Copy Number Varia/on Detec/on. Alex Mawla UCD Genome Center Bioinforma5cs Core Tuesday June 16, 2015

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1 Copy Number Varia/on Detec/on Alex Mawla UCD Genome Center Bioinforma5cs Core Tuesday June 16, 2015

2 Today s Goals Understand the applica5on and capabili5es of using targe5ng sequencing and CNV calling in a clinical or research sekng In today s exercise, you will: Align raw FASTQ reads using Bow5e2 Generate depth of coverage files from BAM files using targetdepth (Samtools depth wrapper) Learn to set parameters and work with input files (bait probe, regions of interest) for the targeted CNV calling tool, PanelDoC Run PanelDoC using R Script on a subset of data and interpret results 2

3 What are CNVs? Copy Number Varia5ons are altera5ons in the genome that result in either normal or abnormal varia5ons in the number of copies of a gene or region Field has been limited in focusing on exome regions of interest, but with decreases in whole genome sequencing cost, non- coding copy number varia5ons are star5ng to be considered 3

4 What are CNVs? (cont d) Structural varia5ons (unbalanced) Duplica5on (gain) Dele5on (loss) AB CDE FG Dele5on AB FG Duplica5on AB CDE CDE FG 4

5 Mechanisms Different causes: Homologous recombina5on can result in tandem repe55on and dele5on of a gene Non- homologous repair can result in dele5ons Non Allelic Homologous Recombination The Fast Car The Brown Rat The Fast Car The Brown Rat X Homologous Recombination at Incorrect Locus The Fast Car The Fast BrownCar Rat The Brown Rat Tandem Duplication The Brown Rat Deletion Non homologous Repair (e.g. NHEJ) Horses Eat Oats Cats Chase Mice DNA break Horses Eat Oats Cats Chase Mice deleted after repair Horses Eat Mice Deletion 5

6 Consequences Consequences include: dosage effect, gain of func5on, or loss of func5on Dosage Effect Loss Protein B 2X Protein B New Alleles Gain of Function Protein A Fusion Protein Protein 6

7 Why care about CNVs? CNVs have been iden5fied as causes to many human diseases: Au5sm (Marshall et al, 2012) Cancer (Walsh et al, 2010) Crohn s Disease (Schaschl et al, 2009) Down Syndrome (Ramachandran et al, 2014) HIV suscep5bility (Hollox et al, 2014) Idiopathic Learning Disability (Morrow et al, 2010) Lupus (Chen et al, 2014) Schizophrenia (Suleyman et al, 2015) 7

8 Why care about CNVs? (cont d) Ex: GSTM1 gene: involved in coding for glutathione What happens during a null dele5on (0 copies)? Individuals with low number of copies are more suscep5ble to certain cancers, but also have a beder clinical outcome (Mahimkar et al., 2012) Ex: CCL3L1 gene: codes protein that binds to the CCR5 binding site on immune cells Individuals with higher number of copies are less suscep5ble to HIV infec5on (Urban et al., 2009) 8

9 Different Sequencing Methods Whole Genome Sequencing Most expensive Sequences coding and non- coding regions of the genome Less used in clinical and research sekngs, but now emerging Exome Sequencing Moderately expensive Sequences all coding regions of the genome 180,000 exons 1% of en5re human genome Oien used in clinical and research sekngs Targeted Sequencing Cheapest Sequences specific regions of interest, usually within exomes Growing amount of use in clinical and research sekngs 9

10 What is Targeted Sequencing? Target Sequencing is the sequencing of specific regions of the genome, usually within the exome, to specifically look at certain areas of interest Magnitudes cheaper than exome or whole genome sequencing Two primary methods: Bait- probe hybridiza5on capture Uses overlapping modified bait probe sequences to capture specific genes of interest Amplicon- based Uses primers 10

11 Bait Probe Hybridiza5on Capture Modified bait probes designed to capture specific regions of interest from the cdna library Usually designed to 5le to ensure strong capture of all nucleo5des in region of interest crna baits (3x 5ling) Area of Interest Repeat 11

12 Different CNV Tools Whole Genome Sequencing Tools SegSeq cnvhmm CNVnator Exome Sequencing Tools CoNIFER XHMM ExomeCNV Targeted Sequencing Tools Svseq SVDetect PanelDoC Reference: Zhao et al,

