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WO1990004651A1 - Topographie de traits quantitatifs en utilisant des marqueurs genetiques - Google Patents

Topographie de traits quantitatifs en utilisant des marqueurs genetiques Download PDF

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WO1990004651A1
WO1990004651A1 PCT/US1989/004688 US8904688W WO9004651A1 WO 1990004651 A1 WO1990004651 A1 WO 1990004651A1 US 8904688 W US8904688 W US 8904688W WO 9004651 A1 WO9004651 A1 WO 9004651A1
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progeny
qtls
genetic
qtl
strains
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PCT/US1989/004688
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Eric S. Lander
Andrew H. Paterson
Steven D. Tanksley
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Whitehead Institute For Biomedical Research
Cornell Research Foundation, Inc.
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Publication of WO1990004651A1 publication Critical patent/WO1990004651A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6827—Hybridisation assays for detection of mutation or polymorphism
    • C12Q1/683—Hybridisation assays for detection of mutation or polymorphism involving restriction enzymes, e.g. restriction fragment length polymorphism [RFLP]
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6827—Hybridisation assays for detection of mutation or polymorphism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6841—In situ hybridisation

Definitions

  • RFLPs restriction fragment length polymorphisms
  • DNA polymorphisms which are differences in the nucleotide sequence of a region of DNA, are of several types. For example, variation in the nucleotide sequence of DNA which is the result of a point mutation can, in turn, result in gain or loss of a restriction site for a particular restriction endonuclease. Changes in DNA which involve larger regions (deletions, additions, inversions, translocations) change the relative
  • restriction fragments which differ in size distribution from fragments similarly obtained from an unaffected individual (i.e., one in whom such a mutation or
  • RFLP which, in the case of a point mutation, is generated only by enzymes whose recognition sites include the mutation and, in the case of changes in larger regions, is
  • RFLPs have been shown to be stably inherited and, in teh case of nuclear RFLPs, to express codominance and generally lack obvious phenotypic effects. In addition, RFLPs often exhibit multiple alleles. Assigning
  • the present invention relates to a systematic and accurate method for mapping or locating precisely the genomic regions containing polygenic factors controlling a quantitatively inherited trait or traits of interest. Described herein is the first method by which accurate mapping of a QTL to an interval can be carried out. That is, unlike previously-available methods of determining QTLs, the present method makes it possible to determine with a high degree of accuracy that a QTL lies within an interval (e.g., within a region of DNA bounded by two selected markers or a subregion thereof). The accuracy with which mapping can be carried out by the method of the present invention is particularly valuable in that it provides precision in at least two applications: gene cloning and gene transfer.
  • the present method makes it possible to delimit the region in which It is highly likely that DNA encoding a quantitative trait of interest occurs, thus maximizing the likelihood that the DNA of interest is, in fact isolated and minimizing the effort which must be expended in its isolation.
  • the present method is also very valuable in those instances in which transfer of a gene or gene portion from one plant or animal (e.g., an unagricultural product or wild type) to another plant or animal (e.g., an agricultural product) is to be carried out.
  • tight intervals increase the likelihood that the desired gene will be transferred and decrease the effort (and concomitant time and expense) necessary to transfer the gene or gene portion.
  • the method of the present invention makes use of genetic markers to map/locate QTL, to estimate or predict their phenotypic effects and to greatly reduce the number of progeny which must be scored with the DNA markers.
  • RFLPs RFLPs
  • isozymes two specific genes were the genetic markers used.
  • any genetic markers such as any DNA polymorphisms or DNA sequence differences that can be detected, codominant protein polymorphisms or a combination of codominant genetic markers can be used for the same purpose.
  • Any set of scorable genetic markers (codominant or recessive) which cover most of the genome of the plant or animal being assessed can be used for this purpose.
  • isogenic lines can be used, in conjunction with the fundamental tools of genetic and molecular biology for the study of a trait, including testing of complementation and epistasis; characterization of physiological and biochemical
  • the method of the present invention has broad application to breeding of plants and animals for agriculturally valuable traits, particularly because it allows for deterministic breeding.
