1 Estimating chromosomal copy number InCoB2007, Hong Kong, 30 August, 2007

2 Copy number variation (CNV) What is it? A form of human genetic variation: instead of 2 copies of each region of each chromosome (diploid), some people have amplifications or losses (> 1kb) in different regions –this doesn’t include translocations or inversions We all have such regions –the publicly available genome NA15510 has between 5 & 240 by various estimates –they are only rarely harmful (but rare things do happen)

3 Copy number variation Population genomics The genomes of two humans differ more in a structural sense than at the nucleotide level; a recent paper estimates that on average two of us differ by ~ Mb of genetic due to Copy Number Variation ~ 2.5 Mb due to Single Nucleotide Polymorphisms

4 Copy number variation As it relates to human disease Is responsible for a number of rare genetic conditions. For example, Down syndrome ( trisomy 21), Cri du chat syndrome (a partial deletion of 5p). Is implicated in complex diseases. For example:  CCL3L1 CN   HIV/AIDS susceptibility; also, some sporadic (non-inherited) CN variants are strongly associated with autism, while Tumors typically have a lot of chromosomal abnormalities, including recurrent CN changes.

5 Trisomy 21

6 Partial deletion of chr 5p

7 Large amplifications/losses can be seen by eye; smaller ones are hard to see

8 A cytogeneticist’s story “The story is about diagnosis of a 3 month old baby with macrocephaly and some heart problems. The doctors questioned a couple of syndromes which we tested for and found negative. Rather than continue this ‘shot in the dark’ approach, we put the case on an array and found a 2Mb deletion which notably deletes the gene NSD1 on chr 5, mutations in which are known to be cause Sotos syndrome. This is an overgrowth syndrome and fits with the macrocephaly. The bottom line is that we are able to diagnose quicker by this approach and delineate exactly the underlying genetic change.”

9 2Mb deletion Chromosome 5

10 NSD1

11 A lung cancer cell line vs matched normal lymphoblast, from Nannya et al Cancer Res 2005;65: Many tumors have gross CN changes

12 Research into gonad dysfunction: Human sex reversal 20% of 46,XY females have mutations in SRY 80% of 46,XY females unexplained! 90% of 46,XX males due to translocation SRY 10% of 46,XX males unexplained! Suggests loss of function and gain of function mutations in other genes may cause sex reversal. We’re looking at shared deletions.

13 Plan To introduce the Single Nucleotide Polymorphism (SNP) arrays, the probes, and the associated assays. Then I’ll discuss the first bioinformatic aspect of Copy Number (CN) analysis, what I call low-level analyses, then show one way of assessing the outcome. For simplicity I concentrate on Affymetrix arrays, called GeneChips , though similar considerations apply in whole or in part to some other array technologies, including Illumina.

14 Genomic DNA ATCGGTAGCCATTCATGAGTTACTA Perfect Match probe for Allele A ATCGGTAGCCATCCATGAGTTACTA Perfect Match probe for Allele B A SNP G TAGCCATCGGTA GTACTCAATGAT Affymetrix SNP chip terminology Genotyping: answering the question about the two copies of the chromosome on which the SNP is located: Is a sample AA (AA), AB (AG) or BB (GG) at this SNP?

Affymetrix GeneChip  1.28cm 6.4 million features/ chip 1.28cm 5 µ > 1 million identical 25 bp probes / feature * * * * * *

ng Genomic DNA RE Digestion Adaptor Ligation GeneChip ® Mapping Assay Overview Xba Fragmentation and Labeling PCR: One Primer Amplification Complexity Reduction AA BB AB Hyb & Wash

17 Principal low-level analysis steps Background adjustment and normalization at probe level These steps are to remove lab/operator/reagent effects Combining probe level summaries to probe set level summary: best done robustly, on many chips at once This is to remove probe affinity effects and discordant observations (gross errors/non-responding probes, etc) Possibly further rounds of normalization (probe set level) as lab/cohort/batch/other effects are frequently still visible Derive the relevant copy-number quantities Finally, quality assessment is an important low-level task.

18 AA TT AT Our preprocessing for total CN using SNP probe pairs (250K chip) Modification by H Bengtsson of a method due to A Wirapati developed some years ago for microsatellite genotyping; similar to the approach used by Illumina.

