Development and validation of an MPS-based 513-Plex SNP identity panel for degraded forensic samples.

Systematic evaluation of the early access applied biosystems precision ID Globalfiler mixture ID and Globalfiler NGS STR panels for the ion S5 system

Cell-free fetal DNA (cffDNA) from maternal plasma has made it possible to develop noninvasive prenatal paternity testing (NIPPT). However, most studies have focused on customized single nucleotide polymorphism (SNP) typing systems and few have used conventional short tandem repeat (STR) markers. Based on massively parallel sequencing (MPS), this study used a widely-accepted forensic multiplex assay system to evaluate the effect of noninvasive prenatal paternity testing with a combination of well-established SNP and STR markers. Using a ForenSeq DNA Signature Prep Kit, NIPPT was performed in 17 real parentage cases with monovular unborn fetuses at 7 to 24 gestational weeks. Different analytical strategies for the identification of paternally inherited allele (PIA) were developed to deal with SNPs and STRs. Combined paternity index (CPI) for 17 real trios as well as 272 unrelated trios was calculated. With the combination of SNPs and A-STRs, 82.35% (14/17), 88.24% (15/17), 94.12% (16/17), and 94.12% (16/17) of real trios could be accurately determined when the likelihood ratio (LR) threshold for paternity inclusion was set to 10,000, 1000, 100, and 10, respectively. This reveals that simultaneous surveys of SNP and STR markers included in the ForenSeq DNA Signature Prep Kit offer a promising method for NIPPT using MPS technology.

The International Society for Animal Genetics (ISAG) currently advocates for a transition towards single nucleotide polymorphism (SNP) markers as a potential alternative for equine parentage verification. To ascertain the efficacy of this transition, it is imperative to evaluate the performance of parentage testing using SNPs in juxtaposition with short tandem repeats (STRs). As per ISAG's recommendation, we used an equine genotyping-by-sequencing panel with 144 SNPs for this purpose. Equine parentage is currently realized using 16 microsatellites (STRs) excluding the LEX3 marker. In this study, 1074 horses were genotyped using the 144 SNPs panel, including 432 foals, 414 mares, and 228 stallions, from five different breeds: 293 Arabians, 167 Barbs, 189 Thoroughbreds, 73 Anglo-Arabians, and 352 Arabian-Barbs. As a result, two SNPs markers were eliminated from the panel system due to inconsistent amplification across all examined individuals leaving 142 SNPs markers for analysis. A comparative analysis between SNPs and STRs markers revealed that the mean expected heterozygosity was 0.457 for SNPs and 0.76 for STRs, while the mean observed heterozygosity stood at 0.472 for SNPs and 0.72 for STRs. Furthermore, the probability of identity was calculated to be 5.722 × 10-57 for SNPs and 1.25 × 10-15 for STRs markers. In alignment with the Hardy-Weinberg equilibrium in polyploids test, 110 out of the total SNPs were consistent with the Hardy-Weinberg equilibrium in polyploids test (p > 0.05). Employing both SNPs and STRs markers, the mean polymorphic information content was discerned to be 0.351 for SNPs and 0.72 for STRs. The cumulative exclusion probabilities for SNP markers exceeded 99.99%, indicating that the 142 SNPs panel might be adequate for parentage testing. In contrast, when utilizing STRs markers, the combined average exclusion probabilities for one and both parents were determined to be 99.8% and 99.9%, respectively. Our comprehensive study underscores the potential of SNPs in equine parentage verification, especially when compared to STRs in terms of exclusion probabilities. As a corollary, the application of SNPs for parentage verification and identification can significantly contribute to the conservation initiative for the five Moroccan horse breeds. Nonetheless, further research is required to address and replace the deficient SNPs within the panel.