  • diabetes predispositions to cancer and teratomas, alcohol sensitivity, drug sensitivities and some
  • concentration of soluble solids and fruit pH are mapped to within about 20-30 centiMorgans (cM, which - 1% recombination about 10 6 - 10 7 bp) by means of a complete RFLP linkage map Brief Description of the Drawings
  • Figure 1 is the frequency distribution for fruit mass, soluble-solids concentration (oBrix, a standard refractometric measure primarily detecting reducing sugars, but also affected by other soluble constituents;
  • 1oBrix is approximately 1% w/w) and pH in the E parental strain and in the backcross (BC) progeny.
  • Figure 2 is the distribution of recurrent parent (E) genotype in the 237 backcross progeny, estimated on the basis of the marker genotypes and their relative
  • Figure 3 are QTL likelihood maps indicating LOD scores for fruit weight (solid lines and bars), soluble solids concentration (dotted lines and bars) and pH
  • FIG. 4 is a schematic drawing of phenotypic distributions in the A and B parental, F1 hybrid and B1 backcross populations.
  • Figure 5 are graphic representations of LOD scores for a hypothetical quantitative trait, based on simulated data for 250 backcross progeny in an organism with 12 chromosomes of 100 cM each.
  • Figure 6 is a graphic representation of LOD scores for a chromosome containing two QTLs.
  • Figure 7 shows the appropriate LOD threshold so that the chance of a false positive occurring anywhere in the genome is at most 5 % , as a function of genome s ize and density of RFLPs scored.
  • Figure 8 shows that progeny having phenotypes exceeding mean by ⁇ L standard deviations make up a proportion Q(L) of population, but account for a
  • Figure 9 shows that if only individuals having phenotypes exceeding mean by ⁇ L standard deviations, the number of progeny genotyped may be decreased by a factor of g(L) if the number of progeny grown and phenotyped is increased by a factor of h(L).
  • Figure 10 shows the number of backcross progeny that must be genotyped to map a QTL, based on the fraction of the backcross variance explained by the segregation of the QTL.
  • all progeny are genotyped and single markers are analyzed.
  • only progeny with 5% most extreme phenotypes are genotyped and interval mapping is used to analyze the data.
  • Figure 11 shows the number of backcross progeny that must be genotypes to map a QTL, based on the difference D between the strains (measured in environmental standard deviations) and the number K of effective factors.
  • D the difference between the strains (measured in environmental standard deviations)
  • K the number of effective factors.
  • the present invention is based on a method of resolving quantitative traits into discrete Mendelian factors, using genetic markers.
  • a complete RFLP linkage map was available and RFLPs were the genetic markers used.
  • any genetic markers which will generally be codominant markers such as DNA polymorphisms, isozymes or other codominant protein polymorphisms or a combination of such markers, can be used. Described herein is the first use of such a complete RFLP linkage map to resolve
  • mapping QTLs in other plants and in animals, have made it possible, for the first time, to map QTLs to DNA intervals with a high degree of accuracy, thus maximizing the likelihood that a QTL of interest lies within an interval defined by two selected (defined) markers.
  • This approach is broadly applicable to the genetic dissection of quantitative inheritance of
  • interval mapping of the present invention and its application to a higher plant are described in detail herein.
  • the method of interval mapping of the present invention can also be used to locate precisely the genomic regions which contain polygenic factors controlling a quantitatively inherited trait or traits.
  • the subject method entails five steps or procedures: 1) choosing of a pair of interfertile strains (i.e., two types or varieties of a plant or animal which differ as to a trait of interest), which will serve as the parent strains in the initial cross (and one of which may serve as the recurrent parent in subsequent crosses if backcrosses are employed); 2) constructing a genetic linkage map (using RFLPs, isozymes and/or other codominant markers), if an adequate map is not already available; 3) arranging one or more
  • back-crosses or intercrosses using as the recurrent parent the strain or type of plant or animal in which the transferred gene (or genes) is to function; 4) scoring progeny of the back-crosses or intercrosses for the trait or traits of interest and for the genetic markers
  • interval mapping method of the present invention to locate genomic regions containing QTLs of interest in tomato plants is presented in the fo llowing s ec tion .
