High resolution of quantitative traits into multiple loci via interval mapping

Journal Article · Fri Apr 01 04:00:00 EST 1994 · Genetics; (United States)

OSTI ID:7061979

Jansen, R C ^[1]; Stam, P ^[2]

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1. Center for Plant Breeding and Reproduction Research, Wageningen (Netherlands)
2. Center for Plant Breeding and Reproduction Research, Wageningen (Netherlands) Wageningen Agricultural Univ. (Netherlands)

A very general method is described for multiple linear regression of a quantitative phenotype on genotype [putative quantitative trait loci (QTLs) and markers] in segregating generations obtained from line crosses. The method exploits two features, (a) the use of additional parental and F[sub 1] data, which fixes the joint QTL effects and the environmental error, and (b) the use of markers as cofactors, which reduces the genetic background noise. As a result, a significant increase of QTL detection power is achieved in comparison with conventional QTL mapping. The core of the method is the completion of any missing genotypic (QTL and marker) observations, which is embedded in a general and simple expectation maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. The method is described in detail for the analysis of an F[sub 2] generation. Because of the generality of the approach, it is easily applicable to other generations, such as backcross progenies and recombinant inbred lines. An example is presented in which multiple QTLs for plant height in tomato are mapped in an F[sub 2] progeny, using additional data from the parents and their F[sub 1] progeny. 18 refs., 2 figs., 3 tabs.

• OSTI ID: 7061979
• Journal Information: Genetics; (United States), Journal Name: Genetics; (United States) Vol. 136:4; ISSN GENTAE; ISSN 0016-6731
• Country of Publication: United States
• Language: English

Related Subjects

550400* -- Genetics
59 BASIC BIOLOGICAL SCIENCES
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
BIOLOGICAL MARKERS
GENETIC MAPPING
MAPPING
MATHEMATICS
REGRESSION ANALYSIS
STATISTICS