4.7 Article

Identifying chromosomal subpopulations based on their recombination histories advances the study of the genetic basis of phenotypic traits

Journal

GENOME RESEARCH
Volume 30, Issue 12, Pages -

Publisher

COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT
DOI: 10.1101/gr.258301.119

Keywords

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Funding

  1. Supercomputing and Bioinnovation Center (SCBI) of the University of Malaga (Spain)
  2. Common Fund of the Office of the Director of the National Institutes of Health
  3. National Cancer Institute
  4. National Human Genome Research Institute
  5. National Heart, Lung, and Blood Institute
  6. National Institute on Drug Abuse
  7. National Institute of Mental Health
  8. National Institute of Neurological Disorders and Stroke
  9. Spanish Ministry of Economy and Competitiveness [MTM2015-68140-R]
  10. Spanish Ministry of Economy and Competitiveness
  11. Agencia Estatal de Investigacion (AEI)
  12. Departament d'Universitats, Recerca i Societat de la Informacio
  13. European Regional Development Fund (ERDF) [RTI2018-100789-B-I00]
  14. Centro de Excelencia Severo Ochoa 2019-2023 Program [CEX2018-000806-S]
  15. Catalan Government through the CERCA Program
  16. Catalan Government (Agencia de Gestio d'Ajuts Universitaris i de Recerca) [016FI_B 00272]
  17. European Commission [H2020-ERC-2014-CoG-647900]
  18. Ministerio de Ciencia, Innovacion y Universidades/AEI/FEDER [BFU2017-82937-P]
  19. Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya [GRC 2017 SGR 880]

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Recombination is a main source of genetic variability. However, the potential role of the variation generated by recombination in phenotypic traits, including diseases, remains unexplored because there is currently no method to infer chromosomal subpopulations based on recombination pattern differences. We developed recombClust, a method that uses SNP-Yphased data to detect differences in historic recombination in a chromosome population. We validated our method by performing simulations and by using real data to accurately predict the alleles of well-known recombination modifiers, including common inversions in Drosophila melanogaster and human, and the chromosomes under selective pressure at the lactase locus in humans. We then applied recombClust to the complex human 1q21.1 region, where nonallelic homologous recombination produces deleterious phenotypes. We discovered and validated the presence of two different recombination histories in these regions that significantly associated with the differential expression of ANKRD35 in whole blood and that were in high linkage with variants previously associated with hypertension. By detecting differences in historic recombination, our method opens a way to assess the influence of recombination variation in phenotypic traits.

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