Genomic inbreeding depression for climatic adaptation of tropical beef cattle1

Abstract

Inbreeding has the potential to negatively impact animal performance. Strategies to monitor and mitigate inbreeding depression require that it can be accurately estimated. Here, we used genomewide SNP data to explore 3 alternative measures of genomic inbreeding: the diagonal elements of the genomic relationship matrix (FGRM), the proportion of homozygous SNP (FHOM), and the proportion of the genome covered by runs of homozygosity (FROH). We used 2,111 Brahman (BR) and 2,550 Tropical Composite (TC) cattle with phenotypes recorded for 10 traits of relevance to tropical adaptation. We further explored 3 marker densities ranging from a high-density chip (729,068 SNP), a medium-density chip (71,726 SNP) specifically designed for cattle, and a low-density chip (18,860 SNP) associated with the measures of inbreeding. Measures of FGRM were highly correlated across the 3 SNP densities and negatively correlated with FHOM and FROH in the BR population. In both populations, there was a strong positive correlation for each measure of inbreeding across the 3 SNP panels. We found significant ( < 0.01) inbreeding depression for various traits, particularly when using the highest-density SNP chip in the BR population, where inbreeding was negatively associated with coat color and coat type such that inbred animals presented shorter, slicker, and lighter coats. Based on FGRM using the medium-density chip, we found that a 1% increase in inbreeding in the BR and TC populations was associated with a decrease of 0.514 and 0.579 kg BW, respectively, in yearlings. In the TC population, a 1% increase in FHOM was associated with a decrease in BCS of -0.636% ( < 0.001). The low-density chip, comprising SNP associated with inbreeding, captured genes, and regions with pleiotropic effects ( < 0.001). However, it did not improve our ability to identify inbreeding depression, relative to the use of higher-density panels. We conclude that where heterogeneous populations are present, such as in tropical environments where composite animals abound, measures of inbreeding that do not depend on allele frequencies, such as FHOM and FROH, are preferable for estimating genomic inbreeding. Finally, the sustainable intensification of livestock systems in tropical regions will rely on genetic safeguards to ensure that productivity is improved while also adapting animals to cope with climate change. The results of this study are a step toward achieving that goal.

A multivariate analysis with direct additive and inbreeding depression load effects

“…The analysis of the beef cattle datasets indicated that, for most of the individuals, the prediction of their inbreeding load was negative, which is consistent with several studies that have analyzed the inbreeding depression in body weights of beef cattle [7, 37–39]. However, a significant proportion of the individuals (10.52% in Pirenaica, and 22.54% in Rubia Gallega) had a positive prediction of their inbreeding loads.…”

Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method

“…The statistics described above were tested in a real-life dataset. We used genetic and phenotypic resources (for details see Table 1 ) from Brahman cows (N = 995) and bulls (N = 1116) that have been widely described in the recent literature [ 40 – 42 ]. Yearling body weight (YWT) computed from the average of all body weights recorded between 300 and 420 days of age was used as the phenotype.…”

Genomic Breed Composition of Selection Signatures in Brangus Beef Cattle

“…According to FAO Guidelines for in vivo conservation of animal genetic resources (FAO, 2013), the desired inbreeding rate per generation should not exceed 1% (equal to Ne = 50). A 1% increase in inbreeding was associated with decrease of −0.23% in yearling weight and −0.64% in body condition score in a tropical composite beef cattle (Reverter et al, 2017). Therefore, selection pressure and finite population (Weir and Cockerham, 1984 Frontiers in Genetics | www.frontiersin.org FIGURE 3 | Frequency of each SNP in a run of homozygosity (ROH) in Brangus population according to the chromosome and position.…”