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κ°μ‘± κΈ°λ° ν¬κ· λ³μ΄ μ°κ΄ λΆμμ μν λΆμ μκ³ λ¦¬μ¦ κ°λ°
νμλ
Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :μμ°κ³Όνλν νλκ³Όμ μλ¬Όμ 보νμ 곡,2019. 8. μμ±νΈ.μλ§μ μ μ₯μ μ 체μ°κ΄λΆμ(GWAS)μλ λΆκ΅¬νκ³ μ§λ³μ°κ΄ μ μ 체λ³μ΄(DSL)λ μ νμ μΌλ‘λ§ λ°κ²¬λμλλ° μ΄λ μ€μ’
λ μ§λ³μ μ μ±(missing heritability)μ κΈ°μΈνλ€. ν λ²μ κΈ΄ 리λ(read)λ₯Ό μνμ±νλ κΈ°μ μ μ΄λ₯Ό 보μν΄ μ€ κ²μΌλ‘ κΈ°λλμ΄ μμΌλ©°, μ΄ κΈ°μ μ λ°λ¬ λλΆμ μ μ 체μ°κ΄λΆμμ νμ©νμ¬ μ¬λ¬ ν¬κ·(rare) λ° μΌλ°(common) μΈκ³Ό λ³μ΄λ₯Ό λ°κ²¬ν μ μμλ€. κ·Έλ¬λ κ½€ λ§μ μνμ μ΄μ©ν μ€νμμλ λ¨μΌ λ³μ΄λ₯Ό λμμΌλ‘ν μ μ₯μ μ 체μ°κ΄λΆμμ λΆμ μ€λ₯(false negative) λ¬Έμ μμ μμ λ‘μΈ μ μλ€. μ΄μ ν¬κ·λ³μ΄ μ°κ΄ λΆμμ κ²μ λ ₯μ μ¦κ°μν€κΈ° μν΄ μλ¬Όνμ μΌλ‘ μ°κ΄μ΄ μλ μμΉμ μ¬λ¬ μ μ 체λ³μ΄λ₯Ό νλλ‘ ν©μ³μ λΆμνλ λ°©λ²λ€μ΄ μ μλμλ€. λ²λ κ²μ (burden test), λΆμ°κ΅¬μ‘° κ²μ (variance component test), κ²°ν© μ΄λλ²μ€ κ²μ (combined omnibus test) λ±μ μμΉκΈ°λ° μ°κ΄ λΆμμ΄ λ°λ‘ κ·Έκ²μ΄λ€.
ν¬κ·λ³μ΄ μ°κ΄λΆμμ μμ κ°μ λΆμλ°©λ²μ νμ©νλ©΄ κ²μ λ ₯μ΄ ν¬κ² μ¦κ°νμ¬ λ λ§μ μ§λ³μ°κ΄ μ μ 체 λ³μ΄λ₯Ό λ°κ²¬ν μ μμ κ²μΌλ‘ κΈ°λλμ΄μλ€. νμ§λ§ μν κ° μ μ μ μ΄μ§μ±μ μ‘΄μ¬μ μλμ μΌλ‘ μν μκ° μ μ νκ³λ€ λλ¬Έμ λ§€μ° μ μ μμ λ³μ΄ λ§μ΄ λ°κ²¬λμλ€. μ΄λ¬ν λ¬Έμ μ μ ν΄κ²°νκΈ° μν΄ λ€μν λ°©λ²λ€μ΄ κ°λ°λμλλ°, κ·Έ μ€ νλλ κ°μ‘±κΈ°λ° λΆμ λ°©λ²μΌλ‘ μ΄λ μν κ° μ μ μ μ΄μ§μ±κ³Ό μ§λ¨μΈ΅ν λ¬Έμ λ₯Ό λ€λ£¨λλ° μ©μ΄νλ€. λ λ²μ§Έλ‘ μλ‘ λ€λ₯Έ νννμ΄ μλ‘ κ΄λ ¨μ΄ μμ κ²½μ° κ²μ λ ₯μ μ¦κ°μν€κΈ° μν΄ μ΄λ€μ νλ²μ λΆμνλ λ°©λ²μ΄ μλ€. μΈ λ²μ§Έλ λ©νλΆμμ νμ©νμ¬ μ¬λ¬ μ°κ΅¬μ κ²°κ³Όλ₯Ό ν©μΉλ λ°©λ²μΌλ‘ μ΄λ λ§μ μ°κ΅¬λ€μμ ν¨κ³Όμ μμ΄ λ°νμ‘λ€.
