ancGWAS: a post genome-wide association study method for interaction, pathway and ancestry analysis in homogeneous and admixed populations

Chimusa, Emile R.; Mbiyavanga, Mamana; Mazandu, Gaston K.; Mulder, Nicola

doi:10.1093/bioinformatics/btv619

Cited by 35 publications

(30 citation statements)

References 30 publications

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“…Subsequent to the analysis presented here, Chimusa et al (2016) published a post-GWAS methodology utilizing LD information and the human protein–protein interaction network, which identified novel pathways associated with breast cancer [52]. The study by Chimusa et al (2016) reiterates the need for alternative methodologies in the identification of regulatory variants associated with a phenotype.…”

Section: Discussionmentioning

confidence: 99%

“…This could be an artefact due to the low sample size in our study, an increase in error variation or the moderate to low effect sizes associated with the six variants [51]. Susceptibility to TB is a complex disease and it is possible that numerous small/moderate effect variants at a frequency less than 0.05 will play a role in this phenotype [52]. It is also possible that some of the variants identified by previous studies are population-specific susceptibility variants and are therefore unlikely to be involved in disease predisposition in the SAC population.…”

Section: Discussionmentioning

confidence: 99%

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A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility

et al. 2017

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Utilizing data from published tuberculosis (TB) genome-wide association studies (GWAS), we use a bioinformatics pipeline to detect all polymorphisms in linkage disequilibrium (LD) with variants previously implicated in TB disease susceptibility. The probability that these variants had a predicted regulatory function was estimated using RegulomeDB and Ensembl’s Variant Effect Predictor. Subsequent genotyping of these 133 predicted regulatory polymorphisms was performed in 400 admixed South African TB cases and 366 healthy controls in a population-based case-control association study to fine-map the causal variant. We detected associations between tuberculosis susceptibility and six intronic polymorphisms located in MARCO, IFNGR2, ASHAS2, ACACA, NISCH and TLR10. Our post-GWAS approach demonstrates the feasibility of combining multiple TB GWAS datasets with linkage information to identify regulatory variants associated with this infectious disease.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility

et al. 2017

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“…Considering the fact that the largest human genetic diversity lies within the African continent, African genomic data will likely provide unique clinical and biodiversity knowledge [Campbell and Tishkoff, 2008; Chimusa et al, 2015]. However, genomic data poses theoretical challenges through the massive data volumes, dimensionality of the data, and African-specific statistical approaches required for mining and interpreting the data.…”

Section: Discussionmentioning

confidence: 99%

“…For downstream analysis of variants from a Genome Wide Association Study (GWAS) or NGS experiment, we developed ancGWAS [Chimusa et al, 2015] and HUMA. ancGWAS is a network-based tool for analysis of GWAS summary statistics that identifies significant subnetworks in the human protein-protein interaction network by mapping SNPs to genes and genes to pathways and networks.…”

Section: Analysis Toolsmentioning

confidence: 99%

Development of Bioinformatics Infrastructure for Genomics Research

Mulder

Adebiyi

et al. 2017

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Background and objectives Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a Pan African bioinformatics network, was established to build capacity specifically to enable H3Africa researchers to analyse their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet’s role has evolved in response to changing needs from the consortium and the African bioinformatics community. The network set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage and analysis. Methods and results Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrolment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Since H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System and eBiokits. A set of reproducible, portable and cloud scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. Conclusion For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities and training programmes. Here, we describe the infrastructure and how it has impacted genomics and bioinformatics research in Africa.

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“…This is a difficult multi-class classification challenge, with only a few attempts at the problem. This problem is also related to the issue of separating admixture populations [7,35], and recent approaches that have used GWAS (Genome-Wide Association Studies) data [2,3,36]. However, we do not address the problem of admixture in the scope of this work and also, we do not use GWAS datasets.…”

Section: Prior Work and Challengesmentioning

confidence: 99%

Inference of Biogeographical Ancestry Under Resource Constraints

Toma¹

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INFERENCE OF BIOGEPGRAPHICAL ANCESTRY UNDER RESOURCE CONSTRAINTS Tanjin Taher TomaWe study the problem of predicting human biogeographical ancestry using genomic data. While continental level ancestry prediction is relatively simple using genomic information, distinguishing between individuals from closely associated sub-populations (e.g., from the same continent) is still a difficult challenge. In particular, we focus on the case where the analysis is constrained to using single nucleotide polymorphisms (SNPs) from just one chromosome. We thus propose methods to construct ancestry informative SNP panels analyzing variants from a single chromosome, and evaluate the performance of such panels for both continental-level and sub-continental level ancestry prediction.Efficient selection of ancestry informative SNPs is the key to successful ancestry prediction. The removal of redundant and noisy SNP features is essential prior to applying a learning algorithm. Here we propose two distinct methods of SNP selection: one is correlation-based SNP selection which uses a correlation metric to evaluate the usefulness of SNP features, while the other is random subspace projection based SNP selection which uses the learning algorithm itself to evaluate the worth of the SNP features. Correlation-based SNP selection approach can construct a small panel of useful SNPs for both continental level classification as well as binary classification of subpopulations. Unlike the correlation-based selection, random subspace projection based selection can construct efficient panel of SNP markers to address the difficult task of multinomial classification with multiple closely related sub-populations. We include results that demonstrate the performance of both methods, including comparison with other recently published related methods.iii ACKNOWLEDGEMENTS

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ancGWAS: a post genome-wide association study method for interaction, pathway and ancestry analysis in homogeneous and admixed populations

Abstract: The ancGWAS package and documents are available at http://www.cbio.uct.ac.za/~emile/software.html.

Cited by 35 publications

References 30 publications

A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility

A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility

Development of Bioinformatics Infrastructure for Genomics Research

Inference of Biogeographical Ancestry Under Resource Constraints

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