breeds from four countries, so it is difficult to anticipate whether a more extensive sampling could lead to the detection of such genetic cline. In the case of African goats, the existence of a gradient of diversity could be explained, at least in part, by a predominantly overland post-domestication dispersal of goats in Africa due to the paucity of natural harbors and navigable rivers in this continent. In contrast, Europe has a long coastline, a feature that might have favored the maritime diffusion of the Neolithic package. Besides, about two thirds of the African continent are occupied by two formidable geographic (Sahara Desert) and biological (Tsetse fly belt) barriers that restrict the long-distance transportation of livestock, while most of Europe is covered by an uninterrupted plain that goes from the Pyrenees to the Ural Mountains. In this context, it is reasonable to assume that the migratory movements of goats (and other livestock), since domestication to present, were more intense, sustained, and recurrent in Europe than in Africa, a circumstance that might have enhanced the erasure of any genetic signature left by the initial spread of domesticates. The combination of these and other factors might explain why a post-domestication gradient of diversity is still detectable in African goats but not in their European counterparts.

Availability of data and materials

All relevant data are included in the manuscript and in the additional files. Genotype data sets can be accessed at: https://bridgeurl.com/goat-genotype-data-petretto-et-al-2024

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Acknowledgements

Thanks to Dr. Hans Lenstra (Utrecht University, Netherlands) for his valuable contribution to obtain, analyze and discuss the data. We also acknowledge the sample providers of the AdaptMap project: James Kijas, CSIRO, Australia; Bernt Guldbrandtsen, Aarhus University, Denmark; Juha Kantanen, Luke, Finland; Dylan Duby, Museum National d’Histoire Naturelle, France; Pierre Martin, Capgenes, France; Coralie Danchin, Delphine Duclos, Institut de l’Elevage, France; Daniel Allain, Rémy Arquet, Nathalie Mandonnet, Michel Naves, Isabelle Palhière, Rachel Rupp, INRAE, France and CABRICOOP breeders; François Pompanon, LECA, France; Hamid R. Rezaei, Gorgan University of Agricultural Sciences and Natural Resources, Iran; Sean Carolan and Maeve Foran, Old Irish Goats Society, Ireland; Alessandra Stella, IBBA-CNR, Italy; Paolo Ajmone-Marsan, Licia Colli, BioDNA, Università Cattolica del Sacro Cuore, Italian Goat Consortium, Italy; Alessandra Crisà, Council for Agricultural Research and Economics (CREA), Italian Goat Consortium, Italy; Donata Marletta, University of Catania, Italian Goat Consortium, Italy; Paola Crepaldi, University of Milan, Italian Goat Consortium, Italy; Michele Ottino, Parco Nazionale del Gran Paradiso, Italy; Ettore Randi, ISPRA Istituto Superiore per la Protezione e la Ricerca Ambientale, Italy; Badr Benjelloun, INRA Maroc, Morocco; Hans Lenstra, University of Utrecht, The Netherlands; Muhammad Moaeen-ud-Din and Jim Reecy, PMAS-Arid Agriculture University, Pakistan; Isabel Alvarez, Área de Genética y Reproducción Animal del Serida, Spain; Marcel Amills and Armand Sànchez, Centre for Research in Agricultural Genomics (CRAG), Spain; Juan Capote, Instituto Canario de Investigaciones Agrarias (ICIA), Spain; Jordi Jordana, Universitat Autònoma de Barcelona, Spain; Agueda Pons, Serveis de Millora Agrària i Pesquera (SEMILLA), Illes Balears, Spain; Amparo Martínez and Antonio Molina, University of Córdoba, Spain; Benjamin Rosen, USDA/ARS, USA; Carina Visser, Faculty of Natural and Agricultural Sciences, South Africa; Cord Drögemüller, University of Bern, Switzerland; Gordon Luikart, Montana Conservation Genomics lab (MCGL), Division of Biological Sciences, University of Montana, Missoula, USA; Clet Wandui Masiga, Tropical Institute of Development Innovations (TRIDI), Uganda; Denis Fidalis Mujibi, International Livestock Research Institute (ILRI), Nairobi Kenya; Hassan Ally Mruttu, Ministry of Livestock and Fisheries Development, Tanzania; Timothy Gondwe, Department of Animal Science, Lilongwe University of Agriculture and Natural Resources, Malawi; Joseph Sikosana, Department of Research and Specialist Services, Division of Livestock Research, Zimbabwe; Maria Da Gloria Taela, Animal Production Institute, Ministry of Agriculture, Maputo, Mozambique; Oyekan Nash, National Biotechnology Development Agency, Nigeria.

