1. bookVolume 69 (2018): Edizione 2 (June 2018)
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Genomic descriptors of biodiversity – A review

Pubblicato online: 05 Oct 2018
Volume & Edizione: Volume 69 (2018) - Edizione 2 (June 2018)
Pagine: 73 - 83
Ricevuto: 09 Nov 2017
Accettato: 01 Jul 2018
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Rivista
eISSN
2719-5430
Prima pubblicazione
30 Mar 2016
Frequenza di pubblicazione
4 volte all'anno
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Inglese
Introduction

Biological diversity can be described as the extent of variation contained within nature. The overall concept of diversity has been around for some time and is regarded as an important component for ensuring and assessing adaptation mechanisms. Given the constant and consistent need of food, domesticated animals were kept, selected and bred to fulfill these demands. During the process, specialized breeds of livestock were formed, with the desirable, but compared to their wild relatives, narrower genetic diversity. Currently, the needs and challenges within the livestock sector are similar to those of our predecessors. Ideally, we would like to have populations that are well adapted, have high fitness and production. The overall fitness could be broken down to several other aspects such as better survival of the wild and prolonged productive life of the livestock populations, good health and reproduction. These characteristics should be satisfactory in the animals’ current environment, but they should maintain this adaptation when any of the external factors change. Also, the variance within the selected population is essential in achieving the selection response and genetic gain; thus, diversity is a major parameter for genetic improvement.

In order to cope with these challenges, it is essential to monitor and maintain genetic diversity within populations, that is, to ensure allelic variation within breeds. The across breed diversity is equally important, to ensure that there is a large enough genetic distance between breeds to maximize the production potential within different environments.

Until recently, the assessment of genetic diversity relied on the existence and accuracy of pedigree records. The pedigrees were a central tool in the assessment of relatedness between individuals, and consequently, the avoidance of mating overly related individuals that could lead to occurrence of recessive disorders, decline in fertility and overall fitness. Based on the rate of inbreeding (and other factors), the effective population size could be assessed, as a comparison metric of real and idealized populations. This metric then provided a reference on the overall genetic diversity of populations. However, the problem with the pedigrees is that they neither exist nor are accurate for many of the livestock breeds, which negates or seriously hampers any effort to assess genetic diversity.

With the availability of molecular markers such as microsatellites, and more recently single nucleotide polymorphisms (SNP) and whole genome sequence data, it is possible to analyze diversity on a genomic level. The dense marker genotypes negate the necessity to have historical records, and thus, all problems associated with pedigrees. It allows a more precise assessment of relatedness accounting for Mendelian sampling, and consequently, more accurate inbreeding levels. The dense marker data also enables new types of analyses, such as more precise heterozygosity estimates and ability to pinpoint exact regions of the genomes undergoing selection.

The population structure and genetic distances established groundbreaking works of Nei (1977) and Weir and Cockerham (1984) reliant on overall heterozygosity levels that can also be more precisely computed for entire populations or in some cases for individual SNPs. In case of crossbred animals, the precise admixture levels can be computed for individual animals or even chromosomes, instead of reliance on average values.

Given its obvious advantages, the dense molecular marker data should be the method of choice to analyze livestock populations. This paper reviews how SNP based genomic markers could be used to investigate selected aspects of genetic diversity. Chapter 2 introduces the Linkage disequilibrium (LD) as a core concept to all follow up applications. In Chapter 3, the estimation of inbreeding from the classical (pedigree) perspective is introduced, as well as using dense marker data. In Chapter 4, the effective population size is discussed as one of the most often used metric in livestock diversity, with computation options based also on LD and inbreeding levels. In Chapter 5, an overview about changes of allele frequencies in a population are given and Chapter 6 concludes the paper.

Linkage disequilibrium

Linkage disequilibrium (LD) stands for the nonrandom association between alleles of different loci. The term was first used by Lewontin and Kojima (1960). Contrary to its name, however, detecting LD does not ensure linkage nor lack of equilibrium. It could be loosely defined as the measure of connectedness between alleles, a probability to be inherited together or a correlation of occurrence between allele pairs across a given population.

The LD could be expressed using various coefficients, reviewed in Slatkin (2008). The historically first measurement unit was the “disequilibrium coefficient” defined as:

DAB=pABpA×pB$${{D}_{AB}}={{p}_{AB}}-{{p}_{A}}\times {{p}_{B}}$$

which is the difference between the frequency of gametes carrying the pair of alleles A and B at two loci (PAB) and the product of the frequencies of those alleles (PA and PB). The D’ was developed by Lewontin (1964) to make up for some of the difficulties in interpretation of D. The D’ is the ratio of D to its maximum possible absolute value, given the allele frequencies, with values ranging between -1 and 1. The extreme values mean that at least one of the possible allele combinations is missing. However, the results of the D’ could be inflated if the analyzed population is small or one of the alleles is rare.

The mean squared correlation of allele frequencies at different loci (r2) is another metric of LD that was shown to be a more robust measure of LD. It is defined as:

r2=(p^ABp^A×p^B)2p^A×(1p^A)×p^B×(1p^B)$${{r}^{2}}=\frac{{{\left( {{{\hat{p}}}_{AB}}-{{{\hat{p}}}_{A}}\times {{{\hat{p}}}_{B}} \right)}^{2}}}{{{{\hat{p}}}_{A}}\times \left( 1-{{{\hat{p}}}_{A}} \right)\times {{{\hat{p}}}_{B}}\times \left( 1-{{{\hat{p}}}_{B}} \right)}$$

The r2 is the most commonly used descriptor of LD in livestock genomics. Its values range between 0 and 1, with high values indicating that the two alleles are good surrogates for each other. It was found that the mean value of r2 was determined almost entirely by the population size (N), recombination fraction (c) and time, measured proportional to N (Hill and Robertson, 1968).

The linkage disequilibrium is a function of demographic events within the species, mainly bottlenecks as a mechanism underlying selection and genetic drift, and recombination rates. The directly visible outcome of this effect is the on average higher LD between alleles close to each other and its gradual decline with increasing distance on the genome. Regarding the species-specific LD levels, the overall conclusion is that less intensively selected species show lower LD across the genome. This is confirmed by low LD levels in humans, with LD extending to only a few kilo-bases (Hinds et al., 2005). In sheep (Kijas et al., 2012; Al-Mamun et al., 2015) and goats (Brito et al., 2015; Mdladla et al., 2016), the LD levels are generally lower than other livestock due to comparably lower selection intensity. In contrast, the LD is higher in cattle (Bohmanova et al., 2010; Espigolan et al., 2013; Porto-Neto et al., 2014), pigs (Du et al., 2007) and dogs (Lindblad-Toh et al., 2005).

LD is generally estimated within the chromosome. However, LD between chromosomes and breeds can vary due to differences in recombination rates, heterozygosity, genetic drift and effects of selection between chromosomes and breeds (Qanbari et al., 2010). As the inheritance of genomic region is based on haplotypes rather than single nucleotides, one would expect higher LD within established haplotype blocks. Similarly, in an inbreeding scenario, within the runs of homozygosity (ROH) segments, the LD is increased, as any one of these continuous, homozygous segments originated from a single ancestor, shaped by recombination events. Thus, one could expect a connectedness between the higher chromosome-wise LD levels and the haplotype block structure and the ROH distribution, as shown in Al-Mamun et al. (2015).

LD is also a function of inter marker distance, as the LD between adjacent markers is generally high, but decreases quickly with increasing distance. The extent of LD decay could differ between breeds and species. When comparing taurine and indicine cattle breeds, the LD is initially higher in taurine, which is attributed to a smaller effective population size and a stronger bottleneck during breed formation. The LD decays at a faster rate in the taurine breeds, however, with generally higher background LD rates in indicine cattle (Pérez O’Brien et al., 2014; Porto-Neto et al., 2014). A relatively low LD was detected for the Frizarta sheep, with r2 values of 0.18, with average inter marker distance of 31kb. As expected, the LD was decaying with increasing distance. The rate of decay among the chromosomes was variable, with more rapid decay in shorter and slower decay in longer chromosomes (Kominakis et al., 2017).

