The candidate gene approach in plant genetics: a review

The candidate gene (CG) approach has been applied in plant genetics in the past decade for the characterisation and cloning of Mendelian and quantitative trait loci (QTLs). It constitutes a complementary strategy to map-based cloning and insertional mutagenesis. The goal of this paper is to present an overview of CG analyses in plant genetics. CG analysis is based on the hypothesis that known-function genes (the candidate genes) could correspond to loci controlling traits of interest. CGs refer either to cloned genes presumed to affect a given trait (`functional CGs') or to genes suggested by their close proximity on linkage maps to loci controlling the trait (`positional CGs'). In plant genetics, the most common way to identify a CG is to look for map co-segregation between CGs and loci affecting the trait. Statistical association analyses between molecular polymorphisms of the CG and variation in the trait of interest have also been carried out in a few studies. The final validation of a CG will be provided through physiological analyses, genetic transformation and/or sexual complementation. Theoretical and practical applications of validated CGs in plant genetics and breeding are discussed.     

Identifying the molecular basis of QTLs: eQTLs add a new dimension

Natural genetic variation within plant species is at the core of plant science ranging from agriculture to evolution. Whereas much progress has been made in mapping quantitative trait loci (QTLs) controlling this natural variation, the elucidation of the underlying molecular mechanisms has remained a bottleneck. Recent systems biology tools have significantly shortened the time required to proceed from a mapped locus to testing of candidate genes. These tools enable research on natural variation to move from simple reductionistic studies focused on individual genes to integrative studies connecting molecular variation at multiple loci with physiological consequences. This review focuses on recent examples that demonstrate how expression QTL data can be used for gene discovery and exploited to untangle complex regulatory networks.     

The nature of quantittative genetic variation revisited: Lessons from Drosophila bristles

Most characters that distinguish one individual from another, like height or weight, vary continuously in populations. Continuous variation of these ‘quantitative’ traits is due to the simultaneous segregation of multiple quantitative trait loci (QTLs) as well as environmental influences. A major challenge in human medicine, animal and plant breeding and evolutionary genetics is to identify QTLs and determine their genetic properties. Studies of the classic quantitative traits, abdominal and sternopleural bristle numbers of Drosophila, have shown that: (1) many loci have small effects on bristle number, but a few have large effects and cause most of the genetic variation; (2) ‘candidate’ loci involved in bristle development often have large quantitative effects on bristle number; and (3) alleles at QTLs affecting bristle number have variable degrees of dominance, interact with each other, and affect other quantitative traits, including fitness. Lessons learned from this model system will be applicable to studies of the genetic basis of quantitative variation in other species.     

Exploring the genetic characteristics of 93-11 and Nipponbare recombination inbred lines based on the GoldenGate SNP assay

Understanding genetic characteristics in rice populations will facilitate exploring evolutionary mechanisms and gene cloning. Numerous molecular markers have been utilized in linkage map construction and quantitative trait locus (QTL) mappings. However, segregation-distorted markers were rarely considered, which prevented understanding genetic characteristics in many populations. In this study, we designed a 384-marker GoldenGate SNP array to genotype 283 recombination inbred lines (RILs) derived from 93-11 and Nipponbare Oryza sativa crosses. Using 294 markers that were highly polymorphic between parents, a linkage map with a total genetic distance of 1,583.2 cM was constructed, including 231 segregation-distorted markers. This linkage map was consistent with maps generated by other methods in previous studies. In total, 85 significant quantitative trait loci (QTLs) with phenotypic variation explained (PVE) values≥5% were identified. Among them, 34 QTLs were overlapped with reported genes/QTLs relevant to corresponding traits, and 17 QTLs were overlapped with reported sterility-related genes/QTLs. Our study provides evidence that segregation-distorted markers can be used in linkage map construction and QTL mapping. Moreover, genetic information resulting from this study will help us to understand recombination events and segregation distortion. Furthermore, this study will facilitate gene cloning and understanding mechanism of inter-subspecies hybrid sterility and correlations with important agronomic traits in rice.     

