High-density Single Nucleotide Polymorphism Panels Research Articles

Comprehensive analysis of runs of homozygosity and heterozygosity in Holstein cattle on the basis of medium and high density SNP panels and large population sample

Abstract This study reports runs of homozygosity (ROH) and heterozygosity (ROHet) distributed in a large population of Holstein cattle on the basis of two microarrays of medium (50k; 2163 animals; 54 609 SNPs) and high single nucleotide polymorphism (SNP) density (HD; 600 animals; 777 692 SNPs). To assess the inbreeding values of Holstein cattle, the ROH-based genomic inbreeding coefficient (FROH) was calculated. The comparison of SNP panels suggested that FROH values above 4 Mb should be considered for panels of medium densities as a relatively reliable measure of inbreeding. Moreover, ROH hotspots and coldspots were identified and compared between the HD and 50k SNP panels and were carefully examined for association with production and functional traits. The obtained results pinpointed genomic regions presumably under selection pressure in Holstein cattle. The regions overlapped with a large number of genes, including GHR, GBF1, SUMF1, CCL28, NIM1K, U6, BTRC and FABP1, many of which are involved in important Holstein cattle characteristics. We also found that some ROH hotspots and coldspots identified with the HD panel were not detected with the 50k panel, mainly because of insufficient SNP density in certain genomic regions. This suggests that using medium-density panels might not be the best choice when precise identification of ROH patterns is the main goal. In summary, in this work, we confirmed that a high-density SNP panel compared to a medium-density SNP panel allows for more precise identification of ROH patterns, especially in the case of short ROH that could be associated with ancestral inbreeding.

Identification of new major histocompatibility complex-B Haplotypes in Bangladesh native chickens.

The major histocompatibility complex in chicken demonstrates a great range of variations within varities, breeds, populations and that can eventually influence their immuneresponses. The preset study was conducted to understand the major histocompatibility complex-B (MHC-B) variability in five major populations of Bangladesh native chicken: Aseel, Hilly, Junglefowl, Non-descript Deshi, and Naked Neck. These five major populations of Bangladesh native chicken were analyzed with a subset of 89 single nucleotide polymorphisms (SNPs) in the high-density MHC-B SNP panel and Kompetitive Allele-Specific polymerase chain reaction genotyping was applied. To explore haplotype diversity within these populations, the results were analyzed both manually and computationally using PHASE 2.1 program. The phylogenetic investigations were also performed using MrBayes program. A total of 136 unique haplotypes were identified within these five Bangladesh chicken populations, and only one was shared (between Hilly and Naked Neck). Phylogenetic analysis showed no distinct haplotype clustering among the five populations, although they were shared in distinct clades; notably, the first clade lacked Naked Neck haplotypes. The present study discovered a set of unique MHC-B haplotypes in Bangladesh chickens that could possibly cause varied immune reponses. However, further investigations are required to evaluate their relationships with global chicken populations.

Genetic insights into crossbred dairy cattle of Pakistan: exploring allele frequency, linkage disequilibrium, and effective population size at a genome-wide scale.

Linkage disequilibrium (LD) affects genomic studies accuracy. High-density genotyping platforms identify SNPs across animal genomes, increasing LD evaluation resolution for accurate analysis. This study aimed to evaluate the decay and magnitude of LD in a cohort of 81 crossbred dairy cattle using the GGP_HDv3_C Bead Chip. After quality control, 116,710 Single Nucleotide Polymorphisms (SNPs) across 2520.241Mb of autosomes were retained. LD extent was assessed between autosomal SNPs within a 10Mb range using the r2 statistics. LD value declined as inter-marker distance increased. The average r2 value was 0.24 for SNP pairs < 10kb apart, decreasing to 0.13 for 50-100kb distances. Minor allele frequency (MAF) and sample size significantly impact LD. Lower MAF thresholds result in smaller r2 values, while higher thresholds show increased r2 values. Additionally, smaller sample sizes exhibit higher average r2 values, especially for larger physical distance intervals (> 50kb) between SNP pairs. Effective population size and inbreeding coefficient were 150 and 0.028 for the present generation, indicating a decrease in genetic diversity over time. These findings imply that the utilization of high-density SNP panels and customized/breed-specific SNP panels represent a highly favorable approach for conducting genome-wide association studies (GWAS) and implementing genomic selection (GS) in the Bos indicus cattle breeds, whose genomes are still largely unexplored. Furthermore, it is imperative to devise a meticulous breeding strategy tailored to each herd, aiming to enhance desired traits while simultaneously preserving genetic diversity.

