Location: Livestock and Range Research LaboratoryTitle: A weighted genomic relationship matrix based on Fixation Index (Fst) prioritized SNPs for genomic selection
|LING-YUN, CHANG - University Of Georgia|
|Hay, El Hamidi|
|AGGREY, SAMUEL - University Of Georgia|
|REKAYA, ROMDHANE - University Of Georgia|
Submitted to: Genes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/8/2019
Publication Date: 11/12/2019
Publication URL: https://handle.nal.usda.gov/10113/6770394
Citation: Ling-Yun, C., Toghiani, S., Hay, E.A., Aggrey, S., Rekaya, R. 2019. A weighted genomic relationship matrix based on Fixation Index (Fst) prioritized SNPs for genomic selection. Genes. 10(11):922. https://doi.org/10.3390/genes10110922.
Interpretive Summary: The advances in high-throughput genotyping technologies led to a dramatic increase in the amount of genomic information. A practical application of genomic information is its use in the selection of livestock animals which is referred to as genomic selection. This method exploits single-nucleotide polymorphisms (SNP) panels to predict the genetic merit of the animals. However, the increase in the density of the SNP panels did not increase the accuracy of genomic selection. Including all SNP in the model will dramatically increase the dimensionality of the data and thus reducing the statistical power. In this study, an approach was proposed to prioritize and reduce the number of SNP markers. This approach utilizes the FST coefficient as a SNP prioritization tool. This coefficient measures the allele differentiation among sub-populations to identify segments of the genome under selection pressure. The results showed that prioritizing SNPs based on their FST resulted in an improvement of the accuracy of genomic selection by more than 5%.
Technical Abstract: The dramatic increase in the density of marker panels was expected to increase the accuracy of genomic selection (GS). Unfortunately, little to no improvement was observed. Including all variants in the association model will dramatically increase the dimensionality of the problem and it will undoubtedly reduce the statistical power. Using all SNPs to compute the genomic relationship matrix (G) will not increase accuracy as the additive relationships can be accurately estimated using a much smaller number of markers. Due to these limitations, variant prioritization has become a necessity to improve accuracy. FST as a measure of population differentiation has been used to identify genome segments and variants under selection pressure. Using prioritized variants has increased the accuracy of GS. Additionally, FST can be used to weight the relative contribution of prioritized SNPs in computing G. In this study, relative weights based on FST scores were developed and incorporated into the calculation of G and their impact on the estimation of variance components and accuracy was assessed. The results showed that prioritizing SNPs based on their FST scores resulted in an increase in the genetic similarity between training and validations animals and improved the accuracy of GS by more than 5%.