Quantitative trait loci and candidate gene mapping of aluminum tolerance in diploid alfalfa

Aluminum (Al) toxicity in acid soils is a major limitation to the production of alfalfa (Medicago sativa subsp. sativa L.) in the USA. Developing Al-tolerant alfalfa cultivars is one approach to overcome this constraint. Accessions of wild diploid alfalfa (M. sativa subsp. coerulea) have been found to be a source of useful genes for Al tolerance. Previously, two genomic regions associated with Al tolerance were identified in this diploid species using restriction fragment length polymorphism (RFLP) markers and single marker analysis. This study was conducted to identify additional Al-tolerance quantitative trait loci (QTLs); to identify simple sequence repeat (SSR) markers that flank the previously identified QTLs; to map candidate genes associated with Al tolerance from other plant species; and to test for co-localization with mapped QTLs. A genetic linkage map was constructed using EST-SSR markers in a population of 130 BC₁F₁ plants derived from the cross between Al-sensitive and Al-tolerant genotypes. Three putative QTLs on linkage groups LG I, LG II and LG III, explaining 38, 16 and 27% of the phenotypic variation, respectively, were identified. Six candidate gene markers designed from Medicago truncatula ESTs that showed homology to known Al-tolerance genes identified in other plant species were placed on the QTL map. A marker designed from a candidate gene involved in malic acid release mapped near a marginally significant QTL (LOD 2.83) on LG I. The SSR markers flanking these QTLs will be useful for transferring them to cultivated alfalfa via marker-assisted selection and for pyramiding Al tolerance QTLs.     

Molecular-Marker-Facilitated Investigations of Quantitative-Trait Loci in Maize. I. Numbers, Genomic Distribution and Types of Gene Action

Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17-20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800-1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.     

Combined genetic and physiological analysis of a locus contributing to quantitative variation

The natural variation of many traits is controlled by multiple genes, individually referred to as quantitative trait loci (QTL), that interact with the environment to determine the ultimate phenotype of any individual. A QTL has yet to be described molecularly, in part because strategies to systematically identify them are underdeveloped and because the subtle nature of QTLs prevents the application of standard methods of gene identification. Therefore, it will be necessary to develop a systematic approach(es) for the identification of QTLs based upon the numerous positional data now being accumulated through molecular marker analyses. We have characterized a QTL by the following three-step approach: (1) identification of a QTL in complex populations, (2) isolation and genetic mapping of this QTL in near-isogenic lines, and (3) identification of a candidate gene using map position and physiological criteria. Using this approach we have characterized a plant height QTL in maize that maps to chromosome 9 near the centromere. Both map position and physiological criteria suggest the gibberillin biosynthesis gene dwarf3 as a candidate gene for this QTL.     

Geographical variation in selection, from phenotypes to molecules

Molecular technologies now allow researchers to isolate quantitative trait loci (QTLs) and measure patterns of gene sequence variation within chromosomal regions containing important polymorphisms. I develop a simulation model to investigate gene sequence evolution within genomic regions that harbor QTLs. The QTLs influence a trait experiencing geographical variation in selection, which is common in nature and produces obvious differentiation at the phenotypic level. Counter to expectations, the simulations suggest that selection can substantially affect quantitative genetic variation without altering the amount and pattern of molecular variation at sites closely linked to the QTLs. Even with large samples of gene sequences, the likelihood of rejecting neutrality is often low. The exception is situations where strong selection is combined with low migration among demes, conditions that may be common in many plant species. The results have implications for gene sequence surveys and, perhaps more generally, for interpreting the apparently weak connection between levels of molecular and quantitative trait variation within species.     