In population genetics, LD provides information for population history and both natural and artificial selection. The LD in various distances is the basis for calculation of one of the possible measurements of effective population size (Hill, 1981; Hayes et al., 2003; Tenesa et al., 2007; Mészáros et al., 2015). Also, the elevated LD values in specific genomic regions are considered as signatures of selection. The logic underlying this strategy is straightforward. When a mutation arises, it does so on an existing background haplotype characterized by complete LD between the new mutation and the linked polymorphisms (Bamshad and Wooding, 2003). Selection against recurrent deleterious mutations also reduces variation at linked loci (Charlesworth et al., 1993). This mechanism, known as “background selection”, causes the continuous removal of linked sequences along with deleterious mutations, resulting in a reduced effective population size (Kim and Stephan, 2002).

The analysis of dense marker data is based on the assumption that SNPs are in sufficient linkage disequilibrium to regions of interests. In genome wide association studies (GWAS), the significant SNPs are assumed to be in the vicinity of the quantitative trait locus (QTL) or the gene influencing the phenotype under study. This interpretation of the power to correctly define an association on the genome lead to the concept of “useful LD” (Kruglyak, 1999), which should be r2 > 0.33 according to the suggestion of Ardlie et al. (2002) to limit the required sample size. The D’ threshold for the useful LD should be higher, due to the tendency of D’ to overestimate the magnitude of LD.

Inbreeding

Inbreeding is a result of mating between two individuals that share at least one common ancestor. Therefore, the concepts of relatedness and inbreeding are connected to each other. Historically, both measures were evaluated based on pedigrees, based on the path coefficient methodology proposed by Wright (1922).

The offspring of two related individuals would be inbred. The inbreeding coefficient is calculated as:

Fz=aX,Y2=i=1N(12)(ni+ni+1)(1+FA)$${{F}_{z}}=\frac{{{a}_{X,Y}}}{2}={{\sum\limits_{i=1}^{N}{\left( \frac{1}{2} \right)}}^{\left( {{n}_{i}}+{{{{n}'}}_{i}}+1 \right)}}\left( 1+{{F}_{A}} \right)$$

where F is the inbreeding coefficient of an animal Z, aX, Y is the relationship coefficient between the parents of animal Z, n and n’ are the number of generations from sire and dam of animals Z to a common ancestor A. If the common ancestor A is inbred itself, the inbreeding coefficient FA should be worked out from the pedigree (Wright, 1922).

A different measure of inbreeding is the so-called F-statistics, mostly FIS, introduced by Wright (1951). FIS ranges between -1 and 1, with the positive indicating that the mate pair is more related than the average relatedness in the population, thus we would expect lower proportion of heterozygotes as expected from the Hardy-Weinberg proportions. Negative FIS values indicate less related individuals and excess of heterozygotes. However, there are several unexpected properties of FIS showing inbreeding around zero in closely related populations, as the average population relatedness is to be considered. Moreover, the coefficient could be negative in very small populations due to allele frequency differences between males and females (Balloux and Williams, 2004). Due to these features, Kardos et al. (2016) discourages the use of FIS as a measure of individual inbreeding in a population.

With the availability of genotype marker data, the inbreeding levels could also be considered from the genomic perspective. The genomic measure of inbreeding is most commonly described by continuous homozygous segments of the genome, called “runs of homozygosity” (ROH). Such homozygous runs are assumed to be a consequence of inbreeding; thus, the ROH are chromosome segments identical-by-descent. The inbreeding coefficient (FROH) is then calculated as the proportion of the autosomal genome covered with ROH:

FROH=LROH/Lauto$${{F}_{ROH}}=\sum{{{{L}_{ROH}}}/{{{L}_{auto}}}\;}$$

where ΣLROH is the total length of all of an individual’s ROHs above a specified minimum length and Lauto is the length of the autosomal genome covered by SNPs. If the chromosomes of the analyzed organism contain centromeres, these should be excluded from Lauto because they do not contain SNPs and their inclusion might inflate estimates of autozygosity if both flanking SNPs are homozygous (McQuillan et al., 2008).

The length of ROH segments is strongly influenced by the number of generations separating the inbred individual from the common ancestor(s). Inbreeding due to recent ancestors usually generates quite long identical by descent (IBD) chromosome segments, whereas IBD segments deriving from more distant ancestors tend to be shorter on average because of a higher number of meioses, and recombination events, separating the inbred individual from the ancestor (Kardos et al., 2016). Under the assumption of genetic and physical distance of approximately 1 cM = 1 Mb, the minimum ROH lengths denote the age of inbreeding. ROH segment lengths of 4, 8 and 16 Mb refer to a common ancestor 12, 6 and 3 generations ago, respectively (Howrigan et al., 2011; Curik et al., 2014). The possibility to look for ROH segments of different length also means different sets of quality control criteria. However, there is an ongoing discussion and lack of standards on what the criteria should be. The most widely used criteria are reviewed in Peripolli et al. (2016), suggesting cautious and critical interpretation when comparing studies, analyzing the density of the SNP chip used, the minimum length of ROH, the number of genotyping errors allowed, and the minimum number of SNPs allowed in a single ROH, as they are likely to greatly affect ROH-based estimates of autozygosity.

There are several advantages of genomic measures of inbreeding. First and foremost, it is possible to calculate them solely from the genotype data, even in the absence of pedigree records. Contrary to non-genomic approaches, the inbreeding could be calculated for animals, whose pedigree is dubious, incomplete, or entirely missing (Mészáros et al., 2015). The marker-based inbreeding was also shown to be the best measure using computer simulations (Kardos et al., 2015).

Studies involving ROH are increasingly common and provide valuable information about how the genome’s architecture can disclose a population’s genetic background (Peripolli et al., 2016). In addition to determination of inbreeding levels, ROH could be used for a number of other purposes. Perhaps, the most related to inbreeding is the study of genetic regions with adverse effects. As deleterious variants tend to be recessive, and thus required to occur in homozygous state to manifest, the examination of ROH regions in affected and control animals could reveal the approximate location of the deleterious allele, as shown in Drögemüller et al. (2011). Even if the selected variant is not harmful, the selection increases homozygosity around the target locus, fixing the genomic region within the population, and creating the so-called ROH islands.

The ROH regions can also reveal some rare events on the genome, such as uniparental disomy (UPD). In short, UPD is a consequence of karyotypic anomalies during the meiosis and/or mitosis occurring independently or in combination, ultimately affecting chromosome distribution (Eggermann et al., 2015). Another event is the hemizygous deletion, occurring when a part of a chromosome is deleted, and this deletion is inherited by the offspring (Mc-Carroll et al., 2006). As the SNP genotyping algorithm is not capable to detect the partially missing genotype, the results would show as a homozygous region (Huie et al., 2002), with unusual signal intensity statistics.

The genomic perspectives of inbreeding depression in wild populations are further reviewed in Kardos et al. (2016). Current applications of ROH in livestock are reviewed in Peripolli et al. (2016). The inbreeding depression in livestock is reviewed by Leroy (2014).

Effective population size

The effective population size (Ne) is a theoretical concept describing any given population in terms of genetic size, rather than actual number of individuals, that is, the census population (N). Formally, the Ne could be defined as the number of individuals from a Wright-Fisher population (finite and constant population size, random mating, no mutation, no selection and non-overlapping generations) that would manifest the same extent of genetic drift and inbreeding as the population in question. The theory was initially developed and later extended by Sewall Wright (Wright, 1931). The concept of effective population size is used in the population diversity studies as a benchmark to predict the rate of inbreeding and loss of genetic variation. The ratio of effective and census population could also be used to describe the relationship between the estimated and counted population size (Ne/N). Such a ratio could be as low as 0.1, an estimate from wild populations (Frankham, 1995). The Ne/N ratio is about 0.03 in livestock (Hall, 2016), a much lower value given the high census population size in livestock originating from relatively few ancestors. In addition to the extent of genetic variation within the population, the Ne also predicts the effectiveness of spreading both beneficial and harmful alleles, with quicker spread in populations with lower effective size (Charlesworth, 2009). The Ne is affected by various factors, and it could be computed in different ways. The sex ratio in the population (Wright, 1931) is one of the most frequently used definitions of Ne, computed as:

Ne=4NfNmNf+Nm$${{N}_{e}}=\frac{4{{N}_{f}}{{N}_{m}}}{{{N}_{f}}+{{N}_{m}}}$$

Here, the Nf is the number of females, Nm the number of males. The less frequently occurring sex has the biggest influence on Ne. Other demographic estimates of Ne involve characteristics such as reproductive success (number of offspring), effective number of breeders or fluctuations in population size (Ardren and Kapuscinski 2003).

