Quantitative Trait Loci Markers Research Articles

Identification of quantitative trait loci for prolificacy and growth in mice.

Marker-quantitative trait locus (QTL) linkage was evaluated in F2 intercross and backcross mouse populations derived from stocks differing dramatically in prolificacy and mature weight. A highly prolific outbred Quackenbush-Swiss mouse line, or an inbred line derived from it (16.62 +/- 0.22 and 14.64 +/- 0.27 pups per litter, respectively) were used as one of the grandparents in these populations. The less prolific C57BL/6J inbred mouse line (6.67 +/- 0.37 pups per litter) was used as the other grandparent. Linkage was evaluated in a three-step process that involved selective genotyping of F2 intercross progeny representing extremes for prolificacy, genotyping of the full F2 for chromosomal regions potentially associated with prolificacy, and genotyping of the backcross for genomic regions significantly associated with prolificacy in the F2. Segments of Chromosomes (Chrs) 2 and 4 were significantly (P < 0.05, experiment-wise error rate) associated with prolificacy, and LOD scores suggestive of linkage were observed for litter size on Chr 9 and growth on Chrs 4 and 11. Existence of growth QTL was also supported by marker effects that were significant (P < 0.05) or approaching significance (P < 0.10) in the backcross. Additive litter size QTL effects ranged from 0.56 to 0.79 pups per litter, and dominance deviations ranged from -0.56 to 1.19 pups per litter, suggesting overdominance as a possible mode of gene action in some cases. The observation of pleiotropic or linked QTL for growth and prolificacy corresponds well with results from many selection experiments identifying positively correlated responses to selection for these two traits.

Breeding value estimation with incomplete marker data

Incomplete marker data prevent application of marker-assisted breeding value estimation using animal model BLUP. We describe a Gibbs sampling approach for Bayesian estimation of breeding values, allowing incomplete information on a single marker that is linked to a quantitative trait locus. Derivation of sampling densities for marker genotypes is emphasized, because reconsideration of the gametic relationship matrix structure for a marked quantitative trait locus leads to simple conditional densities. A small numerical example is used to validate estimates obtained from Gibbs sampling. Extension and application of the presented approach in livestock populations is discussed. © Inra/Elsevier, Paris

RFLP loci associated with soybean seed protein and oil content across populations and locations

Molecular markers provide the opportunity to identify marker-quantitative trait locus (QTL) associations in different environments and populations. Two soybean [Glycine max (L.) Merr.] populations, 'Young' x PI 416 937 and PI 97100 x 'Coker 237', were evaluated with restriction fragment length polymorphism (RFLP) markers to identify additional QTLs related to seed protein and oil. For the Young x PI 416937 population, 120 F4-derived lines were secored for segregation at 155 RFLP loci. The F4-derived lines and two parents were grown at Plains, G.a., and Windblow and Plymouth, N.C. in 1994, and evaluated for seed protein and oil. For the PI 97100 x Coker 237 population, 111 F2-derived lines were evaluated for segregation at 153 RFLP loci. Phenotypic data for seed protein and oil were obtained in two different locations (Athens, G.a., and Blackville, S.C.) in 1994. Based on single-factor analysis of variance (ANOVA) for the Young x PI 416937 population, five of seven independent markers associated with seed protein, and all four independent markers associated with seed oil in the combined analysis over locations were detected at all three locations. For the PI 97 100 x Coker 237 population, both single-factor ANOVA and interval mapping were used to detect QTLs. Using single-factor ANOVA, three of four independent markers for seed protein and two of three independent markers for seed oil were detected at both locations. In both populations, singlefactor ANOVA, revealed the consistency of QTLs across locations, which might be due to the high heritability and the relatively few QTLs with large effects conditioning these traits. However, interval mapping of the PI 97100 x Coker 237 population indicated that QTLs identified at Athens for seed protein and oil were different from those at Blackville. This might result from the power of QTL mapping being dependent on the level of saturation of the genetic map. Increased seed protein was associated with decreased seed oil in the PI 97100 x Coker 237 population (r = -0.61). There were various common markers (P[Symbol: see text]0.05) on linkage groups (LG) E, G,H,K, and UNK2 identified for both seed protein and oil. One QTL on LG E was associated with seed protein in both populations. The other QTLs for protein and oil were population specific.

Genetic markers linked to quantitative traits in poultry.

