How many markers are enough? Factors influencing parentage testing in different livestock populations

Genetic diversity analysis of big-bracted dogwood (Cornus florida and C. kousa) cultivars, interspecific hybrids, and wild-collected accessions using RADseq

Big-bracted dogwoods are popular ornamental trees known for their beautiful spring blooms with showy bracts and four-season appeal. The two most widely grown species are Cornus florida and Cornus kousa, native to Eastern North America and East Asia. Despite their horticultural prominence, there is little information available regarding genetic diversity, population structure, relatedness, and subspecies origins of dogwood cultivars. In this study, 313 cultivars, wild-collected plants, and Rutgers University breeding selections, focusing on C. florida, C. kousa, and interspecific hybrids, were genotyped using restriction-site associated DNA sequencing (RADseq) generating thousands of single nucleotide polymorphism (SNP) and insertion deletion (Indel) markers. The research results showed high genetic diversity among C. florida and C. kousa wild-collected plants and cultivars. For C. florida, pink-bracted plants formed a distinct clade from those with white-bracts with the Mexican C. florida ssp. urbiniana forming an outgroup. For C. kousa, Chinese-collected plants (ssp. chinensis) were a distinct subspecies with clear separation from Japanese and Korean accessions (ssp. kousa) and cultivars were designated as ssp. chinensis, ssp. kousa, or ssp. hybrid. Using this information, a Kompetitive allele specific PCR (KASP) assay genotyping panel was designed to determine C. kousa trees' subspecies makeup. Results revealed many cases of genetically identical cultivars being sold under different names, especially for pink-bracted cultivars of both species. Additionally, reported parent-progeny relationships were evaluated and either validated or discredited. Finally, the hybrid germplasm analysis validated pedigrees of interspecific F1 hybrids and found many of the recent Rutgers breeding selections contain small regions of pacific dogwood (C. nuttallii) DNA introgressed into C. kousa backgrounds. This diversity study elucidates origins, diversity, and relationships of a large population of big-bracted dogwoods. The results can inform plant breeders, arboreta, and the ornamental plant industry, as most modern cultivars and popular historic cultivars are represented.

Microsatellite imputation using SNP data for parentage verification in four Italian sheep breeds

Microsatellite markers (MS) have been widely used for parentage verification in most of the livestock species over the past decades mainly due to their high polymorphic information content. In the genomic era, the spread of genotype information as single-nucleotide polymorphism (SNP) has raised the question to effectively use SNPs also for parentage testing. Despite the clear advantages of SNP panels in terms of cost, accuracy, and automation, the transition from MS to SNP markers for parentage verification is still very slow and, so far, only routinely applied in cattle. A major difficulty during this transition period is the need of SNP data for parents and offspring, which in most cases is not yet feasible due to the genotyping cost. To overcome the unavailability of same genotyping platform during the transition period, in this study we aimed to assess the feasibility of a MS imputation pipeline from SNPs in four native sheep dairy breeds: Comisana (N = 331), Massese (N = 210), Delle Langhe (N = 59) and Sarda (N = 1003). Those sheep were genotyped for 11 MS and with the Ovine SNP50 Bead Chip. Prior to imputation, a quality control (QC) was performed, and SNPs located within a window of 2 Mb from each MS were selected. The core of the developed pipeline was made up of three steps: (a) storing of both MS and SNP data in a Variant Call Format file, (b) masking MS information in a random sample of individuals (10%), (c) imputing masked MS based on non-missing individuals (90%) using an imputation program. The feasability of the proposed methodology was assessed also among different training - testing split ratio, population size, number of flanking SNPs as well as within and among breeds. The accuracy of the MS imputation was assessed based on the genotype concordance as well as at parentage verification level in a subset of animals in which assigned parents' MS were available. A total of 8 MS passed the QC, and 505 SNPs were located within the ±2 Mb window from each MS, with an average of 63 SNPs per MS. The results were encouraging since when excluding the worst imputed MS (OARAE129), and regardless on the analyses performed (within and across breeds) for all breeds, we achieved an overall concordance rate over 94%. In addition, on average, the imputed offspring MS resulted in equivalent parentage outcome in 94% of the cases when compared to verification using original MS, highlighting both the feasibility and the eventual practical advantage of using this imputation pipeline.

Pedigree reconstruction and population structure using SNP markers in Gir cattle

Our objective was to establish a SNPs panel for pedigree reconstruction using microarrays of different densities and evaluate the genomic relationship coefficient of the inferred pedigree, in addition to analyzing the population structure based on genomic analyses in Gir cattle. For parentage analysis and genomic relationship, 16,205 genotyped Gir animals (14,458 females and 1747 males) and 1810 common markers to the four SNP microarrays were used. For population structure analyses, including linkage disequilibrium, effective population size, and runs of homozygosity (ROH), genotypes from 21,656 animals were imputed. Likelihood ratio (LR) approach was used to reconstruct the pedigree, deepening the pedigree and showing it is well established in terms of recent information. Coefficients for each relationship category of the inferred pedigree were adequate. Linkage disequilibrium showed rapid decay. We detected a decrease in the effective population size over the last 50 generations, with the average generation interval around 9.08 years. Higher ROH-based inbreeding coefficient in a class of short ROH segments, with moderate to high values, was also detected, suggesting bottlenecks in the Gir genome. Breeding strategies to minimize inbreeding and avoid massive use of few proven sires with high genetic value are suggested to maintain genetic variability in future generations. In addition, we recommend reducing the generation interval to maximize genetic progress and increase effective population size.

