A global analysis of Y-chromosomal haplotype diversity for 23 STR loci

In a worldwide collaborative effort, 19,630 Y-chromosomes were sampled from 129 different populations in 51 countries. These chromosomes were typed for 23 short-tandem repeat (STR) loci (DYS19, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS385ab, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635, GATAH4, DYS481, DYS533, DYS549, DYS570, DYS576, and DYS643) and using the PowerPlex Y23 System (PPY23, Promega Corporation, Madison, WI). Locus-specific allelic spectra of these markers were determined and a consistently high level of allelic diversity was observed. A considerable number of null, duplicate and off-ladder alleles were revealed. Standard single-locus and haplotype-based parameters were calculated and compared between subsets of Y-STR markers established for forensic casework. The PPY23 marker set provides substantially stronger discriminatory power than other available kits but at the same time reveals the same general patterns of population structure as other marker sets. A strong correlation was observed between the number of Y-STRs included in a marker set and some of the forensic parameters under study. Interestingly a weak but consistent trend toward smaller genetic distances resulting from larger numbers of markers became apparent.Peer reviewe

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Novel Big Data-Driven Machine Learning Models for Drug Discovery Application

Most contemporary drug discovery projects start with a ‘hit discovery’ phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, ’virtual docking calculations’ are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel ‘ensemble docking’ calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be ’selected’ by chemicals is known as ’conformational selection’ process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process