Genetic Susceptibility to Arrhythmia Phenotypes in a Middle Eastern Cohort of 14,259 Whole-Genome Sequenced Individuals.

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.

Correlations between Resting Electrocardiogram Findings and Disease Profiles: Insights from the Qatar Biobank Cohort.

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.

The QChip1 knowledgebase and microarray for precision medicine in Qatar.

Risk genes for Mendelian (single-gene) disorders (SGDs) are consistent across populations, but pathogenic risk variants that cause SGDs are typically population-private. The goal was to develop "QChip1," an inexpensive genotyping microarray to comprehensively screen newborns, couples, and patients for SGD risk variants in Qatar, a small nation on the Arabian Peninsula with a high degree of consanguinity. Over 108 variants in 8445 Qatari were identified for inclusion in a genotyping array containing 165,695 probes for 83,542 known and potentially pathogenic variants in 3438 SGDs. QChip1 had a concordance with whole-genome sequencing of 99.1%. Testing of QChip1 with 2707 Qatari genomes identified 32,674 risk variants, an average of 134 pathogenic alleles per Qatari genome. The most common pathogenic variants were those causing homocystinuria (1.12% risk allele frequency), and Stargardt disease (2.07%). The majority (85%) of Qatari SGD pathogenic variants were not present in Western populations such as European American, South Asian American, and African American in New York City and European and Afro-Caribbean in Puerto Rico; and only 50% were observed in a broad collection of data across the Greater Middle East including Kuwait, Iran, and United Arab Emirates. This study demonstrates the feasibility of developing accurate screening tools to identify SGD risk variants in understudied populations, and the need for ancestry-specific SGD screening tools.

Qatar Biobank Cohort Study: Study Design and First Results.

We describe the design, implementation, and results of the Qatar Biobank (QBB) cohort study for the first 10,000 participants. QBB is a prospective, population-based cohort study in Qatar, established in 2012. QBB's primary goal was to establish a cohort accessible to the local and international scientific community, providing adequate health data and biological samples to enable evidence-based research. The study design is based on an agnostic hypothesis, collecting data using questionnaires, biological samples, imaging data, and -omics. QBB aims to recruit 60,000 participants, men and women, adult (aged ≥18 years) Qataris or long-term residents (≥15 years living in Qatar) and follow up with them every 5 years. Currently, QBB has reached 28% (n = 17,065) of the targeted enrollee population and more than 2 million biological samples. QBB is a multinational cohort including 33 different nationalities, with a relatively young population (mean age, 40.5 years) of persons who are highly educated (50% university-educated) and have high monthly incomes. The 4 main noncommunicable diseases found among the QBB population are dyslipidemia, diabetes, hypertension, and asthma with prevalences of 30.1%, 17.4%, 16.8%, and 9.1%, respectively. The QBB repository can provide data and biological samples sufficient to demonstrate valid associations between genetic and/or environmental exposure and disease development to scientists worldwide.

The Qatar Biobank: background and methods.

BACKGROUND: The Qatar Biobank aims to collect extensive lifestyle, clinical, and biological information from up to 60,000 men and women Qatari nationals and long-term residents (individuals living in the country for ≥15 years) aged ≥18 years (approximately one-fifth of all Qatari citizens), to follow up these same individuals over the long term to record any subsequent disease, and hence to study the causes and progression of disease, and disease burden, in the Qatari population. METHODS: Between the 11(th)-December-2012 and 20(th)-February-2014, 1209 participants were recruited into the pilot study of the Qatar Biobank. At recruitment, extensive phenotype information was collected from each participant, including information/measurements of socio-demographic factors, prevalent health conditions, diet, lifestyle, anthropometry, body composition, bone health, cognitive function, grip strength, retinal imaging, total body dual energy X-ray absorptiometry, and measurements of cardiovascular and respiratory function. Blood, urine, and saliva were collected and stored for future research use. A panel of 66 clinical biomarkers was routinely measured on fresh blood samples in all participants. Rates of recruitment are to be progressively increased in the coming period and the recruitment base widened to achieve a cohort of consented individuals broadly representative of the eligible Qatari population. In addition, it is planned to add additional measures in sub-samples of the cohort, including Magnetic Resonance Imaging (MRI) of the brain, heart and abdomen. RESULTS: The mean time for collection of the extensive phenotypic information and biological samples from each participant at the baseline recruitment visit was 179 min. The 1209 pilot study participants (506 men and 703 women) were aged between 28-80 years (median 39 years); 899 (74.4%) were Qatari nationals and 310 (25.6%) were long-term residents. Approximately two-thirds of pilot participants were educated to graduate level or above. CONCLUSIONS: The pilot has proven that recruitment of volunteers into the Qatar Biobank project with intensive baseline measurements of behavioural, physical, and clinical characteristics is well accepted and logistically feasible. Qatar Biobank will provide a powerful resource to investigate the major determinants of ill-health and well-being in Qatar, providing valuable insights into the current and future public health burden that faces the country.