Genomic network analysis of environmental and livestock F-type plasmid populations.
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ABSTRACT:
SUBMITTER: Matlock W
PROVIDER: S-EPMC8319146 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Although common datasets are an important resource for the scientific community and can be used to address important questions, genomic datasets of a meaningful size have not generally been available in livestock species. We describe a pig dataset that PIC (a Genus company) has made available for comparing genomic prediction methods. We also describe genomic evaluation of the data using methods that PIC considers best practice for predicting and validating genomic breeding values, and we discuss the impact of data structure on accuracy. The dataset contains 3534 individuals with high-density genotypes, phenotypes, and estimated breeding values for five traits. Genomic breeding values were calculated using BayesB, with phenotypes and de-regressed breeding values, and using a single-step genomic BLUP approach that combines information from genotyped and un-genotyped animals. The genomic breeding value accuracy increased with increased trait heritability and with increased relationship between training and validation. In nearly all cases, BayesB using de-regressed breeding values outperformed the other approaches, but the single-step evaluation performed only slightly worse. This dataset was useful for comparing methods for genomic prediction using real data. Our results indicate that validation approaches accounting for relatedness between populations can correct for potential overestimation of genomic breeding value accuracies, with implications for genotyping strategies to carry out genomic selection programs.
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Project description:BackgroundThis paper describes a combined heuristic and hidden Markov model (HMM) method to accurately impute missing genotypes in livestock datasets. Genomic selection in breeding programs requires high-density genotyping of many individuals, making algorithms that economically generate this information crucial. There are two common classes of imputation methods, heuristic methods and probabilistic methods, the latter being largely based on hidden Markov models. Heuristic methods are robust, but fail to impute markers in regions where the thresholds of heuristic rules are not met, or the pedigree is inconsistent. Hidden Markov models are probabilistic methods which typically do not require specific family structures or pedigree information, making them very flexible, but they are computationally expensive and, in some cases, less accurate.ResultsWe implemented a new hybrid imputation method that combined heuristic and HMM methods, AlphaImpute and MaCH, and compared the computation time and imputation accuracy of the three methods. AlphaImpute was the fastest, followed by the hybrid method and then the HMM. The computation time of the hybrid method and the HMM increased linearly with the number of iterations used in the hidden Markov model, however, the computation time of the hybrid method increased almost linearly and that of the HMM quadratically with the number of template haplotypes. The hybrid method was the most accurate imputation method for low-density panels when pedigree information was missing, especially if minor allele frequency was also low. The accuracy of the hybrid method and the HMM increased with the number of template haplotypes. The imputation accuracy of all three methods increased with the marker density of the low-density panels. Excluding the pedigree information reduced imputation accuracy for the hybrid method and AlphaImpute. Finally, the imputation accuracy of the three methods decreased with decreasing minor allele frequency.ConclusionsThe hybrid heuristic and probabilistic imputation method is able to impute all markers for all individuals in a population, as the HMM. The hybrid method is usually more accurate and never significantly less accurate than a purely heuristic method or a purely probabilistic method and is faster than a standard probabilistic method.
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Project description:Background:Genotyping by sequencing (GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes, dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations. Results:Chip array (Chip) and four depths of GBS data was simulated. After quality control (call rate???0.8 and MAF???0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS (GBSc), true genotypes for the GBS loci (GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth?=?2, 4, 5 and 10, respectively. Conclusions:The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths.
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Project description:Since its emergence in the beginning of the 90's, multidrug-resistant (MDR) Salmonella enterica subsp. enterica serovar Kentucky has become a significant public health problem, especially in East Africa. This study aimed to investigate the antimicrobial resistance profile and the genotypic relatedness of Salmonella Kentucky isolated from animal sources in Ethiopia and Kenya (n=19). We also investigated population evolutionary dynamics through phylogenetic and pangenome analyses with additional publicly available Salmonella Kentucky ST198 genomes (n=229). All the 19 sequenced Salmonella Kentucky isolates were identified as ST198. Among these isolates, the predominant genotypic antimicrobial resistance profile observed in ten (59.7%) isolates included the aac(3)-Id, aadA7, strA-strB, bla TEM-1B, sul1, and tet(A) genes, which mediated resistance to gentamicin, streptomycin/spectinomycin, streptomycin, ampicillin, sulfamethoxazole and tetracycline, respectively; and gyrA and parC mutations associated to ciprofloxacin resistance. Four isolates harbored plasmid types Incl1 and/or Col8282; two of them carried both plasmids. Salmonella Pathogenicity islands (SPI-1 to SPI-5) were highly conserved in the 19 sequenced Salmonella Kentucky isolates. Moreover, at least one Pathogenicity Island (SPI 1-4, SPI 9 or C63PI) was identified among the 229 public Salmonella Kentucky genomes. The phylogenetic analysis revealed that almost all Salmonella Kentucky ST198 isolates (17/19) stemmed from a single strain that has accumulated ciprofloxacin resistance-mediating mutations. A total of 8,104 different genes were identified in a heterogenic and still open Salmonella Kentucky ST198 pangenome. Considering the virulence factors and antimicrobial resistance genes detected in Salmonella Kentucky, the implications of this pathogen to public health and the epidemiological drivers for its dissemination must be investigated.
