Back

Genomic analysis revealed hotspots of genetic adaptation and risk of disappearance in the Brazilian goat populations

Bajay, M.; Sobrinho, F. d. A. D.; Coelho, R. C.; de Oliveira Moura, J.; do Nascimento, I. F.; Carvalho, L. C. B.; Britto, F. B.; Sarmento, J. L. R.; Azevedo, D. M. M. R.; Mastrangelo, S.; de Araujo, A. M.

2024-06-04 genomics
10.1101/2024.06.03.597192 bioRxiv
Show abstract

We accessed a 50K Illumina SNP genotype dataset from two important goat breeds of the Brazilian semi- arid region to analyze the abundance and length of runs of homozygosity (ROH). This analysis aims to elucidate the importance of adaptation history in the genome of the Brazilian goat populations and to measure genomic inbreeding. Heterozygosity-rich regions (HRR) or genome regions of high genetic variability provide clues about how diversity might be associated with increased fitness, avoiding deleterious homozygous alleles. Overall, 22,872 ROH were identified. The average number of ROH per individual ranged from 74.73 (Anglo-Nubian commercial breed) to 173.85 (Marota landrace). Analysis of the distribution of runs of homozygosity according to their size showed that, for both breeds, the majority of ROH were in the short (<2.0 Mb) category (65.6%). ROH-based inbreeding (FROH) revealed low levels in Anglo-Nubian (0.0627) and high levels in Marota (0.1419), likely due to a reduction in effective population size over generations in the Marota landrace. We defined islands of ROH and HRR and identified common regions in the Marota goat, where genes related to various traits such as embryonic development, body growth, lipid homeostasis, and brain functions are located. These results indicate that such regions are associated with many traits and have therefore been under selective pressure in these goat breeds reared for different purposes.

Published in Frontiers in Bioscience-Scholar · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.