Conserv Genet (2016) 17:77–89 DOI 10.1007/s10592-015-0762-9
RESEARCH ARTICLE
Prioritization for conservation of Iranian native cattle breeds based on genome-wide SNP data Karim Karimi1,2 • Ali Esmailizadeh Koshkoiyeh1 • Masood Asadi Fozi1 • Laercio R. Porto-Neto3 • Cedric Gondro4
Received: 26 December 2014 / Accepted: 17 July 2015 / Published online: 26 July 2015 Ó Springer Science+Business Media Dordrecht 2015
Abstract Comprehensive identification of the breed characteristics is essential for the effective management of farm animal genetic resources. The objective of this study was to estimate effective population size, genomic inbreeding coefficients, autozygosity derived from runs of homozygosity, and genetic diversity in Iranian native cattle using dense SNP markers. Ten samples per breed were collected from eight Iranian native cattle breeds representing the Sarabi, Kurdi, Najdi, Taleshi, Mazandarani, Pars, Kermani, and Sistani breeds. Samples were genotyped for 777,962 SNPs using the Illumina BovineHD BeadChip, and after quality control 450,341 SNPs were kept for further analyses. The estimated effective population size (Ne) was relatively small for all breeds, varying
from Ne = 13 (Sarabi) to Ne = 107 (Mazandarani). Analysis of runs of homozygosity in different cut-off lengths was applied to provide information on the recent and ancient inbreeding occurrences in each population. The inbreeding coefficients estimated by runs of homozygosity varied between breeds. Sarabi, Pars, and Sistani breeds had higher proportion of long runs of homozygosity, likely to reflect recent inbreeding, while Kermani breed had the higher number of short runs of homozygosity. Some breeds such as Sarabi, Sistani, Pars, Taleshi, and Kermani are exposed to serious risk of extinction and other breeds, except Mazandarani, have no promising situations. Higher priority should be given to conservation programming in Sarabi and Sistani breeds due to unique genetic composition and critical conservation status.
& Karim Karimi
[email protected]
Keywords Conservation Effective population size Runs of homozygosity Iranian native cattle
Ali Esmailizadeh Koshkoiyeh
[email protected] Masood Asadi Fozi
[email protected] Laercio R. Porto-Neto
[email protected] Cedric Gondro
[email protected] 1
Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, PB 76169-133, Kerman, Iran
2
Young Researchers Society, Shahid Bahonar University of Kerman, PB 76169-133, Kerman, Iran
3
Commonwealth Scientific and Industrial Research Organization, Agriculture Flagship, Brisbane, Australia
4
School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia
Introduction Artificial selection pressure, particularly in cattle, has resulted in reduction of effective population size in economically successful breeds. Fitness of high-producing breeds has been gradually reducing, and the incidence of production related diseases has been increased among them (Taberlet et al. 2008; Medugorac et al. 2011). While several important traits such as disease and parasite tolerance, heat tolerance, and adaptation to low-quality feed resources are attributed to native populations, number of low-input local populations of animals has been continuously declining (Hoffmann 2010). Unforeseeable changes in climate, emerging diseases, and changes in the nutritional needs of human community are some of the future challenges of
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livestock production (FAO 2007; Tiwari et al. 2014). Maintenance of adaptive and neutral diversity must be considered in order to conserve the potential to react to the future challenges (Medugorac et al. 2011). Populations with higher genetic diversity have more chance to survive under new environmental pressures (Markert et al. 2010). Moreover, comprehensive identification of the breed characteristics is essential for effective management of farm animal genetic resources. Integration of different types of data such as demographic characterization, geographical distribution, production environment, and within and between breed genetic diversity will facilitate the assessment of the risk status of livestock breeds and effective management of genetic resources (Groeneveld et al. 2010). Size and structure of the population have prominent impacts on risk status of animal population. Effective population size (Ne) is one of the most important measures in evaluating conservation priorities for a local population (Frankham 2005; Gasca-Pineda et al. 2013). This measure takes into account both current census size of a population and its history. Also, the impact of genetic drift or inbreeding rate on a particular population can be quantified by effective population size (Harmon and Stanton 2010; Theodorou and Couvet 2006). In most cases, local livestock populations of developing countries have no reliable pedigree information, and collecting sufficient demographic information to direct calculation of Ne is difficult within such populations. Availability of molecular markers data, and development of new statistical methods have provided new opportunities to calculate Ne of natural populations, especially useful in endangered small local populations. High-throughput SNP genotyping platforms are useful tools in studying livestock population structure and genetic diversity (Decker et al. 2009; Gautier et al. 2010; Mastrangelo et al. 2014). Efficiency of SNP makers was confirmed in prioritization and diversity estimation of small group of highly related animals (Engelsma et al. 2012; Medugorac et al. 2011; Scraggs et al. 2014). The accumulation of inbreeding and the loss of genetic diversity is an issue of concern in the livestock conservation. Inbreeding increases the risk to heir a rare recessive genetic disease and reduces the population fitness (Charlesworth and Willis 2009; Mc Parland et al. 2007). Completeness and accuracy of the available pedigree records is essential to estimate the inbreeding coefficient based on pedigree data. Furthermore, probabilistic approach of pedigree analysis does not take into account the stochastic nature of recombination (McQuillan et al. 2008). Recently, genome-wide SNP data have been applied to estimate this coefficient with or without pedigree information (Garcia-Ga´mez et al. 2012; Gorbach et al. 2010). Runs of homozygosity (ROH) have been used to estimate inbreeding and to map recessive disease genes in cattle populations (Kim et al. 2013; Biscarini
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et al. 2014). Most precise information about levels of autozygosity can be given using observational approach of ROH in contrast with pedigree analysis. (Ferencakovic et al. 2013a, b; Marras et al. 2015). ROH are contiguous lengths of homozygous genotypes that are present in an animal due to parents transmitting identical haplotypes to their offspring. Longer ROH may provide information on recent inbreeding occurred within a pedigree, while shorter segments may indicate the presence of more ancient relatedness (Bjelland et al. 2013; Kirin et al. 2010). Iranian native cattle have extensive biodiversity and are distributed in different geographical regions of country. Long-term responce to pressures of local environment has caused to develop unique collections of adaptive traits in Iranian indigenous cattle breeds. Occurrence of successive droughts and low production level compared with the exotic breeds has led to diminish interest in raising local breeds. Currently, the most important threats facing native cattle are breed replacement and uncontrolled crossbreeding. The process of population decline and uncontrolled crossbreeding is of concern, and the need to conserve these genetic resources can be felt more than ever. Due to the lack of recording system and accurate census in these populations, the objective of this study was to characterize population genetic parameters such as effective population size and inbreeding rate in local cattle populations using whole-genome SNPs information. These informations can be used to address important questions related to conservation genetics and to set appropriate prioritization programs in different native cattle populations.
