Identification of missing variants by combining multiple analytic pipelines

BMC Bioinformatics

Yingxue Ren¹  Yan W. Asmann¹  Joseph S. Reddy¹  Owen A. Ross²  Cyril Pottier²  Rosa Rademakers²  Minerva M. Carrasquillo²  Nilüfer Ertekin-Taner²  Joanna M. Biernacka³  Shulan Tian³  Shannon K. McDonnell³  Vivekananda Sarangi³  Jason P. Sinnwell³  Matthew Hudson⁴  Liudmila Sergeevna Mainzer⁴ 

[1]Department of Health Sciences Research, Mayo Clinic

[2]Department of Neuroscience, Mayo Clinic

[3]Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic

[4]National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign

关键词: Missing variants;    Combining multiple bioinformatics pipelines;    Rare variants;   

DOI  :  10.1186/s12859-018-2151-0

来源: DOAJ

【 摘 要 】

Abstract Background After decades of identifying risk factors using array-based genome-wide association studies (GWAS), genetic research of complex diseases has shifted to sequencing-based rare variants discovery. This requires large sample sizes for statistical power and has brought up questions about whether the current variant calling practices are adequate for large cohorts. It is well-known that there are discrepancies between variants called by different pipelines, and that using a single pipeline always misses true variants exclusively identifiable by other pipelines. Nonetheless, it is common practice today to call variants by one pipeline due to computational cost and assume that false negative calls are a small percent of total. Results We analyzed 10,000 exomes from the Alzheimer’s Disease Sequencing Project (ADSP) using multiple analytic pipelines consisting of different read aligners and variant calling strategies. We compared variants identified by using two aligners in 50,100, 200, 500, 1000, and 1952 samples; and compared variants identified by adding single-sample genotyping to the default multi-sample joint genotyping in 50,100, 500, 2000, 5000 and 10,000 samples. We found that using a single pipeline missed increasing numbers of high-quality variants correlated with sample sizes. By combining two read aligners and two variant calling strategies, we rescued 30% of pass-QC variants at sample size of 2000, and 56% at 10,000 samples. The rescued variants had higher proportions of low frequency (minor allele frequency [MAF] 1–5%) and rare (MAF < 1%) variants, which are the very type of variants of interest. In 660 Alzheimer’s disease cases with earlier onset ages of ≤65, 4 out of 13 (31%) previously-published rare pathogenic and protective mutations in APP, PSEN1, and PSEN2 genes were undetected by the default one-pipeline approach but recovered by the multi-pipeline approach. Conclusions Identification of the complete variant set from sequencing data is the prerequisite of genetic association analyses. The current analytic practice of calling genetic variants from sequencing data using a single bioinformatics pipeline is no longer adequate with the increasingly large projects. The number and percentage of quality variants that passed quality filters but are missed by the one-pipeline approach rapidly increased with sample size.

【 授权许可】

Unknown