Genetic Variants in REC8, RNF212, and PRDM9 Influence Male Recombination in Cattle

Sandor, Cynthia; Li, Wanbo; Coppieters, Wouter; Druet, Tom; Charlier, Carole; Georges, Michel

doi:10.1371/journal.pgen.1002854

Cited by 128 publications

(177 citation statements)

References 49 publications

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“…The BTA10 (21.23 Mb) QTL was previously attributed to variants in the REC8 gene (Sandor et al 2012). Sequence-based finemapping performed in this study indicates that the association maximizes at 645 kb from REC8.…”

Section: Discussionsupporting

confidence: 50%

“…S30). It is noteworthy that we previously showed that coding variants in an X-linked PRDM9 paralog (with highest similarity to the human PRDM9 gene) strongly affect hotspot usage in males (Sandor et al 2012), but this was not confirmed by Ma et al (2015). Combined with the observation of limited sharing of recombination hotspots between males and females (Ma et al 2015), these observations suggest that hotspot usage may be differentially regulated in both sexes in cattle.…”

Section: −12mentioning

confidence: 71%

“…In model organisms, meiotic arrest or strongly reduced recombination rates have been observed in HFM1 (Lipkin et al 2002;Nakagawa and Kolodner 2002;Mercier et al 2005;Guiraldelli et al 2013), MLH3 (Lipkin et al 2002;Brown et al 2013), RNF212 (Reynolds et al 2013), MSH5 (Hollingsworth et al 1995;Edelmann et al 1999), and MSH4 (Ross-Macdonald and Roeder 1994; Nishant et al 2010) mutants. Naturally occurring variants in RNF212 (Kong et al 2008(Kong et al , 2014Chowdhury et al 2009;Fledel-Alon et al 2011;Sandor et al 2012) and MSH4 (Kong et al 2014;Ma et al 2015) were previously shown to affect GRR in mammals. Intriguingly, four of these genes (MSH4, MSH5, HFM1, RNF212) operate at the same stage of meiotic recombination, namely the processing of recombination intermediates, while MLH3 participates in the downstream resolution to crossovers (e.g., Baudat et al 2013).…”

Section: Discussionmentioning

confidence: 99%

“…These can be used to map quantitative trait loci (QTL) affecting individual variation in genome-wide or global recombination rate (GRR). Such GWAS have been conducted in humans (Kong et al 2008(Kong et al , 2014Chowdhury et al 2009;Fledel-Alon et al 2011), cattle (Sandor et al 2012;Ma et al 2015), and sheep , revealing the role of genes such as RNF212 in the three species. In humans, most identified variants were found to have sexspecific effects (Chowdhury et al 2009;Fledel-Alon et al 2011;Kong et al 2014).…”

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confidence: 99%

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Coding and noncoding variants in HFM1, MLH3, MSH4, MSH5, RNF212, and RNF212B affect recombination rate in cattle

et al. 2016

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We herein study genetic recombination in three cattle populations from France, New Zealand, and the Netherlands. We identify 2,395,177 crossover (CO) events in 94,516 male gametes, and 579,996 CO events in 25,332 female gametes. The average number of COs was found to be larger in males (23.3) than in females (21.4). The heritability of global recombination rate (GRR) was estimated at 0.13 in males and 0.08 in females, with a genetic correlation of 0.66 indicating that shared variants are influencing GRR in both sexes. A genome-wide association study identified seven quantitative trait loci (QTL) for GRR. Fine-mapping following sequence-based imputation in 14,401 animals pinpointed likely causative coding (5) and noncoding (1) variants in genes known to be involved in meiotic recombination (HFM1, MSH4, RNF212, MLH3, MSH5) for 5/7 QTL, and noncoding variants (3) in RNF212B for 1/7 QTL. This suggests that this RNF212 paralog might also be involved in recombination. Most of the identified mutations had significant effects in both sexes, with three of them each accounting for ∼10% of the genetic variance in males.

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Section: Discussionsupporting

confidence: 50%

Section: −12mentioning

confidence: 71%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Coding and noncoding variants in HFM1, MLH3, MSH4, MSH5, RNF212, and RNF212B affect recombination rate in cattle

et al. 2016

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“…As HEI10 belongs to a conserved gene family, which encodes RING domain SUMO/ubiquitin E3 ligases that promote crossovers in diverse eukaryotes, this was the strongest candidate gene for rQTL1 (Supplemental Figs. S2, S3;Bhalla et al 2008;Kong et al 2008;FledelAlon et al 2011;Chelysheva et al 2012;Sandor et al 2012;Serrentino et al 2013;De Muyt et al 2014;Qiao et al 2014;Johnston et al 2016;Rao et al 2017). HEI10 family proteins possess N-terminal RING domains, central coiled-coil domains, and C-terminal regions of unknown function (Supplemental Figs.…”

Section: Genetic Mapping Of Arabidopsis Recombination Quantitative Trmentioning

confidence: 99%

Natural variation and dosage of the HEI10 meiotic E3 ligase control Arabidopsis crossover recombination

et al. 2017

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During meiosis, homologous chromosomes undergo crossover recombination, which creates genetic diversity and balances homolog segregation. Despite these critical functions, crossover frequency varies extensively within and between species. Although natural crossover recombination modifier loci have been detected in plants, causal genes have remained elusive. Using natural Arabidopsis thaliana accessions, we identified two major recombination quantitative trait loci (rQTLs) that explain 56.9% of crossover variation in Col×Ler F 2 populations. We mapped rQTL1 to semidominant polymorphisms in HEI10, which encodes a conserved ubiquitin E3 ligase that regulates crossovers. Null hei10 mutants are haploinsufficient, and, using genome-wide mapping and immunocytology, we show that transformation of additional HEI10 copies is sufficient to more than double euchromatic crossovers. However, heterochromatic centromeres remained recombination-suppressed. The strongest HEI10-mediated crossover increases occur in subtelomeric euchromatin, which is reminiscent of sex differences in Arabidopsis recombination. Our work reveals that HEI10 naturally limits Arabidopsis crossovers and has the potential to influence the response to selection.

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Use of Ancestral Haplotypes in Genome-Wide Association Studies

Druet

Farnir

2013

Methods in Molecular Biology

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We herein present a haplotype-based method to perform genome-wide association studies. The method relies on hidden Markov models to describe haplotypes from a population as a mosaic of a set of ancestral haplotypes. For a given position in the genome, haplotypes deriving from the same ancestral haplotype are also likely to carry the same risk alleles. Therefore, the model can be used in several applications such as haplotype reconstruction, imputation, association studies or genomic predictions. We illustrate then the model with two applications: the fine-mapping of a QTL affecting live weight in cattle and association studies in a stratified cattle population. Both applications show the potential of the method and the high linkage disequilibrium between ancestral haplotypes and causative variants.

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Genetic Variants in REC8, RNF212, and PRDM9 Influence Male Recombination in Cattle

Cited by 128 publications

References 49 publications

Coding and noncoding variants in HFM1, MLH3, MSH4, MSH5, RNF212, and RNF212B affect recombination rate in cattle

Coding and noncoding variants in HFM1, MLH3, MSH4, MSH5, RNF212, and RNF212B affect recombination rate in cattle

Natural variation and dosage of the HEI10 meiotic E3 ligase control Arabidopsis crossover recombination

Use of Ancestral Haplotypes in Genome-Wide Association Studies

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