wtest: an integrated R package for genetic epistasis testing

Abstract Background With the increasing amount of high-throughput genomic sequencing data, there is a growing demand for a robust and flexible tool to perform interaction analysis. The identification of SNP-SNP, SNP-CpG, and higher order interactions helps explain the genetic etiology of human diseases, yet genome-wide analysis for interactions has been very challenging, due to the computational burden and a lack of statistical power in most datasets. Results The wtest R package performs association testing for main effects, pairwise and high order interactions in genome-wide association study data, and cis-regulation of SNP and CpG sites in genome-wide and epigenome-wide data. The software includes a number of post-test diagnostic and analysis functions and offers an integrated toolset for genetic epistasis testing. Conclusions The wtest is an efficient and powerful statistical tool for integrated genetic epistasis testing. The package is available in CRAN: https://CRAN.R-project.org/package=wtest.

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Genome-wide discovery of epistatic loci affecting antibiotic resistance using evolutionary couplings

10.1101/325993 ◽

2018 ◽

Author(s):

Benjamin Schubert ◽

Rohan Maddamsetti ◽

Jackson Nyman ◽

Maha R. Farhat ◽

Debora S. Marks

Keyword(s):

Antibiotic Resistance ◽

Statistical Power ◽

Genome Wide Association Study ◽

Association Studies ◽

Genome Wide Association ◽

Whole Genome Sequencing Data ◽

Genome Wide Association Studies ◽

Sequencing Data ◽

Vast Number ◽

Genome Wide

ABSTRACTThe analysis of whole genome sequencing data should, in theory, allow the discovery of interdependent loci that cause antibiotic resistance. In practice, however, identifying this epistasis remains a challenge as the vast number of possible interactions erodes statistical power. To solve this problem, we extend a method that has been successfully used to identify epistatic residues in proteins to infer genomic loci that are strongly coupled and associated with antibiotic resistance. Our method reduces the number of tests required for an epistatic genome-wide association study and increases the likelihood of identifying causal epistasis. We discovered 38 loci and 250 epistatic pairs that influence the dose needed to inhibit growth for five different antibiotics in 1,102 isolates of Neisseria gonorrhoeae that were confirmed in an independent dataset of 495 isolates. Many known resistance-affecting loci were recovered; however, the majority of loci occurred in unreported genes, including murE which was associated with cefixime. About half of the novel epistasis we report involved at least one locus previously associated with antibiotic resistance, including interactions between gyrA and parC associated with ciprofloxacin. Still, many combinations involved unreported loci and genes. Our work provides a systematic identification of epistasis pairs affecting antibiotic resistance in N. gonorrhoeae and a generalizable method for epistatic genome-wide association studies.

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Eagle for better genome-wide association mapping

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab204 ◽

2021 ◽

Author(s):

Andrew W George ◽

Arunas Verbyla ◽

Joshua Bowden

Keyword(s):

Association Mapping ◽

Genome Wide Association Study ◽

R Package ◽

Study Data ◽

Single Locus ◽

Genome Wide Association ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Trait Associations

Abstract Eagle is an R package for multi-locus association mapping on a genome-wide scale. It is unlike other multi-locus packages in that it is easy-to-use for R users and non-users alike. It has two modes of use, command line and GUI. Eagle is fully documented and has its own supporting website, http://eagle.r-forge.r-project.org/index.html. Eagle is a significant improvement over the method-of-choice, single-locus association mapping. It has greater power to detect SNP-trait associations. It is based on model selection, linear mixed models, and a clever idea on how random effects can be used to identify SNP-trait associations. Through an example with real mouse data, we demonstrate Eagle’s ability to bring clarity and increased insight to single-locus findings. Initially, we see Eagle complementing single-locus analyses. However, over time, we hope the community will make, increasingly, multi-locus association mapping their method-of-choice for the analysis of genome-wide association study data.

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Lack of evidence for association of UQCRC1 with Parkinson′s disease in Europeans

10.1101/2020.09.04.20188243 ◽

2020 ◽

Author(s):

Konstantin Senkevich ◽

Sara Bandres Ciga ◽

Ziv Gan-Or ◽

Lynne Krohn ◽

Keyword(s):

Genome Wide Association Study ◽

Rare Variants ◽

European Population ◽

Study Data ◽

Whole Genome Sequencing Data ◽

Sequencing Data ◽

European Origin ◽

Genome Wide ◽

Rare Genetic Variation ◽

Novel Variant

Recently, a novel variant p.Y314S in UQCRC1 has been implicated as pathogenic in Parkinson′ s disease (PD). In the current study, we aimed to examine the association of UQCRC1 with PD in large cohorts of European origin. We examined common and rare genetic variation in UQCRC1 using genome-wide association study data from the International Parkinson Disease Genomics Consortium (IPDGC), including 14,671 cases and 17,667 controls, and whole-genome sequencing data from the Accelerating Medicines Partnership - Parkinson′ s disease initiative (AMP-PD), including 1,647 PD patients and 1,050 controls. No common variants were consistently associated with PD, and a variety of burden analyses did not reveal an association between rare variants in UQCRC1 and PD. Therefore, our results do not support a major role for UQCRC1 in PD in the European population, and additional studies in other populations are warranted.

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A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS

Biostatistics ◽

10.1093/biostatistics/kxw060 ◽

2017 ◽

Vol 18 (3) ◽

pp. 477-494 ◽

Cited By ~ 5

Author(s):

Jakub Pecanka ◽

Marianne A. Jonker ◽

Zoltan Bochdanovits ◽

Aad W. Van Der Vaart ◽

Keyword(s):

Complex Traits ◽

Multiple Testing ◽

Statistical Power ◽

Genome Wide Association Study ◽

Score Test ◽

Interaction Model ◽

Type I ◽

Two Stage ◽

Genome Wide ◽

Strong Control

Summary For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the “missing heritability” of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson’s disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.

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