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141 results for “population genetics analysis”
Data from: Genome-wide association analysis for blood lipid traits measured in three pig populations revealed a substantial level of genetic heterogeneity
Serum lipids are associated with myocardial infarction and cardiovascular disease in humans. Here we dissected the genetic architecture of blood lipid traits by applying genome-wide association studies (GWAS) in 1,256 pigs from Laiwu, Erhualian and Duroc × (Landrace × Yorkshire) populations, and a meta-analysis of GWAS in more than 2,400 pigs from five diverse populations. A total of 22 genomic loci surpassing the suggestive significance level were detected on 11 pig chromosomes (SSC) for six blood lipid traits. Meta-analysis of GWAS identified 5 novel loci associated with blood lipid traits. Comparison of GWAS loci across the tested populations revealed a substantial level of genetic heterogeneity for porcine blood lipid levels. We further evaluated the causality of nine polymorphisms nearby or within the APOB gene on SSC3 for serum LDL-C and TC levels. Of the 9 polymorphisms, an indel showed the most significant association with LDL-C and TC in Laiwu pigs. But the significant association was not identified in the White Duroc × Erhualian F2 resource population, in which the QTL for LDL-C and TC was also detected on SSC3. This indicates that population-specific signals may exist for the SSC3 QTL. Further investigations are warranted to validate this assumption.
Data from: Comparing G: multivariate analysis of genetic variation in multiple populations
The additive genetic variance–covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design.
Data from: Microsatellite analysis of genetic diversity and population structure of Arabian horse populations
The Arabian horse ignites imagination throughout the world. Populations of this breed exist in many countries, and recent genetic work has examined the diversity and ancestry of a few of these populations in isolation. Here we explore seven different populations of Arabians represented by 682 horses. Three of these are Middle Eastern populations from near the historical origin of the breed, including Syrian, Persian, and Saudi Arabian. The remaining Western populations are found in Europe (the Shagya-Arabian and Polish-Arabian) and America (American-Arabian). Analysis of genetic structure was carried out using 15 microsatellite loci. Genetic distances, AMOVA, factorial correspondence analysis and a Bayesian method were applied. The results consistently show higher level of diversity within the Middle Eastern populations than the Western populations. The Western Arabian populations were the main source of among populations variation. Genetic differentiation was not strong among all Middle Eastern populations, but all American-Arabians showed differentiation from Middle Eastern populations and were somewhat uniform among themselves. Here, we explore the diversities of many different populations of Arabian horses, and find that populations not from the Middle East have noticeably lower levels of diversity, which may adversely affect the health of these populations.
Data from: The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis
The inference of population genetic structures is essential in many research areas in population genetics, conservation biology and evolutionary biology. Recently, unsupervised Bayesian clustering algorithms have been developed to detect a hidden population structure from genotypic data, assuming among others that individuals taken from the population are unrelated. Because of this hypothesis, markers in a sample taken from a subpopulation can be considered to be in Hardy-Weinberg and linkage equilibrium. However, close relatives might be sampled from the same subpopulation, and consequently, might cause Hardy-Weinberg and linkage disequilibrium and thus bias a population genetic structure analysis. In this study, we used simulated and real data to investigate the impact of close relatives in a sample on Bayesian population structure analysis. We also showed that, when close relatives were identified by a pedigree reconstruction approach and removed, the accuracy of a population genetic structure analysis can be greatly improved. The results indicate that unsupervised Bayesian clustering algorithms cannot be used blindly to detect genetic structure in a sample with closely related individuals. Rather, when closely related individuals are suspected to be frequent in a sample, these individuals should be first identified and removed before conducting a population structure analysis.
Data from: Population genomic analysis suggests strong influence of river network on spatial distribution of genetic variation in invasive saltcedar across the southwestern US
Understanding the complex influences of landscape and anthropogenic elements that shape the population genetic structure of invasive species provides insight into patterns of colonization and spread. The application of landscape genomics techniques to these questions may offer detailed, previously undocumented insights into factors influencing species invasions. We investigated the spatial pattern of genetic variation and the influences of landscape factors on population similarity in the invasive riparian shrub saltcedar (Tamarix L.) by analyzing 1,997 genome-wide SNP markers for 259 individuals from 25 populations collected throughout the southwestern US. Our results revealed a broad-scale spatial genetic differentiation of saltcedar populations between the Colorado and Rio Grande river basins and identified potential barriers to population similarity along both river systems. River pathways most strongly contributed to population similarity. In contrast, low temperature and dams likely served as barriers to population similarity. We hypothesize that large-scale geographic patterns in genetic diversity resulted from a combination of early introductions from distinct populations, the subsequent influence of natural selection, dispersal barriers, and founder effects during range expansion.
Data from: Challenges in analysis and interpretation of microsatellite data for population genetic studies
Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (FST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.
