Section: Genomics
Topic: Genetics/Genomics, Evolution, Population biology

Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study

Raynaud, Marie1 ; Gagnaire, Pierre-Alexandre1 ; Galtier, Nicolas1 
1 ISEM, Univ Montpellier, CNRS, IRD, Montpellier, France

Corresponding author(s): Raynaud, Marie (marie.raynaud@umontpellier.fr)

10.24072/pcjournal.254 - Peer Community Journal, Volume 3 (2023), article no. e27.

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Knowledge of recombination rate variation along the genome provides important insights into genome and phenotypic evolution. Population genomic approaches offer an attractive way to infer the population-scaled recombination rate ρ=4Ner using the linkage disequilibrium information contained in DNA sequence polymorphism data. Such methods have been used in a broad range of plant and animal species to build genome-wide recombination maps. However, the reliability of these inferences has only been assessed under a restrictive set of conditions. Here, we evaluate the ability of one of the most widely used coalescent-based programs, LDhelmet, to infer a genomic landscape of recombination with the biological characteristics of a human-like landscape including hotspots. Using simulations, we specifically assessed the impact of methodological (sample size, phasing errors, block penalty) and evolutionary parameters (effective population size (Ne),  demographic history, mutation to recombination rate ratio) on inferred map quality. We report reasonably good correlations between simulated and inferred landscapes, but point to limitations when it comes to detecting recombination hotspots. False positive and false negative hotspots considerably confound fine-scale patterns of inferred recombination under a wide range of conditions, particularly when Ne is small and the mutation/recombination rate ratio is low, to the extent that maps inferred from populations sharing the same recombination landscape appear uncorrelated. We thus address a message of caution for the users of these approaches, at least for genomes with complex recombination landscapes such as in humans.

Published online:
DOI: 10.24072/pcjournal.254
Type: Research article
Mots-clés : Population-scaled recombination rate, LDhelmet, simulations, linkage disequilibrium, recombination hotspots
Mots-clés : Population-scaled recombination rate, LDhelmet, simulations, linkage disequilibrium, recombination hotspots

Raynaud, Marie 1; Gagnaire, Pierre-Alexandre 1; Galtier, Nicolas 1

1 ISEM, Univ Montpellier, CNRS, IRD, Montpellier, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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     title = {Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study},
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Raynaud, Marie; Gagnaire, Pierre-Alexandre; Galtier, Nicolas. Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study. Peer Community Journal, Volume 3 (2023), article  no. e27. doi : 10.24072/pcjournal.254. https://peercommunityjournal.org/articles/10.24072/pcjournal.254/

PCI peer reviews and recommendation, and links to data, scripts, code and supplementary information: 10.24072/pci.genomics.100161

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

[1] Arenas, M. The importance and application of the ancestral recombination graph, Frontiers in Genetics, Volume 4 (2013) | DOI

[2] Auton, A.; Fledel-Alon, A.; Pfeifer, S.; Venn, O.; Ségurel, L.; Street, T.; Leffler, E. M.; Bowden, R.; Aneas, I.; Broxholme, J.; Humburg, P.; Iqbal, Z.; Lunter, G.; Maller, J.; Hernandez, R. D.; Melton, C.; Venkat, A.; Nobrega, M. A.; Bontrop, R.; Myers, S.; Donnelly, P.; Przeworski, M.; McVean, G. A Fine-Scale Chimpanzee Genetic Map from Population Sequencing, Science, Volume 336 (2012) no. 6078, pp. 193-198 | DOI

[3] Auton, A.; McVean, G. Recombination rate estimation in the presence of hotspots, Genome Research, Volume 17 (2007) no. 8, pp. 1219-1227 | DOI

[4] Auton, A.; Rui Li, Y.; Kidd, J.; Oliveira, K.; Nadel, J.; Holloway, J. K.; Hayward, J. J.; Cohen, P. E.; Greally, J. M.; Wang, J.; Bustamante, C. D.; Boyko, A. R. Genetic Recombination Is Targeted towards Gene Promoter Regions in Dogs, PLoS Genetics, Volume 9 (2013) no. 12 | DOI

[5] Axelsson, E.; Webster, M. T.; Ratnakumar, A.; Ponting, C. P.; Lindblad-Toh, K. Death of PRDM9 coincides with stabilization of the recombination landscape in the dog genome, Genome Research, Volume 22 (2011) no. 1, pp. 51-63 | DOI

