Comparison of the effectiveness of microsatellites and SNP panels for genetic identification,traceability and assessment of parentage in an inbred Angus herd

During the last decade, microsatellites (short tandem repeats or STRs) have been successfully used for animal genetic identification, traceability and paternity, although in recent year single nucleotide polymorphisms (SNPs) have been increasingly used for this purpose. An efficient SNP identification system requires a marker set with enough power to identify individuals and their parents. Genetic diagnostics generally include the analysis of related animals. In this work, the degree of information provided by SNPs for a consanguineous herd of cattle was compared with that provided by STRs. Thirty-six closely related Angus cattle were genotyped for 18 STRs and 116 SNPs. Cumulative SNPs exclusion power values (Q) for paternity and sample matching probability (MP) yielded values greater than 0.9998 and 4.32E⁻⁴², respectively. Generally 2–3 SNPs per STR were needed to obtain an equivalent Q value. The MP showed that 24 SNPs were equivalent to the ISAG (International Society for Animal Genetics) minimal recommended set of 12 STRs (MP ∼ 10⁻¹¹). These results provide valuable genetic data that support the consensus SNP panel for bovine genetic identification developed by the Parentage Recording Working Group of ICAR (International Committee for Animal Recording).     

Using blocks of linked single nucleotide polymorphisms as highly polymorphic genetic markers for parentage analysis

Single nucleotide polymorphisms (SNPs) are plentiful in most genomes and amenable to high throughput genotyping, but they are not yet popular for parentage or paternity analysis. The markers are bi-allelic, so individually they contain little information about parentage, and in nonmodel organisms the process of identifying large numbers of unlinked SNPs can be daunting. We explore the possibility of using blocks of between three and 26 linked SNPs as highly polymorphic molecular markers for reconstructing male genotypes in polyandrous organisms with moderate (five offspring) to large (25 offspring) clutches of offspring. Haplotypes are inferred for each block of linked SNPs using the programs Haplore and Phase 2.1. Each multi-SNP haplotype is then treated as a separate allele, producing a highly polymorphic, 'microsatellite-like' marker. A simulation study is performed using haplotype frequencies derived from empirical data sets from Drosophila melanogaster and Mus musculus populations. We find that the markers produced are competitive with microsatellite loci in terms of single parent exclusion probabilities, particularly when using six or more linked SNPs to form a haplotype. These markers contain only modest rates of missing data and genotyping or phasing errors and thus should be seriously considered as molecular markers for parentage analysis, particularly when the study is interested in the functional significance of polymorphisms across the genome.     

Single nucleotide polymorphisms for pig identification and parentage exclusion

Single nucleotide polymorphisms (SNPs) have become an important type of marker for commercial diagnostic and parentage genotyping applications as automated genotyping systems have been developed that yield accurate genotypes. Unfortunately, allele frequencies for public SNP markers in commercial pig populations have not been available. To fulfil this need, SNP markers previously mapped in the USMARC swine reference population were tested in a panel of 155 boars that were representative of US purebred Duroc, Hampshire, Landrace and Yorkshire populations. Multiplex assay groups of 5-7 SNP assays/group were designed and genotypes were determined using Sequenom's massarray system. Of 80 SNPs that were evaluated, 60 SNPs with minor allele frequencies >0.15 were selected for the final panel of markers. Overall identity power across breeds was 4.6 x 10(-23), but within-breed values ranged from 4.3 x 10(-14) (Hampshire) to 2.6 x 10(-22) (Yorkshire). Parentage exclusion probability with only one sampled parent was 0.9974 (all data) and ranged from 0.9594 (Hampshire) to 0.9963 (Yorkshire) within breeds. Sire exclusion probability when the dam's genotype was known was 0.99998 (all data) and ranged from 0.99868 (Hampshire) to 0.99997 (Yorkshire) within breeds. Power of exclusion was compared between the 60 SNP and 10 microsatellite markers. The parental exclusion probabilities for SNP and microsatellite marker panels were similar, but the SNP panel was much more sensitive for individual identification. This panel of SNP markers is theoretically sufficient for individual identification of any pig in the world and is publicly available.     

