1,721,060 research outputs found
APCDR Uganda GWAS: Genome-wide sequence variation and susceptibility loci for cardiometabolic traits in a sub-Saharan African population (UGWAS component)
No full textGenomic studies in African populations provide unique opportunities to understand disease aetiology, human genetic diversity and population history in a regional and a global context. To leverage the relative benefits of different strategies, the study undertook a combined approach of genotyping and whole-genome sequencing (WGS) in a population-based study of 6,400 individuals from a geographically defined rural community in South-West Uganda
APCDR AGV Baganda
No full textAPCDR AGV Project: Array data from 100 Baganda. Raw data, intensity files and post-QC Plink files
APCDR Uganda GWAS - UG2G dataset: Whole genome low depth sequence data for 2000 Ugandans (BAMs)
No full textLow depth (4x) Illumina HiSeq raw sequence data for 2000 Ugandan people, drawn from various ethno-linguistic group in rural South-West Uganda
APCDR Uganda GWAS - High depth sequencing of a Baganda trio
No full textA family trio from Uganda (Baganda ethno-linguistic group) has been sequenced to high depth (ca. 30x) on the Illumina HiSeq 2500 platform
APCDR AGV Project: Low depth (4x) sequence data from an Ugandan population (BAMs)
No full textLow depth (4x) Illumina HiSeq raw sequence data for 100 unrelated Baganda from rural Uganda
The opportunity in African genome resource for precision medicine.
Full text linkThere is a critical need to increase and diversify genomic study in the global efforts to achieving full implementation of precision medicine. Given this central importance of Africa to human origins, genetic diversity, and disease susceptibility, there is a clear scientific and public health need to develop large-scale efforts that examines disease susceptibility across diverse populations within Africa. The marked genomic diversity and allelic differentiation among populations in Africa, in combination with the substantially lower linkage disequilibrium (correlation) among genetic variants, will provide excellent opportunities to gain new insights into disease etiology and genetic fine mapping that have relevance for African populations and globally. Importantly, given varying environments and adaptation, the spectrum and distribution of risk factors for a broad range of non-infectious and infectious diseases, and their individual contribution, may differ in African populations compared with European populations or those of African descent in Europe, North America and elsewhere . However, despite the value of conducting such studies in Africa, there have been relatively few investigations of population diversity and the genetic determinants of non-infectious or infectious traits and diseases across the continent
Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control
Full text linkIncreasing reports of insecticide resistance continue to hamper the gains of vector control strategies in curbing malaria transmission. This makes identifying new insecticide targets or alternative vector control strategies necessary. CLassifier of Essentiality AcRoss EukaRyote (CLEARER), a leave-one-organism-out cross-validation machine learning classifier for essential genes, was used to predict essential genes in Anopheles gambiae and selected predicted genes experimentally validated. The CLEARER algorithm was trained on six model organisms: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and employed to identify essential genes in An. gambiae. Of the 10,426 genes in An. gambiae, 1,946 genes (18.7%) were predicted to be Cellular Essential Genes (CEGs), 1716 (16.5%) to be Organism Essential Genes (OEGs), and 852 genes (8.2%) to be essential as both OEGs and CEGs. RNA interference (RNAi) was used to validate the top three highly expressed non-ribosomal predictions as probable vector control targets, by determining the effect of these genes on the survival of An. gambiae G3 mosquitoes. In addition, the effect of knockdown of arginase (AGAP008783) on Plasmodium berghei infection in mosquitoes was evaluated, an enzyme we computationally inferred earlier to be essential based on chokepoint analysis. Arginase and the top three genes, AGAP007406 (Elongation factor 1-alpha, Elf1), AGAP002076 (Heat shock 70kDa protein 1/8, HSP), AGAP009441 (Elongation factor 2, Elf2), had knockdown efficiencies of 91%, 75%, 63%, and 61%, respectively. While knockdown of HSP or Elf2 significantly reduced longevity of the mosquitoes (p<0.0001) compared to control groups, Elf1 or arginase knockdown had no effect on survival. However, arginase knockdown significantly reduced P. berghei oocytes counts in the midgut of mosquitoes when compared to LacZ-injected controls. The study reveals HSP and Elf2 as important contributors to mosquito survival and arginase as important for parasite development, hence placing them as possible targets for vector control
Generalisation of genomic findings and applications of polygenic risk scores
Full text linkPolygenic Risk Scores (PRS) (also known as polygenic scores, genetic risk scores or polygenic indexes) capture genetic contributions of a multitude of markers that characterise complex traits. Although their likely application to precision medicine remains to be established, promising advances have included their ability to stratify high risk individuals and targeted screening interventions. Current PRS have been mostly optimised for individuals of Northern European ancestries. If PRS are to become widespread as a tool for healthcare applications, more diverse populations and greater capacity for derived interventions need to be accomplished. In this editorial we aim to attract submissions from the research community that highlight current challenges in development of PRS applications at scale. We also welcome manuscripts that delve into the ethical, social and legal implications that the implementation of PRS may generate
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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