Małgorzata Skowron

Evaluation of DNA Variants Associated with Androgenetic Alopecia and Their Potential to Predict Male Pattern Baldness

Androgenetic alopecia, known in men as male pattern baldness (MPB), is a very conspicuous condition that is particularly frequent among European men and thus contributes markedly to variation in physical appearance traits amongst Europeans. Recent studies have revealed multiple genes and polymorphisms to be associated with susceptibility to MPB. In this study, 50 candidate SNPs for androgenetic alopecia were analyzed in order to verify their potential to predict MPB. Significant associations were confirmed for 29 SNPs from chromosomes X, 1, 5, 7, 18 and 20. A simple 5-SNP prediction model and an extended 20-SNP model were developed based on a discovery panel of 305 males from various European populations fitting one of two distinct phenotype categories. The first category consisted of men below 50 years of age with significant baldness and the second; men aged 50 years or older lacking baldness. The simple model comprised the five best predictors: rs5919324 near AR, rs1998076 in the 20p11 region, rs929626 in EBF1, rs12565727 in TARDBP and rs756853 in HDAC9. The extended prediction model added 15 SNPs from five genomic regions that improved overall prevalence-adjusted predictive accuracy measured by area under the receiver characteristic operating curve (AUC). Both models were evaluated for predictive accuracy using a test set of 300 males reflecting the general European population. Applying a 65% probability threshold, high prediction sensitivity of 87.1% but low specificity of 42.4% was obtained in men aged <50 years. In men aged ≥50, prediction sensitivity was slightly lower at 67.7% while specificity reached 90%. Overall, the AUC=0.761 calculated for men at or above 50 years of age indicates these SNPs offer considerable potential for the application of genetic tests to predict MPB patterns, adding a highly informative predictive system to the emerging field of forensic analysis of externally visible characteristics.

Evaluation of the predictive capacity of DNA variants associated with straight hair in Europeans.

DNA-based prediction of hair morphology, defined as straight, curly or wavy hair, could contribute to an improved description of an unknown offender and allow more accurate forensic reconstructions of physical appearance in the field of forensic DNA phenotyping. Differences in scalp hair morphology are significant at the worldwide scale and within Europe. The only genome-wide association study made to date revealed the Trichohyalin gene (TCHH) to be significantly associated with hair morphology in Europeans and reported weaker associations for WNT10A and FRAS1 genes. We conducted a study that centered on six SNPs located in these three genes with a sample of 528 individuals from Poland. The predictive capacity of the candidate DNA variants was evaluated using logistic regression; classification and regression trees; and neural networks, by applying a 10-fold cross validation procedure. Additionally, an independent test set of 142 males from six European populations was used to verify performance of the developed prediction models. Our study confirmed association of rs11803731 (TCHH), rs7349332 (WNT10A) and rs1268789 (FRAS1) SNPs with hair morphology. The combined genotype risk score for straight hair had an odds ratio of 2.7 and these predictors explained ∼ 8.2% of the total variance. The selected three SNPs were found to predict straight hair with a high sensitivity but low specificity when a 10-fold cross validation procedure was applied and the best results were obtained using the neural networks approach (AUC=0.688, sensitivity=91.2%, specificity=23.0%). Application of the neural networks model with 65% probability threshold on an additional test set gave high sensitivity (81.4%) and improved specificity (50.0%) with a total of 78.7% correct calls, but a high non-classification rate (66.9%). The combined TTGGGG SNP genotype for rs11803731, rs7349332, rs1268789 (European frequency=4.5%) of all six straight hair-associated alleles was identified as the best predictor, giving >80% probability of straight hair. Finally, association testing of 44 SNPs previously identified to be associated with male pattern baldness revealed a suggestive association with hair morphology for rs4679955 on 3q25.1. The study results reported provide the starting point for the development of a predictive test for hair morphology in Europeans. More studies are now needed to discover additional determinants of hair morphology to improve the predictive accuracy of this trait in forensic analysis.

Increased risk of developing cutaneous malignant melanoma is associated with variation in pigmentation genes and VDR, and may involve epistatic effects.

