Advances in quantitative trait analysis in yeast.
Advances in quantitative trait analysis in yeast.
复制标题
DOI：
10.1371/journal.pgen.1002912
复制
发表时间：
2012
期刊：
影响因子：
4.5
通讯作者：
Louis EJ
中科院分区：
文献类型：
--
作者：
Liti G;Louis EJ
了解复杂性状的遗传机制是生物学的下一个前沿领域之一。出芽酵母酿酒酵母已成为阐明自然遗传和表型变异机制的重要模型。这种成功的部分原因是由于其固有的生物学特性，如有性繁殖时间短，减数分裂重组率高，基因组大小小。精确的反向遗传学技术允许高通量操纵遗传信息与精致的精度，提供了独特的机会，实验测量遗传变异的表型效应。种群基因组学和现象学研究揭示了不同种群之间的广泛差异、人为环境的特征以及野生菌株的地理集群以及自然发生的重组菌株（马赛克）。在这里，我们回顾了这些最近的研究，并提供了一个视角，这些以前未被认识到的变异水平如何有助于弥合我们对基因型-表型差距的理解，使出芽酵母保持在遗传研究的前沿。不仅定量性状位点（QTL）被高分辨率地定位到核苷酸上，而且首次通过实验以前所未有的水平确定了适度效应的QTL和这些QTL之间以及QTL与环境之间的复杂相互作用，使用了选择的个体池以及多代杂交的下一代深度测序技术。
Understanding the genetic mechanisms underlying complex traits is one of the next frontiers in biology. The budding yeast Saccharomyces cerevisiae has become an important model for elucidating the mechanisms that govern natural genetic and phenotypic variation. This success is partially due to its intrinsic biological features, such as the short sexual generation time, high meiotic recombination rate, and small genome size. Precise reverse genetics technologies allow the high throughput manipulation of genetic information with exquisite precision, offering the unique opportunity to experimentally measure the phenotypic effect of genetic variants. Population genomic and phenomic studies have revealed widespread variation between diverged populations, characteristic of man-made environments, as well as geographic clusters of wild strains along with naturally occurring recombinant strains (mosaics). Here, we review these recent studies and provide a perspective on how these previously unappreciated levels of variation can help to bridge our understanding of the genotype-phenotype gap, keeping budding yeast at the forefront of genetic studies. Not only are quantitative trait loci (QTL) being mapped with high resolution down to the nucleotide, for the first time QTLs of modest effect and complex interactions between these QTLs and between QTLs and the environment are being determined experimentally at unprecedented levels using next generation techniques of deep sequencing selected pools of individuals as well as multi-generational crosses.
登录
查看更多内容
影响因子：
4.5
作者：
Demogines, Ann;Smith, Erin;Kruglyak, Leonid;Alani, Eric
通讯作者：
Alani, Eric
影响因子：
64.8
作者：
Joron, Mathieu;Frezal, Lise;Jones, Robert T.;Chamberlain, Nicola L.;Lee, Siu F.;Haag, Christoph R.;Whibley, Annabel;Becuwe, Michel;Baxter, Simon W.;Ferguson, Laura;Wilkinson, Paul A.;Salazar, Camilo;Davidson, Claire;Clark, Richard;Quail, Michael A.;Beasley, Helen;Glithero, Rebecca;Lloyd, Christine;Sims, Sarah;Jones, Matthew C.;Rogers, Jane;Jiggins, Chris D.;Ffrench-Constant, Richard H.
通讯作者：
Ffrench-Constant, Richard H.
影响因子：
4.5
作者：
Granek JA;Magwene PM
通讯作者：
Magwene PM
影响因子：
12.3
作者：
Cook RK;Christensen SJ;Deal JA;Coburn RA;Deal ME;Gresens JM;Kaufman TC;Cook KR
通讯作者：
Cook KR
影响因子：
10.7
作者：
Illingworth, Christopher J. R.;Parts, Leopold;Mustonen, Ville
通讯作者：
Mustonen, Ville