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

Refining the genomic profiles of North African sheep breeds through meta-analysis of worldwide genomic SNP data

Introduction

The development of reproducible tools for the rapid genotyping of thousands of genetic markers (SNPs) has promoted cross border collaboration in the study of sheep genetic diversity on a global scale.

Methods

In this study, we collected a comprehensive dataset of 239 African and Eurasian sheep breeds genotyped at 37,638 filtered SNP markers, with the aim of understanding the genetic structure of 22 North African (NA) sheep breeds within a global context.

Results and discussion

We revealed asubstantial enrichment of the gene pool between the north and south shores of the Mediterranean Sea, which corroborates the importance of the maritime route in the history of livestock. The genetic structure of North African breeds mirrors the differential composition of genetic backgrounds following the breed history. Indeed, Maghrebin sheep stocks constitute a geographically and historically coherent unit with any breed-level genetic distinctness among them due to considerable gene flow. We detected a broad east-west pattern describing the most important trend in NA fat-tailed populations, exhibited by the genetic closeness of Egyptian and Libyan fat-tailed sheep to Middle Eastern breeds rather than Maghrebin ones. A Bayesian FST scan analysis revealed a set of genes with potentially key adaptive roles in lipid metabolism (BMP2, PDGFD VEGFA, TBX15, and WARS2), coat pigmentation (SOX10, PICK1, PDGFRA, MC1R, and MTIF) and horn morphology RXFP2) in Tunisian sheep. The local ancestry method detected a Merino signature in Tunisian Noire de Thibar sheep near the SULF1gene introgressed by Merino's European breeds. This study will contribute to the general picture of worldwide sheep genetic diversity.

PMSeeker: A Scheme Based on the Greedy Algorithm and the Exhaustive Algorithm to Screen Low-Redundancy Marker Sets for Large-Scale Parentage Assignment with Full Parental Genotyping

Parentage assignment is a genetic test that utilizes genetic characteristics, such as molecular markers, to identify the parental relationships within populations, which, in commercial fish farming, are almost always large and where full information on potential parents is known. To accurately find the true parents, the genotypes of all loci in the parentage marker set (PMS) are required for each individual being tested. With the same accuracy, a PMS containing a smaller number of markers will undoubtedly save experimental costs. Thus, this study established a scheme to screen low-redundancy PMSs using the exhaustive algorithm and greedy algorithm. When screening PMSs, the greedy algorithm selects markers based on the parental dispersity index (PDI), a uniquely defined metric that outperforms the probability of exclusion (PE). With the conjunctive use of the two algorithms, non-redundant PMSs were found for more than 99.7% of solvable cases in three groups of random sample experiments in this study. Then, a low-redundancy PMS can be composed using two or more of these non-redundant PMSs. This scheme effectively reduces the number of markers in PMSs, thus conserving human and experimental resources and laying the groundwork for the widespread implementation of parentage assignment technology in economic species breeding.

Development of a tetra-primer ARMS-PCR for identification of sika and red deer and their hybrids

Accurate identification of deer-derived components is significant in food and drug authenticity. Over the years, several methods have been developed to authenticate these products; however, identifying whether female deer products are hybrids is challenging. In this study, the zinc finger protein X-linked (ZFX) gene sequences of sika deer (Cervus nippon), red deer (Cervus elaphus) and their hybrid offspring were amplified and sequenced, the X221 and X428 species-specific single nucleotide polymorphisms (SNP) loci were verified, and a tetra-primer amplification refractory mutation system (T-ARMS-PCR) assay was developed to identify the parent-of-origin of female sika deer, red deer, and their hybrid deer. The T-ARMS-PCR developed based on the X221 locus could identify sika deer, red deer, and their hybrid offspring according to the presence or absence of PCR product sizes of 486 bp, 352 bp, and 179 bp, respectively, just as X428 locus could identify sika deer, red deer, and their hybrid offspring according to the presence or absence of PCR product sizes of 549 bp, 213 bp, and 383 bp, respectively. Forty products labeled deer-derived ingredients randomly purchased were tested using this assay, and the results showed that the identification results based on the two SNP loci were utterly consistent with the actual sources. In addition, this method was found to be accurate, simple, convenient, and with high specificity, thus providing an essential technical reference for deer product species identification. It is also an important supplement to the identification methods of the original ingredients of existing deer products.

