STATISTICAL METHODS FOR GWAS AND THE IMPACT OF DIABETIC MEDICATION ADHERENCE ON HEALTHCARE COSTS

This dissertation includes three Chapters. In Chapter One, we develop a computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS. In this paper, based on the existing CLC method and ACAT strategy, we develop the ceCLC method to test association between multiple phenotypes and a genetic variant. In Chapter Two, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. In Chapter Three, we investigate the relationship between health service costs (medical cost, pharmacy cost, and total cost) and diabetic medication adherence for patients with diabetes in the UPHP population. This finding indicates that despite higher pharmacy spending, increasing medication adherence can significantly reduce the medical cost. Moreover, medication adherence based on different medicines has different effects on total healthcare cost and medical cost

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared

A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics

There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes

Single infrastructure utility provision to households: Technological feasibility study

This paper contemplates the future of utility infrastructure, and considers whether an “All-in-One” approach could supply all necessary utility services to tomorrow's households. The intention is not to propose infrastructure solutions that are currently technically feasible or justifiable, however; the objective is to present visions of future infrastructure that would only be possible with new advances in science and technology, or significant improvements and adaptations of existing knowledge and techniques. The All-in-One vision is explored using several vignettes, each of which envisions a novel, multi-functional infrastructure for serving future communities. The vignettes were conceived using imaginative exercises and brain-storming activities; each was then rooted in technological and scientific feasibility, as informed by extensive literature searches and the input of domain leaders. The vignettes tell their own stories, and we identify the challenges that would need to be overcome to make these visions into reality. The main aim of this work is to encourage radical approaches to thinking about future infrastructure provision, with a focus on rationalisation, efficiency, sustainability and resilience in preparation for the challenging times ahead. The All-in-One concept introduces the possibility of a unified and singular system for infrastructure service provision; this work seeks to explore the possibility space opened thereby

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared

The Interaction of the Senescent and Adjacent Breast Cancer Cells Promotes the Metastasis of Heterogeneous Breast Cancer Cells through Notch Signaling

Chemotherapy is one of the most common strategies for tumor treatment but often associated with post-therapy tumor recurrence. While chemotherapeutic drugs are known to induce tumor cell senescence, the roles and mechanisms of senescence in tumor recurrence remain unclear. In this study, we used doxorubicin to induce senescence in breast cancer cells, followed by culture of breast cancer cells with conditional media of senescent breast cancer cells (indirect co-culture) or directly with senescent breast cancer cells (direct co-culture). We showed that breast cancer cells underwent the epithelial–mesenchymal transition (EMT) to a greater extent and had stronger migration and invasion ability in the direct co-culture compared with that in the indirect co-culture model. Moreover, in the direct co-culture model, non-senescent breast cancer cells facilitated senescent breast cancer cells to escape and re-enter into the cell cycle. Meanwhile, senescent breast cancer cells regained tumor cell characteristics and underwent EMT after direct co-culture. We found that the Notch signaling was activated in both senescent and non-senescent breast cancer cells in the direct co-culture group. Notably, the EMT process of senescent and adjacent breast cancer cells was blocked upon inhibition of Notch signaling with N-[(3,5-difluorophenyl)acetyl]-l-alanyl-2-phenyl]glycine-1,1-dimethylethyl ester (DAPT) in the direct co-cultures. In addition, DAPT inhibited the lung metastasis of the co-cultured breast cancer cells in vivo. Collectively, data arising from this study suggest that both senescent and adjacent non-senescent breast cancer cells developed EMT through activating Notch signaling under conditions of intratumoral heterogeneity caused by chemotherapy, which infer the possibility that Notch inhibitors used in combination with chemotherapeutic agents may become an effective treatment strategy

Energy use and CO2 emissions in the UK universities : an extended Kaya identity analysis

We investigate the progress of the UK universities in greening their energy sources in line with the UK's goal of becoming a net-zero economy by 2050. Using the HESA estate management data for 116 universities over 2012-13 to 2018–19, we employ a Log Mean Divisa Index decomposition method within an extended Kaya identity framework to decouple the changes in total carbon emissions from a range of variables, with a special focus on the impact of different energy sources on energy use and carbon efficiency measures. Overall, between 2012-13 and 2018–19, universities have reduced emissions by 29% although their energy consumption remained mostly stable, implying that these reductions mostly stemmed from reductions in emission coefficient effect (which measures carbon efficiency of energy generation) by 24% and energy intensity effect by 25%. Consistently, estimated correlation coefficients confirm that emission coefficient, intensity, and affluence effects are major contributors behind the annual change in total emissions, with estimated correlation coefficients being 0.42, 0.66, and −0.24, respectively. The share of renewable energy sources was reduced by 2.2%, which is a major reason, in addition to increased number of students, behind the sector's overall failure achieve the 2020 goal of reducing emissions by 43% from the 2005 level. Finally, our results also expose considerable regional variations in mitigating and worsening factors behind emissions that calls for stronger coordination and supervision by policymakers