Clinical and genetic ancestry profile of a large multi-centre sickle cell disease cohort in Brazil.
Anna B F Carneiro-ProiettiShannon KellyCarolina Miranda TeixeiraEster C SabinoCecilia S AlencarLigia CapuaniTassila P Salomon SilvaAderson AraujoPaula LoureiroCláudia MáximoClarisse LoboMiriam V Flor-ParkDaniela O W RodriguesRosimere A MotaThelma T GonçalezCarolyn HoppeJoão E FerreiraMina OzahataGrier P PageYuelong GuoLiliana R PreissDonald BrambillaMichael P BuschBrian S Custernull nullPublished in: British journal of haematology (2018)
Approximately 3500 children with sickle cell disease (SCD) are born in Brazil each year, but the burden of SCD morbidity is not fully characterised. A large, multi-centre cohort was established to characterise clinical outcomes in the Brazilian SCD population and create the infrastructure to perform genotype-phenotype association studies. Eligible patients were randomly selected from participating sites and recruited at routine visits. A biorepository of blood samples was created and comprehensive demographic and clinical outcome data were entered in a centralized electronic database. Peripheral blood genome-wide single nucleotide polymorphism (SNP) genotyping was performed using a customized Transfusion Medicine (TM) Array. A total of 2795 participants at six Brazilian sites were enrolled between 2013 and 2015. The cohort included slight predominance of children <18 years (55·9%) and females (53·0%). Haemoglobin (Hb) SS was the most common SCD genotype (70·7%), followed by HbSC (23%), Sβ0 (3·0%) and Sβ+ (2·9%). SNP data from the TM Array were analysed to evaluate the genetic ancestry of the cohort and revealed significant admixture among the population. Demographics and clinical complications, stratified by age and SCD genotype, are summarized and future studies in this cohort are discussed.
Keyphrases
- genome wide
- sickle cell disease
- dna methylation
- peripheral blood
- young adults
- copy number
- high throughput
- high resolution
- newly diagnosed
- electronic health record
- acute kidney injury
- emergency department
- cardiac surgery
- gene expression
- prognostic factors
- preterm infants
- machine learning
- chronic kidney disease
- gestational age