Methods Samples collection, genotyping, and quality control In total, 80 hair samples from eight Iranian native cattle populations were collected. The sample of approximately 20–40 hairs (have follicle bulbs on the end) was pulled from the bottom of the tail switch in each animal. Phenotype characteristics, pedigree (if was available), farmers information, and history background were considered in order to select non-crossbred (with exotic breeds) and unrelated individuals in each breed. The number of animals per each population, geographic origin, and production system of each breed is given in Table 1. DNA was extracted from the hair roots using Gentra Puregene Tissue Kit (Qiagen Inc., Valencia, California, USA) and genotyped for 777,962 SNPs across the genome using the BovineHD (Illumina, Inc, San Diego, CA, USA). PLINK 1.07 (Purcell et al. 2007) program was used to perform quality control on the data set. We excluded SNPs with greater than 10 % missing genotype, SNPs with minor
Conserv Genet (2016) 17:77–89 Table 1 Number of samples and characteristics of geographic locations, production type, production system, and ecological zones in Iranian cattle populations
79
Breeds
n
Geographic location
Production type
Production system
Ecological zone Cool highland
Sarabi
10
Azerbaijan
Dairy
Mixed crop-livestock
Najdi
10
Khuzestan
Dual purpose
Pastoral
Hot plain
Taleshi
10
Guilan
Dual purpose
Pastoral
Temperate forest
Mazandarani
10
Mazandaran
Dual purpose
Pastoral
Temperate forest
Pars
10
Fars
Dual purpose
Mixed crop-livestock
Temperate plain
Kermani
10
Kerman
Dual purpose
Mixed crop-livestock
Hot desert
Kurdi
10
Kurdistan
Dual purpose
Mixed crop-livestock
Cool highland
Sistani
10
Sistan
Beef
Pastoral
Hot desert
allele frequency (MAF) less than 0.01 and non-autosomal SNPs. Hardy–Weinberg equilibrium was evaluated, and sites with p value less than 10-7 were also removed from the analyses. Also individuals with call rate less than 0.7 were excluded. Genomic relationship matrix was computed using GCTA software (Yang et al. 2011) and level of relatedness among individuals was investigated. However, any closely related individual was not found to be removed in the data set. Application of these filtering criteria left 450,341 SNPs and 65 animals available for further analyses. Genetic diversity and inbreeding coefficients The proportion of polymorphic SNPs (MAF C 0.01), and the expected heterozygosity (He) were determined as the metrics of genetic diversity for each breed. Filtered dataset was pruned to be in approximate linkage equilibrium using the –indeppairwise option (–indep-pairwise 50 10 0.2) in PLINK. This resulted in a pruned set of 74,843 SNPs. Inbreeding coefficient was estimated in the pruned data set based on the observed versus expected number of homozygous genotypes implemented in PLINK. Also, a maximum likelihood estimation of genomic inbreeding coefficient was calculated using SNPRelate package in R (Zheng et al. 2012). Genetic structure analysis Principal component analysis was applied to visualize relationships between populations using SNPRelate package of R (Zheng et al. 2012). STRUCTURE 2.3.4 (Pritchard et al. 2000) was used to perform clustering of the breeds into genetic groups based on the LD pruned data set included 74,843 SNPs. The structure algorithm included the admixture model and correlated allele frequencies. The number of clusters was inferred using four independent runs with 35,000 iterations and a burn-in period of 15,000 with K values ranging from 2 to 10. The convergence of structure runs was examined by equilibrium of alpha and likelihood scores. Most suitable K was selected according to Evanno method (Evanno et al. 2005). Also, Nei’s genetic distances
(Nei 1972) among different populations were used to construct a Neighbor-joining (NJ) tree in Ape package of R (Paradis et al. 2004). Effective population size Linkage disequilibrium (LD) is a known function of population size, and can be used to estimate effective population size. We used a method described by Waples (2006) to implement the bias correction to the original LD method for estimate of effective population size (Hill 1981). This method was improved to account for missing data and was applied in NE ESTIMATOR (v2) software (Do et al. 2014). Effective population sizes were estimated according to the random mating model of linkage disequilibrium method in NEESTIMATOR (v2) software. Effective population size for each chromosome was calculated using only alleles with a frequency of 1 % or more, and average of these values was accounted as overall Ne for different populations.
Runs of homozygosity (ROH) PLINK 1.07 software was applied to detect ROH in different individuals. While minimum number of SNPs needed to define a segment as a ROH and minimum length to define ROH were different among various analyses, the following settings were in common to all analyses: number of missing calls allowed (n = 5), minimum allowed density of SNPs inside a run (1 SNP for every 50 kb), number of heterozygous calls allowed (n = 1), and maximum gap between consecutive homozygous SNPs (1 Mb). In order to investigate the effect of small autozygous segments on genomic inbreeding rate, the minimum size of ROH segments and the minimum number of SNPs required per ROH were set to 100 Kb and 60 SNPs, respectively. In the next step, minimum number of SNPs per ROH was set to 100 SNPs and ROH with different minimum lengths ([0.5, [1, [2, [8, and [16 Mb) were detected in each individual. The inbreeding coefficient based on ROH (FROH) was calculated for each animal by the following formula:
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P FROH ¼
K
ðLengthðROHk ÞÞ ; L
where the numerator of equation is the sum of ROH per animal above a certain criterion length and L is total length of the genome. Total length of the bovine genome was considered 2,510,611 Kb (Purfield et al. 2012). Mean of FROH was calculated for each breed in different length categories separately.
