Gene expression responds to the environment and can also evolve rapidly in response to altered selection regimes. Little is known, however, about the extent to which evolutionary adaptation to a particular type of stress involves changes in the within-generation ('plastic') responses of gene expression to the stress. We used microarrays to quantify gene expression plasticity in response to ethanol in laboratory populations of Drosophila melanogaster differing in their history of ethanol exposure. Two populations ('R' populations) were maintained on regular medium, two ('E') were maintained on medium supplemented with ethanol, and two ('M') were maintained in a mixed regime in which half of the population was reared on one medium type, and half on the other, each generation. After more than 300 generations, embryos from each population were collected and exposed to either ethanol or water as a control, and RNA was extracted from the larvae shortly after hatching. Nearly 2000 transcripts showed significant within-generation responses to ethanol exposure. Evolutionary history also affected gene expression: the E and M populations were largely indistinguishable in expression, but differed significantly in expression from the R populations for over 100 transcripts, the majority of which did not show plastic responses. Notably, in no case was the interaction between selection regime and ethanol exposure significant after controlling for multiple comparisons, indicating that adaptation to ethanol in the E and M populations did not involve substantial changes in gene expression plasticity. The results give evidence that expression plasticity evolves considerably more slowly than mean expression.

Extensive research has been devoted to evaluating potential genotype-environment interactions. However, plant breeders are still in need of a simple way to describe how genotypes respond to different locations and years. In an environmentally diverse state like Oregon, significant genotype-environment interactions do occur The resulting lack of association between actual and genotypic potential yield performance makes it difficult to select genotypically superior lines. This study was prompted to evaluate the extent of such an interaction and compare various yield stability models. A significant genotype-environment interaction encompassing lines, environments, and years was discovered for each individual year analyzed and for the combined analysis of 1992, 1994 and 1995, and 1989 through 1994. Most lines evaluated during 1992, 1994 and 1995 were adapted to low yielding environments. However, two genotypes (OR880172 and OR880525) exhibited broad adaptation. Stephens and Mac Vicar were less adapted to the relatively high yielding Chambers site than the other genotypes tested during 1992, 1994 and 1995 due to Septoria tritici infections. The most stable genotypes during the combined 1992, 1994 and 1995 and 1989-1994 seasons were OR870831, Madsen and OR8500933H. Gene was the most desirable genotype based on stability and yield for both the combined 1992, 1994 and 1995 and 1989-1994 seasons. Due to an inability to adapt to higher yielding environments, the cultivar Rohde was the least stable genotype during the same combined periods. High and low temperatures and precipitation had minor yet significant effects on yield responses at all three sites during various periods identified. Advanced winter wheat selections and cultivars were grown in three diverse environments and compared over different time periods. Due to trial design and the objective of identifying superior genotypes from a set tested in target environments a combination of two methods, stability variance and a selection index, emerged as the most appropriate techniques. These approaches are considered the most appropriate because they use the mean of the trial as a gauge for measuring stability. / Graduation date: 1997

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references. Also available on the Internet.

The ability to metabolise drugs and achieve positive therapeutic outcomes is dependent on both genetic and environmental factors. The focus of this study was to determine the distribution and frequency of clinically relevant DME alleles and to assess the impact of these DME alleles on therapeutic outcomes in a cohort of 50 HIV-TB co-infected Zulu participants. PCR-RFLP was used to generate a genotypic profile of CYPIA2, 2C9, 2C19, 2E1, 3A4, MDR-1 and NAT-2. The distributions of the allelic frequencies were as follows. The CYPIA2 (A) - 50.7%, CYP2C9*2 — 100% and *3 — 56.2%, CYP2C19*2 — 35.4%, CYP2E1 (C2) — 28.4%, CYP3A4*1B (G) — 58.2%, MDR-1 (C3435T) - 16% and NAT-2 slow acetylators — 6.5%. Seventy-three percent of participants had prolonged TB therapy. Within this group, 82.9% of patient displayed wild type and 17.2% variant allele for CYP2E1 gene (p= 0.04) profile. In addition, all the slow acetylators in this study had prolonged TB therapy. In the MDR-1 gene, 87.5% showed wild type allele and 12.5% displayed the variant allele. Unsuccessful TB outcomes were also noted in 22% of this study population. In this group the variant allele was found to be dominant in CYPIA2, CYP3A4 and NAT-2, the opposite was seen in CYP2E1 and MDR-1. It was also interesting to note a similar genetic profile in the group that showed successful TB therapy outcomes. All participants had positive ARV treatment outcomes despite DME genotypic variations. However, 26% of all study participants experienced liver enzyme abnormalities. These findings concur with other studies regarding the ethnic distribution of DME alleles and evidence of an association between ART and TB therapeutic outcomes and DME genotype variation was inconclusive. / Thesis (M.Med.Sc.)-University of KwaZulu-Natal, Durban, 2007.

Two studies were conducted on gene mapping analysis. For the first study, genetic simulation experiments were conducted to address the effects of marker density, method of mapping analysis, and gaps in a marker map on the efficiency of QTL detection and the accuracy of QTL parameter estimation. The simulated genome consisted of seven chromosomes with seven or eight segregating QTL affecting the simulated quantitative trait. A set of six randomly segregating QTL outside the test region was consistently used to represent 40% of phenotypic variation. An individual QTL or a linkage block of two QTL on a target chromosome contributed 10% of phenotypic variation. The marker map was either dense (with markers every 4 cM) or sparse (with markers every 20 cM). The gap in the marker map was either 32 cM or 56 cM. Interval mapping and composite interval mapping were used to map QTL on the target chromosome. A dense map provided more power of QTL detection, better accuracy of QTL parameter estimation, and higher false-positive error rates for the target chromosome than a sparse map. Composite interval mapping provided more power of QTL detection, better accuracy of QTL parameter estimation, and lower false-positive error rates than interval mapping. Presence of a large gap in a marker map affected QTL detection and QTL parameter estimation for a QTL inside or near the gap. The use of a dense map with composite interval mapping was the most efficient combination tested in this study. For the second study, a mixed factorial regression model for interval mapping was developed for conducting QTL-by-environment interaction analysis and for providing inferences about QTL that are applicable beyond the environments used in the experiments. Genetic simulation was used to test the model for the power of detecting QTL-by-environment interaction and identifying the types of such interaction as crossover or non-crossover, and for the accuracy of estimating QTL parameters. The model prov

Thesis (Ph.D.)--University of Melbourne, Dept. of Zoology, 2007. / Typescript. Includes bibliographical references (leaves 218-237).

Thesis (M. Agr. Sc.)--University of Adelaide, 1966. / At foot of title page: Joint Coconut Research Scheme, Yandina, British Solomon Islands. Includes bibliographical references (p. 111-119).

Thesis (Ph.D.)--University of Western Australia, 2006.

Thesis--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references (p.66-78).