Publications by authors named "Alexey Antonik"

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (-value = 1.

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Article Synopsis
  • Research on gene variants related to HIV/AIDS mostly focuses on U.S. and European populations, with limited studies on sub-Saharan African populations, where HIV infections are most prevalent.
  • A genome-wide association study involving 766 participants in Botswana identified three significant gene associations with HIV-1 subtype C (HIV-1C) acquisition, which were also supported by findings in other cohorts.
  • The study not only replicated thirteen previously identified AIDS restriction genes but also contributes to understanding the genetic factors influencing HIV acquisition and progression in the HIV-1C affected population of Botswana.
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The Russian Federation is the largest and one of the most ethnically diverse countries in the world, however no centralized reference database of genetic variation exists to date. Such data are crucial for medical genetics and essential for studying population history. The Genome Russia Project aims at filling this gap by performing whole genome sequencing and analysis of peoples of the Russian Federation.

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Black and white rhinoceros (Diceros bicornis and Ceratotherium simum) are iconic African species that are classified by the International Union for the Conservation of Nature (IUCN) as Critically Endangered and Near Threatened (http://www.iucnredlist.org/), respectively [1].

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A new approach for statistical association signal identification is developed in this paper. We consider a strategy for nonprecise signal identification by extending the well-known signal detection and signal identification methods applicable to the multiple testing problem. Collection of statistical instruments under the presented approach is much broader than under the traditional signal identification methods, allowing more efficient signal discovery.

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