FIGURES 1-2. 1 in Taxonomic diagnosis of Dicyrtomina ornata and D. saundersi (Collembola: Dicyrtomidae) and analysis of their population genetic structure
FIGURES 1-2. 1, Dicyrtomina ornata, habitus; 2, Dicyrtomina saundersi, habitus.
STR data from: Temporal analysis shows relaxed genetic erosion following improved stocking practices in a subarctic transnational brown trout population
<p><span>Maintaining standing genetic variation is a challenge in human-dominated landscapes. We used genetic (i.e., 16 short tandem repeats) and morphological (i.e., length and weight) measurements of 593 contemporary and historical brown trout (<i>Salmo trutta</i>) samples to study fine-scale and short-term impacts of different management practices. These had changed from traditional breeding practices, using the same broodstock for several years, to modern breeding practices, including annual broodstock replacement, in the transnational subarctic Pasvik River. Using population genetic structure analyses (i.e., Bayesian assignment tests, DAPCs, and PCAs), four historical genetic clusters (E2001A-D), likely representing family lineages resulting from different crosses, were found in zone E. These groups were characterized by consistently lower genetic diversity, higher within-group relatedness, lower effective population size, and significantly smaller body size than contemporary stocked (E2001E) and wild fish (E2001F). However, even current breeding practices are insufficient to prevent genetic diversity loss and morphological changes as demonstrated by on average smaller body sizes and recent genetic bottleneck signatures in the modern breeding stock compared to wild fish. Conservation management must evaluate breeding protocols for stocking programs and assess if these can preserve remaining natural genetic diversity and morphology in brown trout for long-term preservation of freshwater fauna. </span></p>
Data from: A longitudinal analysis of the growth rate and mass of tail feathers in a great tit population: ontogeny, genetic effects and relationship between traits
<p class="MsoNoSpacing">Feathers have a diversity of functions in birds and are costly to produce, so their growth rate and mass can be reliable indicators of nutritional condition at the time of production. Despite the potential for feather metrics to advance our understanding of foraging, they are underused in avian ecology. One reason for this is the difficulty of interpreting whether individual variation is driven by ontogenetic, genetic, or environmental effects, which is exacerbated by the fact that most analyses have been done on cross-sectional data. We addressed this deficit using a longitudinal dataset of tail feathers collected from Great tits <em>Parus major</em> to test for ontogenetic and genetic effects on growth rate, mass and length, while controlling for body/feather size differences and other confounding factors. First, we found that the type of moult episode and experimentally-induced replacement differentially affected the length, mass and growth of feathers, providing evidence of an ontogenetic effect that should be considered when comparing these feather traits across individuals as a measure of condition. Second, we detected moderate to high repeatability and heritability values from parent-offspring regression for these three feather traits, which are suggestive of an underlying genetic component of variation. Third, we used a mean centring within-individual approach to test whether feather growth rate and feather mass (length-corrected) are indeed positively correlated with each other as overlapping indicators of body condition in birds, and found that this association, although positive, is weak and only significant between individuals. This suggests that both metrics are not so intimately linked as originally thought, and probably have different sensitivities to variation in foraging performance and ecological conditions. Together with the higher plasticity of feather growth rate compared to feather mass, our results support the idea that feather growth rate is better suited for examining short-term responses to environmental variation.</p>
Fig. 1 in Genetic diversity and population structure of endangered Neofinetia falcata (Orchidaceae) in South Korea based on microsatellite analysis
Fig. 1. Geographic distribution of N. falcata populations. Abbreviations are shown in Table 1. Pie charts represent assignment probability of belonging to each K = 2 clusters identified by STRUCTURE based on microsatellite allele frequencies, with probability values normalized using CLUMPP.
Data from: Population genetic analysis of a parasitic mycovirus to infer the invasion history of its fungal host
Hymenoscyphus fraxineus mitovirus 1 (HfMV1) occurs in the fungus Hymenoscyphus fraxineus, an introduced plant pathogen responsible for the devastating ash dieback epidemic in Europe. Here, we explored the prevalence and genetic structure of HfMV1 in order to elucidate the invasion history of both the virus and the fungal host. A total of 1298 H. fraxineus isolates (181 from Japan and 1117 from Europe) were screened for the presence of this RNA virus and 301 virus-positive isolates subjected to partial sequence analysis of the viral RNA polymerase gene. Our results indicate a high mean prevalence (78.7%) of HfMV1 across European H. fraxineus isolates, which is supported by the observed high transmission rate (average 83.8%) of the mitovirus into sexual spores of its host. In accordance with an expected founder effect in the introduced population in Europe, only 1.1% of the Japanese isolates were tested virus positive. In Europe, HfMV1 shows low nucleotide diversity but a high number of haplotypes, which seem to be subjected to strong purifying selection. Phylogenetic and clustering analysis detected two genetically distinct HfMV1 groups, both present throughout Europe. This pattern supports the hypothesis that only two (mitovirus-carrying) H. fraxineus individuals were introduced into Europe as previously suggested from the bi-allelic nature of the fungus. Moreover, our data points to reciprocal mating events between the two introduced individuals, which presumably initiated the ash dieback epidemic in Europe.
