Large-scale analysis of DNA quantification metrics and SNP sequencing performance in unidentified human remains.

Sample mix-ups are a threat to the validity of clinical laboratory test results. To detect serum sample mix-ups we developed a single nucleotide polymorphism (SNP) profiling test. SNPs are frequent sequence variations in the human genome. Each individual has a unique combination of these nucleotide variations. Predeveloped SNP amplification assays are commercially available. We recently discovered that these SNP assays could be applied to serological samples, which is not self-evident because a key step in serum preparation is removal of white blood cells, the major source of DNA, from blood. DNA was extracted from serum samples. Real-time polymerase chain reaction (PCR) analysis of the purified DNA using a selection of 10 SNP assays provided SNP profiles. The applicability of the SNP profiling test was demonstrated by means of a case where hepatitis E virus serological determinations of four serum samples of one patient seemed inconsistent. SNP profiling of the samples demonstrated that this was due to the enzyme-linked immunosorbent assay test instead of sample mix-up. We have developed an SNP profiling assay that provides a way to link human serum samples to a source, without post-PCR processing. The chance for two randomly chosen individuals to have an identical profile is 1 in 18 000. Solving potential serum sample mix-ups will secure downstream evaluations and critical decisions concerning the patients involved.

Age-related macular degeneration (AMD) has been discovered to have strong genetic associations. Complement factor H and LOC387715/ARMS2 variants are shown to have strong association with development and progression of AMD (Seddon et al. 2007). Other loci in multiple chromosomes have also been associated with genetic predisposition to AMD, including five key gene variants to advanced AMD (Chen et al. 2010). Correlation between individual genetic profiles or cumulative measures of genetic load with specific AMD subtypes is still being elucidated. We performed a case–control study using RetnaGene AMD Test (Perlee et al. 2013) to elucidate single nucleotide polymorphism (SNP) profiles of three common phenotypes of advanced AMD [bilateral geographic atrophy (GA), Type-1 exudative AMD (wAMD) and Type-2 wAMD] to determine whether certain genetic variants are more frequently linked with specific phenotypes. Our secondary aim was to compare SNP profiles of good with poor responders to anti-vascular endothelial growth factor (VEGF) treatment. A total of 157 eyes met our inclusion criteria (37 eyes with GA, 63 eyes with Type-1 wAMD and 57 eyes with Type-2 wAMD). The demographic profiles among tested subjects associated with the 3 phenotypes of advanced AMD were comparable in terms of age, gender, race (all Caucasian) and vision. Complete eye examination, fundus photography, fluorescein angiography, indocyanine-green angiography and spectral domain ocular coherence tomography of the macula were performed at baseline. Buccal mucosal swabs were genotyped with a panel of 12 AMD-associated SNPs using matrix-assisted laser desorption ionization-time of flight mass spectrometry system, at Sequenom Center for Molecular Medicine, San Diego, CA. SNP frequencies and allelic odds ratios were analysed for significance using Fisher’s exact test. Good responders were defined as ≥10 letters improvement or ≥50% reduction of central subfield thickness in response to anti-vascular endothelial growth factor therapy. All study phenotypes of advanced AMD showed similar risk of neovascular development (p > 0.05, Pearson chi-square). Of the tested SNP’s, only C3 risk variant rs2230199 (Arg102Gly) was observed to be significantly more common in eyes with bilateral GA than Type-1 nAMD (p = 0.03, Fisher’s exact test). Furthermore, we found no differences in SNP profiles between good and poor responders when assessing entire cohort of Type-1 and 2 eyes, or stratifying according to Type-1 or Type-2 wAMD, or to type of anti-VEGF drug used (i.e. bevacizumab, ranibizumab, aflibercept). Despite distinct phenotypes, there appears to be a common genetic load shared among GA and Type-1 and Type-2 wAMD eyes. This may explain why our cohort showed similar or greater risk (categorical [chi-square] and Cox hazard ratio) for development of new choroidal neovascularization in GA eyes compared to Type-1 and Type-2 wAMD eyes. The inherent predisposition of GA eyes for exudative changes is consistent with frequent clinical observation of exudative development in certain GA eyes. The higher frequency of C3 risk variant rs2230199 observed in GA compared to eyes Type-1 wAMD suggests that inflammatory and immune mechanisms, which are frequently linked to the alternative complement pathway, may possibly contribute to GA formation in nonexudative AMD (Tan et al. 2016). The results of our study are intriguing, but limited by small sample size and lack of multiplicity testing. Additionally, comparison of results from readily available AMD genetic tests for the same subject appear to have considerable variation of estimated risks, raising questions regarding reliability of the data (Buitendijk et al. 2014). Therefore, our study conclusions are suggestive and appropriate for generating hypothesis. Further studies including much larger sample size and multiplicity testing are necessary to validate our findings.