Funding

This research was funded by Grant PID2019-105805RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by Grant PID2022-136834OB-I00 funded by MCIIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe. We also acknowledge the support of the CERCA Program of the Generalitat de Catalunya and the Center of Excellence Severo Ochoa 2020–2023 (CEX2019–000902-S) Grant funded by MCIN/AEI/10.13039/501100011033 and awarded to the Centre for Research in Agricultural Genomics (CRAG, Bellaterra, Spain). Elena Petretto was funded with a fellowship from the European Social Fund Regional Operational Program (POR-FSE 2014/2020) of the Autonomous Region of Sardinia (Italy). Maria Luigi-Sierra was funded with a PhD fellowship Formación de Personal Investigador BES-C-2017-079709 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement No 945043 through the Postdoctoral Fellowship awarded to Antonia Noce by the AGenT H2020-MSCA-COFUND-2019 programme. Antonia Noce was also awarded with a Beatriu de Pinós postdoctoral fellowship (2020 BP 00101) funded by the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya.

Author information

Authors and Affiliations

  1. Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain

    Elena Petretto, María Gracia Luigi-Sierra, Antonia Noce & Marcel Amills

  2. Department of Veterinary Medicine, University of Sassari, 07100, Sassari, Italy

    Elena Petretto, Maria Luisa Dettori, Michele Pazzola & Giuseppe Massimo Vacca

  3. Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain

    Elena Petretto, María Gracia Luigi-Sierra & Marcel Amills

  4. Department of Genetics, University of Cordoba, 14071, Córdoba, Spain

    Antonio Molina & Amparo Martínez

  5. Área de Genética y Reproducción Animal, SERIDA-Deva, Camino de Rioseco 1225, 33394, Gijón, Spain

    Félix Goyache

  6. Old Irish Goat Society, Mulranny, Ireland

    Sean Carolan

Authors
  1. Elena Petretto
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  2. Maria Luisa Dettori
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  3. María Gracia Luigi-Sierra
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  11. Marcel Amills
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Consortia

The AdaptMap Consortium

  • James Kijas
  • , Bernt Guldbrandtsen
  • , Juha Kantanen
  • , Dylan Duby
  • , Pierre Martin
  • , Coralie Danchin
  • , Delphine Duclos
  • , Daniel Allain
  • , Rémy Arquet
  • , Nathalie Mandonnet
  • , Michel Naves
  • , Isabelle Palhière
  • , Rachel Rupp
  • , François Pompanon
  • , Hamid R. Rezaei
  • , Sean Carolan
  • , Maeve Foran
  • , Alessandra Stella
  • , Paolo Ajmone-Marsan
  • , Licia Colli
  • , Alessandra Crisà
  • , Donata Marletta
  • , Paola Crepaldi
  • , Michele Ottino
  • , Ettore Randi
  • , Badr Benjelloun
  • , Hans Lenstra
  • , Muhammad Moaeen-ud-Din
  • , Jim Reecy
  • , Isabel Alvarez
  • , James Kijas
  • , Armand Sànchez
  • , Juan Capote
  • , Jordi Jordana
  • , Agueda Pons
  • , Amparo Martínez
  • , Antonio Molina
  • , Benjamin Rosen
  • , Carina Visser
  • , Cord Drögemüller
  • , Gordon Luikart
  • , Clet Wandui Masiga
  • , Denis Fidalis Mujibi
  • , Hassan Ally Mruttu
  • , Timothy Gondwe
  • , Joseph Sikosana
  • , Maria Da Gloria Taela
  •  & Oyekan Nash