Relative Contribution of Genetic and Nongenetic Modifiers to Intestinal Obstruction in Cystic Fibrosis

Analysis of DNA from post-blast pipe bomb fragments for identification and determination of ancestry

Background: DNA profiling with sets of highly polymorphic autosomal short tandem repeat (STR) markers has been applied in various aspects of human identification in forensic casework for nearly 20 years. However, in some cases of complex kinship investigation, the information provided by the conventionally used STR markers is not enough, often resulting in low likelihood ratio (LR) calculations. In these cases, it becomes necessary to increment the number of loci under analysis to reach adequate LRs. Recently, it has been proposed that single nucleotide polymorphisms (SNPs) could be used as a supportive tool to STR typing, eventually even replacing the methods/markers now employed. Methods: In this work, we describe the results obtained in 7 revised complex paternity cases when applying a battery of STRs, as well as 52 human identification SNPs (SNPforID 52plex identification panel) using a SNaPshot methodology followed by capillary electrophoresis. Results: Our results show that the analysis of SNPs, as complement to STR typing in forensic casework applications, would at least increase by a factor of 4 total PI values and correspondent Essen-Möllerʼs W value. Conclusions: We demonstrated that SNP genotyping could be a key complement to STR information in challenging casework of disputed paternity, such as close relative individualization or complex pedigrees subject to endogamous relations.

A look at DFNB16 markers and their application in the genetic study of hearing loss in Iranian deaf families.

Genomic selection in farm animals: accuracy of prediction and applications with imputed whole-genome sequencing data in chicken

BackgroundParentage verification by molecular markers is mainly based on short tandem repeat markers. Single nucleotide polymorphisms (SNPs) as bi-allelic markers have become the markers of choice for genotyping projects. Thus, the subsequent step is to use SNP genotypes for parentage verification as well. Recent developments of algorithms such as evaluating opposing homozygous SNP genotypes have drawbacks, for example the inability of rejecting all animals of a sample of potential parents. This paper describes an algorithm for parentage verification by constrained regression which overcomes the latter limitation and proves to be very fast and accurate even when the number of SNPs is as low as 50. The algorithm was tested on a sample of 14,816 animals with 50, 100 and 500 SNP genotypes randomly selected from 40k genotypes. The samples of putative parents of these animals contained either five random animals, or four random animals and the true sire. Parentage assignment was performed by ranking of regression coefficients, or by setting a minimum threshold for regression coefficients. The assignment quality was evaluated by the power of assignment (P_{text {a}}) and the power of exclusion (P_{text {e}}).ResultsIf the sample of putative parents contained the true sire and parentage was assigned by coefficient ranking, P_{text {a}} and P_{text {e}} were both higher than 0.99 for the 500 and 100 SNP genotypes, and higher than 0.98 for the 50 SNP genotypes. When parentage was assigned by a coefficient threshold, P_{text {e}} was higher than 0.99 regardless of the number of SNPs, but P_{text {a}} decreased from 0.99 (500 SNPs) to 0.97 (100 SNPs) and 0.92 (50 SNPs). If the sample of putative parents did not contain the true sire and parentage was rejected using a coefficient threshold, the algorithm achieved a P_{text {e}} of 1 (500 SNPs), 0.99 (100 SNPs) and 0.97 (50 SNPs).ConclusionThe algorithm described here is easy to implement, fast and accurate, and is able to assign parentage using genomic marker data with a size as low as 50 SNPs.

