Integrative Statistical Modelling in Genetics and Genomics

精心设计和仔细分析的科学研究产生了更有力的发现，保护了宝贵的资源，并加速了生物知识的发现。这项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女性队列中收集大量数据。易感性研究涉及遗传遗传变异&乳腺癌诊断的早期年龄。为了发现新的易感基因，解释家族性乳腺癌的主要原因，我们正在分析从安大略省家族性乳腺癌登记中心(OFBCR)挑选的早发性患病姐妹的全基因组测序；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父母之一继承相同的基因突变。这一结果将有助于在国际财团中设计强有力的研究，以确认候选变种。预后研究将肿瘤组织中的分子变化与疾病自然病史的变化相关联。腋窝淋巴结阴性乳腺癌研究是对新诊断的疾病复发/死亡15年的女性进行的前瞻性队列研究。肿瘤样本阵列被用来表征每个女性肿瘤中不同生物标志物的蛋白质水平，但我们观察到的生存数据中的模式与传统的疾病复发时间分析的数学假设不一致。一种允许一定比例的“治愈”患者的混合治疗模型更好地解释了观察结果。虽然这些阵列产生了大多数女性中任何一个生物标记物的测量结果，但当我们一起调查多个生物标记物时，一些信息会因为那些至少有一个缺失值的生物标记物的积累而丢失。填充不完整数据的计算技术称为“多重补偿”，是解决这一问题的一种实用方法，但需要专门用于混合固化模型。

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.