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HPV negativity was associated with a very low long-term risk of cervical cancer. Persistent detection of HPV among cytologically normal women greatly increased risk. Thus, it is useful to perform repeated HPV testing following an initial positive test.

A genetic risk score could be beneficial in assisting clinical diagnosis for complex diseases with high heritability. With large-scale genome-wide association (GWA) data, the current study constructed a genetic risk model with a machine learning approach for bipolar disorder (BPD). The GWA dataset of BPD from the Genetic Association Information Network was used as the training data for model construction, and the Systematic Treatment Enhancement Program (STEP) GWA data were used as the validation dataset. A random forest algorithm was applied for pre-filtered markers, and variable importance indices were assessed. 289 candidate markers were selected by random forest procedures with good discriminability; the area under the receiver operating characteristic curve was 0.944 (0.935–0.953) in the training set and 0.702 (0.681–0.723) in the STEP dataset. Using a score with the cutoff of 184, the sensitivity and specificity for BPD was 0.777 and 0.854, respectively. Pathway analyses revealed important biological pathways for identified genes. In conclusion, the present study identified informative genetic markers to differentiate BPD from healthy controls with acceptable discriminability in the validation dataset. In the future, diagnosis classification can be further improved by assessing more comprehensive clinical risk factors and jointly analysing them with genetic data in large samples.

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder with strong genetic components. Several recent genome-wide association (GWA) studies in Caucasian samples have reported a number of gene regions and loci correlated with the risk of ASD—albeit with very little consensus across studies.MethodsA two-stage GWA study was employed to identify common genetic variants for ASD in the Taiwanese Han population. The discovery stage included 315 patients with ASD and 1,115 healthy controls, using the Affymetrix SNP array 6.0 platform for genotyping. Several gene regions were then selected for fine-mapping and top markers were examined in extended samples. Single marker, haplotype, gene-based, and pathway analyses were conducted for associations.ResultsSeven SNPs had p-values ranging from 3.4~9.9*10−6, but none reached the genome-wide significant level. Five of them were mapped to three known genes (OR2M4, STYK1, and MNT) with significant empirical gene-based p-values in OR2M4 (p = 3.4*10−5) and MNT (p = 0.0008). Results of the fine-mapping study showed single-marker associations in the GLIS1 (rs12082358 and rs12080993) and NAALADL2 (rs3914502 and rs2222447) genes, and gene-based associations for the OR2M3-OR2T5 (olfactory receptor genes, p = 0.02), and GLIPR1/KRR1 gene regions (p = 0.015). Pathway analyses revealed important pathways for ASD, such as olfactory and G protein–coupled receptors signaling pathways.ConclusionsWe reported Taiwanese Han specific susceptibility genes and variants for ASD. However, further replication in other Asian populations is warranted to validate our findings. Investigation in the biological functions of our reported genetic variants might also allow for better understanding on the underlying pathogenesis of autism.

A causal-pie modeling based on a women cohort in Taiwan successfully disentangles the roles of virus factors and reproductive factors at study entry, independently or interactively, on subsequent cervical cancer risk.

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Many susceptibility genes for complex traits were identified without conclusive findings. There is a strong need to integrate rapidly accumulated genomic data from multi-dimensional platforms, and to conduct risk evaluation for potential therapeutic and diagnostic usages. We set up an algorithm to computationally search for optimal weight-vector for various data sources, while minimized potential noises. Through gene-prioritization framework, combined scores for the resulting prioritized gene-set were calculated using a genome-wide association (GWA) dataset, following with evaluation using weighted genetic risk score and risk-attributed information using an independent GWA dataset. The significance of association of GWA data was corrected for gene length. Enriched functional pathways were identified for the prioritized gene-set using the Gene Ontology analysis. We illustrated our framework with bipolar disorder. 233 prioritized genes were identified from 10,830 candidates that curated from six platforms. The prioritized genes were significantly enriched (P(adjusted) < 1 × 10(-5)) in 18 biological functions and molecular mechanisms including membrane, synaptic transmission, transmission of nerve impulse, integral to membrane, and plasma membrane. Our risk evaluation demonstrated higher weighted genetic risk score in bipolar patients than controls (P-values ranged from 0.002 to 3.8 × 10(-6)). Substantial risk-information (71%) was extracted from prioritized genes for bipolar illness than other candidate-gene sets. Our evidence-based prioritized gene-set provides opportunity to explore the complex network and to conduct follow-up basic and clinical studies for complex traits.

MicroRNAs (miRNAs) are known to be important post-transcriptional regulators that are involved in the etiology of complex psychiatric traits. The present study aimed to incorporate miRNAs information into pathway analysis using a genome-wide association dataset to identify relevant biological pathways for bipolar disorder (BPD). We selected psychiatric- and neurological-associated miRNAs (N = 157) from PhenomiR database. The miRNA target genes (miTG) predictions were obtained from . Canonical pathways (N = 4,051) were downloaded from the Molecule Signature Database. We employed a novel weighting scheme for miTGs in pathway analysis using methods of gene set enrichment analysis and sum-statistic. Under four statistical scenarios, 38 significantly enriched pathways (P-value < 0.01 after multiple testing correction) were identified for the risk of developing BPD, including pathways of ion channels associated (e.g., gated channel activity, ion transmembrane transporter activity, and ion channel activity) and nervous related biological processes (e.g., nervous system development, cytoskeleton, and neuroactive ligand receptor interaction). Among them, 19 were identified only when the weighting scheme was applied. Many miRNA-targeted genes were functionally related to ion channels, collagen, and axonal growth and guidance that have been suggested to be associated with BPD previously. Some of these genes are linked to the regulation of miRNA machinery in the literature. Our findings provide support for the potential involvement of miRNAs in the psychopathology of BPD. Further investigations to elucidate the functions and mechanisms of identified candidate pathways are needed.