Towards the cloning of genes underlying murine QTLs

Many quantitative trait loci (QTLs), including those for ethanol-related traits, have been mapped in the mouse. In light of rapidly developing tools and resources, we briefly review the strategy for identifying the genes underlying these QTLs. We note that positional cloning will soon be a matter of testing candidate genes rather than discovering genes; therefore, we describe a ``congenic test' to support that a candidate gene is indeed a QTL. Considering the rapid development of congenics and mutants, we also identify four areas of investigation—phenotypes, ethanol specificity, environment, and gene interactions—that might be exploited during the course of positional cloning to gain insights into QTL pathways. In particular, we note that multiple mutants of nearly every major neurotransmitter pathway have now been made. These mutants are not only useful for phenotypic tests, but also could be used to conduct ``gene dependence' tests of QTLs. We also consider potential applications for the very recently developed ability to clone mice. Received: 15 September 1998 / Accepted: 8 October 1998     

Believe it or not, QTLs are accurate!

It is generally believed that mapping quantitative trait loci (QTLs) does not accurately position genes underlying polygenic traits on the genome, which limits the application of QTL analysis in marker-assisted selection and gene discovery. However, now that a few plant QTLs have been cloned or accurately tagged, it appears that they might be accurate to within 2cM or less. This means that there will be circumstances when map-based cloning using only original mapping data would be a realistic option that avoids time-consuming and expensive fine mapping. Acceptance of this view would enhance the value of past and future mapping experiments, particularly those revealing small and environmentally sensitive QTLs that are often considered intractable at the molecular level.     

Genetic and molecular dissection of naturally occurring variation

Recent progress in plant genome analysis has made it possible to examine the naturally occurring allelic variation underlying complex traits. Many studies have described the genetic mapping of quantitative trait loci for several kinds of complex phenotypic traits. Some researchers have taken up the challenge of performing the molecular cloning of genes at these loci, and examples of cloning have recently been reported. Naturally occurring allelic variation could be a new resource for the functional analysis of plant genes.     

Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.     

Looking into flowering time in almond (Prunus dulcis (Mill) D. A. Webb): the candidate gene approach

Blooming time is one of the most important agronomic traits in almond. Biochemical and molecular events underlying flowering regulation must be understood before methods to stimulate late flowering can be developed. Attempts to elucidate the genetic control of this process have led to the identification of a major gene (Lb) and quantitative trait loci (QTLs) linked to observed phenotypic differences, but although this gene and these QTLs have been placed on the Prunus reference genetic map, their sequences and specific functions remain unknown. The aim of our investigation was to associate these loci with known genes using a candidate gene approach. Two almond cDNAs and eight Prunus expressed sequence tags were selected as candidate genes (CGs) since their sequences were highly identical to those of flowering regulatory genes characterized in other species. The CGs were amplified from both parental lines of the mapping population using specific primers. Sequence comparison revealed DNA polymorphisms between the parental lines, mainly of the single nucleotide type. Polymorphisms were used to develop co-dominant cleaved amplified polymorphic sequence markers or length polymorphisms based on insertion/deletion events for mapping the candidate genes on the Prunus reference map. Ten candidate genes were assigned to six linkage groups in the Prunus genome. The positions of two of these were compatible with the regions where two QTLs for blooming time were detected. One additional candidate was localized close to the position of the Evergrowing gene, which determines a non-deciduous behaviour in peach.     

Discovery and genetic mapping of single nucleotide polymorphisms in candidate genes for pathogen defence response in perennial ryegrass (<Emphasis Type="Italic">Lolium perenne</Emphasis> L.)

Susceptibility to foliar pathogens commonly causes significant reductions in productivity of the important temperate forage perennial ryegrass. Breeding for durable disease resistance involves not only the deployment of major genes but also the additive effects of minor genes. An approach based on in vitro single nucleotide polymorphism (SNP) discovery in candidate defence response (DR) genes has been used to develop potential diagnostic genetic markers. SNPs were predicted, validated and mapped for representatives of the pathogenesis-related (PR) protein-encoding and reactive oxygen species (ROS)-generating gene classes. The F(1)(NA(6) x AU(6)) two-way pseudo-test cross population was used for SNP genetic mapping and detection of quantitative trait loci (QTLs) in response to a crown rust field infection. Novel resistance QTLs were coincident with mapped DR gene SNPs. QTLs on LG3 and LG7 also coincided with both herbage quality QTLs and candidate genes for lignin biosynthesis. Multiple DR gene SNP loci additionally co-located with QTLs for grey leaf spot, bacterial wilt and crown rust resistance from other published studies. Further functional validation of DR gene SNP loci using methods such as fine-mapping and association genetics will improve the efficiency of parental selection based on superior allele content.     