Practical forensic use of kinship determination using high-density SNP profiling based on a microarray platform, focusing on low-quantity DNA

Tropical bed bugs, Cimex hemipterus, which commonly feeds on human blood, may be useful in forensic applications. However, unlike the common bed bug, Cimex lectularius, there is no information regarding tropical bed bug, C. hemipterus, being studied for its applications in forensics. Thus, in this study, lab-reared post-feeding tropical bed bugs were subjected to Short Tandem Repeat (STR) and Single Nucleotide Polymorphism (SNP) analyses to establish the usage of tropical bed bugs in forensics. Several post-feeding times (0, 5, 14, 30, and 45 days) were tested to determine when a complete human DNA profile could still be obtained after the bugs had taken the blood meal. The results showed that complete STR and SNP profiles could only be obtained from the D0 sample. The profile completeness decreased over time, and partial STR and SNP profiles could be obtained up to 45 days post-blood meal. The generated SNP profiles, complete or partial, were also viable for HIrisPlex-S phenotype prediction. In addition, field-collected bed bugs were also used to examine the viability of the tested STR markers, and the STR markers detected mixed profiles. The findings of this study established that the post-blood meal of tropical bed bugs is a suitable source of human DNA for forensic STR and SNP profiling. Human DNA recovered from bed bugs can be used to identify spatial and temporal relations of events.

Medium-density single nucleotide polymorphism (SNP) profiles enable law enforcement to infer close and distant genetic relationships. Part one of this study demonstrated that the ForenSeq® Kintelligence Kit, which targets 10,230 SNPs, can facilitate extended kinship inference through kinship likelihood ratio (LR) and identical by descent (IBD) segment matching methods. However, if SNPs are not detected, or are incorrectly called, the ability to detect genetic relatives and accurately classify the relationship may be compromised. The Kintelligence profiles for the central individuals of two pedigrees described in Part One were edited to simulate information loss through locus and allele dropout. LRs were calculated with DBLR™ and SNP profiles were uploaded to GEDmatch PRO™ for database searching or direct comparison. The LRs decreased with increasing information loss but still provided strong statistical support for relatedness. LRs exceeded 100,000 for all full sibling to fifth degree relationships for up to 30% locus and allele dropout. Locus dropout did not significantly impact the ability to infer first to fifth degree relationships with IBD segment matching. Allele dropout had a greater impact, with 30% allele dropout impairing the ability to classify relationships to their correct degree. When allele dropout was greater than 10%, the fifth degree relative was no longer detected in the database search. This study highlights the robustness of LR calculations and the GEDmatch PRO™ IBD segment matching algorithms and the suitability of the Kintelligence Kit for medium-range kinship inference, with the algorithm maintaining the ability to infer relationships despite increasing information loss.

