Non-invasive prenatal tests are available for chromosomal aneuploidy but not for monogenic diseases yet. This study aimed to develop analysis method for detecting fetal mutation status by using only maternal blood sample. We introduce two different approaches to detect a fetal mutation of the Duchene Muscular Dystrophy (DMD) gene. The algorithms are consisted of two steps: detection of mutation types and regions and determination of fetal mutation status. For the second step, one approach uses allele frequencies of heterozygous sites and the other approach uses relative read depth that is the read depths of cfDNA divided by gDNA of the mother.

Since most of the cancer markers that have been reported are obtained directly from cancer tissues, it is difficult to use them for early diagnosis of cancer without surgery. Thus, development of markers that can be detected by blood is crucial for making early diagnosis of cancer easier. One of the most feasible types of markers that can be detected by blood is a protein marker. Here, we focus on building prediction methods using the protein markers for early diagnosis of cancer. To develop a prediction model with high prediction ability, it is critical to choose appropriate markers first. Here, we consider a stepwise selection method using area under the receiver operating characteristic curve (Step-AUC) in order to construct a multi-protein prediction model. We showed that the performance of Step-AUC highly depends on the tuning parameter. We compared our proposed Step-AUC method to stepwise selection using information criteria and support vector machine recursive feature extraction (SVM-RFE). We observed that Step-AUC and stepwise selection using Bayesian information criteria (Step-BIC) perform better than other methods. The importance of each marker can be chosen using a new stepwise selection consistency (SSC) measure. The final models include the markers with high SSC measures. We applied our stepwise procedure to pancreatic cancer data and found two markers of interest.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, which has dismal prognosis because of its silent early symptoms, high metastatic potential, and resistance to conventional therapies. Although a PDAC patient who is diagnosed at an early stage would have a substantial increase in chance of survival, the survival rate is poor because there is no efficient non-invasive diagnostic test in the early stage. In this study, we developed an efficient prediction models to detect PDAC in its early stages. Our prediction models use both mRNA and miRNA expression data from 104 PDAC tissues and 17 normal pancreatic tissues using microarray technology. After quality control, we built prediction models based on support vector machine (SVM) from mRNA and miRNA expressions for detecting early PDAC. To prevent over-fitting effect, we conducted leave-one-out cross validation (LOOCV) and 5-fold cross validation (CV). For independent validation of prediction models, we performed evaluation on independent datasets from Gene Expression Omnibus (GEO). After the validation, we identified 28 single markers and 231 combinations of markers with powerful prediction performance. In addition, the marker candidates are annotated with cancer pathways using gene ontology analysis. Our prediction models for PDAC may have potential for early diagnosis of PDAC.

MOTIVATION: Gene-gene interactions are important contributors to complex biological traits. Multifactor dimensionality reduction (MDR) is a method to analyze gene-gene interactions and has been applied to many genetics studies of complex diseases. In order to identify the best interaction model associated with disease susceptibility, MDR classifiers corresponding to interaction models has been constructed and evaluated as a predictor of disease status via a certain measure such as balanced accuracy (BA). It has been shown that the performance of MDR tends to depend on the choice of the evaluation measures. RESULTS: In this article, we introduce two types of new evaluation measures. First, we develop weighted BA (wBA) that utilizes the quantitative information on the effect size of each multi-locus genotype on a trait. Second, we employ ordinal association measures to assess the performance of MDR classifiers. Simulation studies were conducted to compare the proposed measures with BA, a current measure. Our results showed that the wBA and tau(b) improved the power of MDR in detecting gene-gene interactions. Noticeably, the power increment was higher when data contains the greater number of genetic markers. Finally, we applied the proposed evaluation measures to real data.