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Many complex diseases are influenced by genetic variations in multiple genes, each with only a small marginal effect on disease susceptibility. Pathway analysis, which identifies biological pathways associated with disease outcome, has become increasingly popular for genome-wide association studies (GWAS). In addition to combining weak signals from a number of SNPs in the same pathway, results from pathway analysis also shed light on the biological processes underlying disease. We propose a new pathway-based analysis method for GWAS, the supervised principal component analysis (SPCA) model. In the proposed SPCA model, a selected subset of SNPs most associated with disease outcome is used to estimate the latent variable for a pathway. The estimated latent variable for each pathway is an optimal linear combination of a selected subset of SNPs; therefore, the proposed SPCA model provides the ability to borrow strength across the SNPs in a pathway. In addition to identifying pathways associated with disease outcome, SPCA also carries out additional within-category selection to identify the most important SNPs within each gene set. The proposed model operates in a well-established statistical framework and can handle design information such as covariate adjustment and matching information in GWAS. We compare the proposed method with currently available methods using data with realistic linkage disequilibrium structures and we illustrate the SPCA method using the Wellcome Trust Case-Control Consortium Crohn Disease (CD) dataset.

Abstract. The overexpression of human epidermal growth factor receptor 2 (HER2) is associated with increased breast cancer recurrence and worse prognosis. Effective treatments such as trastuzumab and lapatinib for patients with HER2 overexpression target the blockade of HER2 signaling activities but are often limited by the emergence of acquired drug resistance. This study applied Raman spectroscopy to differentially identify the amplification status of HER2 in cells and to characterize the biochemical composition of lapatinib resistant and sensitive HER2+ breast cancer cells in response to the drug. Raman spectra from BT474 (HER2+ breast cancer cell), MCF-10A (HER2− control), and HER2+ MCF-10A (HER2+ control) were analyzed using lasso and elastic-net regularized generalized linear models (glmnet) for multivariate statistical analysis and were discriminated to groups of different HER2 expression status with an overall 99% sensitivity and specificity. Enhanced lipid content and decreased proteome were observed in HER2+ cells. With lapatinib treatment, lapatinib-resistant breast cancer cells demonstrated sustained lipogenesis compared with the sensitive cells. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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The most popular RNA library used for RNA sequencing is the poly(A) captured RNA library. This library captures RNA based on the presence of poly(A) tails at the 3′ end. Another type of RNA library for RNA sequencing is the total RNA library which differs from the poly(A) library by capture method and price. The total RNA library costs more and its capture of RNA is not dependent on the presence of poly(A) tails. In practice, only ribosomal RNAs and small RNAs are washed out in the total RNA library preparation. To evaluate the ability of detecting RNA for both RNA libraries we designed a study using RNA sequencing data of the same two breast cancer cell lines from both RNA libraries. We found that the RNA expression values captured by both RNA libraries were highly correlated. However, the number of RNAs captured was significantly higher for the total RNA library. Furthermore, we identify several subsets of protein coding RNAs that were not captured efficiently by the poly(A) library. One of the most noticeable is the histone-encode genes, which lack the poly(A) tail.