μ΄ λ
Όλ¬Έμμλ νμ¬ λ§μ΄ μ¬μ©λκ³ μλ μ¬λ¬ κ°μ‘±κΈ°λ° ν¬κ·λ³μ΄ μ°κ΄ λΆμ λ°©λ²μ λΉκ΅νμκ³ λ€λ₯Έ λ°©λ²λ€μ λΉν΄ FARVAT μ΄ ν΅κ³μ μΌλ‘ κ²¬κ³ νλ©° κ³μ° ν¨μ¨μ μΈ λ°©λ²μμ 보μλ€. λ λμκ° μ΄λ₯Ό λ€μ€ ννν λΆμ λ°©λ²(mFARVAT)κ³Ό λ©νλΆμ λ°©λ²(metaFARVAT)μΌλ‘ νμ₯νμλ€. mFARVATμ μ μ¬μ°λν¨μ κΈ°λ° μ€μ½μ΄ ν
μ€νΈ(quasi-likelihood-based score test)λ₯Ό λ€μμ νννμ μ μ©νλ ν¬κ·μ§ν μ°κ΄λΆμ λ°©λ²μΌλ‘ νννλ€μ λν κ° λ³μ΄μ λμ§μ± λ° μ΄μ§μ± ν¨κ³Όλ₯Ό κ²μ¦νλ€. metaFARVATμ μ¬λ¬ μ°κ΅¬μμμ μ λν¨μ μ€μ½μ΄λ₯Ό κ²°ν©νμ¬ λ²λ ν΅κ³λ, λ³μ΄ μκ³(variable threshold) ν΅κ³λ, λΆμ°κ΅¬μ‘° ν΅κ³λ, κ²°ν© μ΄λλ²μ€ ν΅κ³λμ μμ±νλ€. μ΄λ μ¬λ¬ μ°κ΅¬λ€μ κ²°κ³Όλ₯Ό μ΄μ©νμ¬ λ³μ΄λ€μ λμ§μ± λ° μ΄μ§μ± ν¨κ³Όλ₯Ό κ²μ¦νλ©°, μ λ ννν λ° μ΄λΆ νννμ μ μ©μ΄ κ°λ₯νλ€. λ€μν μλλ¦¬μ€ νμμμ κ΄λ²μν λͺ¨μ μ€νμ ν΅ν΄ μ μν λ°©λ²λ€μ΄ μΌλ°μ μΌλ‘ κ²¬κ³ νκ³ ν¨μ¨μ μ΄λΌλ κ²μ 보μλ€. λν μ΄ λ°©λ²μ νμ©νμ¬DLEC1 λ±μ λ§μ±νμμ±νμ§ν(COPD) κ΄λ ¨ ν보 μ μ μλ₯Ό λ°κ²¬νμλ€.Despite of tens of thousands of genome wide association studies (GWASs), the so-called missing heritability reveals that analyses of common variants identified only a limited number of disease susceptibility loci and a substantial amount of causal variants remain undiscovered by GWASs. Sequencing technology was expected to supply this additional information by obtaining large stretches of DNA spanning the entire genome, and improvements in this technology have enabled genetic association analysis of rare/common causal variants. However, single variant association tests commonly used by GWAS result in false negative findings unless very large samples are available. Alternatively, aggregation of association signals across multiple genetic variants in a biology relevant region is expected to boost statistical power for rare variant analysis. Numerous statistical methods have been proposed for region-based rare variant association studies, such as burden, variance component, and combined omnibus tests.
Region-based association tests are expected to substantially improve statistical power for rare variant analyses and to identify additional disease susceptibility loci. However, very few significant results have been identified due to genetic heterogeneity and relatively small sample sizes. To address the limitations, various approaches have been developed. First, family-based designs play an important role in controlling genetic heterogeneity and population stratification. Second, disease status are often diagnosed by the outcomes of different but related phenotypes, and thus multiple phenotype analysis is supposed to provide additional information and increase power. Third, for the small sample issue, combining results from multiple studies using meta-analysis has been repeatedly addressed as an effective strategy.
