KEY POINTS * Powerful statistical methods are available for the analysis of quantitative traits in experimental species. To a large extent, the development of these methods has been driven

by the increased availability of molecular genetic markers and the generation of high-resolution genetic maps. * Three broadly different types of statistical analysis can be used in the

search for quantitative trait loci (QTL): single-marker tests, interval mapping and composite interval mapping. The advantage of composite interval mapping methods is that they accommodate

multiple QTL. * However, none of the existing methods can accommodate the full complexity of multifactorial traits that arise because of extensive gene-by-gene (epistatic) and

trait. Computational simulation and non-parametric resampling are two methods that are being used. * Despite the power of the statistical methods, and the wealth of genetic markers, there

are few examples in which the genetic basis of a quantitative trait has been thoroughly dissected. * One exciting view of the future is to consider functional genomics approaches.

Transcriptional profiling in particular could provide a way to move rapidly towards a more comprehensive view of gene interactions and epistasis. * Lessons learned from the statistical (QTL)

analysis of complex phenotypes have the potential to be applied to an analysis of the variation of gene expression, and in this way a more complete understanding of functional networks of

genes and their action might arise. This knowledge could benefit our current ability to understand the connection between phenotype and genotype. ABSTRACT Simple statistical methods for the

study of quantitative trait loci (QTL), such as analysis of variance, have given way to methods that involve several markers and high-resolution genetic maps. As a result, the mapping

community has been provided with statistical and computational tools that have much greater power than ever before for studying and locating multiple and interacting QTL. Apart from their

immediate practical applications, the lessons learnt from this evolution of QTL methodology might also be generally relevant to other types of functional genomics approach that are aimed at

the dissection of complex phenotypes, such as microarray assessment of gene expression. Access through your institution Buy or subscribe This is a preview of subscription content, access via

your institution ACCESS OPTIONS Access through your institution Subscribe to this journal Receive 12 print issues and online access $209.00 per year only $17.42 per issue Learn more Buy

this article * Purchase on SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local taxes which are calculated during checkout ADDITIONAL ACCESS OPTIONS: *

Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS PLEIOTROPY, EPISTASIS AND THE GENETIC ARCHITECTURE OF

QUANTITATIVE TRAITS Article 02 April 2024 CORRELATIONAL SELECTION IN THE AGE OF GENOMICS Article 15 April 2021 FROM MENDEL TO QUANTITATIVE GENETICS IN THE GENOME ERA: THE SCIENTIFIC LEGACY

OF W. G. HILL Article 11 July 2022 REFERENCES * Sax, K. The association of size difference with seed-coat pattern and pigmentation in _Phaseolus vulgaris_. _Genetics_ 8, 552–560 (1923).THE

FIRST PAPER ON MAPPING A QTL USING MENDELIAN MARKERS. Article  CAS  PubMed  PubMed Central  Google Scholar  * Thoday, J. M. Location of polygenes. _Nature_ 191, 368–370 (1961).CLASSIC PAPER

THAT IS WELL KNOWN FOR BEING THE FIRST TO DESCRIBE INTERVAL MAPPING AND FOR STATING THAT “THE MAIN PRACTICAL LIMITATION OF THE [GENETIC MAPPING] TECHNIQUE IS THE AVAILABILITY OF SUITABLE

MARKERS.” Article  Google Scholar  * Fisher, R. _The Design of Experiments_ 3rd edn (Oliver & Boyd, London, 1935). Google Scholar  * Watson, J. & Crick, F. A structure for

deoxyribose nucleic acid. _Nature_ 171, 737–738 (1953). Article  CAS  PubMed  Google Scholar  * Southern, E. M. Detection of specific sequences among DNA fragments separated by gel

electrophoresis. _J. Mol. Biol._ 98, 503–517 (1975). Article  CAS  PubMed  Google Scholar  * Sanger, F., Nilken, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors.

