Dataset Information

Coupling high-throughput genetics with phylogenetic information reveals an epistatic interaction on the influenza A virus M segment.

ABSTRACT:

Background

Epistasis is one of the central themes in viral evolution due to its importance in drug resistance, immune escape, and interspecies transmission. However, there is a lack of experimental approach to systematically probe for epistatic residues.

Results

By utilizing the information from natural occurring sequences and high-throughput genetics, this study established a novel strategy to identify epistatic residues. The rationale is that a substitution that is deleterious in one strain may be prevalent in nature due to the presence of a naturally occurring compensatory substitution. Here, high-throughput genetics was applied to influenza A virus M segment to systematically identify deleterious substitutions. Comparison with natural sequence variation showed that a deleterious substitution M1 Q214H was prevalent in circulating strains. A coevolution analysis was then performed and indicated that M1 residues 121, 207, 209, and 214 naturally coevolved as a group. Subsequently, we experimentally validated that M1 A209T was a compensatory substitution for M1 Q214H.

Conclusions

This work provided a proof-of-concept to identify epistatic residues by coupling high-throughput genetics with phylogenetic information. In particular, we were able to identify an epistatic interaction between M1 substitutions A209T and Q214H. This analytic strategy can potentially be adapted to study any protein of interest, provided that the information on natural sequence variants is available.

SUBMITTER: Wu NC

PROVIDER: S-EPMC4710013 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Coupling high-throughput genetics with phylogenetic information reveals an epistatic interaction on the influenza A virus M segment.

Wu Nicholas C NC Du Yushen Y Le Shuai S Young Arthur P AP Zhang Tian-Hao TH Wang Yuanyuan Y Zhou Jian J Yoshizawa Janice M JM Dong Ling L Li Xinmin X Wu Ting-Ting TT Sun Ren R

BMC genomics 20160112

<h4>Background</h4>Epistasis is one of the central themes in viral evolution due to its importance in drug resistance, immune escape, and interspecies transmission. However, there is a lack of experimental approach to systematically probe for epistatic residues.<h4>Results</h4>By utilizing the information from natural occurring sequences and high-throughput genetics, this study established a novel strategy to identify epistatic residues. The rationale is that a substitution that is deleterious i ...[more]

PMID: 26754751

Similar Datasets

Project description:BACKGROUND:Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal of the present study was to apply MDR to mining high-order epistatic interactions in a population-based genetic study of tuberculosis (TB). RESULTS:The study used a previously published data set consisting of 19 candidate single-nucleotide polymorphisms (SNPs) in 321 pulmonary TB cases and 347 healthy controls from Guniea-Bissau in Africa. The ReliefF algorithm was applied first to generate a smaller set of the five most informative SNPs. MDR with 10-fold cross-validation was then applied to look at all possible combinations of two, three, four and five SNPs. The MDR model with the best testing accuracy (TA) consisted of SNPs rs2305619, rs187084, and rs11465421 (TA?=?0.588) in PTX3, TLR9 and DC-Sign, respectively. A general 1000-fold permutation test of the null hypothesis of no association confirmed the statistical significance of the model (p?=?0.008). An additional 1000-fold permutation test designed specifically to test the linear null hypothesis that the association effects are only additive confirmed the presence of non-additive (i.e. nonlinear) or epistatic effects (p?=?0.013). An independent information-gain measure corroborated these results with a third-order epistatic interaction that was stronger than any lower-order associations. CONCLUSIONS:We have identified statistically significant evidence for a three-way epistatic interaction that is associated with susceptibility to TB. This interaction is stronger than any previously described one-way or two-way associations. This study highlights the importance of using machine learning methods that are designed to embrace, rather than ignore, the complexity of common diseases such as TB. We recommend future studies of the genetics of TB take into account the possibility that high-order epistatic interactions might play an important role in disease susceptibility.

Dataset Information

Coupling high-throughput genetics with phylogenetic information reveals an epistatic interaction on the influenza A virus M segment.

Background

Results

Conclusions

Publications

Coupling high-throughput genetics with phylogenetic information reveals an epistatic interaction on the influenza A virus M segment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets