Identifying the causal variants from a GWAS generally involves identifying the haplotype blocks that contain your variant of interest, rather than the variant and the gene it is affecting itself. To find the actual genes involved, you need to consider all variants in LD with your identified associations. Ensembl Post-GWAS analysis pipeline (PostGAP) can provide automatic fine-tuning of your GWAS variants, incorporating regulatory information and population-wide LD calculations, along with your VEP results.
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Most of the time when we talk about variant annotation, we talk about the effects of variants on genes, but did you know that the VEP can also tell you how variants affect the genomic features that regulate gene expression, such as promoter and enhancers?
If a variant hits a splice site, you want to know if splicing is going to occur as normal, or if you can expect a different protein isoform. We have a few cool tools with the VEP that will help you to assess that for your own variants.
In this blog we catch up with Ensembl’s 2018 Google Summer of Code (GSoC) students and hear about their now completed projects, and their reflections on the experience. You may have already seen our previous blog post which we published as they were just beginning their projects. Read on to find out how they went, what they learnt and what valuable advice they can pass on to aspiring GSoC students.
A common use case for the VEP is as a first step towards identifying the causal genetic variant of a rare phenotype from whole genome/exome sequencing. The VEP tells you which genes are hit, what effects they have on them, and you have to begin the long laborious process of filtering those down. Things you might consider include allele frequency, association with genes known to be involved in rare disease and whether both genes in a diploid organism are affected. Rather than faffing about doing this manually, you can use the G2P (genotype to phenotype) plugin instead, which was recently published as a preprint.
Rating variants for their potential deleteriousness is vital for solving the link between genotypes and phenotypes. There are many different algorithms for predicting how likely it is that a human variant would affect the function of a protein, and in release 94 of Ensembl, we’ll be making more of these available.
We’re excited to be trying a new conference this year: the African Society of Human Genetics (AfSHG) conference in collaboration with H3Africa, in Kigali Rwanda, 19th-21st September. The conference is a fantastic opportunity for African scientists to showcase their work, build collaborations and learn more about their field of research. For us, it’s great to see what research is going on outside of our usual sphere, as well as to promote our free database and training to researchers who could benefit from it.
If you don’t want to analyse your variants on external servers or have more than 1000 or so to annotate, you probably want to use the VEP script. Setting it up might not always be straightforward as there are dependencies you need, but the installation script takes away a lot of the trouble.
It’s probably reasonable to assume that the coding sequence (CDS) of a protein-coding transcript model is the feature that is of primary interest to most people who use Ensembl. However, both the 5’ and 3’ untranslated regions (UTRs) are important biological entities in their own right, and it is vital that we in Ensembl do the best we can to represent them accurately. However, the annotation of these UTRs is complicated, so we’re going to focus on exploring the annotation process for 3’ UTRs in this article (Figure 1).
Ensembl produce high quality gene annotation for a number of species, but getting it to the high quality we expect takes time. This means there are many species and strains where we don’t have annotation yet. If you’re working with a species without Ensembl annotation (like Trixie the Triceratops here) or even a specific strain that we don’t have, you can still make use of VEP for predicting the effect of variants on genes and transcripts, using your own annotation. All you need is a GFF or GTF of the transcripts, and a FASTA file of the genome.