The inbreeding, here interpreted as the correlation between maternal and paternal alleles of an individual, could also be used to compute Ne. The change in inbreeding levels per generation (ΔF) computed either from pedigree or genomic data is used to estimate Ne as:

Ne=12ΔF$${{N}_{e}}=\frac{1}{2\Delta F}$$

Another possibility of calculating Ne of a genotyped population based on LD in autosomes (Hill, 1981; McEvoy et al., 2011) is:

NeLD14c*(1rLD2α)$$N{{e}_{LD}}\approx \frac{1}{4c}*\left( \frac{1}{r_{LD}^{2}}-\alpha \right)$$

where r2LD is the squared correlation of allele frequencies at a pair of loci, α is 2 when the impact of mutation is considered and 1 otherwise. Variable c is the genetic distance between loci in Morgans. Assuming that the population has been constant in size, the approximation of NeLD is true for t generations ago, where t = 1/(2c) (Hayes et al., 2003). In case of unlinked loci, the recombination frequency between them is 50%, that is, c = 0.5, pointing towards contemporary effective population size (Sved et al., 2013; Waples et al., 2016). Additional factors, such as the mode of inheritance (autosomal or sex linked), population age structure, changes in population size, migration and selection processes also influence Ne (Charlesworth, 2009).

The Ne calculation based on the numbers of males and females is referenced by the Food and Agriculture Organization of the United Nations (FAO). The recommendation, as a rule of thumb, is to keep the Ne above 50 to ensure that inbreeding levels are below F = 0.01 per year. This inbreeding threshold, however, should be viewed as a short-term criterion, rather than the ultimate goal, as continuous inbreeding leads to a gradual attrition of genetic variation. A population with consistent Ne around 50 will lose about one-fourth of its genetic variation after 20 to 30 generations, and along with it, much of its capacity to adapt to the changing conditions (Barthelmes, 1983).

The 50/500 concept was proposed as a benchmark when considering risk of extinction. Populations with Ne below 50 are at an extreme risk of extinction, while those with Ne less than 500 are in a long-term risk of extinction. In a recent study, however, Frankham et al. (2014) argue that a threshold with Ne > 50 is too low to prevent inbreeding depression, and Ne > 100 should be considered instead. Furthermore, the authors argue that Ne > 1000 would be required to retain evolutionary potential. While Frankham et al. (2014) refer to wild populations with considerably higher Ne, the thresholds are stunningly high for livestock. The contradiction between the suggested and observed Ne values demonstrates well the elusive concept of the Ne. While the Ne is much lower than 1000 in all commercial livestock populations, they achieve considerable genetic gains each year, which could be compared to evolutionary potential of wild populations. The question arises where the biological limits of any given species are. With the selection based on genetic merit using conventional breeding values, genomic selection or the recent promise of new frontiers uncovered by genome editing reminds us time and time again about our poor understanding of what is possible to achieve.

Apart from the presented methods, the effective population size could be assessed by the changes of allele frequencies over time (Pollak, 1983; Waples, 1989; Jorde and Ryman, 1995) or heterozygote excess in small populations (Balloux and Williams, 2004). Further perspectives of Ne estimation are summarized for pedigree-based estimators in Caballero (1994), wildlife specific aspects in Frankham (1995), the effects of spatial-temporal stratifications in Waples (2010) and the molecular genetic approaches in Charlesworth et al. (1993), Charlesworth (2009) and Lanfear et al. (2014).

Allele frequency changes in populations

On the population level, there is an ongoing competition between the genetic drift, that is, the random fluctuation of allele frequencies, and selection, that is, the tendency to fix certain alleles in the population. While the genetic drift is likely to affect small populations by the spread of non-beneficial mutations, the fixation of selected regions of the genome in large populations likely contains beneficial effects (Hallatschek et al., 2007).

These two counteracting processes, together with inbreeding, played a central role in the domestication processes. While the genetic drift led to random changes, the partially controlled processes of relaxed natural selection, natural selection in captivity and the fully controlled artificial selection led to the formation of livestock breeds as we know them today (Mignon-Grasteau et al., 2005).

The genetic drift was described as one of the major effects on changes of allele frequencies, counteracted by selection, if this is more intensive than commonly observed in natural populations (Lacy, 1987). In livestock, we usually observe artificial selection, but many small breeds are typically vulnerable to the effects of genetic drift (Willi et al., 2006). This is because of the small Ne, which is inversely correlated with the increase of evolutionary constraints, inbreeding and drift load. From the evolutionary perspective, there are two immediate consequences for small breeds with a small Ne: 1. In breeds without genetic variation, the evolution is constrained, no matter of the intensity of selection, unless it removes the newly arisen deleterious alleles; 2. even if genetic variation is present, only small selection responses could be expected (Kristensen et al., 2015). This happens because with a decrease of Ne, the impact of genetic drift increases, and loci start to behave as neutral, when selection coefficients become smaller or equal to 1/[2Ne] (Wright, 1931). We have to note, however, that some of the major breeds, for example, Holstein, were estimated to have a strikingly low Ne around 100, without any detrimental consequences.

All livestock breeds constantly face challenges within their environment, such as diseases, climatic conditions and pressure to maintain/increase production. In order to cope with their environment, the individuals express particular traits in response to local conditions (phenotypic plasticity). When the changed conditions persist, the response is the evolution of the breed/species via genetic selection leading to adaptation. These detectable responses compared to a situation expected purely by chance are called “selection signatures” (Kim and Stephan, 2002; Pertoldi et al., 2016).

The selection pressure increases the frequency of favorable alleles that increase the chances of survival, but also the frequency of neutral alleles in linkage disequilibrium with the favorable alleles ( and Haigh, 1974; Flori et al., 2009). Theoretical and empirical research of selection signatures indicates that selection substantially affects the levels of neutral polymorphism, either acting against deleterious mutations or favoring advantageous mutations. This means that we can use patterns of polymorphism at neutral sites to detect selection acting at the molecular level (Payseur and Nachman, 2002). Population genetic theory predicts that beneficial mutations are either lost by genetic drift or increase in frequency until they eventually become fixed in a population (Schlötterer, 2003).

A selection signature tends to drastically reduce the variation within a population but will not lead to a reduction in species specific differences. Conversely, negative selection acting on multiple loci will reduce variability between species more drastically than variability within species (Nielsen, 2005). Neutral variants linked to deleterious mutations will indirectly experience selective pressure to be removed from populations. This idea, termed as background selection (Charlesworth et al., 1993), predicts that genomic regions of reduced recombination will exhibit decreased polymorphism levels. Such genetic hitchhiking (Smith and Haigh, 1974) also predicts a reduction of polymorphism in regions of low recombination.

Apart from a within population analysis, it is possible to evaluate selection signatures by comparing genome patters in multiple populations. Because background selection is a balancing process that involves recurrent deleterious mutations, all populations are expected to respond in a roughly similar fashion. Alternatively, genetic hitchhiking may involve the fixation of beneficial mutations in one population only, or the fixation of different beneficial mutations in different populations. Thus, population-specific deviations from neutrality at particular loci may identify candidate regions for genetic hitchhiking (Payseur and Nachman 2002). Analysis of these similarities or differences between the selection signatures leads to a better understanding of the genotype-phenotype map. The study of a large number of polymorphisms spread across the genome reveals aspects of the genetic structure of the population, including, in some cases, evidence of adaptive selection across the genome (Gibbs et al., 2003; Weir et al., 2005).

