Dataset Information

NetView: a high-definition network-visualization approach to detect fine-scale population structures from genome-wide patterns of variation.

ABSTRACT: High-throughput sequencing and single nucleotide polymorphism (SNP) genotyping can be used to infer complex population structures. Fine-scale population structure analysis tracing individual ancestry remains one of the major challenges. Based on network theory and recent advances in SNP chip technology, we investigated an unsupervised network clustering method called Super Paramagnetic Clustering (Spc). When applied to whole-genome marker data it identifies the natural divisions of groups of individuals into population clusters without use of prior ancestry information. Furthermore, we optimised an analysis pipeline called NetView, a high-definition network visualization, starting with computation of genetic distance, followed clustering using Spc and finally visualization of clusters with Cytoscape. We compared NetView against commonly used methodologies including Principal Component Analyses (PCA) and a model-based algorithm, Admixture, on whole-genome-wide SNP data derived from three previously described data sets: simulated (2.5 million SNPs, 5 populations), human (1.4 million SNPs, 11 populations) and cattle (32,653 SNPs, 19 populations). We demonstrate that individuals can be effectively allocated to their correct population whilst simultaneously revealing fine-scale structure within the populations. Analyzing the human HapMap populations, we identified unexpected genetic relatedness among individuals, and population stratification within the Indian, African and Mexican samples. In the cattle data set, we correctly assigned all individuals to their respective breeds and detected fine-scale population sub-structures reflecting different sample origins and phenotypes. The NetView pipeline is computationally extremely efficient and can be easily applied on large-scale genome-wide data sets to assign individuals to particular populations and to reproduce fine-scale population structures without prior knowledge of individual ancestry. NetView can be used on any data from which a genetic relationship/distance between individuals can be calculated.

SUBMITTER: Neuditschko M

PROVIDER: S-EPMC3485224 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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NetView: a high-definition network-visualization approach to detect fine-scale population structures from genome-wide patterns of variation.

Neuditschko Markus M Khatkar Mehar S MS Raadsma Herman W HW

PloS one 20121031 10

High-throughput sequencing and single nucleotide polymorphism (SNP) genotyping can be used to infer complex population structures. Fine-scale population structure analysis tracing individual ancestry remains one of the major challenges. Based on network theory and recent advances in SNP chip technology, we investigated an unsupervised network clustering method called Super Paramagnetic Clustering (Spc). When applied to whole-genome marker data it identifies the natural divisions of groups of ind ...[more]

PMID: 23152744

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Project description:Despite considerable excitement over the potential functional significance of copy number variants (CNVs), we still lack knowledge of the fine-scale architecture of the large majority of CNV regions in the human genome. In this study, we used a high-resolution array-based comparative genomic hybridization (aCGH) platform that targeted known CNV regions of the human genome at ~1 kb resolution to interrogate the genomic DNAs of 30 individuals from four HapMap populations. Our results revealed that 1,020 of 1,153 CNV loci (88%) were actually smaller in size than what is recorded in the Database of Genomic Variants based on previously published studies. A reduction in size of more than 50% was observed for 876 CNV regions (76%). We conclude that the total genomic content of common human CNVs may be smaller than previously thought. In addition, approximately 8% of the CNV regions observed in multiple individuals exhibited genomic architectural complexity in the form of smaller CNVs within larger ones and CNVs with inter-individual variation in breakpoints. Future association studies that aim to capture the potential influences of CNVs on disease phenotypes will need to consider how to best ascertain this previously underappreciated complexity. The DNA samples are a panel of 30 Hapmap samples, and a female sample NA15510 of undetermined geographic origin, which has been well characterized by fosmid-end sequencing (Tuzun et al. 2005, Nature Genetics 10, p1038). The DNA for this set of 16 female and 15 male samples are all supplied by the Coriell Cell Repository, and is comprised of samples from four populations: 10 unrelated Yoruban, 10 unrelated European-American individuals from Utah (CEPH), 5 unrelated Chinese, and 5 unrelated Japanese. The reference sample, NA10851 is also a male CEPH also from the HapMap Sample set (Corriel Cell Repository). Each of these samples was hybridized in pairs with the reversed labeling polarities. Additionally, 3 self-self control hybridizations were carried out for the reference sample, NA10851, one on each hybridization date. In the Title for each of the samples, each hybridization session is indicated by the letters A, B or C.

Dataset Information

NetView: a high-definition network-visualization approach to detect fine-scale population structures from genome-wide patterns of variation.

Publications

NetView: a high-definition network-visualization approach to detect fine-scale population structures from genome-wide patterns of variation.

Similar Datasets

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