Academic journal article Genetics

Accuracy of Genomic Prediction in Synthetic Populations Depending on the Number of Parents, Relatedness, and Ancestral Linkage Disequilibrium

Academic journal article Genetics

Accuracy of Genomic Prediction in Synthetic Populations Depending on the Number of Parents, Relatedness, and Ancestral Linkage Disequilibrium

Article excerpt

(ProQuest: ... denotes formulae omitted.)

SYNTHETIC populations, known as synthetics, have played an important role in quantitative genetic research on gene action in complex heterotic traits and comparison of selection methods (cf. Hallauer et al. 2010). In many crops, synthetics also serve as cultivars in agricultural production or as source populations for recurrent selection programs (cf. Bradshaw 2016). Synthetics are usually created by crossing a small number of parents (NP) and subsequently cross-pollinating the F1 individuals for one or several generations (Falconer and Mackay 1996). A prominent example is the "Iowa Stiff Stalk Synthetic" (BSSS) generated from 16 parents of maize, from which numerous successful elite inbred lines such as B73 have been derived (Hagdorn et al. 2003). Further examples of synthetics include composite crosses (Suneson 1956) and multi-parental advanced intercross (MAGIC, see Supplemental Material, Table S1 for list of abbreviations) populations (Cavanagh et al. 2008) advocated for breeding purposes in crops (Bandillo et al. 2013). Importantly, two-way and four-way crosses, widely employed as source material in recycling breeding (Mikel and Dudley 2006), can be viewed as special cases of synthetics when NP = 2 and 4, respectively.

Genomic prediction (GP) proposed by Meuwissen et al. (2001) led to a paradigm-shift in animal breeding during the past decade (Hayes et al. 2009a; de Koning 2016), and has also been widely adopted in plant breeding (Lin et al. 2014). In cattle breeding, GP is predominantly applied within closed breeds and training sets (TS) commonly encompass thousands of individuals. By comparison, in plant breeding the TS sizes are much smaller (e.g., hundreds or fewer of individuals) and populations are usually structured into multiple segregating families or subpopulations. Numerous studies addressed the implementation of GP in structured plant breeding populations (cf. Lorenzana and Bernardo 2009; Albrecht et al. 2011; Lehermeier et al. 2014; Technow and Totir 2015), but systematic investigations on the prospects of GP in synthetics are lacking so far, although they were proposed as particularly suitable source material for recurrent genomic selection (Windhausen et al. 2012; Gorjanc et al. 2016).

Genomic best linear unbiased prediction (GBLUP), a modification of the traditional pedigree BLUP devised by Henderson (1984), is a widely used method to implement GP in animal and plant breeding (Mackay et al. 2015). Here, the pedigree relationship matrix is replaced by a marker-derived genomic relationship matrix to estimate actual relationships at QTL (Hayes et al 2009c). The success of this approach depends on three sources of information, namely (i) pedigree relationships captured by markers, (ii) cosegregation of QTL and markers, and (iii) population-wide linkage disequilibrium between QTL and markers (Habier et al. 2007, 2013; Wientjes et al. 2013).

In classical quantitative genetics, pedigree relationships between individuals are calculated as twice the probability of identity-by-descent (IBD) of alleles at a locus, conditional on their pedigree (Wright 1922; Falconer and Mackay 1996). However, actual IBD relationships at QTL deviate from pedigree relationships-which correspond to expected IBD relationships-due to Mendelian sampling (Hill and Weir 2011). In GP, pedigree relationships are captured best with a large number of stochastically independent markers (Habier et al. 2007), whereas capturing the Mendelian sampling term requires cosegregation of QTL and markers (Hayes et al. 2009c; Habier etal 2013).

In pedigree analysis, the founders of the pedigree are, by definition, assumed to be unrelated (i.e., IBD = 0), but in reality, there usually exist latent similarities at QTL contributing to variation in identity-by-state (IBS) relationships among these individuals. Markers enable the capture of these IBS relationships if they are in population-wide linkage disequilibrium (LD) with the QTL in an ancestral population of founders. …

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