Genetic Spectrum and Characteristics of Hereditary Optic Neuropathy in Taiwan

Hereditary optic neuropathy (HON) is a group of genetically heterogeneous diseases that cause optic nerve atrophy and lead to substantial visual impairment. HON may present with optic nerve atrophy only or in association with various systemic abnormalities. Although a genetic survey is indispensable for diagnosing HON, conventional sequencing techniques could render its diagnosis challenging. In this study, we attempted to explore the genetic background of patients with HON in Taiwan through capture-based next-generation sequencing targeting 52 HON-related genes. In total, 57 patients from 48 families were recruited, with 6 patients diagnosed as having Leber hereditary optic neuropathy through initial screening for three common variants (m.3460G>A, m.11778G>A, m.14484T>C). Disease-causing genotypes were identified in 14 (33.3%) probands, and OPA1 variants were the most prevalent cause of autosomal HON. Exposure to medications such as ethambutol could trigger an attack of autosomal dominant optic atrophy. WFS1 variants were identified in three probands with variable clinical features in our cohort. Hearing impairment could occur in patients with OPA1 or WFS1 variants. This is the first comprehensive study investigating the genetic characteristics of HON in Taiwan, especially for autosomal HON. Our results could provide useful information for clinical diagnosis and genetic counseling in this field.

A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels

Malaria larvae embrace unpredictable variable life periods as they spread across many stratospheres of the mosquito vectors. There are transcriptomes of a thousand distinct species. Ribonucleic acid sequencing (RNA-seq) is a ubiquitous gene expression strategy that contributes to the improvement of genetic survey recognition. RNA-seq measures gene expression transcripts data, including methodological enhancements to machine learning procedures. Scientists have suggested many addressed learning for the study of biological evidence. An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms. The efficacy of this assay is tested, and the outcome of the experiment obtained an accuracy metric of 93% and 96% respectively.

Pharmacogenomic Biomarkers of Follicle-Stimulating Hormone Receptor Malfunction in Females with Impaired Ovarian Response—A Genetic Survey

Follicle-stimulating hormone receptor (FSHR) plays an essential role as one of the most important molecules in response to some of infertility related medications. Impaired ovarian reserve and poor response to such treatments are partially dependent on the FSHR molecule itself. However, the function and drug sensitivity for this receptor may change due to various allele and polymorphisms in the FSHR gene. Studies indicated some of the FSHR-mediated treatments utilized in clinical centers display different outcomes in specific populations, which may arise from FSHR altered genotypes in certain patients. To support the increased demands for reaching the personalized drug and hormone therapy in clinics, focusing on actionable variants through Pharmacogenomic analysis of this receptor may be necessary. The current study tries to display a perspective view on genetic assessments for Pharmacogenomic profiling of the FSHR gene via providing a systematic and critical overview on the genetics of FSHR and its diverse responses to ligands for infertility treatment in females with impaired ovarian responses and show the potential effects of the patient genetic make-up on related binding substances efficacy. All identified functional drug-related alleles were selected through a comprehensive literature search and analyzed. Advanced technologies for the genetic evaluation of them are also discussed properly.