Inference Of Genetic Structure Research Articles

Information-based summary statistics for spatial genetic structure inference.

The measurement of biodiversity at all levels of organization is an essential first step to understand the ecological and evolutionary processes that drive spatial patterns of biodiversity. Ecologists have explored the use of a large range of different summary statistics and have come to the view that information-based summary statistics, and in particular so-called Hill numbers, are a useful tool to measure biodiversity. Population geneticists, on the other hand, have focused largely on summary statistics based on heterozygosity and measures of allelic richness. However, recent studies proposed the adoption of information-based summary statistics in population genetics studies. Here, we performed a comprehensive assessment of the power of this family of summary statistics to inform regarding spatial patterns of genetic diversity and we compared it with that of traditional population genetics approaches, namely measures based on allelic richness and heterozygosity. To give an unbiased evaluation, we used three machine learning methods to test the performance of different sets of summary statistics to discriminate between spatial scenarios. We defined three distinct sets, (i) one based on allelic richness measures which included the Jaccard index, (ii) a set based on heterozygosity that included FST and (iii) a set based on Hill numbers derived from Shannon entropy, which included the recently proposed Shannon differentiation, ΔD. The results showed that the last of these performed as well or, under some specific spatial scenarios, even better than the traditional population genetics measures. Interestingly, we found that a rarely or never used genetic differentiation measure based on allelic richness, Jaccard dissimilarity (J), showed the highest discriminatory power to discriminate among spatial scenarios, followed by Shannon differentiation ΔD. We concluded, therefore, that information-based measures as well as Jaccard dissimilarity represent excellent additions to the population genetics toolkit.

Multi-level patterns of genetic structure and isolation by distance in the widespread plant Mimulus guttatus.

An understanding of genetic structure is essential for answering many questions in population genetics. However, complex population dynamics and scale-dependent processes can make it difficult to detect if there are distinct genetic clusters present in natural populations. Inferring discrete population structure is particularly challenging in the presence of continuous genetic variation such as isolation by distance. Here, we use the plant species Mimulus guttatus as a case study for understanding genetic structure at three spatial scales. We use reduced-representation sequencing and marker-based genotyping to understand dispersal dynamics and to characterise genetic structure. Our results provide insight into the spatial scale of genetic structure in a widespread plant species, and demonstrate how dispersal affects spatial genetic variation at the local, regional, and range-wide scale. At a fine-spatial scale, we show dispersal is rampant with little evidence of spatial genetic structure within populations. At a regional-scale, we show continuous differentiation driven by isolation by distance over hundreds of kilometres, with broad geographic genetic clusters that span major barriers to dispersal. Across Western North America, we observe geographic genetic structure and the genetic signature of multiple postglacial recolonisation events, with historical gene flow linking isolated populations. Our genetic analyses show M. guttatus is highly dispersive and maintains large metapopulations with high intrapopulation variation. This high diversity and dispersal confounds the inference of genetic structure, with multi-level sampling and spatially-explicit analyses required to understand population history.

Genetic Diversity in broccoli rabe (Brassica rapa L. subsp. sylvestris (L.) Janch.) from Southern Italy

Broccoli rabe (Brassica rapa L. subsp. sylvestris (L.) Janch.) is a vegetable crop cultivated in southern Italy where it is part of local traditional recipes and heritage. Despite its economic importance, there are no cultivars patented, giving rise to many cultivated landraces that are characterized by wide phenotypic variability and named according to their harvesting time or the size and shape of their primary inflorescence. We report the first study of genetic diversity of 67 landraces of broccoli rabe collected in Apulia region and genotyped by means of 18 very informative SSR markers. A total of 243 alleles were detected in 552 genotypes, with a mean of 13.5 alleles per locus. The molecular variance was partitioned for 64% within accessions and for 36% among accessions, revealing a high level of genetic variation within landraces and a significant genetic differentiation across themselves. Remarkably, the results of genetic structure inference, as well as principal coordinates analysis and hierarchical clustering indicated as the clustering was largely in accordance to the germplasm origin, highlighting the presence of geographic stratification. We established a comprehensive germplasm collection of Apulian broccoli rabe landraces, currently stored at the Genebanks of IBBR-CNR and University of Bari. Finally, our study lays a foundation for future bio-agronomic characterizations addressing the identification of superior accessions for breeding purposes and solving cases of homonymy and synonymy.