In animal breeding, directional selection results in loss of variation within breeds, but at the same time increases between-breed differences, given the diverse breeding goals. Selection will also increase the frequency of alleles of neutral markers in linkage disequilibrium with the favorable alleles (Smith and Haigh, 1974). In earlier works, microsatellite markers were used to detect the selection signatures in Drosophila melanogaster (Kauer et al., 2003; Schöfl and Schlötterer, 2004). When commercial SNP chips became available also for non-model animals, the research was quickly expanded within animal breeding. Selection signatures based on SNP markers were discovered in dairy cattle (Flori et al., 2009; Qanbari et al., 2010; Stella et al., 2010; Glick et al., 2012; Ramey et al., 2013; Pérez O’Brien et al., 2014; Pintus et al., 2014; Randhawa et al., 2014). Other works compared the differences in selection signatures of dairy and beef cattle (Prasad et al., 2008; Hayes et al., 2009) or Bos Taurus and Bos Indicus (Porto-Neto et al., 2014) using SNP markers or restricted genomic regions. Significant efforts were made to combine different methodologies to increase the power to detect selection signatures (Schwarzenbacher et al., 2012; Utsunomiya et al., 2013; Qanbari and Simianer, 2014; Randhawa et al., 2014).

A comprehensive overview of the selection signature methods from the last fifty years, explaining their conceptual motivations and statistical interpretations is outlined in Vitti et al. (2013). Strategies and approaches to detect positive signatures of selection are assessed in Qanbari and Simianer (2014).

Conclusion

The concept of evaluation of biological diversity is central to proper management of livestock breeds. An accurate assessment of such diversity characteristics enables to make qualified decisions on the application of protective measures.

This paper reviews the genomic measures of inbreeding, effective population size and allele frequency changes within the population in the form of genetic drift and selection. These and other indicators of genetic diversity could be accurately assessed, given the availability of dense molecular marker across the genome. The SNP marker based genomic data is widely considered to be the most accurate and affordable source to estimate such indicators, as it does not rely on any other requirement than the availability of a DNA sample. As a direct consequence, the approach also enables to characterize populations that would be otherwise extremely challenging, due to the lack of conventional information sources.

Al-Mamun, H.A., Clark, S.A., Kwan, P. and C. Gondro (2015): Genome-Wide Linkage Disequilibrium and Genetic Diversity in Five Populations of Australian Domestic Sheep. Genetics Selection Evolution 47, 90.Al-MamunH.A.ClarkS.A.KwanP.GondroC.2015Genome-Wide Linkage Disequilibrium and Genetic Diversity in Five Populations of Australian Domestic SheepGenetics Selection Evolution479010.1186/s12711-015-0169-6465920726602211Search in Google Scholar

Ardlie, K.G., Kruglyak, L. and M. Seielstad (2002): Patterns of Linkage Disequilibrium in the Human Genome. Nature Reviews Genetics 3, 299–309.ArdlieK.G.KruglyakL.SeielstadM.2002Patterns of Linkage Disequilibrium in the Human GenomeNature Reviews Genetics329930910.1038/nrg77711967554Search in Google Scholar

Ardren, W.R. and A.R. Kapuscinski (2003): Demographic and Genetic Estimates of Effective Population Size (Ne) Reveals Genetic Compensation in Steelhead Trout. Molecular Ecology 12, 35–49.ArdrenW.R.KapuscinskiA.R.2003Demographic and Genetic Estimates of Effective Population Size (Ne) Reveals Genetic Compensation in Steelhead TroutMolecular Ecology12354910.1046/j.1365-294X.2003.01705.xSearch in Google Scholar

Balloux, F. and R. Williams (2004): Heterozygote Excess in Small Populations and the Heterozygote-Excess Effective Population Size. Evolution 58, 1891–1900.BallouxF.WilliamsR.2004Heterozygote Excess in Small Populations and the Heterozygote-Excess Effective Population SizeEvolution581891190010.1111/j.0014-3820.2004.tb00477.x15521449Search in Google Scholar

Bamshad, M. and S.P. Wooding (2003): Signatures of Natural Selection in the Human Genome. Nature Reviews Genetics 4, 99–111.BamshadM.WoodingS.P.2003Signatures of Natural Selection in the Human GenomeNature Reviews Genetics49911110.1038/nrg99912560807Search in Google Scholar

Barthelmes, D. (1983): FAO Fisheries Technical Paper No 217: Conservation of the Genetic Resources of Fish: Problems and Recommendations. Report of the Expert Consultation on the Genetic Resources of Fish, Rome 9–13 June 1980. FAO, Rome, ISBN 92-5-101173-7.BarthelmesD.1983FAO Fisheries Technical Paper No 217: Conservation of the Genetic Resources of Fish: Problems and Recommendations. Report of the Expert Consultation on the Genetic Resources of Fish, Rome 9–13 June 1980FAO, RomeISBN 92-5-101173-7Search in Google Scholar

Bohmanova, J., Sargolzaei, M. and F.S. Schenkel (2010): Characteristics of Linkage Disequilibrium in North American Holsteins. BMC Genomics 11, 421.BohmanovaJ.SargolzaeiM.SchenkelF.S.2010Characteristics of Linkage Disequilibrium in North American HolsteinsBMC Genomics1142110.1186/1471-2164-11-421299694920609259Search in Google Scholar

Brito, L.F., Jafarikia, M., Grossi, D.A., Kijas, J.W., Porto-Neto, L.R., Ventura, R.V., Salgorzaei, M. and F.S. Schenkel (2015): Characterization of Linkage Disequilibrium, Consistency of Gametic Phase and Admixture in Australian and Canadian Goats. BMC Genetics 16, 67.BritoL.F.JafarikiaM.GrossiD.A.KijasJ.W.Porto-NetoL.R.VenturaR.V.SalgorzaeiM.SchenkelF.S.2015Characterization of Linkage Disequilibrium, Consistency of Gametic Phase and Admixture in Australian and Canadian GoatsBMC Genetics166710.1186/s12863-015-0220-1447906526108536Search in Google Scholar

Caballero, A. (1994): Developments in the Prediction of Effective Population Size. Heredity 73, 657–679.CaballeroA.1994Developments in the Prediction of Effective Population SizeHeredity7365767910.1038/hdy.1994.1747814264Search in Google Scholar

Charlesworth, B. (2009): Effective Population Size and Patterns of Molecular Evolution and Variation. Nature Reviews Genetics 10, 195–205.CharlesworthB.2009Effective Population Size and Patterns of Molecular Evolution and VariationNature Reviews Genetics1019520510.1038/nrg252619204717Search in Google Scholar

Charlesworth, B., Morgan, M.T. and D. Charlesworth (1993): The Effect of Deleterious Mutations on Neutral Molecular Variation. Genetics 134, 1289–1303.CharlesworthB.MorganM.T.CharlesworthD.1993The Effect of Deleterious Mutations on Neutral Molecular VariationGenetics1341289130310.1093/genetics/134.4.128912055968375663Search in Google Scholar

Curik, I., Ferenčaković, M. and J. Sölkner (2014): Inbreeding and Runs of Homozygosity: A Possible Solution to an Old Problem. Livestock Science 166, 26–34.CurikI.FerenčakovićM.SölknerJ.2014Inbreeding and Runs of Homozygosity: A Possible Solution to an Old ProblemLivestock Science166263410.1016/j.livsci.2014.05.034Search in Google Scholar

Drögemüller, C., Reichart, U., Seuberlich, T., Oevermann, A., Baumgartner, M., Boghenbor, K.K., Stoffel, M.H. et al. (2011): An Unusual Splice Defect in the Mitofusin 2 Gene (MFN2) Is Associated with Degenerative Axonopathy in Tyrolean Grey Cattle. PLoS ONE 6, e18931.DrögemüllerC.ReichartU.SeuberlichT.OevermannA.BaumgartnerM.BoghenborK.K.StoffelM.H.et al2011An Unusual Splice Defect in the Mitofusin 2 Gene (MFN2) Is Associated with Degenerative Axonopathy in Tyrolean Grey CattlePLoS ONE6e1893110.1371/journal.pone.0018931307813721526202Search in Google Scholar

Du, F.X., Clutter, A.C. and M.M. Lohuis (2007): Characterizing Linkage Disequilibrium in Pig Populations. International Journal of Biological Sciences 3, 166–178.DuF.X.ClutterA.C.LohuisM.M.2007Characterizing Linkage Disequilibrium in Pig PopulationsInternational Journal of Biological Sciences316617810.7150/ijbs.3.166180201817384735Search in Google Scholar