13 Why PanelDoC? Designed to work well with bait- probe hybridiza5on capture Works with exome sequencing as well Can be used on blood samples, or tumor popula5ons Sensi5ve to small CNVs (31 bp) Very good with larger CNV (>1 kb) detec5on Verified against breast cancer study data Future updates: Complementary hidden markov model will be added to improve robustness Func5onality to handle amplicon- based capture will also be added 13

14 PanelDoC Background Developed by Dr. Alex Nord at University of Washington in 2011 (Nord et al, 2011) Verified by evalua5ng 94 pa5ents for CNVs related to breast and ovarian cancer Correctly iden5fied all known muta5ons Iden5fied 10 CNVs (5 gains and 5 losses) in four cancer- related genes Confirmed a 31 bp homozygous dele5on in BRCA2 Detected 200 bp CNV with 87% sensi5vity and 100 bp CNV with 80% sensi5vity at high signal- to- noise ra5os 14

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''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''',,,,.,,++,+..,-..,--+-., ,.,..+-+,,+.+.,-.-,+.+,+..+,--.+,,.-,+.,.-,.+''''''' ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''',,,,.,,++,+..,-..,--+-., ,.,..+-+,,+.+.,-.-,+.+,+..+,--.+,,.-,+.,.-,.+' Methods Pre- PanelDoC Map reads to genome Generate coverage Normalize coverage Sample specific effect GC content bias Bait probe 5ling Call CNVs with sliding window approach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

16 Methods (cont d) Bait- probe hybridiza5on capture used to capture regions of interest at minimum of 50x- 100x depth 16

17 Methods (cont d) Reads can be mapped with aligners such as: BWA Bow5e/Bow5e2 Coverage files can be generated using a tool in the Samtools suite: Samtools depth targetdepth is a wrapper that will automate this process for each region of interest 17

18 PanelDoC Methods Raw depth of coverage is noisy Raw Coverage Example (chr17: ) Coverage Large varia5ons in coverage Processing steps necessary for CNV analysis 1. Coverage normaliza5on & correc5on 2. Analyze rela5ve coverage vs. expected diploid coverage Gaps where repeats are present 18

19 PanelDoC Methods (cont d) Raw coverage is normalized against median coverage per base posi5on across all samples Raw coverage counts from alignment Normaliza5on to median coverage 19

20 PanelDoC Methods (cont d) Normalized coverage is corrected for GC content bias and bait probe 5ling 20

21 PanelDoc Methods (cont d) Bait Probe Tiling Normaliza5on: Overlap will lead to skewed depth of coverage values crna baits (3x 5ling) Area of Interest Repeat 21

22 PanelDoc Methods (cont d) GC content and capture bias correc5on Raw coverage counts from alignment Normaliza5on to median coverage Correc5on for GC and capture bias 22

23 PanelDoc Methods (cont d) Raw coverage counts from alignment Normaliza5on to median coverage Correc5on for GC content and capture bias Rela5ve ra5o calcula5on and QC 23

24 PanelDoc Methods (cont d) CNV called using sliding window algorithm CNV calling 24

25 PanelDoC Applica5on: Breast Cancer 25

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''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''',,,,.,,++,+..,-..,--+-., ,.,..+-+,,+.+.,-.-,+.+,+..+,--.+,,.-,+.,.-,.+''''''' ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''',,,,.,,++,+..,-..,--+-., ,.,..+-+,,+.+.,-.-,+.+,+..+,--.+,,.-,+.,.-,.+' Methods: Review Pre- PanelDoC Map reads to genome Generate coverage Normalize coverage Sample specific effect GC content bias Bait probe 5ling Call CNVs with sliding window approach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

27 Exercise Goals In today s exercise, you will: Align raw FASTQ reads using Bow5e2 Generate depth of coverage from BAM files using targetdepth Learn to set parameters and work with input files (bait probe, regions of interest) in PanelDoC Run PanelDoC through R Script on a subset of data and interpret results 27

28 Acknowledgements Dr. Alex Nord UCD Genome Center Bioinforma5cs Core Team Dr. Monica Bridon Dr. Joseph Fass Dr. Nikhil Joshi Dr. Ian Korf 28

29 Ques5ons? 29

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