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

Abstract

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.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

“…Moreover, it has been recently reported that recombination patterns can define distinct chromosomal subpopulations that may influence phenotypic traits. RecombClust [ 102 ] is a tool that is able to detect chromosomal subpopulations based on recombination histories using SNP array data. RecombClust can be used to detect inversions, regions under selection or where recombination is under regulation in a subgroup of individuals.…”

The Rat Genome Database (RGD) facilitates genomic and phenotypic data integration across multiple species for biomedical research

“…The study of genomics and disease is greatly benefitted by the inclusion of multiple species for comparative analyses (Prentice and Webster 2004;Howe et al 2018; Alliance of Genome Resources Consortium 2020; Baldridge et al 2021). This concept is widely accepted and has led to the development of a series of comparative tools (for example, Harris et al 2004;Buels et al 2016;Mungall et al 2017;Tholey et al 2017;Dunn et al 2019;Ruiz-Arenas et al 2020;Smith et al 2020;Foley et al 2021)). From its inception, RGD has championed comparative genomics, initially providing comparative maps and genomic and phenotypic data for rat, mouse, and human (Table 1) (Kwitek et al 2001;Twigger et al 2002Twigger et al , 2004 with the goal of better understanding human disease and pathophysiology by integrating data from rat, the major historical physiological and pharmacological model, and mouse, the major genetic model.…”