Population genomic structures and signatures of selection define the genetic uniqueness of several fancy and meat rabbit breeds.

Following the recent domestication process of the European rabbit (Oryctolagus cuniculus), many different breeds and lines, distinguished primarily by exterior traits such as coat colour, fur structure and body size and shape, have been constituted. In this study, we genotyped, with a high-density single-nucleotide polymorphism panel, a total of 645 rabbits from 10 fancy breeds (Belgian Hare, Champagne d'Argent, Checkered Giant, Coloured Dwarf, Dwarf Lop, Ermine, Giant Grey, Giant White, Rex and Rhinelander) and three meat breeds (Italian White, Italian Spotted and Italian Silver). ADMIXTURE analysis indicated that breeds with similar phenotypic traits (e.g. coat colour and body size) shared common ancestries. Signatures of selection using two haplotype-based approaches (iHS and XP-EHH), combined with the results obtained with other methods previously reported that we applied to the same breeds, we identified a total of 5079 independent genomic regions with some signatures of selection, covering about 1777 Mb of the rabbit genome. These regions consistently encompassed many genes involved in pigmentation processes (ASIP, EDNRA, EDNRB, KIT, KITLG, MITF, OCA2, TYR and TYRP1), coat structure (LIPH) and body size, including two major genes (LCORL and HMGA2) among many others. This study revealed novel genomic regions under signatures of selection and further demonstrated that population structures and signatures of selection, left into the genome of these rabbit breeds, may contribute to understanding the genetic events that led to their constitution and the complex genetic mechanisms determining the broad phenotypic variability present in these untapped rabbit genetic resources.

Haplotype analysis of genomic prediction by incorporating genomic pathway information based on high-density SNP marker in Chinese yellow-feathered chicken

Genomic selection using single nucleotide polymorphism (SNP) markers is now intensively investigated in breeding and has been widely utilized for genetic improvement. Currently, several studies have used haplotype (consisting of multiallelic SNPs) for genomic prediction and revealed its performance advantage. In this study, we comprehensively evaluated the performance of haplotype models for genomic prediction in 15 traits, including 6 growth, 5 carcass, and 4 feeding traits in a Chinese yellow-feathered chicken population. We adopted 3 methods to define haplotypes from high-density SNP panels, and our strategy included combining Kyoto Encyclopedia of Genes and Genomes pathway information and considering linkage disequilibrium (LD) information. Our results showed an increase in prediction accuracy due to haplotypes ranging from -0.04∼27.16% in all traits, where the significant improvements were found in 12 traits. The estimates of haplotype epistasis heritability were strongly correlated with the accuracy increase by haplotype models. In addition, incorporating genomic annotation information could further increase the accuracy of the haplotype model, where the further increase in accuracy is significantly relative to the increase of relative haplotype epistasis heritability. The genomic prediction using LD information for constructing haplotypes has the best prediction performance among the 4 traits. These results uncovered that haplotype methods were beneficial for genomic prediction, and the accuracy could be further increased by incorporating genomic annotation information. Moreover, using LD information would potentially improve the performance of genomic prediction.

Identification of Candidate Genes Regulating Carcass Depth and Hind Leg Circumference in Simmental Beef Cattle Using Illumina Bovine Beadchip and Next-Generation Sequencing Analyses.

