Dominant effects of the Huntington's disease HTT CAG repeat length are captured in gene-expression data sets by a continuous analysis mathematical modeling strategy.

In Huntington's disease (HD), the size of the expanded HTT CAG repeat mutation is the primary driver of the processes that determine age at onset of motor symptoms. However, correlation of cellular biochemical parameters also extends across the normal repeat range, supporting the view that the CAG repeat represents a functional polymorphism with dominant effects determined by the longer allele. A central challenge to defining the functional consequences of this single polymorphism is the difficulty of distinguishing its subtle effects from the multitude of other sources of biological variation. We demonstrate that an analytical approach based upon continuous correlation with CAG size was able to capture the modest (∼21%) contribution of the repeat to the variation in genome-wide gene expression in 107 lymphoblastoid cell lines, with alleles ranging from 15 to 92 CAGs. Furthermore, a mathematical model from an iterative strategy yielded predicted CAG repeat lengths that were significantly positively correlated with true CAG allele size and negatively correlated with age at onset of motor symptoms. Genes negatively correlated with repeat size were also enriched in a set of genes whose expression were CAG-correlated in human HD cerebellum. These findings both reveal the relatively small, but detectable impact of variation in the CAG allele in global data in these peripheral cells and provide a strategy for building multi-dimensional data-driven models of the biological network that drives the HD disease process by continuous analysis across allelic panels of neuronal cells vulnerable to the dominant effects of the HTT CAG repeat.

Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer.

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.