19 Background adjustment and normalization Outcome similar to that achieved by quantile normalization

20 Low-level analysis problems haven’t been solved once and for all; why? The feature size keeps  and so the # features/chip keeps  ; Fewer and fewer features are used for a given measurement, allowing more measurements to be made using a single chip These considerations all place more and more demands on the low-level analysis: to maintain the quality of existing measurements, and to obtain good new ones.

21 SNP probe tiling strategy TAGCCATCGGTA N SNP 0 position A / G GTACTCAATGAT* ATCGGTAGCCAT T ATCGGTAGCCAT C ATCGGTAGCCAT G ATCGGTAGCCAT A CATGAGTTACTA PM MM PM MM A A B B 0 Allele Central probe quartet

22 SNP probe tiling strategy, 2 TAGCCATCGGTA N SNP +4 Position A / G GTA C TCAATGATCAGCT* GTAGCCAT T GTAGCCAT C GTAGCCAT T CAT G AGTTACTAGTCG CAT C AGTTACTAGTCG CAT G AGTTACTAGTCG CAT C AGTTACTAGTCG PM MM PM MM A A B B +4 Allele +4 offset probe quartet

SNP probe tiling strategy, PM A MM A PM B MM B Central quartet Offset quartets This was repeated on the opposite strand giving 56 probes for the 10K chip. The 100K chip had 40 chosen from offsets and strands by performance. The 5.0 chip had 8 well chosen probes/SNP; no MMs. The current 6.0 chip has just 6: 3 replicates of a PMA and 3 of a PMB. Also, there are a large # of unreplicated non-polymorphic probes for CN inference.

24 What comes next? Using SNP chips to identify change in total copy number (i.e. CN ≠ 2) Outline a new method (CRMA) Evaluate and compare it with other methods Make some closing remarks on further issues

25 Copy-number estimation using Robust Multichip Analysis (CRMA) CRMA Preprocessing (probe signals) allelic crosstalk (or quantile) Total CNPM=PM A +PM B Summarization (SNP signals  ) log-additive PM only Post-processingfragment-length (GC-content) Raw total CNs R = Reference M ij = log 2 (  ij /  Rj ) chip i, probe j A few details are passed over. Ask me later if you care about them.

26 CRMA, 1 CRMA Preprocessing (probe signals) allelic crosstalk (or quantile) Total CNPM=PM A +PM B Summarization (SNP signals  ) log-additive PM only Postprocessingfragment-length (GC-content) Raw total CNs M ij = log 2 (  ij /  Rj ) Already briefly described.

27 CRMA, 2 CRMA Preprocessing (probe signals) allelic crosstalk (quantile) Total CNPM=PM A +PM B Summarization (SNPsignals  ) log-additive PM only Postprocessingfragment-length (GC-content) Raw total CNs M ij = log 2 (  ij /  Rj )  That’s it!

28 CRMA, 3 CRMA Preprocessing (probe signals) allelic crosstalk (quantile) Total CNsPM=PM A +PM B Summarization (SNP signals  ) log-additive PM only Postprocessingfragment-length (GC-content) Raw total CNs M ij = log 2 (  ij /  Rj ) log 2 (PM ijk ) = log 2  ij + log 2  jk +  ijk Fit using rlm

29 CRMA, 4a CRMA Preprocessing (probe signals) allelic crosstalk (quantile) Total CNPM=PM A +PM B Summarization (SNP signals  ) log-additive PM-only Postprocessingfragment-length (GC-content) Raw total CNs M ij = log 2 (  ij /  Rj ) 100K Longer fragments get less well amplified by PCR and so give weaker SNP signals

30 CRMA, 4b CRMA Preprocessing (probe signals) allelic crosstalk (quantile) Total CNPM=PM A +PM B Summarization (SNP signals  ) log-additive PM-only Postprocessingfragment-length (GC-content) Raw total CNs M ij = log 2 (  ij /  Rj ) 500K Longer fragments get less well amplified by PCR and so give weaker SNP signals

