Population genomic approaches, which take advantages of high-throughput genotyping, are powerful yet costly methods to scan for selective sweeps. DNA-pooling strategies have been widely used for association studies be...Population genomic approaches, which take advantages of high-throughput genotyping, are powerful yet costly methods to scan for selective sweeps. DNA-pooling strategies have been widely used for association studies because it is a cost-effective alternative to large-scale individual genotyping. Here, we performed an SNP-MaP （single nucleotide polymorphism microarrays and pooling） analysis using samples from Eurasia to evaluate the efficiency of pooling strategy in genome-wide scans for selection. By conducting simulations of allelotype data, we first demonstrated that the boxplot with average heterozygosity （HET） is a promising method to detect strong selective sweeps with a moderate level of pooling error. Based on this, we used a sliding window analysis of HET to detect the large contiguous regions （LCRs） putatively under selective sweeps from Eurasia datasets. This survey identified 63 LCRs in a European population. These signals were further supported by the integrated haplotype score （iHS） test using HapMap II data. We also confirmed the European-specific signatures of positive selection from several previously identified genes （KEL, TRPV5, TRPV6, EPHB6）. In summary, our results not only revealed the high credibility of SNP-MaP strategy in scanning for selective sweeps, but also provided an insight into the population differentiation.展开更多
[目的]小麦遗传图谱是进行小麦染色体分析和研究表型变异的遗传基础。通过利用传统分子标记和现代基因芯片技术相结合,构建高密度遗传图谱,重点开展主要产量主要构成要素——粒重的初级基因定位,确定影响粒重的主效QTL位点,为开发粒重C...[目的]小麦遗传图谱是进行小麦染色体分析和研究表型变异的遗传基础。通过利用传统分子标记和现代基因芯片技术相结合,构建高密度遗传图谱,重点开展主要产量主要构成要素——粒重的初级基因定位,确定影响粒重的主效QTL位点,为开发粒重CAPS分子标记及在分子标记辅助育种提供依据和指导,并为利用小麦粒重次级群体进行精细定位和基因挖掘奠定基础。[方法]利用90 K小麦SNP基因芯片、DArt芯片技术及传统的分子标记技术,以包含173个家系的RIL群体(F9:10重组自交系)为材料,构建高密度遗传图谱,并利用QTL network2.0进行了3年共4环境粒重QTL分析。[结果]构建了覆盖小麦21条染色体的高密度遗传图谱,该图谱共含有6 244个多态性标记,其中SNP标记6 001个、DAr T标记216个、SSR标记27个,覆盖染色体总长度4 875.29 c M,标记间平均距离0.78 c M。A、B、D染色体组分别有2 390、3 386和468个标记,分别占总标记数的38.3%、54.3%和7.5%;3个染色体组标记间平均距离分别为0.80、0.75和0.80 c M。用该分子遗传图谱对4个环境下粒重进行QTL分析,检测到位于1B、4B、5B、6A染色体上9个加性QTL,效应值大于10%的QTL位点有QGW4B-17、QGW4B-5、QGW4B-2、QGW6A-344、QGW6A-137;其中QGW4B-17在多个环境下检测到,其贡献率为16%—33.3%,可增加粒重效应值2.30-2.97g,该位点是稳定表达的主效QTL。9个QTL的加性效应均来自大粒母本山农01-35,单个QTL位点加性效应可增加千粒重1.09—2.97 g。[结论]构建的覆盖小麦21条染色体的分子遗传图谱共含有6 241个多态性标记,标记间平均距离为0.77 c M。利用该图谱检测到位于1B、4B、5B、6A染色体上9个控制粒重的加性QTL,其中QGW4B-17是稳定表达的主效QTL位点,贡献率为16.5%—33%,可增加粒重效应值2.30—2.97 g。展开更多
基金supported by the National Natural Science Foundation of China (No. 30871348 and 30700470)Educational Department of Jiangxi Province (No. GJJ10303)National Key Laboratory Specific Fund (No. 2060204)
文摘Population genomic approaches, which take advantages of high-throughput genotyping, are powerful yet costly methods to scan for selective sweeps. DNA-pooling strategies have been widely used for association studies because it is a cost-effective alternative to large-scale individual genotyping. Here, we performed an SNP-MaP （single nucleotide polymorphism microarrays and pooling） analysis using samples from Eurasia to evaluate the efficiency of pooling strategy in genome-wide scans for selection. By conducting simulations of allelotype data, we first demonstrated that the boxplot with average heterozygosity （HET） is a promising method to detect strong selective sweeps with a moderate level of pooling error. Based on this, we used a sliding window analysis of HET to detect the large contiguous regions （LCRs） putatively under selective sweeps from Eurasia datasets. This survey identified 63 LCRs in a European population. These signals were further supported by the integrated haplotype score （iHS） test using HapMap II data. We also confirmed the European-specific signatures of positive selection from several previously identified genes （KEL, TRPV5, TRPV6, EPHB6）. In summary, our results not only revealed the high credibility of SNP-MaP strategy in scanning for selective sweeps, but also provided an insight into the population differentiation.
文摘[目的]小麦遗传图谱是进行小麦染色体分析和研究表型变异的遗传基础。通过利用传统分子标记和现代基因芯片技术相结合,构建高密度遗传图谱,重点开展主要产量主要构成要素——粒重的初级基因定位,确定影响粒重的主效QTL位点,为开发粒重CAPS分子标记及在分子标记辅助育种提供依据和指导,并为利用小麦粒重次级群体进行精细定位和基因挖掘奠定基础。[方法]利用90 K小麦SNP基因芯片、DArt芯片技术及传统的分子标记技术,以包含173个家系的RIL群体(F9:10重组自交系)为材料,构建高密度遗传图谱,并利用QTL network2.0进行了3年共4环境粒重QTL分析。[结果]构建了覆盖小麦21条染色体的高密度遗传图谱,该图谱共含有6 244个多态性标记,其中SNP标记6 001个、DAr T标记216个、SSR标记27个,覆盖染色体总长度4 875.29 c M,标记间平均距离0.78 c M。A、B、D染色体组分别有2 390、3 386和468个标记,分别占总标记数的38.3%、54.3%和7.5%;3个染色体组标记间平均距离分别为0.80、0.75和0.80 c M。用该分子遗传图谱对4个环境下粒重进行QTL分析,检测到位于1B、4B、5B、6A染色体上9个加性QTL,效应值大于10%的QTL位点有QGW4B-17、QGW4B-5、QGW4B-2、QGW6A-344、QGW6A-137;其中QGW4B-17在多个环境下检测到,其贡献率为16%—33.3%,可增加粒重效应值2.30-2.97g,该位点是稳定表达的主效QTL。9个QTL的加性效应均来自大粒母本山农01-35,单个QTL位点加性效应可增加千粒重1.09—2.97 g。[结论]构建的覆盖小麦21条染色体的分子遗传图谱共含有6 241个多态性标记,标记间平均距离为0.77 c M。利用该图谱检测到位于1B、4B、5B、6A染色体上9个控制粒重的加性QTL,其中QGW4B-17是稳定表达的主效QTL位点,贡献率为16.5%—33%,可增加粒重效应值2.30—2.97 g。
