The effect of stage of lactation on milk yield, somatic cell counts, mineral and fatty acid profiles in pasture-based Friesian, Jersey and Friesian × Jersey cows was investigated. Twenty Friesian, twenty Jersey and twenty Friesian × Jersey cows were randomly selected from a dairy herd. A total of 202 milk samples were collected and analysed in three stages of lactation. Genotypic differences were observed in milk yield and fat content. Friesian cows produced the highest yield and lowest fat content whereas the opposite was true for Jersey cows (P<0.01). No significant differences were observed in SCC in the different genotypes, but SCC levels were higher in mid and late lactation (P<0.001). There was no effect (P<0.005) of genotype and stage of lactation interaction on Ca, P, Mg, Na, Mn and Bo concentration. Jersey cows had the least concentration of Fe and Cu in all stages of lactation. Strong positive correlations were observed among Ca and P, Mg and Zn. Aluminium had a strong positive relationship with Bo, Fe, Mn and Zn (P<0.001). Generally, SCC had a weak positive relationship with macro elements but a significant negative relationship with microelements. Yield levels were negatively correlated with Mg, Na, Al, Mn and SCC. Pasture ALA, SFA, n-3, n-6/n-3 and PUFA/MUFA concentration did not differ across the study period. Linoleic acid was highest in the second phase which coincides with mid lactation in cows (P<0.001). Highest moisture content coincided with the least fat free dry matter content in early lactation (P<0.001). Significantly high fat content was observed in late lactation than in early lactation. Highest butyric, caproic, linoleic, n-6 and PUFA were observed for Friesian cows. All other fatty acids ratios were not significantly different among different genotypes. Highest CLA, ALA, LA, SFA, PUFA, n-6, and n-3 and atherogenicity index were observed in early lactation whereas desaturase activity indices were highest in late lactation. Strong positive correlations were observed among milk vaccenic, ALA, LA and CLA concentrations. Inverse relationships were observed between SFA and long chain fatty acids. It can be concluded, it may be of advantage to consume milk from early stage of lactation poses a lower risk to coronary diseases and are much safer to consume.

Gene-environment (GE) interactions involving the IGF pathway may affect childhood obesity. Detecting such interactions using longitudinal family studies requires accounting for individual and familial correlations. Simulations were performed to study three methods to test for GE interactions in longitudinal family data using repeated outcomes (linear mixed model) or individual outcome averages as summary statistics (twin model, partition based score I test). Interactions between the IGF pathway genes (IGF-1, IGFALS) and environmental factors (physical activity, daycare attendance and sleep duration) were tested using the Quebec Newborn Twin Study data. The twin model yielded the best performance. Results from the QNTS analysis showed suggestive association for an IGF-1 variant at position 102791894 of chromosome 12 interacting with physical activity. However, this association was not statistically significant after multiple testing correction. More robust methods and studies are needed to better understand the IGF pathway’s role in childhood obesity.

Objective: To investigate the association of previously reported single nucleotide polymorphisms (SNPs) in relation to orofacial clefts and assess their interaction with environmental factors. Methods: Genome-wide SNP genotypes were obtained for case-parent triads from the EUROCRAN and ITALCLEFT studies. Candidate SNPs were selected from a previous genome-wide association study (Shi et al., 2012) along with surrounding SNPS for a total of 2142 genotyped and imputed SNPs. A total of 411 case-parent triads and 25 case-parent dyads were analyzed using log-linear models to test for maternal and parent-of-origin effects along with their interaction with maternal smoking and maternal folic acid consumption. Results: A significant association (q = 0.025) was detected for a region in the ATXN3 gene. This significance refers to the interaction between maternal periconceptional smoking and maternal genetic effects. Nominally significant associations in genes relating to the brain were also detected. Conclusion: SNPs in the ATXN3 region warrant further investigation.

Thesis (M. A. (Agricultural Management)) -- University of Limpopo, 2020 / Field experiments were conducted at two locations; University of Limpopo (Syferkuil) and University of Venda (Thohoyandou) during 2015 and 2016 winter cropping seasons. The objectives of this study were to determine; the effect of genotype (ACC# 1, 3, 4 and 7) and planting density (33, 25 and 20 plants/m2) on four chickpea genotypes having contrasting growth patterns and also to determine the effect genotype and planting density on water use and water use efficiency of four chickpea genotypes having contrasting growth patterns. The experimental design was randomized complete block design in factorial arrangement with three replications. Plant height, number of primary and secondary branches, grain yield and yield components (number of pods per plant, number of seeds per pod, Harvest Index and 100 seed weight [100-SW] and above ground biomass, and were determined at different growth stages. Data obtained was subjected to analyses of variance using the general linear model of Genstat 17th edition. Significant differences between the treatments means were compared using the standard error of difference (LSD) of the means at 5% level. Correlation analyses were performed to assess the relationship between parameters. Plant height varied with genotype from 41 cm (84 DAE) to 44 cm (118 cm) at Syferkuil and 41 (56 DAE) to 44 cm (63 DAE) at Thohoyandou. Primary branches was not significantly affected by genotype and planting density at both locations and seasons. Planting density had significant effect on number of secondary branches, greater number was recorded at low (32, 6) density at Syferkuil in 2016. Above ground biomass was significantly affected by planting density at Syferkuil during in 2015 (5344 kg ha-1) and 2016 (3701 kg ha-1) growing seasons. Genotype and planting density did not affect number of pods plant-1, number of seeds plant-1, 100 SW (100 seed weight), and Harvest index were not significant at both locations and seasons. Grain yield was significantly affected by planting density at Syferkuil in 2015 and Thohoyandou in 2016. Grain yield increased with the increase in planting density at both locations. Two field experiments were conducted at University of Venda (Thohoyandou) during 2015 and 2016 winter cropping seasons. This study aimed at assessing the effect of genotype v and planting density on water use efficiency of four chickpea genotypes with contrasting growth patterns. Crop water use (WU) was determined by monitoring soil water content at 7-day intervals using a neutron probe and, water use efficiency (WUE) was determined as a ratio of crop biomass and grain yield to WU. Genotype and planting density had no significant effect on WU in 2015 and 2016. Genotype and planting density had no significant effect on biomass production (WUEb) and grain yield production (WUEg) in 2015. In contrast, WUEb and WUEg was significantly affected by planting density in 2016. WUEb was 43.2% greater at high density compared to low density. Similarly, WUEg was 39.3% greater at high density compared to low density. WUEb and WUEg increased with the increase with planting density. Therefore, manipulation of management practices such as planting density may increase chickpea production. Keywords: Planting density, genotype, grain yield and yield components, water use efficiency. / National Research Foundation (NRF) and University of Venda Capacity Development

Investigators have discovered thousands of genetic variants associated with various traits using genome-wide association studies (GWAS). These discoveries have substantially improved our understanding of the genetic architecture of many complex traits. Despite the striking success, these trait-associated loci collectively explain relatively little of disease risk. Many reasons for this unexplained heritability have been suggested and two understudied components are hypothesized to have an impact in complex disease etiology: rare variants and gene-environment (GE) interactions. Advances in next generation sequencing have offered the opportunity to comprehensively investigate the genetic contribution of rare variants on complex traits. Such diseases are multifactorial, suggesting an interplay of both genetics and environmental factors, but most GWAS have focused on the main effects of genetic variants and disregarded GE interactions. In this dissertation, we develop statistical methods to detect GE interactions for rare variant analysis for various types of outcomes in both independent and related samples. We leverage the joint information across a set of rare variants and implement variance component score tests to reduce the computational burden. First, we develop a GE interaction test for rare variants for binary and continuous traits in related individuals, which avoids having to restrict to unrelated individuals and thereby retaining more samples. Next, we propose a method to test GE interactions in rare variants for time-to-event outcomes. Rare variant tests for survival outcomes have been underdeveloped, despite their importance in medical studies. We use a shrinkage method to impose a ridge penalty on the genetic main effects to deal with potential multicollinearity. Finally, we compare different types of penalties, such as least absolute shrinkage selection operator and elastic net regularization, to examine the performance of our second method under various simulation scenarios. We illustrate applications of the proposed methods to detect gene x smoking interaction influencing body mass index and time-to-fracture in the Framingham Heart Study. Our proposed methods can be readily applied to a wide range of phenotypes and various genetic epidemiologic studies, thereby providing insight into biological mechanisms of complex diseases, identifying high-penetrance subgroups, and eventually leading to the development of better diagnostics and therapeutic interventions.

Genome-wide association studies(GWAS) are used to find associations betweengenetic markers and diseases. One part of GWAS is to study interactions be-tween markers which can play an important role in the risk for the disease. Thesearch for interactions can be computationally intensive. The aim of this thesiswas to improve the performance of software used for gene-environment interac-tion by using parallel programming techniques on graphical processors. A studyof the new programs performance, speedup and efficiency was made using mul-tiple simulated datasets. The program shows significantly better performancecompared with the older program.

Synthetic xenoestrogens have differential estrogenic properties. Research has shown that exposures to xenoestrogens could promote breast cancer by disrupting normal function of the human epidermal growth factor receptor 2 (Her2) gene. Although animal models demonstrated a connection between xenoestrogen exposure and Her2 activity, no study using human cells has systematically examined their carcinogenic potential influencing the Her2 gene expression. Furthermore, breast cancer cells are phenotypically disparate (ER+, Her2+), with some phenotypes (Her2+), leading to more aggressive disease. This study aimed to dosimetrically assess the carcinogenic potential of commonly used xenoestrogens influencing Her2 gene expression, and delineate cellular phenotypes at greater risk of more aggressive disease. The study assessed whether the composition, concentrations, and exposure duration of BPA, EE, NPH, and DDT significantly altered Her2 copy numbers in estrogen and Her2 receptor positive or negative breast cancer lines. Each line was randomly assigned to cases (exposed) and control (unexposed) groups using a randomized block design. Fluorescent in-situ hybridization measured Her2 gene copies. Mann Whitney, Kruskal Wallis, and Incidence Rate Ratios revealed Her2 copy gains in all 4 xenoestrogens and receptor types with persistent exposures. A 44% increase in Her2 was observed in the normal ER and Her2 line, marking a shift in its Her2 status, and a 30-times greater risk was noted in the Her2+ lines. These findings promote positive social change by revealing all 4 xenoestrogens as risk factors for breast cancer. This information can be used by breast cancer advocacy groups, health educators, and steering committees to educate women and formulating policies.

No description available.

The missing heritability in genome-wide association studies (GWAS) is an intriguing open scientific problem which has attracted great recent interest. The interaction effects among risk factors, both genetic and environmental, are hypothesized to be one of the main missing heritability sources. Moreover, detection of multilocus interaction effect may also have great implications for revealing disease/biological mechanisms, for accurate risk prediction, personalized clinical management, and targeted drug design. However, current analysis of GWAS largely ignores interaction effects, partly due to the lack of tools that meet the statistical and computational challenges posed by taking into account interaction effects. Here, we propose a novel statistically-based framework (Significant Conditional Association) for systematically exploring, assessing significance, and detecting interaction effect. Further, our SCA work has also revealed new theoretical results and insights on interaction detection, as well as theoretical performance bounds. Using in silico data, we show that the new approach has detection power significantly better than that of peer methods, while controlling the running time within a permissible range. More importantly, we applied our methods on several real data sets, confirming well-validated interactions with more convincing evidence (generating smaller p-values and requiring fewer samples) than those obtained through conventional methods, eliminating inconsistent results in the original reports, and observing novel discoveries that are otherwise undetectable. The proposed methods provide a useful tool to mine new knowledge from existing GWAS and generate new hypotheses for further research. Microarray gene expression studies provide new opportunities for the molecular characterization of heterogeneous diseases. Multiclass gene selection is an imperative task for identifying phenotype-associated mechanistic genes and achieving accurate diagnostic classification. Most existing multiclass gene selection methods heavily rely on the direct extension of two-class gene selection methods. However, simple extensions of binary discriminant analysis to multiclass gene selection are suboptimal and not well-matched to the unique characteristics of the multi-category classification problem. We report a simpler and yet more accurate strategy than previous works for multicategory classification of heterogeneous diseases. Our method selects the union of one-versus-everyone phenotypic up-regulated genes (OVEPUGs) and matches this gene selection with a one-versus-rest support vector machine. Our approach provides even-handed gene resources for discriminating both neighboring and well-separated classes, and intends to assure the statistical reproducibility and biological plausibility of the selected genes. We evaluated the fold changes of OVEPUGs and found that only a small number of high-ranked genes were required to achieve superior accuracy for multicategory classification. We tested the proposed OVEPUG method on six real microarray gene expression data sets (five public benchmarks and one in-house data set) and two simulation data sets, observing significantly improved performance with lower error rates, fewer marker genes, and higher performance sustainability, as compared to several widely-adopted gene selection and classification methods. / Ph. D.

This research is a detailed look at intercultural competence, an issue showing escalating importance in today's higher education and the society at large. In this study, intercultural competence was defined in light of the concept of culture and the contact hypothesis. Person-environment interaction theory and college impact theories were incorporated as theoretical foundations for the operationalization of this research. The development of students' intercultural competence was examined from two perspectives--intercultural competence is viewed both as a desirable outcome of college education and as an active environment component that exerts important influence on students' self-reported gains. Findings of this study evidenced that a variety of college activities, especially those emphasizing cooperative and associated learning, play important role in students' intercultural competence development. Intercultural competence, in turn, has substantially positive effect in student gains in multiple realms. The intrinsic influences of student characteristics were also examined. In the end, previous research was drawn upon to scrutinize the findings of this study. Implications to future practice and policy as well as the values and limitations of this study were also presented. / Ph. D.