No abstract

Genetic connectedness refers to a measure of genetic relatedness across management units (e.g., herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between estimated genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attention has been paid to using genomic information to measure connectedness. In this study, we assessed genome-based connectedness across management units by applying prediction error variance of difference (PEVD), coefficient of determination (CD), and prediction error correlation r to a combination of computer simulation and real data (mice and cattle). We found that genomic information (boldnormalG) increased the estimate of connectedness among individuals from different management units compared to that based on pedigree (boldnormalA). A disconnected design benefited the most. In both datasets, PEVD and CD statistics inferred increased connectedness across units when using boldnormalG- rather than boldnormalA-based relatedness, suggesting stronger connectedness. With r once using allele frequencies equal to one-half or scaling boldnormalG to values between 0 and 2, which is intrinsic to A, connectedness also increased with genomic information. However, PEVD occasionally increased, and r decreased when obtained using the alternative form of G, instead suggesting less connectedness. Such inconsistencies were not found with CD. We contend that genomic relatedness strengthens measures of genetic connectedness across units and has the potential to aid genomic evaluation of livestock species.

With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms ( i.e. , Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.

Genetic connectedness assesses the extent to which estimated breeding values can be fairly compared across management units. Ranking of individuals across units based on best linear unbiased prediction (BLUP) is reliable when there is a sufficient level of connectedness due to a better disentangling of genetic signal from noise. Connectedness arises from genetic relationships among individuals. Although a recent study showed that genomic relatedness strengthens the estimates of connectedness across management units compared with that of pedigree, the relationship between connectedness measures and prediction accuracies only has been explored to a limited extent. In this study, we examined whether increased measures of connectedness led to higher prediction accuracies evaluated by a cross-validation (CV) based on computer simulations. We applied prediction error variance of the difference, coefficient of determination (CD), and BLUP-type prediction models to data simulated under various scenarios. We found that a greater extent of connectedness enhanced accuracy of whole-genome prediction. The impact of genomics was more marked when large numbers of markers were used to infer connectedness and evaluate prediction accuracy. Connectedness across units increased with the proportion of connecting individuals and this increase was associated with improved accuracy of prediction. The use of genomic information resulted in increased estimates of connectedness and improved prediction accuracies compared with those of pedigree-based models when there were enough markers to capture variation due to QTL signals.

Genetic connectedness refers to a measure of genetic relatedness across management units (e.g., herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attention has been paid to using genomic information to measure connectedness. In this study, we assessed genome-based connectedness across management units by applying prediction error variance of difference (PEVD), coefficient of determination (CD), and prediction error correlation (r) to a combination of computer simulation and real data (mice and cattle). We found that genomic information (G) increased the estimate of connectedness among individuals from different management units compared to that based on pedigree (A). A disconnected design benefited the most. In both datasets, PEVD and CD statistics inferred increased connectedness across units when using G- rather than A-based relatedness suggesting stronger connectedness. With r once using allele frequencies equal to one-half or scaling G to values between 0 and 2, which is intrinsic to A, connectedness also increased with genomic information. However, PEVD occasionally increased, and r decreased when obtained using the alternative form of G, instead suggesting less connectedness. Such inconsistencies were not found with CD. We contend that genomic relatedness strengthens measures of genetic connectedness across units and has the potential to aid genomic evaluation of livestock species.The problem of connectedness or disconnectedness is particularly important in genetic evaluation of managed populations such as domesticated livestock. When selecting among animals from different management units (e.g., herds and flocks), caution is needed; choosing one animal over others across management units may be associated with greater uncertainty than selection within management units. Such uncertainty is reduced if individuals from different management units are genetically linked or connected. In such a case, best linear unbiased prediction (BLUP) offers meaningful comparison of the breeding values across management units for genetic evaluation (e.g., Kuehn et al., 2007).Structures of breeding programs have a direct influence on levels of connectedness. Wide use of artificial insemination (AI) programs generally increases genetic connectedness across management units. For example, dairy cattle populations are considered highly connected due to dissemination of genetic material from a small number of highly selected sires. The situation may be different for species with less use of AI and more use of natural service mating such as for beef cattle or sheep populations. Under these scenarios, the magnitude of connectedness across management units is reduced and genetic links are largely confined within management units.Pedigree-based genetic connectedness has been evaluated and applied in practice (e.g., Kuehn et al., 2009; Eikje and Lewis, 2015). However, there is a relative paucity of use of genomic information such as single nucletide polymorphisms (SNPs) to ascertain connectedness. It still remains elusive in what scenarios genomics can strengthen connectedness and how much gain can be expected relative to use of pedigree information alone. Connectedness statistics have been used to optimize selective genotyping and phenotyping in simulated livestock (Pszczola et al., 2012) and plant populations (Maenhout et al., 2010), and in real maize (Rincent et al., 2012; Isidro et al., 2015), and real rice data (Isidro et al., 2015). These studies concluded that the greater the connectedness between the reference and validation populations, the greater the predictive performance. However, 1) connectedness among different management units and 2) differences in connectedness measures between pedigree and genomic relatedness were not explored in those studies. For better understanding of genome-based connectedness, it is critical to examine how the presence of management units comes into play. For instance, genomic relatedness provides relationships between distant individuals that appear disconnected according to the pedigree information. In addition, it captures Mendelian sampling that is not present in pedigree relationships (Hill and Weir, 2011). Thus, genomic information is expected to strengthen measures of connectedness, which in turn refines comparisons of genetic values across different management units. The objective of this study was to assess measures of genetic connectedness across management units with use of genomic information. We leveraged the combination of real data and computer simulation to compare gains in measures of connectedness when moving from pedigree to genomic relationships. First, we studied a heterogenous mice dataset stratified by cage. Then we investigated approaches to measure connectedness using real cattle data coupled with simulated management units to have greater control over the degree of confounding between fixed management groups and genetic relationships.