Considerations for Analyzing Targeted NGS Data HLA

1. Considerations for Analyzing Targeted NGS DataHLA Tim Hague, CTO

2. Introduction • Human leukocyte antigen (HLA) is the major histocompatibility complex (MHC) in humans. • Group of genes ('superregion') on chromosome 6 • Essentially encodes cell-surface antigen-presenting proteins.

3. Functions HLA genes have functions in: • combating infectious diseases • graft/transplant rejection • autoimmunity • cancer

4. Alleles • Large number of alleles (and proteins). • Many alleles are already known. The number of known alleles is increasing

5. HLA Class I Gene A B C Alleles 2013 2605 1551 Proteins 1448 1988 1119 HLA Class II Gene DRA DRB* DQA1 DQB1 DPA1 DPB1 Alleles 7 1260 47 176 34 155 Proteins 2 901 29 126 17 134 HLA Class II - DRB Alleles Gene DRB1 DRB3 DRB4 DRB5 Alleles 1159 58 15 20 Proteins 860 46 8 17

6. Analysis Challenges HLA genes have specific analysis challenges regardless of the sequencing technology.

7. High Polymorphism High rate of polymorphism – up to 100 times the average human mutation rate. • The HLA-DRB1 and HLA-B loci have the highest sequence variation rate within the human genome. • High degree of heterozygosity – homozygotes are the exception in this region.

9. Duplications • High level of segmental duplications • Lots of similar genes and lots of very similar pseudegenes. • Duplicated segments can be more similar to each other within an individual than they are similar to the corresponding segments of the reference genome.

11. Complex Genetics • Particularly HLA-DRB* • The DR β-chain is encoded by 4 loci, however only no more than 3 functional loci are present in a single individual, and only a maximum of 2 per chromosome.

13. Mitigating Factors It's not all bad news: • Many HLA alleles are already well known – both in terms of sequence and frequencies within the population. • The HLA region is fairly small so there a high degree of linkage disequilibrium, and therefore lots of known haplotypes.

14. Traditional Typing • SSO – low resolution, high throughput, cheap • SSP – very fast results, low resolution • SBT – sequence-based typing, high resolution, usually done by Sanger sequencing.

15. NGS Typing High resolution, an alternative to Sanger-based SBT Why is it needed?

16. Sanger and HLA • Sanger data is still the gold standard in the genomic sequencing industry, even though it is very expensive compared to NGS. • 1 in 1'000 base error rate, if forward and reverse typing are done, error rate drops to 1 in 1'000'000. So why is it bad for HLA?

17. Phase Resolution • 2x chromosome 6 • Many loci, many alleles • Lots of heterozygosity

18. Allele Phasing problem reference sequence G/T consensus sequence T/ A OR??? Allele 1 Allele 2 T A T Allele 1 A Allele 2

19. The Problem with Sanger • There is only one signal • High degree of heterozygosity = high degree of ambiguity • Requires statistical techniques based on known allele frequencies, plus manual intervention by trained operators • Ambiguity can only be resolved statistically, which can lead to wrong assignment for rare types

21. HLA typing by Sanger method GGACSGGRASACACGGAAWGTGAAGGCCCACTCACAGACTSACCGAGYGRACCTGGGGACCCTGCGCGGCTACTACAACCAGAGCGAGGMCGGT Number of potential alleles

22. NGS Advantages • Can reduce ambiguity • Phase resolution - two signals, but lots of short reads • Cheaper and faster than Sanger • Less manual intervention required

23. NGS Data - Unphased

24. NGS Data - Phased

25. NGS Approaches • HLA*IMP – chip based imputation engine • Reference-based alignment, followed by a HLA call based on the variants detected during alignment • Search against database of known alleles

26. NGS Reference-based • Fraught with difficulties • Very hard to align reads to this region • The variant/HLA call is only as good as the alignment • No coverage = no call Has been attempted by Broad Institute (HLA Caller) and Roche

27. Alignment Efforts RainDance provide a targeted HLA amplification kit call HLAseq. Target: the whole MHC superregion (except for some tandem repeat regions) Goal: align this data, before doing variant/HLA call.

28. Diverse variant “density” in the MHC superregion Based on a single sample

29. Default BWA alignment – No coverage at an exon of HLA-DMB

30. Low coverage and orphaned reads at a HLA-DRB1 exon

31. BWA vs more permissive alignment: higher coverage = higher noise

32. Large targeted region without usable coverage

33. NGS Reference-based Not providing enough coverage everywhere What about de novo?

34. De novo assembly (MIRA) 287 contigs (longest contig: 2199 bp) Mean contig size: 268 bp Median contig size: 209 bp Total consensus: 77084 bp RainDance target: ~ 3800000 bp

35. De novo assembly (MIRA)

36. NGS De Novo Alignment Not enough contigs produced, not enough coverage of the target region. What about a hybrid approach?

37. De novo assembly with “backbone” First, alignment to backbone, then de novo assembly Backbone: 2220 contigs from HG19 chr 6 (sum: 3554852 bps) → almost whole RainDance target Results: Max reads / backbone contig: 197 Max coverage: 71

38. De novo assembly with “backbone”

39. NGS Typing - Alignment Based We tried: • Burrows Wheeler aligner • More sensitive, seed and extend aligner • De novo aligner • 'Hybrid' de novo aligner • The variant/HLA call is only as good as the alignment • The alignments were not good enough

40. NGS Database Based • Search against 'database' of known alleles • Such as IMGT/HLA database, available from EBI web site Stanford, Connexio, JSI Medical, BC Cancer Agency and Omixon have all tried this approach.

42. DB Based Approach Advantages • Less mapping headaches • Unambiguous results • Potential to be fast Difficulties • Novel allele detection • Homozygous alleles

45. Results with Exome data

46. Exon level detail

47. Detailed results - short read pileup

48. Conclusions • DB based approach to HLA typing is new but very promising • NGS approaches can resolve much of the ambiguity of Sanger SBT • DB based approach can also overcome the limitations of NGS reference-based alignment

49. Conclusions Available DB based HLA typing tools differ in: • Speed • Sequencers supported • Types of sequencing data supported (targeted, exome, whole genome) • Ease of use • Ambiguity of results • Degree of manual intervention required • Novel allele detection capabilities