Use of mitochondrial sequencing to detect gene doping in horses via gene editing and somatic cell nuclear transfer

Gene editing and subsequent cloning techniques offer great potential not only in genetic disease correction in domestic animals, but also in livestock production by enhancement of desirable traits. The existence of the technology, however, leaves it open to potential misuse in performance-led sports such as horseracing and other equestrian events. Recent advances in equine gene editing, regarding the generation of gene-edited embryos using CRISPR/Cas9 technology and somatic cell nuclear transfer, has highlighted the need to develop tools to detect potential prohibited use of the technology. One possible method involves the characterisation of the mitochondrial genome (which is not routinely preserved during cloning) and comparing it to the sequence of the registered dam. We present here our approach to whole-mitochondrial sequencing using tiled long-range PCR and next-generation sequencing. To determine whether the background mutation rate in the mitochondrial genome could potentially confound results, we sequenced ten sets of dam and foal duos. We found variation between duos but none within duos, indicating that this method is feasible for future screening systems. Analysis of WGS data from over one hundred Thoroughbred horses revealed wide variation in the mitochondria sequence within the breed, further displaying the utility of this approach.

Discovery of hidden pedigree errors combining genomic information with the genomic relationship matrix in Texel sheep

Genomic variants such as Single Nucleotide Polymorphisms and animal pedigree are now used widely in routine genetic evaluations of livestock in many countries. The use of genomic information not only can be used to enhance the accuracy of prediction but also to verify pedigrees for animals that are extensively managed using natural mating and enabling multiple-sire mating groups to be used. By so doing, the rate of genetic gain is enhanced, and any bias associated with incorrect pedigrees is removed. This study used a set of 8 764 sheep genotypes to verify the pedigree based on both the conventional opposing homozygote method as well as a novel method when combined with the inclusion of the genomic relationship matrix (GRM). The genomic relationship coefficients between verified pairs of animals showed on average a relationship of 0.50 with parent, 0.25 with grandparent, 0.13 with great grandparent, 0.50 with full-sibling and 0.27 with half-sibling. Minimum obtained values from these verified pairs were then used as thresholds to determine the pedigree for unverified pairs of animals, to detect potential errors in the pedigree. Using a case study from a population partially genotyped UK sheep, the results from this study illustrate a powerful way to resolve parentage inconsistencies, when combining the conventional 'opposing homozygote' method using genomic information together with GRM for pedigree checking. In this way, previously undetected pedigree errors can be resolved.

First-degree relationships and genotyping errors deciphered by a high-density SNP array in a Duroc × Iberian pig cross

BACKGROUND

Two individuals with a first-degree relationship share about 50 percent of their alleles. Parent-offspring relationships cannot be homozygous for alternative alleles (genetic exclusion).

METHODS

Applying the concept of genetic exclusion to HD arrays typed in animals for experimental purposes or genomic selection allows estimation of the rate of rejection of first-degree relationships as the rate at which two individuals typed for a large number of Single Nucleotide Polymorphisms (SNPs) do not share at least one allele. An Expectation-Maximization algorithm is applied to estimate parentage. In addition, genotyping errors are estimated in true parent-offspring relationships. Samples from nine candidate Duroc sires and 55 Iberian dams producing 214 Duroc × Iberian barrows were typed for the HD porcine Affymetrix array.

RESULTS

We were able to establish paternity and maternity of 75 and 85 piglets, respectively. Rate of rejection in true parent-offspring relationships was estimated as 0.000735. This is a lower bound of the genotyping error since rate of rejection depends on allele frequencies. After accounting for allele frequencies, our estimate of the genotyping error is 0.6%. A total of 7,744 SNPs were rejected in five or more true parent-offspring relationships facilitating identification of "problematic" SNPs with inconsistent inheritance in multiple parent-offspring relationships.

CONCLUSIONS

This study shows that animal experiments and routine genotyping in genomic selection allow to establish or to verify first-degree relationships as well as to estimate genotyping errors for each batch of animals or experiment.

Testing the effectiveness of genetic monitoring using genetic non-invasive sampling

Effective conservation requires accurate data on population genetic diversity, inbreeding, and genetic structure. Increasingly, scientists are adopting genetic non-invasive sampling (gNIS) as a cost-effective population-wide genetic monitoring approach. gNIS has, however, known limitations which may impact the accuracy of downstream genetic analyses. Here, using high-quality single nucleotide polymorphism (SNP) data from blood/tissue sampling of a free-ranging koala population (n = 430), we investigated how the reduced SNP panel size and call rate typical of genetic non-invasive samples (derived from experimental and field trials) impacts the accuracy of genetic measures, and also the effect of sampling intensity on these measures. We found that gNIS at small sample sizes (14% of population) can provide accurate population diversity measures, but slightly underestimated population inbreeding coefficients. Accurate measures of internal relatedness required at least 33% of the population to be sampled. Accurate geographic and genetic spatial autocorrelation analysis requires between 28% and 51% of the population to be sampled. We show that gNIS at low sample sizes can provide a powerful tool to aid conservation decision-making and provide recommendations for researchers looking to apply these techniques to free-ranging systems.