Simple SummaryThe performance of genome-wide association studies with high-density single-nucleotide polymorphism panels and next-generation sequencing is a robust approach to the identification of genetic variants that can elucidate variation in complex diseases and economically important traits in farm animals. We identified many markers associated with crucial traits, such as muscle development in Simmental beef cattle. This information can be used to pinpoint candidate genes affecting traits of interest. Our results contribute to the clarification of molecular mechanisms underlying processes associated with hind-leg meat yield and carcass depth in beef cattle.Genome-wide association studies are a robust means of identifying candidate genes that regulate economically important traits in farm animals. The aim of this study is to identify single-nucleotide polymorphisms (SNPs) and candidate genes potentially related to carcass depth and hind leg circumference in Simmental beef cattle. We performed Illumina Bovine HD Beadchip (~670 k SNPs) and next-generation sequencing (~12 million imputed SNPs) analyses of data from 1252 beef cattle, to which we applied a linear mixed model. Using a statistical threshold (p = 0.05/number of SNPs identified) and adopting a false discovery rate (FDR), we identified many putative SNPs on different bovine chromosomes. We identified 12 candidate genes potentially annotated with the markers identified, including CDKAL1 and E2F3, related to myogenesis and skeletal muscle development. The identification of such genes in Simmental beef cattle will help breeders to understand and improve related traits, such as meat yield.

Impact of Marker Pruning Strategies Based on Different Measurements of Marker Distance on Genomic Prediction in Dairy Cattle.

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of 'marker density' seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of 'marker density' based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean (d¯) and variance (σd2) of the physical distance between SNPs and the mean (r2¯) and variance (σr22) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d¯ of all panels is basically the same, but the σd2, r2¯ and σr22 are different. Therefore, we only investigated the effects of σd2, r2¯ and σr22 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σd2, but not with r2¯ and σr22. Compared with GenD and RanD, the σd2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large σd2, which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20-30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data.

Concordance rate in cattle and sheep between genotypes differing in Illumina GenCall quality score.

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12428767 genotypes in total post-editing) and 80 sheep (1557360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of <0.300, 0.300-0.549, and ≥0.550 in the cattle (sheep in parenthesis) was 0.9467 (0.9864), 0.9707 (0.9953), and 0.9994 (0.99997) respectively; the respective allele concordance rate was 0.9730 (0.9930), 0.9849 (0.9976), and 0.9997 (0.99998). Hence, concordance eroded as the GC score of the called genotype reduced, albeit the impact was not dramatic and was not very noticeable until a GC score of <0.55. Moreover, the impact was greater and more consistent in the cattle population than in the sheep population. Furthermore, an impact of GC score on genotype concordance rate existed even for the same SNP GenTrain value; the GenTrain value is a statistical score that depicts the shape of the genotype clusters and the relative distance between the called genotype clusters.

Genome-wide identification of major genes and genomic prediction using high-density and text-mined gene-based SNP panels in Hanwoo (Korean cattle).

It was hypothesized that single-nucleotide polymorphisms (SNPs) extracted from text-mined genes could be more tightly related to causal variant for each trait and that differentially weighting of this SNP panel in the GBLUP model could improve the performance of genomic prediction in cattle. Fitting two GRMs constructed by text-mined SNPs and SNPs except text-mined SNPs from 777k SNPs set (exp_777K) as different random effects showed better accuracy than fitting one GRM (Im_777K) for six traits (e.g. backfat thickness: + 0.002, eye muscle area: + 0.014, Warner-Bratzler Shear Force of semimembranosus and longissimus dorsi: + 0.024 and + 0.068, intramuscular fat content of semimembranosus and longissimus dorsi: + 0.008 and + 0.018). These results can suggest that attempts to incorporate text mining into genomic predictions seem valuable, and further study using text mining can be expected to present the significant results.

