TESTING VARIANCE COMPONENTS BY TWO JACKKNIFE METHODS

The jackknife method, a resampling technique, has been widely used for statistical tests for years. The pseudo value based jackknife method (defined as pseudo jackknife method) is commonly used to reduce the bias for an estimate; however, sometimes it could result in large variation for an estimate and thus reduce the power for parameters of interest. In this study, a non-pseudo value based jackknife method (defined as non-pseudo jackknife method) was used for testing variance components under mixed linear models. We compared this non-pseudo value based jackknife method and the pseudo value based method by simulation regarding their biases, Type I errors, and powers. Our simulated results showed that biases obtained by the two jackknife methods are very similar; however, the non-pseudo value based method had higher testing powers than the pseudo value based method while the non-pseudo value based method had lower Type I error rates than the preset nomial probability values. Thus, we concluded that the non-pseudo value based jackknife method is superior to the pseudo value based method for testing variance components under a general mixed linear model

A GENERALIZED APPROACH AND COMPUTER TOOL FOR QUANTITATIVE GENETICS STUDY

Quantitative genetics is one of the most important components to provide valuable genetic information for improving production and quality of plants and animals. The research history of quantitative genetics study could be traced back more than one hundred years. Since the Analysis of Variance (ANOVA) methods were proposed by Fisher in 1925, several useful genetic models have been proposed and have been widely applied in both plant and animal quantitative genetics studies. Useful examples included various North Carolina (NC) and diallel cross mating designs. However, many genetic models derived from these mating designs are ANOVA method based, so there are several major limitations. For example, ANOVA based methods are constricted to simple genetic models and specific mating designs and require balanced data structures. Though mixed linear model approaches were proposed in the 1960s, their applications in quantitative genetics study were limited until the early 1990s. The advantages of the mixed linear model approaches include the flexibility for unbalanced genetic data structures and complex genetic model systems. In the past years the mixed linear models have been applied to analyze various useful genetic models and a number of computer programs have been developed. In addition, researchers are not only interested in finding appropriate data structures needed for specific genetic models but also want to identify appropriate genetic models suitable for a specific data structure. Therefore, a generalized computer tool has been developed for both model evaluations and actual data analyses. In this paper, various genetic models will be detailed and generalized by mixed linear model approaches and the features of the new computer tool GenMod will be described

DISTRIBUTION OF BOLL NUMBER AND LINT YIELD BY TIME AND POSITION IN UPLAND COTTON CULTIVATORS

The time period and position which make the major contribution to total yield and to its variation is important for the field management and breeding for upland cotton, Gossypium hirsutum, L. Two-year end-of-season plant mapping data from 11 upland cotton cultivars were analyzed by position and by week. The data showed that the first position in the second and third weeks made the largest contribution to the total boll number and lint yield. The eleven cultivars differed with respect to the earliness but they had similar lint yield at harvest. The early season cultivars produce more yield and more bolls than late season cultivars in the first week of blooming, while the late season cultivars produce more yield and more bolls in the fourth week and later. The genotypic variance was the largest in week 5 and later for both lint yield and boll number. Thus, these results suggested that appropriate field management is required to maintain high yield in weeks 2 and 3 and to obtain maximum yield at late season, especially for late season cultivars. Breeders could be able to cross two cultivars which differ in earliness to obtain high yielding lines

VARIATION ANALYSIS FOR FIBER QUALITY TRAITS AMONG DIFFERENT POSITIONSIN EIGHT UPLAND COTTON CULTIVARS

Equivalencyof fiber quality within a plant of upland cotton, Gossypium hirsutum L., is very important. There are several traits within a plant that can be used to measure fiber quality and five of those traits will be investigated. Eight representative upland cultivars were grown at the Plant Science Research Farm at Mississippi State University in 1986 and five fiber traits: micronaire, fiber elongation, 2.5% and 50% span length, and fiber strength, were measured at different plant locations. The analysis of the study was modeled after a crop stability analysis with plant locations being treated as environments in the analysis. Three methodsof stability analyses were investigated:Francis and Kannenberg’s (F-K), Finlay and Wilkinson’s (F-W), and additive main effect and multiplication interaction (AMMI).The results showed that cultivar ST213 was stable for micronaire, MC235 for fiber span length, DPNSL and DES119 for fiber elongation, and CAMD-E for fiber strength

