Biomedical subjects
Publications and source records attributed to Yuan-Ming Zhang.
A CRISPR-derived 36-bp in-frame deletion in GmOIL2 maintains fertility and seed weight while altering seed-related traits. This allele provides a genetic resource for improving soybean quality without affecting seed weight.
Molecular markers have been widely used to map quantitative trait loci (QTL). The QTL mapping partly relies on accurate linkage maps. The non-Mendelian segregation of markers, which affects not only the estimation of genetic distance between two markers but also the order of markers on a same linkage group, is usually observed in QTL analysis. However, these distorted markers are often ignored in the real data analysis of QTL mapping so that some important information may be lost. In this paper, we developed a multipoint approach via Hidden Markov chain model to reconstruct the linkage maps given a specified gene order while simultaneously making use of distorted, dominant and missing markers in an F(2) population. The new method was compared with the methods in the MapManager and Mapmaker programs, respectively, and verified by a series of Monte Carlo simulation experiments along with a working example. Results showed that the adjusted linkage maps can be used for further QTL or segregation distortion locus (SDL) analysis unless there are strong evidences to prove that all markers show normal Mendelian segregation.
In this article, shrinkage estimation method for multiple-marker analysis and for mapping multiple quantitative trait loci (QTL) was reviewed. For multiple-marker analysis, Xu (Genetics, 2003, 163:789-801) developed a Bayesian shrinkage estimation (BSE) method. The key to the success of this method is to allow each marker effect have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, a large number of markers can be handled although most of them may have negligible effects. Under epistatic genetic model, however, the running time is very long. To overcome this problem, a novel method of incorporating the idea described above into maximum likelihood, known as penalized likelihood method, was proposed. A simulated study showed that this method can handle a model with multiple effects, which are ten times larger than the sample size. For multiple QTL analysis, two modified versions for the BSE method were introduced: one is the fixed-interval method and another is the variable-interval method. The former deals with markers with intermediate density, and the latter can handle markers with extremely high density as well as model with epistatic effects. For the detection of epistatic effects, penalized likelihood method and the variable-interval approach of the BSE method are available.
For mapping QTLs, phenotypes of the traits in segregating population derived from the cross between two isogenic lines of the targeted QTL may reflect its genotype if the effect of the QTL is relatively large. In order to map the QTL, it is necessary to use a large sample under the high density of markers around the QTL. However, it increases experimental costs. In order to save the costs, it is possible to map the QTL using the sub-population that consists of plants with homogenous recessive. In this paper, the sub-population was used to estimate the recombination fraction between the marker and the QTL, and its standard error for F2, backcross (BC), double haploid (DH) and recombinant inbred lines (RIL) populations, respectively. The results from Monte Carlo simulation showed that the estimation of recombination fraction based on the sub-population is consistent with that obtained from the full population, and the precision of the former is same as that of the later under the same sample size.
Three sets of data for the P1, P2, F1, and F2 populations derived from three crosses between the normal fertility wheat (Triticum aestivum L.) cultivars with different ecotypes and the female sterile line (XND126) were used to investigate the inheritance of female fertility in wheat using mixed major gene plus polygenes inheritance model in 2005 and 2006. The results from the joint segregation analysis of the four generations showed that female fertility in wheat is controlled by two major genes plus polygenes, and the interaction between the two major genes is also detected.
Three crosses between NG94-156 (brachytic stem) and three varieties (normal stem) were made, and F2 segregative population and two recombined inbred line populations(F(7:8)) were obtained. Genetic analysis indicated that the brachytic stem of NG94-156 was controlled by two duplicate recessive genes. In searching for RAPD marker linked to the genes controlling brachytic stem, 260 RAPD primers were applied to screen four parents of three combinations and RIL. Polymorphic bands revealed by the primer S-506 exhibited the best repeatability among all primers. Linkage analysis indicated the genetic distance between S-506(1600) and brachytic stem gene was 6.94 cM.
Mapping multiple QTL is a typical problem of variable selection in an oversaturated model because the potential number of QTL can be substantially larger than the sample size. Currently, model selection is still the most effective approach to mapping multiple QTL, although further research is needed. An alternative approach to analyzing an oversaturated model is the shrinkage estimation in which all candidate variables are included in the model but their estimated effects are forced to shrink toward zero. In contrast to the usual shrinkage estimation where all model effects are shrunk by the same factor, we develop a Bayesian method that allows the shrinkage factor to vary across different effects. The new shrinkage method forces marker intervals that contain no QTL to have estimated effects close to zero whereas intervals containing notable QTL have estimated effects subject to virtually no shrinkage. We demonstrate the method using both simulated and real data for QTL mapping. A simulation experiment with 500 backcross (BC) individuals showed that the method can localize closely linked QTL and QTL with effects as small as 1% of the phenotypic variance of the trait. The method was also used to map QTL responsible for wound healing in a family of a (MRL/MPJ x SJL/J) cross with 633 F(2) mice derived from two inbred lines.