Exclusion probabilities of 22 bovine microsatellite markers in fluorescent multiplexes for semiautomated parentage testing

Six multiplexes developed for semiautomated fluorescence genotyping were evaluated for parentage testing. These multiplexes contained primer pairs for the amplification of 22 microsatellites on 17 bovine autosomes. Exclusion probabilities were determined from genotypes of 1022 Holstein cattle and 311 beef cattle belonging to five breeds. Two cases were considered: case 1, genotypes are known for an alleged parent and an offspring but genotypes of a confirmed parent are unknown; and case 2, genotypes are known for an alleged parent, a confirmed parent and an offspring. If the alleged parent is not the true parent, then the 22 markers will exclude the alleged parent with a probability of >0·9986 for case 1 and with a probability of >0·99999 for case 2. On the basis of these exclusion probabilities, the probability that an alleged parent will be falsely included as a parent is in the range of 1/716 to 1/2845 for case 1 and 1/1·2 million to 1/159753 for case 2. In addition to these results, a rapid and efficient non-organic method for extraction of DNA from semen is described.     

Power of exclusion for parentage verification and probability of match for identity in American Kennel Club breeds using 17 canine microsatellite markers

DNA analysis of microsatellite markers has become a common tool for verifying parentage in breed registries and identifying individual animals that are linked to a database or owner. Panels of markers have been developed in canines, but their utility across and within a wide range of breeds has not been reported. The American Kennel Club (AKC) authorized a study to determine the power to exclude non-parents and identify individuals using DNA genotypes of 17 microsatellite markers in two panels. Cheek swab samples were voluntarily collected at Parent Breed Club National Specialty dog shows and 9561 samples representing 108 breeds were collected, averaging 88.5 dogs per breed. The primary panel of 10 markers exceeded 99% power of exclusion for canine parentage verification of 61% of the breeds. In combination with the secondary panel of seven markers, 100% of the tested breeds exceeded 99% power of exclusion. The minimum probability match rate of the first panel was 3.6 x 10(-5) averaged across breeds, and with the addition of the second panel, the probability match rate was 3.2 x 10(-8); thus the probability of another random, unrelated dog with the same genotype is very low. The results of this analysis indicated that, on average, the primary panel meets the AKC's needs for routine parentage testing, but that a combination of 10-15 genetic markers from the two panels could yield a universal canine panel with enhanced processing efficiency, reliability and informativeness.     

Estimation of the number of genetic markers required for individual animal identification accounting for genotyping errors

Nearly all studies that consider the power of exclusion for individual identification using genetic markers ignore the possibility of erroneous genotypes, although individual genotype error rates are approximately 1% for microsatellites. Single nucleotide polymorphisms (SNPs) have lower error rates, but because of their lower information content, more SNPs than microsatellites will be required to obtain the same power of exclusion for traceability. In this study, we accounted for genotyping mistakes by requiring at least two discrepancies to reject a match. Exclusion probabilities were computed analytically and by simulation. A microsatellite with five alleles was approximately comparable in exclusion power to 2-2.25 SNPs. At least eight SNPs were required to achieve a 99% probability of rejection for a match between two individuals, while with 25 SNPs there was a <1% chance for a match between any of five million individuals.     

Pedigree reconstruction from SNP data: parentage assignment,sibship clustering and beyond

Data on hundreds or thousands of single nucleotide polymorphisms (SNPs) provide detailed information about the relationships between individuals, but currently few tools can turn this information into a multigenerational pedigree. I present the r package sequoia , which assigns parents, clusters half‐siblings sharing an unsampled parent and assigns grandparents to half‐sibships. Assignments are made after consideration of the likelihoods of all possible first‐, second‐ and third‐degree relationships between the focal individuals, as well as the traditional alternative of being unrelated. This careful exploration of the local likelihood surface is implemented in a fast, heuristic hill‐climbing algorithm. Distinction between the various categories of second‐degree relatives is possible when likelihoods are calculated conditional on at least one parent of each focal individual. Performance was tested on simulated data sets with realistic genotyping error rate and missingness, based on three different large pedigrees (N = 1000–2000). This included a complex pedigree with overlapping generations, occasional close inbreeding and some unknown birth years. Parentage assignment was highly accurate down to about 100 independent SNPs (error rate <0.1%) and fast (<1 min) as most pairs can be excluded from being parent–offspring based on opposite homozygosity. For full pedigree reconstruction, 40% of parents were assumed nongenotyped. Reconstruction resulted in low error rates (<0.3%), high assignment rates (>99%) in limited computation time (typically <1 h) when at least 200 independent SNPs were used. In three empirical data sets, relatedness estimated from the inferred pedigree was strongly correlated to genomic relatedness.     

