- journal article metadata
European Mathematical Society Publishing House
2016-09-19 17:05:22
Oberwolfach Reports
Oberwolfach Rep.
OWR
1660-8933
1660-8941
General
10.4171/OWR
http://www.ems-ph.org/doi/10.4171/OWR
subscribers
European Mathematical Society Publishing House
Zuerich, Switzerland
© Mathematisches Forschungsinstitut Oberwolfach
12
2015
4
Mini-Workshop: Recent Developments in Statistical Methods with Applications to Genetics and Genomics
Iuliana
Ionita-Laza
Columbia University, NEW YORK, UNITED STATES
Michael
Krawczak
Christian-Albrechts-Universität zu Kiel, KIEL, GERMANY
Xihong
Lin
Harvard School of Public Health, BOSTON, UNITED STATES
Michael
Nothnagel
Universität zu Köln, 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.
Statistics
Biology and other natural sciences
2969
3005
10.4171/OWR/2015/52
http://www.ems-ph.org/doi/10.4171/OWR/2015/52