Genomics

Dataset Information

0

Prediction of pigmentation phenotypes by SNP typing in a Northern German population


ABSTRACT: Human pigmentation traits are of great interest to many research areas, from ancient DNA analysis to forensic science. We aimed to develop a gene-based predictive model for pigmentation phenotypes in a realistic target population for forensic case work from Northern Germany. Our aim was to determine whether better prediction accuracy can be achieved, or fewer genetic markers may suffice, than in previously studied, genetically more heterogeneous populations. We investigated the association between eye, hair and skin colour, and 12 candidate single nucleotide polymorphisms (SNPs) from six genes. Our study comprised two samples of 300 and 100 individuals from Northern Germany who were carefully characterized with regard to pigmentation phenotypes. The first sample was used to select trait-associated SNPs whereas the second sample served to estimate odds ratios (ORs) and to quantify the predictive capability of the respective SNP genotypes. SNP rs12913832 in HERC2 was found to be strongly associated with blue eye colour (OR=15.6, p<1.2•10-4) and to yield reasonable predictive power (90% sensitivity, 63% specificity). SNP associations with hair and skin colour were weaker and genotypes less predictive. A comparison to two recently published sets of markers to predict eye and hair colour revealed that the consideration of additional SNPs with weak to moderate effect increases the predictive power in Northern Germans for eye colour, but not for hair colour. In addition, fine phenotyping and differentiation of hair colour (light / dark and red tint / no red tint) were found to increase the number of significant genotype-phenotype associations.

PROVIDER: EGAS00001001174 | EGA |

REPOSITORIES: EGA

Similar Datasets

| EGAD00001001315 | EGA
2020-05-27 | PXD016156 | Pride
| PRJEB18110 | ENA
2021-01-06 | MSV000086665 | MassIVE
2020-06-10 | PXD016169 | Pride
2019-04-04 | PXD011732 | Pride
2017-02-23 | PXD004435 | Pride
2017-03-30 | MSV000080810 | MassIVE
2023-12-01 | GSE190219 | GEO
| PRJEB25630 | ENA