Results Rate of polymorphic loci (MAF [0.01), heterozygosity values, and two different estimations of inbreeding coefficient for each breed are presented in Table 2. The highest rate of polymorphic loci was observed in Kurdi breed (87.62 %), while Sistani breed had the minimum rate of polymorphic loci (74.53 %). Based on expected heterozygosity, Kurdi (0.339) and Sarabi (0.338) had the highest genetic diversity and the least genetic diversity was observed in Sistani breed (0.246). High accordance was observed between heterozygosity values and polymorphism level in different populations. Highest value of inbreeding coefficients was observed in Sistani breed (FHOM = 0.193 and FML = 0.165). According to FHOM, Kurdi had the least inbreeding coefficient (0.02), but minimum FML coefficient (0.048) was observed in Mazandarani breed. Principal component analysis could well reflect discrete genetic characteristics among the Iranian cattle (Fig. 1). The proportions of total variation, which have been explained by first and second principal components, were equal to 46.6 and 6.3 %, respectively. Individuals of each population have evidently grouped in the separate clusters. Sarabi and Kurdi populations have created distinct groups. However, bounds between Taleshi with Mazandarani,
Sistani with Kermani, and Pars with Najdi were unlimited and they could be accounted as the same groups. The bayesian clustering outputs for eight values of K in eight Iranian native cattle are shown in Fig. 2. At K = 2, Sarabi (97.6 %) and Kurdi (94.3 %) cattle shared the same cluster, while Sistani breed (92 %) clustered in the separate group but it appears that other populations were admixture from these two clusters. Kurdi population appeared as an independent cluster at K = 3. Sistani and Sarabi could keep uniqueness at higher K values but after K = 3, signature of admixture appeared in Kurdi cluster. Differentiation among Taleshi and Mazandarani with other populations was first observed at K = 4 such as 79.6 % of Taleshi and 68.9 % of Mazandarani individuals assigned to the same cluster. The method described by Evanno et al. (2005) was used to estimate the number of ancestral populations (K) that were subsequently admixed to form these breeds. The highest DK was observed at K = 3 and indicated that K = 3 was the most suitable number of clusters. Nei’s genetic distances for each pair of cattle populations are represented in Table 3. The highest value of Nei’s genetic distance (0.158) was found between Sistani and Kurdi populations. Nei’s genetic distance was lowest between Pars and Kermani populations (0.032). Based on the average genetic distance calculated for each breed, we found that Sarabi, Sistani, and Kurdi were the most divergent breeds. The relative placement of breeds in the NJ tree showed that Kurdi and Sarabi shared a common node, and position of Taleshi was closer to Mazandarani, while other breeds have occupied relative close placements together on the tree (Fig. 3). Effective population size (Ne) was inferred for each autosomal chromosome based on linkage disequilibrium method and an average of Ne over chromosomes was calculated for each breed (Table 4). Overall effective
Table 2 Level of polymorphism, observed heterozygosity, expected heterozygosity, and inbreeding coefficients (FHOM and FML) for each Iranian native cattle population Observed heterozygosity
Expected heterozygosity
FMLb
Analyzed samples number
Sarabi
10
85.1
0.322
0.338
0.055
0.053
Taleshi
7
84.25
0.306
0.317
0.065
0.069
10 7
87.23 84.05
0.320 0.291
0.326 0.314
0.032 0.161
0.048 0.146
Kermani
9
86.90
0.283
0.312
0.100
0.092
Najdi
7
83.21
0.295
0.307
0.054
0.051
Sistani
8
74.53
0.229
0.246
0.193
0.165
Kurdi
7
87.62
0.330
0.339
0.020
0.081
Total
65
84.11
0.297
0.312
0.085
0.088
Mazandarani Pars
% SNPs with MAF [ 0.01
FHOMa
Breed
a
Estimation based on the observed versus expected number of homozygous genotypes implemented in PLINK
b
Maximum likelihood estimation implemented in SNPRealte package of R
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Fig. 1 Principal component analysis of SNPs data set from Iranian native cattle. The first PC accounts for 46.6 % of the underlying variation, the second PC condenses 6.30 % of the variation
population size ranged from 13 to 107. Mazandarani breed had the highest value of Ne among other breeds, while the lowest effective population size was observed in Sarabi (13) and Sistani (17) populations. Close Ne values were observed in Kermani (23), Taleshi (22), and Najdi (22) breeds. Also, Ne values were equal to 29 and 45 in Kurdi and Pars breeds, respectively. We applied different minimum size lengths and minimum number of homozygous SNPs to define a ROH. In order to investigate the effect of small homozygous segments on the measure of homozygosity (FROH), ROH was defined as the tracts of homozygous genotypes that were [100 Kb in the length and [60 in the number of homozygous SNPs. These results indicated that FROH [ 100 Kb was maximum in Sistani breed (0.182), while Kurdi had the lowest FROH among other populations (FROH [ 100 Kb = 0.045). After that, minimum number of SNP per each ROH was set to 100, and FROH was calculated in different criterion lengths (0.5, 1, 2, 8, and 16 Mb). The highest inbreeding level was observed in the Pars breed for all genomic inbreeding coefficients, which ranged from 0.068 (FROH [ 16 Mb) to 0.132 (FROH [ 500 Kb). On the other hand, the lowest values of FROH were observed in Kurdi breed for all ROH length classes with values ranging from 0.001 (FROH [ 16 Mb) to 0.018 (FROH [ 500 Kb). Figure 4 represents the trend in FROH values in different cut-off lengths for each population. Trend of FROH values varied widely among populations. The highest difference between FROH [ 100 Kb and FROH [ 16 Mb was observed in Sistani (0.131) and Kermani (0.1) breeds that indicated high proportion of short ROH in these breeds. Obviously, gradual decreasing trend was observed in distance from FROH [ 500 Kb to FROH [ 2 Mb in Sarabi and Pars breeds. On the basis of FROH [ 16 Mb (16 Mb segments are expected to present inbreeding up to three generations ago),
most recent inbreeding rates were observed in Pars (0.068), Sistani (0.051), Taleshi (0.039), and Sarabi (0.028) breeds. Figure 5 gives a comparison among average of ROH lengths and number of ROH found in different criterion lengths for various breeds. As expected, a number of ROH and FROH values were greater in the analysis of shorter length thresholds. Total length of ROH for Kermani breed is composed of higher number of shorter ROH segments, while total length of ROH in Pars and Sarabi breeds mainly included large segments. Approximately, 72, 70, 63, 43, 33, 30, 28, and 17 % of cattle in Pars, Sarabi, Sistani, Najdi, Kermani, Mazandarani, Taleshi, and Kurdi breeds had long ROH ([16 Mb), respectively. Trend of correlation coefficients between FHOM and FML values with FROH values in different criterion lengths is represented in Fig. 6. Along with increase in cut-off lengths, correlation of the FROH values with both FML and FHOM decreased. However, FHOM was in more accordance with FROH obtained from different cut-off lengths.
