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Microbial genome-wide association studies: lessons from human GWAS

Nature Reviews Genetics volume 18, pages 41–50 (2017)Cite this article

Subjects

Key Points

  • Genome-wide association studies (GWAS) have been highly successful in the analyses of human genomic data. The increased availability of microorganism whole genomes provides the opportunity for microbial GWAS.

  • Initial microbial GWAS have had success identifying variants for traits under strong selection, such as drug resistance, in a range of bacteria, viruses and protozoa.

  • Several challenges to microbial GWAS exist that could hinder identifying variants under moderate selection. The primary challenge is the increased population stratification in microorganisms owing to selection and complex recombination patterns.

  • Novel software that is tailored to the needs of microbial GWAS would greatly expedite progress in the field. In particular, the application of polygenic methods has yet to be evaluated in microorganisms.

  • An exciting future area of research is the generation of host and microbial genomics data within the same samples. This will allow for genome-to-genome analyses to test for host–microorganism interactions.

Abstract

The reduced costs of sequencing have led to whole-genome sequences for a large number of microorganisms, enabling the application of microbial genome-wide association studies (GWAS). Given the successes of human GWAS in understanding disease aetiology and identifying potential drug targets, microbial GWAS are likely to further advance our understanding of infectious diseases. These advances include insights into pressing global health problems, such as antibiotic resistance and disease transmission. In this Review, we outline the methodologies of GWAS, the current state of the field of microbial GWAS, and how lessons from human GWAS can direct the future of the field.

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Figure 1: Phenotype prediction as GWAS sample sizes increase.
Figure 2: Potential models for microbial GWAS.

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Acknowledgements

Research supported by a South African MRC Flagship grant (MRC-RFA-UFSP-01-2013/UKZN HIVEPI), Wellcome Trust grants (098051 and 201355/Z/16/Z) and a Royal Society Newton Advanced Fellowship.

Author information

Authors and Affiliations

  1. Africa Centre for Population Health, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa

    Robert A. Power & Tulio de Oliveira

  2. Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, Cambridge, UK

    Julian Parkhill & Tulio de Oliveira

  3. Centre for the AIDS Programme of Research in South Africa (CAPRISA), Private Bag X7, Durban, 4013, South Africa

    Tulio de Oliveira

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  1. Robert A. Power
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  3. Tulio de Oliveira
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Correspondence to Robert A. Power.

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PowerPoint slides

Glossary

Genome-wide association studies

(GWAS). A hypothesis-free method that tests hundreds of thousands of variants across the genome to identify alleles that are associated with a phenotype.

Single-nucleotide polymorphisms

(SNPs). A base position where two alleles exist with a frequency of >1% in the population.

Heritability

The proportion of phenotypic variance that is due to inherited genetic variation.

Beta

The standardized regression coefficient, derived from linear regressions in genome-wide association studies of continuous traits. It is reported as an estimate of the effect size of a single-nucleotide polymorphism (SNP), and reflects the change in phenotype expected from carrying a copy of the reference allele of the SNP.

Odds ratio

(OR). The typical means of reporting the effect size of a single-nucleotide polymorphism in a case–control (or other binary phenotype) genome-wide association study. It is derived from a logistic regression, and represents the odds of the phenotype when carrying the reference allele, compared with the odds of the phenotype in the absence of the reference allele.

Main effects

The effects of a variant on the phenotype without accounting for any possible interactions with other variants or environmental factors.

Epistatic interactions

Interactions between variants at different locations in the genome.

Power

The probability that an analysis will reject the null hypothesis when the alternative hypothesis is true. It is influenced by numerous factors, such as the effect size and sample size.

Linkage disequilibrium

(LD). Correlations between variants due to co-inheritance. LD is usually higher between variants that are closer together, and is broken down by recombination.

Phred scores

A measure of the quality of sequencing at a given locus, specifically the confidence in the calling of alleles at that locus.

K-mers

A sequence of bases of length k that, in microbial genome-wide association studies, can be used as the genetic variant tested for association with the phenotype.

Superinfection

When an individual is infected with multiple strains of the same microorganism.

False positives

Variants, or any other predictors, that are identified as significantly associated with a phenotype but that are not causal. In the case of genome-wide association studies, this is usually due to confounding from population structure or insufficient quality control.

Clonal

The case in which reproduction produces genetically identical organisms, and so does not introduce novel variants or recombination.

Panmictic

A population in which clonal structure has been lost due to frequent recombination.

Genome-wide significance

The P value cut-off for declaring a variant significantly associated with a phenotype, accounting for the number of variants tested and the correlations between them.

Effect size

The proportion of variance in a phenotype predicted by a variant.

Polygenic methods

Statistical approaches that focus on the combined effects of many genetic variants rather than on the effect of any individual variant.

Pleiotropic

Pleiotropic variants are those that have an effect on multiple distinct phenotypes.

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Power, R., Parkhill, J. & de Oliveira, T. Microbial genome-wide association studies: lessons from human GWAS. Nat Rev Genet 18, 41–50 (2017). https://doi.org/10.1038/nrg.2016.132

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