BST227 Introduction to Statistical Genetics. Lecture 8: Variant calling from high-throughput sequencing data

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1 BST227 Introduction to Statistical Genetics Lecture 8: Variant calling from high-throughput sequencing data 1

2 PC recap

3 typical genome Differs from the reference genome at 4-5 million sites ~85% SNPs ~15% Indels <0.1% Structural variants ~20 million bp affected Includes ~1000 large deletions ~150 copy number variants ~10 inversions global reference for human genetic variation, The 1000 Genomes Project Consortium, Nature (2015)

4 Pie area indicates the number of polymorphisms within populations

5 Decline in genome sequencing costs First generation sequencers Second (next) generation sequencers Wetterstrand K. DN Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) vailable at: ccessed Nov 14, 2017

6 Short reads: ~50-300bp per read Illumina sequencing

7 Illumina sequencing Illumina

8 DN adapters 1. Prepare genomic DN 2. ttach DN to surface 3. Bridge amplification 4. Fragments become double stranded 5. Denature the doublestranded molecules 6. Complete amplification Randomly fragment genomic DN and ligate adapters to both ends of the fragments pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

9 adapter DN fragment 1. Prepare genomic DN adapter dense lawn of primers 2. ttach DN to surface 3. Bridge amplification 4. Fragments become double stranded 5. Denature the doublestranded molecules 6. Complete amplification pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

10 1. Prepare genomic DN 2. ttach DN to surface 3. Bridge amplification 4. Fragments become double stranded 5. Denature the doublestranded molecules 6. Complete amplification pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

11 1. Prepare genomic DN 2. ttach DN to surface ttached terminus free terminus ttached terminus 3. Bridge amplification 4. Fragments become double stranded 5. Denature the doublestranded molecules 6. Complete amplification pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

12 ttached ttached 1. Prepare genomic DN 2. ttach DN to surface 3. Bridge amplification 4. Fragments become double stranded 5. Denature the doublestranded molecules 6. Complete amplification Denaturation leaves singlestranded templates anchored to the substrate pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

13 1. Prepare genomic DN 2. ttach DN to surface 3. Bridge amplification 4. Fragments become double stranded Clusters 5. Denature the doublestranded molecules 6. Complete amplification Several million dense clusters of double-stranded DN are generated in each channel of the flow cell pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

14 7. Determine first base 8. Image first base 9. Determine second base 10. Image second chemistry cycle 11. Sequencing over multiple chemistry cycles Laser 12. lign data The first sequencing cycle begins by adding four labeled reversible terminators, primers, and DN polymerase pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

15 7. Determine first base 8. Image first base 9. Determine second base 10. Image second chemistry cycle 11. Sequencing over multiple chemistry cycles 12. lign data fter laser excitation, the emitted fluorescence from each cluster is captured and the first base is identified pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

16 7. Determine first base 8. Image first base 9. Determine second base 10. Image second chemistry cycle 11. Sequencing over multiple chemistry cycles Laser 12. lign data The next cycle repeats the incorporation of four labeled reversible terminators, primers, and DN polymerase pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

17 7. Determine first base 8. Image first base 9. Determine second base 10. Image second chemistry cycle 11. Sequencing over multiple chemistry cycles 12. lign data fter laser excitation the image is captured as before, and the identity of the second base is recorded. pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

18 7. Determine first base 8. Image first base 9. Determine second base 10. Image second chemistry cycle 11. Sequencing over multiple chemistry cycles 12. lign data The sequencing cycles are repeated to determine the sequence of bases in a fragment, one base at a time. pevsnerlab.kennedykrieger.org/wiley/3e/chapter9/bfg_chapter09_ngs_v04.pptx

19 FSTQ format Raw sequencing reads: Basecalls and quality scores 4 lines per read Read ID Sequence Base quality } GCCTTGGCCTCCCGCTGCTGGGTT + } TGTCTGTCCTCTCTGGTCTTGCCGT + FFFFJJFJJJJJJJJJJJJJJJJJJJ Read 1 Read 2