This study utilized DNA fingerprints and crosses of two genetically distinct lines of layer-type chickens to identify genetic markers linked to quantitative trait loci (QTL). In phase I, backcross (BC1) hens were separately ranked for each of eight traits and then blood pools were produced in groups along each phenotypic distribution. The DNA was isolated from the blood pools and used in a gradient analysis to screen for DNA fingerprint bands that exhibited intensity gradients associated with the phenotypic traits. To identify linkage of bands with QTL and to estimate band effects, F2 progeny were produced in phase II from the phase I BC1 population. A single-trait animal model was used for analysis of associations of all individual DNA fingerprint bands of sires and their progeny phenotypic performance. Twenty fingerprint bands, only two of which had shown trait-associated gradients in phase I, were identified by the animal model analysis of the progeny test as QTL linked (P < or = 0.05) to specific traits of growth, reproduction and egg quality. These 20 bands warrant further study as potentially valuable molecular markers for QTL.

Association of Alleles for the Class I Bovine Lymphocyte Antigens with Conformation, Semen Traits, and Growth Rate of Young Bulls

The 279 Norwegian bulls that were performance tested during 1988 and 1989 were typed for bovine lymphocyte antigens of class I, and relationships with performance test results of these bulls were investigated. A single-trait animal model was used to estimate the gene substitution effects of bovine lymphocyte antigens on conformation as well as semen volume, density, and quality; a fixed linear model was also applied to the breeding values of growth rate. Allelic frequencies ranged from .2 to 28%; alleles w16 (28%), A2 (15%), A10(w50) (12%), and A8 (10%) were the most frequent. Allele A12(A30) was significantly associated with decreased semen density and decreased growth rate, and allele A10(w50) was significantly associated with increased growth rate. No antigen showed a significant association with conformation, semen density, or semen quality. Heterozygosity at the locus for bovine lymphocyte antigen of class I did not show significant advantage in any of the traits. The bovine lymphocyte antigens appeared to be potential markers for quantitative trait loci controlling semen density and growth rate; however, further research is necessary to confirm these findings.

Covariance between relatives for a marked quantitative trait locus

Summary - Best linear unbiased prediction (BLUP) can be applied to marker-assisted selection. This application requires computation of the inverse of the conditional covariance matrix (Gof additive effects for the quantitative trait locus (QTL) linked to the marker locus (ML), given marker genotypes. This paper presents theory and algorithms to construct G and to obtain its inverse efficiently. These algorithms are suf&ciently general to accommodate situations (1) where paternal or maternal origin of marker alleles cannot be determined and (2) where the marker genotypes of some individuals in the pedigree are unknown. genetic marker / marker-assisted selection / best linear unbiased prediction / covariance between relatives / gametic relationship

Covariance between relatives for a marked quantitative trait locus*

Covariance between relatives for a marked quantitative trait locus*

Selective DNA pooling for determination of linkage between a molecular marker and a quantitative trait locus.

Selective genotyping is a method to reduce costs in marker-quantitative trait locus (QTL) linkage determination by genotyping only those individuals with extreme, and hence most informative, quantitative trait values. The DNA pooling strategy (termed: "selective DNA pooling") takes this one step further by pooling DNA from the selected individuals at each of the two phenotypic extremes, and basing the test for linkage on marker allele frequencies as estimated from the pooled samples only. This can reduce genotyping costs of marker-QTL linkage determination by up to two orders of magnitude. Theoretical analysis of selective DNA pooling shows that for experiments involving backcross, F2 and half-sib designs, the power of selective DNA pooling for detecting genes with large effect, can be the same as that obtained by individual selective genotyping. Power for detecting genes with small effect, however, was found to decrease strongly with increase in the technical error of estimating allele frequencies in the pooled samples. The effect of technical error, however, can be markedly reduced by replication of technical procedures. It is also shown that a proportion selected of 0.1 at each tail will be appropriate for a wide range of experimental conditions.

Prediction of the power of detection of marker-quantitative trait locus linkages using analysis of variance

Analysis of variance can be used to detect the linkage of segregating quantitative trait loci (QTLs) to molecular markers in outbred populations. Using independent full-sib families and assuming linkage equilibrium, equations to predict the power of detection of a QTL are described. These equations are based on an hierarchical analysis of variance assuming either a completely random model or a mixed model, in which the QTL effect is fixed. A simple prediction of power from the mean squares is used that assumes a random model so that in the mixed-model situation this is an approximation. Simulation is used to illustrate the failure of the random model to predict mean squares and, hence, the power. The mixed model is shown to provide accurate prediction of the mean squares and, using the approximation, of power.

Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map.

