<scp>netview p</scp>: a network visualization tool to unravel complex population structure using genome‐wide <scp>SNP</scp>s

Steinig, Eike J.; Neuditschko, Markus; Khatkar, Mehar S.; Raadsma, Herman W.; Zenger, Kyall R.

doi:10.1111/1755-0998.12442

Cited by 100 publications

(106 citation statements)

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“…Samples from the same breed do not necessarily appear together, although that does not imply whether breeds capture substantial and useful characteristics of bulls. Similarly, mutual k-nearest neighbour graphs (mkNNGs) were created by applying NetView1112 for k = 6 and 12, where samples from different breeds are clustered together (Supplementary Fig. 2).…”

Section: Resultsmentioning

confidence: 99%

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Population Structure Analysis of Bull Genomes of European and Western Ancestry

Chung

Szyda

Frąszczak

et al. 2017

Sci Rep

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Since domestication, population bottlenecks, breed formation, and selective breeding have radically shaped the genealogy and genetics of Bos taurus. In turn, characterization of population structure among diverse bull (males of Bos taurus) genomes enables detailed assessment of genetic resources and origins. By analyzing 432 unrelated bull genomes from 13 breeds and 16 countries, we demonstrate genetic diversity and structural complexity among the European/Western cattle population. Importantly, we relaxed a strong assumption of discrete or admixed population, by adapting latent variable models for individual-specific allele frequencies that directly capture a wide range of complex structure from genome-wide genotypes. As measured by magnitude of differentiation, selection pressure on SNPs within genes is substantially greater than that on intergenic regions. Additionally, broad regions of chromosome 6 harboring largest genetic differentiation suggest positive selection underlying population structure. We carried out gene set analysis using SNP annotations to identify enriched functional categories such as energy-related processes and multiple development stages. Our population structure analysis of bull genomes can support genetic management strategies that capture structural complexity and promote sustainable genetic breadth.

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Section: Resultsmentioning

confidence: 99%

“…When visualizing a resulting dendrogram, nodes are colored by breed codes. Alternatively, we applied to create mutual k-nearest neighbour graphs (mkNNGs) based on the same set of SNPs1112. Unlike hierarchical clustering, mKNNGs assign discrete memberships, which are visualized in a force-directed graph (as implemented in ).…”

Section: Methodsmentioning

confidence: 99%

Population Structure Analysis of Bull Genomes of European and Western Ancestry

Chung

Szyda

Frąszczak

et al. 2017

Sci Rep

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“…In order to express the strength of relationship between two individuals, the line width of an edge is proportional to the genetic relatedness between them (IBS). This approach is described in Neuditschko et al (2012) and a recent implementation of this workflow is now available as R package (Steinig et al, 2015) posted at https://github.com/ esteinig/netview.…”

Section: High-resolution Population Networkmentioning

confidence: 99%

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

et al. 2016

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Pollination is a key ecosystem service for agricultural systems and Western honey bees, Apis mellifera, are the most important managed pollinators. Major losses of managed honey bee colonies reinforced the need to take advantage of locally adapted subspecies and ecotypes to buffer populations against various stressors. However, introductions of non-native honey bees from distant lineages are likely to undermine respective conservation efforts unless reliable and cost effective tools can be used to identify hybridization. The purpose of this study is to characterize current population structure and genetic diversity, and to assess the degree of admixture between native and introduced honey bees. Moreover, we aim to select a reduced number of genetic markers to improve conservation management strategies. We take advantage of recent developments in next-generation sequencing and network-based clustering to investigate conservation efforts for the native European Dark honey bee, A. m. mellifera, which is threatened by introgression in most of its range. We collected whole-genome sequence information from haploid drones of A. m. mellifera, A. m. carnica, and Buckfast sampled throughout Switzerland (N = 81), as well as from four Swiss A. m. mellifera conservation areas (N = 39) and from one conservatory in the French Alps (N = 31). Population structure analyses based upon 3.375 M genome-wide SNPs discerned samples by subspecies and geographic origin (Switzerland or France). Ancestry inference indicated admixed individuals in all of the protected areas, calling for improved management efforts. After testing different subsets of ancestry informative SNPs using three different selection strategies (F ST , PCA-based or at random), as few as 50 SNPs are found to be sufficient to differentiate native from introduced honey bees. Therefore, our data suggests that a low-density SNP panel can be a precise and cost-effective tool to support conservation management efforts for managed pollinators.

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“…PCA) [20]. Recently, network-based cluster approaches are regaining favor for uncovering population structures like N et V iew [24, 34]. We extended the recent N et V iew approach with A dmixture [19] and the identification of the key contributors into an integrated three-step procedure that provides a high-resolution analysis and visualization of population structures (Fig 1).…”

Section: Discussionmentioning

confidence: 99%

“…An implementation of N et V iew pipeline is also posted at http://sydney.edu.au/vetscience/reprogen/netview/ and was recently described as a Python pipeline by Steining et al . [34] https://github.com/esteinig/netview. In order to retain well-structured population networks, we used the open graph visualization platform C ytoscape v.2.83 [35] and the plugin MultiColoredNodes [36] for the final network visualization.…”

Section: Methodsmentioning

confidence: 99%

Identification of key contributors in complex population structures

et al. 2017

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Evaluating the genetic contribution of individuals to population structure is essential to select informative individuals for genome sequencing, genotype imputation and to ascertain complex population structures. Existing methods for the selection of informative individuals for genomic imputation solely focus on the identification of key ancestors, which can lead to a loss of phasing accuracy of the reference population. Currently many methods are independently applied to investigate complex population structures. Based on the Eigenvalue Decomposition (EVD) of a genomic relationship matrix we describe a novel approach to evaluate the genetic contribution of individuals to population structure. We combined the identification of key contributors with model-based clustering and population network visualization into an integrated three-step approach, which allows identification of high-resolution population structures and substructures around such key contributors. The approach was applied and validated in four disparate datasets including a simulated population (5,100 individuals and 10,000 SNPs), a highly structured experimental sheep population (1,421 individuals and 44,693 SNPs) and two large complex pedigree populations namely horse (1,077 individuals and 38,124 SNPs) and cattle (2,457 individuals and 45,765 SNPs). In the simulated and experimental sheep dataset, our method, which is unsupervised, successfully identified all known key contributors. Applying our three-step approach to the horse and cattle populations, we observed high-resolution population substructures including the absence of obvious important key contributors. Furthermore, we show that compared to commonly applied strategies to select informative individuals for genotype imputation including the computation of marginal gene contributions (Pedig) and the optimization of genetic relatedness (Rel), the selection of key contributors provided the highest phasing accuracies within the selected reference populations. The presented approach opens new perspectives in the characterization and informed management of populations in general, and in areas such as conservation genetics and selective animal breeding in particular, where assessing the genetic contribution of influential and admixed individuals is crucial for research and management applications. As such, this method provides a valuable complement to common applied tools to visualize complex population structures and to select individuals for re-sequencing.

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netview p: a network visualization tool to unravel complex population structure using genome‐wide SNPs

Cited by 100 publications

References 50 publications

Population Structure Analysis of Bull Genomes of European and Western Ancestry

Population Structure Analysis of Bull Genomes of European and Western Ancestry

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

Identification of key contributors in complex population structures

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