干旱胁迫和正常灌溉条件下玉米产量性状的QTL分析

产量及其产量因子是衡量玉米耐旱能力的重要性状。本研究利用Lo1067×Y i72的F2∶3家系进行产量性状的数量性状位点(QTL)的分析。结果表明,在正常水分条件和开花期干旱胁迫条件下,分别有14个QTL与产量性状穗重、粒重、轴重、百粒重、穗数、穗粒数有关。此外,还检测到7个与抗旱指数(TI)相关的QTL。各QTL所解释的表型变异在1%~78%;这些QTL以部分显性和超显性为主。不同胁迫条件下检测到的QTL不一致,说明存在显著的QTL与环境互作。     

油葵苗期抗旱相关性状的QTL定位及候选基因筛选

干旱是限制向日葵生长发育的重要因素之一。为探究向日葵苗期抗旱性分子机制,该研究以向日葵K55与K58杂交构建的150个F₇重组自交系群体为材料,对其在正常浇水和干旱胁迫两种水分处理条件下的叶片相对电导率、叶绿素含量、叶面积、叶片相对含水量、根长进行表型测定,利用前期建立的SNP、SSR分子标记遗传连锁图谱,通过复合区间作图法对5个抗旱相关的性状进行QTL定位。结果表明：(1)共定位到向日葵QTL位点11个,其中正常浇水条件下5个,干旱胁迫条件下6个,表型贡献率为0.768%~7.547%,且5号连锁群上定位到的QTL位点最多(3个)。(2)QTL置信区间内共筛选到62个与干旱相关的候选基因,包括位于qLA 8 1上的rna23019、rna23004、rna22661、rna22193、rna23294、rna22783和位于qCC 13 1上的rna40140,这些基因可作为后续基因克隆及功能研究的重点候选基因。该研究结果为向日葵抗旱性研究及其遗传改良奠定了基础。     

Mapping quantitative trait loci associated with southern leaf blight resistance in maize (Zea mays L.)

Southern leaf blight (SLB) caused by the fungus Cochliobolus heterostrophus (Drechs.) Drechs. is a major foliar disease of maize worldwide. Our objectives were to identify quantitative trait loci (QTL) for resistance to SLB and flowering traits in recombinant inbred line (RIL) population derived from the cross of inbred lines LM5 (resistant) and CM140 (susceptible). A set of 207 RILs were phenotyped for resistance to SLB at three time intervals for two consecutive years. Four putative QTL for SLB resistance were detected on chromosomes 3, 8 and 9 that accounted for 54% of the total phenotypic variation. Days to silking and anthesis–silking interval (ASI) QTL were located on chromosomes 6, 7 and 9. A comparison of the obtained results with the published SLB resistance QTL studies suggested that the detected bins 9.03/02 and 8.03/8.02 are the hot spots for SLB resistance whereas novel QTL were identified in bins 3.08 and 8.01/8.04. The linked markers are being utilized for marker‐assisted mobilization of QTL conferring resistance to SLB in elite maize backgrounds. Fine mapping of identified QTL will facilitate identification of candidate genes underlying SLB resistance.     

Natural genetic variation as a tool for discovery in Caenorhabditis nematodes

Over the last 20 years, studies of Caenorhabditis elegans natural diversity have demonstrated the power of quantitative genetic approaches to reveal the evolutionary, ecological, and genetic factors that shape traits. These studies complement the use of the laboratory-adapted strain N2 and enable additional discoveries not possible using only one genetic background. In this chapter, we describe how to perform quantitative genetic studies in Caenorhabditis, with an emphasis on C. elegans. These approaches use correlations between genotype and phenotype across populations of genetically diverse individuals to discover the genetic causes of phenotypic variation. We present methods that use linkage, near-isogenic lines, association, and bulk-segregant mapping, and we describe the advantages and disadvantages of each approach. The power of C. elegans quantitative genetic mapping is best shown in the ability to connect phenotypic differences to specific genes and variants. We will present methods to narrow genomic regions to candidate genes and then tests to identify the gene or variant involved in a quantitative trait. The same features that make C. elegans a preeminent experimental model animal contribute to its exceptional value as a tool to understand natural phenotypic variation.     