Effects of genotyping errors on parentage exclusion analysis

Genetic markers are widely used to determine the parentage of individuals in studies of mating systems, reproductive success, dispersals, quantitative genetic parameters and in the management of conservation populations. These markers are, however, imperfect for parentage analyses because of the presence of genotyping errors and undetectable alleles, which may cause incompatible genotypes (mismatches) between parents and offspring and thus result in false exclusions of true parentage. Highly polymorphic markers widely used in parentage analyses, such as microsatellites, are especially prone to genotyping errors. In this investigation, I derived the probabilities of excluding a random (related) individual from parentage and the probabilities of Mendelian-inconsistent errors (mismatches) and Mendelian-consistent errors (which do not cause mismatches) in parent-offspring dyads, when a marker having null alleles, allelic dropouts and false alleles is used in a parentage analysis. These probabilities are useful in evaluating the impact of various types of genotyping errors on the information content of a set of markers in and thus the power of a parentage analysis, in determining the threshold number of genetic mismatches that is appropriate for a parentage exclusion analysis and in estimating the rates of genotyping errors and frequencies of null alleles from observed mismatches between known parent-offspring dyads. These applications are demonstrated by numerical examples using both hypothetical and empirical data sets and discussed in the context of practical parentage exclusion analyses.     

Probability of random sire exclusion using microsatellite markers for parentage verification

Many microsatellite sequences have been described in the bovine genome. Being highly polymorphic these have been suggested as markers for parentage verification and individual identification in cattle. We have evaluated the use of five highly polymorphic microsatellite markers for parentage verification in 14 breeds of cattle in the UK. Three of the microsatellite loci occur within introns in genes: BoLA DRB3 , steroid 21-hydroxylase, and the beta subunit of the follicle-stimulating hormone. The other two are anonymous sites ETH131 and HEL6. Results were analysed by a statistical approach that takes in to account deviations from Hardy-Wienberg equilibrium and linkage disequilibrium for multiple loci. The method of determining the probability of random sire exclusion uses observed genotype frequencies instead of allele frequencies. Independently, the markers used have a probability of between 0.72 and 0.62 of identifying a parentage error, while used together the five markers give, on average across breeds, a probability of 0.99 of excluding an incorrect sire.     

Implementation of a parentage control system in Portuguese beef-cattle with a panel of microsatellite markers

A study was conducted to assess the feasibility of applying a panel of 10 microsatellite markers in parentage control of beef cattle in Portugal. In the first stage, DNA samples were collected from 475 randomly selected animals of the Charolais, Limousin and Preta breeds. Across breeds and genetic markers, means for average number of alleles, effective number of alleles, expected heterozygosity and polymorphic information content, were 8.20, 4.43, 0.733 and 0.70, respectively. Enlightenment from the various markers differed among breeds, but the set of 10 markers resulted in a combined probability above 0.9995 in the ability to exclude a random putative parent. The marker-set thus developed was later used for parentage control in a group of 140 calves from several breeds, where there was the suspicion of possible faulty parentage recording. Overall, 76.4% of the calves in this group were compatible with the recorded parents, with most incompatibilities due to misidentification of the dam. Efforts must be made to improve the quality of pedigree information, with particular emphasis on information recorded at the calf's birth.     

