The struggle to find reliable results in exome sequencing data: filtering out Mendelian errors

Abstract

Next Generation Sequencing studies generate a large quantity of genetic data in a relatively cost and time efficient manner and provide an unprecedented opportunity to identify candidate causative variants that lead to disease phenotypes. A challenge to these studies is the generation of sequencing artifacts by current technologies. To identify and characterize the properties that distinguish false positive variants from true variants, we sequenced a child and both parents (one trio) using DNA isolated from three sources (blood, buccal cells, and saliva). The trio strategy allowed us to identify variants in the proband that could not have been inherited from the parents (Mendelian errors) and would most likely indicate sequencing artifacts. Quality control measurements were examined and three measurements were found to identify the greatest number of Mendelian errors. These included read depth, genotype quality score, and alternate allele ratio. Filtering the variants on these measurements removed ~95% of the Mendelian errors while retaining 80% of the called variants. These filters were applied independently. After filtering, the concordance between identical samples isolated from different sources was 99.99% as compared to 87% before filtering. This high concordance suggests that different sources of DNA can be used in trio studies without affecting the ability to identify causative polymorphisms. To facilitate analysis of next generation sequencing data, we developed the Cincinnati Analytical Suite for Sequencing Informatics (CASSI) to store sequencing files, metadata (eg. relatedness information), file versioning, data filtering, variant annotation, and identify candidate causative polymorphisms that follow either de novo, rare recessive homozygous or compound heterozygous inheritance models. We conclude the data cleaning process improves the signal to noise ratio in terms of variants and facilitates the identification of candidate disease causative polymorphisms.

Copb2is essential for embryogenesis and hypomorphic mutations cause human microcephaly

“…Quality control and data filtering were performed on VCF files in Golden Helix's SNP and Variation Suite. Non-synonymous coding variants were compared to three control databases, including NHLBI's ESP6500 exome data (20), the 1000 genomes project (21), and an internal CCHMC control cohort (22). The initial bioinformatics analysis identified 126,752 variants (Table 3).…”

“…The initial bioinformatics analysis identified 126,752 variants (Table 3). After filtering for quality control using filters previously described (22), coding variants, non-synonymous variants, and minor allele frequencies, we identified one single mutation which followed a homozygous recessive inheritance pattern. The identified variant was compared to known disease genes in the OMIM and Human Gene Mutation (HGMD) (23) databases, and to reported variants in dbSNP (24) and the Exome Aggregation Consortium (ExAC; (25).…”

A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree

“…A significant limitation of this approach is that some of the variants called by just one method may be correct and may provide valuable insights on how to improve variant calling, but these variants are excluded from further consideration by this approach. A third approach is to sequence parent-parent-child trios and test for Mendelian consistency (Boland et al 2013;Patel et al 2014). Although this approach can detect a subset of errors, it falls short of identifying genotyping errors that do not violate inheritance in a trio (Supplemental Tables S3, S4).…”

The Foundation of Precision Medicine: Integration of Electronic Health Records with Genomics Through Basic, Clinical, and Translational Research

“…The third group of papers Cronin et al, 2014;Namjou et al, 2014;Patel et al, 2014;Sun et al, 2014) in this special issue focused on more complex analyses of the genome including copy number variants (CNV), pleiotropy combined with phenome-wide association studies (PheWAS), and epistasis (gene-gene interactions). The first paper by Namjou et al (2014) describes the first PheWAS in a pediatric cohort based on 4268 samples and 2476 sSNPs selected from previously published GWAS studies.…”

“…Finally, how CNVs might be evaluated and used with medical records is discussed. The fifth paper of this group is by Patel et al (2014) and describes quality control processes for whole exome sequencing data, specifically using Mendelian errors as a filtering strategy to minimize errors. The group developed the Cincinnati Analytical Suite for Sequencing Informatics (CASSI) to store sequencing files, metadata, and others.…”