An assessment of genomic connectedness measures in Nellore cattle

Abstract

An important criterion to consider in genetic evaluations is the extent of genetic connectedness across management units (MU), especially if they differ in their genetic mean. Reliable comparisons of genetic values across MU depend on the degree of connectedness; the higher the connectedness, the more reliable the comparison. Traditionally, genetic connectedness was calculated through pedigree-based methods; however, in the era of genomic selection, this can be better estimated utilizing new approaches based on genomics. Most procedures consider only additive genetic effects, which may not accurately reflect the underlying gene action of the evaluated trait, and little is known about the impact of non-additive gene action on connectedness measures. The objective of this study was to investigate the extent of genomic connectedness measures, for the first time, in Brazilian field data by applying additive and non-additive relationship matrices using a fatty acid profile dataset from seven farms located in the three regions of Brazil, which are part of the three breeding programs. Myristic acid (C14:0) was used due to its importance for human health, and reported presence of non-additive gene action. The pedigree included 427,740 animals and 925 of them were genotyped using the Bovine high-density genotyping chip. Six relationship matrices were constructed, parametrically and non-parametrically capturing additive and non-additive genetic effects from both pedigree and genomic data. We assessed genome-based connectedness across MU using the prediction error variance of difference (PEVD) and the coefficient of determination (CD). PEVD values ranged from 0.540 - 1.707, and CD from 0.146 - 0.456. Genomic information consistently enhanced the measures of connectedness compared to the numerator relationship matrix by at least 63%. Combining additive and non-additive genomic kernel relationship matrices or a non-parametric relationship matrix increased the capture of connectedness. Overall, the Gaussian kernel yielded the largest measure of connectedness. Our findings showed that connectedness metrics can be extended to incorporate genomic information and non-additive genetic variation using field data. We propose that different genomic relationship matrices can be designed to capture additive and non-additive genetic effects, increase the measures of connectedness, and to more accurately estimate the true state of connectedness in herds

International genetic and genomic evaluations of beef cattle

“…The higher the exchange of CB, the more animals between populations became related, and the more evident were the decreases in CD across generations due to the penalization applied in the CD measure (Yu et al 2017;Amorim et al 2020). These results agree with Yu et al (2017) and Amorim et al (2020) and show that CD can capture increases in connectedness between populations due to genomic data. However, the computational costs associated with the computation and inversion of the coefficient matrix may limit its application to large datasets and international evaluations (Fouilloux and Laloë 2001;Fouilloux et al 2006).…”

“…Moreover, when moving from A to H, CD between populations increased also between animals in G18-G20 and animals in G13-G17 (Figure 7.4) due to H better capturing the relationships between genotyped and non-genotyped animals (Legarra et al 2014). The higher the exchange of CB, the more animals between populations became related, and the more evident were the decreases in CD across generations due to the penalization applied in the CD measure (Yu et al 2017;Amorim et al 2020). These results agree with Yu et al (2017) and Amorim et al (2020) and show that CD can capture increases in connectedness between populations due to genomic data.…”

GCA: an R package for genetic connectedness analysis using pedigree and genomic data

“…Individual average PEVD : Alternatively, we can first compute PEVD at the individual level using Eq. ( 5 ) and then aggregate and summarize at the unit level to obtain the average of PEVD between individuals across two units [ 13 ] …”

“…Individual average CD : Individual average CD is derived from the average of CD between individuals across two units [ 13 ]. …”

“…B3: Individual average CD is calculated by scaling indvidual average PEVD (PEVD _ IdAve) with the average of pairwise relationship differences of individuals. The CD of contrast (CD _ Contrast) is shown in C. CD _ Contrast is calculated by scaling the prediction error variance of the differences (PEVD) of contrast with the product of the transpose of the contrast vector (x), the relationship matrix (K), and the contrast vector across two units [13].…”