Contributions

MA, MLD, MP and GMV designed the experiment. AdaptMap Consortium members, AMa, FG, SC, and AMo performed sampling activities. All members of the AdaptMap Consortium, AMa, FG, SC, and AMo were involved in the genotyping of goats. EP analyzed the data with the help of MGL and AN. EP and MA drafted the manuscript with the help of FG. All authors revised and approved the final manuscript.

Corresponding author

Correspondence to Marcel Amills.

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Not applicable. All research was based on previously published data sets, so no ethics approval was needed.

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Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

12711_2024_923_MOESM1_ESM.pptx

Additional file 1: Figure S1. Geographic distribution of (a) European, (b) African and (c) Asian goat breeds. It shows the geographic locations and acronyms corresponding to all goat populations under study, which have been coloured according to their country of origin. Figure S2. Principal Component Analysis (PCA) plot of the first two components for 38 European goat breeds. We reported the centroids of principal components 1 and 2 for each breed under study. The percentages of variation explained by the two main components of the PCA are shown in brackets. Individuals are coloured according to their subregion of sampling. Breed acronyms are as follows: ALP_CH= Alpine, APP=Appenzell, ARG= Argentata, ASP= Aspromontana, BEY= Bermeya, BIO= Bionda dell’Adamello, CCG= Ciociara Grigia, CHA= Swiss Chamois, CRP= Carpathian goat, CRS= Corse, MAL= Mallorquina, MLG= Malagueña, MLT=Maltese, NIC= Nicastrese, NVE= Nera Verzasca, ORO= Orobica, PEA= Peacock, PTV= Poitevine, PVC= Provençale, PYR= Pyrenean, DIT= Di Teramo, ENG= Old English Goat, FSS= Fosses, GAR= Garganica, GGT= Girgentana, GST= Grisons striped, IRL= Old Irish Goat, LNR_DK= Landrace Goat (Denmark), LNR_FI= Landrace Goat (Finland), LNR_NL= Landrace Goat (Netherlands), RAS= Blanca de Rasquera, RME= Rossa Mediterranea, SAA= Saanen, SAR= Sarda, SGB= Booted goat, TGR= Tessin grey goat, TOG= Toggenburg, VAG= Valais, VAL= Valdostana,VSS= Valpassiria. Figure S3. Principal Component Analysis (PCA) plot of the first two components for 43 African goat breeds. We reported the centroids of principal components 1 and 2 for each breed under study. The percentages of variation explained by the two main components of the PCA are shown in brackets. Individuals are coloured according to their subregions of sampling. Breed acronyms are as follows: ABR= Abergelle, ALG= Arabia,Makatia, and M'Zabite,Kabyle, BOE=Boer, BRK=Barki, BUR= Burundi goat, CAM= Cameroon Goat, DESE=Desert, DZD= Dedza, MAA= Maasai, MEN= Malagasy goat (Menabe), MOR= Barcha,Draa,Ghazalia, Moroccan goats, Noire de l'Atlas,Nord, MSH= Mashona, MTB= Matebele, MUB= Mubende, NI= Nilotic, OSS= Oasis, SDN= Soudanaise, SEA= Small East African, SEB= Sebei, SHL= Sahel, SID= Saidi, SNJ=Sonjo, SOF= Malagasy goat (Sofia), TAGG= Taggar, GAL= Galla, GUE= Guera, GUM= Gumez, KAR= Karamonja, KEF= Keffa, KHAR= Kalahari Red, LND= Landin, PAL= Palmera, PEU= Peulh, PRW= Pare White, RSK= Red Sokoto, SAFR= South African Local breeds (from Limpopo, Freestate,Gauteng,Nortwest), SAH= Sahel, SAV= Savanna. TAR= Targui, TUN= Tunisian, WAD_CM= West African Dwarf (Cameroon), WAD= West African Dwarf (Nigeria), WYG= Woyito Guji. Figure S4. Principal Component Analysis (PCA) plot of the first two components for 22 Asian goat breeds. We reported the centroids of principal components 1 and 2 for each breed under study. The percentages