Accepted Posters

Category 'Q'- Population Genetics and Variation'
Poster Q01
User friendly cluster computing for QTL analysis on XGAP
Derk Arends- Rijksuniversiteit Groningen
Joeri Velde (University of Groningen, GBIC (Bioinformatics)); Bruno Tesson (University of Groningen, GBIC (Bioinformatics)); Morris Swertz (European Bioinformatics Centre, EMBL); Karl Broman (University of Wisconsin-Madison, Biostatistics & Medical Informatics ); Ritsert Jansen (University of Groningen, GBIC (Bioinformatics));
Short Abstract: Modern technologies provide researchers with huge amounts of raw data. QTL analysis combines experimental breeding techniques with the analysis of quantitative traits. To solve some of the BigData issues we present an extension to an existing datamanagment tool (Molgenis with XGAP) to distribute QTLcalculations across a cluster of computers
Long Abstract:Click Here

Poster Q02
Evoker: a visualization tool for genotype intensity data
James Morris- Wellcome Trust Sanger Institute
Joshua C Randall (University of Oxford, Wellcome Trust Centre for Human Genetics); Julian B Maller (University of Oxford, Wellcome Trust Centre for Human Genetics); Jeffrey C Barrett ( Wellcome Trust Sanger Institute, Human genetics);
Short Abstract: Evoker is a tool for visualizing genotype cluster plots from genome-wide association studies (GWAS). This important quality control step verifies sensible genotype calling in putatively associated SNPs. Evoker provides an easy to use solution to the computational and storage problems related to working with the huge datasets created by GWAS.
Long Abstract:Click Here

Poster Q03
Multi-SNP models for complex phenotypes
Alberto Riva- University of Florida
Alireza Nazarian (University of Florida, Molecular Genetics and Microbiology); Heike Sichtig (University of Florida, Molecular Genetics and Microbiology);
Short Abstract: We describe the initial implementation and evaluation of a method to elucidate the genetic basis of complex diseases. The method builds and optimizes models based on multiple genetic markers (typically SNPs) selected on the basis of preexisting knowledge, and can be used to compare different models with each other.
Long Abstract:Click Here

Poster Q04
Haplotyping Population Samples of Short Fragment Data
Shawn O'Neil- University of Notre Dame
Amitabh Chaudhary (University of Notre Dame, Computer Science and Engineering); Scott Emrich (University of Notre Dame, Computer Science and Engineering);
Short Abstract: Haplotyping from multiple alignments of sequences typically assumes that the data represent exactly two haplotypes. However, sequencing pools of individuals representing unknown numbers of haplotypes has become common, particularly in ecological settings. We discuss graph theoretic results for haplotyping such datasets, which we call "population haplotype assembly," in polynomial time.
Long Abstract:Click Here

Poster Q05
Identification of Structural Variation in Next-Generation Sequence Data by Multiple-Signal Integration
Suzanne Sindi- Brown University
Selim ├ľnal (Brown University, Department of Computer Science); Luke Peng (Brown University, Department of Computer Science); Anna Ritz (Brown University , Department of Computer Science); Hsin-Ta Wu (Brown University, Department of Computer Science); Benjamin Raphael (Brown University, Department of Computer Science);
Short Abstract: We introduce two probabilistic methods (GASV+CC and GASV-Prob) to identify structural variants from paired-end resequencing data. Our methods integrate two signals: discordantly mapped paired-ends and read depth. We demonstrate the increased sensitivity and specificity of our methods over existing methods on simulated data and multiple next-generation resequencing datasets.
Long Abstract:Click Here

Poster Q06
A permutation method to identify subpopulations in comingled sequences
Dennis Maeder- SAIC Frederick Inc.
Qi Yang (SAIC Frederick Inc., DCEG); Cindy Chang (National Cancer Institute, National Institutes of Health, Division of Cancer Epidemiology & Genetics); Kelly Yu (National Cancer Institute, National Institutes of Health, Division of Cancer Epidemiology & Genetics); Sam Mbulaiteye (National Cancer Institute, National Institutes of Health, Division of Cancer Epidemiology & Genetics); Allan Hildesheim (National Cancer Institute, National Institutes of Health, Division of Cancer Epidemiology & Genetics); Kishor Bhatia (National Cancer Institute, National Institutes of Health, Division of Cancer Epidemiology & Genetics); Ruth Pfeiffer (National Cancer Institute, National Institutes of Health, Division of Cancer Epidemiology & Genetics); Joseph Boland (SAIC Frederick Inc, CGF); Casey Matthews (SAIC Frederick Inc, CGF); Victor Lonsberry (SAIC Frederick Inc, CGF); Meredith Yeager (SAIC Frederick Inc, CGF);
Short Abstract: We present a permutation method to identify subpopulations in deep sequencing experiments. It identifies positions in alignments that share common patterns. This generally applicable method allows us to identify subpopulations of comingled virus that may be associated with a pathological state.
Long Abstract:Click Here

