The development of high-density SNP profiles from next generation sequencing (NGS) data for investigative genetic genealogy (IGG) purposes requires custom bioinformatics workflows. While standard bioinformatics workflows exist for processing NGS data, forensic samples are often of low quantity and/or low quality and are therefore not suitable for these types of workflows. Given the variability of sample conditions, careful evaluation of the quality of both the sequencing data and resulting SNP profiles must be performed. Further, the processing and evaluation of positive and negative controls require additional consideration.

Here we introduce Figg, a Python package containing workflows optimized for processing forensic-type NGS data for IGG. First, specific metrics compiled by Illumina software are evaluated to assess the quality of the sequencing run and the demultiplexing process. After verifying the quality of the run, Figg is run on the demultiplexed FASTQ files. The development of the main Figg workflow involved optimization of the alignment method for low quality samples, targeted genotyping of over 1.3 million SNPs, strict quality filtering of reads and genotype calls, and the inclusion of additional quality control tools. The Figg workflow outputs upload-ready genotypes, as well as an HTML report. The HTML report provides key metrics and plots, and summarizes control data, allowing the analyst to evaluate the quality and accuracy of the resulting SNP profile(s).
Additional tools and workflows are also integrated into Figg, including an imputation workflow for imputing missing genotypes from the 1.3 million SNP panel and a negative control workflow for evaluating a potentially problematic control sample. Finally, Figg contains a module to compare a known or reference set of genotypes to the genotypes generated by the main sample workflow or the imputation workflow. This is an important and useful tool for validation efforts to evaluate the accuracy of the SNP profiles developed by Figg when using known samples.

This work was funded under Contract No. HSHQDC-15-C-00064, awarded by the DHS S&T to NBACC, a DHS federal laboratory operated by BNBI. Views and conclusions contained herein are those of the authors and should not be interpreted to represent policies, expressed or implied, of the DHS or S&T.

Authors

• Rebecca Mitchell (National Bioforensic Analysis Center, United States of America)
• Karla Garcia (National Bioforensic Analysis Center, United States of America)
• Daniel Standage (National Bioforensic Analysis Center, United States of America)
• Sana Enke (National Bioforensic Analysis Center, United States of America)
• Ryan Berger (National Bioforensic Analysis Center, United States of America)
• Rebecca Just (National Bioforensic Analysis Center, United States of America)