<h3>Abstract</h3> Single nucleotide polymorphism (SNP) data generated with microarray technologies have been used to solve murder cases via investigative leads obtained from identifying relatives of the unknown perpetrator included in accessible genomic databases, referred to as investigative genetic genealogy (IGG). However, SNP microarrays were developed for relatively high input DNA quantity and quality, while SNP microarray data from compromised DNA typically obtainable from crime scene stains are largely missing. By applying the Illumina Global Screening Array (GSA) to 264 DNA samples with systematically altered quantity and quality, we empirically tested the impact of SNP microarray analysis of deprecated DNA on kinship classification success, as relevant in IGG. Reference data from manufacturer-recommended input DNA quality and quantity were used to estimate genotype accuracy in the compromised DNA samples and for simulating data of different degree relatives. Although stepwise decrease of input DNA amount from 200 nanogram to 6.25 picogram led to decreased SNP call rates and increased genotyping errors, kinship classification success did not decrease down to 250 picogram for siblings and 1^st cousins, 1 nanogram for 2^nd cousins, while at 25 picogram and below kinship classification success was zero. Stepwise decrease of input DNA quality via increased DNA fragmentation resulted in the decrease of genotyping accuracy as well as kinship classification success, which went down to zero at the average DNA fragment size of 150 base pairs. Combining decreased DNA quantity and quality in mock casework and skeletal samples further highlighted possibilities and limitations. Overall, GSA analysis achieved maximal kinship classification success from 800-200 times lower input DNA quantities than manufacturer-recommended, although DNA quality plays a key role too, while compromised DNA produced false negative kinship classifications rather than false positive ones. <h3>Author Summary</h3> Investigative genetic genealogy (IGG), i.e., identifying unknown perpetrators of crime via genomic database-tracing of their relatives by means of microarray-based single nucleotide polymorphism (SNP) data, is a recently emerging field. However, SNP microarrays were developed for much higher DNA quantity and quality than typically available from crime scenes, while SNP microarray data on quality and quantity compromised DNA are largely missing. As first attempt to investigate how SNP microarray analysis of quantity and quality compromised DNA impacts kinship classification success in the context of IGG, we performed systematic SNP microarray analyses on DNA samples below the manufacturer-recommended quantity and quality as well as on mock casework samples and on skeletal remains. In addition to IGG, our results are also relevant for any SNP microarray analysis of compromised DNA, such as for the DNA prediction of appearance and biogeographic ancestry in forensics and anthropology and for other purposes.

Whole Genome Amplification of DNA from Laser Capture-Microdissected Tissue for High-Throughput Single Nucleotide Polymorphism and Short Tandem Repeat Genotyping

Epistasis between CYP19A1 and ESR1 polymorphisms is associated with premature ovarian failure

Single nucleotide polymorphism (SNP) arrays are widely used for genetic and genomic analyses in cattle breeding. However, the relationship among sample genotyping efficiency (call rate per individual), accuracy of SNP genotypes, and DNA quality (integrity, concentration, and mixture of DNA, i.e., chimerism) remains unknown. We determined the effect of DNA quality on call rate per individual and accuracy of SNP genotypes using artificial DNA samples of various qualities. Integrity and concentration of DNA were less sensitive to call rate per individual and accuracy of genotyping in the SNP array. Chimerism strongly affected call rate per individual and accuracy of SNP genotypes. Artificial chimerism experiments showed that relative to unmixed DNA, the genotypic matching error (%) of mixed DNAs linearly increased with mix ratio, whereas the call rate per individual in some samples at 50% mix ratio was >0.95. However, individuals with higher chimerism were readily identified based on standard deviation of B-allele frequency (BAF) and BAF distribution across the genome from SNP array data. Thus, we effectively managed the balance by maximizing genotyping accuracy and minimizing the number of samples for re-genotyping by using quality control for combining call rate per individual with BAF.

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12428767 genotypes in total post-editing) and 80 sheep (1557360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of <0.300, 0.300-0.549, and ≥0.550 in the cattle (sheep in parenthesis) was 0.9467 (0.9864), 0.9707 (0.9953), and 0.9994 (0.99997) respectively; the respective allele concordance rate was 0.9730 (0.9930), 0.9849 (0.9976), and 0.9997 (0.99998). Hence, concordance eroded as the GC score of the called genotype reduced, albeit the impact was not dramatic and was not very noticeable until a GC score of <0.55. Moreover, the impact was greater and more consistent in the cattle population than in the sheep population. Furthermore, an impact of GC score on genotype concordance rate existed even for the same SNP GenTrain value; the GenTrain value is a statistical score that depicts the shape of the genotype clusters and the relative distance between the called genotype clusters.

A multipurpose panel of microhaplotypes for use with STR markers in casework