Discussion A wide genetic diversity was observed among Iranian native cattle based on observed heterozygosity values and rate of polymorphic loci. Higher diversity was observed in Sarabi and Kurdi breeds, while Sistani had the least diversity (Table 2). Genetic diversity could be originated from various production systems and ecological conditions in which native cattle have been historically preserved. This diversity can be applied to adjust breeding goals to account for some important adaptive traits such as resistance to disease challenge, heat tolerance, and adaptation to low-quality diets. However, small effective population sizes found in this study confirmed threat of extinction in
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Fig. 2 Structure output for eight values of K investigated. Each individual is represented by one vertical line with the proportion of assignment to each cluster shown on the y-axis and colored by cluster
Table 3 Nei’s genetic distance among eight Iranian native cattle
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Population
Sarabi
Kermani
Sistani
Najdi
Kurdi
Taleshi
Pars
Mazandarani
Sarabi
0.000
0.092
0.134
0.087
0.057
0.067
0.091
0.068
Kermani
0.092
0.000
0.035
0.038
0.108
0.062
0.032
0.051
Sistani
0.134
0.035
0.000
0.054
0.158
0.090
0.044
0.074
Najdi
0.087
0.038
0.054
0.000
0.101
0.062
0.041
0.053
Kurdi Taleshi
0.057 0.067
0.108 0.062
0.158 0.090
0.101 0.062
0.000 0.075
0.075 0.000
0.107 0.062
0.076 0.043
Pars
0.091
0.032
0.044
0.041
0.107
0.062
0.000
0.052
Mazandarani
0.068
0.051
0.074
0.053
0.076
0.043
0.052
0.000
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Fig. 3 Unrooted neighbor joining (NJ) tree of Nei’s genetic distance representing genetic relationships of eight Iranian native cattle populations. Bootstrap support calculated from 1000 replicates is shown for major branches
Iranian native cattle. Franklin (1980) suggested that in the short term, a population with a Ne of C50 individuals is required to avoid effects of inbreeding depression. Also, the effective population size should be 500 in order to Table 4 Effective population size (Ne) of Iranian native cattle populations
Chromosome number
maintain adaptive genetic variation over longer periods of time (50/500 rule of thumb). However, Frankham et al. (2014) revised these suggestions and recommended that short-term and long-term Ne should be, respectively, equal to 100 and 1000 individuals in order to control inbreeding rate and to maintain evolutionary potential of population. Unfortunately, accurate census population size was not available in Iranian cattle populations to estimate some criteria such as the ratio of effective-to-census population size. However, considering 50 (or 100) as the minimum acceptable Ne to conserve a population, we observed that, except in Mazandarani breed (Ne = 107), Ne estimations were less than the critical value in other Iranian cattle breeds. Regarding intense breed replacement and crossbreedings, these results confirmed serious risk of extinction in Iranian native cattle. Relative contributions of long and short ROH differed among different populations. Also, declining trend in
Breed Mazandarani
Kurdi
Taleshi
Najdi
Sarabi
Pars
Sistani
30.5
13.7
1
60.8
24.4
20.3
45.8
16.2
2
109.6
16.6
12.9
25.2
22.1
6.9
10.9
29.5
3
194
18.9
11.7
27.7
21.4
125.8
20.3
12.9
4
118.8
35.4
48.7
15.4
15.3
23.3
19.9
13.8
5
68.1
28
32.9
14.4
15.9
11.5
13.5
18.8
6
26
31.6
12.5
15.9
13.4
12.7
22.7
24.4
7
60.1
7.1
18.2
14.6
11.3
16.2
21
8 9
187.3 70.4
27.1 16.1
63.9 24.3
17.2 22
9.2 16.8
8.5 13.7
71.8 24.6
22.5 5.6
10
270.6
20.5
12.8
17.7
15.6
23.3
14.5
14.4
11
117
23.6
9.6
35.2
11.8
93.7
35.9
14.3
12
302.5
22.3
25.4
13.2
8.9
28.3
13.2
9.6
13
371.4
36.6
22.9
20.6
9
164.1
27.3
32.3
14
83.1
14.9
24.6
15.6
16.5
37.3
22.2
15.4
15
216.4
47.2
35.3
11.2
12.9
32.2
17.4
11.2
16
25.4
40.1
13.4
41
12.7
294.6
53.9
12.8
17
54.1
16.1
50.1
53.4
15.7
6.7
21.7
12.2
18
64
37.6
14
17.5
7.1
7.6
16.4
3.4
19
51.7
32
13.1
35.9
11.3
34.2
13.7
18.7
20
37.1
35.2
31.7
7.2
10.3
13.9
22.3
20.9
21
67.4
14.2
20.3
32.9
16.4
10.7
17.7
44.5
22
79.2
14.5
15.1
22.4
11.5
4.3
14.8
25.2
23
72.7
13.8
8.9
17.1
3.1
43.3
16.5
19.9
24 25
60.1 88.3
16 15.7
13.1 19.6
23.4 18.6
18.7 4.9
54.3 34
14.5 17.5
16.8 11.3
26
71.9
14.4
7.3
45.1
21.9
20.2
27
29.3
14
11.6
14.3
10.1
12
13.6
16.9
28
96.6
147.8
30.9
8.4
5.7
5.3
21
11.1
7.2
59.4
47.6
11.2
45
23
17
29 Average
57.9 107
7.3
30
43.3
22.4
13.4
29
22
22
13
65.8
Kermani
7.1
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Sistani
0.18 0.123
0.12
0.099
0.087 0.067
0.06
0.113 0.095
0.09 0.051
0.03
0
0
Kermani
0.068
0.06
0.03
0.18
0.119
0.12
0.09
FROH values from different cut-off lengths
0.132
0.15
0.15
Pars
0.169
0.18
Taleshi
0.18
0.15
0.15 0.106
0.12
0.12
0.09
0.107 0.074
0.09 0.054
0.06
0.033 0.03
0.062
0.06 0.024
0.012
0.006
0.054
0.044
0.039
0.03 0
0
Sarabi
0.18
0.12
0.15 0.12
0.101 0.075
0.09
0.068
0.09
0.063
0.06
0.043
0.03
Najdi
0.011
0.007
0.003
0.001
Mazandarani
0.18 0.15
0.15
0.12 0.091
0.09
0.09 0.03
0.018
0.03 0
0.18
0.06
0.045
0.06 0.028
0
0.12
Kurdi
0.18
0.15
0.054 0.04
0.06 0.034
0.025
0.018
0.092 0.059
0.047
0.037
0.03
0.025
0.019
0
0
Categories of minimum length and minimum SNP number to define ROH Fig. 4 Change trend in FROH values of different Iranian cattle populations. Each point on the plots represents value of a specific FROH. Different cut-off length and minimum SNP number categories to define each FROH was shown on the x-axis of the plots
FROH values from short lengths to long lengths thresholds was not similar among various breeds (Fig. 4). These results indicated different LD patterns and genetic structures in each population and can provide information on
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recent and ancient inbreeding occurrences in the populations. Long ROH are likely found in locations with low recombination and locations of selective sweeps, while small ROH patterns have the characteristics of short
Conserv Genet (2016) 17:77–89
85
ROH >100 Kb
ROH > 500 Kb
20
20
0
0
0
300
100
0
0
200
100
Sarabi Kermani Sistani Pars Najdi Taleshi Mazani Kurdi
0
15
10
5
0
ROH > 16 Mb
ROH > 16 Mb
20
400
15
300
10
ROH > 2 Mb 20
200
5
ROH > 8 Mb Average length of ROH (Mb)
Number of ROH
300
Number of ROH
15
ROH > 8 Mb 400
5
0
20
200
5
100
0
0
15
10
5
0 Sarabi Kermani Sistani Pars Najdi Taleshi Mazani Kurdi
100
400
10
10
ROH > 2 Mb
20
Number of ROH
200
15
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Fig. 5 Comparison of ROH length and number of ROH found in various ROH length classes between different Iranian cattle breeds
haplotype block which are highly associated with high linkage disequilibrium and low recombination rate regions in the genome (Gibson et al. 2006; Pemberton et al. 2012). ROHs with minimum length [1, [2, [8 and [16 Mb can represent relatedness up to 50, 25, 6, and 3
