Single-marker analysis. Effect of a marker linked to a QTL. If M and A are linked, average of MM lines will differ from the average of mm lines. The size of difference can be between 0 and a, depending on the marker-QTL distance.
Means of MM and mm recombinant inbred lines. QTL mapping with molecular markers. QTL mapping strategies. All marker-based mapping experiments have same basic strategy:. F-test for the difference between marker genotype classes. Problems with single-marker analysis:. Doing a t-test at every marker results in many false positives this is a general problem with QTLs. Interval mapping methods use information on values of 2 flanking markers to estimate QTL position.
The probability that the data could be obtained assuming a QTL at several positions between the markers is calculated. Finding the position of QTL with molecular markers. DNA markers can be used to map useful genes using recombination frequencies of linked genes:. Recombinant gametes: M 1 a, m 1 A,. Parental gametes: M 1 A, m 1 a,. What are the problems with interval mapping?
The molecular genetics of crop domestication. Cell , — Google Scholar. Du, L. Plant Cell 26, — Edwards, M. Molecular-marker-facilitated investigations of quantitative-trait loci in maize. Numbers, genomic distribution and types of gene action.
Genetics , — Fan, C. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein.
Feuillet, C. Solving the maze. Science , — Gao, L. Fine mapping and candidate gene analysis of a QTL associated with leaf rolling index on chromosome 4 of maize Zea mays L. Gaut, B. Maize as a model for the evolution of plant nuclear genomes. Han, Y. QTL analysis of soybean seed weight across multi-genetic backgrounds and environments. Helentjaris, T. Construction of genetic linkage maps in maize and tomato using restriction fragment length polymorphisms. Hu, J. A rare allele of GS2 enhances grain size and grain yield in rice.
Plant 8, — Kang, Y. Fine mapping and candidate gene analysis of the quantitative trait locus gw8. Genes Genom. Li, C. Quantitative trait loci mapping for yield components and kernel-related traits in multiple connected RIL populations in maize. Euphytica , — Li, H. Quantitative trait locus analysis of heterosis for plant height and ear height in an elite maize hybrid zhengdan by design III.
BMC Genet Li, J. Fine mapping of a grain-weight quantitative trait locus in the pericentromeric region of rice chromosome 3. Li, Y. Natural variation in GS5 plays an important role in regulating grain size and yield in rice. Correlation analysis and QTL mapping for traits of kernel structure and yield components in maize. Lin, H. Fine mapping and characterization of quantitative trait loci Hd4 and Hd5 controlling heading date in rice.
Liu, M. Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping. Plant Biotechnol. Liu, Y. Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments. Lynch, M.
Genetics and analysis of quantitative traits. Abington: Elsevier. Nie, N. Characterization and fine mapping of qkrnw4, a major QTL controlling kernel row number in maize. Nzuve, F. Genetic variability and correlation studies of grain yield and related agronomic traits in maize. Qiu, L. Novel gene discovery of crops in China: status, challenging, and perspective. Acta Agronom. Qiu, X. Mapping and characterization of the major quantitative trait locus qSS7 associated with increased length and decreased width of rice seeds.
Rafiq, C. Studies on heritability, correlation and path analysis in maize Zea mays L. Ramya, P. QTL mapping of kernel weight, kernel length, and kernel width in bread wheat Triticum aestivum L.
Song, X. Su, C. High density linkage map construction and mapping of yield trait QTLs in maize Zea mays using the genotyping-by-sequencing GBS technology. Sun, X. QTL analysis of kernel shape and weight using recombinant inbred lines in wheat. Euphytica Tian, F.
Genome-wide association study of leaf architecture in the maize nested association mapping population. Trapnell, C. Differential analysis of gene regulation at transcript resolution with RNA-seq.
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Van Ooijen, J. Software for the calculation of genetic linkage maps.
Netherlands: Plant Research International Wageningen. Verma, R. Multiubiquitin chain receptors define a layer of substrate selectivity in the ubiquitin-proteasome system. Cell , 99— Voorrips, R. MapChart: software for the graphical presentation of linkage maps and QTLs. Heredity 93, 77— Wan, X. QTL analysis for rice grain length and fine mapping of an identified QTL with stable and major effects.
Wang, G. One notable area of exception involves applied studies in medicine and agriculture, which are often interested in specific segregating alleles. The number of times that individual genes have been identified following a QTL mapping study remains small. Indeed, Roff lists examples of quantitative traits in which single genes have major effects and their molecular basis has been studied, and he notes that this number is modest relative to the effort invested in QTL studies.
One reason for this discrepancy is that many QTL map to regions of the genome of perhaps 20 centimorgans cM in length, and these regions often contain multiple loci that influence the same trait see, however, Price, Moreover, identifying the actual loci that affect a quantitative trait involves demonstrating causality using techniques like positional cloning see Clee et al.
Frequently, the quest for individual genes within a QTL is assisted by the identification of a priori candidate genes using classical reverse genetics or bioinformatics. A functional relationship between the candidate gene and the QTL must then be demonstrated, such as by using functional complementation the addition of wild-type complementary DNA from the gene in question into the nucleus to rescue a loss-of function mutation or to produce an alternative phenotype; see, for example, Frary et al.
Other techniques, such as deficiency mapping deletion mapping , are available for specific organisms, including Drosophila Mackay, New permutations of QTL mapping build upon the utility of the original premise: locus discovery by co-segregation of traits with markers. Now, however, the definition of a trait can be broadened beyond whole-organism phenotypes to phenotypes such as the amount of RNA transcript from a particular gene expression or eQTL; Schadt et al.
QTL mapping works in these contexts because these phenotypes are polygenic , just like more traditional organismal phenotypes, such as yield in corn. For example, transcript abundance is controlled not just by cis -acting sequences like the promoter , but also by potentially unlinked, trans -acting transcription factors.
Similarly, protein abundance is controlled by "local" variation at the coding gene itself, and by "distant" variation mapping to other regions of the genome.
Local variation is likely to be composed of cis variants controlling transcript levels though the correlation between transcript level and protein abundance is often quite low, so this may represent a minority of cases; see Foss et al. Other local mechanisms might include polymorphisms for the stability or regulation of the protein. In contrast, distant variation could include upstream regulation control regions.
Beyond these examples, further extension of QTL analysis includes mapping the contribution of imprinting to size-related traits Cheverud et al. Historically, the availability of adequately dense markers genotypes has been the limiting step for QTL analysis.
However, high-throughput technologies and genomics have begun to overcome this barrier. Thus, the remaining limitations in QTL analysis are now predominantly at the level of phenotyping, although the use of genomic and proteomic data as phenotypes circumvents this challenge to some extent. Genome-wide association studies GWAS are becoming increasingly popular in genetic research, and they are an excellent complement to QTL mapping.
Whereas QTL contain many linked genes , which are then challenging to separate, GWAS produce many unlinked individual genes or even nucleotides, but these studies are riddled with large expected numbers of false positives. Though GWAS remain limited to organisms with genomic resources, combining the two techniques can make the most of both approaches and help provide the ultimate deliverable: individual genes or even nucleotides that contribute to the phenotype of interest.
Indeed, combining different QTL techniques and technologies has great promise. For example, Hubner and colleagues used data on gene expression in fat and kidney tissue from two previously generated, recombinant rat strains to study hypertension.
Alternatively, samples adapted to different environments may be compared, or other populations of interest might be selected for expression analysis. This approach permits measurement of hundreds or even thousands of traits simultaneously. Other interesting questions concerning gene regulation can be addressed by combining eQTL and QTL, such as the relative contributions of cis -regulatory elements versus trans -regulatory elements.
Regarding hypertension, Hubner et al. These integrated approaches will become more common, and they promise a deeper understanding of the genetic basis of complex traits, including disease Hubner et al. Integrating phenotypic QTL with protein QTL can also give investigators a more direct link between genotype and phenotype via co-localization of candidate protein abundance with a phenotypic QTL De Vienne et al.
Still more kinds of data can be integrated with QTL mapping for a "total information" genomics approach e. QTL studies have a long and rich history and have played important roles in gene cloning and characterization; however, there is still a great deal of work to be done.
Existing data on model organisms need to be expanded to the point at which meta-analysis is feasible in order to document robust trends regarding genetic architecture. Furthermore, QTL studies can inform functional genomics , in which the goal is to characterize allelic variation and how it influences the fitness and function of whole organisms.
Thus, although the map between genotype and phenotype remains difficult to read, QTL analysis and a variety of associated innovations will likely continue to provide key landmarks.
Albert, A. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution 62 , 76—85 Baack, E. Selection on domestication traits and quantitative trait loci in crop-wild sunflower hybrids. Molecular Ecology 17 , — Beavis, W. QTL analyses: Power, precision, and accuracy. In Molecular Dissection of Complex Traits , ed. Casa, A. Proceedings of the National Academy of Sciences 97 , — Cheverud, J.
Genomic imprinting effects on adult body composition in mice. Proceedings of the National Academy of Sciences , — Clee, S. Positional cloning Sorcs1, a type 2 diabetes quantitative trait locus. Nature Genetics 38 , — link to article. Damerval, C. Quantitative trait loci underlying gene product variation—A novel perspective for analysing regulation of genome expression. Genetics , — Darvasi, A. Experimental strategies for the genetic dissection of complex traits in animal models.
Nature Genetics 18 , 19—24 link to article. De Vienne, D. Genetics of proteome variation for QTL characterization: Application to drought-stress responses in maize. Journal of Experimental Botany 50 , — Doebley, J. Teosinte branched 1 and the origin of maize: Evidence for epistasis and the evolution of dominance. Falconer, D. Introduction to Quantitative Genetics , 4th ed.
London, Prentice Hall, Forbes, S. Quantitative trait loci affecting life span in replicated populations of Drosophila melanogaster. Composite interval mapping. Foss, E. Genetic basis of proteome variation in yeast. Nature Genetics 39 , — link to article. Frary, A. Science , 85—88 Gupta, P. Functional and Integrative Genomics 4 , — Hayes, B. The distribution of the effects of genes affecting quantitative traits in livestock.
Genetics Selection Evolution 33 , — Hubner, N. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nature Genetics 37 , — link to article. Novel integrative approaches to the identification of candidate genes in hypertension. Hypertension 47 , 1—5 Kearsey, M. The principles of QTL analysis a minimal mathematics approach.
Journal of Experimental Botany 49 , —
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