There Is No 'Rule of Thumb': Genomic Filter Settings for a Small Plant Population to Obtain Unbiased Gene Flow Estimates

The application of high-density polymorphic single-nucleotide polymorphisms (SNP) markers derived from high-throughput sequencing methods has heralded plenty of biological questions about the linkages of processes operating at micro- and macroevolutionary scales. However, the effects of SNP filtering practices on population genetic inference have received much less attention. By performing sensitivity analyses, we empirically investigated how decisions about the percentage of missing data (MD) and the minor allele frequency (MAF) set in bioinformatic processing of genomic data affect direct (i.e., parentage analysis) and indirect (i.e., fine-scale spatial genetic structure - SGS) gene flow estimates. We focus specifically on these manifestations in small plant populations, and particularly, in the rare tropical plant species Dinizia jueirana-facao, where assumptions implicit to analytical procedures for accurate estimates of gene flow may not hold. Avoiding biases in dispersal estimates are essential given this species is facing extinction risks due to habitat loss, and so we also investigate the effects of forest fragmentation on the accuracy of dispersal estimates under different filtering criteria by testing for recent decrease in the scale of gene flow. Our sensitivity analyses demonstrate that gene flow estimates are robust to different setting of MAF (0.05-0.35) and MD (0-20%). Comparing the direct and indirect estimates of dispersal, we find that contemporary estimates of gene dispersal distance (σ _{r t} = 41.8 m) was ∼ fourfold smaller than the historical estimates, supporting the hypothesis of a temporal shift in the scale of gene flow in D. jueirana-facao, which is consistent with predictions based on recent, dramatic forest fragmentation process. While we identified settings for filtering genomic data to avoid biases in gene flow estimates, we stress that there is no 'rule of thumb' for bioinformatic filtering and that relying on default program settings is not advisable. Instead, we suggest that the approach implemented here be applied independently in each separate empirical study to confirm appropriate settings to obtain unbiased population genetics estimates.

Performance of using opposing homozygotes for paternity testing in Japanese Black cattle

Genome-wide single nucleotide polymorphism (SNP) markers in Japanese Black cattle enable genomic prediction and verifying parent-offspring relationships. We assessed the performance of opposing homozygotes (OH) for paternity testing in Japanese Black cattle, using SNP genotype information of 50 sires and 3,420 fattened animals, 1,945 of which were fathered by the 50 genotyped sires. The number of OH was counted for each sire-progeny pair in 28,764 SNPs with minor allele frequencies of ≥0.05 in this population. Across all pairs of animals, the number of OH tended to increase as the pedigree-based coefficient of relationship decreased. With a threshold of 288 (1% of SNPs) for paternity testing, most sire-progeny pairs were detected as true relationships. The frequency of Mendelian inconsistencies was 2.4%, reflecting the high accuracy of pedigree information in Japanese Black cattle population. The results indicate the utility of OH for paternity testing in Japanese Black cattle.

Polly Wants a Genome: The Lack of Genetic Testing for Pet Parrot Species

Parrots are considered the third most popular pet species, after dogs and cats, in the United States of America. Popular birds include budgerigars, lovebirds and cockatiels and are known for their plumage and vocal learning abilities. Plumage colour variation remains the main driving force behind breeder selection. Despite the birds' popularity, only two molecular genetic tests-bird sexing and pathogen screening-are commercially available to breeders. For a limited number of species, parentage verification tests are available, but are mainly used in conservation and not for breeding purposes. No plumage colour genotyping test is available for any of the species. Due to the fact that there isn't any commercial plumage genotype screening or parentage verification tests available, breeders mate close relatives to ensure recessive colour alleles are passed to the next generation. This, in turn, leads to inbreeding depression and decreased fertility, lower hatchability and smaller clutch sizes, all important traits in commercial breeding systems. This review highlights the research carried out in the field of pet parrot genomics and points out the areas where future research can make a vital contribution to understanding how parrot breeding can be improved to breed healthy, genetically diverse birds.

SNP panels for the estimation of dairy breed proportion and parentage assignment in African crossbred dairy cattle

BACKGROUND

Understanding the relationship between genetic admixture and phenotypic performance is crucial for the optimization of crossbreeding programs. The use of small sets of informative ancestry markers can be a cost-effective option for the estimation of breed composition and for parentage assignment in situations where pedigree recording is difficult. The objectives of this study were to develop small single nucleotide polymorphism (SNP) panels that can accurately estimate the total dairy proportion and assign parentage in both West and East African crossbred dairy cows.

METHODS

Medium- and high-density SNP genotype data (Illumina BovineSNP50 and BovineHD Beadchip) for 4231 animals sampled from African crossbreds, African Bos taurus, European Bos taurus, Bos indicus, and African indigenous populations were used. For estimating breed composition, the absolute differences in allele frequency were calculated between pure ancestral breeds to identify SNPs with the highest discriminating power, and different combinations of SNPs weighted by ancestral origin were tested against estimates based on all available SNPs. For parentage assignment, informative SNPs were selected based on the highest minor allele frequency (MAF) in African crossbred populations assuming two Scenarios: (1) parents were selected among all the animals with known genotypes, and (2) parents were selected only among the animals known to be a parent of at least one progeny.

RESULTS

For the medium-density genotype data, SNPs selected for the largest differences in allele frequency between West African indigenous and European Bos taurus breeds performed best for most African crossbred populations and achieved a prediction accuracy (r2) for breed composition of 0.926 to 0.961 with 200 SNPs. For the high-density dataset, a panel with 70% of the SNPs selected on their largest difference in allele frequency between African and European Bos taurus performed best or very near best across all crossbred populations with r2 ranging from 0.978 to 0.984 with 200 SNPs. In all African crossbred populations, unambiguous parentage assignment was possible with ≥ 300 SNPs for the majority of the panels for Scenario 1 and ≥ 200 SNPs for Scenario 2.

