“Estimating Variance Components in Longitudinal Family Studies with Applications to Genetic Heritability”
A longitudinal familial study with repeated measurements on relatives and can serve as a powerful resource to detect genetic association in the development of complex traits. Modeling the covariance structure in a longitudinal family study is often quite challenging, as one needs to capture three distinct levels of dependencies. The dependency among family members at each time point, the dependency of outcomes in an individual across all time-points and modeling the outcome dependencies between two family members among different time points. Heritability measures the contribution of the additive genetic component in a trait variance. In this talk, we investigate the challenges with joint genetic association testing and heritability estimation with longitudinal family data. We will focus on twin studies as this unique study design with MZs and DZs can separate additive genetic effects from shared environmental component and thus provides means for accurate heritability estimation. We develop a model that enables both heritability estimation and association testing with more flexible assumptions and is an extension of the traditional Falconer's approach. For our model, we propose a rapid two stage estimation procedure and a method of moments approach which can even be used to estimate variance components in a multivariate ACE model. We compare our approach with existing approaches through extensive simulation studies and illustrate our approach on Minnesota twin studies.
*Special Date, Time, and Location:
December 10, 2018, 3:00 - 4:00 pm, M3-A805
Donghui Yan, University of Massachussetts Dartmouth
Randon Projection Forests
In this talk, I will introduce a new tool for data mining and inference — Random Projection Forests (rpForests). rpForests is an ensemble of random projection trees constructed recursively through a series of carefully chosen random projections. rpForests combines the power of ensemble methods and the flexibility of trees; it is simple to implement, highly scalable, and readily adapt to the geometry of the underlying data. The ensemble nature of rpForests makes it easy to run in parallel on multicore or clustered computers, with running time nearly inversely proportional to the number of cores or computers used in the computation. This complements previous development of unsupervised extension to Random Forests-Cluster Forests-which aims at clustering by random feature pursuits. One potential use of rpForests is to leverage the locality of data points captured by rpForests, which has the desired property that the probability of neighboring points being separated decays exponentially fast when the ensemble size increases. We discuss two applications along this line, fast k-nearest neighbor (kNN) search and deep representation learning in the scoring of tissue microarray images.
Neoadjuvant therapy induces a potent inflammatory and corresponding regulatory response within the sarcoma immune microenvironment
Soft tissue sarcoma is a prototype for many solid tumors, which are curable when localized, but prone to recurrence and metastasis, at which point survival is limited and more effective treatment strategies are essential. In the current project, we are utilizing multiple techniques of immunologic investigation, including multiplex immunohistochemistry (mIHC), and transcriptomic analysis with the NanoString platform, to define: (1) the immune response to sarcoma, (2) the effect of preoperative radiation and chemotherapy, and (3) the patterns of immune infiltration that predict treatment response and clinical outcome. We will discuss the benefits of each technique, our findings in sarcoma, and applicability to other solid tumors.
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