Targeted resequencing of coding DNA sequences for SNP discovery in nonmodel species.

Targeted capture coupled with high-throughput sequencing can be used to gain information about nuclear sequence variation at hundreds to thousands of loci. Divergent reference capture makes use of molecular data of one species to enrich target loci in other (related) species. This is particularly valuable for nonmodel organisms, for which often no a priori knowledge exists regarding these loci. Here, we have used targeted capture to obtain data for 809 nuclear coding DNA sequences (CDS) in a nonmodel organism, the Eurasian lynx Lynx lynx, using baits designed with the help of the published genome of a related model organism (the domestic cat Felis catus). Using this approach, we were able to survey intraspecific variation at hundreds of nuclear loci in L.lynx across the species' European range. A large set of biallelic candidate SNPs was then evaluated using a high-throughput SNP genotyping platform (Fluidigm), which we then reduced to a final 96 SNP-panel based on assay performance and reliability; validation was carried out with 100 additional Eurasian lynx samples not included in the SNP discovery phase. The 96 SNP-panel developed from CDS performed very successfully in the identification of individuals and in population genetic structure inference (including the assignment of individuals to their source population). In keeping with recent studies, our results show that genic SNPs can be valuable for genetic monitoring of wildlife species.

Spatio-temporal genetic structure of Anopheles gambiae in the Northwestern Lake Victoria Basin, Uganda: implications for genetic control trials in malaria endemic regions

BackgroundUnderstanding population genetic structure in the malaria vector Anopheles gambiae (s.s.) is crucial to inform genetic control and manage insecticide resistance. Unfortunately, species characteristics such as high nucleotide diversity, large effective population size, recent range expansion, and high dispersal ability complicate the inference of genetic structure across its range in sub-Saharan Africa. The ocean, along with the Great Rift Valley, is one of the few recognized barriers to gene flow in this species, but the effect of inland lakes, which could be useful sites for initial testing of genetic control strategies, is relatively understudied. Here we examine Lake Victoria as a barrier between the Ugandan mainland and the Ssese Islands, which lie up to 60 km offshore. We use mitochondrial DNA (mtDNA) from populations sampled in 2002, 2012 and 2015, and perform Bayesian cluster analysis on mtDNA combined with microsatellite data previously generated from the same 2002 mosquito DNA samples.ResultsHierarchical analysis of molecular variance and Bayesian clustering support significant differentiation between the mainland and lacustrine islands. In an mtDNA haplotype network constructed from this and previous data, haplotypes are shared even between localities separated by the Rift Valley, a result that more likely reflects retention of shared ancestral polymorphism than contemporary gene flow.ConclusionsThe relative genetic isolation of An. gambiae on the Ssese Islands, their small size, level terrain and ease of access from the mainland, the relative simplicity of the vectorial system, and the prevalence of malaria, are all attributes that recommend these islands as possible sites for the testing of genetic control strategies.

A Distinct Genetic Cluster in Cultivated Chickpea as Revealed by Genome-wide Marker Discovery and Genotyping.

The accurate description of plant biodiversity is of utmost importance to efficiently address efforts in conservation genetics and breeding. Herein, we report the successful application of a genotyping-by-sequencing (GBS) approach in chickpea ( L.), resulting in the characterization of a cultivated germplasm collection with 3187 high-quality single nucleotide polymorphism (SNP) markers. Genetic structure inference, principal component analysis, and hierarchical clustering all indicated the identification of a genetic cluster corresponding to black-seeded genotypes traditionally cultivated in Southern Italy. Remarkably, this cluster was clearly distinct at both genetic and phenotypic levels from germplasm groups reflecting commercial chickpea classification into and seed types. Fixation index estimates for individual polymorphisms pointed out loci and genomic regions that might be of significance for the diversification of agronomic and commercial traits. Overall, our findings provide information on genetic relationships within cultivated chickpea and highlight a gene pool of great interest for the scientific community and chickpea breeding, which is limited by the low genetic diversity available in the primary gene pool.