  • accession LA1028 (denoted CL).
  • concentrations of soluble solids E approximately 5%; CL approximately 10%. These are traits of agricultural importance because they jointly determine the yield of tomato paste. Rick, CM., Hilgardia, 42:493-510 (1974).
  • the strains are known to be polymorphic for genes affecting fruit pH, which is important for the optimal preservation of tomato products; the difference in pH between parental strains is, however, small.
  • the map is essentially complete: it has linkage groups covering all 12 tomato chromosomes, with an average spacing of 5 cM between markers (1 cM is the distance along the chromosome which gives a
  • E and CL strains differ in two easily-scored, simply-inherited morphological traits: determinancy (described below) and uniform ripening, controlled by the sp and u genes, respectively. Although a few distal regions did not contain appropriate markers, it is estimated that about 95% of the tomato genome was detectably linked to the markers used.
  • interval mapping allows inference about points throughout the entire genome and avoids confounding phenotypic effects with recombination, by using
  • interval mapping reduces to linear regression.
  • threshold of 2.4 gives a probability of less than 5% that even a single false positive will occur anywhere in the genome.
  • QTL likelihood maps showing how lod scores for fruit mass, soluble-solids concentration and pH change as one moves along the genome, reveal multiple QTLs for each trait and estimate their location to within 20-30 cM.
  • chromosome 1 CD41 on chromosome 5 and TG68 on chromosome 12 may affect soluble-solids concentration and merit further attention in larger populations.
  • the region near, the ⁇ locus on chromosome 10 may contain an additional QTL affecting pH (See Example 1).
  • the region near sp on chromosome 6 has the largest effects on soluble solids and pH, as well as a
  • the sp gene affects plant-growth habit: the dominant CL allele causes continuous apical growth (indeterminate habit), whereas the recessive E allele causes termination in an
  • the QTLs identified here may well differ from those that would be fixed by repeated back-crossing with continuing selection for a trait, a classical method for introgressing quantitative traits.
  • Work on LA1563, a strain with increased soluble solids produced through back-crossing a different strain of E to CL has provided some suggestive evidence. Rick, C.M., Hilgardia,
  • Tanksley and Hewitt By surveying RFLPs, Tanksley and Hewitt recently found that LA1563 has maintained three separate regions from CL: near CD56 on chromosome 10, near Got2 on chromosome 7 and near TG13 on chromosome 7. Here, above-threshold effects were detected In the last of these three regions only (which, interestingly, failed to show effects on soluble solids in a single-environment test by Tanksley and Hewitt).
  • crosses can be used to isolate them in near- isogenic lines. These lines can be used to
  • the method of interval mapping described above allows (i) efficient detection of QTLs while limiting the overall occurrence of false positives; til) accurate estimation of phenotypic effects of QTLs; and (iii) localization of QTLs to specific regions
  • phenotypic effect ⁇ that the cross will be designed to detect.
  • ⁇ a choice of ⁇ in the range of between 1 ⁇ 2(D/k) and (D/k) should ensure that QTLs accounting for much of the phenotypic difference will be detected.
  • ⁇ a choice of ⁇ in the range of between 1 ⁇ 2(D/k) and (D/k) should ensure that QTLs accounting for much of the phenotypic difference will be detected.
  • the same choice of ⁇ can be used, although the presence of QTLs with this effect is not guaranteed.
  • interval mapping and selective genotyping reduce the number of progeny to be genotyped by up to 7-fold.
  • isogenic lines can be rapidly constructed differing only in the region of the QTL by using the RFLPs to select for the desired region and against the remainder of the genome.
