In any of the sequence views shown in the Gene and Transcript tabs, you can view variants on the sequence. You can do this by clicking on Configure this page from any of these views.

Let’s take a look at the Gene sequence view for HBB in human. Search for HBB and go to the Sequence view.

If you can’t see variants marked on this view, click on Configure this page and select Show variants: Yes and show links. You may also wish to add a filter to the variants to allow them to load more quickly, we’ll add Filter variants by evidence status: 1000Genomes.

Find out more about a variant by clicking on it.

You can add variants to all other sequence views in the same way.

You can go to the Variation tab by clicking on the variant ID. For now, we’ll explore more ways of finding variants.

To view all the sequence variants in table form, click the Variant table link at the left of the gene tab.

You can filter the table to only show the variants you’re interested in. For example, click on Consequences: All, then select the variant consequences you’re interested in. For display purposes, the table above has already been filtered to only show missense variants.

You can also filter by the different pathogenicity scores and MAF, or click on Filter other columns for filtering by other columns such as Evidence or Class.

The table contains lots of information about the variants. You can click on the IDs here to go to the Variation tab too.

You can also see the phenotypes associated with a gene. Click on Phenotype in the left hand menu.

Open the transcript table and go to HBB-201 ENST00000335295, then click on Haplotypes in the left hand menu.

The Haplotypes view in the transcript tab shows you the actual protein and CDS sequences in 1000 Genomes individuals. This is possible because the 1000 Genomes study has phased genotypes, so we know which alleles occur on which of the chromosome pairs. The table lists all the versions of the protein that occur along with their frequencies, including the reference sequence and sequences with one or more alternative alleles.

Click on one of the haplotypes, we’ll go for 18K>*,​19del{130}, to find out more about it. Here you will see the frequency in the 1000 Genomes subpopulations, the sequence and the 1000 Genomes individuals where this protein is found.

Let’s have a look at variants in the Location tab. Click on the Location tab in the top bar.

Configure this page and open Variation from the left-hand menu.

There are various options for turning on variants. You can turn on variants by source, by frequency, presence of a phenotype or by individual genome they were isolated from. You can also turn on genotyping chips.

Let’s have a look at a specific variant. If we zoomed in we could see the variant rs334 in this region, however it’s easier to find if we put rs334 into the search box. Click through to open the Variation tab.

The icons show you what information is available for this variant. Click on Genes and regulation, or follow the link on the left.

This page illustrates the genes the variant falls within and the consequences on those genes, including pathogenicity predictors. It also shows data from GTEx on genes that have increased/decreased expression in individuals with this variant, in different tissues. Finally, regulatory features and motifs that the variant falls within are shown.

We can also see the variant in the protein structure by clicking on 3D Protein model.

This is a LiteMol viewer, where you can rotate and zoom in on the structure. The variant location is highlighted, so you can see where it lands within the structure.

Let’s look at population genetics. Click on Population genetics in the left-hand menu.

The population allele frequencies are shown by study, including 1000 Genomes and gnomAD. Where genotype frequencies are available, these are shown in the tables.

There are big differences in allele frequencies between populations. Let’s have a look at the phenotypes associated with this variant to see if they are known to be specific to certain human populations. Click on Phenotype Data in the left-hand menu.

This variant is associated with various phenotypes, including sickle cell and malaria resistance. These phenotype associations come from sources including the GWAS catalog, ClinVar, Orphanet and OMIM. Where available, there are links to the original paper that made the association, the allele that is associated with the phenotype and p-values and other statistics.

The SNP rs1738074 in the 5’ UTR of the human TAGAP gene has been identified as a genetic risk factor for a few diseases. Use Ensembl to answer the following questions:

1. In which transcripts is this SNP found?

2. What is the least frequent genotype for this SNP in the Yoruba (YRI) population from the 1000 Genomes phase 3?

3. What is the ancestral allele? Is it conserved in the 91 eutherian mammals EPO-Extended?

4. With which diseases is this SNP associated? Are there any known risk (or associated) alleles?

The missense variation rs1801133 in the human MTHFR gene has been linked to elevated levels of homocysteine, an amino acid whose plasma concentration seems to be associated with the risk of cardiovascular diseases, neural tube defects, and loss of cognitive function.

1. Find the page with information for rs1801133.

2. Is rs1801133 a missense variant in all transcripts of the MTHFR gene? What is the amino acid change?

3. Why are the alleles for this variation in Ensembl given as G/A and not as C/T, as in the literature?

4. What is the major allele of rs1801133 in different populations?

5. In which paper(s) is the association between rs1801133 and homocysteine levels described?

6. According to the data imported from dbSNP, the ancestral allele for rs1801133 is G. Ancestral alleles in dbSNP are based on a comparison between human and chimp. Does the sequence at this same position in other primates confirm that the ancestral allele is G?

Variable number tandem repeats (VNTRs) show high variation in the number of repeats in the population and are commonly used in forensics (DNA fingerprinting) and to study genetic diversity. (a) Go to the region from 3074666 to 3075100 bp on human chromosome 4. Which gene does it overlap? Which exon of this gene falls in this region?

(b) Configure this page to turn on Repeats (low), Simple repeats (Repeats (low)) and Tandem repeats (TRF) tracks in this view. Can you see any repeats in this exon? What tools were used to annotate the repeats according to the track information?

(c) Zoom in on the (CAG)n to see its sequence. How many CAG repeats can you see in the human reference assembly? Does this track overlap any phenotype-associated variants? What is the identifier of this variant?

(d) Go to the variant tab of the phenotype-associated variant. What is the consequence ontology of this variant? Does the reference allele match the number of repeats you have just counted? What is the shortest and longest allele?

