The FORCE Panel: An All-in-One SNP Marker Set for Confirming Investigative Genetic Genealogy Leads and for General Forensic Applications

The FORensic Capture Enrichment (FORCE) panel is an all-in-one SNP panel for forensic applications. This panel of 5422 markers encompasses common, forensically relevant SNPs (identity, ancestry, phenotype, X- and Y-chromosomal SNPs), a novel set of 3931 autosomal SNPs for extended kinship analysis, and no clinically relevant/disease markers. The FORCE panel was developed as a custom hybridization capture assay utilizing ~20,000 baits to target the selected SNPs. Five non-probative, previously identified World War II (WWII) cases were used to assess the kinship panel. Each case included one bone sample and associated family reference DNA samples. Additionally, seven reference quality samples, two 200-year-old bone samples, and four control DNAs were processed for kit performance and concordance assessments. SNP recovery after capture resulted in a mean of ~99% SNPs exceeding 10X coverage for reference and control samples, and 44.4% SNPs for bone samples. The WWII case results showed that the FORCE panel could predict first to fifth degree relationships with strong statistical support (likelihood ratios over 10,000 and posterior probabilities over 99.99%). To conclude, SNPs will be important for further advances in forensic DNA analysis. The FORCE panel shows promising results and demonstrates the utility of a 5000 SNP panel for forensic applications.

The FORCE panel: An all-in-one SNP marker set for confirming investigative genetic genealogy leads and for general forensic applications

The FORensic Capture Enrichment (FORCE) panel is an all-in-one SNP panel for forensic applications. This panel of 5,422 markers encompasses common, forensically relevant SNPs (identity, ancestry, phenotype, X- and Y-chromosomal SNPs), a novel set of 3,931 autosomal SNPs for extended kinship analysis, and no clinically rele-vant/disease markers. The FORCE panel was developed as a custom hybridization capture assay utilizing ~20,000 baits to target the selected SNPs. Five non-probative, previously identified World War II (WWII) cases were used to assess the kinship panel. Each case included one bone sample and associated family reference DNA samples. Additionally, seven reference quality samples, two 200-year-old bone samples, and four control DNAs were processed for kit performance and concordance assessments. SNP recovery after capture resulted in a mean of ~99% SNPs exceeding 10X coverage for reference and control samples, and 44.4% SNPs for bone samples. The WWII case results showed that the FORCE panel could predict 1st to 5th degree relationships with strong statisti-cal support (likelihood ratios over 10,000 and posterior probabilities over 99.99%). To conclude, SNPs will be important for further advances in forensic DNA analysis. The FORCE panel shows promising results and demonstrates the utility of a 5,000 SNP panel for forensic applications.

41 Using Sequence Data to Increase Accuracy of Genomic Predictions in Livestock: Are We There Yet?

As sequence data is becoming available for many livestock species, there is a question on whether this information can help to boost the accuracy of genomic predictions beyond what has already been achieved with SNP chips. Several studies have been conducted by our group using simulated and real livestock populations that included from 1,000 to 100,000 animals with full or imputed sequence information. For the real datasets, the potential causative variants were identified based on genome-wide association (GWA) and were added to the current SNP chips. Additional scenarios included the use of only causative variants and the use of all sequence SNP. Genomic predictions were obtained based on single-step GBLUP (ssGBLUP), and in some cases, Bayesian regressions. Overall, in real datasets, we observed no significant increase in accuracy by using all sequence SNP, causative variants alone, or combined with SNP currently used for genomic prediction. However, an increase in accuracy of almost 100% was observed in simulated datasets when the causative variants were added to a 60k SNP panel and their simulated variances were accounted for by the prediction model. Our results show that if true causative variants are identified, together with their position and the variance explained, a boost in accuracy can be observed. This raises a question on the effectiveness of the methods and size of the datasets used to select causative variants in real livestock populations. We observed distinct GWA methods work differently depending on the data structure, and the number of genotyped animals with phenotypes. The combination of large-scale sequence and other layers of omics data (e.g., functional data) can help to identify some of the true causative variants. This could possibly promote an increase in the accuracy of genomic predictions in real populations.

Single nucleotide polymorphisms are suitable for assessing the success of restocking efforts of the European lobster (Homarus gammarus, L.)

The European lobster (Homarus gammarus) forms the base of an important fishery along the coasts of Europe. However, stocks have been in decline for many years, prompting new regulations in the fishery and also restocking efforts. An important feature of any restocking effort is the assessment of success in the number of released juveniles that stay and become adult over time. Here, we tested the power of a single nucleotide polymorphism (SNP) DNA marker panel developed for population assignment to correctly infer parentage on the maternal side of lobster larvae, in the absence of known fathers, using lobsters included in a current restocking effort on the Swedish west coast. We also examined the power to reconstruct the unknown paternal genotypes, and examined the number of fathers for each larval clutch. We found that the 96-SNP panel, despite only containing 78 informative markers, allowed us to assign all larvae to the correct mother. Furthermore, with ten genotyped larvae or more, confident paternal genotypes could be reconstructed. We also found that 15 out of 17 clutches were full siblings, whereas two clutches had two fathers. To our knowledge, this is the first time a SNP panel of this size has been used to assess parentage in a crustacean restocking effort. Our conclusion is that the panel works well, and that it could be an important tool for the assessment of restocking success of H. gammarus in the future.

A deep learning model for predicting next-generation sequencing depth from DNA sequence

Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.

Reliable wolf-dog hybrid detection in Europe using a reduced SNP panel developed for non-invasively collected samples

Background Understanding the processes that lead to hybridization of wolves and dogs is of scientific and management importance, particularly over large geographical scales, as wolves can disperse great distances. However, a method to efficiently detect hybrids in routine wolf monitoring is lacking. Microsatellites offer only limited resolution due to the low number of markers showing distinctive allele frequencies between wolves and dogs. Moreover, calibration across laboratories is time-consuming and costly. In this study, we selected a panel of 96 ancestry informative markers for wolves and dogs, derived from the Illumina CanineHD Whole-Genome BeadChip (174 K). We designed very short amplicons for genotyping on a microfluidic array, thus making the method suitable also for non-invasively collected samples. Results Genotypes based on 93 SNPs from wolves sampled throughout Europe, purebred and non-pedigree dogs, and suspected hybrids showed that the new panel accurately identifies parental individuals, first-generation hybrids and first-generation backcrosses to wolves, while second- and third-generation backcrosses to wolves were identified as advanced hybrids in almost all cases. Our results support the hybrid identity of suspect individuals and the non-hybrid status of individuals regarded as wolves. We also show the adequacy of these markers to assess hybridization at a European-wide scale and the importance of including samples from reference populations. Conclusions We showed that the proposed SNP panel is an efficient tool for detecting hybrids up to the third-generation backcrosses to wolves across Europe. Notably, the proposed genotyping method is suitable for a variety of samples, including non-invasive and museum samples, making this panel useful for wolf-dog hybrid assessments and wolf monitoring at both continental and different temporal scales.