DAM: A Bayesian Method for Detecting Genome-wide Associations on Multiple Diseases

Guo, Xuan; Zhang, Jing; Cai, Zhipeng; Du, Dajun; Pan, Yi

doi:10.1007/978-3-319-19048-8_9

Cited by 9 publications

(13 citation statements)

References 20 publications

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“…We use the null simulation to test the Type I error rate of BAM. We compared BAM to two other methods (i.e., DAM [26,34] and SAM [25] ) that can find epistatic interactions associated with multiple diseases. In this comparison, we use a set of simulated datasets in which four types of known disease-specific associations were embedded in each dataset.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

Guo

2020

Tinshhua Sci. Technol.

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Many human diseases involve multiple genes in complex interactions. Large Genome-Wide Association Studies (GWASs) have been considered to hold promise for unraveling such interactions. However, statistic tests for high-order epistatic interactions (2 Single Nucleotide Polymorphisms (SNPs)) raise enormous computational and analytical challenges. It is well known that the block-wise structure exists in the human genome due to Linkage Disequilibrium (LD) between adjacent SNPs. In this paper, we propose a novel Bayesian method, named BAM, for simultaneously partitioning SNPs into LD-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient. We also applied BAM on two GWAS datasets from WTCCC, i.e., Rheumatoid Arthritis and Type 1 Diabetes, and accurately recovered the LD-block structure. Therefore, we believe that BAM is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…As shown in Ref. [34], the number of partitions that a set of L traits can be separated into nonempty subsets is a Bell number [35] . Let Q denote the set of distinct partitions of L traits, Q i denote partition i , and Q .j / i denote nonempty subset j in partition i.…”

Section: Notationmentioning

confidence: 99%

BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

Guo

2020

Tinshhua Sci. Technol.

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“…In addition to examining one trait at a time, a significant number of candidate gene studies and Genome-Wide Association Studies (GWAS) have revealed that a genetic variant might be associated with more than one trait [9][10][11][12][13][14][15][16][17][18][19] . For example, many studies have shown that variants in the DSP gene are associated with chronic obstructive pulmonary disease status and lung function traits [20] ; variants in protein tyrosine phosphatase non-receptor type 22 were identified as being associated with immune-related disorders, including rheumatoid arthritis [21] , systemic lupus erythematosus [22] , and type 1 diabetes [23] ; and a genetic risk factor located in the human leukocyte antigen region was reported as being strongly associated with multiple diseases and traits, including type 1 diabetes mellitus, multiple sclerosis, and rheumatoid arthritis [21,24] .…”

Section: Introductionmentioning

confidence: 99%

A Survey of SNP Data Analysis

Ding

Guo

2018

Big Data Min. Anal.

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Every person differs from every other person regarding their physical appearance, susceptibility to disease, response to medications, and so on. However, 99.9 percent of human DNA is the same. As such, differences in human genomes are very worthy of study. Single-Nucleotide Polymorphisms (SNPs) are the simplest form and most common source of genetic polymorphism. SNPs have been used to successfully identify defective genes that cause Mendelian diseases. However, most common human diseases are complex and are caused by multiple SNPs. Each SNP explains only a small fraction of genetic causes. Experiments on individual SNPs may reveal their non-detectable effects on complex diseases. Pathogenesis is a complicated topic, and it is difficult to correctly predict multiple SNPs. As such, the analysis of SNP data is a critical task in the study of genetic diseases.In this paper, we divide the methods for genome-wide SNP data analysis into two categories: single-trait Genome-Wide Association Studies (GWAS) in which pathology is mined from data of a single phenotype, and multiple-trait GWAS which identifies cross-phenotype associations. For single-trait GWAS, we review methods ranging from the simple to the complex, including TEAM, BOOST, AntEpiSeeker, SNPRuler, EDCF, HiSeeker, ORF, MLR-tagging, MSCD, and MIC. For multiple-trait GWAS, we describe methods in terms of their employed regression models, dimension-reduction methods, and meta-analysis methods. We also list the advantages and disadvantages of these methods. Finally, we discuss the future directions of SNP data analysis for genome-wide association.

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“…Zhang et al [14] proposed an innovative method for investigating mutation interactions of HIV after certain drug treatment. This method has been used in detecting genome-wide associations on multiple diseases [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] . In this paper, we applied a Bayesian model equipped with Metropolis-Hastings algorithm on the data of the interferon-based drug treatment to select mutations associated with drug resistance.…”

Section: Introductionmentioning

confidence: 99%

Investigating genotype 1a HCV drug resistance in NS5A region via Bayesian inference

Fu¹,

Chen²,

et al. 2015

Tinshhua Sci. Technol.

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Hepatitis C virus (HCV) treatment is on the cutting edge of medicine. Due to the high rate of mutations and low fidelity of HCV replication, resistant strains quickly become dominant in a viral population under the selection pressure of a drug. In this paper, we examined the drug resistance mechanism in the NS5A region of genotype 1a HCV virus by comparing the sequence data from interferon-ribavirin treated and untreated patients. To find the drug resistance difference, we used innovative Bayesian probability models to detect mutation combinations and inferred detailed interaction structures of these mutations. We aim to provide reference to drug design and mutation mechanism understanding through our work.

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DAM: A Bayesian Method for Detecting Genome-wide Associations on Multiple Diseases

Cited by 9 publications

References 20 publications

BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

A Survey of SNP Data Analysis

Investigating genotype 1a HCV drug resistance in NS5A region via Bayesian inference

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