Poster Q07
Interactions in Genetic Association
Ronald Schuyler- University of Colorado Denver School of Medicine
No additional authors
Short Abstract: This work compares methods for detecting genetic interactions in genome-wide association datasets. Evaluation is based on the number of statistically significant associations identified by each method, as well as additional insights and testable hypotheses developed in the context of what is known about each condition.
Long Abstract:Click Here

Poster Q08
Genome wide association study of non synonymous Single Nucleotide Polymorphisms with seven common diseaes
Praveen Surendran- University College Dublin
Denis Shields (University College Dublin, School of Medicine and Medical Science); Alice Stanton (Royal College of Surgeons in Ireland, Department of Clinical Pharmacology);
Short Abstract: The study conducted in a large scale genome wide data association dataset identified 85 new possible associations of non synonymous single nucleotide polymorphisms associated with seven common diseases in European population with the development of a quality control pipeline applicable to genome wide association datasets in general.
Long Abstract:Click Here

Poster Q09
Phase Variation - on/off or dimmer switch
Alexander Camenzuli- University of Bath
Alex Jeffries (PhD Supervisor, University of Bath, Biology and Biochemistry);
Short Abstract: Phase Variation frequently modulates genetic variance and virulence of pathogenic populations. I investigate whether subtle genetic mutation can lead to functional modification rather than an absolute on/off switch. Results show repeat sequences are abundant in bacterial populations and that phase variation may be a more common event than previously thought.
Long Abstract:Click Here

Poster Q10
IL-1? polymorphism influences body mass index through its effect on the transcription
Jae-Young Um- Kyung Hee University
Hye-lin Kim (Kyung Hee University, Pharmacology); Hyun-Ji Shin (Kyung Hee University, Pharmacology); Seung-Heon Hong (Wonkwang University, Oriental Pharmacy);
Short Abstract: The IL-1? polymorphisms C-889T (rs1800587) and G+4845T (rs17561) in the promoter region are associated with an increase in BMI in obese healthy women and affect the expression of IL-1?, thereby contributing to weight regulation.
Long Abstract:Click Here

Poster Q11
Steroid Combination Therapy and Detoxification Enzyme Gene Polymorphisms in Sudden Sensorineural Hearing Loss Patients
Jae-Young Um- Kyune Hee University
Hyun-Ji Shin (Kyung Hee University, Pharmacology); Hye-Lin Kim (Kyung Hee University, Pharmacology); Sueng-Heon Hong (Wonkwang University, Oriental Pharmacology);
Short Abstract: The purpose of this study was to evaluate the relationships between the combined therapy with steroid and the detoxification enzyme gene polymorphisms in patients with sudden sensorineural hearing loss (SSNHL). this is the first approach to analyze gene polymorphism and efficacy of clinical treatment of patients with SSNHL
Long Abstract:Click Here

Poster Q12
MAVEN 2.0: a tool for integration analysis of association results and automatic variant annotation
Jing Li- Case Western Reserve University
No additional authors
Short Abstract: This is a freely available web tool for integration analysis and new variant annotation.
Long Abstract:Click Here

Poster Q13
Bayesian Analysis of Ancestral Haplotype and Block Structure Using Reversible Jump Markov Chain Monte Carlo
xiaolin yin- Case Western Reserve University
No additional authors
Short Abstract: We proposed a Bayesian network to represent haplotype descended from ancestral haplotype with block structure, and developed a reversible jump Markov chain Monte Carlo method to sample ancestral haplotype and unknown block structure. Results show inferences based on average of inferred models are more accurate and robust than those based on a single optimized model.
Long Abstract:Click Here

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