Fiuncho: a program for any-order epistasis detection in CPU clusters

Ponte-Fernández, Christian; González-Domínguez, Jorge; Martín, María J.

doi:10.1007/s11227-022-04477-5

Cited by 4 publications

(1 citation statement)

References 16 publications

(31 reference statements)

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“…A wide variety of other methods such as clustering and information theory-based searching methods [17][18][19][20][21], statistical methods [2,[22][23][24][25] from which BOOST [26] is very well known, evolutionary and swarm intelligence-based algorithms such as MTACO-DMSI [27], and highperformance computing approaches [28][29][30][31][32][33][34][35] have also been used for the detection of SNP interactions in literature.…”

Section: Introductionmentioning

confidence: 99%

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns

Esmaeili,

Narimani,

Vasighi

2023

PLoS ONE

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Advances in high-throughput sequencing technologies have made it possible to access millions of measurements from thousands of people. Single nucleotide polymorphisms (SNPs), the most common type of mutation in the human genome, have been shown to play a significant role in the development of complex and multifactorial diseases. However, studying the synergistic interactions between different SNPs in explaining multifactorial diseases is challenging due to the high dimensionality of the data and methodological complexities. Existing solutions often use a multi-objective approach based on metaheuristic optimization algorithms such as harmony search. However, previous studies have shown that using a multi-objective approach is not sufficient to address complex disease models with no or low marginal effect. In this research, we introduce a locus-driven harmony search (LDHS), an improved harmony search algorithm that focuses on using SNP locus information and genetic inheritance patterns to initialize harmony memories. The proposed method integrates biological knowledge to improve harmony memory initialization by adding SNP combinations that are likely candidates for interaction and disease causation. Using a SNP grouping process, LDHS generates harmonies that include SNPs with a higher potential for interaction, resulting in greater power in detecting disease-causing SNP combinations. The performance of the proposed algorithm was evaluated on 200 synthesized datasets for disease models with and without marginal effect. The results show significant improvement in the power of the algorithm to find disease-related SNP sets while decreasing computational cost compared to state-of-the-art algorithms. The proposed algorithm also demonstrated notable performance on real breast cancer data, showing that integrating prior knowledge can significantly improve the process of detecting disease-related SNPs in both real and synthesized data.

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Section: Introductionmentioning

confidence: 99%

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns

Esmaeili,

Narimani,

Vasighi

2023

PLoS ONE

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Distributed transformer for high order epistasis detection in large-scale datasets

Graça,

Nobre,

Sousa

et al. 2024

Sci Rep

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Understanding the genetic basis of complex diseases is one of the most important challenges in current precision medicine. To this end, Genome-Wide Association Studies aim to correlate Single Nucleotide Polymorphisms (SNPs) to the presence or absence of certain traits. However, these studies do not consider interactions between several SNPs, known as epistasis, which explain most genetic diseases. Analyzing SNP combinations to detect epistasis is a major computational task, due to the enormous search space. A possible solution is to employ deep learning strategies for genomic prediction, but the lack of explainability derived from the black-box nature of neural networks is a challenge yet to be addressed. Herein, a novel, flexible, portable, and scalable framework for network interpretation based on transformers is proposed to tackle any-order epistasis. The results on various epistasis scenarios show that the proposed framework outperforms state-of-the-art methods for explainability, while being scalable to large datasets and portable to various deep learning accelerators. The proposed framework is validated on three WTCCC datasets, identifying SNPs related to genes known in the literature that have direct relationships with the studied diseases.

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IPU-EpiDet: Identifying Gene Interactions on Massively Parallel Graph-Based AI Accelerators

Nobre,

Ilic,

Santander-Jiménez

et al. 2024

2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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Fiuncho: a program for any-order epistasis detection in CPU clusters

Cited by 4 publications

References 16 publications

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns

Distributed transformer for high order epistasis detection in large-scale datasets

IPU-EpiDet: Identifying Gene Interactions on Massively Parallel Graph-Based AI Accelerators

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