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PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
COEUS: a semantic web application framework
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The GA and the GWAS: Using Genetic Algorithms to Search for Multilocus Associations
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient Search Methods for Statistical Dependency Rules
Fundamenta Informaticae - Machine Learning in Bioinformatics
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ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Motivation: The sequencing of the human genome has made it possible to identify an informative set of 1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods. Contact: jason.h.moore@dartmouth.edu