Selection and implementation of single nucleotide polymorphism markers for parentage analysis in crossbred cattle population

Crossbreeding is an essential way of improving herd performance. However, frequent parentage record errors appear, which results in the lower accuracy of genetic parameter estimation and genetic evaluation. This study aims to build a single nucleotide polymorphism (SNP) panel with sufficient power for parentage testing in the crossbred population of Simmental and Holstein cattle. The direct sequencing technique in PCR products of pooling DNA along with matrix-assisted laser desorption/ionization time-of-flight MS method for genotyping the individuals was applied. A panel comprising 50 highly informative SNPs for parentage analysis was developed in the crossbred population. The average minor allele frequency for SNPs was 0.43, and the cumulative probability of exclusion for single-parent and both-parent inference met 0.99797 and 0.999999, respectively. The maker-set for parentage verification was then used in a group of 81 trios with aid of the likelihood-based parentage-assignment program of Cervus software. Reconfirmation with on-farm records showed that this 50-SNP system could provide sufficient and reliable information for parentage testing with the parental errors for mother–offspring and sire–offspring being 8.6 and 18.5%, respectively. In conclusion, a set of low-cost and efficient SNPs for the paternity testing in the Simmental and Holstein crossbred population are provided.     

Single nucleotide polymorphisms for DNA typing in the domestic horse

Genetic markers are important resources for individual identification and parentage assessment. Although short tandem repeats (STRs) have been the traditional DNA marker, technological advances have led to single nucleotide polymorphisms (SNPs) becoming an attractive alternative. SNPs can be highly multiplexed and automatically scored, which allows for easier standardization and sharing among laboratories. Equine parentage is currently assessed using STRs. We obtained a publicly available SNP dataset of 729 horses representing 32 diverse breeds. A proposed set of 101 SNPs was analyzed for DNA typing suitability. The overall minor allele frequency of the panel was 0.376 (range 0.304–0.419), with per breed probability of identities ranging from 5.6 × 10^?35 to 1.86 × 10^?42. When one parent was available, exclusion probabilities ranged from 0.9998 to 0.999996, although when both parents were available, all breeds had exclusion probabilities greater than 0.9999999. A set of 388 horses from 35 breeds was genotyped to evaluate marker performance on known families. The set included 107 parent–offspring pairs and 101 full trios. No horses shared identical genotypes across all markers, indicating that the selected set was sufficient for individual identification. All pairwise comparisons were classified using ISAG rules, with one or two excluding markers considered an accepted parent–offspring pair, two or three excluding markers considered doubtful and four or more excluding markers rejecting parentage. The panel had an overall accuracy of 99.9% for identifying true parent–offspring pairs. Our developed marker set is both present on current generation SNP chips and can be highly multiplexed in standalone panels and thus is a promising resource for SNP‐based DNA typing.     

The future of parentage analysis: From microsatellites to SNPs and beyond

Parentage analysis is a cornerstone of molecular ecology that has delivered fundamental insights into behaviour, ecology and evolution. Microsatellite markers have long been the king of parentage, their hypervariable nature conferring sufficient power to correctly assign offspring to parents. However, microsatellite markers have seen a sharp decline in use with the rise of next‐generation sequencing technologies, especially in the study of population genetics and local adaptation. The time is ripe to review the current state of parentage analysis and see how it stands to be affected by the emergence of next‐generation sequencing approaches. We find that single nucleotide polymorphisms (SNPs), the typical next‐generation sequencing marker, remain underutilized in parentage analysis but are gaining momentum, with 58 SNP‐based parentage analyses published thus far. Many of these papers, particularly the earlier ones, compare the power of SNPs and microsatellites in a parentage context. In virtually every case, SNPs are at least as powerful as microsatellite markers. As few as 100–500 SNPs are sufficient to resolve parentage completely in most situations. We also provide an overview of the analytical programs that are commonly used and compatible with SNP data. As the next‐generation parentage enterprise grows, a reliance on likelihood and Bayesian approaches, as opposed to strict exclusion, will become increasingly important. We discuss some of the caveats surrounding the use of next‐generation sequencing data for parentage analysis and conclude that the future is bright for this important realm of molecular ecology.     

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.     

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.     

