Association study designs for complex diseases

2001 ◽  
Vol 2 (2) ◽  
pp. 91-99 ◽  
Author(s):  
Lon R. Cardon ◽  
John I. Bell
Keyword(s):  
Association Study ◽  
Complex Diseases ◽  
Study Designs

Multi-Locus Penetrance Variance Analysis Method for Association Study in Complex Diseases

2005 ◽  
Vol 60 (3) ◽  
pp. 143-149 ◽  
Author(s):  
Xiangqing Sun ◽  
Zhongqi Zhang ◽  
Yulong Zhang ◽  
Xuegong Zhang ◽  
Yanda Li
Keyword(s):  
Association Study ◽  
Complex Diseases ◽  
Variance Analysis ◽  
Analysis Method

scholarly journals Identifying strains that contribute to complex diseases through the study of microbial inheritance

2015 ◽  
Vol 112 (3) ◽  
pp. 633-640 ◽  
Author(s):  
Jeremiah J. Faith ◽  
Jean-Frédéric Colombel ◽  
Jeffrey I. Gordon
Keyword(s):  
Disease Risk ◽  
Complex Diseases ◽  
Bacterial Strains ◽  
Health States ◽  
Cultural Traditions ◽  
Advantages And Disadvantages ◽  
Rdna Sequencing ◽  
Domains Of Life ◽  
Life On Earth ◽  
Study Designs

It has been 35 y since Carl Woese reported in PNAS how sequencing ribosomal RNA genes could be used to distinguish the three domains of life on Earth. During the past decade, 16S rDNA sequencing has enabled the now frequent enumeration of bacterial communities that populate the bodies of humans representing different ages, cultural traditions, and health states. A challenge going forward is to quantify the contributions of community members to wellness, disease risk, and disease pathogenesis. Here, we explore a theoretical framework for studies of the inheritance of bacterial strains and discuss the advantages and disadvantages of various study designs for assessing the contribution of strains to complex diseases.

A Method for Analyzing Two-locus Epistasis of Complex Diseases based on Decision Tree and Mutual Entropy

2019 ◽  
Vol 16 (5) ◽  
pp. 366-373
Author(s):  
Xiong Li ◽  
Hui Yang ◽  
Kaifu Wen ◽  
Xiaoming Zhong ◽  
Xuewen Xia ◽  
...  
Keyword(s):  
Decision Tree ◽  
Low Power ◽  
Association Study ◽  
Ant Colony Algorithm ◽  
Association Studies ◽  
Complex Diseases ◽  
Joint Entropy ◽  
Evaluation Scores ◽  
Combination Space ◽  
Mutual Entropy

Background: Epistasis makes complex diseases difficult to understand, especially when heterogeneity also exists. Heterogeneity of complex diseases makes the distribution of case population more confused. However, the traditional methods proposed to detect epistasis often ignore heterogeneity, resulting in low power of association studies. Methods: In this study, we firstly use rank information in the Classification Decision Tree and Mutual Entropy (CTME) to construct two different evaluation scores, namely multiple objectives. In addition, we improve the calculation of joint entropy between SNPs and disease label, which elevates the efficiency of CTME. Then, the ant colony algorithm is applied to search two-locus epistatic combination space. To handle the potential heterogeneity, all candidate two-locus SNPs are merged to recognize multiple different epistatic combinations. Finally, all these solutions are tested by χ2 test. Results and Conclusion: Experiments show that our method CTME improves the power of association study. More importantly, CTME also detects multiple epistatic SNPs contributing to heterogeneity. The experimental results show that CTME has advantages on power and efficiency.

scholarly journals Optimized Group Sequential Study Designs for Tests of Genetic Linkage and Association in Complex Diseases

2001 ◽  
Vol 69 (3) ◽  
pp. 590-600 ◽  
Author(s):  
Inke R. König ◽  
Helmut Schäfer ◽  
Hans-Helge Müller ◽  
Andreas Ziegler
Keyword(s):  
Genetic Linkage ◽  
Complex Diseases ◽  
Group Sequential ◽  
Sequential Study ◽  
Study Designs
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