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5th RECOMB Satellite Workshop on Computational Methods in Genetics
United States - CA - Los Angeles

Hosted by:University of California, Los Angeles
Venue:UCLA Campus
Dates:Jul 27, 2017 through Jul 28, 2017

Call for Papers or Oral Presentations:2017-03-14 through 2017-04-14
Event Registration:2017-03-01 through 2017-07-27
Early Registration Deadline:2017-05-29
5th RECOMB Satellite Workshop on Computational Methods in Genetics


UCLA Campus, Los Angeles, California

July 27-28, 2017


Over the last decade, large-scale, cost-effective availability of genotype data has led to novel analytical opportunities and computational challenges in statistical genetics. Population genetics allows more refined understanding of the demographic history of our species. Association analysis provides insights into the functional and molecular underpinnings of diseases and traits. Clinical applications suggest genetics is emerging as a trailblazer in personalized medicine. Complex bioinformatics questions arise, ranging from inferring more nuanced statistical models of genetic information to algorithms that overcome the complexity challenges of analyzing millions of SNPs across millions of individuals, to systems level challenges of handling such Big Data repositories of genotypes and phenotypes.


This meeting will focus on current research at the intersection of genetics, computer science, statistics, and related fields in gathering, analyzing, and applying SNP and haplotype data to problems in medicine and basic research. We plan to build on the success of previous RECOMB Satellite Meetings on Computational Methods in Genetics in bringing together leading participants from widely divergent backgrounds to share their expertise and results.


Original research papers (including significant work-in-progress) or state-of-the-art surveys are solicited in all aspects of SNP analysis and genetics, including, but not limited to:

Association analysis pipelines, imputation, phasing, and meta-analysis
Rare variants analyses and burden tests
Risk/phenotype prediction models and heritability analysis
Analysis of molecular and ‘omics phenotypes’ (e.g., gene expression, other epigenetic markers)
Complex associations, including mixed models, epistasis, and normalization
Structured phenotypes: multiple/serial/intermediate phenotypes and pleiotropy
Inference of population structure and demographic history
Models for mutation, recombination, selection, and conservation
Relatedness and identity-by-descent
Structural variation
Additional Information
Event URL:http://computationalgenomics.bioinformatics.ucla.edu
ISCB Member Discount:None
Contact Person:Eleazar Eskin ([javascript protected email address])

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