Figure 2 in Genetic diversity of Atherina hepsetus (Osteichthyes: Atherinidae) populations as determined by RFLP analysis of three mtDNA regions
Figure 2. Neighbor-joining (Saitou and Nei 1987) cladogram, based on the net nucleotide divergence.
The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical datasets
<p class="CxSpFirst">The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE output, the Δ<i>K</i> method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of <i>K</i> and is sensitive to uneven sampling. If this finding holds for empirical datasets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described estimators of <i>K</i> to re-estimate genetic structure in 41 empirical microsatellite datasets; 15 from a broad range of taxa and 26 focused on a diverse phylogenetic group, coral. We compared alternative estimates of <i>K</i> (Puechmaille statistics) with traditional (Δ<i>K</i> and posterior probability) estimates and found widespread disagreement of estimators across datasets. Thus, one estimator alone is insufficient for determining the optimal number of clusters regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) between clustering solutions did not necessarily clarify which solution was best. To better infer population structure, we suggest a combination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to Δ<i>K</i>. Differences between estimators could reveal patterns with important biological implications, such as the potential for more population structure than previously estimated, as was the case for many studies reanalyzed here.</p>
Figure 3 from: Bu Y, Ma Y, Luan Y-X (2016) Paracerella Imadaté in China: the description of a new species and the analysis of genetic differences between populations (Protura, Acerentomata, Nipponentomidae). ZooKeys 604: 1-11. https://doi.org/10.3897/zookeys.604.8737
Figure 3 - Paracerella sinensis sp. n. holotype. A Tergite VII (psm= posterosubmedial) B tergite VIII C sternite I D sternite II E sternite IV F sternite VI (spm= sternal posteromedial) G sternite VII H striate band of abdominal VIII I Tergite VIII–XII J sternites VIII–XII. Arrows indicate pores. Scale bars: 20 μm.
Figure 2 from: Bu Y, Ma Y, Luan Y-X (2016) Paracerella Imadaté in China: the description of a new species and the analysis of genetic differences between populations (Protura, Acerentomata, Nipponentomidae). ZooKeys 604: 1-11. https://doi.org/10.3897/zookeys.604.8737
Figure 2 - Paracerella sinensis sp. n. holotype. A Habitus B ventral side of head (s=sensillum) C pronotum D mesonotum E metanotum F prosternum G mesosternum H metasternum I tergite I, right side J tergite III, right side. Arrows indicate pores. Scale bars: (A)100 μm, others, 20 μm.
Figure 1 from: Bu Y, Ma Y, Luan Y-X (2016) Paracerella Imadaté in China: the description of a new species and the analysis of genetic differences between populations (Protura, Acerentomata, Nipponentomidae). ZooKeys 604: 1-11. https://doi.org/10.3897/zookeys.604.8737
Figure 1 - Paracerella sinensis sp. n. holotype. A Head, dorsal view (cp = clypeal pore, fp = frontal pore) B pseudoculus C canal of maxillary gland D labial palpus E maxillary palpus (d = dorsal sensillum, v = ventral sensillum) F foretarsus, exterior view G foretarsus, interior view H foretarsus, interolateral view (paratype No. LM6-14D) I comb J female quama genitalis. Scale bars: (A, F–H) 50 μm; others, 20 μm.
Sheepnose mussel (P. cyphyus) microsatellite dataset for population genetic analysis
<p class="Body">North American freshwater mussel species have experienced substantial range fragmentation and population reductions. These impacts have the potential to reduce genetic connectivity among populations and increase the risk of losing genetic diversity. Thirteen microsatellite loci and an 883 bp fragment of the mitochondrial ND1 gene were used to assess genetic diversity, population structure, contemporary migration rates, and population size changes across the range of the Sheepnose mussel (<em>Plethobasus cyphyus</em>). Population structure analyses reveal five populations, three in the Upper Mississippi River Basin and two in the Ohio River Basin. Sampling locations exhibit a high degree of genetic diversity and contemporary migration estimates indicate that migration between populations within river basins is occurring, although at low rates. but no migration is occurring between the Ohio and Mississippi river basins. No evidence of bottlenecks was detected, and almost all locations exhibited the signature of population expansion. Our results indicate that although anthropogenic activity has altered the landscape across the range of the Sheepnose, these activities have yet to be reflected in losses of genetic diversity. Efforts to conserve Sheepnose populations should focus on maintaining existing habitats and fostering genetic connectivity between extant demes to conserve remaining genetic diversity for future viable Sheepnose populations.</p>
Data from: A longitudinal analysis of the growth rate and mass of tail feathers in a great tit population: ontogeny, genetic effects and relationship between traits
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Data from: Genome-wide association analysis for blood lipid traits measured in three pig populations revealed a substantial level of genetic heterogeneity
Open the record for dataset details and reuse information.
Data from: The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis
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