_Proc. Natl Acad. Sci. USA_ 74, 5463–5468 (1980). Article  Google Scholar  * Saiki, R. K. et al. Enzymatic amplification of β-globin genomic sequences and restriction site analysis for

diagnosis of sickle-cell anemia. _Science_ 230, 1350–1354 (1985). Article  CAS  PubMed  Google Scholar  * Kerem, B.-S. et al. Identification of the cystic fibrosis gene: genetic analysis.

_Science_ 245, 1073–1080 (1989). Article  CAS  PubMed  Google Scholar  * The Huntington's Disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is

expanded and unstable on Huntington's disease chromosome. _Cell_ 72, 971–983 (1993). * Blumenfeld, A. et al. Localization of the gene of familial dysautomia on chromosome 9 and

definition of DNA markers for genetic diagnosis. _Nature Genet._ 4, 160–163 (1993). Article  CAS  PubMed  Google Scholar  * Georges, M. et al. Microsatellite mapping of a gene affecting horn

development in _Bos taurus_. _Nature Genet._ 4, 206–210 (1993). Article  CAS  PubMed  Google Scholar  * Keim, P. et al. RFLP mapping in soybean: association between marker loci and

variation in quantitative traits. _Genetics_ 126, 735–742 (1990). Article  CAS  PubMed  PubMed Central  Google Scholar  * Edwards, M. D., Stuber, C. W. & Wendel, J. F.

Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action. _Genetics_ 116, 113–125 (1987). Article  CAS 

PubMed  PubMed Central  Google Scholar  * Beckmann, J. S. & Soller, M. Detection of linkage between marker loci and loci affecting quantitative traits in crosses between segregating

populations. _Theor. Appl. Genet._ 76, 228–236 (1988). Article  CAS  PubMed  Google Scholar  * Luo, Z. W. & Kearsey, M. J. Maximum likelihood estimation of linkage between a marker gene

and a quantitative trait locus. _Heredity_ 63, 401–408 (1989). Article  PubMed  Google Scholar  * Lander, E. S. & Botstein, D. Mapping Mendelian factors underlying quantitative traits

using RFLP linkage maps. _Genetics_ 121, 185–199 (1989); erratum 136, 705 (1994).ONE OF THE KEY PAPERS ON QTL MAPPING THAT USED INTERVALS OF MOLECULAR MARKERS. Article  CAS  PubMed  PubMed

Central  Google Scholar  * Knapp, S. J., Bridges, W. C. & Birkes, D. Mapping quantitative trait loci using molecular marker linkage maps. _Theor. Appl. Genet._ 79, 583–592 (1990).

Article  CAS  PubMed  Google Scholar  * Carbonell, E. A., Gerig T. M., Balansard, E. & Asins, M. J. Interval mapping in the analysis of nonadditive quantitative trait loci. _Biometrics_

48, 305–315 (1992). Article  Google Scholar  * Jansen, R. C. A general mixture model for mapping quantitative trait loci by using molecular markers. _Theor. Appl. Genet._ 85, 252–260

(1992).ONE OF MANY INFLUENTIAL PUBLICATIONS BY RITSERT JANSEN DETAILING THE USE OF GENERAL MIXTURE MODELS FOR QTL MAPPING. Article  CAS  PubMed  Google Scholar  * Knott, S. A. & Haley,

C. S. Aspects of maximum likelihood methods for the mapping of quantitative trait loci in line crosses. _Genet. Res._ 60, 139–151 (1992). Article  Google Scholar  * Lincoln, S., Daly, M.

