SummaryIn familial cutaneous malignant melanoma (CMM), disruption of the retinoblastoma (pRB) pathway frequently occurs through inactivating mutations in the p16 (p16 INK4A /CDKN2A/MTS1) gene or activating mutations in the G1-specific cyclin dependent kinase 4 gene (CDK4). Uveal malignant melanoma (UMM) also occurs in a familial setting, or sometimes in association with familial or sporadic CMM. Molecular studies of sporadic UMM have revealed somatic deletions covering the INK4A-ARF locus (encoding P16 INK4A and P14 ARF ) in a large proportion of tumours. We hypothesized that germline mutations in the p16 INK4A , p14 ARF or CDK4 genes might contribute to some cases of familial UMM, or to some cases of UMM associated with another melanoma. Out of 155 patients treated at the Institut Curie for UMM between 1994 and 1997, and interviewed about their personal and familial history of melanoma, we identified seven patients with a relative affected with UMM (n = 6) or CMM (n = 1), and two patients who have had, in addition to UMM, a personal history of second melanoma, UMM (n = 1), or CMM (n = 1). We screened by polymerase chain reaction single-strand conformation polymorphism the entire coding sequence of the INK4A-ARF locus (exon 1α from p16 INK4A , exon 1β from p14 ARF , and exons 2 and 3, common to both genes), as well as the exons 2, 5 and 8 of the CDK4 gene, coding for the functional domains involved in p16 and/or cyclin D1 binding. A previously reported polymorphism in exon 3 of the INK4A-ARF locus was found in one patient affected with bilateral UMM, but no germline mutations were detected, either in the p16 INK4A , p14 ARF or CDK4 genes. Our data support the involvement of other genes in predisposition to uveal melanoma.

Genetic contribution of pre-Slavic populations to gene pools of modern Russia is increasingly relevant, along with genetic footprints of the Golden Horde invasion. The novel genome-wide approaches enable advanced solutions in this field. The study aimed at searching for the footprints of genetic interaction among Finnicspeaking, Slavic and Turkic-speaking populations of Central Russia and Volga Region and their reflection in pharmacogenetic landscape. Modeling ancestral components by ADMIXTURE software and their mapping involved genome-wide genotyping data for 248 individual genomes representing 47 populations of 9 ethnic groups. Of specific ancestral components identified in each of the Finnic-speaking peoples, only Mordovian ancestral components are common for all populations within the studied geographic area, regardless of their linguistic affiliation. Gene pools of Russian populations include 80% of intrinsic component, 19% contribution from Finnic-speaking peoples, and 1% of Central Asian influence. The Tatar gene pool combines all identified ancestral components, including 81% contribution from Finnic-speaking peoples and only 12% of Central Asian influence, which prevents using it as a reference for the assessment of Golden Horde footprints in Russian gene pools. A map of genetic distances from Ryazan Russians based on a panel of 42 pharmacogenetic markers reveals a landscape strikingly independent from the selectively neutral ancestral genomic patterns. For instance, populations of Mordovia, Kaluga, Smolensk, and Kostroma regions are the closest to Ryazan Russians in pharmacogenetic status, whereas populations of Ryazan and Nizhny Novgorod regions have strikingly divergent pharmacogenetic status despite the similarity of the selectively neutral ancestral genomic patterns. These findings confirm the relevance of targeted pharmacogenetic characterization for gene pools of Russia.

Background Predicting the eye and hair color from genotype became an established and widely used tool in forensic genetics, as well as in studies of ancient human populations. However, the accuracy of this tool has been verified on the West and Central Europeans only, while populations from border regions between Europe and Asia (like Caucasus and Ural) also carry the light pigmentation phenotypes. Results We phenotyped 286 samples collected across North Eurasia, genotyped them by the standard HIrisPlex-S markers and found that predictive power in Caucasus/Ural/West Siberian populations is reasonable but lower than that in West Europeans. As these populations have genetic ancestries different from that of West Europeans, we hypothesized they may carry a somewhat different allele spectrum. Thus, for all samples we performed the exome sequencing additionally enriched with the 53 genes and intergenic regions known to be associated with the eye/hair color. Our association analysis replicated the importance of the key previously known SNPs but also identified five new markers whose eye color prediction power for the studied populations is compatible with the two major previously well-known SNPs. Four out of these five SNPs lie within the HERС2 gene and the fifth in the intergenic region. These SNPs are found at high frequencies in most studied populations. The released dataset of exomes from Russian populations can be further used for population genetic and medical genetic studies. Conclusions This study demonstrated that precision of the established systems for eye/hair color prediction from a genotype is slightly lower for the populations from the border regions between Europe and Asia that for the West Europeans. However, this precision can be improved if some newly revealed predictive SNPs are added into the panel. We discuss that the replication of these pigmentation-associated SNPs on the independent North Eurasian sample is needed in the future studies.

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One of the tasks of population-based biobanks is to determine the frequencies of clinically relevant genetic polymorphisms in the population. The population of Russia is very heterogeneous both ethnically and genetically. Therefore, the frequencies of genetic markers are in demand not in one sample, but in a series of samples reflecting the heterogeneity of the gene pool of different peoples and regions.Aim. To divide the population of Russia and neighboring countries into population groups meeting certain conditions, as well as having a representative sample in existing data and biobanks.Material and methods. We developed a method for combining populations into larger groups with maintaining intragroup homogeneity based on the principal components analysis with K-means clustering, followed by refinement of clustering for higher homogeneity and a more equal distribution of group sizes using FST distances. The technology has been adjusted using the example of the Biobank of Northern Eurasia. Therefore, the material was the genome-wide data on 4.5 million genetic markers for 1,883 samples representing 247 populations of Russia and neighboring countries from this biobank. The developed approach, the resulting set of populations and related map can be applied for other collections of biomaterials from Russian populations.Results. Application of this approach made it possible to divide the entire population of Russia and neighboring countries into 29 ethnogeographic groups, characterized by relative genetic homogeneity. This set of populations is recommended as a baseline for population screenings to identify the frequency of any genetic markers among the population of Russia. A map has been constructed showing the division of population into 29 ethnogeographic areas.Conclusion. On the basis of a reliable genome-wide data, the zoning of gene pool of the Russian population was carried out. We identified ethnogeographic groups with intergroup contrasting allele frequencies, but at the same time with relatively homogeneous intragroup parameters. The resulting map and register of groups can be used in population genetic, medical genetic and pharmacogenetic studies.