Academic journal article Genetics

Variation in Recombination Rate and Its Genetic Determinism in Sheep Populations

Academic journal article Genetics

Variation in Recombination Rate and Its Genetic Determinism in Sheep Populations

Article excerpt

(ProQuest: ... denotes formulae omitted.)

Meiotic recombination is a fundamental biological process that brings a major contribution to the genetic diversity and the evolution of eukaryotic genomes (Baudat et al. 2013). During meiosis, recombination enables chromosomal alignment resulting in proper disjunction and segregation of chromosomes, avoiding deleterious outcomes such as aneuploidy (Hassold et al. 2007). Over generations, recombination contributes to shaping genetic diversity in a population by creating new allelic combinations and preventing the accumulation of deleterious mutations. Over large evolutionary timescales, divergence in recombination landscapes can lead to speciation; the action of a key factor in the recombination process in many mammals, the gene PRDM9, has been shown to have a major contribution to the infertility between two mouse species, making it the only known speciation gene in mammals today (Mihola et al. 2009).

Genetics studies on recombination were first used to infer the organization of genes along the genome (Sturtevant 1913). With advances in molecular techniques, more detailed physical maps and eventually whole-genome assemblies are now available in many species. The establishment of highly resolutive recombination maps remains of fundamental importance for the validation of the physical ordering of markers obtained from sequencing experiments (Groenen etal. 2012; Jiang etai. 2014). From an evolutionary perspective, the relevant distance between loci is the genetic distance and recombination maps are essential tools for the genetic studies of a species, for estimation of past demography (Li and Durbin 2011; Boitard et al. 2016), detection of selection signatures (Sabeti et al. 2002; Voight et al. 2006), QTL mapping (Cox et al. 2009), and imputation of genotypes (Howie et al. 2009) for genome-wide association studies (GWAS) or genomic selection. Precise recombination maps can be estimated using different approaches. Meiotic recombination rates can be estimated from the observation of markers' segregation in families. Although this is a widespread approach, its resolution is limited by the number of meioses that can be collected within a population and the number of markers that can be genotyped. Consequently, highly resolutive meiotic maps have been produced in situations where large segregating families can be studied and genotyped densely (Shifman et al. 2006; Mancera et al. 2008; Groenen et al. 2009; Rockman and Kruglyak 2009; Kong et al. 2010) or by focusing on specific genomic regions (Cirulli et al. 2007; Stevison and Noor 2010; Kaur and Rockman 2014). In livestock species, the recent availability of dense genotyping assays has fostered the production of highly resolutive recombination maps (Tortereau et al. 2012; Johnston et al. 2016, 2017), particularly by exploiting reference population data from genomic selection programs (Sandor et al. 2012; Ma et al. 2015; Kadri et al. 2016).

Another approach to study the distribution of recombination on a genome is to exploit patterns of correlation between allele frequencies in a population (i.e., linkage disequilibrium, LD) to infer past (historical) recombination rates (McVean etal. 2002; Li and Stephens 2003; Chan etal. 2012). Because the LD-based approach in essence exploits meioses accumulated over many generations, it can provide more precise estimates of local variation on recombination rate. For example, until recently (Pratto et al. 2014; Lange et al. 2016) this was the only known indirect approach allowing the detection of fine-scale patterns of recombination genome-wide in species with large genomes. Several highly recombining intervals (recombination hotspots) were detected from historical recombination rate maps and confirmed or completed those discovered by sperm-typing experiments (Crawford et al. 2004; Myers et al. 2005). One important caveat of LD-based approaches is that their recombination rate estimates are affected by other evolutionary processes, especially selection that affects LD patterns unevenly across the genome. …

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