This study aimed to identify the oncogenes associated with lung cancer based on the mRNA and single nucleotide polymorphism (SNP) profile data. The mRNA expression profile data of GSE43458 (80 cancer and 30 normal samples) and SNP profile data of GSE33355 (61 pairs of lung cancer samples and control samples) were downloaded from Gene Expression Omnibus database. Common genes between the mRNA profile and SNP profile were identified as the lung cancer oncogenes. Risk subpathways of the selected oncogenes with the SNP locus were analyzed using the iSubpathwayMiner package in R. Moreover, protein-protein interaction (PPI) network of the oncogenes was constructed using the HPRD database and then visualized using the Cytoscape. Totally, 3004 DEGs (1105 up-regulated and 1899 down-regulated) and 125 significant SNPs closely related to 174 genes in the lung cancer samples were identified. Also, 39 common genes, like PFKP (phosphofructokinase, platelet) and DGKH-rs11616202 (diacylglycerol kinase, eta) that enriched in sub-pathways such as galactose metabolism, fructose and mannose metabolism, and pentose phosphate pathway, were identified as the lung cancer oncogenes. Besides, PIK3R1 (phosphoinositide-3-kinase, regulatory subunit 1), RORA (RAR-related orphan receptor A), MAGI3 (membrane associated guanylate kinase, WW and PDZ domain containing 3), PTPRM (protein tyrosine phosphatase, receptor type, M), and BMP6 (bone morphogenetic protein 6) were the hub genes in PPI network. Our study suggested that PFKP and DGKH that enriched in galactose metabolism, fructose and mannose metabolism pathway, as well as PIK3R1, RORA, and MAGI3, may be the lung cancer oncogenes.

BackgroundThe crisis of Misidentified and contaminated cell lines have plagued the biological research community for decades. Some repositories and journals have heeded calls for mandatory authentication of human cell lines, yet misidentification of mouse cell lines has received little publicity despite their importance in sponsored research. Short tandem repeat (STR) profiling is the standard authentication method, but it may fail to distinguish cell lines derived from the same inbred strain of mice. Additionally, STR profiling does not reveal karyotypic changes that occur in some high-passage lines and may have functional consequences. Single nucleotide polymorphism (SNP) profiling has been suggested as a more accurate and versatile alternative to STR profiling; however, a high-throughput method for SNP-based authentication of mouse cell lines has not been described.ResultsWe have developed computational methods (Cell Line Authentication by SNP Profiling, CLASP) for cell line authentication and copy number analysis based on a cost-efficient SNP array, and we provide a reference database of commonly used mouse strains and cell lines. We show that CLASP readily discriminates among cell lines of diverse taxonomic origins, including multiple cell lines derived from a single inbred strain, intercross or wild caught mouse. CLASP is also capable of detecting contaminants present at concentrations as low as 5%. Of the 99 cell lines we tested, 15 exhibited substantial divergence from the reported genetic background. In all cases, we were able to distinguish whether the authentication failure was due to misidentification (one cell line, Ba/F3), the presence of multiple strain backgrounds (five cell lines), contamination by other cells and/or the presence of aneuploid chromosomes (nine cell lines).ConclusionsMisidentification and contamination of mouse cell lines is potentially as widespread as it is in human cell culture. This may have substantial implications for studies that are dependent on the expected background of their cell cultures. Laboratories can mitigate these risks by regular authentication of their cell cultures. Our results demonstrate that SNP array profiling is an effective method to combat cell line misidentification.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-847) contains supplementary material, which is available to authorized users.

Genomic selection using single nucleotide polymorphisms (SNPs) is a powerful new tool for genetic selection. In cattle, SNP profiles for individual animals are generated using a small plastic chip that is diagnostic for up to 50 000 SNPs spaced throughout the genome. Phenotypes, usually averaged over offspring of bulls, are matched with SNP profiles of bulls mathematically so that animals can be ranked for siring desirable phenotypes via their SNP profiles. For many traits in dairy cattle, the rate of genetic improvement can be nearly doubled when SNP information is used in addition to current methods of genetic evaluation. Separate SNP analyses need to be developed for different populations (e.g. the system for Holsteins is not useful for Jerseys). In addition, the value of these systems is very dependent on the number of accurate phenotypes matched with SNP profiles; for example, increasing the number of North American Holstein bulls evaluated from 1151 to 3576 quadrupled the additional genetic gain in net merit from this approach. Thus, the available information will be insufficient to exploit this technology fully for most populations. However, once a valid SNP evaluation system is developed, any animal in that population, including embryos, can be evaluated with similar accuracy. Biopsying embryos and screening them via SNP analysis will greatly enhance the value of this technology by minimising generation intervals.