generations from common ancestor, respectively (Ferencakovic et al. 2013a). While FHOM and FML could not distinguish recent and ancient relatedness, recent inbreeding in each population was well detected according to the ROH analyses.
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Correlation coefficients
1 0.9
correlation coefficient with
FHOM
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Different ROH length classes
Fig. 6 Trend of correlation coefficients between FROH from different length classes with FHOM (dashed line) and FML (solid line)
In accordance with lower genetic diversity observed in Sistani breed, high genomic inbreeding coefficients (see Table 2) and low effective population size (Ne = 17) were also found in this breed. Analysis of FROH in different cut-off lengths showed high occurrence of small homozygous segments in Sistani breed (see Fig. 5). However, high proportion of long runs has also composed total length of ROH in Sistani breed. This result indicated that Sistani breeds have likely had a historical small population size, and inbreeding rate has been continuously increased until recent years. Recent droughts and changes in environment have led to intensify population decline and to increase the rate of inbreeding in Sistani breed. High inbreeding coefficient based on excess SNP homozygosity (0.1) was observed in Kermani breed. Based on FROH [100 Kb (0.106), Kermani breed had relatively high inbreeding rate among other breeds, but according to FROH values derived from ROH with longer length ([0.5,[1,[2,[8 and[16 Mb), Kermani had lower FROH than other populations (Fig. 4). These results indicated the presence of more ancient relatedness in Kermani population which is supported by the estimated Ne = 23. It appears that this population faces almost-extinction due to severe breed replacement. In recent years, Kermani breed has been increasingly replaced by exotic breeds, thereby making it difficult to find pure cattle in Kerman province. As compared to other breeds, Sarabi showed high genetic diversity and had low inbreeding coefficients (Table 2). Surprisingly, low effective population size was observed in this population (Ne = 13). Investigation of FROH values in Sarabi breed indicated that long homozygous segments have contributed more than short segments in the inbreeding rate of this breed (see Fig. 5). Sarabi could be originated from a bottleneck in about 25 generations ago (Fig. 4). The lowest value of Ne among studied breeds was observed in Sarabi breed and this is in accordance with a bottleneck in this breed. On the other hand,
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Sarabi breeds are known as a local dairy breed and selection might have affected some parts of the genome in this breed. However, more studies are needed to investigate bottleneck occurrence or selection effects in this breed. All the investigated parameters indicated serious threat of extinction in Pars population. Pars have experienced intense inbreeding, particularly in recent years. Recent droughts and breed replacement have put this breed to near extinction. High recent inbreeding rate (FROH [ 16 Mb = 0.039) along with small population size (Ne = 22) confirmed endangered status also for Taleshi breed. Regarding high genetic diversity and low genomic inbreeding rates found in Kurdi breed, the lowest FROH values in all length classes were also observed in this population. However, Kurdi breeds have small population size, which per se defines a high risk and sensitize this population to future environmental changes. In contrast, Najdi indicated low inbreeding rate in all studied metrics. It appears that pastoral system in which Najdi cattle are normally bred has helped to prevent relatively close matings. However, effective population size in this breed was equal to 22, which is considered very low, and extinction risk threats this population. Mazandarani had the highest Ne value among other breeds (107). Mazandarani has been kept in the northern forests of the country under pastoral system and have been less affected by environmental changes and economic conditions. In accordance with higher value of Ne, high heterozygosity and low inbreeding coefficients were also observed in Mazandarani population (Table 2). Determination of genetic structure can be valuable in prioritizing populations for conservation and for developing suitable management practices. Output of structure analysis showed at K = 2, Sarabi and Kurdi populations shared the same cluster, while Sistani breed clustered in the separate group and other populations were admixed from these two clusters. The first level of clustering (K = 2) reflected the division of taurine from indicine cattle. The highest DK was observed at K = 3 and indicated that K = 3 was the most suitable number of clusters. Sarabi (taurine) and Sistani (indicine) were two pure breeds that had most distinctive genetic composition as compared to other breeds. Small effective population sizes, high inbreeding coefficients, and high FROH values along with unique genetic composition confirmed that the higher priority should be given to design of conservation programs in Sarabi and Sistani breeds. Also, Kurdi can be proposed as an important breed in conservation programs due to high genetic diversity and unique genetic composition. Differentiation among Taleshi and Mazandarani with other populations was first observed at K = 4. At K = 4, 79.6 % of Taleshi and 68.9 % of Mazandarani individuals assigned to the same cluster. This result was in accordance with close geographical distribution of these populations such that both breeds have been kept in the forested areas in the
Conserv Genet (2016) 17:77–89
