Department of Agronomy, Kansas State University, Manhattan, Kansas 66506, USA.
Genome Res. 2012 Dec;22(12):2436-44. doi: 10.1101/gr.140277.112. Epub 2012 Jun 14.
The complex genomes of many economically important crops present tremendous challenges to understand the genetic control of many quantitative traits with great importance in crop production, adaptation, and evolution. Advances in genomic technology need to be integrated with strategic genetic design and novel perspectives to break new ground. Complementary to individual-gene-targeted research, which remains challenging, a global assessment of the genomic distribution of trait-associated SNPs (TASs) discovered from genome scans of quantitative traits can provide insights into the genetic architecture and contribute to the design of future studies. Here we report the first systematic tabulation of the relative contribution of different genomic regions to quantitative trait variation in maize. We found that TASs were enriched in the nongenic regions, particularly within a 5-kb window upstream of genes, which highlights the importance of polymorphisms regulating gene expression in shaping the natural variation. Consistent with these findings, TASs collectively explained 44%-59% of the total phenotypic variation across maize quantitative traits, and on average, 79% of the explained variation could be attributed to TASs located in genes or within 5 kb upstream of genes, which together comprise only 13% of the genome. Our findings suggest that efficient, cost-effective genome-wide association studies (GWAS) in species with complex genomes can focus on genic and promoter regions.
许多具有重要经济价值的作物基因组非常复杂,这给理解许多对作物生产、适应和进化具有重要意义的数量性状的遗传控制带来了巨大的挑战。基因组技术的进步需要与战略遗传设计和新视角相结合,以开拓新的领域。除了具有挑战性的个别基因靶向研究之外,对从数量性状基因组扫描中发现的与性状相关的 SNP(TAS)的基因组分布进行全面评估,可以深入了解遗传结构,并有助于未来研究的设计。在这里,我们报告了第一个对玉米中不同基因组区域对数量性状变异相对贡献的系统制表。我们发现 TAS 在非基因区域富集,特别是在基因上游的 5kb 窗口内,这突出了调节基因表达的多态性在塑造自然变异中的重要性。与这些发现一致,TAS 共同解释了玉米数量性状总表型变异的 44%-59%,平均而言,可归因于基因内或基因上游 5kb 内 TAS 的解释变异占 79%,而这些区域仅占基因组的 13%。我们的研究结果表明,对于具有复杂基因组的物种,高效、经济的全基因组关联研究(GWAS)可以集中在基因和启动子区域。