SNP characterizaiton and genetic and molecular analysis of mutants affecting fiber development in cotton

Cotton (Gossypium spp.) is the world’s leading textile fiber crop, and an important source of oil and protein. Insufficient candidate gene derived-markers suitable for genetic mapping and limited information on genes that control economically important traits are the major impediments to the genetic improvement of Upland cotton (G. hirsutum L.). The objectives of this study were to develop a SNP marker discovery strategy in tetraploid cotton species, SNP characterization and marker development from fiber initiation and elongation related genes, chromosomal assignment of these genes by SNP marker-based deletion analysis or linkage mapping, and genetic and molecular analysis of mutants affecting cotton fiber development. Phylogenetic grouping and comparision to At- and Dt-genome putative ancestral diploid species of allotetraploid cotton facilitated differentiation between genome specific polymorphisms (GSPs) and marker-suitable locus-specific polymorphisms (LSPs). By employing this strategry, a total of 222 and 108 SNPs were identified and the average frequency of SNP was 2.35% and 1.30% in six EXPANSIN A genes and six MYB genes, respectively. Both gene families showed independent and incongruent evolution in the two subgenomes and a faster evolution rate in Dt-genome than that in At-genome. SNPs were concordantly mapped to different chromsomes, which confirmed their value as candidate gene marker and indicated the reliability of SNP discovery stragey. QTL mapping by two F2 populations developed from fiber mutants detected major QTL which explain 62.8-87.1% of the phenotypic variation for lint percentage or lint index in the vicinity of BNL3482-138 on chromosome 26. Single marker regression analyses indicated STV79-108, which was located to the long arm of chromosome 12 (the known location of N1 and perhaps n2 loci), also had significant association (R2 % value 15.4-30.6) with lint percentage, lint index, embryo protein percentage and micronaire. Additional QTL and significant markers associated with other seed and fiber traits were detected on different chromosomes. Inheritance analysis indicated that both genetic models N1N1n2n2 and n2n2li3lisub\u3e3 could lead to the fiberless phenotype. The observation of fuzzless-short lint phenotype indicated fiber initiation and elongation were controlled by different mechanisms. The penetrance of Li2 gene expression was observed in this study

Quantitative trait loci mapping for agronomic and fiber quality traits in Upland cotton (Gossypium hirsutum L.) using molecular markers

The breeding of Upland cotton (Gossypium hirsutum L.) cultivars that combine high yield and fiber quality is a major challenge to the breeder. The understanding of the quantitative trait loci (QTL) contributing to agronomic and fiber quality traits offers an excellent route to solve this problem. A QTL analysis was carried out after an F2:3 population composed of 138 lines, derived from the intraspecific cross between Paymaster 54 and PeeDee 2165, was developed and a linkage map including 143 AFLP markers was constructed. The F2:3 population was grown in two locations, Alexandria and Baton Rouge in LA. The 143 linked markers were assigned to 13 major and 15 minor linkage groups, the 28 linkage groups cover a genetic distance of 1773.2 cM. This gives coverage of 37.7% of the cotton genome (4700 cM). Single-marker analysis, including simple and logistic regression, and interval marker analysis, including interval mapping (IM) and composite interval mapping (CIM), was used. Interval mapping was used to study QTL interaction effects with the environment. For the agronomic traits, the same five QTL were detected, using a significant threshold of 2 LOD, in both IM and CIM. These include two for lint weight per boll, two for seedcotton weight per plant, and one for lint percentage, which collectively, based on IM analysis, explained 32.5%, 28.6%, and 4.4% of the phenotypic variation, respectively. In total, seven and nine different QTL were detected by IM and CIM, respectively. For the fiber quality traits, the same nine QTL were detected in both IM and CIM. These include one for fiber elongation, one for length, two for uniformity, three for strength, and two for micronaire, which collectively, based on IM analysis, explained 50.9%, 18.7%, 69%, 49.6%, and 25.3% of the phenotypic variation, respectively. In total, nine and 19 different QTL were detected in IM and CIM, respectively. Eleven QTL were found to have significant interaction effects with the two locations. Future efforts in QTL mapping should focus on developing more saturated maps, using larger population sizes, and more powerful statistical algorithms and theories for identifying QTL and elucidating QTL X environment interactions

Characterization of quantitative traits using association genetics tetraploid and genetic linkage mapping in diploid cotton (Gossypium spp.)

