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Cotton is a very important economical crop in the U.S. and throughout the world. The developments in molecular biology offer new and innovative approaches toward evaluating and understanding genetic mechanisms of important agronomical traits. Bacterial artificial chromosome (BAC) libraries have rapidly become the preferred choice for physical mapping. BAC-derived microsatellite or simple sequence repeats (SSRs) markers facilitate the integration of physical and genetic recombination maps. The first objective in this research was to identify chromosome locations of a set of BAC-derived SSR markers in tetraploid cotton. A total of 192 SSR primer pairs were derived from BAC clones of an Upland cotton (Gossypium hirsutum L.) genetic standard line TM-1. Using deletion analysis method, we assigned 39 markers out of the 192 primer pairs to 18 different chromosomes or chromosome arms. Chromosomal assignment of these markers will help to improve the current cotton genetic linkage maps and facilitate positional candidate gene cloning, comparative genome analysis, and the coordination of chromosome-based genome sequencing projects. Wild race stocks (Gossypium spp.) represent valuable resources for genetic improvement. Most primitive accessions are photoperiod sensitive; they do not flower under the long days of the U.S. cotton belt. Molecular markers were used to locate quantitative trait loci (QTLs) for node of first fruiting branch (NFB), node of first open boll (NOB), and fruiting score (FS). An F2 population consisted of 251 plants from the cross of a day neutral cultivar Deltapine 61, and a photoperiod sensitive accession Texas 701, were used in this study. For each trait, three major QTLs were mapped to chromosome 16, 21, and 25. QTL analysis was also conducted in two F2 populations generated from the cross between Deltapine 61 and two photoperiod sensitive accessions (T1107, PI 607174; T1354, PI 530082) of Upland cotton (G. hirsutum L.). QTL analysis indicated that NFB differed between the two F2 populations. Two major QTLs (q-NFB-c21-1 and q-NFB-c25-1) were found in population 1107; whereas, only one (q-NFB-c25-1) was important in population 1354. Discovering QTLs associated with flowering time may have the potential to facilitate day neutral conversion of wild photoperiod sensitive accessions.

Quantitative Trait Loci (QTL) mapping has been widely used to identify genetic loci attributable to the variation observed in complex traits. In recent years, gene expression phenotypes have emerged as a new type of quantitative trait for which QTL can be mapped. Locating sequence variation that has an effect on gene expression (eQTL) is thought to be a promising way to elucidate the genetic architecture of quantitative traits. This thesis explores a number of methodological aspects of eQTL mapping (also known as “genetical genomics”) and considers some practical strategies for applying this approach to livestock populations. One of the exciting prospects of genetical genomics is that the combination of expression studies with fine mapping of functional trait loci can guide the reconstruction of gene networks. The thesis begins with an analysis in which correlations between gene expression and meat quality traits in pigs are investigated in relation to a pork meat quality QTL previously identified. The influence on power due to factors including sample size and records of matched subjects is discussed. An efficient experimental design for two-colour microarrays is then put forward, and it is shown to be an effective use of microarrays for mapping additive eQTL in outbred crosses under simulation. However, designs optimised for detecting both additive and dominance eQTL are found to be less effective. Data collected from livestock populations usually have a pedigreed structure. Many family-based association mapping methods are rather computationally intensive, hence are time-consuming when analysing very large numbers of traits. The application of a novel family-based association method is demonstrated; it is shown to be fast, accurate and flexible for genetical genomics. Furthermore, the results show that multiple testing correction alone is not sufficient to control type I errors in genetical genomics and that careful data filtering is essential. While it is important to limit false positives, it is desirable not to miss many true signals. A multi-trait analysis based on grouping of functionally related genes is devised to detect some of the signals overlooked by a univariate analysis. Using an inbred rat dataset, 13 loci are identified with significant linkage to gene sets of various functions defined by Gene Ontology. Applying this method to livestock species is possible, but the current level of annotations is a limiting factor. Finally, the thesis concludes with some current opinions on the development of genetical genomics and its impact on livestock genetics research.

