A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study
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ABSTRACT:
SUBMITTER: Araujo A
PROVIDER: S-EPMC8551834 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:We report a study of genome-wide, dense SNP (? 900K) and copy number polymorphism data of indigenous southern Africans. We demonstrate the genetic contribution to southern and eastern African populations, which involved admixture between indigenous San, Niger-Congo-speaking and populations of Eurasian ancestry. This finding illustrates the need to account for stratification in genome-wide association studies, and that admixture mapping would likely be a successful approach in these populations. We developed a strategy to detect the signature of selection prior to and following putative admixture events. Several genomic regions show an unusual excess of Niger-Kordofanian, and unusual deficiency of both San and Eurasian ancestry, which were considered the footprints of selection after population admixture. Several SNPs with strong allele frequency differences were observed predominantly between the admixed indigenous southern African populations, and their ancestral Eurasian populations. Interestingly, many candidate genes, which were identified within the genomic regions showing signals for selection, were associated with southern African-specific high-risk, mostly communicable diseases, such as malaria, influenza, tuberculosis, and human immunodeficiency virus/AIDs. This observation suggests a potentially important role that these genes might have played in adapting to the environment. Additionally, our analyses of haplotype structure, linkage disequilibrium, recombination, copy number variation and genome-wide admixture highlight, and support the unique position of San relative to both African and non-African populations. This study contributes to a better understanding of population ancestry and selection in south-eastern African populations; and the data and results obtained will support research into the genetic contributions to infectious as well as non-communicable diseases in the region.
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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:Genetic variability defends us against pathogen-driven antigens; human leucocyte antigens (HLA) is the immunological system in charge of this work. The Mexican mestizo population arises mainly from the mixture of three founder populations; Amerindian, Spaniards, and a smaller proportion of the African population. We describe allele and haplotype frequencies of HLA class I (-A and -B) and class II (-DRB1 and -DQB1), which were analyzed by PCR-SSP in Mexican mestizo from three urban populations of Mexico: Chihuahua-Chihuahua City (n = 88), Mexico City-Tlalpan (n = 330), and Veracruz-Xalapa (n = 84). The variability of the allele HLA class I and class II among the three regions of Mexico are in four alleles: HLA-A*24:02 (36.39%), -B*35:01 (16.04%), -DRB1*04:07 (17.33%), and -DQB1*03:02 (31.47%), these alleles have been previously described in some indigenous populations. We identified 5 haplotypes with a frequency >1%: HLA-A*02:01-B*35:01-DRB1*08:02-DQB1*04:02, A*68:01-B*39:01-DRB1*08:02-DQB1*04:02, A*02:01-B*35:01-DRB1*04:07-DQB1*03:02, A*68:01-B*39:01-DRB1*04:07-DQB1*03:02, and A*01:01-B*08:01-DRB1*03:01-DQB1*02:01. Also, the haplotype A*02:01-B*35:01-DRB1*08:02-DQB1*04:02 was identified in Tlalpan and Xalapa regions. Haplotype A*01:01-B*08:01-DRB1*03:01-DQB1*02:01 was found only in Tlalpan and Chihuahua. In the Xalapa region, the most frequent haplotype was A*24:02-B*35:01-DRB1*04:07-DQB1*03:02. These alleles and haplotypes have been described in Amerindian populations. Our data are consistent with previous studies and contribute to the analysis of the variability in the Mexican population.
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Project description:F-type plasmids are diverse and of great clinical significance, often carrying genes conferring antimicrobial resistance (AMR) such as extended-spectrum β-lactamases, particularly in Enterobacterales. Organising this plasmid diversity is challenging, and current knowledge is largely based on plasmids from clinical settings. Here, we present a network community analysis of a large survey of F-type plasmids from environmental (influent, effluent and upstream/downstream waterways surrounding wastewater treatment works) and livestock settings. We use a tractable and scalable methodology to examine the relationship between plasmid metadata and network communities. This reveals how niche (sampling compartment and host genera) partition and shape plasmid diversity. We also perform pangenome-style analyses on network communities. We show that such communities define unique combinations of core genes, with limited overlap. Building plasmid phylogenies based on alignments of these core genes, we demonstrate that plasmid accessory function is closely linked to core gene content. Taken together, our results suggest that stable F-type plasmid backbone structures can persist in environmental settings while allowing dramatic variation in accessory gene content that may be linked to niche adaptation. The association of F-type plasmids with AMR may reflect their suitability for rapid niche adaptation.