CONCLUSIONS

The identified low-cost SNP assays could overcome incomplete or inaccurate pedigree records in African smallholder systems and allow effective breeding decisions to produce progeny of desired breed composition.

Pedigree reconstruction and distant pairwise relatedness estimation from genome sequence data: A demonstration in a population of rhesus macaques (Macaca mulatta)

A primary challenge in the analysis of free‐ranging animal populations is the accurate estimation of relatedness among individuals. Many aspects of population analysis rely on knowledge of relatedness patterns, including socioecology, demography, heritability and gene mapping analyses, wildlife conservation and the management of breeding colonies. Methods for determining relatedness using genome‐wide data have improved our ability to determine kinship and reconstruct pedigrees in humans. However, methods for reconstructing complex pedigree structures and estimating distant relatedness (beyond third‐degree) have not been widely applied to other species. We sequenced the genomes of 150 male rhesus macaques from the Tulane National Primate Research Center colony to estimate pairwise relatedness, reconstruct closely related pedigrees, estimate more distant relationships and augment colony records. Methods for determining relatedness developed for human genetic data were applied and evaluated in the analysis of nonhuman primates, including identity‐by‐descent‐based methods for pedigree reconstruction and shared segment‐based inference of more distant relatedness. We compared the genotype‐based pedigrees and estimated relationships to available colony pedigree records and found high concordance (95.5% agreement) between expected and identified relationships for close relatives. In addition, we detected distant relationships not captured in colony records, including some as distant as twelfth‐degree. Furthermore, while deep sequence coverage is preferable, we show that this approach can also provide valuable information when only low‐coverage (5×) sequence data is available. Our findings demonstrate the value of these methods for determination of relatedness in various animal populations, with diverse applications to conservation biology, evolutionary and ecological research and biomedical studies.

Accuracy of Imputation of Microsatellite Markers from a 50K SNP Chip in Spanish Assaf Sheep

Simple Summary

Parentage misassignments directly affect genetic gain in traditional breeding programs. The use of genetic markers facilitates parentage verification. In sheep, microsatellite markers and single nucleotide polymorphism (SNP) markers have been proposed by the International Society of Animal Sciences (ISAG) for parentage testing. Since the implementation of genomic selection, the microsatellite information used for parental testing in previous generations is gradually being replaced by SNPs. However, parentage verifications should all be performed using the same technology. A strategy for transitioning from microsatellites to SNP markers, while avoiding extra genotyping costs, is the imputation of microsatellite alleles from SNP haplotypes. This study aims to identify the optimum approach, using a minimum number of SNPs to accurately impute microsatellite markers and developing a low-density SNP chip for parentage verification in the Assaf sheep breed. The imputation approach described here reached high accuracies using a low number of SNP markers, which supports the development of a low-density SNP chip that could avoid the problems of genotyping with both technologies, being a cost-effective method for parentage testing. This study will help sheep breeders to perform parentage verification when different genotyping platforms have been used across generations.

Abstract

Transitioning from traditional to new genotyping technologies requires the development of bridging methodologies to avoid extra genotyping costs. This study aims to identify the optimum number of single nucleotide polymorphisms (SNPs) necessary to accurately impute microsatellite markers to develop a low-density SNP chip for parentage verification in the Assaf sheep breed. The accuracy of microsatellite marker imputation was assessed with three metrics: genotype concordance (C), genotype dosage (length r²), and allelic dosage (allelic r²), for all imputation scenarios tested (0.5–10 Mb microsatellite flanking SNP windows). The imputation accuracy for the three metrics analyzed for all haplotype lengths tested was higher than 0.90 (C), 0.80 (length r²), and 0.75 (allelic r²), indicating strong genotype concordance. The window with 2 Mb length provides the best accuracy for the imputation procedure and the design of an affordable low-density SNP chip for parentage testing. We additionally evaluated imputation performance under two null models, naive (imputing the most common allele) and random (imputing by randomly selecting the allele), which in comparison showed weak genotype concordances (0.41 and 0.15, respectively). Therefore, we describe a precise methodology in the present article to impute multiallelic microsatellite genotypes from a low-density SNP chip in sheep and solve the problem of parentage verification when different genotyping platforms have been used across generations.

Genotype calling of triploid offspring from diploid parents

BACKGROUND

Polyploidy is widespread in animals and especially in plants. Different kinds of ploidies exist, for example, hexaploidy in wheat, octaploidy in strawberries, and diploidy, triploidy, tetraploidy, and pseudo-tetraploidy (partly tetraploid) in fish. Triploid offspring from diploid parents occur frequently in the wild in Atlantic salmon (Salmo salar) and, as with triploidy in general, the triploid individuals are sterile. Induced triploidy in Atlantic salmon is common practice to produce sterile fish. In Norwegian aquaculture, production of sterile triploid fish is an attempt by government and industry to limit genetic introgression between wild and farmed fish. However, triploid fish may have traits and properties that differ from those of diploids. Investigating the genetics behind traits in triploids has proved challenging because genotype calling of genetic markers in triploids is not supported by standard software. Our aim was to develop a method that can be used for genotype calling of genetic markers in triploid individuals.

RESULTS

Allele signals were produced for 381 triploid Atlantic salmon offspring using a 56 K Thermo Fisher GeneTitan genotyping platform. Genotypes were successfully called by applying finite normal mixture models to the (transformed) allele signals. Subsets of markers were filtered by quality control statistics for use with downstream analyses. The quality of the called genotypes was sufficient to allow for assignment of diploid parents to the triploid offspring and to discriminate between maternal and paternal parents from autosomal inheritance patterns. In addition, as the maternal inheritance in triploid offspring is identical to gynogenetic inheritance, the maternal recombination pattern for each chromosome could be mapped by using a similar approach as that used in gene-centromere mapping.