& Lander, E. _Mapping Genes Controlling Quantitative Traits with MAPMAKER/QTL 1.1_. 2nd edition (Whitehead Institute Technical Report, Cambridge, Massachusetts, 1992).THE FIRST TECHNICAL

REPORT DETAILING QTL-MAPPING SOFTWARE FOR INTERVAL MAPPING USING THE LANDER AND BOTSTEIN APPROACH (SEE REFERENCE 16). Google Scholar  * Darvasi, A. & Weller, J. I. On the use of the

moments method of estimation to obtain approximate maximum likelihood estimates of linkage between a genetic marker and a quantitative locus. _Heredity_ 68, 43–46 (1992). Article  PubMed 

Google Scholar  * Doerge, R. W. _Statistical Methods for Locating Quantitative Trait Loci with Molecular Markers_. Ph.D. thesis, Department of Statistics, North Carolina State University,

Raleigh, North Carolina, USA (1993). Google Scholar  * Zeng, Z.-B. Theoretical basis of precision mapping of quantitative trait loci. _Proc. Natl Acad. Sci. USA_ 90, 10972–10976 (1993).THIS

PAPER DETAILS THE THEORETICAL DEVELOPMENT OF COMPOSITE INTERVAL MAPPING. Article  CAS  PubMed  PubMed Central  Google Scholar  * Cooper, M. & DeLacy, I. H. Relationships among analytical

methods used to study genotypic variation and genotype-by-environmental interaction in plant breeding multi-environment experiments. _Theor. Appl. Genet._ 88, 561–572 (1994). Article  CAS 

PubMed  Google Scholar  * Dupuis, J. _Statistical Problems Associated with Mapping Complex and Quantitative Traits from Genomic Mismatch Scanning Data_. Ph.D. thesis, Department of

Statistics, Stanford University, USA (1994). Google Scholar  * Kearsey, M. J. & Hyne, V. QTL analysis: a simple 'marker–regression' approach. _Theor. Appl. Genet._ 8, 698–702

(1994). Article  Google Scholar  * Zeng, Z.-B. Precision mapping of quantitative trait loci. _Genetics_ 136, 1457–1468 (1994).COMPANION PAPER TO ZENG'S 1993 (REFERENCE 24 ) THEORETICAL

DEVELOPMENT OF COMPOSITE INTERVAL MAPPING. Article  CAS  PubMed  PubMed Central  Google Scholar  * Xu, S. & Yi, N. Mixed model analysis of quantitative trait loci. _Proc. Natl Acad. Sci.

USA_ 97, 14542–14547 (2000). Article  CAS  PubMed  PubMed Central  Google Scholar  * Jansen, R. C. Interval mapping of multiple quantitative trait loci. _Genetics_ 135, 205–211

(1993).EXTENDS THE CONCEPTS OF INTERVAL MAPPING FROM A SINGLE QTL ANALYSIS TO MULTIPLE QTL, AND COINS THE TERM 'MULTIPLE QTL MAPPING' (MQM). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Jansen, R. C. & Stam, P. High resolution of quantitative traits into multiple loci via interval mapping. _Genetics_ 136, 1447–1455 (1994). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Jiang, C. & Zeng, Z.-B. Multiple trait analysis of genetic mapping for quantitative trait loci. _Genetics_ 140, 1111–1127 (1995). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Kao, C. H., Zeng, Z.-B. & Teasdale, R. D. Multiple interval mapping for quantitative trait loci. _Genetics_ 152, 1203–1216 (1999). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Ronin, Y. I., Kirzhner, V. M. & Korol, A. B. Linkage between loci of quantitative traits and marker loci: multi-trait analysis with a single marker. _Theor.

Appl. Genet._ 90, 776–786 (1995). Article  CAS  PubMed  Google Scholar  * Korol, A. B., Ronin, Y. I. & Kirzhner, V. M. Interval mapping of quantitative trait loci employing correlated

trait complexes. _Genetics_ 140, 1137–1147 (1995). Article  CAS  PubMed  PubMed Central  Google Scholar  * Korol, A. B., Ronin, Y. I., Nevo, E. & Hayes, P. M. Multi-interval mapping of

correlated trait complexes. _Heredity_ 80, 273–284 (1998). Article  Google Scholar  * Frary, A. et al. A quantitative trait locus key to the evolution of tomato fruit size. _Science_ 289,

