Publications by authors named "S Bolormaa"
Article Synopsis
- Genotype imputation helps in obtaining sequence genotypes for analyses like genome-wide association studies (GWAS), but assessing imputation accuracy is crucial to avoid false positives.
- The study compared three imputation programs—Beagle 5.2, Minimac4, and IMPUTE5—and found that high-density genotypes yield better accuracy than low-density, with notable differences in how each software estimates imputation performance.
- The findings highlight the need for customized Rsq thresholds when filtering data and indicate that INDEL variants tend to have lower accuracy than SNPs; also, the accuracy of X chromosome imputation varies significantly between different regions.
Cancer is a major public health concern, impacting nearly 20 million people each year, and it is responsible for 1 in 6 deaths worldwide. The burden of cancer is increasing rapidly, straining health systems that are unable to prevent and manage the disease. Childhood cancer constitutes a significant and relevant public health challenge; it was the ninth leading cause of childhood disease globally, according to findings by the Global Burden of Disease 2017 study.
View Article and Find Full Text PDFAlthough selection for increased milk production traits has led to a genetic increase in body weight (BW), the genetic gain in milk production has exceeded the gain in BW, so gross feed efficiency has improved. Nonetheless, greater gains may be possible by directly selecting for a measure of feed efficiency. Australia first introduced Feed Saved (FS) estimated breeding value (EBV) in 2015.
View Article and Find Full Text PDFBackground: Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows.
Results: GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE).