Multiclass linear discriminant analysis with ultrahigh-dimensional features

Construction of lung cancer serum markers based on ReliefF feature selection

Serum miRNAs are available clinical samples for cancer screening. Identifying early serum markers in lung cancer (LC) is essential for patients' early diagnosis and clinical treatment. Expression data of serum miRNAs of lung adenocarcinoma (LUAD) patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO). These data were normalized and subjected to differential expression analysis to obtain differentially expressed miRNAs (DEmiRNAs). The DEmiRNAs were subsequently subjected to ReliefF feature selection, and subsets closely related to cancer were screened as candidate feature miRNAs. Thereafter, a Gaussian Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifier were constructed based on these candidate feature miRNAs. Then the best diagnostic signature was constructed through NB combined with incremental feature selection (IFS). Thereafter, these samples were subjected to principal component analysis (PCA) based on miRNAs with optimal predictive performance. Finally, the peripheral serum miRNAs of 64 LUAD patients and 59 normal individuals were extracted for qRT-PCR analysis to validate the performance of the diagnostic model in respect of clinical detection. Finally, according to area under the curve (AUC) and accuracy values, the NB classifier composed of miR-5100 and miR-663a manifested the most outstanding diagnostic performance. The PCA results also revealed that the 2-miRNA diagnostic signature could effectively distinguish cancer patients from healthy individuals. Finally, qRT-PCR results of clinical serum samples revealed that miR-5100 and miR-663a expression in tumor samples was remarkably higher than that in normal samples. The AUC of the 2-miRNA diagnostic signature was 0.968. In summary, we identified markers (miR-5100 and miR-663a) in serum for early LUAD screening, providing ideas for developing early LUAD diagnostic models.

Predicting late-stage age-related macular degeneration by integrating marginally weak SNPs in GWA studies

Introduction: Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of blindness in developed countries. Current genome-wide association studies (GWAS) for late-stage age-related macular degeneration are mainly single-marker-based approaches, which investigate one Single-Nucleotide Polymorphism (SNP) at a time and postpone the integration of inter-marker Linkage-disequilibrium (LD) information in the downstream fine mappings. Recent studies showed that directly incorporating inter-marker connection/correlation into variants detection can help discover novel marginally weak single-nucleotide polymorphisms, which are often missed in conventional genome-wide association studies, and can also help improve disease prediction accuracy. Methods: Single-marker analysis is performed first to detect marginally strong single-nucleotide polymorphisms. Then the whole-genome linkage-disequilibrium spectrum is explored and used to search for high-linkage-disequilibrium connected single-nucleotide polymorphism clusters for each strong single-nucleotide polymorphism detected. Marginally weak single-nucleotide polymorphisms are selected via a joint linear discriminant model with the detected single-nucleotide polymorphism clusters. Prediction is made based on the selected strong and weak single-nucleotide polymorphisms. Results: Several previously identified late-stage age-related macular degeneration susceptibility genes, for example, BTBD16, C3, CFH, CFHR3, HTARA1, are confirmed. Novel genes DENND1B, PLK5, ARHGAP45, and BAG6 are discovered as marginally weak signals. Overall prediction accuracy of 76.8% and 73.2% was achieved with and without the inclusion of the identified marginally weak signals, respectively. Conclusion: Marginally weak single-nucleotide polymorphisms, detected from integrating inter-marker linkage-disequilibrium information, may have strong predictive effects on age-related macular degeneration. Detecting and integrating such marginally weak signals can help with a better understanding of the underlying disease-development mechanisms for age-related macular degeneration and more accurate prognostics.

A Structured Brain-wide and Genome-wide Association Study Using ADNI PET Images

A multi-stage variable selection method is introduced for detecting association signals in structured brain-wide and genome-wide association studies (brain-GWAS). Compared to conventional single-voxel-to-single-SNP approaches, our approach is more efficient and powerful in selecting the important signals by integrating anatomic and gene grouping structures in the brain and the genome, respectively. It avoids large number of multiple comparisons while effectively controls the false discoveries. Validity of the proposed approach is demonstrated by both theoretical investigation and numerical simulations. We apply the proposed method to a brain-GWAS using ADNI PET imaging and genomic data. We confirm previously reported association signals and also find several novel SNPs and genes that either are associated with brain glucose metabolism or have their association significantly modified by Alzheimer's disease status.