Salmonella enterica serovar Typhimurium is one of the leading causes of gastroenteritis in humans. Phage typing has been used for the epidemiological surveillance of S. Typhimurium for over 4 decades. However, knowledge of the evolutionary relationships between phage types is very limited. In this study, we used single nucleotide polymorphisms (SNPs) as molecular markers to determine the relationships between common S. Typhimurium phage types. Forty-four SNPs, including 24 identified in a previous study and 20 from 6 available whole-genome sequences, were used to analyze 215 S. Typhimurium isolates belonging to 45 phage types. Altogether, 215 isolates and 6 genome strains were differentiated into 33 SNP profiles and four distinctive phylogenetic clusters. Fourteen phage types, including DT9, one of the most common phage types in Australia, were differentiated into multiple SNP profiles. These SNP profiles were distributed into different phylogenetic clusters, indicating that they have arisen independently multiple times. This finding suggests that phage typing may not be useful for long-term epidemiological studies over long periods (years) and diverse localities (different countries or continents). SNP typing provided a discriminative power similar to that of phage typing. However, 12 SNP profiles contained more than one phage type, and more SNPs would be needed for further differentiation. SNP typing should be considered as a replacement for phage typing for the identification of S. Typhimurium strains.

Short tandem repeats (STRs) have long been the gold standard in forensic DNA analysis, while single nucleotide polymorphisms (SNPs) have increasingly emerged as valuable complementary markers. The advent of massively parallel sequencing (MPS) technology has significantly enhanced the detection resolution of both STRs and SNPs, providing novel approaches for forensic human identification. In this study, we developed and validated a comprehensive multiplex detection system utilizing MPS technology, which enabled the concurrent analyses of 88 STRs and 348 SNPs. Following the SWGDAM guidelines, validation studies were conducted to assess the forensic applicability of the system. Sensitivity study revealed that 100% STR profiles could be obtained from ≥ 0.125 ng, and 100% SNP profiles were achieved with ≥ 0.0625 ng. The panel showed high accuracy, achieving a 99.78% concordance rate with conventional capillary electrophoresis (CE) DNA profiles. The system exhibited robust performance in the presence of four common PCR inhibitors (including humic acid, melanin, indigo, and hematin). Additionally, 99.42% of STR genotypes and 100% of SNP genotypes were successfully obtained from degraded DNA shorter than 500bp. Furthermore, across gender combinations in mixed samples, both STR and SNP markers performed robustly at 1:1 and 1:3 ratios. At extreme proportions, male-female mixtures (99:1) enable the detection of scarce minor contributors at Y-STR loci. For SNP loci, the ACR value serves as a key evaluative metric for distinguishing mixtures. ROC analysis of these SNP data yielded an AUC of 0.909. Population genetics analyses revealed that the cumulative power of discrimination (CPD) and cumulative probability of exclusion (CPE) both exceeded 0.999999, highlighting its extensive applicability and practical value in forensic genetics. This comprehensive validation study confirmed that the multiplex panel provided high sensitivity, accuracy, and reliability, representing a significant advancement in forensic DNA analysis methodology.

Comparative evaluation of SNP sequencing workflows for identification of the missing in Vietnam.

Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship.ip estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.