Linkage between markers and genes that affect a phenotype of interest may be determined by examining differences in marker allele frequency in the extreme progeny of a cross between two inbred lines. This strategy is usually employed when pooling is used to reduce genotyping costs. When the cross progeny are asexual the extreme progeny may be selected by multiple generations of asexual reproduction and selection. In this thesis I will analyse this method of measuring phenotype in asexual cross progeny. The aim is to examine the behaviour of marker allele frequency due to selection over many generations, and also to identify statistically significant changes in frequency in the selected population. I will show that stochasticity in marker frequency in the selected population arises due the finite initial population size. For Mendelian traits, the initial population size should be at least in the low to mid hundreds to avoid spurious changes in marker frequency in the selected population. For quantitative traits the length of time selection is applied for, as well as the initial population size, will affect the stochasticity in marker frequency. The longer selection is applied for, the more chance of spurious changes in marker frequency. Also for quantitative traits, I will show that the presence of epistasis can hinder changes in marker frequency at selected loci, and consequently make identification of selected loci more difficult. I also show that it is possible to detect epistasis from the marker frequency by identifying reversals in the direction of marker frequency change. Finally, I develop a maximum likelihood based statistical model that aims to identify significant changes in marker frequency in the selected population. I will show that the power of this statistical model is high for detecting large changes in marker frequency, but very low for detecting small changes in frequency.

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Obesity, diabetes, hypertension, and heart disease are highly heritable conditions that in aggregate are the major causes of morbidity and mortality in the developed world and are growing problems in developing countries. To map the causal genes, we conducted a population screen for these conditions on the Pacific Island of Kosrae. Family history and genetic data were used to construct a pedigree for the island. Analysis of the pedigree showed highly significant heritability for the metabolic traits under study. DNA samples from 2,188 participants were genotyped with 405 microsatellite markers with an average intermarker distance of 11 cM. A protocol using LOKI, a Markov chain Monte Carlo sampling method, was developed to analyze the Kosraen pedigree for height, a model quantitative trait. Robust quantitative trait loci for height were found on 10q21 and 1p31. This protocol was used to map a set of metabolic traits, including plasma leptin to chromosome region 5q35; systolic blood pressure to 20p12; total cholesterol to 19p13, 12q24, and 16qter; hip circumference to 10q25 and 4q23; body mass index to 18p11 and 20q13; apolipoprotein B to 2p24-25; weight to 18q21; and fasting blood sugar to 1q31-1q43. Several of these same chromosomal regions have been identified in previous studies validating the use of LOKI. These studies add information about the genetics of the metabolic syndrome and establish an analytical approach for linkage analysis of complex pedigrees. These results also lay the foundation for whole genome scans with dense sets of SNPs aimed to identifying causal genes.

Pre-harvest contamination of maize grain with aflatoxin is a chronic problem worldwide and particularly in the southeastern U.S. Aflatoxin is a mycotoxin produced by the fungus Aspergillus flavus, an opportunistic ear-rot pathogen of maize (Zea mays). Resistance to aflatoxin accumulation is heritable, and resistant germplasm-lines are available. These lines are derived from “exotic” genetic backgrounds and were released as sources of resistance, not parental inbreds. However, all current sources of resistance are quantitative, which complicates conventional efforts to introgress resistance alleles from unadapted but resistant donor lines to adapted but susceptible recipient lines. Mapping quantitative trait loci (QTL) and their linked markers enables targeted introgression of the desired alleles via marker-assisted selection. Quantitative trait loci were identified in two F2:3 mapping populations, derived from crossing resistant inbreds Mp715 and Mp717 to a common susceptible parent (Va35). The Mp715 x Va35 population was phenotyped for aflatoxin accumulation under artificial inoculation in replicated field trials at Mississippi State (MSU) in 2015 and 2016. The Mp717 x Va35 population was phenotyped at MSU and Lubbock, TX in 2016. Populations were genotyped using simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers and linkage maps created in JoinMap4. To locate QTL, linkage maps, genotypes, and phenotypes were analyzed jointly in QTL Cartographer 2.5 using composite interval mapping (CIM) and multiple interval mapping (MIM) procedures. Five QTL with the beneficial allele contributed by Mp715 were identified during CIM in bins 5.01, 6.06, 7.03 10.04 and 10.05. Three QTL with the beneficial allele contributed by Mp717 were identified during CIM in bins 3.07/3.08, 7.02/7.03, and 10.05. In both populations, QTL were identified with the beneficial allele contributed by Va35. Those QTL did not co-locate across populations but four of the six were on chromosome 1. Significant QTL effects from CIM were used as the initial model terms in MIM, where all QTL effects were fit simultaneously and their gene-action and epistatic interactions estimated.