CONCLUSIONS

We show that calling of dense marker genotypes for triploid individuals is feasible. The resulting genotypes can be used in parentage assignment of triploid offspring to diploid parents, to discriminate between maternal and paternal parents using autosomal inheritance patterns, and to map the maternal recombination pattern using an approach similar to gene-centromere mapping. Genotyping of triploid individuals is important both for selective breeding programs and unravelling the underlying genetics of phenotypes recorded in triploids. In principle, the developed method can be used for genotype calling of other polyploid organisms.

RAPTURE (RAD capture) panel facilitates analyses characterizing sea lamprey reproductive ecology and movement dynamics

Genomic tools are lacking for invasive and native populations of sea lamprey (Petromyzon marinus). Our objective was to discover single nucleotide polymorphism (SNP) loci to conduct pedigree analyses to quantify reproductive contributions of adult sea lampreys and dispersion of sibling larval sea lampreys of different ages in Great Lakes tributaries. Additional applications of data were explored using additional geographically expansive samples. We used restriction site-associated DNA sequencing (RAD-Seq) to discover genetic variation in Duffins Creek (DC), Ontario, Canada, and the St. Clair River (SCR), Michigan, USA. We subsequently developed RAD capture baits to genotype 3,446 RAD loci that contained 11,970 SNPs. Based on RAD capture assays, estimates of variance in SNP allele frequency among five Great Lakes tributary populations (mean F ST 0.008; range 0.00-0.018) were concordant with previous microsatellite-based studies; however, outlier loci were identified that contributed substantially to spatial population genetic structure. At finer scales within streams, simulations indicated that accuracy in genetic pedigree reconstruction was high when 200 or 500 independent loci were used, even in situations of high spawner abundance (e.g., 1,000 adults). Based on empirical collections of larval sea lamprey genotypes, we found that age-1 and age-2 families of full and half-siblings were widely but nonrandomly distributed within stream reaches sampled. Using the genomic scale set of SNP loci developed in this study, biologists can rapidly genotype sea lamprey in non-native and native ranges to investigate questions pertaining to population structuring and reproductive ecology at previously unattainable scales.

Parentage assignment with genotyping-by-sequencing data

In this paper, we evaluate using genotype-by-sequencing (GBS) data to perform parentage assignment in lieu of traditional array data. The use of GBS data raises two issues: First, for low-coverage (e.g., <2×) GBS data, it may not be possible to call the genotype at many loci, a critical first step for detecting opposing homozygous markers. Second, the amount of sequencing coverage may vary across individuals, making it challenging to directly compare the likelihood scores between putative parents. To address these issues, we extend the probabilistic framework of Huisman (Molecular Ecology Resources, 2017, 17, 1009) and evaluate putative parents by comparing their (potentially noisy) genotypes to a series of proposal distributions. These distributions describe the expected genotype probabilities for the relatives of an individual. We assign putative parents as a parent if they are classified as a parent (as opposed to e.g., an unrelated individual), and if the assignment score passes a threshold. We evaluated this method on simulated data and found that (a) high-coverage (>2×) GBS data performs similarly to array data and requires only a small number of markers to correctly assign parents and (b) low-coverage GBS data (as low as 0.1×) can also be used, provided that it is obtained across a large number of markers. When analysing the low-coverage GBS data, we also found a high number of false positives if the true parent is not contained within the list of candidate parents, but that this false positive rate can be greatly reduced by hand tuning the assignment threshold. We provide this parentage assignment method as a standalone program called AlphaAssign.

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.

Evaluation of 17 microsatellite markers for parentage testing and individual identification of domestic yak (Bos grunniens)

BACKGROUND

Yak (Bos grunniens) is the most important domestic animal for people living at high altitudes. Yak ordinarily feed by grazing, and this behavior impacts the accuracy of the pedigree record because it is difficult to control mating in grazing yak. This study aimed to evaluate the pedigree system and individual identification in polled yak.

METHODS

A total of 71 microsatellite loci were selected from the literature, mostly from the studies on cattle. A total of 35 microsatellite loci generated excellent PCR results and were evaluated for the parentage testing and individual identification of 236 unrelated polled yaks. A total of 17 of these 35 microsatellite loci had polymorphic information content (PIC) values greater than 0.5, and these loci were in Hardy-Weinberg equilibrium without linkage disequilibrium.

RESULTS

Using multiplex PCR, capillary electrophoresis, and genotyping, very high exclusion probabilities were obtained for the combined core set of 17 loci. The exclusion probability (PE) for one candidate parent when the genotype of the other parent is not known was 0.99718116. PE for one candidate parent when the genotype of the other parent is known was 0.99997381. PE for a known candidate parent pair was 0.99999998. The combined PEI (PE for identity of two unrelated individuals) and PESI (PE for identity of two siblings) were >0.99999999 and 0.99999899, respectively. These findings indicated that the combination of 17 microsatellite markers could be useful for efficient and reliable parentage testing and individual identification in polled yak.

DISCUSSION

Many microsatellite loci have been investigated for cattle paternity testing. Nevertheless, these loci cannot be directly applied to yak identification because the two bovid species have different genomic sequences and organization. A total of 17 loci were selected from 71 microsatellite loci based on efficient amplification, unambiguous genotyping, and high PIC values for polled yaks, and were suitable for parentage analysis in polled yak populations.