  • flanking markers may be used to retain the QTL and the study of the remaining markers may be used to speed progress by identifying individuals with a fortuitously high
  • the tomatoes were grown in the field at Davis, California, in a completely randomized design including 237 BC plants (with E as the recurrent pistillate
  • Figure 2 shows the distribution of percentage of recurrent parent (E) genotype in the 237 back-cross progeny, estimated on teh basis of the marker genotypes and their relative distances. Determination of marker genotypes was as previously described. Tanksley, S. D. and J. Hewitt,
  • the height of the curve indicates the strength of the evidence (log 10 of the odds ratio) for the presence of a QTL at each location and not the magnitude of the inferred allelic effect.
  • the horizontal line at a height of 2.4 indicates the stringent threshold that the lod score must cross to allow the presence of a QTL to be inferred, as described herein.
  • Information about the likely position of the QTL can also be inferred from the curve.
  • the maximum likelihood position of the QTL is the highest point on the curve.
  • Bars below each graph indicate a 10:1 likelihood support interval for the position of the QTL (the range outside which the likelihood falls by a lod score of 1.0), whereas the lines extending out from the bars indicate a
  • Phenotypic effects indicated beside the bars are the inferred effect of substituting a single CL allele for one of the two E alleles at the QTL.
  • telome one near TG19, chromosome five near TG34 and chromosome 12 near TG68 regions show sub-threshold effects on one or more traits (chromosome one near TG19, chromosome five near TG34 and chromosome 12 near TG68) which may represent QTLs; this requires additional testing.
  • the region near TG68 may be particularly interesting, as it is the only instance found where the CL allele seems to decrease soluble-solids concentration (by about
  • the lod score and the maximum likelihood estimate (MLE) of the phenotypic effect at any point in the genome is computed assuming that the distribution of phenotypes in the BC progeny represents a mixture of two normal distributions (of equal variance) with means depending on the genotype at a putative QTL at the given position.
  • QTLs are considered individually and there is no assumption that different QTL effects can be added, except in studying the possibility of two QTLs on chromosome 10 affecting pH .
  • the likelihood function for individual i with quantitative phenotype ⁇ is given by
  • ⁇ 2 is the phenotypic variance not attributable to the QTL and p 1 and p 2 are the probabilities that individual i has genotype E/E and E/CL, respectively, at the QTL (which can be computed on the basis of the genotypes at the flanking markers and the distance to the flanking markers).
  • ⁇ * and ⁇ * denote the MLEs allowing the possibility of a QTL at the
  • QTLs quantitative trait loci
  • Figures 10 and 11 are graphs that allow geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.
  • mapping QTLs The basic methodology for mapping QTLs involves arranging a cross between two inbred strains differing substantially in a quantitative trait: segregating progeny are scored both for the trait and for a number of genetic markers. Typically, the segregating progeny are produced by a B1 backcros s (F1 x Parent) or an F2
  • phenotypic effect ⁇ of a QTL is meant the additive effect of substituting both A alleles by B alleles.
  • a single allele has effect 1 ⁇ 2 ⁇ , since
  • the number of effective factors k may seriously underestimate the number of QTLs.
  • the number of QTLs is unlimited. In this case, must there exist any QTLs affecting the phenotype by (D/k)? More generally, for any 0 ⁇ ⁇ ⁇ 1, must there exist QTLs affecting the phenotype by ⁇ (D/k)? And, how must of the total
  • the traditional approach for detecting a QTL near a genetic marker involves comparing the phenotypic means for two classes of progeny: those with genotype marker AB, and those with marker genotype AA .
  • the difference between the means provides an estimate of the phenotypic effect of substituting a B allele for an A allele at ehe QTL.
  • z is a standard normal variable (i.e., Z ⁇ is the number of standard deviations beyond which the normal curve contains probability ⁇ ).
  • the required progeny size thus essentially scales inversely with the square of the phenotypic effect of the QTL or, equivalently, inversely with the variance explained.
  • mapping the traditional approach has a number of
  • phenotypic effect of the QTL is biased downward by a factor of (1-2 ⁇ ). (ii) If the QTL does not lie at the marker locus, substantially more progeny may be required.
  • the progeny size would need to be increased by 22%, 49%, 82% or 123%, respectively, to account for the possibility that the QTL might lie In the middle of an interval (i.e., at the maximum distance from te nearest RFLP).
