Integrative Statistical Modelling in Genetics and Genomics

遗传学和基因组学的综合统计模型

• 批准号: RGPIN-2020-05896
• 负责人: Bull, Shelley
• 金额: $ 2.26万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764376
• 关键词: Integrative; Statistical; Modelling; Genetics; Genomics

Well-designed & carefully-analysed scientific studies yield stronger findings, preserve precious resources, and accelerate discovery of biological knowledge. The proposed research will develop new ways of statistical learning from family and patient data. It is motivated by large data collections in two cohorts of women with breast cancer. Susceptibility studies are concerned with inherited genetic variation & early age at breast cancer diagnosis. With the aim of discovering new susceptibility genes that account for the substantial unexplained component in familial breast cancer, we are analysing whole genome sequencing in early onset affected sisters selected from Ontario Familial Breast Cancer Registry (OFBCR) families; the OFBCR is part of large national & international consortia. We propose new methods for data analysis that take into account that siblings can inherit the same genetic mutation from one of their parents. The results will help design well-powered studies in international consortia to confirm candidate variants. Prognostic studies correlate molecular alterations in tumour tissues with variation in the natural history of disease. The axillary node-negative breast cancer study is a prospective cohort of newly diagnosed women followed for disease recurrence/death for 15 years. Arrays of tumour samples have been used to characterize the protein level of various biomarkers in each woman's tumour, but the patterns in the survival data we observed were inconsistent with the mathematical assumptions of conventional analysis of time to disease recurrence. A mixture-cure model that allows for a proportion of "cured" patients explains the observations much better. Although the arrays yield measurements of any one biomarker in most women, when we investigate multiple biomarkers together, some information is lost due to the accumulation of those with at least one missing value. Computational techniques known as "Multiple Imputation" which fill-in the incomplete data are a practical way to address this problem, but need to be specialized for the mixture-cure model.

精心设计和仔细分析的科学研究会产生更有力的发现，保护宝贵的资源，并加速生物知识的发现。拟议的研究将开发从家庭和患者数据中进行统计学习的新方法。它的动机是在两个乳腺癌妇女队列中收集大量数据。易感性研究关注遗传的遗传变异和早期乳腺癌诊断。为了发现家族性乳腺癌中大量原因不明的新易感基因，我们正在分析从安大略家族性乳腺癌登记处（OFBCR）家庭中选出的早发受影响姐妹篇的全基因组测序; OFBCR是大型国家和国际财团的一部分。我们提出了新的数据分析方法，考虑到兄弟姐妹可以从他们的父母之一继承相同的基因突变。这些结果将有助于在国际财团中设计强有力的研究，以确认候选变体。预后研究将肿瘤组织中的分子改变与疾病自然史的变化相关联。腋窝淋巴结阴性乳腺癌研究是一项前瞻性队列研究，对新诊断的女性进行了15年的疾病复发/死亡随访。肿瘤样本阵列已被用于表征每个女性肿瘤中各种生物标志物的蛋白质水平，但我们观察到的生存数据模式与疾病复发时间的传统分析的数学假设不一致。一个混合治愈模型，允许一部分“治愈”的患者更好地解释了观察结果。尽管阵列可以测量大多数女性的任何一种生物标志物，但当我们一起研究多种生物标志物时，由于至少有一个缺失值的累积，一些信息会丢失。被称为“多重插补”的计算技术填补了不完整的数据是解决这个问题的一种实用方法，但需要专门用于混合固化模型。