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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.