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

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-phased 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.We thank the Supercomputing and Bioinnovation Center (SCBI) of the University of Malaga (Spain) for their support and resources. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by the National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke. GTEx data were obtained from the GTEx Portal on 06/07/2018 and dbGaP accession number phs000424.v7.p2 on 12/05/2017. This work was partly supported by the Spanish Ministry of Economy and Competitiveness (MTM2015-68140-R) and by the Spanish Ministry of Economy and Competitiveness, the Agencia Estatal de Investigación (AEI), the Departament d'Universitats, Recerca i Societat de la Informació, and the European Regional Development Fund (ERDF) (RTI2018-100789-B-I00). This work also received support from the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S); and the Catalan Government through the CERCA Program. C.R.-A. is funded by the Catalan Government (Agència de Gestió d'Ajuts Universitaris i de Recerca, #016FI_B 00272 to C.R.-A.). J.G. is funded by the European Commission (H2020-ERC-2014-CoG-647900), the Ministerio de Ciencia, Innovación y Universidades/AEI/FEDER (BFU2017-82937-P), and the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880)