Cotton (Gossypium spp.) is the most extensively used natural fiber in the textile industry. Understanding the genetic diversity, population structure and marker trait associations are of great importance in marker assisted selection. Microsatellite, AFLP and TRAP markers were used to construct a linkage map with 94 F2 diploid individuals derived from a cross between G. arboreum x G. herbaceum. A total of 606 polymorphic markers gave rise to 37 linkage groups covering a total of 1109cM with an average distance of 7.92cM between each loci. Discriminant analysis identified three markers each for petal color and seed fuzziness, and four markers for petal spot. For quantitative traits, a total of 19 QTL’s were identified and linked with five fiber traits using composite interval mapping. Markers such as qFL4-1, qFS4-2, qELO1-1 and qSI2-1 were found to be significantly linked with fiber length, strength, elongation and seed index respectively. Association mapping principles were applied to upland cotton genotypes in order to examine population structure and marker trait associations. A set of 232 genotypes were genotyped using AFLP markers. The molecular diversity was in the range of 0.48-0.574 with molecular variance found to be 10% among the groups. Bayesian and MCMC based population structure analysis, there existed six subpopulations, in accordance with their geographical origin. The mixed and mixed-multiple regression (MMR) models identified significant markers for lint yield and fiber traits, showing low AICC, BIC and SBC values and high adj. R2. Two way epistatic interaction analyses further confirmed their strong association. In the similar study, a set of 75 upland cotton genotypes were analyzed for seed quality traits such as seed protein, oil and fiber content. Population structure based mixed models showed 32 significant markers, associated with these seed quality traits. MMR models identified several markers, notably E4M3_440, E4M3_200 and E5M7_195 for seed protein, oil and fiber content respectively. Finally, 60 upland genotypes from RBTN program were screened with AFLP markers. The pairwise kinship estimates were ranging between 0.1-0.88 accounting for most of the shared ancestral alleles. The MMR models improved the efficiency of marker selection with 38 markers associated with eight traits

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance

Artificial analysis of molecular marker loci linked to tree resistance response by an artificial neural network

One of the biggest challenges that software developers face is to make an accurate estimate of the project effort. Radial basis function neural networks have been used to software effort estimation in this work using NASA dataset. This paper evaluates and compares radial basis function versus a regression model. The results show that radial basis function neural network have obtained less Mean Square Error than the regression method

Generalized genetical genomics : advanced methods and applications

Generalized genetical genomics (GGG) is a systems genetics approach that combines the analysis of genetic variation with population-wide assessment of variation in molecular traits in multiple environments to identify genotype-by-environment interactions. This thesis starts by introducing the generalized genetical genomics strategy (Chapter 1). Then, we present a newly developed software, designGG for designing optimal GGG experiments (Chapter 2). Next, two important statistical issues relevant to GGG studies were addressed. We discussed the critical concerns on causal inference with genetic data. In addition, we examined the permutation method used for determining the significance of quantitative trait loci (QTL) hotspots in linkage and association studies (Chapter 3−4). Furthermore, we applied the GGG strategy to three pilot studies: In the first of these, we showed that heritable differences in the plastic responses of gene expression are largely regulated in “trans''. In the second pilot study, we demonstrated that heritable differences in transcript abundance are highly sensitive to cellular differentiation stage. In the third study, we found that the alternative splicing machinery exhibits a general genetic robustness in C. elegans and that only a minor fraction of genes shows heritable variation in splicing forms and relative abundance. (Chapter 5−7). Finally, we conclude by discussing various fundamental issues involved in data preprocessing, QTL mapping, result interpretation and network reconstruction and suggesting future directions yet to be explored in order to expand the reach of systems genetics (Chapter 8).

From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology

The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels

COMPARISON OF METHODS INCORPORATING COVARIATES INTO AFFECTED SIB PAIR LINKAGE ANALYSIS

Complex diseases such as type 2 diabetes, hypertension and psychiatric disorders have been major public health problems in US. In order to increase the power in the linkage analysis of complex traits, genetic heterogeneity has to be taken into account. During the past few years, several methods have been proposed for dealing with this issue by incorporating covariate information into the affected sib pair (ASP) analysis. However, it is still not clear how these approaches perform under different gene-environment (G x E) interactions. The covariate statistics evaluated in this study are: (1) mixture model; (2) general conditional-logistic model (LODPAL); (3) multinomial logistic regression models (MLRM under no dominance, no additive and min-max restriction); (4) extension of the maximum-likelihood-binomial approach (MLB); (5) ordered-subset analysis (OSA with three different rank orders: high-to-low, low-to-high and optimal-slice); (6) logistic regression modeling (COVLINK). Based on the chromosome-based approach, we have written simulation programs to generate data under various G x E models and disease models. We first define the empirical statistical significance thresholds using C2, the environmental risk factor, under the null hypothesis. We then evaluate the power of the covariate statistics when different covariates are used. We also compare the performance of the covariate statistics with the model-free methods (Sall and Spair). In all three G x E interaction models, most covariate methods perform better when using C1, the covariate with G x E interaction effect, than when using C2 or the random noise covariate C3, except for MLB and the low-to-high OSA method. Comparing with the model-free methods (using Sall as the baseline), mixture model and the high-to-low OSA method perform the best of the covariate statistics when using C1. However, when using C2 or C3, most covariate statistics provide less power than Sall. Only MLB has comparable power to Sall across all genetic models. According to our results, in different G x E interactions, one should apply the appropriate covariate statistic and include the suitable type of covariates carefully

Phenotypic and Genotypic Studies in the Peach [Prunus persica (L.) Batsch] and Muscadine Grape (Vitis rotundifolia Michx.)

Peach: Peaches [Prunus persica (L.) Batsch] are routinely chilled to increase shelf-life. Exposure to temperatures of 5° C for two weeks can induce chilling injury (CI) symptoms, including flesh mealiness (or wooliness) and a lack of juiciness. Phenotypic data were collected on seven biparental F1 peach populations maintained at the University of Arkansas Fruit Research Station. A genome wide association study (GWAS) was performed using TASSEL 5 which identified four quantitative trait loci (QTLs) associated with expressible juice, four QTLs for mealiness, five QTLs for soluble solids, and three QTLs for fruit weight. Exploiting these genetic markers could help breeders identify fruit quality traits in seedlings through marker-assisted selection (MAS). Muscadine: Two biparental F1 muscadine (Vitis rotundifolia Michx.) populations were phenotyped for flower sex and berry color, and genotyping-by-sequencing (GBS) was performed to produce high-density genetic linkage maps. A total of 1244 SNP markers in population Black Beauty [BB] x Nesbitt [N] and 2069 SNP markers in population Supreme [S] x Nesbitt [N] were mapped to 20 linkage groups (LG) for each population. The results support previous studies revealing an evolutionary bifurcation of V. vinifera chromosome 7 into two independently segregating linkage groups in the muscadine, or, conversely, a possible fusion of muscadine-derived chromosomes into chromosome 7 of V. vinifera. The locus controlling flower type in muscadine mapped to a region spanning 4.6 – 5.1 Mbp on chromosome 2, while the berry color locus mapped to a region spanning 11.1-11.9 Mbp on chromosome 4. These high-density linkage maps lay the groundwork for marker-assisted selection (MAS) in muscadine and provide clues to the evolutionary relationship of the muscadine with V. vinifera. Colorimetry: Precise color identification is critical in many scientific fields, and horticulture is no exception. Plant breeders must be able to effectively discern colors among plant parts and provide accurate descriptions when applying for legal protections. The RHS Colour Chart is currently recognized as the most universally accepted method of assigning color descriptions in horticulture. The RHS Colour Chart relies on manually matching plant parts with the labeled color chips provided. Color perception in humans is complicated by many factors, including the type and quantity of illumination available as well as the individual’s own physiological abilities and limitations. Scientific colorimeters have been developed to serve as an objective way to study color, and many hypothetical color space models have been created to enhance this field of study. The CIE 1976 L*a*b* (CIELAB) color space is widely recognized as a scientific standard and was used in this study. Traditional colorimeters have been bulky and expensive lab equipment, but a new, portable, inexpensive LED-based color scanner called the Nix Pro Color Sensor™ has recently become available. Multiple studies were conducted comparing the Nix Pro with the Konica Minolta CR-400 colorimeter and the RHS Colour Chart paint chip system. The results indicate the Nix Pro, which is inexpensive, yields consistent results, and features built-in color matching capabilities, could be a very useful tool for horticulturists and plant breeders