scholarly journals ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information

2018 ◽  
Vol 19 (8) ◽  
pp. 2267 ◽  
Author(s):  
Xia Cao ◽  
Guoxian Yu ◽  
Jie Liu ◽  
Lianyin Jia ◽  
Jun Wang
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm ◽  
Association Studies ◽  
Case Control ◽  
High Order ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Optimization Search

Identifying single nucleotide polymorphism (SNP) interactions is considered as a popular and crucial way for explaining the missing heritability of complex diseases in genome-wide association studies (GWAS). Many approaches have been proposed to detect SNP interactions. However, existing approaches generally suffer from the high computational complexity resulting from the explosion of candidate high-order interactions. In this paper, we propose a two-stage approach (called ClusterMI) to detect high-order genome-wide SNP interactions based on significant pairwise SNP combinations. In the screening stage, to alleviate the huge computational burden, ClusterMI firstly applies a clustering algorithm combined with mutual information to divide SNPs into different clusters. Then, ClusterMI utilizes conditional mutual information to screen significant pairwise SNP combinations in each cluster. In this way, there is a higher probability of identifying significant two-locus combinations in each group, and the computational load for the follow-up search can be greatly reduced. In the search stage, two different search strategies (exhaustive search and improved ant colony optimization search) are provided to detect high-order SNP interactions based on the cardinality of significant two-locus combinations. Extensive simulation experiments show that ClusterMI has better performance than other related and competitive approaches. Experiments on two real case-control datasets from Wellcome Trust Case Control Consortium (WTCCC) also demonstrate that ClusterMI is more capable of identifying high-order SNP interactions from genome-wide data.

scholarly journals Association analysis of juvenile idiopathic arthritis genetic susceptibility factors in Estonian patients

2021 ◽  
Author(s):  
Tiit Nikopensius ◽  
Priit Niibo ◽  
Toomas Haller ◽  
Triin Jagomägi ◽  
Ülle Voog-Oras ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Juvenile Idiopathic Arthritis ◽  
Autoimmune Diseases ◽  
Genetic Risk ◽  
Association Studies ◽  
Control Sample ◽  
Case Control ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic condition of childhood. Genetic association studies have revealed several JIA susceptibility loci with the strongest effect size observed in the human leukocyte antigen (HLA) region. Genome-wide association studies have augmented the number of JIA-associated loci, particularly for non-HLA genes. The aim of this study was to identify new associations at non-HLA loci predisposing to the risk of JIA development in Estonian patients. Methods We performed genome-wide association analyses in an entire JIA case–control sample (All-JIA) and in a case–control sample for oligoarticular JIA, the most prevalent JIA subtype. The entire cohort was genotyped using the Illumina HumanOmniExpress BeadChip arrays. After imputation, 16,583,468 variants were analyzed in 263 cases and 6956 controls. Results We demonstrated nominal evidence of association for 12 novel non-HLA loci not previously implicated in JIA predisposition. We replicated known JIA associations in CLEC16A and VCTN1 regions in the oligoarticular JIA sample. The strongest associations in the All-JIA analysis were identified at PRKG1 (P = 2,54 × 10−6), LTBP1 (P = 9,45 × 10−6), and ELMO1 (P = 1,05 × 10−5). In the oligoarticular JIA analysis, the strongest associations were identified at NFIA (P = 5,05 × 10−6), LTBP1 (P = 9,95 × 10−6), MX1 (P = 1,65 × 10−5), and CD200R1 (P = 2,59 × 10−5). Conclusion This study increases the number of known JIA risk loci and provides additional evidence for the existence of overlapping genetic risk loci between JIA and other autoimmune diseases, particularly rheumatoid arthritis. The reported loci are involved in molecular pathways of immunological relevance and likely represent genomic regions that confer susceptibility to JIA in Estonian patients. Key Points• Juvenile idiopathic arthritis (JIA) is the most common childhood rheumatic disease with heterogeneous presentation and genetic predisposition.• Present genome-wide association study for Estonian JIA patients is first of its kind in Northern and Northeastern Europe.• The results of the present study increase the knowledge about JIA risk loci replicating some previously described associations, so adding weight to their relevance and describing novel loci.• The study provides additional evidence for the existence of overlapping genetic risk loci between JIA and other autoimmune diseases, particularly rheumatoid arthritis.

scholarly journals Genome-wide association studies of cardiac electrical phenotypes

2020 ◽  
Vol 116 (9) ◽  
pp. 1620-1634
Author(s):  
Charlotte Glinge ◽  
Najim Lahrouchi ◽  
Reza Jabbari ◽  
Jacob Tfelt-Hansen ◽  
Connie R Bezzina
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Individual Variability ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Challenges And Opportunities ◽  
Electrocardiographic Parameters

Abstract The genetic basis of cardiac electrical phenotypes has in the last 25 years been the subject of intense investigation. While in the first years, such efforts were dominated by the study of familial arrhythmia syndromes, in recent years, large consortia of investigators have successfully pursued genome-wide association studies (GWAS) for the identification of single-nucleotide polymorphisms that govern inter-individual variability in electrocardiographic parameters in the general population. We here provide a review of GWAS conducted on cardiac electrical phenotypes in the last 14 years and discuss the implications of these discoveries for our understanding of the genetic basis of disease susceptibility and variability in disease severity. Furthermore, we review functional follow-up studies that have been conducted on GWAS loci associated with cardiac electrical phenotypes and highlight the challenges and opportunities offered by such studies.

A-MBED: Adaptive Markov Blanket Method for Epitasis Detection in Genome-Wide Association Studies

2014 ◽  
Vol 618 ◽  
pp. 278-282
Author(s):  
Tao Peng ◽  
Hao Wang ◽  
Yi Ran Wang ◽  
Wen Wen Xie ◽  
Jia Wei Luo
Keyword(s):  
Association Studies ◽  
Search Space ◽  
Detection Algorithm ◽  
International Hapmap Project ◽  
Genome Wide Association Studies ◽  
Weak Correlation ◽  
Matching Method ◽  
Markov Blanket ◽  
Genome Wide ◽  
Genome Wide Data

With the completion of the international HapMap project and the development of high-throughput technologies, designing more effective epistasis detection algorithm for genome-wide data poses a significant challenge. This paper proposes a new method based on the Markov blanket to solve the limitations of the existing algorithm, such as a large false-positive proportion and low accuracy. The algorithm uses G2 to judge the strength of correlation between variables of self-adaptive remove strategy and SNP matching method; to effectively eliminate variables that are unrelated to the target, as well as weak correlation between variables; to significantly reduce the search space and time; to prevent unnecessary retrieval analysis; and to improve the accuracy of the detection algorithm to a certain extent.

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