Master of Science / Department of Statistics / Gary L. Gadbury / QTL (quantitative-trait locus) analysis aims to locate and estimate the effects of genes that are responsible for quantitative traits, such as grain protein content and yield, by means of statistical methods that evaluate the association of genetic variation with trait (phenotypic) variation. Quantitative traits are typically polygenic, i.e., controlled by multiple genes, with varying degrees of in uence on the phenotype. Several methods have been developed to increase the accuracy of QTL location and effect estimates. One of them, multiple interval mapping (MIM) (Kao et al. 1999), has been shown to be more accurate than conventional methods such as composite interval mapping (CIM) (Zeng 1994). Other QTL analysis methods have been developed to perform additional analyses that might be useful for breeders, such as of pleiotropy and QTL-by-environment (QxE) interaction. It has been shown (Jiang and Zeng 1995) that these analyses can be carried out with a multivariate extension of CIM (MT-CIM) that exploits the correlation structure in a set of traits. In doing so, this method also improves the accuracy of QTL location detection. This thesis describes the multivariate extension of MIM (MT-MIM) using ideas from MT-CIM. The development of additional multivariate tests, such as of pleiotropy and QxE interaction, and several methods pertinent to the development of MT-MIM are also described. A small simulation study shows that MT-MIM is more accurate than MT-CIM and univariate MIM. Results for real data show that MT-MIM is able to provide a more accurate and precise estimate of QTL location.

Master of Science / Department of Biology / Theodore J. Morgan / The organismal response to temperature represents one of the most ubiquitous processes that occur in the natural world, and this response is critical for survival in most habitats. Increased attention should be focused on how organisms cope with temperature extremes, either through adaptation, plasticity, or a combination of both, as climate models predict increased variations in temperature accompanied by novel thermal extremes. Drosophila melanogaster is an excellent resource for answering questions pertaining to how organisms persist in environmental extremes because they originated in central tropical Africa and have since colonized nearly the entire globe, exposing them to many novel thermal stressors. In this work I elucidated regions of the genome contributing to phenotypic variation in cold tolerance and thermal plasticity. A quantitative trait locus (QTL) approach was used, which involved phenotyping roughly 400 recombinant inbred lines (RILs) of D. melanogaster from the Drosophila Synthetic Population Resource (DSPR). The DSPR captures genetic variation from around the globe, allowing for precision mapping of cold tolerance and thermal plasticity QTL, while simultaneously determining the frequency of the QTL alleles. Upon development at both 18°C and 25°C, RILS were measured for a common cold tolerance metric, chill-coma recovery time (CCR), and a plasticity value was derived as the change in CCR between environments. Analysis of variance revealed significant effects of sex, line (RIL), treatment (temperature), and line by treatment interaction (GxE). Mapped QTL for chill-coma recovery time at 18°C and 25°C spanned the same regions as several studies previously reported, validating the automated phenotyping method used and the mapping power of the DSPR. QTL between CCR at 18°C and 25°C overlapped significantly, and QTL for thermal plasticity shared the similar regions as QTL for CCR, but also exhibited two non-overlapping QTL on the left arm of the third chromosome. This study demonstrated the tremendous amount of variation present in cold tolerance phenotypes and identified candidate regions of the genome that contribute to thermal plasticity and require further investigation.