Cutaneous malignant melanoma (CMM) is a malicious human skin cancer that primarily affects individuals with light pigmentation and heavy sun exposure, but also has a known familial association. Multiple genes and polymorphisms have been reported as low-penetrance susceptibility loci for CMM. Here, we examined 33 candidate polymorphisms located in 11 pigmentation genes and the vitamin D receptor gene (VDR) in a population of 130 cutaneous melanoma patients and 707 healthy controls. The genotypes obtained were evaluated for main association effects and potential gene–gene interactions. MC1R, TYR, VDR and SLC45A2 genes were found to be associated with CMM in our population. The results obtained for major function MC1R mutations were the most significant [with odds ratio (OR)=1.787, confidence interval (CI)=1.320–2.419 and P=1.715−4], followed by TYR (rs1393350) (with OR=1.569, CI=1.162–2.118, P=0.003), VDR (GCCC haplotype in rs2238136–rs4516035–rs7139166–rs11568820 block) (with OR=5.653, CI=1.794–17.811, P=0.003) and SLC45A2 (rs16891982) (with OR=0.238, CI=0.057–0.987, P=0.048). The study also detected significant intermolecular epistatic effects between MC1R and TYR, SLC45A2 and VDR, HERC2 and VDR, OCA2 and TPCN2, as well as intramolecular interactions between variants within the genes MC1R and VDR. In the final multivariate logistic regression model for CMM development, only the gene–gene interactions discovered remained significant, showing that epistasis may be an important factor in the risk of melanoma.

Further evidence for population specific differences in the effect of DNA markers and gender on eye colour prediction in forensics

The genetics of eye colour has been extensively studied over the past few years, and the identified polymorphisms have been applied with marked success in the field of Forensic DNA Phenotyping. A picture that arises from evaluation of the currently available eye colour prediction markers shows that only the analysis of HERC2-OCA2 complex has similar effectiveness in different populations, while the predictive potential of other loci may vary significantly. Moreover, the role of gender in the explanation of human eye colour variation should not be neglected in some populations. In the present study, we re-investigated the data for 1020 Polish individuals and using neural networks and logistic regression methods explored predictive capacity of IrisPlex SNPs and gender in this population sample. In general, neural networks provided higher prediction accuracy comparing to logistic regression (AUC increase by 0.02–0.06). Four out of six IrisPlex SNPs were associated with eye colour in the studied population. HERC2 rs12913832, OCA2 rs1800407 and SLC24A4 rs12896399 were found to be the most important eye colour predictors (p < 0.007) while the effect of rs16891982 in SLC45A2 was less significant. Gender was found to be significantly associated with eye colour with males having ~1.5 higher odds for blue eye colour comparing to females (p = 0.002) and was ranked as the third most important factor in blue/non-blue eye colour determination. However, the implementation of gender into the developed prediction models had marginal and ambiguous impact on the overall accuracy of prediction confirming that the effect of gender on eye colour in this population is small. Our study indicated the advantage of neural networks in prediction modeling in forensics and provided additional evidence for population specific differences in the predictive importance of the IrisPlex SNPs and gender.

Sequence, genome organization, annotation and proteomics of the thermophilic, 47.7-kb Geobacillus stearothermophilus bacteriophage TP-84 and its classification in the new Tp84virus genus

Bacteriophage TP-84 is a well-characterized bacteriophage of historical interest. It is a member of the Siphoviridae, and infects a number of thermophilic Geobacillus (Bacillus) stearothermophilus strains. Its’ 47.7-kbp double-stranded DNA genome revealed the presence of 81 coding sequences (CDSs) coding for polypeptides of 4 kDa or larger. Interestingly, all CDSs are oriented in the same direction, pointing to a dominant transcription direction of one DNA strand. Based on a homology search, a hypothetical function could be assigned to 31 CDSs. No RNA or DNA polymerase-coding genes were found on the bacteriophage genome indicating that TP-84 relies on the host’s transcriptional and replication enzymes. The TP84 genome is tightly packed with CDSs, typically spaced by several-to-tens of bp or often overlapping. The genome contains five putative promoter-like sequences showing similarity to the host promoter consensus sequence and allowing for a 2-bp mismatch. In addition, ten putative rho-independent terminators were detected. Because the genome sequence shows essentially no similarity to any previously characterised bacteriophage, TP-84 should be considered a new species in an undefined genus within the Siphoviridae family. Thus a taxonomic proposal of a new Tp84virus genus has been accepted by the International Committee on Taxonomy of Viruses. The bioinformatics genome analysis was verified by confirmation of 33 TP-84 proteins, which included: a) cloning of a selected CDS in Escherichia coli, coding for a DNA single-stranded binding protein (SSB; gene TP84_63), b) purification and functional assays of the recombinant TP-84 SSB, which has been shown to improve PCR reactions, c) mass spectrometric (MS) analysis of TP-84 bacteriophage capsid proteins, d) purification of TP-84 endolysin activity, e) MS analysis of the host cells from infection time course.

Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA

Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.