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.

"No thanks, it keeps me awake": the genetics of coffee-attributed sleep disturbance.

STUDY OBJECTIVES: Previous genetic investigations of sleep disturbance have shown various measures of sleep quality and sleep pattern to be heritable. But none of these studies have investigated the genetic predisposition to sleep disturbance attributed to caffeine. In this study, the heritability of coffee-attributed sleep disturbance and its relationship with other sleep measures were estimated, and chromosomal regions influencing this trait were identified. DESIGN: A classical twin design was used to estimate the heritability of coffee-attributed sleep disturbance and its genetic covariance with other measures of sleep disturbance (e.g., due to anxiety, depression) and sleep quality (e.g., variability in sleep quality). To locate quantitative trait loci influencing coffee-attributed sleep disturbance, a genome-wide linkage screen of 1395 microsatellite markers was performed. PARTICIPANTS: The study included 3808 Australian adult twin pairs (n = 1799 monozygous pairs; n = 2009 dizygous pairs). A subsample of 1989 individuals from 1175 families was used for the linkage analysis. MEASUREMENTS AND RESULTS: The heritability of coffee-attributed sleep disturbance (measured by self report) was approximately 0.40, with three fourths of this genetic variance explained by genes unrelated to the general sleep disturbance factor. One region of significant linkage to coffee-attributed sleep disturbance was identified on chromosome 2q (LOD score of 2.9). CONCLUSIONS: Although no candidate genes known to be related to caffeine metabolism or sleep disorder were identified in the significant linkage region, 2 candidates were found under a smaller peak on chromosome 17q.