Comparison of economic returns among genetic evaluation strategies in a 2-tiered Charolais-sired beef cattle production system1,2

The objective of this study was to estimate economic returns and costs associated with 4 scenarios of genetic evaluation that combine genotypes, phenotypes, and pedigree information from a vertically integrated purebred (PB) and commercial (CM) beef cattle system. Inference was to a genetic evaluation for a production system producing Charolais terminal sires for 10,000 CM cows. The first genetic evaluation scenario, denoted PB_A, modeled a genetic evaluation in which pedigree information and phenotypes are available for PB seedstock animals. Scenario PB_H contained the same information as PB_A with the addition of 25K density (GeneSeek Genomic Profiler LD) single nucleotide polymorphism (SNP) genotypes from PB animals. Scenario PBCM_A contained pedigree records and phenotypes from PB and CM cattle. Scenario PBCM_H contained phenotypes, pedigree, and genotypes from the PB and CM animals. Estimates of prediction error variance, (co)variance, and selection index parameters were used to estimate accuracy of selection candidates (r_TI) and genetic gain resulting from selection on an economic index in US dollars (ΔG). Annual costs and incomes were used to determine the 30-yr cumulative net present value (CNPV) per CM calf resulting from selection in these genetic evaluation scenarios. Adding genotypes and CM production phenotypes to genetic evaluation increased the r_TI of selection candidates and ΔG across all 4 scenarios. Scenario PBCM_H produced the highest annual ΔG in the PB herd at US$11.91 per head. Including CM phenotypes and parentage testing in the genetic evaluation increased the time to breakeven from 12 yr in PB_A to 19 years in PBCM_A after accounting for the cost of that information. Adding CM phenotypes and genotypes increased the breakeven time from 12 yr in PB_H to 18 yr in PBCM_H. Scenario PB_H produced the highest 30-yr CNPV per slaughtered CM calf at US$371.16. These results using field data indicate that economically relevant r_TI and ΔG can be realized by adding 25K SNP genotypes and CM phenotypes to genetic evaluation, but the additional cost of that data significantly delays the economic return to the enterprise.

Genetic Improvement in South African Livestock: Can Genomics Bridge the Gap Between the Developed and Developing Sectors?

South Africa (SA) holds a unique position on the African continent with a rich diversity in terms of available livestock resources, vegetation, climatic regions and cultures. The livestock sector has been characterized by a dual system of a highly developed commercial sector using modern technology vs. a developing sector including emerging and smallholder farmers. Emerging farmers typically aim to join the commercial sector, but lag behind with regard to the use of modern genetic technologies, while smallholder farmers use traditional practices aimed at subsistence. Several factors influence potential application of genomics by the livestock industries, which include available research funding, socio-economic constraints and extension services. State funded Beef and Dairy genomic programs have been established with the aim of building reference populations for genomic selection with most of the potential beneficiaries in the well-developed commercial sector. The structure of the beef, dairy and small stock industries is fragmented and the outcomes of selection strategies are not perceived as an advantage by the processing industry or the consumer. The indigenous and local composites represent approximately 40% of the total beef and sheep populations and present valuable genetic resources. Genomic research has mostly provided insight on genetic biodiversity of these resources, with limited attention to novel phenotypes associated with adaptation or disease tolerance. Genetic improvement of livestock through genomic technology needs to address the role of adapted breeds in challenging environments, increasing reproductive and growth efficiency. National animal recording schemes contributed significantly to progress in the developed sector with regard to genetic evaluations and estimated breeding values (EBV) as a selection tool over the past three decades. The challenge remains on moving the focus to novel traits for increasing efficiency and addressing welfare and environmental issues. Genetic research programs are required that will be directed to bridge the gap between the elite breeders and the developing livestock sector. The aim of this review was to provide a perspective on the dichotomy in the South African livestock sector arguing that a realistic approach to the use of genomics in beef, dairy and small stock is required to ensure sustainable long term genetic progress.

Genomic Tools and Selective Breeding in Molluscs

The production of most farmed molluscs, including mussels, oysters, scallops, abalone, and clams, is heavily dependent on natural seed from the plankton. Closing the lifecycle of species in hatcheries can secure independence from wild stocks and enables long-term genetic improvement of broodstock through selective breeding. Genomic techniques have the potential to revolutionize hatchery-based selective breeding by improving our understanding of the characteristics of mollusc genetics that can pose a challenge for intensive aquaculture and by providing a new suite of tools for genetic improvement. Here we review characteristics of the life history and genetics of molluscs including high fecundity, self-fertilization, high genetic diversity, genetic load, high incidence of deleterious mutations and segregation distortion, and critically assess their impact on the design and effectiveness of selective breeding strategies. A survey of the results of current breeding programs in the literature show that selective breeding with inbreeding control is likely the best strategy for genetic improvement of most molluscs, and on average growth rate can be improved by 10% per generation and disease resistance by 15% per generation across the major farmed species by implementing individual or family-based selection. Rapid advances in sequencing technology have resulted in a wealth of genomic resources for key species with the potential to greatly improve hatchery-based selective breeding of molluscs. In this review, we catalog the range of genomic resources currently available for molluscs of aquaculture interest and discuss the bottlenecks, including lack of high-quality reference genomes and the relatively high cost of genotyping, as well as opportunities for applying genomics-based selection.

