1. Whole Exome Sequencing for Variant Discovery and Prioritisation

2. First, a recap. What have we learned?
NGS platforms – short and long reads
What the data looks like
How to QC data
General procedures in processing data
How to find biological signal in data - RNA-Seq lectures + practical (in progress)
There’s a LOT more, but it’s not necessarily more complex or very different!

3. Exomes: Publication Trends
Total: 925 (Oct 2012)
2013: ~ 800 papers
2014: ~ 1200 papers
Forero DA, 2012

4. NGS Variation Discovery Workflow (resequencing based)

5. Variant Discovery Application: Disease
An equivalent of the genome would amount almost 2000 books, containing 1.5 million letters each (average books with 200 pages)!
This information is contained in any single cell of the body.

6. Monogenic Diseases Single mutation
How do we find it in all those ‘books’?
A bioinformatics challenge
NGS sequencers can only read small portions
So, the library is fragments of pages of the books!

7. Mendelian Disease Gene Discovery
Gilissen, Genome Biol 2011

8. Mendelian Disease Gene Discovery
Gilissen, Genome Biol 2011

9. Opportunities and Challenges
Enabling technologies: NGS machines, open-source algorithms, capture reagents, lowering cost, big sample collections
Exomes more cost effective: Sequence patient DNA and filter common SNPs; compare parents child trios; compare paired normal cancer
Challenges:
Still can’t interpret many Mendelian disorders
Rare variants need large samples sizes
Exome might miss region (e.g. novel non-coding genes)
Shendure, Genome Biol 2011

10. Why exome sequencing?
WGS still too costly & added value of intergenic mutations is low
WES: targeted sequencing of coding regions (~1% of human genome)
Mendelian disorders  disrupt protein-coding sequences (mostly)
Large fraction of rare non-synonymous variants in human genome are predicted to be deleterious
Splice sites also enriched for highly functional variation
The exome represents a highly enriched subset of the genome in which to search for variants with large effect sizes

11. A representation of the relationship between the size of the mutational target and the frequency of disease for disorders caused by de novo mutations
Gilissen, Genom Biol 2011

12. Majewski, J Med Genet 2011

13. Maximizing chances of finding disease-causing rare variants using exome sequencing
Bamshad, Nat Rev Genet 2011

14. Example: Comparative Sequencing
Somatic mutation detection between normal / cancer pairs
More mutation yield and better causal gene identification than Mendelian disorders
Meyerson et al, Nat Rev Genet 2010

15. BUT Exome Analysis for single patient can be informative
Perrault syndrome (HSD17B4)
Pierce, Am J Hum Genet 2010

16. Exome sequencing procedure

17. Read Mapping
Mapping hundreds of millions of reads the reference genome is CPU and RAM intensive, and ‘slow’
Read quality decreases with length (small single nucleotide mismatches or indels – real or artifact?)
Very few mappers appropriately deal with indels
Mapping output: SAM (BAM) or BED

18. Mapped Data: SAM specification
Generic sequence alignment format
Describes alignment of reads to a reference
Flexible - stores all the alignment information
Simple enough to be easily generated or converted from other existing alignment formats
Keeps track of chromosome position, alignment quality and alignment features (extended cigar)
Includes mate pair / paired end information
Original FASTQ data can be reproduced from SAM (and BAM)

19. SAM FIELDS

20. BAM format Binary version of SAM - more compact
Makes downstream analysis independent from the mapping program
Allows most of operations on alignment to work on a stream without loading the whole alignment into memory
Allows the file to be indexed by genomic position to efficiently retrieve all reads aligning to a locus

21. VCF format Emerging standard for storing variant data
Originally designed for SNPs and short INDELs, it also works for structural variations
Consists of header and data sections
The data section is TAB delimited with each line consisting of at least 8 mandatory fields

22. VCF FIELDS

24. Variant filtering

25. Variant Prioritization
Heuristic filtering to identify novel genes for Mendelian disorders
Stitziel et al, Genome Biol 2011

26. More than just SNVs and ‘short’ indels

27. Structural Variation
BreakDancer Chen et al, Nat Meth 2009 Only looks at anomalous read pairs

28. Copy Number Variation Detection
Change in read coverage

29. Example WES-based variant discovery workflow
Map the reads to a reference genome
index the reference genome
Map (BWA, BOWTIE, NOVOAOLIGN, ETC)
Sort BAM file
Remove PCR duplicates
Realign around indels (‘optional’)
Call variants
Recalibrate quality scores (‘optional’)
Filter variants
Basic variant annotation
Biological interpretation only starts here