Estimating sequencing error rates using families

Abstract

Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

The human “contaminome”: bacterial, viral, and computational contamination in whole genome sequences from 1000 families

“…In order to better understand patterns of contamination in human whole genome sequencing, we analyzed sequences from the iHART dataset 36 . Originally curated to study genetic determinants of autism, the iHART dataset contains whole genome sequences from blood samples from children with autism, their siblings, and their parents, but also stands as an invaluable genomics resource due to its unique family structure 37 , 38 . iHART was sequenced at the New York Genome Sequencing Center, a common site for large sequencing studies, using commonly followed storage, prep, and sequencing protocols 36 , making it a good model dataset to understand common sequencing issues.…”

The Human “Contaminome”: Bacterial, Viral, and Computational Contamination in Whole Genome Sequences from 1,000 Families

“…In order to better understand patterns of contamination in human whole genome sequencing, we analyzed sequences from the iHART dataset[37]. Originally curated to study genetic determinants of autism, the iHART dataset contains whole genome sequences from blood samples from children with autism, their siblings, and their parents, but also stands as an invaluable genomics resource due to its unique family structure [38, 39, 40]. iHART was sequenced at the New York Genome Sequencing Center, a common site for large sequencing studies, using commonly followed storage, prep, and sequencing protocols [37], making it a good model dataset to understand common sequencing issues.…”

An Algorithm for Sequence Location Approximation using Nuclear Families (ASLAN) Validates Regions of the Telomere-to-Telomere Assembly and Identifies New Hotspots for Genetic Diversity

“…We use ASLAN with the iHART dataset [26], a large WGS dataset from families with autistic children (4,501 individuals from 1,010 families), that our group curated originally to study the genetic components of autism. To our knowledge, this is one of the largest familial WGS datasets in the world and offers an unique opportunity for family-based analysis such as ASLAN and others [25, 4, 6]. The iHART collection includes the raw WGS from the individuals, as well as the aligned and variant-called data (VCF format) in reference to GRCh38.…”