Using genomic relationship likelihood for parentage assignment

Background

Parentage assignment is usually based on a limited number of unlinked, independent genomic markers (microsatellites, low-density single nucleotide polymorphisms (SNPs), etc.). Classical methods for parentage assignment are exclusion-based (i.e. based on loci that violate Mendelian inheritance) or likelihood-based, assuming independent inheritance of loci. For true parent–offspring relations, genotyping errors cause apparent violations of Mendelian inheritance. Thus, the maximum proportion of such violations must be determined, which is complicated by variable call- and genotype error rates among loci and individuals. Recently, genotyping using high-density SNP chips has become available at lower cost and is increasingly used in genetics research and breeding programs. However, dense SNPs are not independently inherited, violating the assumptions of the likelihood-based methods. Hence, parentage assignment usually assumes a maximum proportion of exclusions, or applies likelihood-based methods on a smaller subset of independent markers. Our aim was to develop a fast and accurate trio parentage assignment method for dense SNP data without prior genotyping error- or call rate knowledge among loci and individuals. This genomic relationship likelihood (GRL) method infers parentage by using genomic relationships, which are typically used in genomic prediction models.

Results

Using 50 simulated datasets with 53,427 to 55,517 SNPs, genotyping error rates of 1–3% and call rates of ~ 80 to 98%, GRL was found to be fast and highly (~ 99%) accurate for parentage assignment. An iterative approach was developed for training using the evaluation data, giving similar accuracy. For comparison, we used the Colony2 software that assigns parentage and sibship simultaneously to increase the power of the likelihood-based method and found that it has considerably lower accuracy than GRL. We also compared GRL with an exclusion-based method in which one of the parameters was estimated using GRL assignments.This method was slightly more accurate than GRL.

Conclusions

We show that GRL is a fast and accurate method of parentage assignment that can use dense, non-independent SNPs, with variable call rates and unknown genotyping error rates. By offering an alternative way of assigning parents, GRL is also suitable for estimating the expected proportion of inconsistent parent–offspring genotypes for exclusion-based models.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0397-7) contains supplementary material, which is available to authorized users.

SNP Data Quality Control in a National Beef and Dairy Cattle System and Highly Accurate SNP Based Parentage Verification and Identification

A major use of genetic data is parentage verification and identification as inaccurate pedigrees negatively affect genetic gain. Since 2012 the international standard for single nucleotide polymorphism (SNP) verification in Bos taurus cattle has been the ISAG SNP panels. While these ISAG panels provide an increased level of parentage accuracy over microsatellite markers (MS), they can validate the wrong parent at ≤1% misconcordance rate levels, indicating that more SNP are needed if a more accurate pedigree is required. With rapidly increasing numbers of cattle being genotyped in Ireland that represent 61 B. taurus breeds from a wide range of farm types: beef/dairy, AI/pedigree/commercial, purebred/crossbred, and large to small herd size the Irish Cattle Breeding Federation (ICBF) analyzed different SNP densities to determine that at a minimum ≥500 SNP are needed to consistently predict only one set of parents at a ≤1% misconcordance rate. For parentage validation and prediction ICBF uses 800 SNP (ICBF800) selected based on SNP clustering quality, ISAG200 inclusion, call rate (CR), and minor allele frequency (MAF) in the Irish cattle population. Large datasets require sample and SNP quality control (QC). Most publications only deal with SNP QC via CR, MAF, parent-progeny conflicts, and Hardy-Weinberg deviation, but not sample QC. We report here parentage, SNP QC, and a genomic sample QC pipelines to deal with the unique challenges of >1 million genotypes from a national herd such as SNP genotype errors from mis-tagging of animals, lab errors, farm errors, and multiple other issues that can arise. We divide the pipeline into two parts: a Genotype QC and an Animal QC pipeline. The Genotype QC identifies samples with low call rate, missing or mixed genotype classes (no BB genotype or ABTG alleles present), and low genotype frequencies. The Animal QC handles situations where the genotype might not belong to the listed individual by identifying: >1 non-matching genotypes per animal, SNP duplicates, sex and breed prediction mismatches, parentage and progeny validation results, and other situations. The Animal QC pipeline make use of ICBF800 SNP set where appropriate to identify errors in a computationally efficient yet still highly accurate method.

Double-digest RAD sequencing outperforms microsatellite loci at assigning paternity and estimating relatedness: A proof of concept in a highly promiscuous bird

Information on genetic relationships among individuals is essential to many studies of the behaviour and ecology of wild organisms. Parentage and relatedness assays based on large numbers of single nucleotide polymorphism (SNP) loci hold substantial advantages over the microsatellite markers traditionally used for these purposes. We present a double-digest restriction site-associated DNA sequencing (ddRAD-seq) analysis pipeline that, as such, simultaneously achieves the SNP discovery and genotyping steps and which is optimized to return a statistically powerful set of SNP markers (typically 150-600 after stringent filtering) from large numbers of individuals (up to 240 per run). We explore the trade-offs inherent in this approach through a set of experiments in a species with a complex social system, the variegated fairy-wren (Malurus lamberti) and further validate it in a phylogenetically broad set of other bird species. Through direct comparisons with a parallel data set from a robust panel of highly variable microsatellite markers, we show that this ddRAD-seq approach results in substantially improved power to discriminate among potential relatives and considerably more precise estimates of relatedness coefficients. The pipeline is designed to be universally applicable to all bird species (and with minor modifications to many other taxa), to be cost- and time-efficient, and to be replicable across independent runs such that genotype data from different study periods can be combined and analysed as field samples are accumulated.

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.