North temperate zone of the country. Close genetic composition and similar environments indicate that similar and probably related conservation programs can be applied for these breeds. Kermani population has represented a large percentage of assignment to Sistani (at K = 3, 62 %) and this is the reason for having overlapping clusters in PCA analysis. This genetic similarity has good accordance with geographical distribution and both breeds have been bred in the South-East dry and hot areas of Iran. Therefore, Sistani and Kermani can also participate in similar conservation strategy. On the other hand, Pars and Najdi breeds can be accounted as same genetic groups inhabited in the South of Iran. These results confirmed the presence of a gradient between indicine (Sistani) and taurine cattle (Sarabi and Kurdi) from South-East to North-West of the country. Since the adaptability of Iranian native cattle with their environments is the product of long-term selection pressures and cannot be attributed to recent times, admixed cattle should also be considered in the conservation strategies. An appropriate conservation strategy should be able to maintain maximum genetic diversity in the global gene pool while maintaining within breed diversity to reduce inbreeding and preserve genetically differentiated groups (Talle et al. 2005). Ne estimations ranged from 13 (Sarabi) to 107 (Mazandarani) among Iranian native cattle. Flury et al. (2010) estimated LD-based effective population size in the range of 87–149 for indigenous Swiss cattle breed. Also, analysis of effective population size based on SNP data demonstrated that effective population size was declined to 98.1 up to three generations ago in Hanwoo cattle (Lee et al. 2011). On the other hand, very low effective population size (12.32) was found in the Istrian cattle using SNP information (Curik et al. 2014). High genetic diversity was observed among Iranian native cattle based on expected heterozygosity (He). Sistani breed had the lowest expected heterozygosity (0.246) among Iranian cattle. Compared to other cattle breeds, expected heterozygosity values estimated in the Iranian native cattle (With the exception of Sistani) were higher than those obtained in Afrikaner (0.24), Nguni (0.28), Drakensberger (0.30), Bonsmara (0.29) (Makina et al. 2014), Gayal (0.153) (Uzzaman et al. 2014), Kortegaard herd (0.259), and Belgian Blue (0.307) breeds (Pertoldi et al. 2014). However, higher He values were reported in the red Chittagong (0.396), nondescript deshi (0.401) (Uzzaman et al. 2014), Horro (0.388), Danakil (0.370), Borana (0.372), and Hanwoo (0.41) cattle (Edea et al. 2013) compared to Iranian cattle. Level of inbreeding was variable among Iranian cattle breeds and ranged from 0.020 (Kurdi) to 0.193 (Sistani). Makina et al. (2014) found the lower level of inbreeding across the South African cattle in their study ranging from 0.004 (Afrikaner) to -0.002 (Drakensberger). Also, lower inbreeding coefficients were found in Bangladeshi cattle including red
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Chittagong (-0.027) and non-descript deshi (-0.029) breeds (Uzzaman et al. 2014). Furthermore, genomic inbreeding coefficients were also derived from runs of homozygosity (ROH). Based on FROH [ 16 Mb, Pars (0.068), Sistani (0.051), and Taleshi (0.039) had higher levels of inbreeding among Iranian cattle breeds. Compared to other cattle breeds, FROH [ 16 Mb was reported to be equal to 0.034, 0.026, 0.017, 0.004, and 0.007 in Brown, Holstein, Marchigiana, Piedmontese, and Simmental breeds, respectively (Marras et al. 2015). Also, Ferencakovic et al. (2013a) have reported the values of this parameter equal to 0.037, 0.017, 0.02, and 0.025 in Brown Swiss, Fleckvieh, Norwegian Red, and Tyrol Gray cattle breeds, respectively. On the other hand, Curik et al. (2014) obtained higher FROH [ 16 Mb (0.075) in Istrian cattle bulls. However, it should be noted that the difference between SNP densities, sample sizes, and methods applied in various studies can influence the obtained results and any comparison should be conservative. Accurate estimation of genetic parameters might be influenced by the small sample sizes. Therefore, a common question is, ‘whether sampling can provide sufficient information for the analyses conducted in the conservation genetics studies.’ Smith and Wang (2014) confirmed that small sample size can cause little bias in measures of expected heterozygosity, pairwise FST and population structure, but a large downward bias in estimates of allelic diversity. Willing et al. (2012) have reported that population sample size can be significantly reduced (as small as n = 4–6) when using an appropriate estimator and a large number of bi-allelic genetic markers. Similar results were given regarding expected heterozygosity by Pruett and Winker (2008). On the other hand, effective population size might be strongly biased when the sample size is small. Waples (2006) developed a method which is considered a bias correction for estimates of effective population size based on linkage disequilibrium (LD) data. Also, High marker density used in this study can somewhat attenuate sample size effect (Luikart et al. 2010). However, it appears that Ne values were highly variable among chromosomes in some breeds such as Pars. Some of these variations may be attributed to small sample size applied in this breed. However, heterogeneous selection pressures could also cause the variation among LD chromosomes. In general, despite the low number of samples used in this study, these estimates can be useful in assessing the genetic diversity of the Iranian cattle populations. Small population size can increase the effect of inbreeding and genetic drift which could endanger the longterm viability of the populations (Frankham 2005). In this study, small effective population size was inferred for several populations of Iranian cattle breeds. Genetic diversity is under threat within and between these populations and high inbreeding rate was observed within Pars, Sistani, Sarabi,
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and Taleshi populations in recent generations. Some breeds such as Sarabi, Sistani, Pars, Taleshi, and Kermani are exposed with serious risk of extinction and other breeds, except Mazandarani, also have not promising situations. Thus, the efforts should be made to conserve genetic diversity within these populations. Control of crossbreeding, breeding stations development and improvement of recording system are strategies to conserve these breeds under in situ conservation situation. However, improvement of economic production is the most important factor that can affect conservation of Iranian native cattle. It appears that pastoral systems have more economic benefits, but recent droughts have restricted this system of production. Also, ex situ conservation by gene banks can be used to store genetic diversity as insurance for the future. Some methods such as optimal contribution selection (Engelsma et al. 2014) can be used to identify prioritization of animals for incorporation in the gene bank. Globalization of breeding programs and maintaining of local genetic resources should be considered more than ever to perform future breeding objectives. Acknowledgments The authors would like to thank the participant farmers for their collaboration in collecting samples from their animals, and the officials of the cattle breeding stations in Iran for facilitating the sampling process. This work was supported by a grant from the Next Generation BioGreen 21 Program (No. PJ008196), Rural Development Administration, Republic of Korea.