Eggermann, T., Soellner, L., Buiting, K. and D. Kotzot (2015): Mosaicism and Uniparental Disomy in Prenatal Diagnosis. Trends in Molecular Medicine, Special Issue: Nurturing the Next Generation, 21, 77–87.EggermannT.SoellnerL.BuitingK.KotzotD.2015Mosaicism and Uniparental Disomy in Prenatal DiagnosisTrends in Molecular Medicine, Special Issue: Nurturing the Next Generation21778710.1016/j.molmed.2014.11.01025547535Search in Google Scholar

Espigolan, R., Baldi, F., Boligon, A.A., Souza, F.R., Gordo, D.G., Tonussi, R.L., Cardoso, D.F. et al. (2013): Study of Whole Genome Linkage Disequilibrium in Nellore Cattle. BMC Genomics 14 305.EspigolanR.BaldiF.BoligonA.A.SouzaF.R.GordoD.G.TonussiR.L.CardosoD.F.et al2013Study of Whole Genome Linkage Disequilibrium in Nellore CattleBMC Genomics1430510.1186/1471-2164-14-305366263623642139Search in Google Scholar

Flori, L., Fritz, S., Jaffrézic, F., Boussaha, M., Gut, I., Heath, S., Foulley, J.L. and M. Gautier (2009): The Genome Response to Artificial Selection: A Case Study in Dairy Cattle. PLOS ONE 4, e6595.FloriL.FritzS.JaffrézicF.BoussahaM.GutI.HeathS.FoulleyJ.L.GautierM.2009The Genome Response to Artificial Selection: A Case Study in Dairy CattlePLOS ONE4e659510.1371/journal.pone.0006595272272719672461Search in Google Scholar

Frankham, R. (1995): Effective Population Size/adult Population Size Ratios in Wildlife: A Review. Genetics Research 66, 95–107.FrankhamR.1995Effective Population Size/adult Population Size Ratios in Wildlife: A ReviewGenetics Research669510710.1017/S0016672300034455Search in Google Scholar

Frankham, R., Bradshaw, C.J.A. and B.W. Brook (2014): Genetics in Conservation Management: Revised Recommendations for the 50/500 Rules, Red List Criteria and Population Viability Analyses. Biological Conservation 170, 56–63.FrankhamR.BradshawC.J.A.BrookB.W.2014Genetics in Conservation Management: Revised Recommendations for the 50/500 Rules, Red List Criteria and Population Viability AnalysesBiological Conservation170566310.1016/j.biocon.2013.12.036Search in Google Scholar

Glick, G., Shirak, A., Uliel, S., Zeron, Y., Ezra, E., Seroussi, E., Ron, M. and J.I. Weller (2012): Signatures of Contemporary Selection in the Israeli Holstein Dairy Cattle. Animal Genetics 43, 45–55.GlickG.ShirakA.UlielS.ZeronY.EzraE.SeroussiE.RonM.WellerJ.I.2012Signatures of Contemporary Selection in the Israeli Holstein Dairy CattleAnimal Genetics43455510.1111/j.1365-2052.2012.02348.x22742502Search in Google Scholar

Hall, S.J.G. (2016): Effective Population Sizes in Cattle, Sheep, Horses, Pigs and Goats Estimated from Census and Herdbook Data. Animal 10, 1778–1785.HallS.J.G.2016Effective Population Sizes in Cattle, Sheep, Horses, Pigs and Goats Estimated from Census and Herdbook DataAnimal101778178510.1017/S175173111600091427160794Search in Google Scholar

Hallatschek, O., Hersen, P., Ramanathan, S. and D.R. Nelson (2007): Genetic Drift at Expanding Frontiers Promotes Gene Segregation. Proceedings of the National Academy of Sciences 104, 19926–19930.HallatschekO.HersenP.RamanathanS.NelsonD.R.2007Genetic Drift at Expanding Frontiers Promotes Gene SegregationProceedings of the National Academy of Sciences104199261993010.1073/pnas.0710150104214839918056799Search in Google Scholar

Hayes, B.J., Chamberlain, A.J., Maceachern, S., Savin, K., McPartlan, H., MacLeod, I., Sethuraman, L. and M.E. Goddard (2009): A Genome Map of Divergent Artificial Selection between Bos Taurus Dairy Cattle and Bos Taurus Beef Cattle. Animal Genetics 40, 176–184.HayesB.J.ChamberlainA.J.MaceachernS.SavinK.McPartlanH.MacLeodI.SethuramanL.GoddardM.E.2009A Genome Map of Divergent Artificial Selection between Bos Taurus Dairy Cattle and Bos Taurus Beef CattleAnimal Genetics4017618410.1111/j.1365-2052.2008.01815.x19067671Search in Google Scholar

Hayes, B.J., Visscher, P.M., McPartlan, H.C. and M.E. Goddard (2003): Novel Multilocus Measure of Linkage Disequilibrium to Estimate Past Effective Population Size. Genome Research 13, 635–643.HayesB.J.VisscherP.M.McPartlanH.C.GoddardM.E.2003Novel Multilocus Measure of Linkage Disequilibrium to Estimate Past Effective Population SizeGenome Research1363564310.1101/gr.38710343016112654718Search in Google Scholar

Hill, W.G. (1981): Estimation of Effective Population Size from Data on Linkage Disequilibrium. Genetics Research 38, 209–216.HillW.G.1981Estimation of Effective Population Size from Data on Linkage DisequilibriumGenetics Research3820921610.1017/S0016672300020553Search in Google Scholar

Hill, W.G. and A. Robertson (1968): Linkage Disequilibrium in Finite Populations. Theoretical and Applied Genetics 38, 226–231.HillW.G.RobertsonA.1968Linkage Disequilibrium in Finite PopulationsTheoretical and Applied Genetics3822623110.1007/BF0124562224442307Search in Google Scholar

Hinds, D.A., Stuve, L.L., Nilsen, G.B., Halperin, E., Eskin, E., Ballinger, D.G., Frazer, K.A. and D.R. Cox (2005): Whole-Genome Patterns of Common DNA Variation in Three Human Populations. Science 307, 1072–1079.HindsD.A.StuveL.L.NilsenG.B.HalperinE.EskinE.BallingerD.G.FrazerK.A.CoxD.R.2005Whole-Genome Patterns of Common DNA Variation in Three Human PopulationsScience3071072107910.1126/science.110543615718463Search in Google Scholar

Howrigan, D.P., Simonson, M.A. and M.C. Keller (2011): Detecting Autozygosity through Runs of Homozygosity: A Comparison of Three Autozygosity Detection Algorithms. BMC Genomics 12, 460.HowriganD.P.SimonsonM.A.KellerM.C.2011Detecting Autozygosity through Runs of Homozygosity: A Comparison of Three Autozygosity Detection AlgorithmsBMC Genomics1246010.1186/1471-2164-12-460318853421943305Search in Google Scholar

Huie, M.L., Anyane-Yeboa, K., Guzman, E. and R. Hirschhorn (2002): Homozygosity for Multiple Contiguous Single-Nucleotide Polymorphisms as an Indicator of Large Heterozygous Deletions: Identification of a Novel Heterozygous 8-Kb Intragenic Deletion (IVS7–19 to IVS15–17) in a Patient with Glycogen Storage Disease Type II. The American Journal of Human Genetics 70, 1054–1057.HuieM.L.Anyane-YeboaK.GuzmanE.HirschhornR.2002Homozygosity for Multiple Contiguous Single-Nucleotide Polymorphisms as an Indicator of Large Heterozygous Deletions: Identification of a Novel Heterozygous 8-Kb Intragenic Deletion (IVS7–19 to IVS15–17) in a Patient with Glycogen Storage Disease Type IIThe American Journal of Human Genetics701054105710.1086/33969137910211854868Search in Google Scholar

Jorde, P.E. and N. Ryman (1995): Temporal Allele Frequency Change and Estimation of Effective Size in Populations with Overlapping Generations. Genetics 139, 1077–1090.JordeP.E.RymanN.1995Temporal Allele Frequency Change and Estimation of Effective Size in Populations with Overlapping GenerationsGenetics1391077109010.1093/genetics/139.2.107712063587713410Search in Google Scholar