85–88 (2000). Article  CAS  PubMed  Google Scholar  * The Chipping Forecast. _Nature Genet._ 21 (Suppl.) (1999). * Ewing, R. M. et al. Large-scale statistical analyses of rice ESTs reveal

correlated patterns of gene expression. _Genome Res._ 9, 950–959 (1999). Article  CAS  PubMed  PubMed Central  Google Scholar  * Kim, S. K. et al. A gene expression map for _Caenorhabditis

elegans_. _Science_ 293, 2087–2092 (2001). Article  CAS  PubMed  Google Scholar  * Iyer, V. R. et al. The transcriptional program in the response of human fibrolasts to serum. _Science_ 283,

83–87 (1999). Article  CAS  PubMed  Google Scholar  * Perou, C. M. et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. _Proc. Natl Acad. Sci.

USA_ 96, 9212–9217 (1999). Article  CAS  PubMed  PubMed Central  Google Scholar  * Zhu, H. et al. Global analysis of protein activities using proteome chips. _Science_ 293, 2101–2105 (2001).

Article  CAS  PubMed  Google Scholar  * Thiellement, H. et al. Proteomics for genetic and physiological studies in plants. _Electrophoresis_ 20, 2013–2026 (1999). Article  CAS  PubMed 

Google Scholar  * Tanksley, S. D. Mapping polygenes. _Annu. Rev. Genet._ 27, 205–233 (1993). Article  CAS  PubMed  Google Scholar  * Doerge, R. W., Zeng, Z.-B. & Weir, B. S. Statistical

issues in the search for genes affecting quantitative traits in experimental populations. _Stat. Sci._ 12, 195–219 (1997).DETAILED REVIEW OF THE STATISTICAL ISSUES INVOLVED IN QTL MAPPING.

Article  Google Scholar  * Lynch, M. & Walsh, B. _Genetics and Analysis of Quantitative Traits_ (Sinauer Associates, Sunderland, Massachusetts, 1998). Google Scholar  * Liu, B.-H.

_Genomics: Linkage Mapping and QTL Analysis_ (CRC, Boca Raton, Florida, 1998). Google Scholar  * Jansen, R. C. in _Handbook of Statistical Genetics_ (eds Balding, D. et al.) (John Wiley

& Sons, New York, 2001). Google Scholar  * Quackenbush, J. Computational analysis of microarray data. _Nature Rev. Genet._ 2, 418–427 (2001). Article  CAS  PubMed  Google Scholar  *

Schadt, E. Enhancing candidate gene detection via experimental genome annotation and treating transcript abundance as quantitative traits. _Pezcoller Found. J._ 10 (2001). * Jansen, R. C.

& Nap, J.-N. Genetical genomics: the added value from segregation. _Trends Genet._ 17, 388–391 (2001). Article  CAS  PubMed  Google Scholar  * Doerge, R. W. & Craig, B. A. Model

selection for quantitative trait locus analysis in polyploids. _Proc. Natl Acad. Sci. USA_ 97, 7951–7956 (2000). Article  CAS  PubMed  PubMed Central  Google Scholar  * Xie, C. G. & Xu,

S. H. Mapping quantitative trait loci in tetraploid populations. _Genet. Res._ 76, 105–115 (2000). Article  CAS  PubMed  Google Scholar  * Mackay, T. F. C. Quantitative trait loci in

_Drosophila_. _Nature Rev. Genet._ 2, 11–20 (2001). Article  CAS  PubMed  Google Scholar  * Göring, H. H., Terwilliger, J. D. & Blangero, J. Large upward bias in estimation of

locus-specific effects from genomewide scans. _Am. J. Hum. Genet._ 69, 1357–1369 (2001). Article  PubMed  PubMed Central  Google Scholar  * Neter, J., Kutner, M. H., Nachtshiem, C. J. &

Wasserman, W. _Applied Linear Statistical Models_ 4th edn (Irwin, Chicago, Illinois, 1997).DETAILED REFERENCE TEXT ON THE USE OF LINEAR MODELS IN APPLIED REGRESSION. Google Scholar  *

Sourdille, P. et al. Linkage between RFLP markers and genes affecting kernel hardness in wheat. _Theor. Appl. Genet._ 93, 580–586 (1996). Article  CAS  PubMed  Google Scholar  *

Timmerman-Vaughan, G. A., McCallum, J. A., Frew, T. J., Weeden, N. F. & Russell, A. C. Linkage mapping of quantitative trait loci controlling seed weight in pea (_Pisum sativum_ L.).

