A Genome-Wide Association Study Reveals a BDNF-Centered Molecular Network Associated with Alcohol Dependence and Related Clinical Measures.

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 (p-value = 1.33 × 10-8 calculated with the Yates-corrected χ2 test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWASs of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network, where BDNF is the most significant hub gene. This study indicates that several genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The study also reveals a central role of BDNF in the pathogenesis of AD.

Genome-wide association study reveals genetic variants associated with HIV-1C infection in a Botswana study population.

Although there have been many studies of gene variant association with different stages of HIV/AIDS progression in United States and European cohorts, few gene-association studies have assessed genic determinants in sub-Saharan African populations, which have the highest density of HIV infections worldwide. We carried out genome-wide association studies on 766 study participants at risk for HIV-1 subtype C (HIV-1C) infection in Botswana. Three gene associations (AP3B1, PTPRA, and NEO1) were shown to have significant association with HIV-1C acquisition. Each gene association was replicated within Botswana or in the United States-African American or United States-European American AIDS cohorts or in both. Each associated gene has a prior reported influence on HIV/AIDS pathogenesis. Thirteen previously discovered AIDS restriction genes were further replicated in the Botswana cohorts, extending our confidence in these prior AIDS restriction gene reports. This work presents an early step toward the identification of genetic variants associated with and affecting HIV acquisition or AIDS progression in the understudied HIV-1C afflicted Botswana population.

Genome-wide sequence analyses of ethnic populations across Russia.

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. Here we report the characterization of genome-wide variation of 264 healthy adults, including 60 newly sequenced samples. People of Russia carry known and novel genetic variants of adaptive, clinical and functional consequence that in many cases show allele frequency divergence from neighboring populations. Population genetics analyses revealed six phylogeographic partitions among indigenous ethnicities corresponding to their geographic locales. This study presents a characterization of population-specific genomic variation in Russia with results important for medical genetics and for understanding the dynamic population history of the world's largest country.

Robust forensic matching of confiscated horns to individual poached African rhinoceros.

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]. At the end of the 19th century, Southern white rhinoceros (Ceratotherium simum simum) numbers had declined to fewer than 50 animals in the Hluhluwe-iMfolozi region of the KwaZulu-Natal (KZN) province of South Africa, mainly due to uncontrolled hunting [2,3]. Efforts by the Natal Parks Board facilitated an increase in population to over 20,000 in 2015 through aggressive conservation management [2]. Black rhinoceros (Diceros bicornis) populations declined from several hundred thousand in the early 19th century to ∼65,000 in 1970 and to ∼2,400 by 1995 [1] with subsequent genetic reduction, also due to hunting, land clearances and later poaching [4]. In South Africa, rhinoceros poaching incidents have increased from 13 in 2007 to 1,215 in 2014 [1]. This has occurred despite strict trade bans on rhinoceros products and strict enforcement in recent years.

Signal localization: a new approach in signal discovery.

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. Further assessments of maximal value and average statistics in signal discovery are improved. While our method does not attempt to detect individual predictors, it instead detects sets of predictors that are jointly associated with the outcome. Therefore, an important application would be in genome wide association study (GWAS), where it can be used to detect genes which influence the phenotype but do not contain any individually significant single nucleotide polymorphism (SNP). We compare power of the signal identification method based on extremes of single p-values with the signal localization method based on average statistics for logarithms of p-values. A simulation analysis informs the application of signal localization using the average statistics for wide signals discovery in Gaussian white noise process. We apply average statistics and the localization method to GWAS to discover better gene influences of regulating loci in a Chinese cohort developed for risk of nasopharyngeal carcinoma (NPC).