References Biscarini F, Biffani S, Morandi N, Nicolazzi EL, Stella A (2014) Using runs of homozygosity to detect genomic regions associated with susceptibility to infectious and metabolic diseases in dairy cows under intensive farming conditions. ELARES:56 Bjelland DW, Weigel KA, Vukasinovic N, Nkrumah JD (2013) Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. J Dairy Sci 96:4697–4706. doi:10.3168/jds.2012-6435 Charlesworth D, Willis JH (2009) The genetics of inbreeding depression. Nat Rev Genet 10:783–796. doi:10.1038/nrg2664 Curik I, Ferencˇakovic´ M, Karapandza N, Cubric Curik V, So¨lkner J (2014) Estimation of inbreeding and effective population size in Istrian cattle using molecular information. Acta Agrar Kaposva´riensis 18(1):30–34 Decker JE, Pires JC, Conant GC et al (2009) Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. PNAS 106:18644–18649. doi:10.1073/pnas.0904691106 Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2014) NEESTIMATOR v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14(1):209–214. doi:10.1111/ 1755-0998.12157 Edea Z, Dadi H, Kim S, Dessie T, Lee T, Kim H, Kim J, Kim K (2013) Genetic diversity, population structure and relationships in indigenous cattle populations of Ethiopia and Korean Hanwoo breeds using SNP markers. Front Genet 4:35. doi:10.3389/fgene. 2013.00035 Engelsma KA, Veerkamp RF, Calus MPL, Bijma P, Windig JJ (2012) Pedigree and marker based methods in the estimation of genetic
123
Conserv Genet (2016) 17:77–89 diversity in small groups of Holstein cattle. J Anim Breed Genet 129:195–205. doi:10.1111/j.1439-0388.2012.00987.x Engelsma KA, Veerkamp RF, Calus MPL, Windig JJ (2014) Consequences for diversity when animals are prioritized for conservation of the whole genome or of one specific allele. J Anim Breed Genet 131(1):61–70. doi:10.1111/jbg.12052 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620 FAO (2007) The state of the world’s animal genetic resources for food and agriculture. In: Rischkowsky B, Pilling D (eds) United Nations Food and Agriculture Organization (FAO), Rome, Italy Ferencakovic M, Hamzic E, Gredler B, Solberg TR, Klemetsdal G, Curik I, Solkner J (2013a) Estimates of autozygosity derived from runs of homozygosity: empirical evidence from selected cattle populations. J Anim Breed Genet 130:286–293. doi:10.1111/jbg.12012 Ferencakovic M, Solkner J, Curik I (2013b) Estimating autozygosity from high-throughput information: effects of SNP density and genotyping errors. Genet Sel Evol 45(1):42. doi:10.2527/jas20147898 Flury C, Tapio M, Sonstegard T, Drogemuller C, Leeb T, Simianer H, Hanotte O, Rieder S (2010) Effective population size of an indigenous Swiss cattle breed estimated from linkage disequilibrium. J Anim Breed Genet 127:339–347. doi:10.1111/j.14390388.2010.00862.x Frankham R (2005) Genetics and extinction. Biol Conserv 126:131–140. doi:10.1016/j.biocon.2005.05.002 Frankham R, Bradshaw CJA, Brook BW (2014) Genetics in conservation and management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol Conserv 170:56–63 Franklin IR (1980) Evolutionary change in small populations. In: Soule´ ME, Wilcox BA (eds) Conservation biology: an evolutionaryecological perspective. Sinauer Associates, Sunderland, pp 135–150 Garcia-Ga´mez E, Sahana G, Gutie´rrez-Gil B, Arranz JJ (2012) Linkage disequilibrium and inbreeding estimation in Spanish Churra sheep. BMC Genet 13:43. doi:10.1186/1471-2156-13-43 Gasca-Pineda J, Cassaigne I, Alonso RA, Eguiarte LE (2013) Effective population size, genetic variation, and their relevance for conservation: the Bighorn sheep in Tiburon island and comparisons with managed Artiodactyls. PLoS One 8(10):e78120. doi:10.1371/ journal.pone.0078120 Gautier M, Laloe D, Moazami-Goudarzi K (2010) Insights into the genetic history of French cattle from dense SNP data on 47 worldwide breeds. PLoS One 5(9):e13038. doi:10.1371/journal. pone.0013038 Gibson J, Morton NE, Collins A (2006) Extended tracts of homozygosity in outbred human populations. Hum Mol Genet 15:789–795 Gorbach DM, Makgahlela ML, Reecy JM, Kemp SJ et al (2010) Use of SNP genotyping to determine pedigree and breed composition of dairy cattle in Kenya. J Anim Breed Genet 127:348–351. doi:10.1111/j.1439-0388.2010.00864.x Groeneveld LF, Lenstra JA, Eding H, Toro MA, Scherf B et al (2010) Genetic diversity in farm animals–a review. Anim Genet 41:6–31. doi:10.1111/j.1365-2052.2010.02038.x Harmon LJ, Stanton B (2010) Conservation of small populations: Effective population sizes, inbreeding, and the 50/500 rule. In: Braude S, Low BS (eds) An introduction to methods and models in ecology, evolution, and conservation biology. Princeton University Press, Princeton, pp 125–138 Hill WG (1981) Estimation of effective population size from data on linkage disequilibrium. Genet Res 38:209–216 Hoffmann I (2010) Climate change and the characterization, breeding and conservation of animal genetic resources. Anim Genet 41:32–46. doi:10.1111/j.1365-2052.2010.02043.x