  • linear regression solutions (a*, b*, ⁇ 2 *) are, in fact, maximum likelihood estimates (MLEs) for the
  • parameters--thhat are the values which maximize the probability L(a,b, ⁇ 2 ) that the observed data would have occurred.
  • LOD log 10 (L(a*, b*, ⁇ 2 * )/L( ⁇ A , 0, ⁇ 2 BC1 )), essentially indicating how much more likely the data is to have arisen assuming the presence of a QTL than assuming its absence. (The choice of log 10 accords with longstanding practice in human genetics, although log e would be slightly more convenient below). Morton, N.E., Am . J. Hum. Genet., 7 : 211 -318 (1955). If the LOD score exceeds a predetermined threshold T, a QTL is declared to be present. The important issues are: (i) What LOD threshold T should be used, in order to maintain an acceptable low rate of false positives? (ii) What is the expected contribution to the LOD score (called the ELOD) from each additional progeny? The number of progeny required is then T/ELOD, to provide even odds of
  • genotyped ln typical cases, a reduction of up to 7-fold can be achieved by combining two approaches: interval mapping and selective genotyping.
  • Selective genotyping involves growing a larger population, but genotyping only those individuals whose phenotypes deviate substantially from the mean. Additional methods for increasing the power of QTL mapping include reducing environmental noise by progeny testing and reducing genetic noise by studying several genetic regions simultaneously.
  • L(a,b, ⁇ 2 ) ⁇ i [G i (0)L i (0) + G i (1)L i (1)], (7)
  • Finding the maximum likelihood solution (a * , b * , ⁇ 2* ) to (7) can be regarded as a linear regression problem with missing data: none of the independent variables (genotypes) are known; only probability distributions for each are available. Although standard computer programs for linear regressions cannot be used, techniques for maximum likelihood estimation with missing data have been developed in recent years. Little, R.J.A. and D.B.
  • Figure 5 presents a QTL likelihood map, showing how the LOD score varies
  • a LOD score of 2.4 is required (see below) for declaring the presence of a QTL.
  • the four largest QTLs are detected while the fifth does not attain statistical significance.
  • the approximate position of the QTLs is indicated by one-lod confidence intervals, defined by the points on the genetic map at which the likelihood ratio has fallen by a factor of 10 from the maximum; such confidence intervals are frequently used in human genetics to indicate the probable position of genes. (Ott, J., Analysis of Human Genetic Linkage, Johns Hopkins University, Baltimore (1985)).
  • the probable position of the QTL is given by confidence intervals, indicating the range of points for which the likelihood ratio is within a factor of 10 (or 100, if desired) of the maximum.
  • Interval mapping thus decreases the required number of progeny by a factor of (1- ⁇ )--which is exactly the proportion of meioses in which the flanking markers do not recombine.
  • d 10,20,30 and 40 cM
  • Interval mapping has recently been applied to an interspecific backcross in tomato: six QTLs affecting tomato fruit weight, four QTLs affecting the
  • interval mapping should prove valuable for analyzing and pres enting evidence for QTLs, and for decreasing somewhat the number of progeny required to detect QTLs of a given magnitude.
  • a standard chi-square table may be used to calculate the LOD score threshold corresponding to a 5% chance that even a single false positive will occur.
  • LOD thresholds as a function of genome size and marker spacing ( Figure 7).
  • interval mapping increases the efficiency of QTL mapping somewhat, large numbers of progeny may still be required. Additional methods are available to increase the power of QTL mapping, the most important of which is selective genotyping.
  • the individuals that provide the most linkage information are those whose genotype can be most clearly inferred from their phenotype. For example,
  • the highest ELODs are provided by the progeny that deviate most from the phenotypic mean.
  • the cost of growing progeny is less that the cost of complete RFLP genotyping (as is frequently the case), it will thus be more efficient to increase the number of progeny grown but to genotype only those with the most extreme
  • Progeny with phenotypes more than 1 standard deviations from the mean comprise about 33% of the total population but contribute about 81% of the total linkage information. By growing a population that was only about 25% larger and genotyping only these extreme progeny, the same total linkage information would be obtained from genotyping only about 40% as many individuals.