Genome-wide characterisation of regions under intense selection based on runs of homozygosity in Charolais cattle

Submitted 2020-06-22 | Accepted 2020-07-20 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.347-352 In this study, 68 genotyped purebred cows and bulls of Charolais cattle were used to determine runs of homozygosity (ROH) for evaluation of selection effect on the genome structure. ROH analysis was performed for 41,153 SNPs, and homozygous segments considering a minimum of 15 consecutive SNPs. The aim was to identify if regions of the genome with a high frequency of SNPs in ROH include signatures of selection. The most significant outlier SNPs were found on BTA2, 5, 7 and 19 (11 regions), with a sum of ROH length of 15.85 Mb. These regions contained genes included in various biological processes associated with the functioning of the immune system, growth, reproduction and metabolism. Various quantitative traits loci (QTLs) which affected the characteristics of meat production and reproduction have been identified in these regions. Overall, obtained results suggest that the Charolais cattle genome includes selection signatures reflecting the improvement of meat production and reproduction in accordance to breeding objecives. Keywords: artificial selection, autozygosity, beef, bovine, candidate genes References Aspatwar, A. et al. (2014). Carbonic anhydrase related proteins: molecular biology and evolution. Subcell. Biochem. 75: 135–156. Biscarini, F. et al. (2019). detectRUNS: an R package to detect runs of homozygosity and heterozygosity in diploid genomes. The R Project. Retrieved June 8, 2019 from https://cran.r-project.org/web/packages/detectRUNS/vignettes/detectRUNS.vignette.html Dixit, S. P. et al. (2020). Genome-Wide Runs of Homozygosity Revealed Selection Signatures in Bos indicus. Front. Genet. https://doi.org/10.3389/fgene.2020.00092 Gaudet, P. et al. (2011). Phylogenetic-based Propagation of Functional Annotations Within the Gene Ontology Consortium. 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Chinese Fir Breeding in the High-Throughput Sequencing Era: Insights from SNPs

Knowledge on population diversity and structure is of fundamental importance for conifer breeding programs. In this study, we concentrated on the development and application of high-density single nucleotide polymorphism (SNP) markers through a high-throughput sequencing technique termed as specific-locus amplified fragment sequencing (SLAF-seq) for the economically important conifer tree species, Chinese fir (Cunninghamia lanceolata). Based on the SLAF-seq, we successfully established a high-density SNP panel consisting of 108,753 genomic SNPs from Chinese fir. This SNP panel facilitated us in gaining insight into the genetic base of the Chinese fir advance breeding population with 221 genotypes for its genetic variation, relationship and diversity, and population structure status. Overall, the present population appears to have considerable genetic variability. Most (94.15%) of the variability was attributed to the genetic differentiation of genotypes, very limited (5.85%) variation occurred on the population (sub-origin set) level. Correspondingly, low FST (0.0285–0.0990) values were seen for the sub-origin sets. When viewing the genetic structure of the population regardless of its sub-origin set feature, the present SNP data opened a new population picture where the advanced Chinese fir breeding population could be divided into four genetic sets, as evidenced by phylogenetic tree and population structure analysis results, albeit some difference in membership of the corresponding set (cluster vs. group). It also suggested that all the genetic sets were admixed clades revealing a complex relationship of the genotypes of this population. With a step wise pruning procedure, we captured a core collection (core 0.650) harboring 143 genotypes that maintains all the allele, diversity, and specific genetic structure of the whole population. This generalist core is valuable for the Chinese fir advanced breeding program and further genetic/genomic studies.

