Generalisation of genomic findings and applications of polygenic risk scores

Polygenic 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

Developing Ontology Support for Human Malaria Control Initiatives

Malaria is one of the most common infectious diseases and an enormous public health problem in Sub-Sahara Africa, Asia and parts of America. In this paper, we discuss the development of the Human Malaria Control Ontology (HMCO) which contains general information on Malaria and epidemiological information that can help in the formulation of effective malaria control policies. The HMCO is aimed at providing interoperability support for the knowledge management of malaria control initiatives, and serve as an open semantic web infrastructure for malaria research and treatment

A Functional Workbench for Anopheles gambiae Micro Array Analysis

Insecticide resistance, a character inherited that encompasses alteration in one or more of insect’s genes is now a major public health challenge combating world efforts on malaria control strategies. Anopheles has developed heavy resistance to pyrethroids, the only World Health Organization (WHO) recommended class for Indoor Residual Spray (IRS) and Long-Lasting Insecticide Treated Nets (LLITNs) through P450 pathways. We used the biochemical network of Anopheles gambiae (henceforth Ag) to deduce its resistance mechanism(s) using two expression data (when Ag is treated with pyrethroid and when controlled). The employed computational techniques are accessible by a robust, multi-faceted and friendly automated graphic user interface (GUI) tagged ‘workbench’ with JavaFX Scenebuilder. In this work, we introduced a computational platform to determine and also elucidate for the first time resistance mechanism to a commonly used class of insecticide, Pyrethroid. Significantly, our work is the first computational work to identify genes associated or involved in the efflux system in Ag and as a resistance mechanism in the Anophele

Are we nearly there yet? Starts and stops on the road to use of polygenic scores

As technological advancements expand the accessibility and availability of molecular genetic data, excitement over the potential use of genetic risk scores also known as polygenic scores (PGS) or polygenic indices for disease prevention has grown. At the same time, however, the translation of PGS into healthcare and social settings raises a host of social, ethical, and clinically relevant questions

The Anopheles gambiae Insecticidal Targets Made Bare by In-silica Analysis

seveml wot·ks had attempted to use genomics to explain the mode of mosquito t·esistance and pt·edict dmg tat·get. The use of insecticides in val'ious ways has been the majm· malal'ia vectot· conti'Ol stmtegy being deployed lately, mostly pyt·ethi'Oid, the majm· t·ecommended compound class fot· IRS, ITNs and LLITNs. Resistance to dmgs and insecticides has continually obstmcted vectm·/malal'ia contt·ol stntegies. The advet·t effect is so enonnous in the Sub-Sahamn Afl'ican; its socioeconomic impact is unquantifiable in evet·y measm·e. Thus, the quick necessity fm· the development and elucidation of potent, cheap and efficient new potential insecticidal tat·gets, especially those in the class pyt·ethi'Oid fm· the malal'ia vectot·, A. gambiae. In this wm·k, an updated Anopheles gambiae biochemical metabolic netwm·k (AnoCyc vel'l.O), othet·wise known as pathway genome database (PGDB) was extmcted, the database was t·econstt·ucted by developing a computational gmph model in an appi'Oach that modeled the metabolic netwot·k of the m·ganism as a bipat·tite gmph, deployed the concept of choke point, load point and t·eaction without deviation to detet·mine the essential enzymatic t·eactions in the netwm·ks. Each potential dmg tat·get to theit· coiTesponding gene/pi'Otein and such encoding pi'Otein sequences wet·e extmcted. (PDB) was blasted fot· genes that have stmctm·e m· homologue of >= 30 sequence identity. Finally, we deployed Ovet·ton and Bation Scm·e (OB-Scm·e) and Pat·Ct·ys pt·ediction to mnk pi'Oteins by theit' likely success in ct·ystallization. 61 potential insecticidal candidate tat·gets was made bat·e, one clinically validated insecticidal tat·get and othet·s with biological evidence in the litemtnt·e. Seven of these tat·gets ideally stand out and have no homology with othet· vetiebmtes. These in depth dissection of the biochemical metabolic netwm·ks of the Anopheles effectively identified the ideal gene pi'Oducts and specifically extmct essential enzymes as new potential insecticidal tat·get against A. gambiae

Non-communicable diseases pandemic and precision medicine: Is Africa ready?