Parentage and sibship exclusions: higher statistical power with more family members

Parentage exclusion probabilities are now routinely calculated in genetic marker-assisted parentage analyses to indicate the statistical power of the analyses achievable for a given set of markers, and to measure the informativeness of a set of markers for parentage inference. Previous formulas invariably assume that parentage is to be sought for a single offspring, while in practice multiple full siblings might be sampled (for example, seeds, eggs or young from a pair of monogamous parents) and their father, mother or both are to be assigned among a number of candidates. In this study, I derive formulas for parentage exclusion probabilities for an arbitrary number (n) of fullsibs, which reduce to previous equations for the special case of n=1. I also derive sibship exclusion probabilities, and investigate the power of differentiating half-sib, avuncular and grandparent-grandoffspring relationships using unlinked autosomal markers among different numbers of tested individuals. Applications of the formulas are demonstrated using both theoretical and empirical data sets of allele frequencies. The results from the study highlight the conclusion that the power of genealogical relationship inferences can be enhanced enormously by analysing multiple individuals for a given set of markers. The equations derived in this study allow more accurate determination of marker information and of the power of a parentage/sibship analysis. In addition, they can be used to guide experimental designs of parentage analyses in selecting markers and determining the number of offspring to be sampled and genotyped.     

Performance of different SNP panels for parentage testing in two East Asian cattle breeds

The International Society for Animal Genetics (ISAG) proposed a panel of single nucleotide polymorphisms (SNPs) for parentage testing in cattle (a core panel of 100 SNPs and an additional list of 100 SNPs). However, markers specific to East Asian taurine cattle breeds were not included, and no information is available as to whether the ISAG panel performs adequately for these breeds. We tested ISAG's core (100 SNP) and full (200 SNP) panels on two East Asian taurine breeds: the Korean Hanwoo and the Japanese Wagyu, the latter from the Australian herd. Even though the power of exclusion was high at 0.99 for both ISAG panels, the core panel performed poorly with 3.01% false‐positive assignments in the Hanwoo population and 3.57% in the Wagyu. The full ISAG panel identified all sire–offspring relations correctly in both populations with 0.02% of relations wrongly excluded in the Hanwoo population. Based on these results, we created and tested two population‐specific marker panels: one for the Wagyu population, which showed no false‐positive assignments with either 100 or 200 SNPs, and a second panel for the Hanwoo, which still had some false‐positive assignments with 100 SNPs but no false positives using 200 SNPs. In conclusion, for parentage assignment in East Asian cattle breeds, only the full ISAG panel is adequate for parentage testing. If fewer markers should be used, it is advisable to use population‐specific markers rather than the ISAG panel.     

pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

Background  

Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets.     

Fine-scale genetic structure and gene dispersal in Centaurea corymbosa (Asteraceae) I. Pattern of pollen dispersal

Pollen dispersal was characterized within a population of the narrowly endemic perennial herb, Centaurea corymbosa, using exclusion-based and likelihood-based paternity analyses carried out on microsatellite data. Data were used to fit a model of pollen dispersal and to estimate the rates of pollen flow and mutation/genotyping error, by developing a new method. Selfing was rare (1.6%). Pollen dispersed isotropically around each flowering plant following a leptokurtic distribution, with 50% of mating pairs separated by less than 11 m, but 22% by more than 40 m. Estimates of pollen flow lacked precision (0-25%), partially because mutations and/or genotyping errors (0.03-1%) could also explain the occurrence of offspring without a compatible candidate father. However, the pollen pool that fertilized these offspring was little differentiated from the adults of the population whereas strongly differentiated from the other populations, suggesting that pollen flow rate among populations was low. Our results suggest that pollen dispersal is too extended to allow differentiation by local adaptation within a population. However, among populations, gene flow might be low enough for such processes to occur.     

Association models for a multivariate binary response

Models for a multivariate binary response are parameterized by univariate marginal probabilities and dependence ratios of all orders. The w-order dependence ratio is the joint success probability of w binary responses divided by the joint success probability assuming independence. This parameterization supports likelihood-based inference for both regression parameters, relating marginal probabilities to explanatory variables, and association model parameters, relating dependence ratios to simple and meaningful mechanisms. Five types of association models are proposed, where responses are (1) independent given a necessary factor for the possibility of a success, (2) independent given a latent binary factor, (3) independent given a latent beta distributed variable, (4) follow a Markov chain, and (5) follow one of two first-order Markov chains depending on the realization of a binary latent factor. These models are illustrated by reanalyzing three data sets, foremost a set of binary time series on auranofin therapy against arthritis. Likelihood-based approaches are contrasted with approaches based on generalized estimating equations. Association models specified by dependence ratios are contrasted with other models for a multivariate binary response that are specified by odds ratios or correlation coefficients.     

