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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.
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A comparative approach for selecting orthologous candidate genes underlying signal in genome-wide association studies across multiple species
Advances in quantitative genetics have enabled researchers to identify genomic regions associated with changes in phenotype. However, genomic regions can contain hundreds to thousands of genes, and progressing from genomic regions to candidate genes is still challenging. In genome-wide association studies (GWAS) measuring elemental accumulation (ionomic) traits, a mere 5% of loci are associated with a known ionomic gene - indicating that many causal genes are still unknown. To select candidates for the remaining 95% of loci, we developed a method to identify conserved genes underlying GWAS loci in multiple species. For 19 ionomic traits, we identified 14,336 candidates across Arabidopsis, soybean, rice, maize, and sorghum. We calculated the likelihood of candidates with random permutations of the data and determined that most of the top 10% of candidates were orthologous genes linked to GWAS loci across all five species. The candidate list also includes orthologous genes with previously established ionomic functions in Arabidopsis and rice. Our methods highlight the conserved nature of ionomic genetic regulators and enable the identification of previously unknown ionomic genes.
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- Award ID(s):
- 2309932
- PAR ID:
- 10512498
- Publisher / Repository:
- bioRxiv
- Date Published:
- Journal Name:
- bioRxiv
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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