_Theor. Appl. Genet._ 93, 431–439 (1996). Article  CAS  PubMed  Google Scholar  * Varshney, M., Prasad, R., Kumar, N., Harjit-Singh, H. S. & Gupta, P. K. Identification of eight

chromosomes and a microsatellite marker on 1AS associated with QTL for grain weight in bread wheat. _Theor. Appl. Genet._ 8, 1290–1294 (2000). Article  Google Scholar  * Lander, E. S. &

Green, P. Construction of multilocus genetic linkage maps in humans. _Proc. Natl Acad. Sci. USA_ 84, 2363–2367 (1987). Article  CAS  PubMed  PubMed Central  Google Scholar  * Stam, P.

Construction of integrated genetic linkage maps by means of a new computer package: Joinmap. _Plant J._ 5, 739–744 (1993). Article  Google Scholar  * Bailey, D. W. _The Mathematical Theory

of Genetic Linkage_ (Clarendon Press, Oxford, UK, 1961). Google Scholar  * Lincoln, S., Daly, M. & Lander, E. _Constructing Genetic Maps with MAPMAKER/EXP 3.0_ 3rd edn (Whitehead

Institute Technical Report, Cambridge, Massachusetts, 1992).COMPANION TECHNICAL REPORT AND SOFTWARE TO MAPMAKER/QTL. Google Scholar  * Soller, M., Brody, T. & Genizi, A. On the power of

experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. _Theor. Appl. Genet._ 47, 35–39 (1979).ONE OF THE FIRST PAPERS

PROPOSING INTERVAL MAPPING. Article  Google Scholar  * Soller, M., Brody, T. & Genizi, A. The expected distribution of marker-linked quantitative effects in crosses between inbred lines.

_Heredity_ 43, 179–190 (1979). Article  Google Scholar  * Martinez, O. & Curnow, R. N. Estimating the locations and the size of the effects of quantitative trait loci using flanking

markers. _Theor. Appl. Genet._ 85, 480–488 (1992). Article  CAS  PubMed  Google Scholar  * Churchill, G. A. & Doerge, R. W. Empirical threshold values for quantitative trait mapping.

_Genetics_ 138, 963–971 (1994).DETAILS THE USE OF RESAMPLING THROUGH PERMUTATION TO ESTIMATE EXPERIMENT-SPECIFIC QTL THRESHOLD VALUES. Article  CAS  PubMed  PubMed Central  Google Scholar  *

_QTL CARTOGRAPHER: A Reference Manual and Tutorial for QTL Mapping_ Department of Statistics, North Carolina State University, Raleigh, North Carolina (1995–2001). * Jansen, R. C. _Genetic

Mapping of Quantitative Trait Loci in Plants – a Novel Statistical Approach_. Ph.D. thesis, CIP–data Koninklijke Biblotheek, Den Haag, The Netherlands (1995). Google Scholar  * Piepho, H. P.

& Gauch, H. G. Marker pair selection for mapping quantitative trait loci. _Genetics_ 157, 433–444 (2001). Article  CAS  PubMed  PubMed Central  Google Scholar  * Nakamichi, R., Ukai, Y.