High-density SNP markers elucidate the genetic divergence and population structure of Noticiana sheep breed in the Mediterranean context

Among livestock species, sheep have played an early major role in the Mediterranean area. Italy has a long history of sheep breeding and, despite a dramatic contraction in numbers, still raise several local populations that may represent a unique source of genetic diversity. The Noticiana is a breed of the south-eastern part of Sicily appreciated both for its dairy products and for its resistance to harsh environment. In this study, the high-density Illumina Ovine SNP600K BeadChip array was used for the first genome-wide characterization of 48 individuals of Noticiana sheep to investigate its diversity, the genome structure and the relationship within the context of worldwide and Italian breeds. Moreover, the runs of homozygosity (ROH) pattern and the pairwise F_ST-outliers were examined. Noticiana reported moderate levels of genetic diversity. The high percentage of short and medium length ROH segments (93% under 4 Mb) is indicative of a within breed relatedness dating back to ancient times, despite the absence of management for the mating plans and the reduced population size. In the worldwide context, the Southern Italian, Spanish and Albanian breeds overlapped in a macro cluster which also included the Noticiana sheep. The results highlighted ancestral genetic components of Noticiana shared with Comisana breed, and showed the clear separation from the other Italian sheep. This is likely the consequence of the combined effects of genetic drift, small population size and reproductive isolation. ROH islands and F_ST-outliers approaches in Noticiana identified genes and QTLs involved in milk and meat production, as well as related to the local adaptation, and therefore are consistent with the phenotypic traits of the studied breed. Although a wider sampling could be useful to deepen the genomic survey on Noticiana, these results represent a crucial starting point for the characterization of an important local genetic resource, with a view of supporting the local economy and preserving the biodiversity of the sheep species.

Integer programming for selecting set of informative markers in paternity inference

Background

Parentage information is fundamental to various life sciences. Recent advances in sequencing technologies have made it possible to accurately infer parentage even in non-model species. The optimization of sets of genome-wide markers is valuable for cost-effective applications but requires extremely large amounts of computation, which presses for the development of new efficient algorithms.

Results

Here, for a closed half-sib population, we generalized the process of marker loci selection as a binary integer programming problem. The proposed systematic formulation considered marker localization and the family structure of the potential parental population, resulting in an accurate assignment with a small set of markers. We also proposed an efficient heuristic approach, which effectively improved the number of markers, localization, and tolerance to missing data of the set. Applying this method to the actual genotypes of apple (Malus × domestica) germplasm, we identified a set of 34 SNP markers that distinguished 300 potential parents crossed to a particular cultivar with a greater than 99% accuracy.

Conclusions

We present a novel approach for selecting informative markers based on binary integer programming. Since the data generated by high-throughput sequencing technology far exceeds the requirement for parentage assignment, a combination of the systematic marker selection with targeted SNP genotyping, such as KASP, allows flexibly enlarging the analysis up to a scale that has been unrealistic in various species. The method developed in this study can be directly applied to unsolved large-scale problems in breeding, reproduction, and ecological research, and is expected to lead to novel knowledge in various biological fields. The implementation is available at https://github.com/SoNishiyama/IP-SIMPAT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04801-z.

Genome-wide selection of discriminant SNP markers for breed assignment in indigenous sheep breeds

