Data from: Candidate genes and genetic architecture of symbiotic and agronomic traits revealed by whole-genome, sequence-based association genetics in Medicago truncatula

Genome-wide association study (GWAS) has revolutionized the search for the genetic basis of complex traits. To date, GWAS have generally relied on relatively sparse sampling of nucleotide diversity, which is likely to bias results by preferentially sampling high-frequency SNPs not in complete linkage disequilibrium (LD) with causative SNPs. To avoid these limitations we conducted GWAS with >6 million SNPs identified by sequencing the genomes of 226 accessions of the model legume Medicago truncatula. We used these data to identify candidate genes and the genetic architecture underlying phenotypic variation in plant height, trichome density, flowering time, and nodulation. The characteristics of candidate SNPs differed among traits, with candidates for flowering time and trichome density in distinct clusters of high linkage disequilibrium (LD) and the minor allele frequencies (MAF) of candidates underlying variation in flowering time and height significantly greater than MAF of candidates underlying variation in other traits. Candidate SNPs tagged several characterized genes including nodulation related genes SERK2, MtnodGRP3, MtMMPL1, NFP, CaML3, MtnodGRP3A and flowering time gene MtFD as well as uncharacterized genes that become candidates for further molecular characterization. By comparing sequence-based candidates to candidates identified by in silico 250K SNP arrays, we provide an empirical example of how reliance on even high-density reduced representation genomic makers can bias GWAS results. Depending on the trait, only 30–70% of the top 20 in silico array candidates were within 1 kb of sequence-based candidates. Moreover, the sequence-based candidates tagged by array candidates were heavily biased towards common variants; these comparisons underscore the need for caution when interpreting results from GWAS conducted with sparsely covered genomes.全基因组关联分析（Genome-wide association study, GWAS）彻底革新了复杂性状遗传基础的探索研究。迄今为止，GWAS通常依赖相对稀疏的核苷酸多样性采样策略，这种策略会因优先选取与致病单核苷酸多态性（single nucleotide polymorphism, SNP）未处于完全连锁不平衡（linkage disequilibrium, LD）状态的高频SNP，而导致研究结果出现偏倚。为规避这些局限，我们对226份模式豆科植物蒺藜苜蓿（Medicago truncatula）的基因组进行测序，获取了超过600万个SNP，并以此开展GWAS。利用该数据集，我们鉴定出与株高、表皮毛密度、开花时间及结瘤性状的表型变异相关的候选基因与遗传结构。不同性状的候选SNP特征存在差异：开花时间与表皮毛密度的候选SNP位于独立的高连锁不平衡（LD）簇中，且控制开花时间与株高变异的候选SNP的次要等位基因频率（minor allele frequency, MAF）显著高于控制其他性状变异的候选SNP的MAF。候选SNP标记了多个已注释功能的基因，包括结瘤相关基因SERK2、MtnodGRP3、MtMMPL1、NFP、CaML3、MtnodGRP3A以及开花时间基因MtFD，同时也标记了多个尚未注释功能的基因，为后续分子功能鉴定提供了候选靶点。通过将基于测序的候选SNP与通过计算机模拟250K SNP阵列（in silico 250K SNP arrays）鉴定得到的候选SNP进行对比，我们提供了一个实证案例，展示了即便依赖高密度简化基因组标记，仍可能对GWAS结果造成偏倚。针对不同性状，仅30%~70%的排名前20的虚拟阵列候选SNP位于基于测序的候选SNP的1kb范围内。此外，被阵列候选SNP标记的基于测序的候选SNP，高度偏向于常见变异；这些对比结果凸显了在解读基于低覆盖度基因组开展的GWAS结果时，需保持谨慎的必要性。