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The Ensembl Variant Effect Predictor (VEP) is a freely available, open‐source tool for the annotation and filtering of genomic variants. It predicts variant molecular consequences using the Ensembl/GENCODE or RefSeq gene sets. It also reports phenotype associations from databases such as ClinVar, allele frequencies from studies including gnomAD, and predictions of deleteriousness from tools such as Sorting Intolerant From Tolerant and Combined Annotation Dependent Depletion. Ensembl VEP includes filtering options to customize variant prioritization. It is well supported and updated roughly quarterly to incorporate the latest gene, variant, and phenotype association information. Ensembl VEP analysis can be performed using a highly configurable, extensible command‐line tool, a Representational State Transfer application programming interface, and a user‐friendly web interface. These access methods are designed to suit different levels of bioinformatics experience and meet different needs in terms of data size, visualization, and flexibility. In this tutorial, we will describe performing variant annotation using the Ensembl VEP web tool, which enables sophisticated analysis through a simple interface. [ABSTRACT FROM AUTHOR]

A nonsense allele at rs1343879 in human MAGEE2 on chromosome X has previously been reported as a strong candidate for positive selection in East Asia. This premature stop codon causing ∼80% protein truncation is characterized by a striking geographical pattern of high population differentiation: common in Asia and the Americas (up to 84% in the 1000 Genomes Project East Asians) but rare elsewhere. Here, we generated a Magee2 mouse knockout mimicking the human loss-of-function mutation to study its functional consequences. The Magee2 null mice did not exhibit gross abnormalities apart from enlarged brain structures (13% increased total brain area, P  =   0.0022) in hemizygous males. The area of the granular retrosplenial cortex responsible for memory, navigation, and spatial information processing was the most severely affected, exhibiting an enlargement of 34% (P  =   3.4×10−6). The brain size in homozygous females showed the opposite trend of reduced brain size, although this did not reach statistical significance. With these insights, we performed human association analyses between brain size measurements and rs1343879 genotypes in 141 Chinese volunteers with brain MRI scans, replicating the sexual dimorphism seen in the knockout mouse model. The derived stop gain allele was significantly associated with a larger volume of gray matter in males (P  =   0.00094), and smaller volumes of gray (P  =   0.00021) and white (P  =   0.0015) matter in females. It is unclear whether or not the observed neuroanatomical phenotypes affect behavior or cognition, but it might have been the driving force underlying the positive selection in humans. [ABSTRACT FROM AUTHOR]

Genetic studies of type 1 diabetes (T1D) have identified 50 susceptibility regions1,2 (www.T1DBase.org) revealing major pathways contributing to risk3, with some loci shared across immune disorders4–6. In order to make genetic comparisons across autoimmune disorders as informative as possible a dense genotyping array, the ImmunoChip, was developed, from which four novel T1D regions were identified (P < 5 × 10−8). A comparative analysis with 15 immune diseases (www.ImmunoBase.org) revealed that T1D is more similar genetically to other autoantibody-positive diseases, most significantly to juvenile idiopathic arthritis and least to ulcerative colitis, and provided support for three additional novel T1D loci. Using a Bayesian approach, we defined credible sets for the T1D SNPs. These T1D SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34+ stem cells. Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal.