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Recent research effort (measured in number of publications) on genes is biased towards genes that have been studied heavily in the past. Some factors for why this occurs is that many of these historically studied genes are important for survival or there are more tools available that make genetic studies of them much more accessible. Studies of research effort on \textit{Saccharomyces cerevisiae} genes characterized with genetic or protein interactions found that there is an aversion to studying lesser-known genes in networks. As well, in a study of three human protein families, many of the genes that have recently been discovered to have association with complex disease, through methods such as genome wide association studies (GWAS), are understudied in the present compared to the small number of historically heavily studied genes. In this study we explore possible causes of and diversion from this preferential bias with gene conservation and human genes being disease-associated. We find there is some evidence of conservation driving biases in research effort for essential genes in \textit{Saccharomyces cerevisiae}, but inconclusive evidence in other organisms. We look for effects of disease association through Mendelian and complex diseases in a historical, pre-GWAS, and contemporary, post-GWAS, context. Within both contexts we find that Mendelian disease genes may drive preferential study bias. For contemporary research effort we utilize a model of publication rates and find that there are individual GWAS genes that tend to be investigated more than predicted compared to non-GWAS genes. It appears that the proportion of GWAS genes that had highly unexpected increases in publication rate compared to model predictions rose fairly quickly but has been declining. Our analysis suggests that GWAS has had a small impact on what genes some scientists study despite preferential study biases. However GWAS gene-disease association's impact on research effort appears to be declining, possibly due to scientists not being as interested in GWAS results as time goes on.

The accessibility of high-throughput biology data brought a great deal of attention to disease association studies. High density maps of single nucleotide polymorphism (SNP's) as well as massive genotype data with large number of individuals and number of SNP's become publicly available. By now most analysis of the new data is undertaken by the statistics community. In this dissertation, we pursue a different line of attack on genetic susceptibility to complex disease that adheres to the computer science community with an emphasis on design rather than analytical methodology. The main goal of disease association analysis is to identify gene variations contributing to the risk of and/or susceptibility to a particular disease. There are basically two main steps in susceptibility: (i) haplotyping of the population and (ii) predicting the genetic susceptibility to diseases. Although there exist many phasing methods for step (i), phasing and missing data recovery for data representing family trios is lagging behind, and most disease association studies are based on family trios. This study is devoted to the problem of assessing accumulated information targeting to predict genotype susceptibility to complex diseases with significantly high accuracy and statistical power. The dissertation proposes two new greedy and integer linear programming based solution methods for step (i). We also proposed several universal and ad hoc methods for step (ii). The quality of susceptibility prediction algorithm has been assessed using leave-one-out and leave-many-out tests and shown to be statistically significant based on randomization tests. The prediction of disease status can also be viewed as an integrated risk factor. A combinatorial prediction complexity measure has been proposed for case/control studies. The best prediction rate achieved by the proposed algorithms is 69.5% for Crohn's disease and 61.3% for autoimmune disorder, respectively, which are significantly higher than those achieved by universal prediction methods such as Support Vector Machine (SVM) and known statistic methods.

Accessibility of high-throughput genotyping technology makes possible genome-wide association studies for common complex diseases. When dealing with common diseases, it is necessary to search and analyze multiple independent causes resulted from interactions of multiple genes scattered over the entire genome. The optimization formulations for searching disease-associated risk/resistant factors and predicting disease susceptibility for given case-control study have been introduced. Several discrete methods for disease association search exploiting greedy strategy and topological properties of case-control studies have been developed. New disease susceptibility prediction methods based on the developed search methods have been validated on datasets from case-control studies for several common diseases. Our experiments compare favorably the proposed algorithms with the existing association search and susceptibility prediction methods.

We conducted a cross-sectional survey to assess the anxiety, distress, and turnover intention (likelihood to leave their current job) of healthcare workers in Peru during the COVID-19 pandemic. Our results reported that 21.7% healthcare workers in Peru experienced severe anxiety, whereas 26.1% of them experienced severe mental distress. A higher level of education related with a lower level of anxiety. Younger workers had a higher level of turnover intention than their older colleagues did. Healthcare workers in the private sector had a higher turnover intention than those in the public sector. Most importantly, people who were geographically far from Lima, the epicenter in Peru, during the outbreak experienced less anxiety and mental distress, corroborating the ripple effect and disconfirming the typhoon eye theory. However, the direction of these relationships can change depending on the type of institutions (public versus private) and the type of employees' contract (full time versus part time). Our research helps provide insights for clinical professionals in identifying the vulnerable groups to mental disorders in Peru. This is the first study to assess anxiety, mental distress, and turnover intention in healthcare workers in Peru during the COVID-19 pandemic. Copyright

A population of the rare Southern Appalachian endemic species Lilium grayi, (Gray’s lily) Roan Mountain, TN/NC was monitored for 2 years to determine the cause and impact of an early season collapse. High concentrations of the Lilium spp. host-specific fungal phytopathogen, Pseudocercosporella inconspicua (G. Winter) U. Braun were associated with 19/20 symptomatic and 0/30 asymptomatic plants. Strength of the association between pathogen and disease and the replication of disease symptoms in 4/4 healthy hosts showed that P. inconspicua was the causal agent of the disease referred to as lily leaf spot. Disease had a severe impact on the population with 59% of mature and 98% of adolescent plants undergoing early senescence. Only 32% of mature plants produced capsules and they were frequently diseased. A recurring spatiotemporal pattern typical of an infectious disease suggested that the lily leaf spot disease is capable of causing sequential annual epidemics of unknown long-term consequences to the stability of the host population.

Single Nucleotide Polymorphisms (SNPs) are the most frequent genomic variations and the main basis for genetic differences among individuals and many diseases. As genotyping millions of SNPs at once is now possible with the microarrays and advanced sequencing technologies, SNPs are becoming more popular as genomic biomarkers. Like other high-throughput research techniques, genome wide association studies (GWAS) of SNPs usually hit a bottleneck after statistical analysis of significantly associated SNPs, as there is no standardized approach to prioritize SNPs or to select representative SNPs that show association with the conditions under study. In this study, a java based integrated system that makes use of major public databases to prioritize SNPs according to their biological relevance and statistical significance has been constructed. The Analytic Hierarchy Process, has been utilized for objective prioritization of SNPs and a new emerging methodology for second-wave analysis of genes and pathways related to disease associated SNPs based on a combined p-value approach is applied into the prioritization scheme. Using the subset of SNPs that is most representative of all SNPs associated with the diseases reduces the required computational power for analysis and decreases cost of following association and biomarker discovery studies. In addition to the proposed prioritization system, we have developed a novel feature selection method based on Simulated Annealing (SA) for representative SNP selection. The validity and accuracy of developed model has been tested on real life case control data set and produced biologically meaningful results. The integrated desktop application developed in our study will facilitate reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting timely identification of genomic disease biomarkers, and development of personalized medicine approaches and targeted drug discoveries.

Examination of variation at polymorphic microsatellite loci is a widely accepted method for determining parentage and examining genetic diversity within rainbow trout (Oncorhynchus mykiss) breeding programs. Genotyping costs are considerable; therefore, we developed a single-step method of co-amplifying twelve microsatellite loci in two hexaplex reactions. The protocol is explicitly described to ensure reproducible results. I applied the protocol to samples previously analyzed at the National Center for Cool and Coldwater Aquaculture (NCCCWA) with previously reported marker sets for a comparison of results. Each marker within the multiplex system was evaluated for duplication, null alleles, physical linkage, and probability of genotyping errors. Data from four of the 12 markers were excluded from parental analysis based on these criteria. Parental assignments were compared to those of a previous study that used five independently amplified microsatellites. Percentages of progeny assigned to parents were higher using the subset of eight markers from the multiplex system than with five markers used in the previous study (98% vs. 92%). Through multiplexing, use of additional markers improved parental allocation while also improving efficiency by reducing the number of PCR reactions and genotyping runs required. I evaluated the methods further through estimation of F-statistics, pairwise genetic distances, and cluster analysis among brood-years at the NCCCWA facility. These estimates were compared to those from nine independently amplified microsatellites used in a previous study. Fst metrics calculated between brood-years showed similar values of genetic differentiation using both marker sets. Estimates of individual pairwise genetic distances were used for constructing neighbor-joining trees. Both marker-sets yielded trees that showed similar subpopulation structuring and agreed with results from a model-based cluster analysis and available pedigree information. These approaches for detecting population substructure and admixture portions within individuals are particularly useful for new breeding programs where the founders' relatedness is unknown. The 2005 NCCCWA brood-year (75 full-sib families) was challenged with Flavobacterium psychrophilum, the causative agent of bacterial coldwater disease (BCWD). The overall mortality rate was 70%, with large variation among families. Resistance to the disease was assessed by monitoring post-challenge days-to-death. Phenotypic variation and additive genetic variation were estimated using mixed models of survival analysis. The microsatellite markers used were previously isolated from BAC clones that harbor genes of interest and mapped onto the rainbow trout genetic linkage map. A general relationship between UBA gene sequence types and MH-IA-linked microsatellite alleles indicated that microsatellites mapped near or within specific major histocompatibility (MH) loci reliably mark sequence variation at MH genes. The parents and grandparents of the 2005 brood-year families were genotyped with markers linked to the four MH genomic regions (MH-IA, MH-IB, TAP1, and MH-II) to assess linkage disequilibrium (LD) between those genomic regions and resistance to BCWD. Family analysis suggested that MH-IB and MH-II markers are linked to BCWD survivability. Tests for disease association at the population level substantiated the involvement of MH-IB with disease resistance. The impact of MH sequence variation on selective breeding for disease resistance is discussed in the context of aquaculture production. / Master of Science

Background: The role of the placenta in fetal programming has been recognized as a highly significant, yet often neglected area of study. We investigated placental size in relation to psychopathology, in particular attention deficit hyperactivity disorder (ADHD) symptoms, in children at 8 years of age, and later as adolescents at 16 years. Methodology/Principal Findings: Prospective data were obtained from The Northern Finland Birth Cohort (NFBC) 1986. Placental weight, surface area and birth weight were measured according to standard procedures, within 30 minutes after birth. ADHD symptoms, probable psychiatric disturbance, antisocial disorder and neurotic disorder were assessed at 8 years (n = 8101), and ADHD symptoms were assessed again at 16 years (n = 6607), by teachers and parents respectively. We used logistic regression analyses to investigate the association between placental size and mental health outcomes, and controlled for gestational age, birth weight, socio-demographic factors and medical factors, during gestation. There were significant positive associations between placental size (weight, surface area and placental-to-birth-weight ratio) and mental health problems in boys at 8 and 16 years of age. Increased placental weight was linked with overall probable psychiatric disturbance (at 8y, OR = 1.14 [95% CI = 1.04-1.25]), antisocial behavior (at 8 y, OR = 1.14 [95% CI = 1.03-1.27]) and ADHD symptoms (inattention-hyperactivity at 16y, OR = 1.19 [95% CI = 1.02-1.38]). No significant associations were detected among girls. Conclusions/Significance: Compensatory placental growth may occur in response to prenatal insults. Such overgrowth may affect fetal development, including brain development, and ultimately contribute to psychopathology.