  • Progeny with phenotypes more than 2 standard deviations from the mean comprise about 5% of the total population but contribute about 28% of the total linkage information.
  • progeny genotypes for the non-extreme progeny may simply be entered as missing. Using the MAPMAKER-QTL program, the method has been applied to both simulated and experimental data sets.
  • F2 intercrosses and recombinant inbred strains Although the discussion above concerns the backcross, it applies directly to F2 intercrosses and recombinant inbred strains, with the following modifications:
  • multi-generational breeding scheme that is used to construct recombinant inbred strains increases the effective genetic length of the genome. Compared to a backcross, the density of crossovers is doubled in a recombinant inbred produced through selfing and is quadrupled in a recombinant inbred produced by sib mating. Haldane, J.B.S. and CH. Waddington, Genetics, 16 : 357-374 (1931). A genetic length of 2G or 4G must be used in place when computing the appropriate LOD
  • threshold which leads to an increase of about 0.3 or 0.6, respectively, in the threshold required.
  • the higher threshold will increase the number of progeny required, the effect is typically offset by the ability to decrease the number of progeny by reducing the
  • ⁇ *(d) denote the maximum likelihood estimate of the phenotypic effect of a putative QTL at this position
  • LOD(d) denote the corresponding LOD score.
  • LOD(d) is asymptotically proportional to the square of a random normal variable ⁇ (d) (which incidentally proves that LOD is proportional to X 2 ).
  • r(a,b) denote the correlation coefficient for any two random variables a and b.
  • 1 -2 ⁇ e -2d .
  • this is the definition of ORENSTEIN - UHLENBECK diffusion and Proposition 2 follows directly (see LEADERBETTER, LINDGREN and ROOTSZEN 1983. Theorem 12.2.9 and discussion following).
  • the LOD score for a marker at 0 cM can be redefined as the log 10 of
  • This ratio measures how much more likely the data is to have been generated by a QTL with the hypothesized effect located at the marker locus than by a QTL with this same effect but unlinked to the marker.
  • the ELOD can be found by numerical integration over the distribution for ⁇ . In the limit of a QTL with large effect, the expression tends to the traditional LOD score for a qualitative trait used in human genetics.
  • the LOD score (comparing the true hypothesis H 1 :(0.b, ⁇ 2 ) to the alternative H 0 :(0,0, ⁇ 2 )) is
  • LOD ⁇ L ⁇ ⁇ I ⁇ I ⁇ L ⁇ (LOD ⁇ )p( ⁇ )d ⁇ .

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Abstract

Un procédé systématique et précis permettant de topographier ou de localiser des sous-régions ou des régions génomiques contenant des facteurs polygéniques qui régulent un ou des traits ou caractéristiques d'intérêt hérités quantitativement est décrit. Le procédé de l'invention, qui peut s'appliquer aux plantes et aux animaux, permet de déterminer avec un degré élevé de précision qu'un emplacement d'un trait quantitatif (QTL) se trouve dans une région ou sous-région génomique délimitée par des marqueurs génétiques sélectionnés.
