A computational approach for positive genetic identification and relatedness detection from low-coverage shotgun sequencing data.

Several methods exist for detecting genetic relatedness or identity by comparing DNA information. These methods generally require genotype calls, either single-nucleotide polymorphisms or short tandem repeats, at the sites used for comparison. For some DNA samples, like those obtained from bone fragments or single rootless hairs, there is often not enough DNA present to generate genotype calls that are accurate and complete enough for these comparisons. Here, we describe IBDGem, a fast and robust computational procedure for detecting genomic regions of identity-by-descent by comparing low-coverage shotgun sequence data against genotype calls from a known query individual. At less than 1× genome coverage, IBDGem reliably detects segments of relatedness and can make high-confidence identity detections with as little as 0.01× genome coverage.

Systematic evaluation of STRmix™ performance on degraded DNA profile data.

This study examined the DNA degradation modeling capacity of STRmix™, a widely implemented DNA interpretation software program. As a part of the CAL DOJ STRmix™ v2.4 validation, a large volume of STR profile data was generated from intact template DNA exposed to DNase I for a series of increasing time intervals. The resulting degraded profile data was analyzed with STRmix™ v2.4, and the efficacy of the analysis was assessed, both in terms of how the degradation modeling parameter values from the STRmix™ analysis compared to ground truth values, and how the weight-of-evidence statistics calculated for degraded profiles compared to those calculated for corresponding intact profiles. An additional set of differentially degraded mixture data was generated in silico to further challenge the STRmix™ degradation model, as well as to determine the extent to which end-user adjustment of the model's application can assist in resolving analysis problems that arise when high levels of degradation are observed in a profile. This work demonstrates that the degradation model in STRmix™ is capable of addressing a wide range of degraded STR profile data. The assessment expands the range of samples that have been rigorously examined using probabilistic genotyping approaches, as called for by forensic advisory bodies such as the United States President's Council of Advisors on Science and Technology.

Searching for first-degree familial relationships in California's offender DNA database: validation of a likelihood ratio-based approach.

A validation study was performed to measure the effectiveness of using a likelihood ratio-based approach to search for possible first-degree familial relationships (full-sibling and parent-child) by comparing an evidence autosomal short tandem repeat (STR) profile to California's ∼1,000,000-profile State DNA Index System (SDIS) database. Test searches used autosomal STR and Y-STR profiles generated for 100 artificial test families. When the test sample and the first-degree relative in the database were characterized at the 15 Identifiler(®) (Applied Biosystems(®), Foster City, CA) STR loci, the search procedure included 96% of the fathers and 72% of the full-siblings. When the relative profile was limited to the 13 Combined DNA Index System (CODIS) core loci, the search procedure included 93% of the fathers and 61% of the full-siblings. These results, combined with those of functional tests using three real families, support the effectiveness of this tool. Based upon these results, the validated approach was implemented as a key, pragmatic and demonstrably practical component of the California Department of Justice's Familial Search Program. An investigative lead created through this process recently led to an arrest in the Los Angeles Grim Sleeper serial murders.