Conserv Genet (2016) 17:77–89 Kim ES, Cole JB, Huson H, Wiggans GR, Van Tassell CP, Crooker BA, Liu G, Da Y, Sonstegard TS (2013) Effect of artificial selection on runs of homozygosity in U.S. Holstein cattle. PLoS One 8(11):e80813. doi:10.1371/journal.pone.0080813 Kirin M, McQuillan R, Franklin CS, Campbell H, McKeigue PM, Wilson JF (2010) Genomic runs of homozygosity record population history and consanguinity. PLoS One 5(11):e13996. doi:10.1371/journal.pone.0013996.g001 Lee SH, Cho YM, Lim D, Kim HC, Choi BH, Park HS, Kim OH, Kim S, Kim TH, Yoon D, Hong SK (2011) Linkage disequilibrium and effective population size in Hanwoo Korean cattle. Asian Australas J Anim Sci 24:1660–1665. doi:10.5713/ajas.2011. 11165 Luikart G, Ryman N, Tallmon DA, Schwartz MK, Allendorf FW (2010) Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches. Conserv Genet 11:355–373. doi:10.1007/s10592-010-0050-7 Makina SO, Muchadeyi FC, van Marle-Koster E, MacNeil MD, Maiwashe A (2014) Genetic diversity and population structure among six cattle breeds in South Africa using a whole genome SNP panel. Front Genet 5:333. doi:10.3389/fgene.2014.00333 Markert JA, Champlin DM, Gutjahr-Gobell R, Grear JS, Kuhn A, McGreevy TJ, Roth A, Bagley MJ, Nacci DE (2010) Population genetic diversity and fitness in multiple environments. BMC Evol Biol 10:205. doi:10.1186/1471-2148-10-205 Marras G, Gaspa G, Sorbolini S, Dimauro C, Ajmone-Marsan P, Valentini A, Williams JL, Macciotta NP (2015) Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy. Anim Genet 46:110–121. doi:10. 1111/age.12259 (Epub 2014 Dec 22) Mastrangelo S, Saura M, Tolone M, Salces-Ortiz J, Di Gerlando R, Bertolini F, Fontanesi L, Sardina MT, Serrano M, Portolano B (2014) The genome-wide structure of two economically important indigenous Sicilian cattle breeds. J Anim Sci 92(11):4833–4842. doi:10.2527/jas.2014-7898 Mc Parland S, Kearney JF, Rath M, Berry DP (2007) Inbreeding effects on milk production, calving performance, fertility, and conformation in Irish Holstein-Friesians. J Dairy Sci 90:4411–4419. doi:10.3168/jds.2007-0227 McQuillan R, Leutenegger AL, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, Smolej-Narancic N, Janicijevic B, Polasek O, Tenesa A, Macleod AK, Farrington SM, Rudan P, Hayward C, Vitart V, Rudan I, Wild SH, Dunlop MG, Wright AF, Campbell H, Wilson JF (2008) Runs of homozygosity in European populations. Am J Hum Genet 83:359–372 Medugorac I, Veit-Kensch CE, Ramljak J, Brka M, Markovic´ B, Stojanovic´ S et al (2011) Conservation priorities of genetic diversity in domesticated metapopulations: a study in taurine cattle breeds. Ecol Evol 1(3):408–420. doi:10.1002/ece3.39 Nei M (1972) Genetic distance between populations. Am Nat 106:283–292 Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20(2):289–290. doi:10.1093/bioinformatics/btg412 Pemberton TJ, Absher D, Feldman MW, Myers RM, Rosenberg NA, Li JZ (2012) Genomic patterns of homozygosity in worldwide human populations. Am J Hum Genet 91(2):275–292 Pertoldi C, Purfield DC, Berg P, Jensen TH, Bach OS, Vingborg R, Kristensen TN (2014) Genetic characterization of a herd of the
89 endangered Danish Jutland cattle. J Anim Sci 92:2372–2376. doi:10.2527/jas.2013-7206 Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multi locus genotype data. Genetics 155:945–959 Pruett CL, Winker K (2008) The effects of sample size on population genetic diversity estimates in song sparrows Melospiza melodia. J Avian Biol 39:252–256 Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and populationbased linkage analyses. Am J Hum Genet 81:559–575. doi:10. 1086/519795 Purfield D, Berry D, McParland S, Bradley D (2012) Runs of homozygosity and population history in cattle. BMC Genet 13:70. doi:10.1186/1471-2156-13-70 Scraggs E, Zanella R, Wojtowicz A, Taylor JF, Gaskins CT, Reeves JJ, de Avila JM, Neibergs HL (2014) Estimation of inbreeding and effective population size of full-blood Wagyu cattle registered with the American Wagyu cattle association. J Anim Breed Genet 131:3–10. doi:10.1111/jbg.12066 Smith O, Wang J (2014) When can noninvasive samples provide sufficient information in conservation genetics studies? Mol Ecol Resour 14:1011–1023 Taberlet P, Valentini A, Rezaei HR, Naderi S, Pompanon F, Negrini R, Ajmone-Marsan P (2008) Are cattle, sheep, and goats endangered species? Mol Ecol 17:275–284 Talle SB, Chenyabuga WS, Fimland E, Syrstad O, Meuwissen T, Klungland H (2005) Use of DNA technologies for the conservation of animal genetic resources: a review. Acta Agric Scand Sect A Anim Sci 55:1–8. doi:10.1080/09064700510009315 Theodorou K, Couvet D (2006) On the expected relationship between inbreeding, fitness, and extinction. Genet Sel Evol 38(4):371–387. doi:10.1051/gse:2006010 Tiwari R, Dileep Kumar H, Dutt T, Singh BP, Pachaiyappan K, Dhama K (2014) Future challenges of food security and sustainable livestock production in India in the changing climatic scenario. Asian J Anim Vet Adv 9:367–384. doi:10.3923/ajava. 2014.367.384 Uzzaman MR, Edea Z, Bhuiyan MSA, Walker J, Bhuiyan AKFH, Kim KS (2014) Genome-wide single nucleotide polymorphism analyses reveal genetic diversity and structure of wild and domestic cattle in Bangladesh. Asian Australas J Anim Sci 27(10):1381–1386. doi:10.5713/ajas.2014.14160 Waples RS (2006) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv Genet 7(2):167–184. doi:10.1007/s10592-005-9100-y Willing E, Dreyer C, van Oosterhout C (2012) Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many snp markers. PLoS One 7(8):e42649. doi:10.1371/journal.pone.0042649 Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for Genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82. doi:10.1016/j.ajhg.2010.11.011 (Epub 2010 Dec 17) Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012) A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28:3326–3328
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