PCT/US1989/004688 1988-10-19 1989-10-19 Topographie de traits quantitatifs en utilisant des marqueurs genetiques WO1990004651A1 (fr)

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WO1995017524A2 (fr) * 1993-12-23 1995-06-29 Molecular Tool, Inc. Determination automatique de genotype
US5492547A (en) * 1993-09-14 1996-02-20 Dekalb Genetics Corp. Process for predicting the phenotypic trait of yield in maize
WO1999008507A1 (fr) * 1997-08-15 1999-02-25 Forbio Limited Technique de reproduction dirigee permettant de restreindre le developpement de l'appareil genital
WO1999013107A1 (fr) * 1997-09-08 1999-03-18 Warner-Lambert Co. Procede de determination de la fonction in vivo de sequences de codage d'adn
WO2002044422A2 (fr) * 2000-12-01 2002-06-06 University Of North Carolina - Chapel Hill Procede de cartographie de genes, a ultra haute resolution, et determination de reseaux genetiques parmi des caracteres phenotypiques sous-jacents a des genes
WO2010071431A1 (fr) 2008-12-19 2010-06-24 Monsanto Invest N.V. Procédé de reproduction de plants de concombre résistants au virus du jaunissement et du rabougrissement des cucurbitacées (cysdv)
WO2011050296A1 (fr) 2009-10-22 2011-04-28 Seminis Vegetable Seeds, Inc. Procédés et compositions pour identifier des plantes de concombre résistantes au mildiou
EP2511381A1 (fr) 2007-06-08 2012-10-17 Monsanto Technology LLC Procédés de reproductioin moléculaire dirigés par séquence
CN110093406A (zh) * 2019-05-27 2019-08-06 新疆农业大学 一种盘羊及其杂交后代遗传基因研究方法
US10455783B2 (en) 2006-08-15 2019-10-29 Monsanto Technology Llc Compositions and methods of plant breeding using high density marker information

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WO1989007647A1 (fr) * 1988-02-22 1989-08-24 Pioneer Hi-Bred International, Inc. Liaisons genetiques entre des genes importants du point de vue agronomique et polymorphismes de longueurs de fragments a restriction

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US6455758B1 (en) 1991-02-19 2002-09-24 Dekalb Genetics Corporation Process predicting the value of a phenotypic trait in a plant breeding program
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WO1995017524A3 (fr) * 1993-12-23 1995-07-13 Molecular Tool Inc Determination automatique de genotype
EP1352973A2 (fr) * 1993-12-23 2003-10-15 Beckman Coulter, Inc. Détermination automatique de génotype
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US7585466B1 (en) 1993-12-23 2009-09-08 Beckman Coulter, Inc. Automatic genotype determination
WO1999008507A1 (fr) * 1997-08-15 1999-02-25 Forbio Limited Technique de reproduction dirigee permettant de restreindre le developpement de l'appareil genital
WO1999013107A1 (fr) * 1997-09-08 1999-03-18 Warner-Lambert Co. Procede de determination de la fonction in vivo de sequences de codage d'adn
WO2002044422A2 (fr) * 2000-12-01 2002-06-06 University Of North Carolina - Chapel Hill Procede de cartographie de genes, a ultra haute resolution, et determination de reseaux genetiques parmi des caracteres phenotypiques sous-jacents a des genes
WO2002044422A3 (fr) * 2000-12-01 2003-09-25 Univ North Carolina Chapel Hill Procede de cartographie de genes, a ultra haute resolution, et determination de reseaux genetiques parmi des caracteres phenotypiques sous-jacents a des genes
US10455783B2 (en) 2006-08-15 2019-10-29 Monsanto Technology Llc Compositions and methods of plant breeding using high density marker information
EP2511381A1 (fr) 2007-06-08 2012-10-17 Monsanto Technology LLC Procédés de reproductioin moléculaire dirigés par séquence
US10544448B2 (en) 2007-06-08 2020-01-28 Monsanto Technology Llc Methods for sequence-directed molecular breeding
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US10550424B2 (en) 2007-06-08 2020-02-04 Monsanto Technology Llc Methods for sequence-directed molecular breeding
WO2010071431A1 (fr) 2008-12-19 2010-06-24 Monsanto Invest N.V. Procédé de reproduction de plants de concombre résistants au virus du jaunissement et du rabougrissement des cucurbitacées (cysdv)
WO2011050296A1 (fr) 2009-10-22 2011-04-28 Seminis Vegetable Seeds, Inc. Procédés et compositions pour identifier des plantes de concombre résistantes au mildiou
EP3680352A1 (fr) 2009-10-22 2020-07-15 Seminis Vegetable Seeds, Inc. Plants de concombre résistants au mildiou
CN110093406A (zh) * 2019-05-27 2019-08-06 新疆农业大学 一种盘羊及其杂交后代遗传基因研究方法

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