SNP-haplotypes: An accurate approach for parentage and relatedness inference in gilthead sea bream (Sparus aurata)

Pedigree information among individuals has become a key factor in the correct management and conservation of wild populations, as well as captive stocks. Despite the growing interest in the identification and application of single nucleotide polymorphism (SNP) markers, to date, microsatellite loci continues to be considered as the genetic marker par excellence to establish the degree of relationship among individuals. In fact, although high-density SNP panels are a highly accurate tool for inferring kinship relationships, at small-scale, with only a reduced number of SNPs involved, it is difficult to preserve that accuracy due to the bi-allelic nature of this genetic marker.The present study has evaluated the accuracy of SNP-haplotypes (genotypic combination of single SNPs located in the same gene) as parentage and relatedness tool in gilthead sea bream (Sparus aurata). Initially, by direct sequencing of 11 genes in a set of 30 unrelated individuals with heterogeneous origin, a total of 398 putative SNPs were identified and, by means of a filtering procedure to remove false positive and SNPs in a very low frequency, 164 SNPs were selected for the final panel. Using the iPLEX Gold MassARRAY technology, a subset of 58 SNPs located in 7 genes were genotyped in 424 individuals organized into two broodstocks (54 breeders and 370 offspring with pedigree previously inferred by microsatellite loci). From the obtained genotype data, a SNP-haplotype reconstruction was carried out for each gene. According to this approach, 58 biallelic single SNPs were turned into 7 multi-allelic markers (1 gene = 1 marker) whose allele (haplotype) number ranged from 3 to 41. This SNP-haplotypes resolved parentage assignment with an accuracy higher than 99%, resulting in a 10.5% and 3.5% more accuracy than the set of 58 SNPs and 9 microsatellite loci, respectively. Likewise, under a scenario in which ancestral information was not available, SNP-haplotypes showed an accuracy similar than microsatellite loci for discriminating unrelated individuals. Using this approach, a proportion close to 94% of unrelated individuals correctly classified for the coefficient of relationship equal to zero (theoretically expected relatedness value between unrelated individuals) was achieved. The 6% of individuals, misclassified as unrelated, were largely half-sibs since the full-sibs did not exceed 0.03%. In conclusion, we report herein that SNP-haplotypes approach represents a highly efficient alternative to microsatellite loci to infer parentage and assess relatedness.

Short communication: Uncovering quantitative trait loci associated with resistance to Mycobacterium avium ssp. paratuberculosis infection in Holstein cattle using a high-density single nucleotide polymorphism panel

Mycobacterium avium ssp. paratuberculosis (MAP) is the etiological agent of Johne's disease in cattle. Johne's disease is a disease of significant economic, animal welfare, and public health concern around the globe. Therefore, understanding the genetic architecture of resistance to MAP infection has great relevance to advance genetic selection methods to breed more resistant animals. The objectives of this study were to perform a genome-wide association study of previously analyzed 50K genotypes now imputed to a high-density single nucleotide polymorphism panel (777K), aiming to validate previously reported associations and potentially identify additional single nucleotide polymorphisms associated with antibody response to MAP infection. A principal component regression-based genome-wide association study revealed 15 putative quantitative trait loci (QTL) associated with the MAP infection phenotype (serum or milk ELISA tests) on 9 different chromosomes (Bos taurus autosomes 5, 6, 7, 10, 14, 15, 16, 20, and 21). These results validated previous findings and identified new QTL on Bos taurus autosomes 15, 16, 20, and 21. The positional candidate genes NLRP3, IFi47, TRIM41, TNFRSF18, and TNFRSF4 lying within these QTL were identified. Further functional validation of these genes is now warranted to investigate their roles in regulating the immune response and, consequently, cattle resistance to MAP infection.