In this study, I compared the performance of a selection of the popular family-based rare variant association tests and found FARVAT is the most statistically robust and computationally efficient method. Besides, I extended FARVAT for multiple phenotype analysis (mFARVAT), and meta-analysis (metaFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. metaFARVAT combines quasi-likelihood scores from multiple studies and generates burden, variable threshold, variance component, and combined omnibus test statistics. metaFARVAT tests homogeneous and heterogeneous genetic effects of variants among different studies and can be applied to both quantitative and dichotomous phenotypes. With extensive simulation studies under various scenarios, I found that the proposed methods are generally robust and efficient with different underlying genetic architectures, and I identified some promising candidate genes associated with chronic obstructive pulmonary disease, including DLEC1.Abstract i
Contents iv
List of Figures vii
List of Tables viii
1 Introduction 1
1.1 The background on rare variant association studies 1
1.1.1 Overview of rare variant association studies 1
1.1.2 Challenges of rare variant association studies 8
1.2 Purpose of this study 12
1.3 Outline of the thesis 15
2 Overview of family-based rare variant association tests 16
2.1 Overview of family-based association studies 16
2.2 Comparison of the selected family-based rare variant association tests 21
2.2.1 Rare Variant Transmission Disequilibrium Test (RV-TDT) 24
2.2.2 Generalized Estimating Equations based Kernel Machine test (GEE-KM) 25
2.2.3 Combined Multivariate and Collapsing test for Pedigrees (PedCMC) 26
2.2.4 Gene-level kernel and burden tests for Pedigrees (PedGene) 27
2.2.5 FAmily-based Rare Variant Association Test (FARVAT) 28
2.2.6 Comparison of the methods with GAW19 data 30
2.3 Conclusions 38
3 Family-based Rare Variant Association Test for Multivariate Phenotypes 39
3.1 Introduction 39
3.2 Methods 40
3.2.1 Notations and the disease model 40
3.2.2 Choice of offset 42
3.2.3 Score for quasi-likelihood 43
3.2.4 Homogeneous mFARVAT 44
3.2.5 Heterogeneous mFARVAT 47
3.3 Simulation study 51
3.3.1 The simulation model 51
3.3.2 Evaluation of mFARVAT with simulated data 55
3.4 Application to COPD data 78
3.5 Discussion 85
4 Family-based Rare Variant Association Test for Meta-analysis 90
4.1 Introduction 90
4.2 Methods 92
4.2.1 Notation 92
4.2.2 Choices of Offset 93
4.2.3 Score for Quasi-likelihood 94
4.2.4 Homogeneous Model 95
4.2.5 Heterogeneous Model 98
4.3 Simulation study 101
4.3.1 The simulation model 101
4.3.2 Evaluation of metaFARVAT with simulated data 104
4.4 Application to COPD data 124
4.5 Discussion 132
5 Summary & Conclusions 145
Bibliography 149
Abstract (Korean) 156Docto
Insights into the pulmonary vascular complications of heart failure with preserved ejection fraction
Pulmonary hypertension in the setting of heart failure with preserved ejection fraction (PH-HFpEF) is a growing public health problem that is increasing in prevalence. While PH-HFpEF is defined by a high mean pulmonary artery pressure, high left ventricular end-diastolic pressure and a normal ejection fraction, some HFpEF patients develop PH in the presence of pulmonary vascular remodelling with a high transpulmonary pressure gradient or pulmonary vascular resistance. Ageing, increased left atrial pressure and stiffness, mitral regurgitation, as well as features of metabolic syndrome, which include obesity, diabetes and hypertension, are recognized as risk factors for PH-HFpEF. Qualitative studies have documented that patients with PH-HFpEF develop more severe symptoms than those with HFpEF and are associated with more significant exercise intolerance, frequent hospitalizations, right heart failure and reduced survival. Currently, there are no effective therapies for PH-HFpEF, although a number of candidate drugs are being evaluated, including soluble guanylate cyclase stimulators, phosphodiesterase type 5 inhibitors, sodium nitrite and endothelin receptor antagonists. In this review we attempt to provide an updated overview of recent findings pertaining to the pulmonary vascular complications in HFpEF in terms of clinical definitions, epidemiology and pathophysiology. Mechanisms leading to pulmonary vascular remodelling in HFpEF, a summary of pre-clinical models of HFpEF and PH-HFpEF, and new candidate therapeutic strategies for the treatment of PH-HFpEF are summarized
Dynamic Contact Angle on a Surface with Gradient in Wettability
The retention and drainage of water on heat exchangers is extremely important in air-conditioning, refrigeration, and heat-pumping systems. In this work, droplets of varying sizes sliding on an inclined heat exchanger materials with and without a wettability gradient are observed using a high-speed camera. The dynamic contact angles, the shape evolution and the velocity of the droplet are obtained by image processing. Aluminum and copper surfaces are examined and the gradient is created by partially treating the base surface. The hypothesis is that the momentum of a sliding droplet on a treated surface will push the droplet onto the part without any treatment, so that water retention and drainage can be improved with limited surface treatment. It is found that the dynamic contact angle, the shape evolution and the velocity can be very different for droplets sliding in a wettability-increasing direction when compared to those sliding in a wettability-decreasing direction. The results are very important for the design of specialized heat transfer surfaces operating under dehumidification or defrosting conditions
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