Landscape ecology analysis and code for genome-wide association study
These datasets were utilised in a landscape genomic analysis and genome-wide association study (GWAS) of Bonsmara cattle in South Africa. The analyses were further used to investigate growth performance of cattle from the Eastern Cape, Free State and North-West provinces. The single nucleotide polymorphisms (SNP) genotypic data was obtained from Bonsmara stud breeders through SA StudBook and is thus the property of the breeders. The IDs of the animals included in the study were changed to protect the privacy of the breeders.
The SNP genotype and weather datasets were used to conduct a landscape genomic analysis on the 3 provincial groups of cattle. The datasets were analyzed together with a landscape ecology analysis (LEA) package, that was developed for RStudio. The output results consisted of candidate loci that the LEA identified to be associated with the environmental variables included in the weather datasets. The Fst values for each candidate loci list (three lists in total; one for each province) were calculated and the 20 SNPs (20 SNPs per candidate loci list; 60 SNPs in total) with the highest values were chosen for gene annotation. The objective of gene annotation was to determine if any of the associated genes were linked to growth performance in cattle. Nine out of the 60 annotated SNPs were found to be associated with genes that had previously been reported in cattle and linked to growth performance.
The phenotypic datasets were used alongside the SNP genotypic datasets in a genome-wide association study (GWAS). The GWAS was conducted in the PLINK software and the results were plotted on Manhattan Plots using RStudio software packages. The GWAS results were used comparatively to the candidate loci results from the LEA analysis. There were no commonly associated loci identified between the GWAS and the LEA.
The PLINK and RStudio codes used in this study are available in a text file.
Sustainable Development Goals
- 2 Zero Hunger