Genome-wide association analyses in the model rhizobium Ensifer meliloti

Brendan Epstein, Reda A.I. Abou-Shanab, Abdelaal Shamseldin, Margaret R. Taylor, Joseph Guhlin, Liana T. Burghardt, Matthew Nelson, Michael J. Sadowsky, Peter Tiffin

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Association studies therefore provide a powerful complement to approaches that rely on de novo mutations for characterizing gene function. Although bacteria should be amenable to GWAS, few GWAS have been conducted on bacteria, and the extent to which nonindependence among genomic variants (e.g., linkage disequilibrium [LD]) and the genetic architecture of phenotypic traits will affect GWAS performance is unclear. We apply association analyses to identify candidate genes underlying variation in 20 biochemical, growth, and symbiotic phenotypes among 153 strains of Ensifer meliloti. For 11 traits, we find genotype-phenotype associations that are stronger than expected by chance, with the candidates in relatively small linkage groups, indicating that LD does not preclude resolving association candidates to relatively small genomic regions. The significant candidates show an enrichment for nucleotide polymorphisms (SNPs) over gene presence-absence variation (PAV), and for five traits, candidates are enriched in large linkage groups, a possible signature of epistasis. Many of the variants most strongly associated with symbiosis phenotypes were in genes previously identified as being involved in nitrogen fixation or nodulation. For other traits, apparently strong associations were not stronger than the range of associations detected in permuted data. In sum, our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. However, careful evaluation of candidates is necessary to avoid false signals of association.

Original languageEnglish (US)
Article numbere00386-18
JournalmSphere
Volume3
Issue number5
DOIs
StatePublished - Sep 1 2018

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under grant numbers IOS-1237993 and IOS-1724993; additional support came from USDA-HATCH Award MIN-71-030. Computing resources were provided by the Minnesota Supercomputing Institute (MSI) at the University of Minnesota. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Funding Information:
This material is based upon work supported by the National Science Foundation under grant numbers IOS-1237993 and IOS-1724993; additional support came from USDA-HATCH Award MIN-71-030. Computing resources were provided by the Minnesota Supercomputing Institute (MSI) at the University of Minnesota.

Publisher Copyright:
© 2018 Epstein et al.

Keywords

  • BSLMM
  • Bacteria
  • Chip heritability
  • GWAS
  • Genetic architecture
  • Genomics
  • Linkage disequilibrium
  • Medicago
  • Rhizobium
  • Sinorhizobium
  • Symbiosis

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