Oberwolfach Reports


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Volume 12, Issue 4, 2015, pp. 2969–3005
DOI: 10.4171/OWR/2015/52

Published online: 2016-06-27

Mini-Workshop: Recent Developments in Statistical Methods with Applications to Genetics and Genomics

Iuliana Ionita-Laza[1], Michael Krawczak[2], Joel W. Fish[3] and Michael Nothnagel[4]

(1) Columbia University, New York, USA
(2) Christian-Albrechts-Universität zu Kiel, Germany
(3) University of Massachusetts Boston, USA
(4) Universität zu Köln, Germany

Recent progress in high-throughput genomic technologies has revolutionized the field of human genetics and promises to lead to important scientific advances. With new improvements in massively parallel biotechnologies, it is becoming increasingly more efficient to generate vast amounts of information at the genomics, transcriptomics, proteomics, metabolomics etc. levels, opening up as yet unexplored opportunities in the search for the genetic causes of complex traits. Despite this tremendous progress in data generation, it remains very challenging to analyze, integrate and interpret these data. The resulting data are high-dimensional and very sparse, and efficient statistical methods are critical in order to extract the rich information contained in these data. The major focus of the mini-workshop, entitled “Recent Developments in Statistical Methods with Applications to Genetics and Genomics”, has been on integrative methods. Relevant research questions included the optimal study design for integrative genomic analyses; appropriate handling and pre-processing of different types of omics data; statistical methods for integration of multiple types of omics data; adjustment for confounding due to latent factors such as cell or tissue heterogeneity; the optimal use of omics data to enhance or make sense of results identified through genetic studies; and statistical and computational strategies for analysis of multiple types of high-dimensional data.

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Ionita-Laza Iuliana, Krawczak Michael, Fish Joel, Nothnagel Michael: Mini-Workshop: Recent Developments in Statistical Methods with Applications to Genetics and Genomics. Oberwolfach Rep. 12 (2015), 2969-3005. doi: 10.4171/OWR/2015/52