A hybrid design for studying genetic influences on risk of diseases with onset early in life

Studies of genetic contributions to risk can be family-based, such as the case-parents design, or population-based, such as the case-control design. Both provide powerful inference regarding associations between genetic variants and risks, but both have limitations. The case-control design requires identifying and recruiting appropriate controls, but it has the advantage that nongenetic risk factors like exposures can be assessed. For a condition with an onset early in life, such as a birth defect, one should also genotype the mothers of cases and the mothers of controls to avoid potential confounding due to maternally mediated genetic effects acting on the fetus during gestation. The case-parents approach is less vulnerable than the case-mother/control-mother approach to biases due to population structure and self-selection. The case-parents approach also allows access to epigenetic phenomena like imprinting, but it cannot evaluate the role of nongenetic cofactors like exposures. We propose a hybrid design based on augmenting a set of affected individuals and their parents with a set of unaffected, unrelated individuals and their parents. The affected individuals and their parents are all genotyped, whereas only the parents of unaffected individuals are genotyped, although exposures are ascertained for both affected and unaffected offspring. The proposed hybrid design, through log-linear, likelihood-based analysis, allows estimation of the relative risk parameters, can provide more power than either the case-parents approach or the case-mother/control-mother approach, permits straightforward likelihood-ratio tests for bias due to mating asymmetry or population stratification, and admits valid alternative analyses when mating is asymmetric or when population stratification is detected.     

Computationally efficient sibship and parentage assignment from multilocus marker data

Quite a few methods have been proposed to infer sibship and parentage among individuals from their multilocus marker genotypes. They are all based on Mendelian laws either qualitatively (exclusion methods) or quantitatively (likelihood methods), have different optimization criteria, and use different algorithms in searching for the optimal solution. The full-likelihood method assigns sibship and parentage relationships among all sampled individuals jointly. It is by far the most accurate method, but is computationally prohibitive for large data sets with many individuals and many loci. In this article I propose a new likelihood-based method that is computationally efficient enough to handle large data sets. The method uses the sum of the log likelihoods of pairwise relationships in a configuration as the score to measure its plausibility, where log likelihoods of pairwise relationships are calculated only once and stored for repeated use. By analyzing several empirical and many simulated data sets, I show that the new method is more accurate than pairwise likelihood and exclusion-based methods, but is slightly less accurate than the full-likelihood method. However, the new method is computationally much more efficient than the full-likelihood method, and for the cases of both sexes polygamous and markers with genotyping errors, it can be several orders faster. The new method can handle a large sample with thousands of individuals and the number of markers limited only by the computer memory.     

Fine mapping of quantitative trait loci affecting female fertility in dairy cattle on BTA03 using a dense single-nucleotide polymorphism map

Fertility quantitative trait loci (QTL) are of high interest in dairy cattle since insemination failure has dramatically increased in some breeds such as Holstein. High-throughput SNP analysis and SNP microarrays give the opportunity to genotype many animals for hundreds SNPs per chromosome. In this study, due to these techniques a dense SNP marker map was used to fine map a QTL underlying nonreturn rate measured 90 days after artificial insemination previously detected with a low-density microsatellite marker map. A granddaughter design with 17 Holstein half-sib families (926 offspring) was genotyped for a set of 437 SNPs mapping to BTA3. Linkage analysis was performed by both regression and variance components analysis. An additional analysis combining both linkage analysis and linkage-disequilibrium information was applied. This method first estimated identity-by-descent probabilities among base haplotypes. These probabilities were then used to group the base haplotypes in different clusters. A QTL explaining 14% of the genetic variance was found with high significance (P < 0.001) at position 19 cM with the linkage analysis and four sires were estimated to be heterozygous (P < 0.05). Addition of linkage-disequilibrium information refined the QTL position to a set of narrow peaks. The use of the haplotypes of heterozygous sires offered the possibility to give confidence in some peaks while others could be discarded. Two peaks with high likelihood-ratio test values in the region of which heterozygous sires shared a common haplotype appeared particularly interesting. Despite the fact that the analysis did not fine map the QTL in a unique narrow region, the method proved to be able to handle efficiently and automatically a large amount of information and to refine the QTL position to a small set of narrow intervals. In addition, the QTL identified was confirmed to have a large effect (explaining 13.8% of the genetic variance) on dairy cow fertility as estimated by nonreturn rate at 90 days.     