Estimation of migration rates from marker‐based parentage analysis

Coupled with rapid developments of efficient genetic markers, powerful population genetic methods were proposed to estimate migration rates (m) in natural populations in much broader spatial and temporal scales than the traditional mark‐release‐recapture (MRR) methods. Highly polymorphic (e.g. microsatellites) and genomic‐wide (e.g. SNPs) markers provide sufficient information to assign individuals to their populations or parents of origin and thereby to estimate directly m in a way similar to MRR. Such direct estimates of current migration rates are particularly useful in understanding the ecology and microevolution of wild populations and in managing the populations in the future. In this study, I proposed and implemented, in the software MigEst, a likelihood method to use marker‐based parentage assignments in jointly estimating m and candidate parent sampling proportions (x) in a subset of populations, investigated its power and accuracy using data simulated in various scenarios of population properties (e.g. the actual m, number, size and differentiation of populations) and sampling properties (e.g. the numbers of sampled parent candidates, offspring and markers), compared it with the population assignment approach implemented in the software BayesAss and demonstrated its usefulness by analysing a microsatellite data set from three natural populations of Brazilian bats. Simulations showed that MigEst provides unbiased and accurate estimates of m and performs better than BayesAss except when populations are highly differentiated with very small and ecologically insignificant migration rates. A valuable property of MigEst is that in the presence of unsampled populations, it gives good estimates of the rate of migration among sampled populations as well as of the rate of migration into each sampled population from the pooled unsampled populations.     

The probability of matching genetic markers in different samples

Hypervariable DNA polymorphisms in humans have been introduced in forensic science for the exclusion of innocent persons, and possibily for the identification of guilty ones, through mismatches and matches of DNA patterns in incriminating samples. Under the assumption of random mating and linkage equilibrium, it is observed that the probability of mismatch, then of exclusion of innocent persons, is very high. The probability of a match on the contrary may be very low, particularly when several hypervariable DNA polymorphisms are used for the DNA pattern. When a match is observed, and the probability of match is calculated, and it is lower than one in five billions, this might be considered incriminating by a judge. It is concluded that an innocent person has all advantages in submitting to the DNA fingerprinting test.     

Characterization of 18 SNP markers for sperm whale (Physeter macrocephalus)

We report the characterization of 18 new single nucleotide polymorphism (SNP) markers for an endangered species, the sperm whale (Physeter macrocephalus), developed using a targeted gene approach. SNP markers were derived from autosomal regions of the genome using primers originally characterized for genome mapping in other mammals. These SNP markers are the first to be designed for genotyping sperm whale populations and will provide a necessary addition to the genetic tools employed for understanding population structure on a global scale and for developing a conservation management strategy for this endangered species.     

Detection and characterization of SNPs useful for identity control and parentage testing in major European dairy breeds

We propose the use of single nucleotide polymorphisms (SNPs) instead of polymorphic microsatellite markers for individual identification and parentage control in cattle. To this end, we present an initial set of 37 SNP markers together with a gender-specific SNP for identity control and parentage testing in the Holstein, Fleckvieh and Braunvieh breeds. To obtain suitable SNPs, a total of 91.13 kb of random genomic DNA was screened yielding 531 SNPs. These, and 43 previously identified SNPs, were subjected to the following selection criteria: (1) the frequency of the minor allele must be larger than 0.1 in at least two of the three examined breeds, and (2) markers should not be linked closely. Allele frequencies were estimated by analysing sequencing traces of pooled DNA or by genotyping individual DNA samples. The selected SNP loci were physically mapped by radiation hybrid mapping or by fluorescence in situ hybridization, and tested against the neutral mutation hypothesis. The presented marker set theoretically allows probabilities of identity less than 10(-13) for individual verification and exclusion powers exceeding 99.99% for parentage testing.     

Genomic and genetic variability of six chicken populations using single nucleotide polymorphism and copy number variants as markers