Friends and family: A software program for identification of unrelated individuals from molecular marker data.

The identification of related and unrelated individuals from molecular marker data is often difficult, particularly when no pedigree information is available and the data set is large. High levels of relatedness or inbreeding can influence genotype frequencies and thus genetic marker evaluation, as well as the accurate inference of hidden genetic structure. Identification of related and unrelated individuals is also important in breeding programmes, to inform decisions about breeding pairs and translocations. We present Friends and Family, a Windows executable program with a graphical user interface that identifies unrelated individuals from a pairwise relatedness matrix or table generated in programs such as coancestry and genalex. Friends and Family outputs a list of samples that are all unrelated to each other, based on a user-defined relatedness cut-off value. This unrelated data set can be used in downstream analyses, such as marker evaluation or inference of genetic structure. The results can be compared to that of the full data set to determine the effect related individuals have on the analyses. We demonstrate one of the applications of the program: how the removal of related individuals altered the Hardy-Weinberg equilibrium test outcome for microsatellite markers in an empirical data set. Friends and Family can be obtained from https://github.com/DeondeJager/Friends-and-Family.

Genotyping-by-Sequencing SNP Identification for Crops without a Reference Genome: Using Transcriptome Based Mapping as an Alternative Strategy.

Next-generation sequencing opens the way for genomic studies of diversity even for non-model crops and animals. Genome reduction techniques are becoming progressively more popular as they allow a fraction of the genome to be sequenced for multiple individuals and/or populations. These techniques are an efficient way to explore genome diversity in non-model crops and animals for which no reference genome is available. Genome reduction techniques emerged with the development of specific pipelines such as UNEAK (Universal Network Enabled Analysis Kit) and Stacks. However, even for non-model crops and animals, transcriptomes are easier to obtain, thereby making it possible to directly map reads. We investigate the direct use of transcriptome as an alternative strategy. Our specific objective was to compare SNPs obtained from the UNEAK pipeline as well as SNPs obtained by directly mapping genotyping-by-sequencing reads on a transcriptome. We assessed the feasibility of both SNP datasets, UNEAK and transcriptome mapping, to investigate the diversity of 91 samples of wild pearl millet sampled across its distribution area. Both approaches produced several tens of thousands of single nucleotide variants, but differed in the way the variants were identified, leading to differences in the frequency spectrum associated with marked differences in the assessment of diversity. Difference in the frequency spectrum significantly biased a large set of diversity analyses as well as detection of selection approaches. However, whatever the approach, we found very similar inference of genetic structure, with three major genetic groups from West, Central, and East Africa. For non-model crops, using transcriptome data as a reference is thus a particularly promising way to obtain a more thorough analysis of datasets generated using genome reduction techniques.

Improving the inference of population genetic structure in the presence of related individuals.

It is well known that the presence of related individuals can affect the inference of population genetic structure from molecular data. This has been verified, for example, on the unsupervised Bayesian clustering algorithm implemented in the software STRUCTURE. This methodology assumes, among others, Hardy-Weinberg and linkage equilibrium within subpopulations. The existence of groups of close relatives, such as full-sib families, may prevent these assumptions to be fulfilled, causing the algorithm to work suboptimally. The purpose of this study was to evaluate the effect of the presence of related individuals on a different methodology (implemented in CLUSTER_DIST) for population genetic structure inference. This approach arranges individuals to maximize the genetic distance between groups and does not make Hardy-Weinberg and linkage equilibrium assumptions. We study the robustness of this approach to the presence of close relatives in a sample using simulated scenarios involving combinations of several factors, including the number of subpopulations, the level of differentiation between them, the number, size and type (full or half-sibs) of families in a sample, and the type and number of molecular markers available for clustering analysis. Results indicate that the methodology that maximizes the genetic distance between subpopulations is less influenced by the presence of related individuals than the program STRUCTURE. Therefore, the former can be used, in combination with the program STRUCTURE, to analyse population genetic structure when related individuals are suspected to be present in a sample.

