Reliable genotyping of recombinant genomes using a robust hidden Markov model

Abstract

Meiotic recombination is an essential mechanism during sexual reproduction and includes the exchange of chromosome segments between homologous chromosomes. New allelic combinations are transmitted to the new generation, introducing novel genetic variation in the offspring genomes. With the improvement of high-throughput whole-genome sequencing technologies, large numbers of recombinant individuals can now be sequenced with low sequencing depth at low costs, necessitating computational methods for reconstructing their haplotypes. The main challenge is the uncertainty in haplotype calling that arises from the low information content of a single genomic position. Straightforward sliding window-based approaches are difficult to tune and fail to place recombination breakpoints precisely. Hidden Markov Model-based approaches, on the other hand, tend to over-segment the genome. Here, we present RTIGER, an HMM-based model that exploits in a mathematically precise way the fact that true chromosome segments typically have a certain minimum length. We further separate the task of identifying the correct haplotype sequence from the accurate placement of haplotype borders, thereby maximizing the accuracy of border positions. By comparing segmentations based on simulated data with known underlying haplotypes, we highlight the reasons for RTIGER outperforming traditional segmentation approaches. We then analyze the meiotic recombination pattern of segregants of two Arabidopsis (Arabidopsis thaliana) accessions and a previously described hyper-recombining mutant. RTIGER is available as an R-package with an efficient Julia implementation of the core algorithm.

Meiotic recombination dynamics in plants with repeat-based holocentromeres shed light on the primary drivers of crossover patterning

“…Next, SNPs of each F1 sample were input to TIGER 77 for genotyping and generating potential CO positions. In addition, RTIGER 78 was also used to identify the genotypes of chromosomal segments by utilizing the corrected markers resulted from TIGER. Only the COs that agreed by both tools were kept.…”

Meiotic recombination dynamics in plants with repeat-based holocentromeres shed light on the primary drivers of crossover patterning

“…Next, SNPs of each F 1 sample were input to TIGER 91 for genotyping and to generate potential CO positions. In addition, RTIGER 92 was used to identify the genotypes of chromosomal segments by utilizing the corrected markers that resulted from TIGER. Only COs that agreed using both tools were kept.…”

Expression quantitative trait locus mapping in recombinant gametes using single nucleus RNA sequencing

“…We adapted the recently described rigid hidden Markov model (rHMM) approach for recombination mapping, for use in haploid nuclei (Campos-Martin et al, 2023). The rHMM method uses sparsely connected states, meaning that any path through the model passes through multiple states representing the same haplotype.…”

“…This method solves the challenges unique to demultiplexing heterogeneous gamete populations originating from different heterozygous hybrids. We then modified the recently developed rHMM method to predict meiotic recombination patterns between the two parental genotypes, using SNPs and indels as markers (Campos-Martin et al, 2023). The benefit of the rHMM method is that it uses sparse transitions to prevent over-segmentation of chromosomes which is not biologically plausible due to crossover interference (Durand et al, 2022).…”