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

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.