| S-EPMC9251186 | biostudies-literature
Project description:It is difficult to understand plasmid maintenance in the absence of selection and theoretical models predict the conditions for plasmid persistence to be limited. Plasmid-associated fitness costs decrease bacterial competitivity, while imperfect partition allows the emergence of plasmid-free cells during cell division. Although plasmid conjugative transfer allows mobility into plasmid-free cells, the rate of such events is generally not high enough to ensure plasmid persistence. Experimental data suggest several factors that may expand the conditions favorable for plasmid maintenance, such as compensatory mutations and accessory genes that allow positive selection. Most of the previous studies focus on bacteria that carry a single plasmid. However, there is increasing evidence that multiple plasmids inhabit the same bacterial population and that interactions between them affect their transmission and persistence. Here, we adapt previous mathematical models to include multiple plasmids and perform computer simulations to study how interactions among them affect plasmid maintenance. We tested the contribution of different plasmid interaction parameters that impact three biological features: host fitness, conjugative transfer and plasmid loss - which affect plasmid persistence. The interaction affecting conjugation was studied in the contexts of intracellular and intercellular interactions, i.e., the plasmids interact when present in the same cell or when in different cells, respectively. First, we tested the effect of each type of interaction alone and concluded that only interactions affecting fitness (epistasis) prevented plasmid extinction. Although not allowing plasmid maintenance, intracellular interactions increasing conjugative efficiencies had a more determinant impact in delaying extinction than the remaining parameters. Then, we allowed multiple interactions between plasmids and concluded that, in a few cases, a combined effect of (intracellular) interactions increasing conjugation and fitness lead to plasmid maintenance. Our results show a hierarchy among these interaction parameters. Those affecting fitness favor plasmid persistence more than those affecting conjugative transfer and lastly plasmid loss. These results suggest that interactions between different plasmids can favor their persistence in bacterial communities.
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Project description:Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness. Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (R o 1.287), hence regarded as "slow" pathogen, and evaluate the effects of disease interventions. Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network. Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public-private partnerships, would serve to limit its spread.
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Project description:Clostridium perfringens, one of the most common causes of food poisonings, can carry the enterotoxin gene, cpe, in its chromosome or on a plasmid. C. perfringens food poisonings are more frequently caused by the chromosomal cpe-carrying strains, while the plasmid-borne cpe-positive genotypes are more commonly found in the human feces and environmental samples. Different tolerance to food processing conditions by the plasmid-borne and chromosomal cpe-carrying strains has been reported, but the reservoirs and contamination routes of enterotoxin-producing C. perfringens remain unknown. A comparative genomic hybridization (CGH) analysis with a DNA microarray based on three C. perfringens type A genomes was conducted to shed light on the epidemiology of C. perfringens food poisonings caused by plasmid-borne and chromosomal cpe-carrying strains by comparing chromosomal and plasmid-borne cpe-positive and cpe-negative C. perfringens isolates from human, animal, environmental, and food samples. The chromosomal and plasmid-borne cpe-positive C. perfringens genotypes formed two distinct clusters. Variable genes were involved with myo-inositol, ethanolamine and cellobiose metabolism, suggesting a new epidemiological model for C. perfringens food poisonings. The CGH results were complemented with growth studies, which demonstrated different myo-inositol, ethanolamine, and cellobiose metabolism between the chromosomal and plasmid-borne cpe-carrying strains. These findings support a ubiquitous occurrence of the plasmid-borne cpe-positive strains and their adaptation to the mammalian intestine, whereas the chromosomal cpe-positive strains appear to have a narrow niche in environments containing degrading plant material. Thus the epidemiology of the food poisonings caused by two populations appears different, the plasmid-borne cpe-positive strains probably contaminating foods via humans and the chromosomal strains being connected to plant material.
| S-EPMC3477167 | biostudies-literature
Project description:Livestock breeding has shifted during the past decade toward genomic selection. For the estimation of breeding values in purebred breeding schemes, genomic best linear unbiased prediction has become the method of choice. Systematic crossbreeding with the aim to utilize heterosis and breed complementary effects is widely used in livestock breeding, especially in pig and poultry breeding. The goal is to improve the performance of the crossbred animals. Due to genotype-by-environment interactions, imperfect linkage disequilibrium, and the existence of dominance and imprinting, purebred and crossbred performances are not perfectly correlated. Hence, more complex genomic models are required for crossbred populations. This study reviews and compares such models. Compared to purebred genomic models, the reviewed models were of much higher complexity due to the inclusion of dominance effects, breed-specific effects, imprinting effects, and the joint evaluation of purebred and crossbred performance data. With the model assessment work conducted until now, it is not possible to come to a clear recommendation as to which existing method is most suitable for a specific breeding program and a specific genetic trait architecture. Since it is expected that a superior method includes all the different genetic effects in a single model, a dominance model with imprinting and breed-specific SNP effects is proposed. Further progress could be made by assuming realistic covariance structures between the genetic effects of the different breeding lines, and by using larger marker panels and mixture distributions for the SNP effects.
| S-EPMC7332767 | biostudies-literature
Project description:BackgroundHeritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.ResultsHere, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).ConclusionsHeritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.
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