[6] Baker, Z.; Schumer, M.; Haba, Y.; Bashkirova, L.; Holland, C.; Rosenthal, G. G.; Przeworski, M. Repeated losses of PRDM9-directed recombination despite the conservation of PRDM9 across vertebrates, eLife, Volume 6 (2017) | DOI

[7] Baudat, F.; Buard, J.; Grey, C.; Fledel-Alon, A.; Ober, C.; Przeworski, M.; Coop, G.; de Massy, B. PRDM9 Is a Major Determinant of Meiotic Recombination Hotspots in Humans and Mice, Science, Volume 327 (2010) no. 5967, pp. 836-840 | DOI

[8] Baudat, F.; de Massy, B. Regulating double-stranded DNA break repair towards crossover or non-crossover during mammalian meiosis, Chromosome Research, Volume 15 (2007) no. 5, pp. 565-577 | DOI

[9] Booker, T. R.; Ness, R. W.; Keightley, P. D. The Recombination Landscape in Wild House Mice Inferred Using Population Genomic Data, Genetics, Volume 207 (2017) no. 1, pp. 297-309 | DOI

[10] Briggs, S. D.; Strahl, B. D. Unraveling heterochromatin, Nature Genetics, Volume 30 (2002) no. 3, pp. 241-242 | DOI

[11] Buard, J.; de Massy, B. Playing hide and seek with mammalian meiotic crossover hotspots, Trends in Genetics, Volume 23 (2007) no. 6, pp. 301-309 | DOI

[12] Capilla, L.; Garcia Caldés, M.; Ruiz-Herrera, A. Mammalian Meiotic Recombination: A Toolbox for Genome Evolution, Cytogenetic and Genome Research, Volume 150 (2016) no. 1, pp. 1-16 | DOI

[13] Carrington, M.; Cullen, M. Justified chauvinism: advances in defining meiotic recombination through sperm typing, Trends in Genetics, Volume 20 (2004) no. 4, pp. 196-205 | DOI

[14] Chan, A. H.; Jenkins, P. A.; Song, Y. S. Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster, PLoS Genetics, Volume 8 (2012) no. 12 | DOI

[15] Choi, K.; Henderson, I. R. Meiotic recombination hotspots - a comparative view, The Plant Journal, Volume 83 (2015) no. 1, pp. 52-61 | DOI

[16] Clark, A. G.; Wang,, X.; Matise, T. Contrasting Methods of Quantifying Fine Structure of Human Recombination, Annual Review of Genomics and Human Genetics, Volume 11 (2010) no. 1, pp. 45-64 | DOI

[17] Cromie, G. A.; Connelly, J. C.; Leach, D. R. Recombination at Double-Strand Breaks and DNA Ends, Molecular Cell, Volume 8 (2001) no. 6, pp. 1163-1174 | DOI

[18] Danecek, P.; Auton, A.; Abecasis, G.; Albers, C. A.; Banks, E.; DePristo, M. A.; Handsaker, R. E.; Lunter, G.; Marth, G. T.; Sherry, S. T.; McVean, G.; Durbin, R. The variant call format and VCFtools, Bioinformatics, Volume 27 (2011) no. 15, pp. 2156-2158 | DOI

[19] Danguy des Déserts, A.; Bouchet, S.; Sourdille, P.; Servin, B. Evolution of recombination landscapes in diverging populations of bread wheat, Genome Biology and Evolution (2021) | DOI

[20] Dapper, A. L.; Payseur, B. A. Effects of Demographic History on the Detection of Recombination Hotspots from Linkage Disequilibrium, Molecular Biology and Evolution, Volume 35 (2017) no. 2, pp. 335-353 | DOI

[21] Delaneau, O.; Zagury, J.-F.; Robinson, M. R.; Marchini, J. L.; Dermitzakis, E. T. Accurate, scalable and integrative haplotype estimation, Nature Communications, Volume 10 (2019) no. 1 | DOI

[22] Delignette-Muller, M. L.; Dutang, C. fitdistrplus: An R Package for Fitting Distributions, Journal of Statistical Software, Volume 64 (2015) no. 4 | DOI