Kardos, M., Luikart, G. and F.W. Allendorf (2015): Measuring Individual Inbreeding in the Age of Genomics: Marker-Based Measures Are Better than Pedigrees. Heredity 115, 63–72.KardosM.LuikartG.AllendorfF.W.2015Measuring Individual Inbreeding in the Age of Genomics: Marker-Based Measures Are Better than PedigreesHeredity115637210.1038/hdy.2015.17481549526059970Search in Google Scholar

Kardos, M., Taylor, H.R., Ellegren, H., Luikart, G. and F.W. Allendorf (2016): Genomics Advances the Study of Inbreeding Depression in the Wild. Evolutionary Applications 9, 1205–1218.KardosM.TaylorH.R.EllegrenH.LuikartG.AllendorfF.W.2016Genomics Advances the Study of Inbreeding Depression in the WildEvolutionary Applications91205121810.1111/eva.12414510821327877200Search in Google Scholar

Kauer, M.O., Dieringer, D. and C. Schlötterer (2003): A Microsatellite Variability Screen for Positive Selection Associated with The ‘out of Africa’ habitat Expansion of Drosophila Melanogaster. Genetics 165, 1137–1148.KauerM.O.DieringerD.SchlöttererC.2003A Microsatellite Variability Screen for Positive Selection Associated with The ‘out of Africa’ habitat Expansion of Drosophila MelanogasterGenetics1651137114810.1093/genetics/165.3.1137146282014668371Search in Google Scholar

Kijas, J.W., Lenstra, J.A., Hayes, B., Boitard, S., Porto Neto, L.R., San Cristobal, M., Servin, B. et al. (2012): Genome-Wide Analysis of the World’s Sheep Breeds Reveals High Levels of Historic Mixture and Strong Recent Selection. PLoS Biology 10, e1001258.KijasJ.W.LenstraJ.A.HayesB.BoitardS.Porto NetoL.R.San CristobalM.ServinB.et al2012Genome-Wide Analysis of the World’s Sheep Breeds Reveals High Levels of Historic Mixture and Strong Recent SelectionPLoS Biology10e100125810.1371/journal.pbio.1001258327450722346734Search in Google Scholar

Kim, Y. and W. Stephan (2002): Detecting a Local Signature of Genetic Hitchhiking Along a Recombining Chromosome. Genetics 160, 765–777.KimY.StephanW.2002Detecting a Local Signature of Genetic Hitchhiking Along a Recombining ChromosomeGenetics16076577710.1093/genetics/160.2.765146196811861577Search in Google Scholar

Kominakis, A., Hager-Theodorides, A.L., Saridaki, A., Antonakos, G. and G. Tsiamis (2017): Genome-Wide Population Structure and Evolutionary History of the Frizarta Dairy Sheep. Animal 3, 1–9.KominakisA.Hager-TheodoridesA.L.SaridakiA.AntonakosG.TsiamisG.2017Genome-Wide Population Structure and Evolutionary History of the Frizarta Dairy SheepAnimal31910.1017/S175173111700042828274293Search in Google Scholar

Kristensen, T.N., Hoffmann, A.A., Pertoldi, C. and A.V. Stronen (2015): What Can Livestock Breeders Learn from Conservation Genetics and Vice Versa? Frontiers in Genetics 5, 00038.KristensenT.N.HoffmannA.A.PertoldiC.StronenA.V.2015What Can Livestock Breeders Learn from Conservation Genetics and Vice Versa?Frontiers in Genetics50003810.3389/fgene.2015.00038432273225713584Search in Google Scholar

Kruglyak, L. (1999): Prospects for Whole-Genome Linkage Disequilibrium Mapping of Common Disease Genes. Nature Genetics 22, 139–144.KruglyakL.1999Prospects for Whole-Genome Linkage Disequilibrium Mapping of Common Disease GenesNature Genetics2213914410.1038/964210369254Search in Google Scholar

Lacy, R.C. (1987): Loss of Genetic Diversity from Managed Populations: Interacting Effects of Drift, Mutation, Immigration, Selection, and Population Subdivision. Conservation Biology 1, 143–158.LacyR.C.1987Loss of Genetic Diversity from Managed Populations: Interacting Effects of Drift, Mutation, Immigration, Selection, and Population SubdivisionConservation Biology114315810.1111/j.1523-1739.1987.tb00023.xSearch in Google Scholar

Lanfear, R., Kokko, H. and A. Eyre-Walker (2014): Population Size and the Rate of Evolution. Trends in Ecology & Evolution 29, 33–41.LanfearR.KokkoH.Eyre-WalkerA.2014Population Size and the Rate of EvolutionTrends in Ecology & Evolution29334110.1016/j.tree.2013.09.00924148292Search in Google Scholar

Leroy, G. (2014): Inbreeding Depression in Livestock Species: Review and Meta-Analysis. Animal Genetics 45, 618–628.LeroyG.2014Inbreeding Depression in Livestock Species: Review and Meta-AnalysisAnimal Genetics4561862810.1111/age.1217824975026Search in Google Scholar

Lewontin, R.C. (1964): The Interaction of Selection and Linkage. I. General Considerations; Heterotic Models. Genetics 49, 49–67.LewontinR.C.1964The Interaction of Selection and LinkageI. General Considerations; Heterotic Models. Genetics49496710.1093/genetics/49.1.49Search in Google Scholar

Lewontin, R.C. and K. Kojima (1960): The Evolutionary Dynamics of Complex Polymorphisms. Evolution 14, 458–472.LewontinR.C.KojimaK.1960The Evolutionary Dynamics of Complex PolymorphismsEvolution14458472Search in Google Scholar

Lindblad-Toh, K., Wade, C.M., Mikkelsen, T.S., Karlsson, E.K., Jaffe, D.B., Kamal, M., Clamp, M. et al. (2005): Genome Sequence, Comparative Analysis and Haplotype Structure of the Domestic Dog. Nature 438, 803–819.Lindblad-TohK.WadeC.M.MikkelsenT.S.KarlssonE.K.JaffeD.B.KamalM.ClampM.et al2005Genome Sequence, Comparative Analysis and Haplotype Structure of the Domestic DogNature43880381910.1038/nature04338Search in Google Scholar

McCarroll, S.A., Hadnott, T.N., Perry, G.H., Sabeti, P.C., Zody, M.C., Barrett, J.C., Dallaire, S. et al. (2006): Common Deletion Polymorphisms in the Human Genome. Nature Genetics 38, 86–92.McCarrollS.A.HadnottT.N.PerryG.H.SabetiP.C.ZodyM.C.BarrettJ.C.DallaireS.et al2006Common Deletion Polymorphisms in the Human GenomeNature Genetics38869210.1038/ng1696Search in Google Scholar

McEvoy, B.P., Powell, J.E., Goddard, M.E. and P.M. Visscher (2011): Human Population Dispersal ‘Out of Africa’ Estimated from Linkage Disequilibrium and Allele Frequencies of SNPs. Genome Research 21, 821–829.McEvoyB.P.PowellJ.E.GoddardM.E.VisscherP.M.2011Human Population Dispersal ‘Out of Africa’ Estimated from Linkage Disequilibrium and Allele Frequencies of SNPsGenome Research2182182910.1101/gr.119636.110Search in Google Scholar

McQuillan, R., Leutenegger, A.L., Abdel-Rahman, R., Franklin, C.S., Pericic, M., Barac-Lauc, L., Smolej-Narancic, N. et al. (2008): Runs of Homozygosity in European Populations. The American Journal of Human Genetics 83, 359–372.McQuillanR.LeuteneggerA.L.Abdel-RahmanR.FranklinC.S.PericicM.Barac-LaucL.Smolej-NarancicN.et al2008Runs of Homozygosity in European PopulationsThe American Journal of Human Genetics8335937210.1016/j.ajhg.2008.08.007Search in Google Scholar