A High Throughput Single Nucleotide Polymorphism Multiplex Assay for Parentage Assignment in New Zealand Sheep

Accurate pedigree information is critical to animal breeding systems to ensure the highest rate of genetic gain and management of inbreeding. The abundance of available genomic data, together with development of high throughput genotyping platforms, means that single nucleotide polymorphisms (SNPs) are now the DNA marker of choice for genomic selection studies. Furthermore the superior qualities of SNPs compared to microsatellite markers allows for standardization between laboratories; a property that is crucial for developing an international set of markers for traceability studies. The objective of this study was to develop a high throughput SNP assay for use in the New Zealand sheep industry that gives accurate pedigree assignment and will allow a reduction in breeder input over lambing. This required two phases of development- firstly, a method of extracting quality DNA from ear-punch tissue performed in a high throughput cost efficient manner and secondly a SNP assay that has the ability to assign paternity to progeny resulting from mob mating. A likelihood based approach to infer paternity was used where sires with the highest LOD score (log of the ratio of the likelihood given parentage to likelihood given non-parentage) are assigned. An 84 “parentage SNP panel” was developed that assigned, on average, 99% of progeny to a sire in a problem where there were 3,000 progeny from 120 mob mated sires that included numerous half sib sires. In only 6% of those cases was there another sire with at least a 0.02 probability of paternity. Furthermore dam information (either recorded, or by genotyping possible dams) was absent, highlighting the SNP test’s suitability for paternity testing. Utilization of this parentage SNP assay will allow implementation of progeny testing into large commercial farms where the improved accuracy of sire assignment and genetic evaluations will increase genetic gain in the sheep industry.     

Odds ratio-based genetic algorithms for generating SNP barcodes of genotypes to predict disease susceptibility

Genome-wide association analysis involving many single nucleotide polymorphisms (SNPs) data is challenging mathematically and computationally. It is time consuming to classify the combination of multilocus genotypes into high- and low-risk groups without false positive and negative errors. Hence, we propose the odds ratio-based genetic algorithms (OR-GA) method that uses the odds ratio as a new quantitative measure of disease risk among many SNP combinations. Genetic algorithms (GA) are applied to generate SNP "barcodes" of genotypes, which propose the maximal difference of occurrence between the case and control groups, to predict disease susceptibility (e.g., osteoporosis). When individuals are grouped into a low and high bone mass density (BMD) range, different SNP barcode patterns may occur several times in each of these two groups. Our results showed that a GA can effectively identify a specific SNP barcode with an optimized fitness value. SNP barcodes with a low fitness value will naturally be discarded from the population. A representative SNP barcode with a variable number of SNPs is processed by odds ratio analysis to determine the maximum difference between the low and high BMD groups in a statistical manner. Therefore, this paper introduces a powerful procedure for analysis of disease-associated SNP barcode in genome-wide genes.     

Assessment of microsatellite and SNP markers for parentage assignment in ex situ African Penguin (Spheniscus demersus) populations

Captive management of ex situ populations of endangered species is traditionally based on pedigree information derived from studbook data. However, molecular methods could provide a powerful set of complementary tools to verify studbook records and also contribute to improving the understanding of the genetic status of captive populations. Here, we compare the utility of single nucleotide polymorphisms (SNPs) and microsatellites (MS) and two analytical methods for assigning parentage in ten families of captive African penguins held in South African facilities. We found that SNPs performed better than microsatellites under both analytical frameworks, but a combination of all markers was most informative. A subset of combined SNP (n = 14) and MS loci (n = 10) provided robust assessments of parentage. Captive or supportive breeding programs will play an important role in future African penguin conservation efforts as a source of individuals for reintroduction. Cooperation among these captive facilities is essential to facilitate this process and improve management. This study provided us with a useful set of SNP and MS markers for parentage and relatedness testing among these captive populations. Further assessment of the utility of these markers over multiple (>3) generations and the incorporation of a larger variety of relationships among individuals (e.g., half‐siblings or cousins) is strongly suggested.     

Robust Demographic Inference from Genomic and SNP Data

We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets.     

Detection of disease genes by use of family data. II. Application to nuclear families

Two likelihood-based score statistics are used to detect association between a disease and a single diallelic polymorphism, on the basis of data from arbitrary types of nuclear families. The first statistic, the nonfounder statistic, extends the transmission/disequilibrium test to accommodate affected and unaffected offspring and missing parental genotypes. The second statistic, the founder statistic, compares observed or inferred parental genotypes with those of some reference population. In this comparison, the genotypes of affected parents or of those with many affected offspring are weighted more heavily than are the genotypes of unaffected parents or of those with few affected offspring. Genotypes of single unrelated cases and controls can be included in this analysis. We illustrate the two statistics by applying them to data on a polymorphism of the SDR5A2 gene in nuclear families with multiple cases of prostate cancer. We also use simulations to compare the power of the nonfounder statistic with that of the score statistic, on the basis of the conditional logistic regression of offspring genotypes.