Novel mutation detection in large sample pools using Population Calling
•
1 like•1,358 views
This document describes different methods for genotype calling in large population studies, including single sample calling, batch calling, and joint/population calling. It presents a case study comparing the Genalice population calling module to traditional stepwise calling using BWA/GATK on a dataset of 3,000 rice genomes. The population calling approach was over 30 times faster than stepwise calling and scaled linearly with sample size. While it produced fewer total variants than BWA/GATK, over 80% of shared sites had identical genotypes between the two methods. The document concludes that population calling is an efficient approach for large datasets but that more analysis is needed to understand differences from other methods.
Recommended
Recommended
More Related Content
What's hot
What's hot (14)
Similar to Novel mutation detection in large sample pools using Population Calling
Similar to Novel mutation detection in large sample pools using Population Calling (20)
Recently uploaded
Recently uploaded (20)
Novel mutation detection in large sample pools using Population Calling
- 1. Population Calling a powerful tool for novel mutation detection in larger sample pools January 12, 2016 Antoine Janssen antoine.janssen@keygene.com June 2015/ Antoine JanssenThe crop innovation company
- 2. The crop innovation company 2 Overview • Genotype calling strategies • Infrastructure setup • Show case 3,000 rice genomes • Genalice stepwise vs Population calling • BWA/GATK joint genotyping vs Population calling • Conclusions
- 3. The crop innovation company Overview Genotype calling strategies 3 • Single sample calling • Batch calling • Joint calling Pros • Distinction between homozygous reference vs. missing data • Greater sensitivity for low-frequency variants • Greater ability to filter out false positives Cons • Scaling and infrastructure • Incremental analysis
- 4. The crop innovation company Single sample calling Genotype calling strategies 4
- 5. The crop innovation company The classic route: BWA/GATK preprocessing read mapping genotypingpostprocessing • python script • sickle • BWA • samtools • Picard MarkDuplicates • GATK IndelRealigner • GATK UnifiedGenotyper• several python scripts Genotype calling strategies
- 6. The crop innovation company Map per sample Mapping of all samples against reference Genotype per sample Genotyping of all samples Store variant positions Create BED file of all variation over all samples Genotype calling strategies Stepwise approach (1)
- 7. The crop innovation company Adjust VCF per sample Adjust VCF files to results in samples with equal amounts of positions – forcing unknown (./.) Merge VCFs & Post process Merge VCFs & Post-process all samples Genotype calling strategies Stepwise approach (2) Genotype per sample Genotyping of all samples with a BED file – force calls on all positions
- 8. The crop innovation company Genalice Population Calling Genotype calling strategies 8 Map per sample Map all samples against reference Genotype per sample Genotype all samples Pop call & post process Population calling & post-processing of all samples
- 9. The crop innovation company Genalice Population Calling: Process Genotype calling strategies 9 gaPopulation add gaPopulation merge gaPopulation commit gaPopulation extract XMLgaMap GAR GAR GAR GAR fastq fastq fastq fastq GVM VCF
- 10. The crop innovation company Infrastructure setup 10 3,000 rice accessions ~17 Tb fastq.gz format Streaming fastq.gz over NFS Desktop client (VM) SSH connection Illumina HiSeq2500Intel Xeon 2620 (12 cores, 2Ghz) 96Gb RAM
- 11. The crop innovation company 3,000 rice genome project Show case 11 • Rice (Oryza sativa L) • 3,000 rice accessions • 89 countries • Avg seq depth of 14X • Mapped to Nipponbare (IRGSP-1.0) o 374 Mbp o 12 chromosomes • BWA and GATK (DNANexus) • Data on o Gygascience o EBI o Amazon
- 12. The crop innovation company Test set 1: 131 rice accessions Show case 12 • Subset of 131 accessions (out of 3,000) selected • All major rice types are represented • Mapped to Nipponbare (IRGSP-1.0) Type # Aus/boro 5 Basmati/sadri 1 Indica 19 Intermediate type 7 Japonica 11 Temperate japonica 71 Tropical japonica 17 Grand Total 131
- 13. The crop innovation company Genalice ‘stepwise’ vs. Population calling Show case 13 Map per sample Genotype per sample Store variant positions 9h Adjust VCF per sample Merge VCFs & Post process Genotype per sample 1h 0.5h 3h 7h 116h Map per sample Pop call & post process 3h 5m Genotype per sample 1h Total time: 136:30h 8,227,780 variants Total time: 4:05h 8,137,366 variants Stepwise Pop call 6,147,072 positions shared 75%/76% Map: 1:22m / sample Total: 1:43m / sample
- 14. The crop innovation company Genalice ‘stepwise’ vs. Population calling Show case 14 • Genalice population calling route is straight forward and does not require external tools • Major performance increase from stepwise to population calling approach (factor 34) • Overlap of approaches on position ~75% • Further qualitative research required
- 15. The crop innovation company BWA/GATK vs. Genalice Population calling Show case 15 BWA / GATK from https://aws.amazon.com/public-data- sets/3000-rice-genome No details on compute available gaMap gaPopulation merge 75h 1.5h gaPopulation add 25h Pop call Map per sample Pop call & post process Genotype per sample BWA/GATK gaPopulation extract 1h gaPopulation commit 0.5h Map: 1:30m / sample Total: 2:04m / sample Analysis of 3,000 rice accessions
- 16. The crop innovation company BWA/GATK vs. Genalice Population calling (2) Show case 16 • Format of VCF on Amazon was different from Genalice MAP o --output_mode EMIT_ALL_SITES o One gVCF file per accession (~2Gb each) o Call for all positions of the reference o Multiple calls for same genomic position • Datasets are cleaned and merged • Variant count 131 samples: 14,838,819 vs 8,137,366 • Minimum allele depth 2 vs 5 • 6,031,002 positions shared (74%) -> 26% novels • 83.5% of shared positions have identical genotypes • Further analysis required
- 17. The crop innovation company 17 Conclusions • The new Population Calling module is extremely fast • > 30 time faster then stepwise approach • Analysis time scales linear with # samples • Differences in content should be further analyzed • The module is highly flexible o Incremental addition of samples • Extracting variants from GVM is very efficient • The VCF output is conform standard but misses details required in follow up research (depth / quality) • This feature is requested and will be added soon
- 18. The crop innovation company 18 Thank you Cueleneare Koen Nijbroek Hans Karten Tim Rudie Antonise Bas Tolhuis