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Genome-wide association studies for microbial genomes

This overview presents a synthesis of genome-wide association studies (GWAS) focused on microbial genomes. It elaborates on the methodologies utilized to correlate protein functions and phenotypic traits, highlighting the crucial role of comparative genomics in linking genes to phenotypes. The synthesis covers data from various studies, discussing the importance of standardizing phenotype measurements and the need for comprehensive databases. Insights into the influence of environmental factors and the utility of genomic sequences for understanding microbial behavior are also addressed, emphasizing future directions for research.

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Genome-wide association studies for microbial genomes

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  1. Genome-wide association studies for microbial genomes Bas E. Dutilh March 4th 2013

  2. Protein function • Phenotypicfunction • E.g. apoptosis • GO: Biologicalprocess • Cellularfunction • E.g. ribosome • GO: Cellular component • Molecularfunction • E.g. transcriptionfactor • GO: Molecularfunction Bork et al. J. Mol. Biol. 1998 GO Consortium Genome Res. 2001

  3. Molecular function ↔ phenotype • Molecular systems biology • First determine protein functions • … then model how functions lead to phenotype • Comparative genomics • First sequence a set of genomes with different phenotypes • … then link genes to phenotype

  4. 1998: differential genome analysis • Virulence factors • De-acidifiers Huynenet al.FEBS Lett. 1998

  5. 2013: microbial GWAS • 210 Vibrio cholerae genomes • 3 niche dimensions • Time • Space • Habitat • 24,000+ variables • Proteinfamilies • Functions • Prophages • SNPs • Largepsmalln • Risk of over-trainingmanygenotypes to few samples Dutilh et al. in preparation

  6. Pre-processing • Genotypes • Highlycorrelatinggenotypes • E.g. genes in one operon • Delete monotonous features • E.g. housekeepinggenes • Phenotypes • Discard ambiguousphenotypes (noise) • E.g. growth: Yes / No / Partial / Unknown • Decreaseclassimbalancebybagging • Largestclass ≤ 2x smallestclass PhenoLink Bayjanovet al. BMC Genomics 2012

  7. Random Forest Training Testing V4 V2 V2 V4

  8. Importance score • Space (continent) • Phagepackagingmachinery • Bacteriophage P4 cluster • R1t-like Streptococcalphages • PhagefamilyInoviridae • Integrons • CBSS.350688.3.peg.1509 • Potassiumhomeostasis • Phenazinebiosynthesis • Cyanophage • Outermembraneproteins Importance →

  9. Fromstatistics to biology • GO termsenrichment • Visualization • Metabolic map • STRING database Franceschiniet al.Nucl. Acids Res. 2013

  10. Bottlenecks formicrobial GWAS? • Genome sequencing and annotation • SNPs: mutationsorindels • Presence/absence of orthologs (gene content) • Phages • Phenotypesmeasuredconsistently • Standard phenotypemicroarray • Specializedphenotypemicroarray (e.g. forspecies) to bring out differences (e.g. betweenstrains) • Make these data available • Central database Dutilh et al. Brief. Funct. Genomics 2013

  11. Transcriptome-traitmapping in L. plantarum • ± NaCl • Aminoacids • Temperature • pH • Oxic / anoxic van Bokhorst-van de Veen et al.PLoS ONE 2012

  12. Survival in simulated GI tract Goodsurvivors Bad survivors van Bokhorst-van de Veen et al.PLoS ONE 2012

  13. Geneswhoseexpressionpredicts survival Positive correlationwithsurvival Negativecorrelationwith survival van Bokhorst-van de Veen et al.PLoS ONE 2012

  14. Conclusions • The many (draft) genomes can be exploited for linking phenotype to genotype on a genomic scale • Consistently measured phenotypes across a series of sequenced strains are still rare • Phenotype microarrays should be measured for every sequenced genome (cultured) • Central repository for PM data is needed • Transcriptome-trait mapping within one species • Metagenome-trait mapping for communities

  15. Thank you

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