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An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data

Jun, Goo, Wing, Mary Kate, Abecasis, Gonçalo R., Kang, Hyun Min·2015·Genome Research
DOI 10.1101/gr.176552.114PMC PMC4448687
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Abstract

The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies.

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Figures

Figure 1. Outline of GotCloud variant calling pipeline.
Figure 1. Outline of GotCloud variant calling pipeline.CC BY-NC · PubMed Central
Figure 2. Computational costs of GATK UnifiedGenotyper and GotCloud pipelines. (A) Runtime estimated for whole-genome (6×) and whole-exome (60×) sequence data running with 40 parallel sessions on a four-node minicluster with 48 physical CPU cores. For GATK, runtimes for 1000 samples are extrapolated from analyses of a single 5-Mb block of Chromosome 20. For all other analyses, no extrapolation was used. (B) Peak memory usage estimates averaged over Chromosome 20 chunks.
Figure 2. Computational costs of GATK UnifiedGenotyper and GotCloud pipelines. (A) Runtime estimated for whole-genome (6×) and whole-exome (60×) sequence data running with 40 parallel sessions on a four-node minicluster with 48 physical CPU cores. For GATK, runtimes for 1000 samples are extrapolated from analyses of a single 5-Mb block of Chromosome 20. For all other analyses, no extrapolation was used. (B) Peak memory usage estimates averaged over Chromosome 20 chunks.CC BY-NC · PubMed Central
Figure 3. Variant discovery sensitivity comparison of GotCloud and GATK using HumanExome BeadChip excluding the SNPs contained in Omni2.5 array, because Omni2.5 variants are used to train variant filters in GotCloud and GATK. GotCloud results are shown for unfiltered (raw) and SVM-filtered sets, and GATK results are shown for unfiltered and VQSR-filtered sets, across low-coverage genome (A) and exome data (B).
Figure 3. Variant discovery sensitivity comparison of GotCloud and GATK using HumanExome BeadChip excluding the SNPs contained in Omni2.5 array, because Omni2.5 variants are used to train variant filters in GotCloud and GATK. GotCloud results are shown for unfiltered (raw) and SVM-filtered sets, and GATK results are shown for unfiltered and VQSR-filtered sets, across low-coverage genome (A) and exome data (B).CC BY-NC · PubMed Central
Figure 4. Comparison of SVM filtering with hard filtering based on a single feature. (A) Ts/Tv of filtered-in (PASS) and filtered-out (FAIL) variants using different filters. Variants are ordered by a single variant feature and a fixed fraction of variants (8%) are filtered out to match the variant counts with the default SVM filter. Absolute values are used for StrandBias correlation and CycleBias correlation. (B) Percentage of filtered-out HumanExome BeadChip (Omni2.5) variants among those that are polymorphic in the array genotypes.
Figure 4. Comparison of SVM filtering with hard filtering based on a single feature. (A) Ts/Tv of filtered-in (PASS) and filtered-out (FAIL) variants using different filters. Variants are ordered by a single variant feature and a fixed fraction of variants (8%) are filtered out to match the variant counts with the default SVM filter. Absolute values are used for StrandBias correlation and CycleBias correlation. (B) Percentage of filtered-out HumanExome BeadChip (Omni2.5) variants among those that are polymorphic in the array genotypes.CC BY-NC · PubMed Central
Figure 5. Impact of model-transfer filtering on the variant quality. The vertical axis represents Ts/Tv for novel SNPs for model-transferred SVM and self-trained SVM filters. Ts/Tv for known SNPs are ∼3.5, which is higher than novel SNPs because known SNPs contain a larger fraction of synonymous SNPs. The horizontal axis represents the size of targeted regions randomly selected from 500 whole-exome sequences. The transferred model is trained on nonoverlapping 500 exome data.
Figure 5. Impact of model-transfer filtering on the variant quality. The vertical axis represents Ts/Tv for novel SNPs for model-transferred SVM and self-trained SVM filters. Ts/Tv for known SNPs are ∼3.5, which is higher than novel SNPs because known SNPs contain a larger fraction of synonymous SNPs. The horizontal axis represents the size of targeted regions randomly selected from 500 whole-exome sequences. The transferred model is trained on nonoverlapping 500 exome data.CC BY-NC · PubMed Central
Figure 6. Comparison of known and novel Ts/Tv with and without trimming overlapping reads for 1000 low-coverage Chromosome 20 sequences from the 1000 Genomes Project. Overlapping fragments lowers novel Ts/Tv and the effect is more eminent in the singletons (with allele count of one).
Figure 6. Comparison of known and novel Ts/Tv with and without trimming overlapping reads for 1000 low-coverage Chromosome 20 sequences from the 1000 Genomes Project. Overlapping fragments lowers novel Ts/Tv and the effect is more eminent in the singletons (with allele count of one).CC BY-NC · PubMed Central
Figure 7. Nonreference genotype concordance for low-pass genome data calculated using Omni2.5 array genotypes. The haplotype-aware refinement steps significantly improve genotype accuracy, especially with larger sample sizes.
Figure 7. Nonreference genotype concordance for low-pass genome data calculated using Omni2.5 array genotypes. The haplotype-aware refinement steps significantly improve genotype accuracy, especially with larger sample sizes.CC BY-NC · PubMed Central

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Cites

Research works printed but not linked: 11

  1. 10.1016/S0893-6080(99)00032-5 · DOI
  2. 10.1016/j.ajhg.2009.11.004 · DOI
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  8. 10.1101/gr.117259.110 · DOI
  9. 2013, QPLOT: a quality assessment tool for next generation sequencing data, Biomed Res Int, 2013, 865181
  10. 10.1101/gr.146084.112 · DOI
  11. 10.1371/journal.pone.0082138 · DOI

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Metadata

Authors
Jun, G., Wing, M. K., Abecasis, G. R., & Kang, H. M.
Venue
Genome Research
Publisher
Cold Spring Harbor Laboratory
Published
2015-04-16
Volume
25
Issue
6
Pages
918-925
ISSN
  • 1088-9051
  • 1549-5469
DOI
10.1101/gr.176552.114
PMC
PMC4448687
Cited by
536
References
6
Language
en
Funders
  • National Institutes of Health
  • National Human Genome Research Institute (U01 HG006513)

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