Volume 6 Supplement 6
Optimizing genotype quality metrics for individual exomes and cohort analysis
© Gordon et al; licensee BioMed Central Ltd. 2012
Published: 1 October 2012
Few evidence-based best practice bioinformatics guidelines exist for genotyping using next-generation sequencing data, especially colorspace data produced by Life Technologies sequencers. Dozens of software packages can perform the various steps required, and genome features such as pseudogenes or large paralogous gene families are problematic. High false positive and negative rates can compound the difficulty of cohort analysis.
Materials and methods
Using a Sanger-validated set of 32 BRCA gene regions from 16 patients, high-throughput colorspace (Life Technologies) sequencing performance was optimized by comparing various combinations of sequence aligners, re-aligners, de-duplicators, quality re-calibrators and genotype callers. Independently, six exomes were captured using the Agilent SureSelect v3 kit. The optimized pipeline was applied, and results were compared to microarray genotyping to characterize false positives and negatives. A further four exomes were pair-end sequenced on both the Life Technologies 5500x1 and Illumina HiSeq sequencers to check platform concordance. Variant metrics for each exome were compared to the literature.
In the clinic, individual exomes are manually triaged by a medical geneticist, and salient variants are confirmed by Sanger sequencing. For disease cohorts, software was developed to isolate variants possibly causing monogenic rare diseases, taking likely false positives into account.
Using results from Life Technologies' reference genome aligner, the intersection of single nucleotide polymorphism (SNP) calls from FreeBayes  (with SamTools  de-duplication) and Life Technologies' diBayes (with Picard de-duplication) was optimal. Using reads realigned by the Broad Institute Genome Analysis Toolkit (GATK) , the intersection of insertion and deletion calls from FreeBayes and Atlas2  was optimal. A threshold of 14% variant reads for true heterozygous calls was observed.
For bases with 10× coverage, variant calls are on average 98.9% concordant with SNP microarrays (versus 99.2% microarray technical reproducibility ). False positive and negative variant rates are each approximately 0.5%, with all false positives called heterozygous. Concordance with Illumina variant calls from a standard GATK pipeline was 95.2%. GATK produced more novel variants, especially in non-unique genomic regions: such variants are flagged with caveats in the colorspace pipeline. In a dominant heterozygous model analysis of five Nager syndrome patients, our cohort analysis software excluded 15 of 19 candidate genes, based mainly on a preponderance of genotype caveats.
Quality metrics reported in the literature, and the optimized colorspace genotyping results.
Low false positive and negative rates using colorspace data can be achieved by: first, reporting only concurrent variants from ultiple methods; and second, reporting caveats where the reference sequence is not unique. Accurate calls and caveats enable major cohort gene triage when modeling diseases caused by monogenic rare variants.
We thank Dr Richard Pon's laboratory for producing the high-quality colorspace data. We also thank the FORGE Consortium for the HiSeq-derived genotypes.
- Garrison E, Marth G: Haplotype-based variant detection from short-read sequencing. [http://arxivorg/abs/1207.3907]
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-9. 10.1093/bioinformatics/btp352.PubMed CentralView ArticlePubMedGoogle Scholar
- McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA: The Genome Analysis Toolkit:a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20: 1297-303. 10.1101/gr.107524.110.PubMed CentralView ArticlePubMedGoogle Scholar
- Challis D, Yu J, Evani US, Jackson AR, Paithankar S, Coarfa C, Milosavljevic A, Gibbs RA, Yu F: An integrative variant analysis suite for whole exome next-generation sequencing data. BMC Bioinformatics. 2012, 13: 8-10.1186/1471-2105-13-8.PubMed CentralView ArticlePubMedGoogle Scholar
- Woo JG, Sun G, Haverbusch M, Indugula S, Martin LJ, Broderick JP, Deka R, Woo D: Quality assessment of buccal versus blood genomic DNA using Affymetrix 500K GeneChip. BMC Genet. 2007, 8: 79-PubMed CentralView ArticlePubMedGoogle Scholar
- Tennessen JA, Bigham AW, O'Connor TD, Fu W, Kenny EE, Gravel S, McGee S, Do R, Liu X, Jun G, Kang HM, Jordan D, Leal SM, Gabriel S, Rieder MJ, Abecasis G, Altshuler D, Nickerson DA, Boerwinkle E, Sunyaev S, Bustamante CD, Bamshad MJ, Akey JM, Broad GO, Seattle GO, NHLBI Exome Sequencing Project: Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science. 2012, 337: 64-9. 10.1126/science.1219240.PubMed CentralView ArticlePubMedGoogle Scholar
- Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, Shaffer T, Wong M, Bhattacharjee A, Eichler EE, Bamshad M, Nickerson DA, Shendure J: Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009, 461: 272-6. 10.1038/nature08250.PubMed CentralView ArticlePubMedGoogle Scholar
- Pelak K, Shianna KV, Ge D, Maia JM, Zhu M, Smith JP, Cirulli ET, Fellay J, Dickson SP, Gumbs CE, Heinzen EL, Need AC, Ruzzo EK, Singh A, Campbell CR, Hong LK, Lornsen KA, McKenzie AM, Sobreira NL, Hoover-Fong JE, Milner JD, Ottman R, Haynes BF, Goedert JJ, Goldstein DB: The characterization of twenty sequenced human genomes. PLoS Genet. 2010, 6: e1001111-10.1371/journal.pgen.1001111.PubMed CentralView ArticlePubMedGoogle Scholar
- Pattnaik S, Vaidyanathan S, Pooja DG, Deepak S, Panda B: Customisation of the exome data analysis pipeline using a combinatorial approach. PLoS ONE. 2012, 7: e30080-10.1371/journal.pone.0030080.PubMed CentralView ArticlePubMedGoogle Scholar