Accuracy of genotype imputation and genomic predictions in a two-generation farmed Atlantic salmon population using high-density and low-density SNP panels

The objectives of this study were: (i) to assess genotype imputation accuracy in different scenarios using genome-wide single nucleotide polymorphisms (SNP) data from a population comprising two generations of farmed Atlantic salmon and (ii) to assess the accuracy of genomic predictions for a quantitative trait (body weight) using the imputed genotypes. The pedigree consisted of 53 parents and 1069 offspring genotyped using a high-density SNP panel (50 K). Two groups were created: Group A: 90% of the offspring were included into training and 10% into validation sets; Group B: 10% of the offspring were included into training and 90% into validation sets. Different scenarios of available genotypic information from relatives were tested for the two groups previously described. Imputation was performed using three in silico low-density panels (0.5, 3 and 6 K) with all markers except the markers present on the low-density panel masked in the validation sets. The accuracy of genomic selection was tested using the scenarios that resulted in the best and the worst imputation accuracy for the three low density panels and were compared to accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions using the 50 K SNP panel. In general, imputation accuracy ranged from 0.74 to 0.98 depending on scenario. For the best scenario with the highest number of animals in reference population (Group A), the accuracy of imputation ranged from 0.95 to 0.98 depending on the low-density panel used. For the best scenario with the lowest number of animals in reference population (Group B), the accuracy of imputation ranged from 0.94 to 0.98 depending on the low-density panel used. In general, the number of SNPs in the low-density panels had a greater influence on the accuracy of imputation than the size of the reference set. The accuracies of genomic predictions using imputed genotypes, ranging from 0.71 to 0.73, outperformed PBLUP (0.66) and were identical or very similar to the use of all true genotype data (0.73). The high imputation and genomic prediction accuracy suggest that the imputation of genotypes from low density (0.5 to 3 K) to high density (50 K) could be a cost-effective strategy for the feasibility of the practical implementation of genomic selection in Atlantic salmon.

Genomic differentiation between swamp and river buffalo using a cattle high-density single nucleotide polymorphisms panel

Buffalo (Bubalus bubalis) is an important livestock species in many tropical and subtropical regions. In recent decades, the interest in buffalo’s milk have expanded and intensive buffalo farms start to emerge. However, breeding programs and population genetics information for this species is scarce or inexistent. The present study aims to test the suitability of the commercial high-density single nucleotide polymorphisms (SNP) genotyping panel, the Illumina BovineHD BeadChip, to estimate population genetics parameters, pedigree control and identification of common variants in major production candidate genes. From a total of 777 962 SNPs included in the panel, 20 479 were polymorphic in water buffalo at a call rate of 86% and an average expected heterozygosity (HE) of 0.306. From these, 357 were mapped within or around the flanking regions of several major candidate genes. A principal components analysis identified three different clusters, each representing pure swamp buffalo type, pure river buffalo type and admixed river buffalo. The hybrids between swamp and river buffalo were clearly identified as an intermediary cluster. The suitability of these SNPs data set for parentage and identity testing demonstrated that the combination of just 30 to 50 SNPs were enough to attain high probabilities of parentage exclusion (0.9999) in both types and identity (2.3×10−5 and 2.0×10−7) for river and swamp buffalo, respectively. Our analysis confirms the suitability of the BovineHD BeadChip to assess population structure, hybridization and identity of the water buffalo populations.

Selection signatures in Canchim beef cattle

BackgroundRecent technological advances in genomics have allowed the genotyping of cattle through single nucleotide polymorphism (SNP) panels. High-density SNP panels possess greater genome coverage and are useful for the identification of conserved regions of the genome due to selection, known as selection signatures (SS). The SS are detectable by different methods, such as the extended haplotype homozygosity (EHH); and the integrated haplotype score (iHS), which is derived from the EHH. The aim of this study was to identify SS regions in Canchim cattle (composite breed), genotyped with high-density SNP panel.ResultsA total of 687,655 SNP markers and 396 samples remained for SS analysis after the genotype quality control. The iHS statistic for each marker was transformed into piHS for better interpretation of the results. Chromosomes BTA5 and BTA14 showed piHS > 5, with 39 and nine statistically significant SNPs (P < 0.00001), respectively. For the candidate selection regions, iHS values were computed across the genome and averaged within non-overlapping windows of 500 Kb. We have identified genes that play an important role in metabolism, melanin biosynthesis (pigmentation), and embryonic and bone development.ConclusionsThe observation of SS indicates that the selection processes performed in Canchim, as well as in the founder breeds (i.e. Charolais), are maintaining specific genomic regions, particularly on BTA5 and BTA14. These selection signatures regions could be associated with Canchim characterization.Electronic supplementary materialThe online version of this article (doi:10.1186/s40104-016-0089-5) contains supplementary material, which is available to authorized users.

