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One of the aims of population genetics is to identify genetic differences/similarities among individuals of multiple ancestries. Many approaches including principal component analysis, clustering, and maximum likelihood techniques can be used to assign individuals to a given ancestry based on their genetic makeup. Although there are several tools that implement such algorithms, there is a lack of interactive visual platforms to run a variety of algorithms in one place. Therefore, we developed PopMLvis, a platform that offers an interactive environment to visualize genetic similarity data using several algorithms, and generate figures that can be easily integrated into scientific articles.

Background: Statins are central to primary and secondary prevention of atherosclerotic cardiovascular disease but are often underutilized due to myopathy and intolerance. While individual pharmacogenetic (PGx) variants, particularly in SLCO1B1, are linked to statin-associated muscle symptoms, the real-world impact of both clinical and cumulative PGx burden on regimen modification and adverse outcomes remains unclear. We aimed to evaluate the existing uncertainty regarding whether combined PGx scores can effectively guide statin dose titration and regimen modification, thereby filling a key clinical gap. Methods: A retrospective cohort study of 911 statin-treated patients with coronary artery disease was conducted from the Qatar Cardiovascular Biorepository with available whole-genome sequencing data. Variants in SLCO1B1, ABCG2, and CYP2C9 were combined into a functional PGx burden score, and their associations with statin regimen modification, intolerance, myopathy, liver injury, adherence, and composite adverse events were evaluated. The composite adverse events were defined as the occurrence of any statin-related adverse event, including statin-associated myopathy, liver injury, or poor medication adherence, during the follow-up period. Patients were classified as having experienced the composite outcome if at least one of these events occurred. Results: Over 12 months following statin initiation, 10.2% of patients underwent dose escalation, 11.4% de-escalation, and 78.4% remained on the same regimen. PGx burden is not statistically significantly associated with statin intolerance (OR 1.14; 95% CI: 0.73–1.76), composite adverse outcome (OR 1.08; 95% CI 0.82–1.42), or time to regimen change (HR 1.02; 95% CI 0.77–1.35). However, higher PGx burden showed a directional tendency toward dose de-escalation (RRR 1.18, 95% CI 0.76–1.84) and lower likelihood of escalation (RRR 0.93, 95% CI 0.56–1.54). Conclusions: Clinical factors, particularly statin intensity and myopathy, were the primary determinants of regimen modification. The PGx burden contributes to vulnerability to statin-related adverse effects in a context-dependent manner but does not independently drive statin regimen modification in routine clinical practice. Prospective studies are warranted to assess the clinical utility of PGx-guided workflows in statin therapy.

Disparities in the genetic risk of cancer among various ancestry groups and populations remain poorly defined. This challenge is even more acute for Middle Eastern populations, where the paucity of genomic data could affect the clinical potential of cancer genetic risk profiling. We used data from the phase 1 cohort of the Qatar Genome Programme to investigate genetic variation in cancer-susceptibility genes in the Qatari population. The Qatar Genome Programme generated high-coverage genome sequencing on DNA samples collected from 6142 native Qataris, stratified into six distinct ancestry groups: general Arab, Persian, Arabian Peninsula, Admixture Arab, African, and South Asian. In this population-based, cohort study, we evaluated the performance of polygenic risk scores for the most common cancers in Qatar (breast, prostate, and colorectal cancers). Polygenic risk scores were trained in The Cancer Genome Atlas (TCGA) dataset, and their distributions were subsequently applied to the six different genetic ancestry groups of the Qatari population. Rare deleterious variants within 1218 cancer susceptibility genes were analysed, and their clinical pathogenicity was assessed by ClinVar and the CharGer computational tools. The cohort included in this study was recruited by the Qatar Biobank between Dec 11, 2012, and June 9, 2016. The initial dataset comprised 6218 cohort participants, and whole genome sequencing quality control filtering led to a final dataset of 6142 samples. Polygenic risk score analyses of the most common cancers in Qatar showed significant differences between the six ancestry groups (p<0·0001). Qataris with Arabian Peninsula ancestry showed the lowest polygenic risk score mean for colorectal cancer (−0·41), and those of African ancestry showed the highest average for prostate cancer (0·85). Cancer-gene rare variant analysis identified 76 Qataris (1·2% of 6142 individuals in the Qatar Genome Programme cohort) carrying ClinVar pathogenic or likely pathogenic variants in clinically actionable cancer genes. Variant analysis using CharGer identified 195 individuals carriers (3·17% of the cohort). Breast cancer pathogenic variants were over-represented in Qataris of Persian origin (22 [56·4%] of 39 BRCA1/BRCA2 variant carriers) and completely absent in those of Arabian Peninsula origin. We observed a high degree of heterogeneity for cancer predisposition genes and polygenic risk scores across ancestries in this population from Qatar. Stratification systems could be considered for the implementation of national cancer preventive medicine programmes. Qatar Foundation.