& Kishino, H. Detection of closely linked multiple quantitative trait loci using a genetic algorithm. _Genetics_ 158, 463–475 (2001). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Sen, S. & Churchill, G. A. A statistical framework for quantitative trait mapping. _Genetics_ 159, 371–387 (2001). Article  CAS  PubMed  PubMed Central  Google Scholar  *

Green, P. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. _Biometrika_ 82, 711–732 (1995). Article  Google Scholar  * Carlborg, O., Andersson, L. &

Kinghorn, B. The use of a genetic algorithm for simultaneous mapping of multiple interacting quantitative trait loci. _Genetics_ 155, 2003–2010 (2000). Article  CAS  PubMed  PubMed Central

  Google Scholar  * Foster, J. A. Evolutionary computation. _Nature Rev. Genet._ 428–436 (2001). * Holland, J. _Adaption in Natural and Artifical Systems_ (Univ. of Michigan Press, Ann

Arbor, Michigan, 1975). Google Scholar  * Baker, J. E. in _Genetic Algorithms and their Applications_, _Proc. 2nd Intl Conf._ (ed. Grefenstette, J. J.) 14–21 (LEA, Cambridge, Massachusetts,

1987). Google Scholar  * Ghosh, J. K. & Sen, P. K. On the asymptotic performance of the log likelihood ratio statistic for the mixture model and related results. _Proc. Berkeley Conf._

2, 789–807 (1985). Google Scholar  * Hartigan, J. A. A failure of likelihood asymptotics for normal distributions. _Proc. Berkeley Conf._ 2, 807–810 (1985). Google Scholar  * Self, S. G.

& Liang, K.-Y. Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. _J. Am. Stat. Assoc._ 82, 605–610 (1987). Article  Google

Scholar  * Rebaï, A., Goffinet, B. & Mangin, B. Approximate thresholds of interval mapping tests for QTL detection. _Genetics_ 138, 235–240 (1994). Article  PubMed  PubMed Central 

Google Scholar  * Lander, E. S. & Kruglyak, L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. _Nature Genet._ 11, 241–247 (1995).

Article  CAS  PubMed  Google Scholar  * Efron, B. & Tibshirani, R. J. _An Introduction to the Bootstrap_ (Chapman & Hall, New York, 1993). Book  Google Scholar  * Good, I. P.

_Permutation Tests: a Practical Guide to Resampling Methods for Testing Hypothesis_ (Springer, New York, 2000). Book  Google Scholar  * Piepho, H.-P. A quick method for computing approximate

thresholds for quantitative trait loci detection. _Genetics_ 157, 425–432 (2001). Article  CAS  PubMed  PubMed Central  Google Scholar  * Davies, R. B. Hypothesis testing when a nuisance

parameter is present only under the alternative. _Biometrika_ 64, 247–254 (1977). Article  Google Scholar  * Davies, R. B. Hypothesis testing when a nuisance parameter is present only under

the alternative. _Biometrika_ 74, 33–53 (1987). Google Scholar  * Van Ooijen, J. W. LOD significance thresholds for QTL analysis in experimental populations of diploid species. _Heredity_

83, 613–624 (1999). Article  PubMed  Google Scholar  * Doerge, R. W. & Rebai, A. Significance thresholds for QTL mapping tests. _Heredity_ 76, 459–464 (1996). Article  Google Scholar  *

Wilson, I. W., Schiff, C. L., Hughes, D. E. & Somerville, S. C. Quantitative trait loci analysis of powdery mildew disease resistance in the _Arabidopsis thaliana_ accession Kashmir-1.

_Genetics_ 158, 1301–1309 (2001). Article  CAS  PubMed  PubMed Central  Google Scholar  * Kerr, M. K. & Churchill, G. A. Statistical design and the analysis of gene expression

microarrays. _Genet. Res._ 77, 123–128 (2001). Article  CAS  PubMed  Google Scholar  * Kerr, M. K., Martin, M. & Churchill, G. A. Analysis of variance for gene expression microarray

data. _J. Comput. Biol._ 7, 819–837 (2001). Article  Google Scholar  * Newton, M. A. et al. On differential variability of expression ratios: improving statistical inference about gene

expression changes from microarray data. _J. Comput. Biol._ 8, 37–52 (2001). Article  CAS  PubMed  Google Scholar  * Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster

analysis of genome-wide expression patterns. _Proc. Natl Acad. Sci. USA_ 95, 14863–14868 (1998). Article  CAS  PubMed  PubMed Central  Google Scholar  * Holter, N. S. et al. Fundamental

patterns underlying gene expression profiles: simplicity from complexity. _Proc. Natl Acad. Sci. USA_ 97, 8409–8414 (2000). Article  CAS  PubMed  PubMed Central  Google Scholar  * Dudoit,