Quantitative trait loci mapping for fatty acid contents in the backfat on porcine chromosomes 1, 13, and 18

A partial genome scan using microsatellite markers was conducted in order to detect quantitative trait loci (QTLs) for 10 fatty acid contents of the backfat in a pig reference population. Two QTLs were found by studying SSC1, SSC13, and SSC18, where QTLs had already been identified for backfat thickness. A QTL was located between marker loci S0113 and SW974 on chromosome 1; this QTL was only significantly detected (P < 0.05) for linoleic acid. The other QTL was discovered between markers S0062 and S0120 on chromosome 18, and its significance only showed (P < 0.05) for myristic acid. The two QTLs mapped to the same location as the backfat thickness QTL. A third of the phenotypic variation was explained for linoleic acid by the QTL on chromosome 1, and a quarter for myristic acid by the QTL on chromosome 18. Further studies on fine mapping and positional comparative candidate gene analyses will be the next step toward a better understanding of the genetic architecture of fatty acid contents.     

玉米抗甘蔗花叶病毒基因的比较定位

收集了玉米抗甘蔗花叶病毒基因/QTL定位信息, 借助玉米遗传图谱IBM2 2005 Neighbors进行了整合。在国内外研究中, 累计报道81个抗病毒基因位点, 分布在玉米7条染色体上, 比较定位发现这些位点集中分布于第3和6染色体。采用元分析技术, 确定3个“一致性”抗病毒QTL, 其中1个位于第3染色体, 在遗传图谱IBM2 2005 Neighbors上覆盖的范围为6.44 cM; 2个位于第6染色体, 覆盖范围分别为6.16 cM和27.48 cM。借助比较基因组学策略, 在第3染色体“一致性”QTL区间内筛选出4个抗病位置候选基因。该研究结果为确定和克隆抗病主效基因提供了基础。     

玉米株高和穗位高的遗传基础与分子机制*

株高和穗位高是玉米重要育种性状,直接影响植株的养分利用效率及抗倒伏性,进而影响玉米产量。玉米株高和穗位高属于典型数量性状,目前通过数量性状位点(quantitative trait loci mapping,QTL)定位和全基因组关联分析(genome-wide association study, GWAS)等方法已挖掘到较多相关遗传位点,通过QTL精细定位及利用突变体克隆了一些调控株高和穗位高关键基因。但是由于各研究组所利用的群体类型和大小、标记类型和密度以及统计方法不同,所鉴定QTL差异较大,单个研究难以揭示玉米株高和穗位高遗传结构。早期QTL定位的结果多以遗传距离来展示,不同时期GWAS研究所使用参考基因组版本不同,这进一步增加了借鉴和利用前人研究结果的难度。首次将目前已鉴定株高和穗位高遗传定位信息统一锚定至玉米自交系B73参考基因组V4版本,构建了株高和穗位高性状定位的一致性图谱,并鉴定出可被多个独立研究定位的热点区间。进一步对已克隆玉米株高和穗位高调控基因进行总结与分类,揭示株高和穗位高性状调控机制,对深度解析株高和穗位高遗传结构、指导基因克隆和利用分子标记辅助选择优化玉米株高和穗位高性状均具有重要意义。     

植物抗病遗传育种中的QTL定位和克隆(综述)

对植物抗病遗传育种中QTL定位与克隆研究进行综述。主要阐述了数量抗性的遗传学基础、作物抗病性QTL的定位作图、QTL作图的可靠性及应对措施、QTLs候选基因的证实和定位克隆等,并对植物抗病遗传育种未来的研究方向予以讨论。