We have identified five variants on human chromosome nine, C-> A at 128203516, an A deletion at 128328461, C->A at 128322349, C->G at 128323079 and G->A at 128322917.

We will use the Ensembl VEP to determine:

• Have my variants already been annotated in Ensembl?
• What genes are affected by my variants?
• Do any of my variants affect gene regulation?

Go to the front page of Ensembl and click on the Variant Effect Predictor.

This page contains information about the VEP, including links to download the script version of the tool. Click on Launch VEP to open the input form:

The data is in VCF format:
chromosome coordinate id reference alternative

Put the following into the Paste data box:
9 128328460 var1 TA T
9 128322349 var2 C A
9 128323079 var3 C G
9 128322917 var4 G A
9 128203516 var5 C A

The VEP will automatically detect that the data is in VCF.

There are further options that you can choose for your output. These are categorised as Identifiers, Variants and frequency data, Additional annotations, Predictions, Filtering options and Advanced options. Let’s open all the menus and take a look.

Hover over the options to see definitions.

We’re going to select some options:

• HGVS, annotation of variants in terms of the transcripts and proteins they affect, commonly-used by the clinical community
• Phenotypes
• Protein domains

When you’ve selected everything you need, scroll right to the bottom and click Run.

The display will show you the status of your job. It will say Queued, then automatically switch to Done when the job is done, you do not need to refresh the page. You can edit or discard your job at this time. If you have submitted multiple jobs, they will all appear here.

Click View results once your job is done.

In your results you will see a graphical summary of your data, as well as a table of your results.

The results table is enormous and detailed, so we’re going to go through the it by section. The first column is Uploaded variant. If your input data contains IDs, like ours does, the ID is listed here. If your input data is only loci, this column will contain the locus and alleles of the variant. You’ll notice that the variants are not neccessarily in the order they were in in your input. You’ll also see that there are multiple lines in the table for each variant, with each line representing one transcript or other feature the variant affects.

You can mouse over any column name to get a definition of what is shown.

The next few columns give the information about the feature the variant affects, including the consequence. Where the feature is a transcript, you will see the gene symbol and stable ID and the transcript stable ID and biotype. Where the feature is a regulatory feature, you will get the stable ID and type. For a transcription factor binding motif (labelled as a MotifFeature) you will see just the ID. Most of the IDs are links to take you to the gene, transcript or regulatory feature homepage.

This is followed by details on the effects on transcripts, including the position of the variant in terms of the exon number, cDNA, CDS and protein, the amino acid and codon change, transcript flags, such as MANE, on the transcript, which can be used to choose a single transcript for variant reporting, and pathogenicity scores. The pathogenicity scores are shown as numbers with coloured highlights to indicate the prediction, and you can mouse-over the scores to get the prediction in words. Two options that we selected in the input form are the HGVS notation, which is shown to the left of the image below and can be used for reporting, and the Domains to the right. The Domains list the proteins domains found, and where there is available, provide a link to the 3D protein model which will launch a LiteMol viewer, highlighting the variant position.

Where the variant is known, the ID of the existing variant is listed, with a link out to the variant homepage. In this example, only rsIDs from dbSNP are shown, but sometimes you will see IDs from other sources such as COSMIC. The VEP also looks up the variant in the Ensembl database and pulls back the allele frequency (AF in the table), which will give you the 1000 Genomes Global Allele Frequency. In our query, we have not selected allele frequencies from the continental 1000 Genomes populations or from gnomAD, but these could also be shown here. We can also see ClinVar clinical significance and the phenotypes associated with known variants or with the genes affected by the variants, with the variant ID listed for variant associations and the gene ID listed for gene associations, along with the source of the association.

For variants that affect transcription factor binding motifs, there are columns that show the effect on motifs (you may need to click on Show/hide columns at the top left of the table to display these). Here you can see the position of the variant in the motif, if the change increases or decreases the binding affinity of the motif and the transcription factors that bind the motif.

Above the table is the Filter option, which allows you to filter by any column in the table. You can select a column from the drop-down, then a logic option from the next drop-down, then type in your filter to the following box. We’ll try a filter of Consequence, followed by is then missense_variant, which will give us only variants that change the amino acid sequence of the protein. You’ll notice that as you type missense_variant, the VEP will make suggestions for an autocomplete.

You can export your VEP results in various formats, including VCF. When you export as VCF, you’ll get all the VEP annotation listed under CSQ in the INFO column. After filtering your data, you’ll see that you have the option to export only the filtered data. You can also drop all the genes you’ve found into the Gene BioMart, or all the known variants into the Variation BioMart to export further information about them.

Resequencing of the genomic region of the human CFTR (cystic fibrosis transmembrane conductance regulator (ATP-binding cassette sub-family C, member 7) gene (ENSG00000001626) has revealed the following variants. The alleles defined in the forward strand:

• G/A at 7: 117,530,985
• T/C at 7: 117,531,038
• T/C at 7: 117,531,068

Use the VEP tool in Ensembl and choose the options to see SIFT and PolyPhen predictions. Do these variants result in a change in the proteins encoded by any of the Ensembl genes? Which gene? Have the variants already been found?

7	117530985	117530985	G/A
7	117531038	117531038	T/C  
7	117531068	117531068	T/C

We have details of a genomic deletion in a breast cancer sample in VCF format:

13 32307062 sv1 . <DEL> . . SVTYPE=DEL;END=32908738

Use VEP in Ensembl to find out the following information:

1.  How many genes have been affected?

2.  Does the structural variant (SV) cause deletion of any complete transcripts?

3.  Map your variant in the Ensembl browser on the Region in detail view.