O uso de haplóides de algodoeiro em cruzamentos interespecíficos

Haploid cotton plants (n = 26 chromossomes) from the semigametic virescent lines 73-4003 and 74-4116 (Gossypium barbadense L.) with genetic marker virescent 7 were used as female in crosses with normal (2n = 4x = 52 chromossomes) Upland varieties (Gossypium hirsutum L.). All crosses were feasible and produced trilocular bolls with one seeded boll but one which produced two seeds. These seeds had normal germination and the F1 plants exhibit good development. The phenotype of the F2generation resembled normal interspecific hybrids and the segregation of three plants normal green to one virescent 7, indicated that crosses involving haploids plants as female with Upland (Gossypium hirsutum L.) diploid plants yielded a hybrid progeny like in crosses with diploid x diploid. Plantas haplóides (n = 26 cromossomos) de algodoeiro oriundas das linhagens semigaméticas de Gossypium barbadense L. 73-4003 e 74-4116 com marcador genético "virescent 7" foram empregadas como fêmeas em cruzamentos com variedades diplóides (2n = 4x = 52 cromossomos) de algodão Upland (Gossypium hirsutum L.). Todos os cruzamentos foram viáveis e produziram capulhos triloculares de tamanho reduzido, com uma semente, a exceção de um, com duas sementes. As sementes tiveram germinação normal sendo que a geração F2 apresentou plantas vigorosas e bem desenvolvidas. A população F1 exibiu características de híbridos interespecíficos sendo que a segregação monoíbrida observada em três plantas verdes normais para uma "virescent 7" demonstrou que os cruzamentos com haplóides produziram descendência híbrida semelhante a cruzamentos entre plantas diplóides.

SEPARATION OF SINGLE GENE EFFECTS FROM ADDITIVE-DOMINANCE GENETIC MODELS

Separation of single gene and polygenic effects would be useful in crop improvement. In this study, additive-dominance model with a single qualitative gene based on diallel crosses of parents and progeny F1s (or F2s) was examined. The mixed linear model approach, minimum norm quadratic unbiased estimation (MINQUE), was used to estimate the variance and covariance components and single gene effects. Monte Carlo simulation was used to evaluate the efficiency of each parameter estimated from the MINQUE approach for this genetic model. The results of 200 simulations indicated that estimates of variance components and single gene effects were unbiased when setting different single gene effects for parents and F1s (or F2s). Results also indicated that estimates of variances and single gene effects were very similar for both genetic populations. Therefore, single gene effects could be effectively separated and estimated by this approach. This research should aid the extension of this model to cases that involve multiple linked or unlinked genes (or genetic markers) and other complex ploygenic models. For illustration, a real data set comprised of eight parents of upland cotton (Gossypium hirsutum L.) with normal leaf and one parent with okra leaf, and their 44 F2s were used to estimate the variance components and the genetic effects of the okra leaf gene on fiber traits

A Cotton-Fiber-Associated Cyclin-Dependent Kinase A Gene: Characterization and Chromosomal Location

A cotton fiber cDNA and its genomic sequences encoding an A-type cyclin-dependent kinase (GhCDKA) were cloned and characterized. The encoded GhCDKA protein contains the conserved cyclin-binding, ATP binding, and catalytic domains. Northern blot and RT-PCR analysis revealed that the GhCDKA transcript was high in 5–10 DPA fibers, moderate in 15 and 20 DPA fibers and roots, and low in flowers and leaves. GhCDKA protein levels in fibers increased from 5–15 DPA, peaked at 15 DPA, and decreased from 15 t0 20 DPA. The differential expression of GhCDKA suggested that the gene might play an important role in fiber development. The GhCDKA sequence data was used to develop single nucleotide polymorphism (SNP) markers specific for the CDKA gene in cotton. A primer specific to one of the SNPs was used to locate the CDKA gene to chromosome 16 by deletion analysis using a series of hypoaneuploid interspecific hybrids

A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton

Title: Quantile-quantile (Q-Q) Plot of six fiber traits generated from GWAS analysis following mixed linear model (MLM) using GAPIT software. A) Fiber elongation (ELO), B) Micronaire (MIC), C) Short fiber content (SFC), D) Fiber strength (STR), E) Upper half mean fiber length (UHM), and F) Uniformity index (UI). Description of data: Q-Q plots of six fiber traits generated from GWAS analysis following MLM are included in this figure. The X and Y axis have the expected and observed negative logarithm 10 of p value, respectively generated during GWAS analysis. (DOCX 207 kb