A simulation study was carried out on a backcross population in order to determine the effect of marker spacing, gene effect and population size on the power of marker-quantitative trait loci (QTL) linkage experiments and on the standard error of maximum likelihood estimates (MLE) of QTL gene effect and map location. Power of detecting a QTL was virtually the same for a marker spacing of 10 cM as for an infinite number of markers and was only slightly decreased for marker spacing of 20 or even 50 cM. The advantage of using interval mapping as compared to single-marker analysis was slight. "Resolving power" of a marker-QTL linkage experiment was defined as the 95% confidence interval for the QTL map location that would be obtained when scoring an infinite number of markers. It was found that reducing marker spacing below the resolving power did not add appreciably to narrowing the confidence interval. Thus, the 95% confidence interval with infinite markers sets the useful marker spacing for estimating QTL map location for a given population size and estimated gene effect.

Elimination of Quantitative Trait Loci Equations in an Animal Model Incorporating Genetic Marker Data

An animal model for BLUP of additive effects at marked quantitative trait loci and breeding values does not require quantitative trait loci equations for animals that were not marker genotyped and do not provide relationship ties among genotyped descendants. An animal model in quantitative trait loci effects and breeding values is equivalent to a previous model in quantitative trait loci effects and residual breeding values. With no marker data, absorption of quantitative trait loci into breeding value equations reduces to the standard animal model for phenotypic data. With marker data on some animals, quantitative trait loci equations for animals without marker data and not providing relationship ties among genotyped descendants can be eliminated. Resulting mixed model equations contain the inverse of a variance-covariance matrix among breeding values and remaining quantitative trait loci effects, which can be computed directly. Quantitative trait loci effects of progeny with parental quantitative trait loci effects eliminated are linked to breeding values of parents. Animals without marker data may be ancestors and nonelite members of the current population. Hence, a substantial fraction of a population may not be genotyped. Then, the number of equations is reduced considerably.

Simulation of marker-assisted selection utilizing linkage disequilibrium: the effects of several additional factors

Use was made of our published model and methods to investigate the effects of several additional factors on marker-assisted selection (MAS) utilizing linkage disequilibrium. The additional factors were: size of the sample used to estimate the marker quantitative trait locus (MQTL) association effects, the method used to estimate the MQTL effects, use of the average of the top MQTL estimates in selection rather than individual estimates, size of the selection population, and the crossing of duplicate selection lines to generate further linkage disequilibrium and further selection response. The average map distance between the quantitative trait loci (QTLs) and their nearest marker was 0.15 Morgans. Use of estimates of MQTL effects derived by least squares yielded smaller selection responses than estimates derived by mixed-model methods. Selection responses were also reduced by using a smaller sample for estimating the associations because MQTL effects were less well estimated. This applied to selection on the MQTL effects themselves and to selection combining the MQTL with phenotypic information. Thus, poorly estimated MQTL effects added noise to the system and reduced selection response in combined selection. Using the average of the top MQTL estimates, rather than individual estimates, also reduced selection response. New linkage disequilibrium, generated by crossing two lines selected from the same population, did not lead to additional selection response in the cross line. These results show limitations to MAS using linkage disequilibrium until close linkages of markers and QTLs are available.

Theory for identification of marker locus-QTL associations in population by line crosses

The objective of this paper is to present genetic theory demonstrating the conditions under which it should be possible to identify molecular marker-quantitative trait locus (QTL) associations in crosses of random-mating populations to inbreds. Using as an example the cross of a corn (Zea mays L.) population to an inbred, the expected disequilibrium for testcross and per se performance of F2, F3, BC1 (to the inbred) and recombinant inbred generations was derived for cases where a marker allele is linked to an unfavorable QTL allele in the inbred and where the marker allele is linked to a favorable QTL allele in the inbred. Disequilibrium in segregating generations was shown to be a function of disequilibrium in the parent population, the frequency of marker and QTL alleles in the parent population, and the recombination distance between the marker and the QTL. To maximize the opportunity to identify a favorable QTL the following procedures are suggested: (1) Select marker loci with alleles in the parent population which are not present in the inbred. (2) Select populations known to have favorable QTL alleles not present in the inbred. (3) Use as many marker loci as possible to enhance the probability of tight linkage between the marker and the QTL.

