Bayesian inference of epistatic interactions in case-control studies

By Yu Zhang, Jun S. Liu. 2007

518 citations recorded by the source corpus.

Subfields: Genetics, Ophthalmology, Rheumatology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Nelson L. S. Tang
2. Michael Q. Zhang
3. Rui Jiang
4. Qiang Yang
5. Can Yang
6. Jason G. Mezey
7. Xiang Wan
8. Zexian Zeng

In Ophthalmology

1. Rui Jiang
2. Qiang Yang
3. Can Yang
4. Jason G. Mezey
5. Xiang Wan
6. Zexian Zeng
7. Wenhui Fu
8. Benjamin A. Logsdon

In Rheumatology

1. Rui Jiang
2. Qiang Yang
3. Can Yang
4. Jason G. Mezey
5. Xiang Wan
6. Zexian Zeng
7. Wenhui Fu
8. Benjamin A. Logsdon

Citations in the corpus

• Discovering causal interactions using Bayesian network scoring and information gain (2016, methodology): “…substantially outperformed nine epistasis learning methods including MBS [29], LEAP [31], logistic regression [18], MDR [33] combined with a heuristic search, full interaction modeling [21], information gain alone [22], SNP Harvester [23], BEAM [27], and a technique that uses maximum entropy [28].”
• MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study (2009, methodology): “To make the fair comparison, we use the simulation program provided in BEAM package and follow the same procedure in [1] to generate the data based on two epistatic models (additive effect and multiplicative effect).”
• Network-based global inference of human disease genes (2008, background): “algorithms are proposed to uncover these joint effects in population association studies (Ritchie et al, 2001; Zhang and Liu, 2007).”
• SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies (2009, methodology): “, 2001) and the B-statistic value (Zhang and Liu, 2007).”
• A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis (2010, methodology): “V-Bay therefore has better computational scaling properties than most currently proposed multiple locus algorithms for full likelihood or exact MCMC Bayesian analysis, when simultaneously considering all markers in a GWA study [9,10,16-18].”
• A random forest approach to the detection of epistatic interactions in case-control studies (2009, methodology): “Averaging this difference over all trees, one obtains a measure for the interaction of every pair of features [17].”