Who’s who? Detecting and resolving sample anomalies in human DNA sequencing studies with peddy

Abstract

The potential for genetic discovery in human DNA sequencing studies is greatly diminished if DNA samples from the cohort are mislabelled, swapped, contaminated, or include unintended individuals. Unfortunately, the potential for such errors is significant since DNA samples are often manipulated by several protocols, labs or scientists in the process of sequencing. We have developed peddy to identify and facilitate the remediation of such errors via interactive visualizations and reports comparing the stated sex, relatedness, and ancestry to what is inferred from each individual's genotypes. Peddy predicts a sample's ancestry using a machine learning model trained on individuals of diverse ancestries from the 1000 Genomes Project reference panel. Peddy 's speed, text reports and web interface facilitate both automated and visual detection of sample swaps, poor sequencing quality and other indicators of sample problems that, were they left undetected, would inhibit discovery.

Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

“…Sample identity was verified by comparing sex and genotype between the WGS and RNA-seq data. In the WGS data, sex was determined from chromosome X heterozygosity using Peddy (v0.3.2; (Pedersen and Quinlan, 2017)), with the Peddy hg19.sites converted to GRCh38 using the UCSC Genome Browser LiftOver utility. High-quality variants with an allele frequency ≥1% were exported from the VCF using Hail for input into Peddy.…”

Large, three-generation CEPH families reveal post-zygotic mosaicism and variability in germline mutation accumulation

“…We sequenced the genomes of 603 individuals from 33, three-generation CEPH/Utah pedigrees to a genome-wide median depth of ~30X (Figure 1-figure supplement 1, Supplementary File 1), and removed 10 samples from further analysis following quality control using peddy (Pedersen and Quinlan 2017). After standard quality filtering, we identified a total of 4,671 germline de novo mutations in 70 second-generation individuals, each of which was transmitted to at least one offspring in the third generation (Figure 1a, Supplementary File 2).…”

“…We used peddy (Pedersen and Quinlan 2017) to perform relatedness and sample sequencing quality checks on all CEPH/Utah samples. We discovered a total of 10 samples with excess levels of heterozygosity (ratio of heterozygous to homozygous alternate calls > 0.2).…”

“…We identified high-confidence de novo mutations from the joint-called VCF in the second and third generations as follows, using cyvcf2 (Pedersen and Quinlan 2017). For each variant, we required that the child possessed a unique genotyped allele absent from both parents; when identifying de novo variants on the X chromosome, we required male offspring genotypes to be homozygous.…”

“…Given the filters we employed to identify high-confidence de novo mutations, we needed to calculate the fraction of the genome that was considered in our analysis. To this end, we used mosdepth (Pedersen and Quinlan 2018) to calculate per-base genome coverage in all CEPH/Utah samples, excluding low-complexity regions (Li 2014) and reads with mapping quality < 20 (the minimum mapping quality threshold used by GATK HaplotypeCaller in this analysis). For each second-and third-generation child, we then calculated the number of all genomic positions that had at least 12 aligned sequence reads in the child's, mother's, and father's genome (excluding the X chromosome).…”