Genome-wide association studies for agronomical traits in a world wide spring barley collection.
Genome-wide association studies for agronomical traits in a world wide spring barley collection.
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DOI：
10.1186/1471-2229-12-16
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发表时间：
2012-01-27
影响因子：
5.3
通讯作者：
Graner A
中科院分区：
文献类型：
--
作者：
Pasam RK;Sharma R;Malosetti M;van Eeuwijk FA;Haseneyer G;Kilian B;Graner A
基于连锁不平衡（LD）的全基因组关联研究（GWAS）为复杂农艺性状的数量性状位点（QTL）检测和精细定位提供了一种有前景的工具。本研究探讨了224个不同品种春大麦抽穗期、株高、千粒重、淀粉含量和粗蛋白质含量等性状变异的遗传基础。使用Illumina的GoldenGate技术，使用包含1536个snp的定制寡核苷酸池测定对整个面板进行基因分型，最终获得957个成功的snp，覆盖所有染色体。形态学特征“行型”（二排与六排）证实了该方法的高选择性和敏感性。本研究利用GWAS技术对上述农艺性状的QTL进行了检测。用不同的方法和6个亚群对种群结构进行了调查，这些亚群主要基于它们的穗形和起源区域。我们探索了所有7条大麦染色体的连锁不平衡（LD）模式。在5-10 cM的地图距离内，观察到平均LD衰减到临界水平（r2值0.2）以下。面板内所有性状的表型变异都相当大。在多环境试验中，各性状的遗传率在0.90-0.95之间。检验了不同的统计模型来控制种群结构引起的虚假LD，并计算了标记-性状关联的p值。利用带有亲缘关系的混合线性模型控制伪LD效应，共发现171个显著的标记性状关联，共划分为107个QTL区域。在所有性状中，这些QTL可以分为57个新的QTL和50个与先前绘制的QTL位置一致的QTL。结果表明，在适当考虑群体结构的前提下，所描述的多样化大麦群体可以有效地用于各种数量性状的GWAS。观察到的显著标记性状关联为大麦重要农艺性状的遗传结构提供了精细的见解。然而，单个QTL只占表型变异的一小部分，这可能是由于标记覆盖率不足和/或在分析之前消除了罕见的等位基因。组合SNP效应无法解释完整的表型变异，这一事实可能支持这样的假设，即数量性状的表达是由大量逃避检测的非常小的效应引起的。尽管存在这些局限性，GWAS与双亲本连锁图谱的整合以及不断增加的基因组序列信息将有助于系统地分离农学上重要的基因，并随后分析它们的等位基因多样性。
Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS. Population structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions. Our results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversity.
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影响因子：
3.3
作者：
Breseghello, F;Sorrells, ME
通讯作者：
Sorrells, ME
DOI：
10.1101/sqb.2003.68.69
发表时间：
2003-01-01
期刊：
COLD SPRING HARBOR SYMPOSIA ON QUANTITATIVE BIOLOGY
影响因子：
--
作者：
Fan, JB;Oliphant, A;Chee, MS
通讯作者：
Chee, MS
影响因子：
5.8
作者：
Bradbury, Peter J.;Zhang, Zhiwu;Buckler, Edward S.
通讯作者：
Buckler, Edward S.
影响因子：
3.3
作者：
Faure, Sebastien;Higgins, Janet;Laurie, David A.
通讯作者：
Laurie, David A.
影响因子：
56.9
作者：
Buckler, Edward S.;Holland, James B.;McMullen, Michael D.
通讯作者：
McMullen, Michael D.