Limitations of molecular-marker-aided selection in forest tree breeding

The advances to date with quantitative trait locus identification in agronomic crops, which have mostly been with studies of inter- and intra-specific hybrids, are of little relevance to assessing the potential for marker-aided selection in nonhybrid forest tree populations. Although molecular markers provide great opportunities for dissection of quantitative traits in experimental populations, we expect that their near-term usefulness in most operational tree breeding programs will be limited. In addition to cost, this limitation results from quantitative trait locus–marker associations being limited to specific genetic backgrounds as a result of linkage equilibrium, interactions of quantitative trait locus effects with genetic backgrounds, genotype by environment interaction, and changes of quantitative trait locus allele frequencies among generations. Marker-aided selection within individually mapped full-sib families can substantially aid phenotypic selection, but only where large restrictions of genetic base are tolerated, trait heritabilities are low, markers are able to explain much of the additive variance, selection intensities within families are high compared with that among families, and very large numbers of progeny are examined. Broad use of marker-aided selection in the longer term will require substantial technical advances in a number of areas, including means for precise quantitative trait locus identification; reduction of large-scale mapping and genotyping costs; and changes in breeding and propagation systems. Consideration of trait characteristics suggests that marker-aided selection will be most efficient in direct selection with high-value, low-heritability traits such as height and diameter growth. These traits, however, often show genotype by environment interaction and unfavorable genetic correlations with other desirable traits, and are likely to be controlled by a large number of minor genes rather than relatively few major ones. Traits with the most potential for marker-aided selection in nonhybrid tree populations will therefore be strongly inherited ones for which phenotypic assay is difficult; examples might include wood quality, resistance to biotrophic pathogens, and resistance to air pollutants. Because of the large disequilibrium generated during hybridization and the great phenotypic variance that segregates in F2 and backcross generations, interspecific hybrid programs lend themselves much more readily to marker-aideed selection. Segregation distortion and related meiotic aberrations, however, may substantially hamper precise estimation of quantitative trait locus locations and phenotypic effects. Nonadditive quantitative trait locus effects will likely be greater in hybrid populations than in intraspecific populations. Rapid decay of disequilibrium due to recombination, and allele frequency shifts due to selective breeding and natural selection during early generations after hybridization, are likely to cause instability for quantitative trait locus - marker associations and quantitative trait locus phenotypic effects. Finally, interspecific hybridization of highly heterozygous individuals from species in linkage equilibrium will impede marker-aided selection.

Using marker-aided selection to improve tree growth response to abiotic stress

The ability to locate quantitative trait loci (QTL) on a restriction fragment length polymorphism (RFLP) genetic linkage map will provide tree breeders the means to overcome some currently intractable problems inherent to breeding long-lived tree species. These RFLP–QTL maps will allow early, indirect, marker-aided selection (MAS) in breeding programs. This paper explores the opportunities and limitations of MAS, an approach to developing an RFLP–QTL marker map, and use of the map in breeding loblolly pine (Pinustaeda L.) for improved growth response to abiotic stress; specifically, drought stress. Growth responses under moderate drought stress are important to long-term forest productivity, because on most sites loblolly pine is subjected to growth-limiting moisture stress during part of almost every growing season. MAS provides new precision and reliability in identifying and breeding trees that are able to continue growth under moderate stress, and perhaps provides a unique opportunity for improving tree productivity on moderately stressful sites. Traits potentially related to growth under stress are identified, and their suitability for placement and use on an RFLP–QTL linkage map is discussed.

Computer simulation of marker-assisted selection utilizing linkage disequilibrium

The value of marker-assisted selection (MAS) using linkage disequilibrium between genetic markers and quantitative trait loci (QTL) was examined. To simulate the disequilibrium, four base populations were created, F2, F5, F10 and F20, by random mating from a cross between two inbred lines. Selections were on breeding values estimated from: (1) marker QTL (MQTL) associations (MAS); (2) conventional best linear unbiased prediction (BLUP) methods; and (3) a combination of 1 and 2 (COMB). Alternative cases were studied by varying the parameters (heritability, initial linkage disequilibrium, and distribution of QTL effects). A genome with 100 QTL and 100 markers randomly (but equally) spread over 20 chromosomes, each 100 centiMorgans (cM) in length, was generated. Linkage disequilibrium (over 30 replicates) of QTLs with their nearest marker averaged 0.153, 0.104, 0.068, and 0.047 for the four base populations, and fell to 0.035, 0.025, 0.021, and 0.018, respectively, after ten generations of MAS selection (heritability 0.25). The initial linkage disequilibrium had the greatest effect on the genetic gain by MAS with the responses for the base populations F2>F5>F10>F20. Genetic gains by conventional BLUP selection were usually greater than by MAS. However, MAS contributed to the combined selection (COMB) to give appreciably higher genetic responses. Hybridization of selected lines after several generations of selection contributed little to generating further linkage disequilibrium. Detection of markers closer to the QTL will increase the linkage disequilibrium available for selection. Eventually with very close linkage each QTL allele can be uniquely identified in selection, and selection will then be equivalent to selection on the QTLs themselves.