20 FSTQ quality scores The FSTQ format uses Phred quality scores: a logarithmic function of base calling error (Pe) Q PHRED = 10 log 10 (P e )

21 FSTQ quality scores Stored as single SCII characters: GCCTTGGCCTCCCGCTGCTGGGTT + #FFFJJJJJJFJJJJJJJJJJJJJJJJ

22 ssembly vs lignment If no reference genome exists, assembly is required For most well-studied organisms, especially humans, high quality references do exist and we can instead align reads to the reference genome The current human reference is Genome Reference Consortium Human Build 38 (GRCh38) Simplified genome assembly Step 1 Step 2

23 Mapping/ligning reads to the reference genome Reference genome sequence Depth 100 bp Unknown sequence 100 bp Source: Wikimedia, file:mapping Reads.png

24 Visualizing reads aligned to a reference genome Non-reference variant Non-reference variant Integrative Genomics Viewer (IGV)

25 How to map reads to a reference genome? Comparing every read with every position in the genome is problematic: Very slow Won t allow for even slight mismatches Instead, we create an index of the reference genome sequence Index lookups are much faster than whole text search n example index:

26 SM/BM SM: Sequence lignment/map format. plain text file that contains reads, mapping positions, quality scores Read ID Chromosome Position (bp) Mapping quality BM: binary version of SM

27 Causes for mismatches between reads and the reference real variant Sequencing library generation error (i.e. error in amplifying original genomic DN) Base call error Mapping error (i.e. the read belongs somewhere else in the reference genome) Reference genome sequence error

28 How do we call SNPs? Heuristic methods based on hard thresholds: Reference T T T G= G=T G=?

29 SNP calling: Probabilistic methods We want to estimate the probability of each genotype, G, given the read data, D. P (G D) In this case: D =[T ] Let s consider: P (G = D) P (G = T D) (The other genotypes are very unlikely) T G=?

30 SNP calling: Probabilistic methods Use Bayes rule: Prior probability of genotype (e.g. use HWE) P (G D) = P (G, D) P (D) = P (D G)P (G) P (D) = P (D G)P (G) P i P (D G i)p (G i ) First assume no errors in reads: So: P (D G = ) =0 P (D G = T )= D =[T ] P (G = T D) = P (D G = T ) P (D G = T )+P (D G = ) =1 T G=?

31 SNP calling: Probabilistic methods Now let s allow for 1% sequencing error The probability of observing a given base is: Probability that base was present in the read and sequenced correctly plus the probability that another base was present but sequenced incorrectly. D =[T ] T G=?

32 SNP calling: Probabilistic methods Now let s allow for 1% sequencing error P (D G = ) = s 1 T P (D G = T )=5 ( ) 4 ( ) 0.16 So: P(real base is ) P (G = T D) P(correctly called) P(real base is T) =0.76 P(incorrectly called) D =[T ] T G=?

33 Importance of base quality scores Recall, FSTQ format: Pe=0.63 GCCTTGGCCTCCCGCTGCTGGGTTCGGCGTG + F#FJJJJJJFJJJJJJJJJJJJJJJJJJJJJJJJJ Proprietary algorithms

34 Base Quality Score Recalibration Empirically adjust base quality scores based on actual observed error rates ssume non-reference bases in reads that are not present in dbsnp are errors Stratify by base position in read. Calculate adjustment as: - verage reported phred score, minus - verage empirical phred score lso stratify by other covariates: reference base, neighbor bases, reported quality score,

35 Calling structural variants e.g. Deletions: 4 types of evidence Denovo Read pairs too far apart SV class ssembly assembly Read pair Read depth Shallow read Split reads Split end Ref mapping Deletion Reads Contig ssemble lkan et al., Genome structural variation discovery and genotyping. Nat Rev Genet. May;12(5): (2011)

36 Variant Call Format (VCF) files ~100,000 rows per exome ~4 million rows per whole genome Used to generate per variant genotype counts for association testing

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