Genetic tests for estimating dairy breed proportion and parentage assignment in East African crossbred cattle

BACKGROUND

Smallholder dairy farming in much of the developing world is based on the use of crossbred cows that combine local adaptation traits of indigenous breeds with high milk yield potential of exotic dairy breeds. Pedigree recording is rare in such systems which means that it is impossible to make informed breeding decisions. High-density single nucleotide polymorphism (SNP) assays allow accurate estimation of breed composition and parentage assignment but are too expensive for routine application. Our aim was to determine the level of accuracy achieved with low-density SNP assays.

METHODS

We constructed subsets of 100 to 1500 SNPs from the 735k-SNP Illumina panel by selecting: (a) on high minor allele frequencies (MAF) in a crossbred population; (b) on large differences in allele frequency between ancestral breeds; (c) at random; or (d) with a differential evolution algorithm. These panels were tested on a dataset of 1933 crossbred dairy cattle from Kenya/Uganda and on crossbred populations from Ethiopia (N = 545) and Tanzania (N = 462). Dairy breed proportions were estimated by using the ADMIXTURE program, a regression approach, and SNP-best linear unbiased prediction, and tested against estimates obtained by ADMIXTURE based on the 735k-SNP panel. Performance for parentage assignment was based on opposing homozygotes which were used to calculate the separation value (sv) between true and false assignments.

RESULTS

Panels of SNPs based on the largest differences in allele frequency between European dairy breeds and a combined Nelore/N'Dama population gave the best predictions of dairy breed proportion (r² = 0.962 to 0.994 for 100 to 1500 SNPs) with an average absolute bias of 0.026. Panels of SNPs based on the highest MAF in the crossbred population (Kenya/Uganda) gave the most accurate parentage assignments (sv = -1 to 15 for 100 to 1500 SNPs).

CONCLUSIONS

Due to the different required properties of SNPs, panels that did well for breed composition did poorly for parentage assignment and vice versa. A combined panel of 400 SNPs was not able to assign parentages correctly, thus we recommend the use of 200 SNPs either for breed proportion prediction or parentage assignment, independently.

Development of a SNP panel dedicated to parentage assignment in French sheep populations

BACKGROUND

The efficiency of breeding programs partly relies on the accuracy of the estimated breeding values which decreases when pedigrees are incomplete. Two reproduction techniques are mainly used by sheep breeders to identify the sires of lambs: animal insemination and natural matings with a single ram per group of ewes. Both methods have major drawbacks, notably time-consuming tasks for breeders, and are thus used at varying levels in breeding programs. As a consequence, the percentage of known sires can be very low in some breeds and results in less accurate estimated breeding values.

RESULTS

In order to address this issue and offer an alternative strategy for obtaining parentage information, we designed a set of 249 SNPs for parentage assignment in French sheep breeds and tested its efficiency in one breed. The set was derived from the 54 K SNP chip that was used to genotype the thirty main French sheep populations. Only SNPs in Hardy-Weinberg equilibrium, displaying the highest Minor Allele Frequency across all the thirty populations and not associated with Mendelian errors in verified family trios were selected. The panel of 249 SNPs was successfully used in an on-farm test in the BMC breed and resulted in more than 95% of lambs being assigned to a unique sire.

CONCLUSION

In this study we developed a SNP panel for assignment that achieved good results in the on-farm testing. We also raised some conditions for optimal use of this panel: at least 180 SNPs should be used and a minute preparation of the list of candidate sires. Our panel also displays high levels of MAF in the SheepHapMap breeds, particularly in the South West European breeds.

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.

Recent development of allele frequencies and exclusion probabilities of microsatellites used for parentage control in the German Holstein Friesian cattle population

Background

Methods for parentage control in cattle have changed since their initial implementation in the late 1950’s from blood group typing to more current single nucleotide polymorphism determination. In the early 1990’s, 12 microsatellites were selected by the International Society for Animal Genetics based on their informativeness and robustness in a variety of different cattle breeds. Since then this panel is used as standard in cattle herd book breeding and its application is accompanied by recurrent international comparison tests ensuring permanent validity for the most common commercial dairy and beef cattle breeds for example Holstein Friesian, Simmental, Angus, and Hereford. Although, nearly every parentage can be resolved using these microsatellites, cases with very close relatives became an emerging resolution problem during recent years. This is mainly due to an increase of monomorphism and a trend to the fixation of alleles, although no direct selection against their variability was applied. Thus other effects must be presumed resulting in a loss of polymorphism information content, heterozygosity, and exclusion probabilities.

Results

To determine changes of allele frequencies and exclusion probabilities, we analyzed the development of these parameters for the 12 microsatellites from 2004 to 2014. One hundred sixty eight thousand recorded Holstein Friesian cattle genotypes were evaluated. During this period certain alleles of nine microsatellites increased significantly (t-values >5). When calculating the exclusion probabilities for 11 microsatellites, reduction was determined for the three situations, i.e. one parent is wrongly identified (p = 0.01), both parents are wrongly identified (p = 0.005), and the genotype of one parent is missing (p = 0.048). With the addition of BM1818 to the marker set in 2009, this development was corrected leading to significant increases in exclusion probabilities. Although, the exclusion probabilities for the three family situations using the 12 microsatellites are >99 %, the clarification of 142 relationships in 40,000 situations where one parent is missing will still be impossible.

Twenty-five sires were identified that are responsible for the most significant microsatellite allele increases in the population. The corresponding alleles are mainly associated with milk protein and fat yield, body weight at birth and weaning, as well as somatic cell score, milk fat percentage, and longissimus muscle area.

Conclusions

Our data show that most of the microsatellites used for parentage control in cattle show directional changes in allele frequencies consistent with the history of artificial selection in the German Holstein population.