Inferences of evolutionary history of a widely distributed mangrove species, Bruguiera gymnorrhiza, in the Indo‐West Pacific region

Inference of genetic structure and demographic history is fundamental issue in evolutionary biology. We examined the levels and patterns of genetic variation of a widespread mangrove species in the Indo-West Pacific region, Bruguiera gymnorrhiza, using ten nuclear gene regions. Genetic variation of individual populations covering its distribution range was low, but as the entire species it was comparable to other plant species. Genetic differentiation among the investigated populations was high. They could be divided into two genetic clusters: the West and East clusters of the Malay Peninsula. Our results indicated that these two genetic clusters derived from their ancestral population whose effective size of which was much larger compared to the two extant clusters. The point estimate of speciation time between B. gymnorrhiza and Bruguiera sexangula was two times older than that of divergence time between the two clusters. Migration from the West cluster to the East cluster was much higher than the opposite direction but both estimated migration rates were low. The past Sundaland and/or the present Malay Peninsula are likely to prevent gene flow between the West and East clusters and function as a geographical or land barrier.

Bayesian statistical treatment of the fluorescence of AFLP bands leads to accurate genetic structure inference

Ever since the introduction of allozymes in the 1960s, evolutionary biologists and ecologists have continued to search for more powerful molecular markers to estimate important parameters such as effective population size and migration rates and to make inferences about the demographic history of populations, the relationships between individuals and the genetic architecture of phenotypic variation (Bensch & Akesson 2005; Bonin et al. 2007). Choosing a marker requires a thorough consideration of the trade-offs associated with the different techniques and the type of data obtained from them. Some markers can be very informative but require substantial amounts of start-up time (e.g. microsatellites), while others require very little time but are much less polymorphic. Amplified fragment length polymorphism (AFLP) is a firmly established molecular marker technique that falls in this latter category. AFLPs are widely distributed throughout the genome and can be used on organisms for which there is no a priori sequence information (Meudt & Clarke 2007). These properties together with their moderate cost and short start-up time have made them the method of choice for many molecular ecology studies of wild species (Bensch & Akesson 2005). However, they have a major disadvantage, they are dominant. This represents a very important limitation because many statistical genetics methods appropriate for molecular ecology studies require the use of codominant markers. In this issue, Foll et al. (2010) present an innovative hierarchical Bayesian method that overcomes this limitation. The proposed approach represents a comprehensive statistical treatment of the fluorescence of AFLP bands and leads to accurate inferences about the genetic structure of natural populations. Besides allowing a quasi-codominant treatment of AFLPs, this new method also solves the difficult problems posed by subjectivity in the scoring of AFLP bands.

High genetic divergence characterizes populations of the endemic plant Lithophragma maximum (Saxifragaceae) on San Clemente Island

Narrowly-ranging species frequently harbor less genetic variability relative to widespread relatives and face graver extinction threats due to the heightened impacts of stochastic events on ecological and genetic diversity. In this study, we examined the impact of historical and cur- rent threats to the maintenance of genetic variation in Lithophragma maximum (Saxifragaceae), a perennial herb endemic to San Clemente Island, California. This species exists as small populations confined to canyons along 4 km of the southeast coastline of the island. In 15 populations analyzed with 10 microsatellite markers, we identified an average of 2.05 alleles per locus and 58.7% polymorphic loci. Significant departures from Hardy-Weinberg equi- librium existed in six populations; five of these exhibited heterozygote deficiency. Bayesian inference of genetic structure indicated a significant amount of structure among populations and canyons and infrequent gene flow even over very short distances. We also identified a significant and positive correlation between genetic and geographic distances, indicative of isolation by distance. There was no evidence of recent bottlenecks in any of the sampled populations, but older bottlenecks were detected in two populations. These results suggest that populations of L. maximum have historically been small and isolated, which is likely due to the rugged habitat in which this species occurs and limited pollen and seed dispersal. Given the high degree of structure observed across populations, we suggest that conservation efforts should focus on pre- serving populations in multiple canyons, maintaining large population sizes to preserve genetic variation, and controlling the spread of invasive species in areas where L. maximum occurs.