The human major histocompatibility complex (MHC) is characterized by polymorphic multicopy gene families, such as HLA and MIC (PERB11); duplications; insertions and deletions (indels); and uneven rates of recombination. Polymorphisms at the antigen recognition sites of the HLA class I and II genes and at associated neutral sites have been attributed to balancing selection and a hitchhiking effect, respectively. We, and others, have previously shown that nucleotide diversity between MHC haplotypes at non-HLA sites is unusually high (>10%) and up to several times greater than elsewhere in the genome (0.08%-0.2%). We report here the most extensive analysis of nucleotide diversity within a continuous sequence in the genome. We constructed a single nucleotide polymorphism (SNP) profile that reveals a pattern of extreme but interrupted levels of nucleotide diversity by comparing a continuous sequence within haplotypes in three genomic subregions of the MHC. A comparison of several haplotypes within one of the genomic subregions containing the HLA-B and -C loci suggests that positive selection is operating over the whole subgenomic region, including HLA and non-HLA genes. [The sequence data for the multiple haplotype comparisons within the class I region have been submitted to DDBJ/EMBL/GenBank under accession nos. AF029061, AF029062, and AB031005-AB031010. Additional sequence data have been submitted to the DDBJ data library under accession nos. AB031005-AB03101 and AF029061-AF029062.]

Single-nucleotide polymorphisms (SNPs) represent a significant genetic variation influencing individual responses to cosmetic dermatology treatments. SNP profiling offers a pathway to personalized skincare by enabling practitioners to predict patient outcomes, customize interventions, and mitigate risks. The integration of genetic insights into dermatology has gained traction, with SNP analysis revealing predispositions in skin characteristics, such as collagen degradation, pigmentation, and inflammatory responses. Key SNPs, including MMP1, SOD2, TYR, and IL-6, are pivotal in determining skin health and treatment outcomes. Despite its promise, the adoption of SNP profiling in cosmetic dermatology is in its infancy, requiring further exploration of its practical applications. SNPs significantly influence skin responses to aesthetic treatments, offering insights for personalized care. Variations in MMP1 correlate with collagen degradation, suggesting collagen-stimulating therapies, while SOD2 SNPs highlight the need for antioxidant support. TYR variations affect pigmentation risks in light-based treatments, and IL-6 SNPs reveal inflammatory predispositions, guiding anti-inflammatory protocols. AI integration enhances SNP profiling by improving prediction accuracy and treatment customization. Challenges remain, including standardization, ethical considerations, and cost-effectiveness. Combining genetic insights with epigenetics and leveraging AI technologies can amplify precision and safety in dermatologic care. SNP profiling marks a transformative step toward precision medicine in cosmetic dermatology, enabling tailored treatments that enhance efficacy and minimize adverse effects. Integrating AI-driven SNP analysis with epigenetic insights provides a comprehensive approach to patient care, fostering a new era of personalized skincare that respects genetic and environmental interactions. This paradigm shift holds the potential to redefine dermatologic practices, improving outcomes and patient satisfaction.

This chapter reviews developments in forensic DNA that enable case clearance using genetic data from rootless hair. Advances in Massively Parallel Sequencing (MPS) have enabled the production of DNA profiles of Single-Nucleotide Polymorphisms (SNPs). The resulting SNP profiles can then be used to identify persons of interest – usually unidentified decedents or suspects in criminal cases – using Forensic Investigative Genetic Genealogy (FIGG). FIGG uses genetic genealogy databases of SNP profiles to generate relatedness estimates to identify persons of interest. Once candidates are identified through FIGG, the identification is normally confirmed through the direct comparison of Short Tandem Repeat (STR) forensic DNA profiles – either through direct comparison for crime scene samples, or through kinship testing for human remains. However, SNP profiles can be generated in cases in which the DNA evidence exists in too low quantities or is too degraded to generate a traditional (STR) forensic DNA profile. This allows for cases to be cleared, even where an STR DNA profile is not available to be uploaded and searched against CODIS databases. Novel statistical methods are available for direct comparison between SNP profiles, which enables the probative value of the evidence to be assessed by triers of fact in criminal cases. In this chapter, we focus on the clearance of cases using rootless hair, as these samples are commonly found in criminal and human remains investigations but have resisted conventional DNA analyses. Forensic DNA analysis is therefore poised to move beyond CODIS, and this poses novel challenges for courts and legal professionals.