抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

https://cdr.lib.unc.edu/downloads/v692t8920

A global reference for human genetic variation.

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

/pdf/analysis-of-protein-coding-genetic-variation-in-60-706-43wjw7d23z.pdf

Analysis of protein-coding genetic variation in 60,706 humans

For a long time, superoxide generation by an NADPH oxidase was considered as an oddity only found in professional phagocytes. Over the last years, six homologs of the cytochrome subunit of the phag...

/pdf/the-nox-family-of-ros-generating-nadph-oxidases-physiology-zjc88zvh59.pdf

The NOX Family of ROS-Generating NADPH Oxidases: Physiology and Pathophysiology

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Numerous independent susceptibility variants have been identified for Age-related macular degeneration (AMD) by genome-wide association studies (GWAS). Since advanced AMD is currently incurable, an accurate prediction of a person’s AMD risk using genetic information is desirable for early diagnosis and clinical management. In this study, genotype data of 32,215 Caucasian individuals with age above 50 years from the International AMD Genomics Consortium in dbGAP were used to establish and validate prediction models for AMD risk using four different machine learning approaches: neural network, lasso regression, support vector machine, and random forest. A standard logistic regression model was also considered using a genetic risk score. To identify feature SNPs for AMD prediction models, we selected the genome-wide significant SNPs from GWAS. All methods achieved good performance for predicting normal controls versus advanced AMD cases (AUC=0.81∼0.82 in a separate test dataset) and normal controls versus any AMD (AUC=0.78∼0.79). By applying the state-of-art machine learning approaches on the large AMD GWAS data, the predictive models we established can provide an accurate estimation of an individual’s AMD risk profile across the person’s lifespan based on a comprehensive genetic information.

/pdf/gwas-based-machine-learning-for-prediction-of-age-related-57m4hyn9xt.pdf

Anand Swaroop

Papers

GWAS-based Machine Learning for Prediction of Age-Related Macular Degeneration Risk

Nutrition and Nutraceutical Supplements for the Treatment of Hypertension

Transgenic Analysis of Nrl Promoter: Towards Identification of Rod Precursors

Isolated population helps tease out a third locus underlying a multigenic form of canine RPGRIP1 cone-rod dystrophy

Identification of putative small open reading frame peptides in the retinal transcriptome