31 CRMA, 4c CRMA Preprocessing (probe signals) allelic crosstalk (quantile) Total CNPM=PM A +PM B Summarization (SNP signals  ) log-additive PM-only Postprocessingfragment-length (GC-content) Raw total CNs M ij = log 2 (  ij /  Rj ) 500K Longer fragments get less well amplified by PCR and so give weaker SNP signals

32 CRMA, 5 CRMA Preprocessing (probe signals) allelic crosstalk (quantile) Total CNPM=PM A +PM B Summarization (SNP signals  ) log-additive PM-only Postprocessingfragment-length (GC-content) Raw total CNs M ij = log 2 (  ij /  Rj ) Care required with the number and nature of Reference samples used

33 Summary comparison of 4 methods CRMAdChip (Li & Wong 2001) CNAG* (Nannya et al 2005) CNAT v4 (Affymetrix 2006) Preprocessing (probe signals) allelic crosstalk (quantile) quantilescalequantile Total CNPM=PM A +PM B PM=PM A +PM B MM=MM A +MM B PM=PM A +PM B “log-additive” PM-only Summarization (SNP signals  ) Log additive PM only Multiplicative PM-MM =A+B=A+B Post-processingfragment-length (GC-content ) fragment-length (GC-content) fragment-length (GC-content) Raw total CNs M ij = log 2 (  ij /  Rj )

34 Evaluation: how well can we differentiate between one and two copies? HapMap: Mapping 250K Nsp data 30 males and 29 females (no children; one bad data set) Chromosome X is known: Males (CN=1) & females (CN=2) 5,608 SNPs Classification rule: M ij < threshold  CN ij =1, otherwise CN ij =2. Number of calls: 59  5,608 = 330,872

35 Calling samples for SNP_A # males: 30 # females: 29 Call rule: If M i < threshold, a male Calling a male male: #True positives: 30 Calling a female male: #False positives : 5 TP rate: 30/30 = 100% FP rate: 5/29 = 17% M = log 2 (  /  R )

36 Receiver Operator Characteristic (ROC) increasing threshold FP rate TP rate

37 Single-SNP comparison: a random SNP TP rate FP rate

38 A non-differentiating SNP

39 Distribution (density) of TP rates when controlling for FP rate (5,608 SNPs) TP rate (correctly calling males male) FP rate: 1.0% (incorrectly calling females male) CNAT: 10% SNPs poor density

40 CRMA & dChip perform better for an average SNP (common threshold) Number of calls: 59  5,608 = 330,872 zoom in

41 Average across R SNPs non-overlapping windows threshold A false-positive (or real?!?)

42 Better detection rate when averaging (with risk of missing short regions) R=1 (no av) R=2 R=3 R=4

43 CRMA does a bit better than dChip CRMA dChip Control for FP rate: 1.0% CRMA:R=169.6% R=296.0% R=398.7% R=499.8% …

44 Comparing methods by “resolution” CRMA dChip CNAG* CNAT FP rate 1%

45 Several further bioinformatic issues Estimating copy number: needs calibration data Segmentation (of chromosomes into constant copy number regions): an HMM-like algorithm Analysing family CN data: a different HMM Incorporating non-polymorphic probes: independent HMM observations to be weighted and combined Dealing with mixed normal-abnormal samples Utilizing poor quality DNA samples Estimating allele-specific copy number ……and more

46 Some results using trios Data: one of seven trios, 250K, results from Jeremy Silver

47 Conclusions/comments Using chromosome X permits us to: –test how well a method detects deletions –compare methods –get a sense of resolution We plan to do further tests with known CN changes to see how well this generalizes We are working on some of the issues other mentioned There is room for contributions from you!

48 Available in aroma.affymetrix ("google it") “Infinite” number of arrays: 1- 1,000s Requirements: 1-2GB RAM Arrays: SNP, exon, expression, (tiling). Dynamic HTML reports Import/export to existing methods Open source: R Cross platform: Windows, Linux, Mac

49 Acknowledgements Henrik Bengtsson, UC Berkeley Andrew Sinclair & Howard Slater, MCRI Nusrat Rabbee, Genentech Simon Cawley, Francois Collin & Srinka Ghosh, Affymetrix Rafael Irizarry & Benilton Carvalho, Johns Hopkins Nancy Zhang, Stanford Jeremy Silver, WEHI

50 Thank you!