A Particular Focus on the Prevalence of α - and β -Thalassemia in Western Sicilian Population from Trapani Province in the COVID-19 Era.
Rossella DaidoneAntonella CarolloMaria Patrizia PerriconeRenato MessinaCarmela Rita BalistreriPublished in: International journal of molecular sciences (2023)
Thalassemia is a Mendelian inherited blood disease caused by α- and β-globin gene mutations, known as one of the major health problems of Mediterranean populations. Here, we examined the distribution of α- and β-globin gene defects in the Trapani province population. A total of 2,401 individuals from Trapani province were enrolled from January 2007 to December 2021, and routine methodologies were used for detecting the α- and β-globin genic variants. Appropriate analysis was also performed. Eight mutations in the α globin gene showed the highest frequency in the sample studied; three of these genetic variants represented the 94% of the total α-thalassemia mutations observed, including the -α3.7 deletion (76%), and the tripling of the α gene (12%) and of the α2 point mutation IVS1-5nt (6%). For the β-globin gene, 12 mutations were detected, six of which constituted 83.4% of the total number of β-thalassemia defects observed, including codon β039 (38%), IVS1.6 T > C (15.6%), IVS1.110 G > A (11.8%), IVS1.1 G > A (11%), IVS2.745 C > G (4%), and IVS2.1 G > A (3%). However, the comparison of these frequencies with those detected in the population of other Sicilian provinces did not demonstrate significant differences, but it contrarily revealed a similitude. The data presented in this retrospective study help provide a picture of the prevalence of defects on the α and β-globin genes in the province of Trapani. The identification of mutations in globin genes in a population is required for carrier screening and for an accurate prenatal diagnosis. It is important and necessary to continue promoting public awareness campaigns and screening programs.
Keyphrases
- south africa
- genome wide
- genome wide identification
- copy number
- mental health
- healthcare
- public health
- risk factors
- sickle cell disease
- genome wide analysis
- dna methylation
- bioinformatics analysis
- machine learning
- transcription factor
- clinical practice
- single cell
- risk assessment
- gene expression
- climate change
- big data
- social media
- data analysis