A high-density SNP panel reveals extensive diversity, frequent recombination and multiple recombination hotspots within the chicken major histocompatibility complex B region between BG2 and CD1A1.

BackgroundThe major histocompatibility complex (MHC) is present within the genomes of all jawed vertebrates. MHC genes are especially important in regulating immune responses, but even after over 80 years of research on the MHC, much remains to be learned about how it influences adaptive and innate immune responses. In most species, the MHC is highly polymorphic and polygenic. Strong and highly reproducible associations are established for chicken MHC-B haplotypes in a number of infectious diseases. Here, we report (1) the development of a high-density SNP (single nucleotide polymorphism) panel for MHC-B typing that encompasses a 209,296 bp region in which 45 MHC-B genes are located, (2) how this panel was used to define chicken MHC-B haplotypes within a large number of lines/breeds and (3) the detection of recombinants which contributes to the observed diversity.MethodsA SNP panel was developed for the MHC-B region between the BG2 and CD1A1 genes. To construct this panel, each SNP was tested in end-point read assays on more than 7500 DNA samples obtained from inbred and commercially used egg-layer lines that carry known and novel MHC-B haplotypes. One hundred and one SNPs were selected for the panel. Additional breeds and experimentally-derived lines, including lines that carry MHC-B recombinant haplotypes, were then genotyped.ResultsMHC-B haplotypes based on SNP genotyping were consistent with the MHC-B haplotypes that were assigned previously in experimental lines that carry B2, B5, B12, B13, B15, B19, B21, and B24 haplotypes. SNP genotyping resulted in the identification of 122 MHC-B haplotypes including a number of recombinant haplotypes, which indicate that crossing-over events at multiple locations within the region lead to the production of new MHC-B haplotypes. Furthermore, evidence of gene duplication and deletion was found.ConclusionsThe chicken MHC-B region is highly polymorphic across the surveyed 209-kb region that contains 45 genes. Our results expand the number of identified haplotypes and provide insights into the contribution of recombination events to MHC-B diversity including the identification of recombination hotspots and an estimation of recombination frequency.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0181-x) contains supplementary material, which is available to authorized users.

Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection

Availability of high-density single nucleotide polymorphism (SNP) genotyping platforms provided unprecedented opportunities to enhance breeding programmes in livestock, poultry and plant species, and to better understand the genetic basis of complex traits. Using this genomic information, genomic breeding values (GEBVs), which are more accurate than conventional breeding values. The superiority of genomic selection is possible only when high-density SNP panels are used to track genes and QTLs affecting the trait. Unfortunately, even with the continuous decrease in genotyping costs, only a small fraction of the population has been genotyped with these high-density panels. It is often the case that a larger portion of the population is genotyped with low-density and low-cost SNP panels and then imputed to a higher density. Accuracy of SNP genotype imputation tends to be high when minimum requirements are met. Nevertheless, a certain rate of genotype imputation errors is unavoidable. Thus, it is reasonable to assume that the accuracy of GEBVs will be affected by imputation errors; especially, their cumulative effects over time. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation and the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between the reference and testing sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by about 0.5% per generation. In fact, after 25 generations, the accuracy was only 7% lower than the first generation. When the reference population was updated by either 1% or 5% of the top animals in the previous generations, decay of imputation accuracy was substantially reduced. These results indicate that low-density panels are useful, especially when the generational interval between reference and testing population is small. As the generational interval increases, the imputation accuracies decay, although not at an alarming rate. In absence of updating of the reference population, accuracy of GEBVs decays substantially in one or two generations at the rate of 20% to 25% per generation. When the reference population is updated by 1% or 5% every generation, the decay in accuracy was 8% to 11% after seven generations using true and imputed genotypes. These results indicate that imputed genotypes provide a viable alternative, even after several generations, as long the reference and training populations are appropriately updated to reflect the genetic change in the population.