QTL mapping for two commercial traits in farmed saltwater crocodiles (Crocodylus porosus)

The recent generation of a genetic linkage map for the saltwater crocodile (Crocodylus porosus) has now made it possible to carry out the systematic searches necessary for the identification of quantitative trait loci (QTL) affecting traits of economic, as well as evolutionary, importance in crocodilians. In this study, we conducted genome‐wide scans for two commercially important traits, inventory head length (which is highly correlated with growth rate) and number of scale rows (SR, a skin quality trait), for the existence of QTL in a commercial population of saltwater crocodiles at Darwin Crocodile Farm, Northern Territory, Australia. To account for the uncommonly large difference in sex‐specific recombination rates apparent in the saltwater crocodile, a duel mapping strategy was employed. This strategy employed a sib‐pair analysis to take advantage of our full‐sib pedigree structure, together with a half‐sib analysis to account for, and take advantage of, the large difference in sex‐specific recombination frequencies. Using these approaches, two putative QTL regions were identified for SR on linkage group 1 (LG1) at 36 cM, and on LG12 at 0 cM. The QTL identified in this investigation represent the first for a crocodilian and indeed for any non‐avian member of the Class Reptilia. Mapping of QTL is an important first step towards the identification of genes and causal mutations for commercially important traits and the development of selection tools for implementation in crocodile breeding programmes for the industry.     

Possible consequences of genes of major effect: transient changes in the G-matrix

Understanding the process of evolutionary divergence requires knowledge of the strength, form, and targets of selection, as well as the genetic architecture of the divergent traits. Quantitative genetic approaches to understanding multivariate selection and genetic response to selection have proven to be powerful tools in this endeavor, particularly with respect to short-term evolution. However, the application of quantitative genetic theory over periods of substantial phenotypic change is controversial because it requires that the requisite genetic parameters remain constant over the period of time in question. We show herein how attempts to determine the stability of key genetic parameters may be misled by the many genes of small effect type of genetic architecture generally assumed in quantitative genetics. The presence of genes of major effect (GOMEs) can alter the genetic variance-covariance matrix dramatically for brief periods of time, significantly alter the rate and trajectory of multivariate evolution, and thereby mislead attempts to reconstruct or predict long term evolution.     

Mapping of post-flowering drought resistance traits in grain sorghum: association between QTLs influencing premature senescence and maturity

The identification of genetic factors underlying the complex responses of plants to drought stress provides a solid basis for improving drought resistance. The stay-green character in sorghum (Sorghum bicolor L. Moench) is a post-flowering drought resistance trait, which makes plants resistant to premature senescence under drought stress during the grainfilling stage. The objective of this study was to identify quantitative trait loci (QTLs) that control premature senescence and maturity traits, and to investigate their association under post-flowering drought stress in grain sorghum. A genetic linkage map was developed using a set of recombinant inbred lines (RILs) obtained from the cross B35 × Tx430, which were scored for 142 restriction fragment length polymorphism (RFLP) markers. The RILs and their parental lines were evaluated for post-flowering drought resistance and maturity in four environments. Simple interval mapping identified seven stay-green QTLs and two maturity QTLs. Three major stay-green QTLs (SGA, SGD and SGG) contributed to 42% of the phenotypic variability (LOD 9.0) and four minor QTLs (SGB, SGI.1, SGI.2, and SGJ) significantly contributed to an additional 25% of the phenotypic variability in stay-green ratings. One maturity QTL (DFB) alone contributed to 40% of the phenotypic variability (LOD 10.0), while the second QTL (DFG) significantly contributed to an additional 17% of the phenotypic variability (LOD 4.9). Composite interval mapping confirmed the above results with an additional analysis of the QTL × Environment interaction. With heritability estimates of 0.72 for stay-green and 0.90 for maturity, the identified QTLs explained about 90% and 63% of genetic variability for stay-green and maturity traits, respectively. Although stay-green ratings were significantly correlated (r=0.22, P ≤ 0.05) with maturity, six of the seven stay-green QTLs were independent of the QTLs influencing maturity. Similarly, one maturity QTL (DFB) was independent of the stay-green QTLs. One stay-green QTL (SGG), however, mapped in the vicinity of a maturity QTL (DFG), and all markers in the vicinity of the independent maturity QTL (DFB) were significantly (P ≤ 0.1) correlated with stay-green ratings, confounding the phenotyping of stay-green. The molecular genetic analysis of the QTLs influencing stay-green and maturity, together with the association between these two inversely related traits, provides a basis for further study of the underlying physiological mechanisms and demonstrates the possibility of improving drought resistance in plants by pyramiding the favorable QTLs. Received: 10 October 1998 / Accepted: 12 July 1999     

