scholarly journals Optimized Selection of Unrelated Subjects for Whole-Genome Sequencing Studies of Rare High-Penetrance Alleles

2012 ◽  
Vol 36 (5) ◽  
pp. 472-479 ◽  
Author(s):  
Todd L. Edwards ◽  
Chun Li
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
High Penetrance ◽  
Sequencing Studies ◽  
Selection Of

scholarly journals eSCAN: Scan Regulatory Regions for Aggregate Association Testing using Whole Genome Sequencing Data

2020 ◽  
Author(s):  
Yingxi Yang ◽  
Yuchen Yang ◽  
Le Huang ◽  
Jai G. Broome ◽  
Adolfo Correa ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
New Technologies ◽  
Real Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Association Testing ◽  
Wide Range ◽  
Sequencing Studies

AbstractWith advances in whole genome sequencing (WGS) technology, multiple statistical methods for aggregate association testing have been developed. Many common approaches aggregate variants in a given genomic window of a fixed/varying size and are not reliant on existing knowledge to define appropriate test units, resulting in most identified regions not being clearly linked to genes, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to the genes they affect, can be leveraged to predefine variant sets for aggregate testing in WGS. Therefore, in this paper we propose the eSCAN (Scan the Enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG with the advantages of increased incorporation of genomic annotation. eSCAN searches biologically meaningful searching windows, increasing power and aiding biological interpretation, as demonstrated by simulation studies under a wide range of scenarios. We also apply eSCAN for association analysis of blood cell traits using TOPMed WGS data from Women’s Health Initiative (WHI) and Jackson Heart Study (JHS). Results from this real data example show that eSCAN is able to capture more significant signals, and these signals are of shorter length and drive association of larger regions detected by other methods.

scholarly journals Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies

2019 ◽  
Vol 104 (2) ◽  
pp. 260-274 ◽  
Author(s):  
Han Chen ◽  
Jennifer E. Huffman ◽  
Jennifer A. Brody ◽  
Chaolong Wang ◽  
Seunggeun Lee ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Mixed Model ◽  
Whole Genome ◽  
Association Tests ◽  
Binary Traits ◽  
Sequencing Studies

scholarly journals Selection of unique Escherichia coli clones by random amplified polymorphic DNA (RAPD): Evaluation by whole genome sequencing

2014 ◽  
Vol 103 ◽  
pp. 101-103 ◽  
Author(s):  
Karen L. Nielsen ◽  
Paul A. Godfrey ◽  
Marc Stegger ◽  
Paal S. Andersen ◽  
Michael Feldgarden ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Random Amplified Polymorphic Dna ◽  
Whole Genome ◽  
Selection Of

scholarly journals Public attitudes in Japan toward participation in whole genome sequencing studies

2018 ◽  
Vol 12 (1) ◽  
Author(s):  
Taketoshi Okita ◽  
Noriko Ohashi ◽  
Daijiro Kabata ◽  
Ayumi Shintani ◽  
Kazuto Kato
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Public Attitudes ◽  
Whole Genome ◽  
Sequencing Studies

scholarly journals Nonsynonymous Polymorphism Counts in Bacterial Genomes: a Comparative Examination

2020 ◽  
Vol 87 (1) ◽  
Author(s):  
Sara L. Loo ◽  
Anna Ong ◽  
Wunna Kyaw ◽  
Loïc M. Thibaut ◽  
Ruiting Lan ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Negative Selection ◽  
Bacterial Species ◽  
Host Population ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Chronic Infections ◽  
Snp Data ◽  
Sequencing Studies

ABSTRACT Genomic data reveal single-nucleotide polymorphisms (SNPs) that may carry information about the evolutionary history of bacteria. However, it remains unclear what inferences about selection can be made from genomic SNP data. Bacterial species are often sampled during epidemic outbreaks or within hosts during the course of chronic infections. SNPs obtained from genomic analysis of these data are not necessarily fixed. Treating them as fixed during analysis by using measures such as the ratio of nonsynonymous to synonymous evolutionary changes (dN/dS) may lead to incorrect inferences about the strength and direction of selection. In this study, we consider data from a range of whole-genome sequencing studies of bacterial pathogens and explore patterns of nonsynonymous variation to assess whether evidence of selection can be identified by investigating SNP counts alone across multiple WGS studies. We visualize these SNP data in ways that highlight their relationship to neutral baseline expectations. These neutral expectations are based on a simple model of mutation, from which we simulate SNP accumulation to investigate how SNP counts are distributed under alternative assumptions about positive and negative selection. We compare these patterns with empirical SNP data and illustrate the general difficulty of detecting positive selection from SNP data. Finally, we consider whether SNP counts observed at the between-host population level differ from those observed at the within-host level and find some evidence that suggests that dynamics across these two scales are driven by different underlying processes. IMPORTANCE Identifying selection from SNP data obtained from whole-genome sequencing studies is challenging. Some current measures used to identify and quantify selection acting on genomes rely on fixed differences; thus, these are inappropriate for SNP data where variants are not fixed. With the increase in whole-genome sequencing studies, it is important to consider SNP data in the context of evolutionary processes. How SNPs are counted and analyzed can help in understanding mutation accumulation and trajectories of strains. We developed a tool for identifying possible evidence of selection and for comparative analysis with other SNP data. We propose a model that provides a rule-of-thumb guideline and two new visualization techniques that can be used to interpret and compare SNP data. We quantify the expected proportion of nonsynonymous SNPs in coding regions under neutrality and demonstrate its use in identifying evidence of positive and negative selection from simulations and empirical data.

scholarly journals Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Kristopher A. Standish ◽  
Tristan M. Carland ◽  
Glenn K. Lockwood ◽  
Wayne Pfeiffer ◽  
Mahidhar Tatineni ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Variant Calling ◽  
Whole Genome ◽  
Next Generation ◽  
Sequencing Studies
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