The assignment of an individual to the true population of origin is one of the most important applications of genomic data for practical use in animal breeding. The aim of this study was to develop a statistical method and then, to identify the minimum number of informative SNP markers from high-throughput genotyping data that would be able to trace the true breed of unknown samples in indigenous sheep breeds. The total numbers of 217 animals were genotyped using Illumina OvineSNP50K BeadChip in Zel, Lori-Bakhtiari, Afshari, Moqani, Qezel and a wild-type Iranian sheep breed. After SNP quality check, the principal component analysis (PCA) was used to determine how the animals allocated to the groups using all genotyped markers. The results revealed that the first principal component (PC1) separated out the two domestic and wild sheep breeds, and all domestic breeds were separated from each other for PC2. The genetic distance between different breeds was calculated using FST and Reynold methods and the results showed that the breeds were well differentiated. A statistical method was developed using the stepwise discriminant analysis (SDA) and the linear discriminant analysis (LDA) to reduce the number of SNPs for discriminating 6 different Iranian sheep populations and K-fold cross-validation technique was employed to evaluate the potential of a selected subset of SNPs in assignment success rate. The procedure selected reduced pools of markers into 201 SNPs that were able to exactly discriminate all sheep populations with 100% accuracy. Moreover, a discriminate analysis of principal components (DAPC) developed using 201 linearly independent SNPs revealed that these markers were able to assign all individuals into true breed. Finally, these 201 identified SNPs were successfully used in an independent out-group breed consisting of 96 samples of Baluchi sheep breed and the results indicated that these markers are able to correctly allocate all unknown samples to true population of origin. In general, the results of this study indicated that the combined use of the SDA and LDA techniques represents an efficient strategy for selecting a reduced pool of highly discriminant markers.

SNPs with intermediate minor allele frequencies facilitate accurate breed assignment of Indian Tharparkar cattle

Tharparkar cattle breed is widely known for its superior milch quality and hardiness attributes. This study aimed to develop an ultra-low density breed-specific single nucleotide polymorphism (SNP) genotype panel to accurately quantify Tharparkar populations in biological samples. In this study, we selected and genotyped 72 Tharparkar animals randomly from Cattle & Buffalo Farm of IVRI, India. This Bovine SNP50 BeadChip genotypic datum was merged with the online data from six indigenous cattle breeds and five taurine breeds. Here, we used a combination of pre-selection statistics and the MAF-LD method developed in our laboratory to analyze the genotypic data obtained from 317 individuals of 12 distinct breeds to identify breed-informative SNPs for the selection of Tharparkar cattle. This methodology identified 63 unique Tharparkar-specific SNPs near intermediate gene frequencies. We report several informative SNPs in genes/QTL regions affecting phenotypes or production traits that might differentiate the Tharparkar breed.

The levels of artificial insemination and missing sire information make genomic selection not always beneficial in meat sheep

Numerous meat sheep breeding programs in developed and developing countries are characterized by incomplete sire information and a predominant use of natural matings. These two parameters potentially affect the benefit of genomic selection (GS), especially for the selection of a late-in-life trait. Using stochastic simulations, the genetic gains obtained using genomic and conventional strategies for a maternal trait were evaluated in meat sheep population. Natural mating and artificial insemination (AI)-based designs, inspired by the current diversity of designs used for French meat sheep breeds, were modeled and three genomic strategies were tested and compared with a conventional selection strategy: parentage assignment, GS based on a male or a male and female reference population. Genomic selection based on a male reference population did not always outperform conventional selection. Its benefit depended on the design, the level of missing information on dam sires, and the level of AI. Genomic selection based on a male and female reference population always outperformed the conventional selection strategy, even if only 25 % of the females in the nucleus were genotyped.

Development of SNP-Based Genomic Tools for the Canadian Bison Industry: Parentage Verification and Subspecies Composition

Genomic technologies have been increasingly applied in livestock production due to their utility in production management and animal genetic improvement. The current project aimed to develop genomic resources for the Canadian bison industry, specifically a parentage verification tool and a subspecies composition tool. Both products stand to help with building and maintaining purebred and crossbred bison populations, and in turn bison conservation and production. The development of this genomic toolkit proceeded in two stages. In the single-nucleotide polymorphism (SNP) discovery and selection stage, raw sequence information from 41 bison samples was analyzed, and approximately 52.5 million candidate biallelic SNPs were discovered from 21 samples with high sequence quality. A set of 19,954 SNPs (2,928 for parentage verification and 17,026 for subspecies composition) were then selected for inclusion on an Axiom myDesign custom array. In the refinement and validation stage, 480 bison were genotyped using the custom SNP panel, and the resulting genotypes were analyzed to further filter SNPs and assess tool performance. In various tests using real and simulated genotypes, the two genomic tools showed excellent performance for their respective tasks. Final SNP sets consisting of 191 SNPs for parentage and 17,018 SNPs for subspecies composition are described. As the first SNP-based genomic toolkit designed for the Canadian bison industry, our results may provide a new opportunity in improving the competitiveness and profitability of the industry in a sustainable manner.

