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Genetic landscape of recessive diseases in the Vietnamese population from large-scale clinical exome sequencing.

Ngoc Hieu TranThanh-Huong Nguyen ThiHung-Sang TangLe-Phuc HoangTrung-Hieu Le NguyenNhat-Thang TranThu-Huong Nhat TrinhVan Thong NguyenBao-Han Huu NguyenHieu Trong NguyenLoc Phuoc DoanNgoc-Minh PhanKim-Huong Thi NguyenHong-Dang Luu NguyenMinh-Tam Thi QuachThanh-Phuong Thi NguyenVu Uyen TranDinh-Vinh TranQuynh-Tho Thi NguyenThanh-Thuy Thi DoNien Vinh LamPhuong Cao Thi NgocDinh Kiet TruongHoai-Nghia NguyenMinh Duy PhanHoa Giang
Published in: Human mutation (2021)
Accurate profiling of population-specific recessive diseases is essential for the design of cost-effective carrier screening programs. However, minority populations and ethnic groups, including Vietnamese, are still underrepresented in existing genetic studies. Here, we reported the first comprehensive study of recessive diseases in the Vietnamese population. Clinical exome sequencing data of 4503 disease-associated genes obtained from a cohort of 985 Vietnamese individuals was analyzed to identify pathogenic variants, associated diseases and their carrier frequencies in the population. A total of 118 recessive diseases associated with 164 pathogenic or likely pathogenic variants were identified, among which 28 diseases had carrier frequencies of at least 1% (1 in 100 individuals). Three diseases were prevalent in the Vietnamese population with carrier frequencies of 2-12 times higher than in the world populations, including beta-thalassemia (1 in 23), citrin deficiency (1 in 31), and phenylketonuria (1 in 40). Seven novel pathogenic and two likely pathogenic variants associated with nine recessive diseases were discovered. The comprehensive profile of recessive diseases identified in this study enables the design of cost-effective carrier screening programs specific to the Vietnamese population.
Keyphrases
  • copy number
  • single cell
  • public health
  • muscular dystrophy
  • gene expression
  • machine learning
  • dna methylation
  • mass spectrometry
  • artificial intelligence
  • smoking cessation
  • data analysis