基于基因型选择提高QTL作图的精度——以一个RIL群体为例

以PCR为基础的分子标记以及其他检测技术的发展,使得大规模的标记分析成为现实。这也为通过大群体标记分析,然后基于基因型选择挑选合适的小群体,从而提高QTL定位准确性和精度提供了可能。以一个包含294个家系的重组自交系（RIL）群体为例,通过基因型选择和随机选择的办法产生了一系列大小不等的亚群体,比较了两类群体QTL定位的结果。分析表明：相同大小的基因型选择群体与随机群体相比性状的表型分布都符合正态分布;标记的偏分离情况也没有明显的差别,都随着群体大小的增大,偏分离的比例也逐渐增大。但同等大小的基因型选择群体比随机群体的交换富集率（CE）要大,且随着选择强度的增大不断增大,如群体大小为270时,CE=1．04,群体大小为30时,CE=1．45。总体上,随着群体大小的增加,不管是随机群体还是选择群体,其QTL检测能力、灵敏性和特异性也随之增加,但选择群体的检测能力、灵敏性和特异性总体上要好于随机群体。当群体大于或等于240时,其QTL检测能力基本没有差别;群体大小大于或等于210时,其QTL检测的灵敏性和特异性也没有什么差别。这也说明：选择强度越大,效果越明显。以QTLI—LOD区间作为衡量QTL精度的一个指标,结果显示所有基因型选择群体都比相同大小随机群体的QTL定位精度高。目前QTL定位研究中,基因型数据较表型数据而言更容易准确获得,因此通过基因型选择可以更好的优化群体结构,减少田间实验的工作量,提高全基因组水平QTL作图的精度,为随后的QTL辅助选择和精细定位以及克隆提供帮助。     

大豆重组自交系群体（ZKS-HX）遗传图谱的构建和形态标记的定位

以大豆品种‘合丰25’为母本,半野生大豆‘新民6号’为父本杂交得到的F2-9代122个重组自交系为试验材料,构建了含有124个SSR标记、1个EST标记、3个形态学标记的大豆遗传图谱。此图谱覆盖的基因组长度为2348．3cM．标记间平均距离为18．3cM。每个连锁群长度范围为15．1～195．9cM之间,标记数范围2—10个。本文将控制茸毛色（Pb）基因定位于LG06-C2连锁群上,与Sat_40x2的遗传距离为39．6cM;控制叶耳g（Le）、花色（4W,）基因定位于LG12-F连锁群上,它们之间的遗传距离为9．9cM,与两边的Satt348、Sat_240标记遗传距离分别为13．3cM和10．5cM。     

Sowing (and mapping) the wild oats

Both ecological and molecular methods have been applied to the study of locally adapted populations. While Clausen et al . (1940) were the pioneers of field transplant studies, the work of Robert Allard et al . on the invasive species Avena barbata is a classic example of using molecular evidence to infer adaptation via correlation of particular alleles with environmental gradients. In a new study published in this issue of Molecular Ecology, Latta (2009) combines ecological methods (reciprocal environment studies) with quantitative genetic techniques (recombinant inbred lines and quantitative trait analysis) to provide new evidence for local adaptation within this well known system. The conclusions are orthogonal to the original hypothesis, and instead, provide evidence that factors other than local adaptation were likely responsible for the historically observed patterns. This new evidence suggests that one ecotype is generally more fit than the other in both a moist and a dry environment, and accordingly, it appears to be increasing in frequency in historically surveyed populations.     

Improving QTL Mapping Resolution Based on Genotypic Sampling—a Case Using a RIL Population

The QTL mapping results were compared with the genotypically selected and random samples of the same size on the base of a RIL population. The results demonstrated that there were no obvious differences in the trait distribution and marker segregation distortion between the genotypically selected and random samples with the same population size. However, a significant increase in QTL detection power, sensitivity, specificity, and QTL resolution in the genotypically selected samples were observed. Moreover, the highly significant effect was detected in small size of genotypically selected samples. In QTL mapping, phenotyping is a more sensitive limiting factor than genotyping so that the selection of samples could be an attractive strategy for increasing genome-wide QTL mapping resolution. The efficient selection of samples should be more helpful for QTL maker assistant selection, fine mapping, and QTL cloning.