Comparative Map and Trait Viewer (CMTV): an integrated bioinformatic tool to construct consensus maps and compare QTL and functional genomics data across genomes and experiments

In the past few decades, a wealth of genomic data has been produced in a wide variety of species using a diverse array of functional and molecular marker approaches. In order to unlock the full potential of the information contained in these independent experiments, researchers need efficient and intuitive means to identify common genomic regions and genes involved in the expression of target phenotypic traits across diverse conditions. To address this need, we have developed a Comparative Map and Trait Viewer (CMTV) tool that can be used to construct dynamic aggregations of a variety of types of genomic datasets. By algorithmically determining correspondences between sets of objects on multiple genomic maps, the CMTV can display syntenic regions across taxa, combine maps from separate experiments into a consensus map, or project data from different maps into a common coordinate framework using dynamic coordinate translations between source and target maps. We present a case study that illustrates the utility of the tool for managing large and varied datasets by integrating data collected by CIMMYT in maize drought tolerance research with data from public sources. This example will focus on one of the visualization features for Quantitative Trait Locus (QTL) data, using likelihood ratio (LR) files produced by generic QTL analysis software and displaying the data in a unique visual manner across different combinations of traits, environments and crosses. Once a genomic region of interest has been identified, the CMTV can search and display additional QTLs meeting a particular threshold for that region, or other functional data such as sets of differentially expressed genes located in the region; it thus provides an easily used means for organizing and manipulating data sets that have been dynamically integrated under the focus of the researchers specific hypothesis.     

Genetic basis of pre-harvest sprouting tolerance using single-locus and two-locus QTL analyses in bread wheat

Quantitative trait loci (QTL) analysis for pre-harvest sprouting tolerance (PHST) in bread wheat was conducted following single-locus and two-locus analyses, using data on a set of 110 recombinant inbred lines (RILs) of the International Triticeae Mapping Initiative population grown in four different environments. Single-locus analysis following composite interval mapping (CIM) resolved a total of five QTLs with one to four QTLs in each of the four individual environments. Four of these five QTLs were also detected following two-locus analysis, which resolved a total of 14 QTLs including 8 main effect QTLs (M-QTLs), 8 epistatic QTLs (E-QTLs) and 5 QTLs involved in QTL × environment (QE) or QTL × QTL × environment (QQE) interactions, some of these QTLs being common. The analysis revealed that a major fraction (76.68%) of the total phenotypic variation explained for PHST is due to M-QTLs (47.95%) and E-QTLs (28.73%), and that only a very small fraction of variation (3.24%) is due to QE and QQE interactions. Thus, more than three-quarters of the genetic variation for PHST is fixable and would contribute directly to gains under selection. Two QTLs that were detected in more than one environment and at LOD scores above the threshold values were located on 3BL and 3DL presumably in the vicinity of the dormancy gene TaVp1. Another QTL was found to be located on 3B, perhaps in close proximity to the R gene for red grain colour. However, these associations of QTLs for PHST with genes for dormancy and grain colour are only suggestive. The results obtained in the present study suggest that PHST is a complex trait controlled by large number of QTLs, some of them interacting among themselves or with the environment. These QTLs can be brought together through marker-aided selection, leading to enhanced PHST.