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.IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in Ensifer meliloti, an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations.
Highlights
Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation
GWAS have identified both previously known and novel operons associated with nickel tolerance in Mesorhizobium [15], copy-number variants associated with alginate metabolism in Vibrio [16], presence-absence variation (PAV) of known and novel virulence factors associated with infectivity in Listeria [17], antibiotic resistance genes in several lineages [18, 19], and both gene presence-absence and nucleotide variants associated with host range of Campylobacter [20]
We used the Illumina platform to sequence 153 Ensifer meliloti strains to a mean depth of 19.5ϫ, and aligned reads to E. meliloti strain USDA1106 to identify genomic variants
Summary
Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. Association analyses, or genome-wide association studies (GWAS), provide a complementary approach for identifying gene function by using statistical approaches to associate naturally occurring allelic variation with phenotypic variation [1]. GWAS have identified both previously known and novel operons associated with nickel tolerance in Mesorhizobium [15], copy-number variants associated with alginate metabolism in Vibrio [16], presence-absence variation (PAV) of known and novel virulence factors associated with infectivity in Listeria [17], antibiotic resistance genes in several lineages [18, 19], and both gene presence-absence and nucleotide variants associated with host range of Campylobacter [20]
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