mpirical Comparisons of Different Statistical Models to Identify and Validate Kernel Row Number-Associated Variants from Structured Multiparent Mapping Populations of Maize

Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and false discovery rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant maize (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and Bayesian MV is effective, through identification and cross-validation of trait associated loci. In this study, kernel row MPP number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (MV) models. Using SV, STR, and MV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the developmental steps involved in ear development.

新一代测序技术与统计分析方法的进步，使得研究者能够通过全基因组关联分析（genome-wide association study, GWAS）实现对表型性状的全基因组解析。尽管当前已有多种可用于GWAS的统计分析方法，但多数方法的统计效力与错误发现率大多仅通过模拟数据完成验证。此前尚未有研究通过性状关联位点的鉴定与交叉验证，对比检验单一分析方法，或单变异位（single variant, SV）与贝叶斯玉米多变异位（multi-variant maize, MV）组合方法是否具备更佳分析效果。本研究从嵌套关联作图（Nested Association Mapping, NAM）群体及相关群体衍生的6230份材料中，收集了穗行数（kernel row MPP number, KRN）表型数据。本研究共开展三类GWAS分析：1）单变异位分析（single-variant, SV）；2）逐步回归分析（stepwise regression, STR）；3）基于贝叶斯框架的多变异位模型（Bayesian-based multi-variant, MV）。通过SV、STR与MV模型，初始GWAS分析分别鉴定得到257、300及442个与KRN相关的变异位点（KRN-associated variants, KAVs）。其中，231个KAVs通过初始GWAS未涉及的3个独立群体开展了遗传验证。遗传验证结果表明，三类GWAS分析方法具备互补性。值得注意的是，相较于重组活跃区域的KAVs，低重组区域的KAVs在独立群体中更易检测到关联信号，这一现象可能由连锁不平衡（linkage disequilibrium, LD）所致。本研究鉴定得到的KAVs，有助于加深我们对玉米雌穗发育相关过程的理解。