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Statistical Methods in Genetic Association Studies and a Genetic Risk Score for Predictive Modeling of Disease Risk: from Gene Discovery to Translation.

机译：遗传关联研究中的统计方法和疾病风险预测模型的遗传风险评分：从基因发现到翻译。

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Fueled by rapid technological and methodological development, research in the area of genetic epidemiology has brought many successes as well as analytical challenges in identification of genetic risk factors for disease risk. Many have predicted that the detection of heritable disease susceptibility variants could eventually lead to stable models for prediction of disease risk. This dissertation is devoted to the statistical methodological consideration and development in genetic association studies, as well as the use of weighted genetic risk score in predictive modeling of disease risk.;We begin the work by reviewing a brief background into genetic association studies, predictive modeling and relevant analytic challenges. In Chapter 2, we discuss the theoretical relationship between classical multiple linear regression (MLR) and two-stage residual-outcome regression analysis (2SR) in terms of confounding adjustment. We demonstrate that the 2SR would introduce bias and loss of power in the presence of confounding, and thus remind researchers to be cautious in applying the 2SR in genetic association studies. In Chapter 3, we propose an adaptive permutation procedure in testing the significance of susceptibility genetic variants. Results show that the adaptive test is statistically valid as well as computationally feasible in genome-wide association studies (GWAS). Recommendations are made for the implementation of adaptive permutation in real studies. In Chapter 4, we develop an explained-variance based genetic risk score (GRS) for predictive modeling of disease risk. Extensive simulation studies suggest that this new weighted GRS is a robust risk score approach that consistently outperforms simple count and odds ratio based GRS approaches. In Chapter 5, we further explore the performance of GRS approaches in the presence of interactions, including statistical interaction, dependence and linkage disequilibrium (LD). Results emphasize the advantage of weighted GRS even in more complicated settings, motivating its application in predictive modeling. This work concludes with several practical guidelines in genetic association studies and predictive modeling, and discussions of future perspectives.
机译：在快速的技术和方法学发展的推动下，遗传流行病学领域的研究在识别疾病风险的遗传风险因素方面带来了许多成功以及分析挑战。许多人已经预测，遗传性疾病易感性变异的检测最终可能会形成用于预测疾病风险的稳定模型。本论文致力于遗传关联研究的统计学方法论研究和发展，以及加权遗传风险评分在疾病风险预测模型中的应用。以及相关的分析挑战。在第二章中，我们从混杂调整的角度讨论了经典多元线性回归（MLR）和两阶段残差结果回归分析（2SR）之间的理论关系。我们证明了2SR在混淆的情况下会引入偏见和动力丧失，因此提醒研究人员在将2SR用于遗传关联研究时要谨慎。在第3章中，我们提出了一种适应性置换程序，以测试易感性遗传变异的重要性。结果表明，在全基因组关联研究（GWAS）中，自适应测试在统计上是有效的，并且在计算上是可行的。提出了在实际研究中实施自适应置换的建议。在第4章中，我们为疾病风险的预测模型开发了基于解释方差的遗传风险评分（GRS）。大量的模拟研究表明，这种新的加权GRS是可靠的风险评分方法，始终优于基于简单计数和优势比的GRS方法。在第5章中，我们将进一步探讨GRS方法在存在交互作用（包括统计交互作用，依赖性和连锁不平衡（LD））的情况下的性能。结果强调了加权GRS的优势，即使在更复杂的环境中，也激发了其在预测建模中的应用。这项工作以遗传关联研究和预测建模中的一些实用指南作为结束，并讨论了未来的观点。

著录项

  • 作者

    Che, Ronglin.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Biology Bioinformatics.;Biology Genetics.;Statistics.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 228 p.
  • 总页数 228
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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