Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota, USA.
Biotechnology Institute, University of Minnesota, Saint Paul, Minnesota, USA.
mSphere. 2018 Oct 24;3(5):e00386-18. doi: 10.1128/mSphere.00386-18.
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 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 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. 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 , 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.
全基因组关联研究 (GWAS) 可以鉴定导致自然发生和数量表型变异的遗传变异。因此,关联研究为依赖于突变来描述基因功能的方法提供了有力的补充。尽管细菌应该适合进行 GWAS,但很少有对细菌进行 GWAS,并且基因组变异之间的非独立性(例如连锁不平衡 [LD])和表型性状的遗传结构将如何影响 GWAS 性能尚不清楚。我们应用关联分析来确定 153 株 中 20 种生化、生长和共生表型变异的候选基因。对于 11 个性状,我们发现与基因型相关的表型关联比偶然预期的要强,候选基因位于相对较小的连锁群中,这表明 LD 不会排除将关联候选基因解析到相对较小的基因组区域。显著的候选基因显示核苷酸多态性 (SNP) 比基因存在缺失变异 (PAV) 更为丰富,对于五个性状,候选基因在大的连锁群中富集,这可能是上位性的一个特征。与共生表型最密切相关的许多变异都在以前被确定为参与固氮或结瘤的基因中。对于其他性状,明显的强关联并不比在随机数据中检测到的关联范围更强。总之,我们的数据表明,细菌中的 GWAS 可能是一种用于描述遗传结构和鉴定负责表型变异的基因的有力工具。然而,需要仔细评估候选基因,以避免关联的虚假信号。全基因组关联分析是一种用于鉴定基因功能的强大方法。这些分析在人类、家养动物和农作物的研究中已经很常见,但在细菌中很少进行。我们将关联分析应用于在与豆科植物共生时固氮的农业和生态上重要的细菌 中测量的 20 个性状。我们确定了共生性状、抗生素抗性和利用各种碳源的变异的候选等位基因和基因存在缺失变异;其中一些候选基因在以前已知影响这些性状的基因中,而其他基因则在尚未很好表征的基因中。我们的结果指出了关联分析在细菌中的潜在力量,但也需要仔细评估虚假关联的可能性。