Genomic and genetic variation among six Italian chicken native breeds (Livornese, Mericanel della Brianza, Milanino, Bionda Piemontese, Bianca di Saluzzo and Siciliana) were studied using single nucleotide polymorphism (SNP) and copy number variants (CNV) as markers. A total of 94 DNA samples genotyped with Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix) were used in the analyses. The results showed the genetic and genomic variability occurring among the six Italian chicken breeds. The genetic relationship among animals was established with a principal component analysis. The genetic diversity within breeds was calculated using heterozygosity values (expected and observed) and with Wright’s F-statistics. The individual-based CNV calling, based on log R ratio and B-allele frequency values, was done by the Hidden–Markov Model (HMM) of PennCNV software on autosomes. A hierarchical agglomerative clustering was applied in each population according to the absence or presence of definite CNV regions (CNV were grouped by overlapping of at least 1 bp). The CNV map was built on a total of 1003 CNV found in individual samples, after grouping by overlaps, resulting in 564 unique CNV regions (344 gains, 213 losses and 7 complex), for a total of 9.43 Mb of sequence and 1.03% of the chicken assembly autosome. All the approaches using SNP data showed that the Siciliana breed clearly differentiate from other populations, the Livornese breed separates into two distinct groups according to the feather colour (i.e. white and black) and the Bionda Piemontese and Bianca di Saluzzo breeds are closely related. The genetic variability found using SNP is comparable with that found by other authors in the same breeds using microsatellite markers. The CNV markers analysis clearly confirmed the SNP results.     

基于高密度SNP标记的肉牛人工选择痕迹筛查

近年来随着遗传改良工作的实施,人工选择大大提高了肉牛的生产性能并使其遗传基础发生巨大改变。文章基于Illumina BovineSNP50(54K)和BovineHD(770K)两款芯片数据,采用FST检验方法分析牛群的遗传分化,并筛查人工选择在牛的基因组留下的印记。通过全基因组范围内的扫描,共发现47 104个"离群"位点和3064个群体特异的人工选择"候选基因",如CLIC5、TG、CACNA2D1、FSHR等。通过基因注释对基因的生物学过程和分子功能进行富集分析。文章构建了我国肉牛的全基因组的选择信号图谱,为深入研究人工选择和理解生物进化提供线索,且研究结果也显示人工选择对基因组的影响在牛品种遗传改良中发挥了重要作用。     

Comparison of microsatellites and amplified fragment length polymorphism markers for parentage analysis

This study compares the properties of dominant markers, such as amplified fragment length polymorphisms (AFLPs), with those of codominant multiallelic markers, such as microsatellites, in reconstructing parentage. These two types of markers were used to search for both parents of an individual without prior knowledge of their relationships, by calculating likelihood ratios based on genotypic data, including mistyping. Experimental data on 89 oak trees genotyped for six microsatellite markers and 159 polymorphic AFLP loci were used as a starting point for simulations and tests. Both sets of markers produced high exclusion probabilities, and among dominant markers those with dominant allele frequencies in the range 0.1-0.4 were more informative. Such codominant and dominant markers can be used to construct powerful statistical tests to decide whether a genotyped individual (or two individuals) can be considered as the true parent (or parent pair). Gene flow from outside the study stand (GFO), inferred from parentage analysis with microsatellites, overestimated the true GFO, whereas with AFLPs it was underestimated. As expected, dominant markers are less efficient than codominant markers for achieving this, but can still be used with good confidence, especially when loci are deliberately selected according to their allele frequencies.     

Bayesian parentage analysis reliably controls the number of false assignments in natural populations

Parentage analysis in natural populations is a powerful tool for addressing a wide range of ecological and evolutionary questions. However, identifying parent–offspring pairs in samples collected from natural populations is often more challenging than simply resolving the Mendelian pattern of shared alleles. For example, large numbers of pairwise comparisons and limited numbers of genetic markers can contribute to incorrect assignments, whereby unrelated individuals are falsely identified as parent–offspring pairs. Determining which parentage methods are the least susceptible to making false assignments is an important challenge facing molecular ecologists. In a recent paper, Harrison et al. (2013a) address this challenge by comparing three commonly used parentage methods, including a Bayesian approach, in order to explore the effects of varied proportions of sampled parents on the accuracy of parentage assignments. Unfortunately, Harrison et al. made a simple error in using the Bayesian approach, which led them to incorrectly conclude that this method could not control the rate of false assignment. Here, I briefly outline the basic principles behind the Bayesian approach, identify the error made by Harrison et al., and provide detailed guidelines as to how the method should be correctly applied. Furthermore, using the exact data from Harrison et al., I show that the Bayesian approach actually provides greater control over the number of false assignments than either of the other tested methods. Lastly, I conclude with a brief introduction to solomon , a recently updated version of the Bayesian approach that can account for genotyping error, missing data and false matching.