Many commercial inbred lines are available in crops. A large amount of genetic variation is preserved among these lines. The genealogical history of the inbred lines is usually well documented. However, quantitative trait loci (QTL) responsible for the genetic variances among the lines are largely unexplored due to lack of statistical methods. In this study, we show that the pedigree information of the lines along with the trait values and marker information can be used to map QTL without the need of further crossing experiments. We develop a Monte Carlo method to estimate locus-specific identity-by-descent (IBD) matrices. These IBD matrices are further incorporated into a mixed-model equation for variance component analysis. QTL variance is estimated and tested at every putative position of the genome. The actual QTL are detected by scanning the entire genome. Applying this new method to a well-documented pedigree of maize (Zea mays L.) that consists of 404 inbred lines, we mapped eight QTL for the maize male flowering trait, growing degree day heat units to pollen shedding (GDUSHD). These detected QTL contributed >80% of the variance observed among the inbred lines. The QTL were then used to evaluate all the inbred lines using the best linear unbiased prediction (BLUP) technique. Superior lines were selected according to the estimated QTL allelic values, a technique called marker-assisted selection (MAS). The MAS procedure implemented via BLUP may be routinely used by breeders to select superior lines and line combinations for development of new cultivars.
In plants and laboratory animals, QTL mapping is commonly performed using F(2) or BC individuals derived from the cross of two inbred lines. Typical QTL mapping statistics assume that each F(2) individual is genotyped for the markers and phenotyped for the trait. For plant traits with low heritability, it has been suggested to use the average phenotypic values of F(3) progeny derived from selfing F(2) plants in place of the F(2) phenotype itself. All F(3) progeny derived from the same F(2) plant belong to the same F(2:3) family, denoted by F(2:3). If the size of each F(2:3) family (the number of F(3) progeny) is sufficiently large, the average value of the family will represent the genotypic value of the F(2) plant, and thus the power of QTL mapping may be significantly increased. The strategy of using F(2) marker genotypes and F(3) average phenotypes for QTL mapping in plants is quite similar to the daughter design of QTL mapping in dairy cattle. We study the fundamental principle of the plant version of the daughter design and develop a new statistical method to map QTL under this F(2:3) strategy. We also propose to combine both the F(2) phenotypes and the F(2:3) average phenotypes to further increase the power of QTL mapping. The statistical method developed in this study differs from published ones in that the new method fully takes advantage of the mixture distribution for F(2:3) families of heterozygous F(2) plants. Incorporation of this new information has significantly increased the statistical power of QTL detection relative to the classical F(2) design, even if only a single F(3) progeny is collected from each F(2:3) family. The mixture model is developed on the basis of a single-QTL model and implemented via the EM algorithm. Substantial computer simulation was conducted to demonstrate the improved efficiency of the mixture model. Extension of the mixture model to multiple QTL analysis is developed using a Bayesian approach. The computer program performing the Bayesian analysis of the simulated data is available to users for real data analysis.
In this article, a new algorithm for obtaining the maximum likelihood estimators (MLEs) of parameters in the joint segregation analysis (JSA) of multiple generations of P1, F1, P2, F2 and F2:3 (MG5) for quantitative traits was set up. Firstly, owing to the fact that the component variance of the heterogeneous genotype in F2:3 included both the first-order genetic parameters (denoted by the means of distributions) and the second-order parameters, a simple closed form for the MLEs of the means of component distributions did not exist while the expectation and maximization (EM) algorithm was used. To simplify the estimation of parameters, the first partial derivative of the above variance on the mean in the sample log-likelihood function was omitted. However, this would be remedied by the iterated method. Then, variances of component distributions for segregating populations were partitioned into major-gene, polygenic and environmental variances so that the generally iterated formulae for estimating the means as well as polygenic and environmental variances of component distributions in the maximization step (M-step) of the EM algorithm were obtained. Therefore, the EM algorithm for estimating parameters in the JSA model for the MG5 was simplified. This is called the expectation and iterated maximization (EIM) algorithm. Finally, an example of the inheritance of the resistance of soybean to beanfly showed that the results of mixed inheritance analysis in this paper coincided with those in both Wang & Gai (2001) and Wei et al. (1989), so the EIM algorithm was appropriate.
It has been a long haul but photoperiod- and thermo-sensitive genic male sterile (PTGMS) rice has not been freely used in hybrid rice production because there are two perplexing problems corresponding to the critical sterility temperature point (CSTP): the uncertainty of the CSTP segregating pattern and the instability of CSTP for every originally useful line. N5088S, the most widely commercialized japonica-type PTGMS line in China, also saw that its CSTP variants have been isolated but with all other agronomic characteristics unchanged. In this report we analyzed the genetic basis of CSTP, by employing the iterated expectation and conditional maximization (IECM) algorithm on four tiller-splitting-formed sets of seven generations from N5088S and its CSTP-variant H5088S, each set treated with one temperature regime. The main results indicated that there are two dominant major genes and polygene, as well as their respective epistasis conditioning the CSTP in the 23.5 degrees C regime. Based on the results obtained, the strategy for breeding of PTGMS lines with stable low CSTP was outlined.
While there is linkage between molecular marker and trait controlled by two duplicate genes in the DH or RIL population, the recombination rate (RR) between molecular marker and one gene controlling the above trait may be estimated by the maximum likelihood method. Moreover, the standard deviation of RR was also obtained in this paper. Finally, the results from Monte Carlo simulation with 3000 replications showed that the unbiasedness of RR for various sample size and RR was good, and the variation of the estimated value of RR decreased with the increase of sample size or RR.