MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Heinrich, Felix; Ramzan, Faisal; Rajavel, Abirami; Schmitt, Armin O.; Gültaş, Mehmet

doi:10.3390/biology10090921

Cited by 5 publications

(10 citation statements)

References 123 publications

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“…The use of entropies in different fields, especially in the field of information theory and its connection to communication, allowed the development of several useful information measures, such as mutual information, symmetry of information, and information distances. See, for example, References [17][18][19] for some recent work related to the aforementioned information measures.…”

Section: Introductionmentioning

confidence: 99%

On Conditional Tsallis Entropy

Teixeira

Souto

Antunes

2021

Entropy

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There is no generally accepted definition for conditional Tsallis entropy. The standard definition of (unconditional) Tsallis entropy depends on a parameter α that converges to the Shannon entropy as α approaches 1. In this paper, we describe three proposed definitions of conditional Tsallis entropy suggested in the literature—their properties are studied and their values, as a function of α, are compared. We also consider another natural proposal for conditional Tsallis entropy and compare it with the existing ones. Lastly, we present an online tool to compute the four conditional Tsallis entropies, given the probability distributions and the value of the parameter α.

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Section: Introductionmentioning

confidence: 99%

On Conditional Tsallis Entropy

Teixeira

Souto

Antunes

2021

Entropy

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“…Afterwards, I describe the framework that I developed to identify regulatory SNPs based on genotyping-by-sequencing data and, finally, I present the MIDESP algorithm for the detection of epistatic SNP pairs using mutual information. The content in this chapter is mostly taken from my published papers [52,165,187] (see Appendices A.1, A.2 and A.3).…”

Section: Methodsmentioning

confidence: 99%

“…Large parts of this chapter are taken from my publication [165] (see Appendix A.3) or are based on the reviews given in [53,121].…”

Section: Detection Of Epistatic Interactionsmentioning

confidence: 99%

“…Finally, the removal of the estimated background from the measured epistasis values leads to the detection of correct epistatic signals arising from functional interactions. MIDESP has been published in [165] (see Appendix A.3). The method consists of several steps, which I explain in detail in the following.…”

Section: Mutual Information Based Detection Of Epistatic Snp Pairsmentioning

confidence: 99%

“…Based on the N x and m S i values, the mutual information can be estimated. Images are taken from [165].…”

Section: Mutual Information Based Detection Of Epistatic Snp Pairsmentioning

confidence: 99%

See 2 more Smart Citations

Identification of regulatory SNPs and epistatic SNP pairs using deep learning and information theory

Heinrich¹

Self Cite

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In the last two decades, new technologies have made DNA genotyping and sequencing far more time and cost efficient. The resulting tremendous increase in the amount of available genomic data allows for a deeper understanding of the relationship between the genotype and the phenotype. In this thesis, I present two novel frameworks which analyze specific aspects of the relationship between the genotype and the phenotype, namely the identification of regulatory SNPs (rSNPs) as well as the detection of epistatic SNP pairs. Additional thanks go to the group of Prof. Wolfgang Link whose collaboration helped to facilitate my Master's thesis and first publication.Finally, I would like to thank my parents. They supported me during my studies and in my daily life and they have made it possible for me to concentrate fully on my studies. I dedicate this thesis to them.Last but not least, I thank my sister Jasmin who, despite her own work and studies, found the time to proofread my publications and this thesis. Contents x Material and methods

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GWLD: an R package for genome-wide linkage disequilibrium analysis

Zhang

et al. 2023

G3: Genes, Genomes, Genetics

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Linkage disequilibrium (LD) analysis is fundamental to the investigation on genetic architecture of complex traits (e.g. human disease, animal and plant breeding), and population structure and evolution dynamics. However, until now, studies primarily focus on LD status between genetic variants located on the same chromosome. Moreover, genome (re-)sequencing produces unprecedented numbers of genetic variants, and fast LD computation becomes a challenge. Here, we have developed GWLD, a parallelized and generalized tool designed for the rapid genome-wide calculation of LD values, including conventional D/D’, r2 and (reduced) mutual information (MI and RMI) measures. LD between genetic variants within and across chromosomes can be rapidly computed and visualized in either R package or standalone C++ software. To evaluate the accuracy and speed of LD calculation, we conducted comparisons using four real datasets. Interchromosomal LD patterns observed potentially reflect levels of selection intensity across different species. Both versions of GWLD, the R package (https://github.com/Rong-Zh/GWLD-R) and the standalone C++ software (https://github.com/Rong-Zh/GWLD-C++), are freely available on GitHub.

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MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Cited by 5 publications

References 123 publications

On Conditional Tsallis Entropy

On Conditional Tsallis Entropy

Identification of regulatory SNPs and epistatic SNP pairs using deep learning and information theory

GWLD: an R package for genome-wide linkage disequilibrium analysis

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