Mdladla, K., Dzomba, E.F., Huson, H.J. and F.C. Muchadeyi (2016): Population Genomic Structure and Linkage Disequilibrium Analysis of South African Goat Breeds Using Genome-Wide SNP Data. Animal Genetics 47, 471–482.MdladlaK.DzombaE.F.HusonH.J.MuchadeyiF.C.2016Population Genomic Structure and Linkage Disequilibrium Analysis of South African Goat Breeds Using Genome-Wide SNP DataAnimal Genetics4747148210.1111/age.12442Search in Google Scholar

Mészáros, G., Boison, S.A., Pérez O’Brien, A.M., Ferenčaković, M., Curik, I., Da Silva, M.V.B., Utsunomiya, Y.T., Garcia, J.F. and J. Sölkner (2015): Genomic Analysis for Managing Small and Endangered Populations: A Case Study in Tyrol Grey Cattle. Frontiers in Genetics 6, 173.MészárosG.BoisonS.A.Pérez O’BrienA.M.FerenčakovićM.CurikI.Da SilvaM.V.B.UtsunomiyaY.T.Garcia, J.F. and J. Sölkner2015Genomic Analysis for Managing Small and Endangered Populations: A Case Study in Tyrol Grey CattleFrontiers in Genetics6173Search in Google Scholar

Mignon-Grasteau, S., Boissy, A., Bouix, J., Faure, J.M., Fisher, A.D., Hinch, G.N., Jensen, P. et al. (2005): Genetics of Adaptation and Domestication in Livestock. Livestock Production Science 93, 3–14.Mignon-GrasteauS.BoissyA.BouixJ.FaureJ.M.FisherA.D.HinchG.N.JensenP.et al2005Genetics of Adaptation and Domestication in LivestockLivestock Production Science9331410.1016/j.livprodsci.2004.11.001Search in Google Scholar

Nei, M. (1977): F-Statistics and Analysis of Gene Diversity in Subdivided Populations. Annals of Human Genetics 41, 225–233.NeiM.1977F-Statistics and Analysis of Gene Diversity in Subdivided PopulationsAnnals of Human Genetics4122523310.1111/j.1469-1809.1977.tb01918.xSearch in Google Scholar

Nielsen, R. (2005): Molecular Signatures of Natural Selection. Annual Review of Genetics 39, 197–218.NielsenR.2005Molecular Signatures of Natural SelectionAnnual Review of Genetics3919721810.1146/annurev.genet.39.073003.112420Search in Google Scholar

Payseur, B.A. and M.W. Nachman (2002): Natural Selection at Linked Sites in Humans. Gene 300, 31–42.PayseurB.A.NachmanM.W.2002Natural Selection at Linked Sites in HumansGene300314210.1016/S0378-1119(02)00849-1Search in Google Scholar

Pérez O’Brien, A.M., Utsunomiya, Y.T., Mészáros, G., Bickhart, D.M., Liu, G.E., Van Tassell, C.P., Sonstegard, T.S., Da Silva, M.V.B., Garcia, J.F. and J. Sölkner (2014): Assessing Signatures of Selection through Variation in Linkage Disequilibrium between Taurine and Indicine Cattle. Genetics, Selection, Evolution 46, 19.Pérez O’BrienA.M.UtsunomiyaY.T.MészárosG.BickhartD.M.LiuG.E.Van TassellC.P.SonstegardT.S.Da SilvaM.V.B.GarciaJ.F.SölknerJ.2014Assessing Signatures of Selection through Variation in Linkage Disequilibrium between Taurine and Indicine CattleGenetics, Selection, Evolution461910.1186/1297-9686-46-19401480524592996Search in Google Scholar

Peripolli, E., Munari, D.P., Da Silva, M.V.B., Lima, A.L.F., Irgang, R. and F. Baldi (2016): Runs of Homozygosity: Current Knowledge and Applications in Livestock. Animal Genetics 48, 255–271.PeripolliE.MunariD.P.Da SilvaM.V.B.LimaA.L.F.IrgangR.BaldiF.2016Runs of Homozygosity: Current Knowledge and Applications in LivestockAnimal Genetics4825527110.1111/age.1252627910110Search in Google Scholar

Pertoldi, C., Randi, E., Ruiz-Gonzalez, A., Vergeer, P. and J. Ouborg (2016): How Can Genomic Tools Contribute to the Conservation of Endangered Organisms. International Journal of Genomics 12, e4712487.PertoldiC.RandiE.Ruiz-GonzalezA.VergeerP.OuborgJ.2016How Can Genomic Tools Contribute to the Conservation of Endangered OrganismsInternational Journal of Genomics12e471248710.1155/2016/4712487512641927975048Search in Google Scholar

Pintus, E., Sorbolini, S., Albera, A., Gaspa, G., Dimauro, C., Steri, R., Marras, G. and N.P. Macciotta (2014): Use of Locally Weighted Scatterplot Smoothing (LOWESS) Regression to Study Selection Signatures in Piedmontese and Italian Brown Cattle Breeds. Animal Genetics 45, 1–11.PintusE.SorboliniS.AlberaA.GaspaG.DimauroC.SteriR.MarrasG.MacciottaN.P.2014Use of Locally Weighted Scatterplot Smoothing (LOWESS) Regression to Study Selection Signatures in Piedmontese and Italian Brown Cattle BreedsAnimal Genetics4511110.1111/age.1207623889699Search in Google Scholar

Pollak, E. (1983): A New Method for Estimating the Effective Population Size from Allele Frequency Changes. Genetics 104, 531–548.PollakE.1983A New Method for Estimating the Effective Population Size from Allele Frequency ChangesGenetics10453154810.1093/genetics/104.3.531120209317246147Search in Google Scholar

Porto-Neto, L.R., Kijas, J.W. and A. Reverter (2014): The Extent of Linkage Disequilibrium in Beef Cattle Breeds Using High-Density SNP Genotypes. Genetics, Selection, Evolution 46, 22.Porto-NetoL.R.KijasJ.W.ReverterA.2014The Extent of Linkage Disequilibrium in Beef Cattle Breeds Using High-Density SNP GenotypesGenetics, Selection, Evolution462210.1186/1297-9686-46-22402122924661366Search in Google Scholar

Prasad, A., Schnabel, R.D., McKay, S.D., Murdoch, B., Stothard, P., Kolbehdari, D., Wang, Z., Taylor, J.F. and S.S. Moore (2008): Linkage Disequilibrium and Signatures of Selection on Chromosomes 19 and 29 in Beef and Dairy Cattle. Animal Genetics 39, 597–605.PrasadA.SchnabelR.D.McKayS.D.MurdochB.StothardP.KolbehdariD.WangZ.TaylorJ.F.MooreS.S.2008Linkage Disequilibrium and Signatures of Selection on Chromosomes 19 and 29 in Beef and Dairy CattleAnimal Genetics3959760510.1111/j.1365-2052.2008.01772.x265938818717667Search in Google Scholar

Qanbari, S. and H. Simianer (2014): Mapping Signatures of Positive Selection in the Genome of Livestock. Livestock Science, Genomics Applied to Livestock Production 166, 133–143.QanbariS.SimianerH.2014Mapping Signatures of Positive Selection in the Genome of LivestockLivestock Science, Genomics Applied to Livestock Production16613314310.1016/j.livsci.2014.05.003Search in Google Scholar

Qanbari, S., Pimentel, E.C.G., Tetens, J., Thaller, G., Lichtner, P., Sharifi, A.R. and H. Simianer (2010): The Pattern of Linkage Disequilibrium in German Holstein Cattle. Animal Genetics 41, 346–56.QanbariS.PimentelE.C.G.TetensJ.ThallerG.LichtnerP.SharifiA.R.SimianerH.2010The Pattern of Linkage Disequilibrium in German Holstein CattleAnimal Genetics413465610.1111/j.1365-2052.2009.02011.x20055813Search in Google Scholar

Ramey, H.R., Decker, J.E., McKay, S.D., Rolf, M.M., Schnabel, R.D. and J.F. Taylor (2013): Detection of Selective Sweeps in Cattle Using Genome-Wide SNP Data. BMC Genomics 14, 382.RameyH.R.DeckerJ.E.McKayS.D.RolfM.M.SchnabelR.D.TaylorJ.F.2013Detection of Selective Sweeps in Cattle Using Genome-Wide SNP DataBMC Genomics1438210.1186/1471-2164-14-382368155423758707Search in Google Scholar

