Whole Exome Sequencing for Variant Discovery and Prioritisation

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 (resequencingbased)

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, GenomBiol 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 Perraultsyndrome (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