S., Yang, Y. H., Callow, M. J. & Speed, T. P. _Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments_. Technical Report #578,

Stanford University (2000). Google Scholar  * Munneke, B. _Null Model Methods for Cluster Analysis of Gene Expression Data_. Ph.D. thesis, Department of Statistics, Purdue University, West

Lafayette, Indiana (2001). Google Scholar  * Butterfield, R. J. et al. Genetic analysis of disease subtype and sexual dimorphism in mouse EAE: relapsing–remitting and monophasic

remitting/non-relapsing EAE are immunogenetically distinct. _J. Immunol._ 162, 3096–3102 (1999). CAS  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS This work is partially

supported by the USDA-IFAFS. An extended thank you to the Teuscher group for allowing access and use of their data, and to the anonymous reviewers for many challenging opinions. AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Department of Statistics, and Department of Agronomy, and Computational Genomics, Purdue University, West Lafayette, 47907-1399, Indiana, USA Rebecca

W. Doerge Authors * Rebecca W. Doerge View author publications You can also search for this author inPubMed Google Scholar RELATED LINKS RELATED LINKS DATABASES OMIM multiple sclerosis

FURTHER INFORMATION An alphabetical list of genetic analysis software Minitab QTL mapping software QTL references Quantitative genetics resources SAS Splus GLOSSARY * COMPLEX TRAIT A trait

determined by many genes, almost always interacting with environmental influences. * QUANTITATIVE TRAIT LOCUS A genetic locus identified through the statistical analysis of complex traits

(such as plant height or body weight). These traits are typically affected by more than one gene, and also by the environment. * EPISTASIS In the broad sense used here, it refers to any

genetic interaction in which the combined phenotypic effect of two or more loci exceeds the sum of effects at individual loci. * RECOMBINANT INBRED LINES A population of fully homozygous

individuals that is obtained by repeated selfing from an F1 hybrid, and that comprises ∼50% of each parental genome in different combinations. * SEGREGATION DISTORTION The non-random

segregation of alleles. Apparent segregation distortion can result from incorrect genotype classification. * CLOSED FORM ESTIMATE An estimate of a population parameter that can be calculated

directly from an equation to obtain an exact solution. * SIGNIFICANCE LEVEL A probability for a test statistic that gives the maximum acceptable value of rejecting a 'true' null

hypothesis. * GENETIC INTERFERENCE The presence of a recombinational event in one region affects the occurrence of recombinational events in adjacent regions. * GHOST QTL Quantitative trait

locus (QTL) effects that occur as artefacts due to real QTL in surrounding intervals. * GENETIC ALGORITHM Numerical optimization procedures based on evolutionary principles, such as

mutation, deletion and selection. * DENDROGRAM A branching 'tree' diagram that represents a hierarchy of categories on the basis of degree of similarity or number of shared

characteristics, especially in biological taxonomy. The results of hierarchical clustering are presented as dendrograms, in which the distance along the tree from one element to the next

represents their relative degree of similarity in terms of gene expression. * NON-PARAMETRIC Statistical procedures that are not based on models, or assumptions pertaining to the

distribution of the quantitative trait. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Doerge, R. Mapping and analysis of quantitative trait loci in

experimental populations. _Nat Rev Genet_ 3, 43–52 (2002). https://doi.org/10.1038/nrg703 Download citation * Issue Date: 01 January 2002 * DOI: https://doi.org/10.1038/nrg703 SHARE THIS

ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard

Provided by the Springer Nature SharedIt content-sharing initiative