Analysis of Skin Pigmentation and Genetic Ancestry in Three Subpopulations from Pakistan: Punjabi, Pashtun, and Baloch

A Mendelian randomization study of genetic liability to cutaneous melanoma and sunburns

Background

Some studies have reported that sunburns and cutaneous melanoma (CM) risk is increasing, but a clear causal link has yet to be established.

Methods

This current study conducted a two-sample Mendelian randomization (MR) approach to clarify the association and causality between sunburn history and CM using large-scale genome-wide association study data.

Results

The inverse-variance weighted method result showed that sunburn might be associated with the risk of CM increasing (p = 2.21 × 10-23, OR = 1.034, 95% CI= 1.027-1.041), causally. The MR-Egger regression, weighted median method, simple mode method, and weighted mode method results showed similar results.

Conclusion

This study offers evidence of sunburn history and increased risk of CM, and it shows that there might be common genetic basics regarding sunburns and CM susceptibility in Caucasian, European, or British ethnic groups.

Applications and Performance of Precision ID GlobalFiler NGS STR, Identity, and Ancestry Panels in Forensic Genetics

Short Tandem Repeat (STR) testing via capillary electrophoresis is undoubtedly the most popular forensic genetic testing method. However, its low multiplexing capabilities and limited performance with challenging samples are among the factors pushing scientists towards new technologies. Next-generation sequencing (NGS) methods overcome some of these limitations while also enabling the testing of Single-Nucleotide Polymorphisms (SNPs). Nonetheless, these methods are still under optimization, and their adoption into practice is limited. Among the available kits, Thermo Fisher Scientific (Waltham, MA, USA) produces three Precision ID Panels: GlobalFiler NGS STR, Identity, and Ancestry. A clear review of these kits, providing information useful for the promotion of their use, is, however, lacking. To close the gap, a literature review was performed to investigate the popularity, applications, and performance of these kits. Following the PRISMA guidelines, 89 publications produced since 2015 were identified. China was the most active country in the field, and the Identity Panel was the most researched. All kits appeared robust and useful for low-quality and low-quantity samples, while performance with mixtures varied. The need for more population data was highlighted, as well as further research surrounding variables affecting the quality of the sequencing results.

Association of TYR SNP rs1042602 with Melanoma Risk and Prognosis

Cutaneous melanoma is the most aggressive of skin tumors. In order to discover new biomarkers that could help us improve prognostic prediction in melanoma patients, we have searched for germline DNA variants associated with melanoma progression. Thus, after exome sequencing of a set of melanoma patients and healthy control individuals, we identified rs1042602, an SNP within TYR, as a good candidate. After genotyping rs1042602 in 1025 patients and 773 healthy donors, we found that the rs1042602-A allele was significantly associated with susceptibility to melanoma (CATT test: p = 0.0035). Interestingly, we also observed significant differences between patients with good and bad prognosis (5 years of follow-up) (n = 664) (CATT test for all samples p = 0.0384 and for men alone p = 0.0054). Disease-free-survival (DFS) analyses also showed that patients with the A allele had shorter DFS periods. In men, the association remained significant even in a multivariate Cox Proportional-hazards model, which was adjusted for age at diagnosis, Breslow thickness, ulceration and melanoma subtype (HR 0.4; 95% confidence interval (CI) 0.20-0.83; p = 0.0139). Based on our results, we propose that rs1042602-A is a risk allele for melanoma, which also seems to be responsible for a poorer prognosis of the disease, particularly in men.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.