Review: High-performance computing to detect epistasis in genome scale data sets

Upton, Alex; Trelles, Oswaldo; Cornejo-García, José Antonio; Perkins, James R.

doi:10.1093/bib/bbv058

Cited by 43 publications

(30 citation statements)

References 120 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although efficient and scalable computational tools have been developed for searching for interactions amongst genome wide SNPs 26–28, 108 , detecting them with statistical significance remains a major challenge. There are previous methods that have approached this problem, although from different perspectives than the method proposed here.…”

Section: Methodsmentioning

confidence: 99%

Discovering genetic interactions bridging pathways in genome-wide association studies

Fang

Wang

Paunić

et al. 2017

Preprint

View full text Add to dashboard Cite

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data.Applying BridGE broadly, we discovered significant interactions in Parkinson's disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/182741 doi: bioRxiv preprint first posted online Aug. 30, 2017; 3 Genome-wide association studies (GWAS) have been increasingly successful at identifying single-nucleotide polymorphisms (SNPs) with statistically significant association to a variety of diseases [1][2][3][4][5] and gene sets significantly enriched for SNPs with moderate association [6][7][8][9][10] . However, for most diseases, there remains a substantial disparity between the disease risk explained by the discovered loci and the estimated total heritable disease risk based on familial aggregation [11][12][13][14][15][16] . While there are a number of possible explanations for this "missing heritability", including many loci with small effects or rare variants [11][12][13][14][15]17 , genetic interactions between loci are one potential culprit 13,14,16,18,19 . Genetic interactions generally refer to a combination of two or more genes whose contribution to a phenotype cannot be completely explained by their independent effects 16,20,21 , For example, one example of an extreme genetic interaction is synthetic lethality, which is the case where two mutations, neither of which is lethal on its own, combines to generate a lethal double mutant phenotype. Genetic interactions allow relatively benign variation to combine and generate more extreme phenotypes, including complex human diseases [11][12][13]16,22 .While several studies have reported interactions between genetic variants in various disease contexts 20,[23][24][25][26] , and though efficient and scalable computational tools have been developed for searching for interactions amongst genome wide SNPs 20,[26][27][28] , discovering them systematically with statistical significance remains a major challenge. For examp...

show abstract

Section: Methodsmentioning

confidence: 99%

Discovering genetic interactions bridging pathways in genome-wide association studies

Fang

Wang

Paunić

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, according to the listings in the Genomes OnLine Database (GOLD) as of March 8, 2016, there were 79,650 genome sequencing projects of which 8018 were completed projects, 33,489 were permanent drafts, 35,609 were incomplete projects, and 1553 were targeted projects [81]. There are 73,000 organism, including archaea (1201), bacteria (55,303), eukaryotes (11,990), and viruses (4473), listed for sequencing. These numbers should be increased if the sequencing of the 100,000 whole-human genomes [82] is added.…”

Section: Bioinformatics Help In Handling and Analysis Of The Genomicsmentioning

confidence: 99%

Bioinformatics: Basics, Development, and Future

Abdurakhmonov¹

2016

Bioinformatics - Updated Features and Applications

View full text Add to dashboard Cite

Abstract"ioinformatics is an interdisciplinary scientific field of life sciences. "ioinformatics research and application include the analysis of molecular sequence and genomics data genome annotation, gene/protein prediction, and expression profiling molecular folding, modeling, and design building biological networks development of databases and data management systems development of software and analysis tools bioinformatics services and workflow mining of biomedical literature and text and bioinformatics education and training. "stronomical accumulation of genomics, proteomics, and metabolomics data as well as a need for their storage, analysis, annotation, organization, systematization, and integration into biological networks and database systems were the main driving forces for the emergence and development of bioinformatics. Current critical needs for bioinformatics among others highlighted in this chapter, however, are to understand basics and specifics of bioinformatics as well as to prepare new generation scientists and specialists with integrated, interdisciplinary, and multilingual knowledge who can use modern bioinformatics resources powered with sophisticated operating systems, software, and database/networking technologies. In this introductory chapter, I aim to give an overall picture on basics and developments of the bioinformatics field for readers with some future perspectives, highlighting chapters published in this book.

show abstract

“…Epistasis is defined as the deviation of the combined allele effect at two or more loci from the sum of their individual effects (Fisher, 1918). It is considered a promising approach to understanding genetic causes of complex traits (Mackay and Moore, 2014; Phillips, 2008; Upton, et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects

Wang

Tang

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

SummaryWe have developed a rapid mixed model algorithm for exhaustive genome-wide epistatic association analysis by controlling multiple polygenic effects. Our model can simultaneously handle additive by additive epistasis, dominance by dominance epistasis and additive by dominance epistasis, and account for intrasubject fluctuations due to individuals with repeated records. Furthermore, we suggest a simple but efficient approximate algorithm, which allows examination of all pairwise interactions in a remarkably fast manner of linear with population size. Application to publicly available yeast and human data has showed that our mixed model-based method has similar performance with simple linear model-based Plink on computational efficiency. It took less than 40 hours for the pairwise analysis of 5,000 individuals genotyped with roughly 350,000 SNPs with five threads on Intel Xeon E5 2.6GHz CPU.Availability and implementationSource codes are freely available at https://github.com/chaoning/GMAT.

show abstract

Review: High-performance computing to detect epistasis in genome scale data sets

Cited by 43 publications

References 120 publications

Discovering genetic interactions bridging pathways in genome-wide association studies

Discovering genetic interactions bridging pathways in genome-wide association studies

Bioinformatics: Basics, Development, and Future

Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects

Contact Info

Product

Resources

About