[23] Dréau, A.; Venu, V.; Avdievich, E.; Gaspar, L.; Jones, F. C. Genome-wide recombination map construction from single individuals using linked-read sequencing, Nature Communications, Volume 10 (2019) no. 1 | DOI

[24] Duret, L.; Galtier, N. Biased Gene Conversion and the Evolution of Mammalian Genomic Landscapes, Annual Review of Genomics and Human Genetics, Volume 10 (2009) no. 1, pp. 285-311 | DOI

[25] Ellegren, H.; Galtier, N. Determinants of genetic diversity, Nature Reviews Genetics, Volume 17 (2016) no. 7, pp. 422-433 | DOI

[26] Gagnaire, P.; Raynaud, M. Singularity-Recipes: Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study. (v0.1.0) [Collection]., Zenodo | DOI

[27] Grey, C.; Baudat, F.; de Massy, B. PRDM9, a driver of the genetic map, PLOS Genetics, Volume 14 (2018) no. 8 | DOI

[28] Haenel, Q.; Laurentino, T. G.; Roesti, M.; Berner, D. Meta-analysis of chromosome-scale crossover rate variation in eukaryotes and its significance to evolutionary genomics, Molecular Ecology, Volume 27 (2018) no. 11, pp. 2477-2497 | DOI

[29] Hunter, C. M.; Huang, W.; Mackay, T. F. C.; Singh, N. D. The Genetic Architecture of Natural Variation in Recombination Rate in Drosophila melanogaster, PLOS Genetics, Volume 12 (2016) no. 4 | DOI

[30] Jensen-Seaman, M. I.; Furey, T. S.; Payseur, B. A.; Lu, Y.; Roskin, K. M.; Chen, C.-F.; Thomas, M. A.; Haussler, D.; Jacob, H. J. Comparative Recombination Rates in the Rat, Mouse, and Human Genomes, Genome Research, Volume 14 (2004) no. 4, pp. 528-538 | DOI

[31] Kamm, J. A.; Spence, J. P.; Chan, J.; Song, Y. S. Two-Locus Likelihoods Under Variable Population Size and Fine-Scale Recombination Rate Estimation, Genetics, Volume 203 (2016) no. 3, pp. 1381-1399 | DOI

[32] Kelleher, J.; Etheridge, A. M.; McVean, G. Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes, PLOS Computational Biology, Volume 12 (2016) no. 5 | DOI

[33] Kim, S.; Plagnol, V.; Hu, T. T.; Toomajian, C.; Clark, R. M.; Ossowski, S.; Ecker, J. R.; Weigel, D.; Nordborg, M. Recombination and linkage disequilibrium in Arabidopsis thaliana, Nature Genetics, Volume 39 (2007) no. 9, pp. 1151-1155 | DOI

[34] Kodama, M.; Brieuc, M. S. O.; Devlin, R. H.; Hard, J. J.; Naish, K. A. Comparative Mapping Between Coho Salmon (Oncorhynchus kisutch) and Three Other Salmonids Suggests a Role for Chromosomal Rearrangements in the Retention of Duplicated Regions Following a Whole Genome Duplication Event, G3 Genes|Genomes|Genetics, Volume 4 (2014) no. 9, pp. 1717-1730 | DOI

[35] Kong, A.; Gudbjartsson, D. F.; Sainz, J.; Jonsdottir, G. M.; Gudjonsson, S. A.; Richardsson, B.; Sigurdardottir, S.; Barnard, J.; Hallbeck, B.; Masson, G.; Shlien, A.; Palsson, S. T.; Frigge, M. L.; Thorgeirsson, T. E.; Gulcher, J. R.; Stefansson, K. A high-resolution recombination map of the human genome, Nature Genetics, Volume 31 (2002) no. 3, pp. 241-247 | DOI

[36] Lander, E. S.; Green, P. Construction of multilocus genetic linkage maps in humans., Proceedings of the National Academy of Sciences, Volume 84 (1987) no. 8, pp. 2363-2367 | DOI

[37] Latrille, T.; Duret, L.; Lartillot, N. The Red Queen model of recombination hot-spot evolution: a theoretical investigation, Philosophical Transactions of the Royal Society B: Biological Sciences, Volume 372 (2017) no. 1736 | DOI