Recombination locations and rates in beef cattle assessed from parent-offspring pairs.

BackgroundRecombination events tend to occur in hotspots and vary in number among individuals. The presence of recombination influences the accuracy of haplotype phasing and the imputation of missing genotypes. Genes that influence genome-wide recombination rate have been discovered in mammals, yeast, and plants. Our aim was to investigate the influence of recombination on haplotype phasing, locate recombination hotspots, scan the genome for Quantitative Trait Loci (QTL) and identify candidate genes that influence recombination, and quantify the impact of recombination on the accuracy of genotype imputation in beef cattle.Methods2775 Angus and 1485 Limousin parent-verified sire/offspring pairs were genotyped with the Illumina BovineSNP50 chip. Haplotype phasing was performed with DAGPHASE and BEAGLE using UMD3.1 assembly SNP (single nucleotide polymorphism) coordinates. Recombination events were detected by comparing the two reconstructed chromosomal haplotypes inherited by each offspring with those of their sires. Expected crossover probabilities were estimated assuming no interference and a binomial distribution for the frequency of crossovers. The BayesB approach for genome-wide association analysis implemented in the GenSel software was used to identify genomic regions harboring QTL with large effects on recombination. BEAGLE was used to impute Angus genotypes from a 7K subset to the 50K chip.ResultsDAGPHASE was superior to BEAGLE in haplotype phasing, which indicates that linkage information from relatives can improve its accuracy. The estimated genetic length of the 29 bovine autosomes was 3097 cM, with a genome-wide recombination distance averaging 1.23 cM/Mb. 427 and 348 windows containing recombination hotspots were detected in Angus and Limousin, respectively, of which 166 were in common. Several significant SNPs and candidate genes, which influence genome-wide recombination were localized in QTL regions detected in the two breeds. High-recombination rates hinder the accuracy of haplotype phasing and genotype imputation.ConclusionsSmall population sizes, inadequate half-sib family sizes, recombination, gene conversion, genotyping errors, and map errors reduce the accuracy of haplotype phasing and genotype imputation. Candidate regions associated with recombination were identified in both breeds. Recombination analysis may improve the accuracy of haplotype phasing and genotype imputation from low- to high-density SNP panels.

Fine mapping of a quantitative trait locus for bovine milk fat composition on Bos taurus autosome 19

A major quantitative trait locus (QTL) for milk fat content and fatty acids in both milk and adipose tissue has been detected on Bos taurus autosome 19 (BTA19) in several cattle breeds. The objective of this study was to refine the location of the QTL on BTA19 for bovine milk fat composition using a denser set of markers. Opportunities for fine mapping were provided by imputation from 50,000 genotyped single nucleotide polymorphisms (SNP) toward a high-density SNP panel with up to 777,000 SNP. The QTL region was narrowed down to a linkage disequilibrium block formed by 22 SNP covering 85,007 bp, from 51,303,322 to 51,388,329 bp on BTA19. This linkage disequilibrium block contained 2 genes: coiled-coil domain containing 57 (CCDC57) and fatty acid synthase (FASN). The gene CCDC57 is minimally characterized and has not been associated with bovine milk fat previously, but is expressed in the mammary gland. The gene FASN has been associated with bovine milk fat and fat in adipose tissue before. This gene is a likely candidate for the QTL on BTA19 because of its involvement in de novo fat synthesis. Future studies using sequence data of both CCDC57 and FASN, and eventually functional studies, will have to be pursued to assign the causal variant(s).