Non-communicable diseases (NCDs) kill more than 41 million people every year, accounting for 71% of all deaths globally. The prevalence of NCDs is estimated to be higher than that of infectious diseases in Africa by 2030. Precision medicine may help with early identification of cases, resulting in timely prevention and improvement in the efficacy of treatments. However, Africa has been lagging behind in genetic research, a key component of the precision medicine initiative. A number of genomic research initiatives which could lead to translational genomics are emerging on the African continent which includes the Non-communicable Diseases Genetic Heritage Study (NCDGHS) and the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network. These offer a promise that precision medicine can be applied in African countries. This review evaluates the advances of genetic studies for cancer, hypertension, type 2 diabetes and body mass index (BMI) in Africa

Transcription-translation error: In-silico investigation of the structural and functional impact of deleterious single nucleotide polymorphisms in GULP1 gene

Nonsynonymous single nucleotide polymorphisms (nsSNPs) are one of the most common forms of mutations known to disrupt the product of translation thereby altering the protein structure-function relationship. GULP1 (PTB domain-containing engulfment adaptor protein 1) is an evolutionarily conserved adaptor protein that has been associated with glycated hemoglobin (HbA1c) in Genome-Wide Association Studies (GWAS). In order to understand the role of GULP1 in the etiology of diabetes, it is important to study some functional nsSNPs present within the GULP1 protein. We, therefore, used a SNPinformatics approach to retrieve, classify, and determine the stability effect of some nsSNPs. Y27C, G142D, A144T, and Y149C were jointly predicted by the pathogenic-classifying tools to be disease-causing, however, only G142D, A144T, and Y149C had their structural architecture perturbed as predicted by I-MUTANT and MuPro. Interestingly, G142D and Y149C occur at positions 142 and 149 of GULP1 which coincidentally are found within the binding site of GULP1. Protein-Protein interaction analysis also revealed that GULP1 interacted with 10 proteins such as Cell division cycle 5-like protein (CDC5L), ADP-ribosylation factor 6 (ARF6), Arf-GAP with coiled-coil (ACAP1), and Multiple epidermal growth factor-like domains protein 10 (MEGF10), etc. Taken together, rs1357922096, rs1264999716, and rs128246649 could be used as genetic biomarkers for the diagnosis of diabetes. However, being a computational study, these nsSNPs require experimental validation to explore their metabolic involvement in the pathogenesis of diseases

Complimentary Methods for Multivariate Genome-Wide Association Study Identify New Susceptibility Genes for Blood Cell Traits.

Genome-wide association studies (GWAS) have found hundreds of novel loci associated with full blood count (FBC) phenotypes. However, most of these studies were performed in a single phenotype framework without putting into consideration the clinical relatedness among traits. In this work, in addition to the standard univariate GWAS, we also use two different multivariate methods to perform the first multiple traits GWAS of FBC traits in ∼7000 individuals from the Ugandan General Population Cohort (GPC). We started by performing the standard univariate GWAS approach. We then performed our first multivariate method, in this approach, we tested for marker associations with 15 FBC traits simultaneously in a multivariate mixed model implemented in GEMMA while accounting for the relatedness of individuals and pedigree structures, as well as population substructure. In this analysis, we provide a framework for the combination of multiple phenotypes in multivariate GWAS analysis and show evidence of multi-collinearity whenever the correlation between traits exceeds the correlation coefficient threshold of r 2 >=0.75. This approach identifies two known and one novel loci. In the second multivariate method, we applied principal component analysis (PCA) to the same 15 correlated FBC traits. We then tested for marker associations with each PC in univariate linear mixed models implemented in GEMMA. We show that the FBC composite phenotype as assessed by each PC expresses information that is not completely encapsulated by the individual FBC traits, as this approach identifies three known and five novel loci that were not identified using both the standard univariate and multivariate GWAS methods. Across both multivariate methods, we identified six novel loci. As a proof of concept, both multivariate methods also identified known loci, HBB and ITFG3. The two multivariate methods show that multivariate genotype-phenotype methods increase power and identify novel genotype-phenotype associations not found with the standard univariate GWAS in the same dataset