[38] Lesecque, Y.; Glémin, S.; Lartillot, N.; Mouchiroud, D.; Duret, L. The Red Queen Model of Recombination Hotspots Evolution in the Light of Archaic and Modern Human Genomes, PLoS Genetics, Volume 10 (2014) no. 11 | DOI

[39] Li, N.; Stephens, M. Modeling Linkage Disequilibrium and Identifying Recombination Hotspots Using Single-Nucleotide Polymorphism Data, Genetics, Volume 165 (2003) no. 4, pp. 2213-2233 | DOI

[40] Li, R.; Bitoun, E.; Altemose, N.; Davies, R. W.; Davies, B.; Myers, S. R. A high-resolution map of non-crossover events reveals impacts of genetic diversity on mammalian meiotic recombination, Nature Communications, Volume 10 (2019) no. 1 | DOI

[41] Mancera, E.; Bourgon, R.; Brozzi, A.; Huber, W.; Steinmetz, L. M. High-resolution mapping of meiotic crossovers and non-crossovers in yeast, Nature, Volume 454 (2008) no. 7203, pp. 479-485 | DOI

[42] McVean, G. A. T.; Myers, S. R.; Hunt, S.; Deloukas, P.; Bentley, D. R.; Donnelly, P. The Fine-Scale Structure of Recombination Rate Variation in the Human Genome, Science, Volume 304 (2004) no. 5670, pp. 581-584 | DOI

[43] Melamed-Bessudo, C.; Shilo, S.; Levy, A. A. Meiotic recombination and genome evolution in plants, Current Opinion in Plant Biology, Volume 30 (2016), pp. 82-87 | DOI

[44] Morris, G. P.; Ramu, P.; Deshpande, S. P.; Hash, C. T.; Shah, T.; Upadhyaya, H. D.; Riera-Lizarazu, O.; Brown, P. J.; Acharya, C. B.; Mitchell, S. E.; Harriman, J.; Glaubitz, J. C.; Buckler, E. S.; Kresovich, S. Population genomic and genome-wide association studies of agroclimatic traits in sorghum, Proceedings of the National Academy of Sciences, Volume 110 (2012) no. 2, pp. 453-458 | DOI

[45] Myers, S.; Spencer, C.; Auton, A.; Bottolo, L.; Freeman, C.; Donnelly, P.; McVean, G. The distribution and causes of meiotic recombination in the human genome, Biochemical Society Transactions, Volume 34 (2006) no. 4, pp. 526-530 | DOI

[46] Myers, S.; Bottolo, L.; Freeman, C.; McVean, G.; Donnelly, P. A Fine-Scale Map of Recombination Rates and Hotspots Across the Human Genome, Science, Volume 310 (2005) no. 5746, pp. 321-324 | DOI

[47] Myers, S.; Bowden, R.; Tumian, A.; Bontrop, R. E.; Freeman, C.; MacFie, T. S.; McVean, G.; Donnelly, P. Drive Against Hotspot Motifs in Primates Implicates the PRDM9 Gene in Meiotic Recombination, Science, Volume 327 (2010) no. 5967, pp. 876-879 | DOI

[48] Nachman, M. Variation in recombination rate across the genome: evidence and implications, Current Opinion in Genetics & Development, Volume 12 (2002) no. 6, pp. 657-663 | DOI

[49] Oliver, P. L.; Goodstadt, L.; Bayes, J. J.; Birtle, Z.; Roach, K. C.; Phadnis, N.; Beatson, S. A.; Lunter, G.; Malik, H. S.; Ponting, C. P. Accelerated Evolution of the Prdm9 Speciation Gene across Diverse Metazoan Taxa, PLoS Genetics, Volume 5 (2009) no. 12 | DOI

[50] Paigen, K.; Petkov, P. M. PRDM9 and Its Role in Genetic Recombination, Trends in Genetics, Volume 34 (2018) no. 4, pp. 291-300 | DOI

[51] Parvanov, E. D.; Petkov, P. M.; Paigen, K. Prdm9 Controls Activation of Mammalian Recombination Hotspots, Science, Volume 327 (2010) no. 5967, p. 835-835 | DOI

[52] Peñalba, J. V.; Wolf, J. B. W. From molecules to populations: appreciating and estimating recombination rate variation, Nature Reviews Genetics, Volume 21 (2020) no. 8, pp. 476-492 | DOI

