Assessing the Effects of Multiple Markers in Genetic Association Studies

88.6K

views

54

authors

19

articles

Assessing the Effects of Multiple Markers in Genetic Association Studies

88.6K

views

54

authors

19

articles

Editorial

24 February 2015

Assessing the effects of multiple markers in genetic association studies

Xuefeng Wang

and

Joanna M. Biernacka

4,191 views

4 citations

Editors

3

Xuefeng Wang

Moffitt Cancer Center

Joanna Biernacka

Mayo Clinic

Liming Liang

School of Public Health, Harvard University

Impact

About

It is believed that human diseases and their underlying causal pathways involve a complex interplay between multiple genetic risk factors from different genes as well as within the same gene. The search for such genetic factors, including interactions with environmental conditions, is both a goal and a challenge in modern human genetics and genetic epidemiology. Although methods and software packages that consider multi-marker models and gene-gene interaction effects have become available, they have been slow to become widely adopted in practice and the results produced by them are yet to be verified. On the other hand, an ever expanding amount of data has been yielded by today’s low cost and high throughput array and sequencing technology.

The articles in this Research Topic aim to investigate the extent to which we can better exploit the currently available data with appropriate statistical approaches, as well as the technical challenges and computational issues that remain in practical data analysis.

We are particularly interested in accommodating contributions that explore any of the following research topics (but certainly not limited to):

(1) Statistical analysis strategies, methods and applications for association studies involving (multiple) rare variants.
(2) Methods and applications in detecting or incorporating gene-gene and gene-environment interactions.
(3) Machine learning and nonparametric approaches and their applications in association studies.
(4) Novel model selection, estimation and prediction procedures in high-dimensional genomic data analysis.

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Editors

Xuefeng Wang

Moffitt Cancer Center

Joanna Biernacka

Mayo Clinic

Liming Liang

School of Public Health, Harvard University

Impact

88,634 Total views

63,334 Article views

22,857 Article downloads

2,443 Topic views

Published In

Frontiers in Genetics

Statistical Genetics and Methodology

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