<p>South Asian populations remain underrepresented in cancer genomics, despite elevated risk for certain malignancies and distinct clinical profiles. This gap is especially pronounced for British Bangladeshi and Pakistani communities. We analyse data from 57,416 individuals of Bangladeshi and Pakistani ancestry in the UK-based <i>Genes &amp; Health</i> cohort, integrating electronic health records, cancer registry data, and whole-exome sequencing (n = 43,462). Among them, 2,782 (4.8%) has cancer, with earlier onset across multiple types compared to national benchmarks. A phenome-wide case–control analysis identifies 132 significant cancer–comorbidity associations, with larger effect sizes observed for topographically concordant cancer-comorbidity pairs, and stronger associations for systemic disorders such as obesity and hypertension. Exome-wide analyses reveal 39 variant-level and 31 gene-level associations, over 60% absent from major genomic databases assessed and enriched for ultra-rare coding variations. Notable loci included <i>ARHGAP45</i>, <i>CBR1</i>, <i>FKBP6</i>, <i>NF1</i>, and <i>ZNF155</i>, with several ancestry-specific associations. These findings highlight distinct cancer risk architectures in this South Asian subpopulation, emphasising the need for population-tailored risk models and screening strategies.</p>

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Genetic and health determinants of cancer risk in Bangladeshi and Pakistani individuals in the UK

  • Abu Zafer Mohammed Dayem Ullah,
  • Ashitha Joby,
  • Graeme John Thorn,
  • Lewis George Emin James,
  • Isabelle Sanders,
  • David A. van Heel,
  • Shabana Chaudhary,
  • Joseph Gafton,
  • Karen A. Hunt,
  • Shapna Hussain,
  • Kamrul Islam,
  • Mohammed Bodrul Mazid,
  • Elizabeth Owor,
  • Jessry Russell,
  • Nishat Safa,
  • John Solly,
  • Marie Spreckley,
  • David A. van Heel,
  • Jan Whalley,
  • Ishevanhu Zengeya,
  • Emily Mantle,
  • Eamonn Maher,
  • Ana Angel,
  • Saeed Bidi,
  • Fabiola Eto,
  • Sarah Finer,
  • Chris Griffiths,
  • Sam Hodgson,
  • Benjamin M. Jacobs,
  • Rohini Mathur,
  • Caroline Morton,
  • Asma Qureshi,
  • Stuart Rison,
  • Annum Salman,
  • Miriam Samuel,
  • Moneeza K. Siddiqui,
  • Daniel Stow,
  • Sabina Yasmin,
  • Julia Zöllner,
  • Sheik Dowlut,
  • Shaheen Akhtar,
  • Samina Ashraf,
  • Dan Mason,
  • John Wright,
  • Michael Simpson,
  • Richard C. Trembath,
  • Gerome Breen,
  • Raymond Chung,
  • Sang Hyuck Lee,
  • Daniel MacArthur,
  • Omar Asgar,
  • Joanne Harvey,
  • Karen Tricker,
  • Caroline Winckley,
  • Hanifa Khatun,
  • Amna Asif,
  • Claudia Langenberg,
  • Grainne Colligan,
  • Ceri Durham,
  • Bill Newman,
  • Ahsan Khan,
  • Hilary Martin,
  • Teng Heng,
  • Matt Hurles,
  • Vivek Iyer,
  • Georgios Kalantzis,
  • Vladimir Ovchinnikov,
  • Iaroslav Popov,
  • Klaudia Walter,
  • Panos Deloukas,
  • David Collier,
  • Claude Chelala

摘要

South Asian populations remain underrepresented in cancer genomics, despite elevated risk for certain malignancies and distinct clinical profiles. This gap is especially pronounced for British Bangladeshi and Pakistani communities. We analyse data from 57,416 individuals of Bangladeshi and Pakistani ancestry in the UK-based Genes & Health cohort, integrating electronic health records, cancer registry data, and whole-exome sequencing (n = 43,462). Among them, 2,782 (4.8%) has cancer, with earlier onset across multiple types compared to national benchmarks. A phenome-wide case–control analysis identifies 132 significant cancer–comorbidity associations, with larger effect sizes observed for topographically concordant cancer-comorbidity pairs, and stronger associations for systemic disorders such as obesity and hypertension. Exome-wide analyses reveal 39 variant-level and 31 gene-level associations, over 60% absent from major genomic databases assessed and enriched for ultra-rare coding variations. Notable loci included ARHGAP45, CBR1, FKBP6, NF1, and ZNF155, with several ancestry-specific associations. These findings highlight distinct cancer risk architectures in this South Asian subpopulation, emphasising the need for population-tailored risk models and screening strategies.