Characterization of genetic variants in children with refractory epilepsy / 中华医学遗传学杂志

OBJECTIVE@#To analyze the characteristics of genetic variants among children with refractory epilepsy (RE).@*METHODS@#One hundred and seventeen children with RE who had presented at the Affiliated Jinhua Hospital of Zhejiang University School of Medicine from January 1, 2018 to November 21, 2019 were selected as the study subjects. The children were divided into four groups according to their ages of onset: < 1 year old, 1 ~ 3 years old, 3 ~ 12 years old, and >= 12 years old. Clinical data and results of trio-whole exome sequencing were retrospectively analyzed.@*RESULTS@#In total 67 males and 50 females were included. The age of onset had ranged from 4 days to 14 years old. Among the 117 patients, 33 (28.21%) had carried pathogenic or likely pathogenic variants. The detection rates for the < 1 year old, 1 ~ 3 years old and >= 3 years old groups were 53.85% (21/39), 12.00% (3/25) and 16.98% (9/53), respectively, with a significant difference among the groups (χ2 = 19.202, P < 0.001). The detection rates for patients with and without comorbidities were 33.33% (12/36) and 25.93% (21/81), respectively (χ2 = 0.359, P = 0.549). Among the 33 patients carrying genetic variants, 27 were single nucleotide polymorphisms (SNPs) or insertion/deletions (InDels), and 6 were copy number variations (CNVs). The most common mutant genes were PRRT2 (15.15%, 5/33) and SCN1A (12.12%, 4/33). Among children carrying genetic variants, 72.73% (8/11) had attained clinical remission after adjusting the medication according to the references.@*CONCLUSION@#28.21% of RE patients have harbored pathogenic or likely pathogenic variants or CNVs. The detection rate is higher in those with younger age of onset. PRRT2 and SCN1A genes are more commonly involved. Adjusting medication based on the types of affected genes may facilitate improvement of the remission rate.

A practical guide to amplicon and metagenomic analysis of microbiome data

Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.