The HIV-1 Env spike is the main protein complex that facilitates HIV-1 entry into CD4+ host cells. HIV-1 entry is a multistep process that is not yet completely understood. This process involves several protein-protein interactions between HIV-1 Env and a variety of host cell receptors along with many conformational changes within the spike. HIV-1 Env developed due to high mutation rates and plasticity escape strategies from immense immune pressure and entry inhibitors. We applied a coevolution and residue-residue contact detecting method to identify coevolution patterns within HIV-1 Env protein sequences representing all group M subtypes. We identified 424 coevolving residue pairs within HIV-1 Env. The majority of predicted pairs are residue-residue contacts and are proximal in 3D structure. Furthermore, many of the detected pairs have functional implications due to contributions in either CD4 or coreceptor binding, or variable loop, gp120-gp41, and interdomain interactions. This study provides a new dimension of information in HIV research. The identified residue couplings may not only be important in assisting gp120 and gp41 coordinate structure prediction, but also in designing new and effective entry inhibitors that incorporate mutation patterns of HIV-1 Env.

Coronary heart disease (CHD) is a major cause of death in Middle Eastern (ME) populations, with current studies of the metabolic fingerprints of CHD lacking in diversity. Identification of specific biomarkers to uncover potential mechanisms for developing predictive models and targeted therapies for CHD is urgently needed for the least-studied ME populations. A case-control study was carried out in a cohort of 1001 CHD patients and 2999 controls. Untargeted metabolomics was used, generating 1159 metabolites. Univariate and pathway enrichment analyses were performed to understand functional changes in CHD. A metabolite risk score (MRS) was developed to assess the predictive performance of CHD using multivariate analysis and machine learning. A total of 511 metabolites were significantly different between the CHD patients and the controls (FDR p < 0.05). The enriched pathways (FDR p < 10−300) included D-arginine and D-ornithine metabolism, glycolysis, oxidation and degradation of branched chain fatty acids, and sphingolipid metabolism. MRS showed good discriminative power between the CHD cases and the controls (AUC = 0.99). In this first study in the Middle East, known and novel circulating metabolites and metabolic pathways associated with CHD were identified. A small panel of metabolites can efficiently discriminate CHD cases and controls and therefore can be used as a diagnostic/predictive tool.

When performing a Genome-Wide Association Study (GWAS), one attempts to associate a phenotype with some genomic information, commonly a gene or set of genes. Often we wish to have more accuracy and attempt to identify a Single Nucleotide Polymorphism (SNP) or Single Nucleotide Polymorphisms (SNPs) that are associated with the phenotype. Sometimes a GWAS is also used to associate other kinds of genetic data, like methylation or Copy Number Variations (CNVs) with the phenotype. The phenotype in such studies is often a disease, e.g. Type II Diabetes Melitus (T2D), Coronary Heart Disease (CHD), cancer, or others, but can be other traits as well, for instance, height, weight, eye color, or intelligence. In order to perform a GWAS it is necessary to sequence the Deoxyribonucleic Acid (DNA) of the individuals in the study. This sequencing is much cheaper than it once was, but is still very expensive for large scale studies. Large scale studies are needed in order to achieve the necessary statistical power to reliably identify associations. By performing imputation we are able to increase the size of studies in two ways. Individual studies are able to sequence more individuals on their budget because they can sequence individuals for only certain sites and impute the rest of the sites to recover part of the power. Also, large scale meta-studies can impute in order to have full sequences for all the individuals in the smaller studies in order to make them comparable, this is the approach taken by Fuchsberger et al [33]. Imputation for genetic data is done in two main ways. The first way is population-based imputation, which depends on Linkage Disequilibrium (LD) and knowing the allele frequencies for a reference population that the study population is believed to be similar to. The second main way to impute is Identity By Descent (IBD)-based imputation, in which we infer genotypes based on the familial relationships in pedigree data. In this thesis, we focus on IBD-based imputation. Imputing on pedigree data can be quite time consuming, for instance, the original implementation of GIGI (Genome Imputation Given Inheritance), Cheung et al [15], took around 17 days to impute chromosome 2 (2,402,346 SNPs) of a pedigree with 189 members, using 28 GB of RAM [53]. Being able to complete family (IBD)-based imputation in a timely manner with high accuracy is of great value to researchers around the world, especially now as this data becomes more available to those without large budgets for sheer computing power. The basis for phasing and imputation along with the details of the calculations involved and exploration of ways to increase the speed for imputing large pedigree data are described in this thesis.

