BioMed Research International

Statistical Analysis of High-Dimensional Genetic Data in Complex Traits


Status
Published

Lead Editor

1Seoul National University, Seoul, Republic of Korea

2University of Alabama at Birmingham, Birmingham, USA

3Université de Liège-Montefiore Institute, Liège, Belgium

4The University of Texas Health Science Center at Houston, Houston, USA


Statistical Analysis of High-Dimensional Genetic Data in Complex Traits

Description

With the recent development of high-throughput DNA microarray and next-generation sequencing techniques for detecting various genomic variants (SNVs, CNVs, INDELs, etc.), genome-wide association studies (GWAS) have become a popular strategy to discover genetic factors affecting common complex diseases. Many GWAS have successfully identified genetic risk factors associated with common diseases and have achieved substantial success in unveiling genomic regions responsible for the various aspects of phenotypes.

However, identifying the underlying mechanism of disease susceptible loci has proven to be difficult due to the complex genetic architecture of common diseases. The previously associated variants through GWAS only explain a small portion of the genetic factors in complex diseases. This rather limited finding is partly ascribed to the lack of intensive analysis on undiscovered genetic determinants such as rare variants and gene-gene interactions. Unfortunately, standard methods used to test for association with single common genetic variants are underpowered for detection of rare variants and genetic interactions.

This special issue will be dedicated to presenting state-of-the-art statistical and computational methods for finding missing heritability underlying complex traits with massive genetic data including GWAS, next-generation sequencing, and DNA microarray data, as well as other multiomics data. The main focus of this special issue will be on data mining and machine learning for revealing hidden association structure of rare variant-phenotype relationship. This special issue will provide a platform to the researchers with expertise in data mining to discuss recent advancements in analytic approach of rare variant association and genetic interaction in the field of statistics and bioinformatics.

Potential topics include, but are not limited to:

  • Data mining of GWAS and rare variant association results
  • Knowledge based prioritizing analysis of rare variant analysis
  • Constructing biological network from GWAS and rare variant association
  • Biological interpretation and visualization of GWAS and rare variant association
  • Gene-gene interaction analysis for rare variant association
  • Gene-environment interaction for rare variant association
  • Multiple-gene based analysis for rare variant association
  • Pathway/gene set based test for rare variant analysis
  • Integration analysis with genomic variants
  • Rare variant analysis with family-based design

Articles

  • Special Issue
  • - Volume 2015
  • - Article ID 461593
  • - Research Article

Robust Association Tests for the Replication of Genome-Wide Association Studies

Jungnam Joo | Ju-Hyun Park | ... | Nancy L. Geller
  • Special Issue
  • - Volume 2015
  • - Article ID 916352
  • - Research Article

Dynamic Model for RNA-seq Data Analysis

Lerong Li | Momiao Xiong
  • Special Issue
  • - Volume 2015
  • - Article ID 852341
  • - Research Article

Clique-Based Clustering of Correlated SNPs in a Gene Can Improve Performance of Gene-Based Multi-Bin Linear Combination Test

Yun Joo Yoo | Sun Ah Kim | Shelley B. Bull
  • Special Issue
  • - Volume 2015
  • - Article ID 135782
  • - Research Article

Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data

Haiming Xu | Beibei Jiang | ... | Liyong Cao
  • Special Issue
  • - Volume 2015
  • - Article ID 605891
  • - Research Article

Evaluation of Penalized and Nonpenalized Methods for Disease Prediction with Large-Scale Genetic Data

Sungho Won | Hosik Choi | ... | Sunghoon Kwon
  • Special Issue
  • - Volume 2015
  • - Article ID 564273
  • - Editorial

Statistical Analysis of High-Dimensional Genetic Data in Complex Traits

Taesung Park | Kristel Van Steen | ... | Momiao Xiong
  • Special Issue
  • - Volume 2015
  • - Article ID 671859
  • - Research Article

A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype

Seungyeoun Lee | Yongkang Kim | ... | Taesung Park
  • Special Issue
  • - Volume 2015
  • - Article ID 671349
  • - Research Article

On the Estimation of Heritability with Family-Based and Population-Based Samples

Youngdoe Kim | Young Lee | ... | Juyoung Lee
  • Special Issue
  • - Volume 2015
  • - Article ID 462549
  • - Research Article

Identifying and Assessing Interesting Subgroups in a Heterogeneous Population

Woojoo Lee | Andrey Alexeyenko | ... | Yudi Pawitan
  • Special Issue
  • - Volume 2015
  • - Article ID 523641
  • - Research Article

Detecting Genetic Interactions for Quantitative Traits Using -Spacing Entropy Measure

Jaeyong Yee | Min-Seok Kwon | ... | Mira Park
BioMed Research International
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Acceptance rate31%
Submission to final decision81 days
Acceptance to publication54 days
CiteScore2.410
Impact Factor2.197
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