Abstract
Previous research has shown that using population-specific reference panels has a significant effect on downstream population genomic analyses like haplotype phasing, genotype imputation, and association, especially in the context of population isolates. Here, we developed a high-resolution recombination rate mapping at 10 and 50 kb scale using high-coverage (20–30×) whole-genome sequenced data of 55 family trios from Finland and compared it to recombination rates of non-Finnish Europeans (NFE). We tested the downstream effects of the population-specific recombination rates in statistical phasing and genotype imputation in Finns as compared to the same analyses performed by using the NFE-based recombination rates. We found that Finnish recombination rates have a moderately high correlation (Spearman’s ρ = 0.67–0.79) with NFE, although on average (across all autosomal chromosomes), Finnish rates (2.268 ± 0.4209 cM/Mb) are 12–14% lower than NFE (2.641 ± 0.5032 cM/Mb). Finnish recombination map was found to have no significant effect in haplotype phasing accuracy (switch error rates ~2%) and average imputation concordance rates (97–98% for common, 92–96% for low frequency and 78–90% for rare variants). Our results suggest that haplotype phasing and genotype imputation mostly depend on population-specific contexts like appropriate reference panels and their sample size, but not on population-specific recombination maps. Even though recombination rate estimates had some differences between the Finnish and NFE populations, haplotyping and imputation had not been noticeably affected by the recombination map used. Therefore, the currently available HapMap recombination maps seem robust for population-specific phasing and imputation pipelines, even in the context of relatively isolated populations like Finland.
Highlights
Recombination is not uniform across the human genome with large areas having lower recombination rates, so-called ‘coldspots’, which are interspersed by shorter regions marked by a high recombinational activity called ‘hotspots’ [1]
We present a direct comparison between the two maps, of the recombination rates at 5 Mb scales, which presents a similar visual pattern of rates across the genome (Supplementary Fig. 1)
Population isolates like Finland, have had a divergent demographic history as compared to the outbred NonFinnish European populations, with lower migration rates, more fluctuating population sizes and higher incidences of bottleneck events and founder effects [35, 36] This unique demographic history affects different population genetic parameters, like recombination rates [37]
Summary
Recombination is not uniform across the human genome with large areas having lower recombination rates, so-called ‘coldspots’, which are interspersed by shorter regions marked by a high recombinational activity called ‘hotspots’ [1]. A less time-consuming but computationally intensive alternative is to use the LD patterns surrounding the SNPs [5] Such methods have been used in the past decade or so, to create fine-scale recombination maps [6]. Besides the International HapMap project that focused on capturing common variants and haplotypes in diverse populations, international wholegenome sequencing (WGS)-based collaborations like the 1000 Genomes Project, provided genetic variation data for 20 worldwide populations [7]. This led to further refinement of the recombination maps coupled with methodological advances of using coalescent methods for recombination rate [8, 9]
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