| S-EPMC8319146 | biostudies-literature
Project description:Malaria is one of the strongest selective pressures in recent human evolution. African populations have been and continue to be at risk for malarial infections. However, few studies have re-sequenced malaria susceptibility loci across geographically and genetically diverse groups in Africa. We examined nucleotide diversity at Intercellular adhesion molecule-1 (ICAM-1), a malaria susceptibility candidate locus, in a number of human populations with a specific focus on diverse African ethnic groups. We used tests of neutrality to assess whether natural selection has impacted this locus and tested whether SNP variation at ICAM-1 is correlated with malaria endemicity. We observe differing patterns of nucleotide and haplotype variation in global populations and higher levels of diversity in Africa. Although we do not observe a deviation from neutrality based on the allele frequency distribution, we do observe several alleles at ICAM-1, including the ICAM-1 (Kilifi) allele, that are correlated with malaria endemicity. We show that the ICAM-1 (Kilifi) allele, which is common in Africa and Asia, exists on distinct haplotype backgrounds and is likely to have arisen more recently in Asia. Our results suggest that correlation analyses of allele frequencies and malaria endemicity may be useful for identifying candidate functional variants that play a role in malaria resistance and susceptibility.
| S-EPMC3916218 | biostudies-literature
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:Accurate identification of somatic mutations and allele frequencies in cancer has critical research and clinical applications. Several computational tools have been developed for this purpose but, in the absence of comprehensive 'ground truth' data, assessing the accuracy of these methods is challenging. We created a computational framework to simulate tumour and matched normal sequencing data for which the source of all loci that contain non-reference bases is known, based on a phased, personalized genome. Unlike existing methods, we account for sampling errors inherent in the sequencing process. Using this framework, we assess accuracy and biases in inferred mutations and their frequencies in an established somatic mutation calling pipeline. We demonstrate bias in existing methods of mutant allele frequency estimation and show, for the first time, the observed mutation frequency spectrum corresponding to a theoretical model of tumour evolution. We highlight the impact of quality filters on detection sensitivity of clinically actionable variants and provide definitive assessment of false positive and false negative mutation calls. Our simulation framework provides an improved means to assess the accuracy of somatic mutation calling pipelines and a detailed picture of the effects of technical parameters and experimental factors on somatic mutation calling in cancer samples.
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Project description:BackgroundSkeeter Buster is a stochastic, spatially explicit simulation model of Aedes aegypti populations, designed to predict the outcome of vector population control methods. In this study, we apply the model to two specific locations, the cities of Iquitos, Peru, and Buenos Aires, Argentina. These two sites differ in the amount of field data that is available for location-specific customization. By comparing output from Skeeter Buster to field observations in these two cases we evaluate population dynamics predictions by Skeeter Buster with varying degrees of customization.Methodology/principal findingsSkeeter Buster was customized to the Iquitos location by simulating the layout of houses and the associated distribution of water-holding containers, based on extensive surveys of Ae. aegypti populations and larval habitats that have been conducted in Iquitos for over 10 years. The model is calibrated by adjusting the food input into various types of containers to match their observed pupal productivity in the field. We contrast the output of this customized model to the data collected from the natural population, comparing pupal numbers and spatial distribution of pupae in the population. Our results show that Skeeter Buster replicates specific population dynamics and spatial structure of Ae. aegypti in Iquitos. We then show how Skeeter Buster can be customized for Buenos Aires, where we only had Ae. aegypti abundance data that was averaged across all locations. In the Argentina case Skeeter Buster provides a satisfactory simulation of temporal population dynamics across seasons.ConclusionsThis model can provide a faithful description of Ae. aegypti populations, through a process of location-specific customization that is contingent on the amount of data available from field collections. We discuss limitations presented by some specific components of the model such as the description of food dynamics and challenges that these limitations bring to model evaluation.
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Project description:Theory predicts that genetic erosion in small, isolated populations of endangered species can be assessed using estimates of neutral genetic variation, yet this widely used approach has recently been questioned in the genomics era. Here, we leverage a chromosome-level genome assembly of an endangered rattlesnake (Sistrurus catenatus) combined with whole genome resequencing data (N = 110 individuals) to evaluate the relationship between levels of genome-wide neutral and functional diversity over historical and future timescales. As predicted, we found positive correlations between genome-wide estimates of neutral genetic diversity (π) and inferred levels of adaptive variation and an estimate of inbreeding mutation load, and a negative relationship between neutral diversity and an estimate of drift mutation load. However, these correlations were half as strong for projected future levels of neutral diversity based on contemporary effective population sizes. Broadly, our results confirm that estimates of neutral genetic diversity provide an accurate measure of genetic erosion in populations of a threatened vertebrate. They also provide nuance to the neutral-functional diversity controversy by suggesting that while these correlations exist, anthropogenetic impacts may have weakened these associations in the recent past and into the future.
| S-EPMC10614936 | biostudies-literature