Background: The current study explores the genetic underpinnings of cardiac arrhythmia phenotypes within Middle Eastern populations, which are under-represented in genomic medicine research. Methods: Whole-genome sequencing data from 14,259 individuals from the Qatar Biobank were used and contained 47.8% of Arab ancestry, 18.4% of South Asian ancestry, and 4.6% of African ancestry. The frequency of rare functional variants within a set of 410 candidate genes for cardiac arrhythmias was assessed. Polygenic risk score (PRS) performance for atrial fibrillation (AF) prediction was evaluated. Results: This study identified 1196 rare functional variants, including 162 previously linked to arrhythmia phenotypes, with varying frequencies across Arab, South Asian, and African ancestries. Of these, 137 variants met the pathogenic or likely pathogenic (P/LP) criteria according to ACMG guidelines. Of these, 91 were in ACMG actionable genes and were present in 1030 individuals (~7%). Ten P/LP variants showed significant associations with atrial fibrillation p < 2.4 × 10−10. Five out of ten existing PRSs were significantly associated with AF (e.g., PGS000727, p = 0.03, OR = 1.43 [1.03, 1.97]). Conclusions: Our study is the largest to study the genetic predisposition to arrhythmia phenotypes in the Middle East using whole-genome sequence data. It underscores the importance of including diverse populations in genomic investigations to elucidate the genetic landscape of cardiac arrhythmias and mitigate health disparities in genomic medicine.

Background: Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool used in clinical medicine to assess the electrical activity of the heart while the patient is resting. Abnormalities in ECG may be associated with clinical biomarkers and can predict early stages of diseases. In this study, we evaluated the association between ECG traits, clinical biomarkers, and diseases and developed risk scores to predict the risk of developing coronary artery disease (CAD) in the Qatar Biobank. Methods: This study used 12-lead ECG data from 13,827 participants. The ECG traits used for association analysis were RR, PR, QRS, QTc, PW, and JT. Association analysis using regression models was conducted between ECG variables and serum electrolytes, sugars, lipids, blood pressure (BP), blood and inflammatory biomarkers, and diseases (e.g., type 2 diabetes, CAD, and stroke). ECG-based and clinical risk scores were developed, and their performance was assessed to predict CAD. Classical regression and machine-learning models were used for risk score development. Results: Significant associations were observed with ECG traits. RR showed the largest number of associations: e.g., positive associations with bicarbonate, chloride, HDL-C, and monocytes, and negative associations with glucose, insulin, neutrophil, calcium, and risk of T2D. QRS was positively associated with phosphorus, bicarbonate, and risk of CAD. Elevated QTc was observed in CAD patients, whereas decreased QTc was correlated with decreased levels of calcium and potassium. Risk scores developed using regression models were outperformed by machine-learning models. The area under the receiver operating curve reached 0.84 using a machine-learning model that contains ECG traits, sugars, lipids, serum electrolytes, and cardiovascular disease risk factors. The odds ratio for the top decile of CAD risk score compared to the remaining deciles was 13.99. Conclusions: ECG abnormalities were associated with serum electrolytes, sugars, lipids, and blood and inflammatory biomarkers. These abnormalities were also observed in T2D and CAD patients. Risk scores showed great predictive performance in predicting CAD.

Background The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso. Results Using the original PSICOV benchmark dataset, COUSCOus achieves mean accuracies of 0.74, 0.62 and 0.55 for the top L /10 predicted long, medium and short range contacts, respectively. In addition, COUSCOus attains mean areas under the precision-recall curves of 0.25, 0.29 and 0.30 for long, medium and short contacts and outperforms PSICOV. We also observed that COUSCOus outperforms PSICOV w.r.t. Matthew’s correlation coefficient criterion on full list of residue contacts. Furthermore, COUSCOus achieves on average 10% more gain in prediction accuracy compared to PSICOV on an independent test set composed of CASP11 protein targets. Finally, we showed that when using a simple random forest meta-classifier, by combining contact detecting techniques and sequence derived features, PSICOV predictions should be replaced by the more accurate COUSCOus predictions. Conclusion We conclude that the consideration of superior covariance shrinkage approaches will boost several research fields that apply the GLasso procedure, amongst the presented one of residue-residue contact prediction as well as fields such as gene network reconstruction.