[53] Pratto, F.; Brick, K.; Khil, P.; Smagulova, F.; Petukhova, G. V.; Camerini-Otero, R. D. Recombination initiation maps of individual human genomes, Science, Volume 346 (2014) no. 6211 | DOI

[54] Ramos-Onsins, S. How to interpret the inference of recombination landscapes on methods based on linkage disequilibrium?, Peer Community In Genomics (2023) | DOI

[55] Rastas, P. Lep-MAP3: robust linkage mapping even for low-coverage whole genome sequencing data, Bioinformatics, Volume 33 (2017) no. 23, pp. 3726-3732 | DOI

[56] Raynaud, M. Performance_LD_methods_recombination: Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study. (v0.1.0) [Collection], Zenodo, 2023 | DOI

[57] Raynaud, M.; Gagnaire, P.-A.; Galtier, N. Supplementary Materials from: Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study. [Dataset], Zenodo, 2023 | DOI

[58] Samuk, K.; Noor, M. A. F. Gene flow biases population genetic inference of recombination rate, G3 Genes|Genomes|Genetics, Volume 12 (2022) no. 11 | DOI

[59] Schield, D. R.; Pasquesi, G. I. M.; Perry, B. W.; Adams, R. H.; Nikolakis, Z. L.; Westfall, A. K.; Orton, R. W.; Meik, J. M.; Mackessy, S. P.; Castoe, T. A. Snake Recombination Landscapes Are Concentrated in Functional Regions despite PRDM9, Molecular Biology and Evolution, Volume 37 (2020) no. 5, pp. 1272-1294 | DOI

[60] Shanfelter, A. F.; Archambeault, S. L.; White, M. A. Divergent Fine-Scale Recombination Landscapes between a Freshwater and Marine Population of Threespine Stickleback Fish, Genome Biology and Evolution, Volume 11 (2019) no. 6, pp. 1552-1572 | DOI

[61] Singhal, S.; Leffler, E. M.; Sannareddy, K.; Turner, I.; Venn, O.; Hooper, D. M.; Strand, A. I.; Li, Q.; Raney, B.; Balakrishnan, C. N.; Griffith, S. C.; McVean, G.; Przeworski, M. Stable recombination hotspots in birds, Science, Volume 350 (2015) no. 6263, pp. 928-932 | DOI

[62] Spence, J. P.; Song, Y. S. Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations, Science Advances, Volume 5 (2019) no. 10 | DOI

[63] Stapley, J.; Feulner, P. G. D.; Johnston, S. E.; Santure, A. W.; Smadja, C. M. Variation in recombination frequency and distribution across eukaryotes: patterns and processes, Philosophical Transactions of the Royal Society B: Biological Sciences, Volume 372 (2017) no. 1736 | DOI

[64] Stevison, L. S.; Woerner, A. E.; Kidd, J. M.; Kelley, J. L.; Veeramah, K. R.; McManus, K. F.; Bustamante, C. D.; Hammer, M. F.; Wall, J. D. The Time Scale of Recombination Rate Evolution in Great Apes, Molecular Biology and Evolution, Volume 33 (2015) no. 4, pp. 928-945 | DOI

[65] Stumpf, M. P. H.; McVean, G. A. T. Estimating recombination rates from population-genetic data, Nature Reviews Genetics, Volume 4 (2003) no. 12, pp. 959-968 | DOI

[66] Sun, H.; Rowan, B. A.; Flood, P. J.; Brandt, R.; Fuss, J.; Hancock, A. M.; Michelmore, R. W.; Huettel, B.; Schneeberger, K. Linked-read sequencing of gametes allows efficient genome-wide analysis of meiotic recombination, Nature Communications, Volume 10 (2019) no. 1 | DOI

[67] The International HapMap Consortium A second generation human haplotype map of over 3.1 million SNPs, Nature, Volume 449 (2007) no. 7164, pp. 851-861 | DOI

[68] Wallberg, A.; Glémin, S.; Webster, M. T. Extreme Recombination Frequencies Shape Genome Variation and Evolution in the Honeybee, Apis mellifera, PLOS Genetics, Volume 11 (2015) no. 4 | DOI

[69] Watterson, G. On the number of segregating sites in genetical models without recombination, Theoretical Population Biology, Volume 7 (1975) no. 2, pp. 256-276 | DOI

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