Inbreeding and effective population size in French dairy sheep: Comparison between genomic and pedigree estimates

Before availability of dense SNP data, genetic diversity was characterized and managed with pedigree-based information. Besides this classical approach, 2 methodologies have been proposed in recent years to characterize and manage diversity from dense SNP data: the SNP-by-SNP approach and the alternative based on runs of homozygosity (ROH). The establishment of criteria to identify ROH is a current constraint in the literature dealing with ROH. The objective of this study was, using a medium-density SNP chip, to quantify by 3 methods (pedigree, SNP-by-SNP, and ROH) the genetic diversity on 5 selected French dairy sheep subpopulations and breeds and to assess the effect of the definition of ROH on these estimates. The data set available included individuals from the breeds Basco-Béarnaise, Manech Tête Noire, Manech Tête Rousse, and 2 subpopulations of Lacaune: Lacaune Confederation and Lacaune Ovitest. Animals were genotyped with the Illumina OvineSNP50 BeadChip (Illumina Inc., San Diego, CA). After filtering, the genomic data included 38,287 autosomal SNP and 8,700 individuals, which comprised 72,803 animals in the pedigree. The results indicated that no significant differences were observed in effective population size estimates obtained from pedigree or genomic (SNP-by-SNP or ROH) information. In general, estimates of effective population size were above 200 in Lacaune Confederation and Lacaune Ovitest subpopulations and below 200 in Basco-Béarnaise, Manech Tête Noire, and Manech Tête Rousse breeds. The minimum length that constituted a ROH, the minimum number of SNP that constituted a ROH, as well as the minimum density and the maximum distance allowed between 2 homozygous SNP are ROH-defining factors with important implications in the estimation of the rate of inbreeding. The ROH-based rates of inbreeding in concordance with those obtained from pedigree information require a specific set of values. This particular set of values is different from that identified to obtain ROH-based rates of inbreeding similar to those obtained on a SNP-by-SNP basis. Factors to define ROH do not change the results much unless extreme values are considered, although further research on ROH-based inbreeding is still required.

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.

Functional SNP panel for parentage assessment and assignment in worldwide goat breeds

BACKGROUND

International standard panels of single nucleotide polymorphisms (SNPs) have replaced microsatellites in several species for parentage assessment and assignment (PA) purposes. However, such a resource is still lacking in goats. The application of a cheap tool for PA would help the management of goat populations by improving the reliability of pedigree registration and, consequently, allow a better implementation of breeding schemes or conservation programs.

RESULTS

Using data from the current GoatSNP50 chip, starting from a worldwide dataset of more than 4000 animals belonging to more than 140 breeds and populations from the AdaptMap initiative, we selected a panel of 195 SNPs. The assignment rate of this panel was up to 100% on an additional dataset that included 2000 Alpine and Saanen animals and highly related candidate sires.

CONCLUSIONS

In this study, we defined a highly informative SNP panel, which will be publicly available to worldwide breeders and laboratories. Its development on such a large number of breeds and populations, together with validation on a second set of cosmopolitan breeds, makes it a promising and important genomic tool for the goat species.

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.

Using a very low-density SNP panel for genomic selection in a breeding program for sheep

Background

Building an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs).

Methods

A population under selection for a maternal trait was simulated using real genotypes. Genetic gains realized from classical selection and genomic selection designs were compared. Genomic selection scenarios that differed in reference population structure (whether or not dams were included in the reference) and genotype quality (medium-density or imputed to medium-density from very low-density) were evaluated.

Results

The genomic design increased genetic gain by 26% when the reference population was based on sire medium-density genotypes and by 54% when the reference population included both sire and dam medium-density genotypes. When medium-density genotypes of male candidates and dams were replaced by imputed genotypes from very low-density SNP genotypes (1000 SNPs), the increase in gain was 22% for the sire reference population and 42% for the sire and dam reference population. The rate of increase in inbreeding was lower (from − 20 to − 34%) for the genomic design than for the classical design regardless of the genomic scenario.

Conclusions

We show that very low-density genotypes of male candidates and dams combined with an imputation process result in a substantial increase in genetic gain for small sheep breeding programs.