Randhawa, I.A.S., Khatkar, M.S., Thomson, P.C. and H.W. Raadsma (2014): Composite Selection Signals Can Localize the Trait Specific Genomic Regions in Multi-Breed Populations of Cattle and Sheep. BMC Genetics 15, 34.RandhawaI.A.S.KhatkarM.S.ThomsonP.C.RaadsmaH.W.2014Composite Selection Signals Can Localize the Trait Specific Genomic Regions in Multi-Breed Populations of Cattle and SheepBMC Genetics153410.1186/1471-2156-15-34Search in Google Scholar

Schlötterer, C. (2003): Hitchhiking Mapping – Functional Genomics from the Population Genetics Perspective. Trends in Genetics 19, 32–38.SchlöttererC.2003Hitchhiking Mapping – Functional Genomics from the Population Genetics PerspectiveTrends in Genetics19323810.1016/S0168-9525(02)00012-4Search in Google Scholar

Schöfl, G. and C. Schlötterer (2004): Patterns of Microsatellite Variability Among X Chromosomes and Autosomes Indicate a High Frequency of Beneficial Mutations in Non-African D. Simulans. Molecular Biology and Evolution 21, 1384–1390.SchöflG.SchlöttererC.2004Patterns of Microsatellite Variability Among X Chromosomes and Autosomes Indicate a High Frequency of Beneficial Mutations in Non-African DSimulans. Molecular Biology and Evolution211384139010.1093/molbev/msh13215044592Search in Google Scholar

Schwarzenbacher, H., Dolezal, M., Flisikowski, K., Seefried, F., Wurmser, C., Schlötterer, C. and R. Fries (2012): Combining Evidence of Selection with Association Analysis Increases Power to Detect Regions Influencing Complex Traits in Dairy Cattle. BMC Genomics 13, 48.SchwarzenbacherH.DolezalM.FlisikowskiK.SeefriedF.WurmserC.SchlöttererC.FriesR.2012Combining Evidence of Selection with Association Analysis Increases Power to Detect Regions Influencing Complex Traits in Dairy CattleBMC Genomics134810.1186/1471-2164-13-48330558222289501Search in Google Scholar

Slatkin, M. (2008): Linkage Disequilibrium — Understanding the Evolutionary Past and Mapping the Medical Future. Nature Reviews Genetics 9, 477–485.SlatkinM.2008Linkage Disequilibrium — Understanding the Evolutionary Past and Mapping the Medical FutureNature Reviews Genetics947748510.1038/nrg2361512448718427557Search in Google Scholar

Smith, J.M. and J. Haigh (1974): The Hitch-Hiking Effect of a Favourable Gene. Genetical Research 23, 23–35.SmithJ.M.HaighJ.1974The Hitch-Hiking Effect of a Favourable GeneGenetical Research23233510.1017/S0016672300014634Search in Google Scholar

Stella, A., Ajmone-Marsan, P., Lazzari, B. and P. Boettcher (2010): Identification of Selection Signatures in Cattle Breeds Selected for Dairy Production. Genetics 185, 1451–1461.StellaA.Ajmone-MarsanP.LazzariB.BoettcherP.2010Identification of Selection Signatures in Cattle Breeds Selected for Dairy ProductionGenetics1851451146110.1534/genetics.110.116111292776920479146Search in Google Scholar

Sved, J. A., Cameron, E.C. and A.S. Gilchrist (2013): Estimating Effective Population Size from Linkage Disequilibrium between Unlinked Loci: Theory and Application to Fruit Fly Outbreak Populations. PLoS ONE 8, e69078.SvedJ. A.CameronE.C.GilchristA.S.2013Estimating Effective Population Size from Linkage Disequilibrium between Unlinked Loci: Theory and Application to Fruit Fly Outbreak PopulationsPLoS ONE8e6907810.1371/journal.pone.0069078372088123894410Search in Google Scholar

Tenesa, A., Navarro, P., Hayes, B.J., Duffy, D.L., Clarke, G.M., Goddard, M.E and P.M. Visscher (2007): Recent Human Effective Population Size Estimated from Linkage Disequilibrium. Genome Research 17, 520–526.TenesaA.NavarroP.HayesB.J.DuffyD.L.ClarkeG.M.GoddardM.EVisscherP.M.2007Recent Human Effective Population Size Estimated from Linkage DisequilibriumGenome Research1752052610.1101/gr.6023607183209917351134Search in Google Scholar

Utsunomiya, Y.T., do Carmo, A.S., Carvalheiro, R., Neves, H.H.R., Matos, M.C., Zavarez, L.B., Pérez O’Brien, A.M. et al. (2013): Genome-Wide Association Study for Birth Weight in Nellore Cattle Points to Previously Described Orthologous Genes Affecting Human and Bovine Height. BMC Genetics 14, 52.UtsunomiyaY.T.do CarmoA.S.CarvalheiroR.NevesH.H.R.MatosM.C.ZavarezL.B.Pérez O’BrienA.M.et al2013Genome-Wide Association Study for Birth Weight in Nellore Cattle Points to Previously Described Orthologous Genes Affecting Human and Bovine HeightBMC Genetics145210.1186/1471-2156-14-52368332723758625Search in Google Scholar

Vitti, J.J., Grossman, S.R. and P.C. Sabeti (2013): Detecting Natural Selection in Genomic Data. Annual Review of Genetics 47, 97–120.VittiJ.J.GrossmanS.R.SabetiP.C.2013Detecting Natural Selection in Genomic DataAnnual Review of Genetics479712010.1146/annurev-genet-111212-13352624274750Search in Google Scholar

Waples, R.S. (1989): A Generalized Approach for Estimating Effective Population Size from Temporal Changes in Allele Frequency. Genetics 121, 379–391.WaplesR.S.1989A Generalized Approach for Estimating Effective Population Size from Temporal Changes in Allele FrequencyGenetics12137939110.1093/genetics/121.2.37912036252731727Search in Google Scholar

Waples, R.S. (2010): Spatial-Temporal Stratifications in Natural Populations and How They Affect Understanding and Estimation of Effective Population Size. Molecular Ecology Resources 10, 785–796.WaplesR.S.2010Spatial-Temporal Stratifications in Natural Populations and How They Affect Understanding and Estimation of Effective Population SizeMolecular Ecology Resources1078579610.1111/j.1755-0998.2010.02876.x21565090Search in Google Scholar

Waples, R.K., Larson, W.A. and R.S. Waples (2016): Estimating Contemporary Effective Population Size in Non-Model Species Using Linkage Disequilibrium across Thousands of Loci. Heredity 117, 233–240.WaplesR.K.LarsonW.A.WaplesR.S.2016Estimating Contemporary Effective Population Size in Non-Model Species Using Linkage Disequilibrium across Thousands of LociHeredity11723324010.1038/hdy.2016.60502675827553452Search in Google Scholar

Weir, B.S. and C.C. Cockerham (1984): Estimating F-Statistics for the Analysis of Population Structure. Evolution 38, 1358–1370.WeirB.S.CockerhamC.C.1984Estimating F-Statistics for the Analysis of Population StructureEvolution381358137010.1111/j.1558-5646.1984.tb05657.x28563791Search in Google Scholar

Willi, Y., Van Buskirk, J. and A.A. Hoffmann (2006): Limits to the Adaptive Potential of Small Populations. Annual Review of Ecology, Evolution, and Systematics 37, 433–458.WilliY.Van BuskirkJ.HoffmannA.A.2006Limits to the Adaptive Potential of Small PopulationsAnnual Review of Ecology, Evolution, and Systematics3743345810.1146/annurev.ecolsys.37.091305.110145Search in Google Scholar

Wright, S. (1922): Coefficients of Inbreeding and Relationship. The American Naturalist 56, 330–338.WrightS.1922Coefficients of Inbreeding and RelationshipThe American Naturalist5633033810.1086/279872Search in Google Scholar

Wright, S. (1931): Evolution in Mendelian Populations. Genetics 16, 97–159.WrightS.1931Evolution in Mendelian PopulationsGenetics169715910.1093/genetics/16.2.97120109117246615Search in Google Scholar

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