of variation explained by the two main components of the PCA are shown in brackets. Individuals are coloured according to their subregions of sampling. Breed acronyms are as follows: ANK= Ankara (Turkey), KIL= Kol (Turkey), KLS= Kilis (Turkey), IRA_KUR= Markhoz (Iran), BAB= Barbari (Pakistan), BRI= Bari (Pakistan), BUT= Bugituri (Pakistan), DDP= Dera Din Panah (Pakistan), KAC= Kachan (Pakistan), KAM= Kamori (Pakistan), LOH= Lohri (Pakistan), PAH= Pahari (Pakistan), PAT= Pateri (Pakistan), TAP= Tapri (Pakistan), TED= Teddi (Pakistan), THA= Thari (Pakistan), NJ= Nanjiang (China), QG= Qinggeda (China), AC= Aarbas Cashmere (China), JN= Jining Grey (China), LP= Luoping Yellow (China), GF= Guangfeng (China). Figure S5. Correlation graph between the distance (km) from Ganj Dareh to sampling locations of European, African and Asian goat breeds in relation to their FST values with regard to the Iranian Markhoz population. We report plots representing Pearson correlations (with their P-values) between FST values (differentiation between all populations and the Iranian Markhoz population) and distance between Ganj Dareh and sampling location of (a) European breeds (not including and including insular breeds); (b) African populations (not including and including insular populations); and (c) Asian populations. Breed acronyms can be found in Tables 1, 2 and 3, and the country of origin of goats is indicated with coloured points. Figure S6. Correlation plot depicting the relationship between the distance (measured in kilometers) from Ganj Dareh and the sampling locations of European, African, and Asian goat breeds, with regard to their FROH values. We report graphs representing Pearson correlations (with their P-values) between FROH coefficients and distance from Ganj Dareh to sampling locations of (a) European breeds (not including and including insular breeds); (b) African populations (not including and including insular populations); and (c) Asian populations. Breed acronyms can be found in Tables 1, 2 and 3, and the country and subregions of origin of goats is indicated with coloured points. Figure S7. Interpolation map of Fis values measured in European, African and Asian goat populations. Interpolation map showing the distribution of inbreeding coefficient Fis in African, European and Asian breeds. Red points represent sampling localities. In Europe we mostly observe negative and low Fis values, as well as in Africa, except for the North where positive values are noticeable (presence of an intense blue colour). In Asia, especially in the south we also have positive values, a potential signature of inbreeding.

12711_2024_923_MOESM2_ESM.xlsx

Additional file 2: Table S1. List of European, African and Asian goat breeds of the Adaptmap data set and their heterozygosity (Ho and He) and FROH values. FROH values were calculated by Bertolini et al. (2018) for the set of AdaptMap goat populations. Breed code including breed name, subregions of provenance, observed heterozygosity (HO), expected heterozygosity (He) values, average fraction of the genome that contains ROH (FROH) and distance (km) from Ganj Dareh.

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Petretto, E., Dettori, M.L., Luigi-Sierra, M.G. et al. Investigating the footprint of post-domestication dispersal on the diversity of modern European, African and Asian goats. Genet Sel Evol 56, 55 (2024). https://doi.org/10.1186/s12711-024-00923-5

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  • DOI: https://doi.org/10.1186/s12711-024-00923-5

Genetics Selection Evolution

ISSN: 1297-9686