Genome-wide association studies (GWAS) and genomic prediction (GP) are two fundamental tools in genomics to study the relationships between the genome and phenotypes. GWAS scans all available markers, such as single nucleotide polymorphisms (SNPs), and has successfully identified many signals associated with complex traits. GP uses all available markers to predict the genetic merit of individuals, which has revolutionized the breeding industry. However, GWAS alone usually cannot identify causal mutations and the accuracy of GP is far away from 100%.
Emerging evidence has shown that several types of additional information beyond SNPs can assist GWAS to identify causal mutations and/or increase the accuracy of GP: 1) using functional annotation of SNPs, such as the gene regulation information; 2) conducting GWAS and/or GP for more than one trait which leverages the information of pleiotropy and 3) conducting GWAS and/or GP in multiple breeds which adds power in finding and utilizing variants segregating in multiple breeds. Therefore, we proposed the topic of “Multi-layered genome-wide association/prediction in livestock and animal species” to examine to what extent the additional information beyond SNPs described above can improve GWAS and GP.
We welcome manuscript formats including Mini-Reviews, full-length Reviews, and Original Research. Our topic welcomes, but is not necessarily limited to:
· GWAS with functional information from the transcriptome, epigenome, metabolome, or any functional annotation of the genome
· GWAS with more than one trait, either fitting multiple traits in one model or using meta-analysis to combine results from single-trait studies
· GWAS with more than one breed, either using multi-breed regression or using meta-analysis to combine results from single-breed analyses
· Genomic prediction with functional information of the genome
· Genomic prediction with multiple traits, either using multi-variable training/prediction or using post hoc methods to combine results from single-trait analyses
· Genomic prediction with multiple breeds, either using multiple breeds in the training/prediction or using post hoc methods to combine results from single-breed analyses
· GWAS and/or genomic prediction that cover more than one of above-described areas, e.g., GWAS + multi-omics + multi-breeds
Genome-wide association studies (GWAS) and genomic prediction (GP) are two fundamental tools in genomics to study the relationships between the genome and phenotypes. GWAS scans all available markers, such as single nucleotide polymorphisms (SNPs), and has successfully identified many signals associated with complex traits. GP uses all available markers to predict the genetic merit of individuals, which has revolutionized the breeding industry. However, GWAS alone usually cannot identify causal mutations and the accuracy of GP is far away from 100%.
Emerging evidence has shown that several types of additional information beyond SNPs can assist GWAS to identify causal mutations and/or increase the accuracy of GP: 1) using functional annotation of SNPs, such as the gene regulation information; 2) conducting GWAS and/or GP for more than one trait which leverages the information of pleiotropy and 3) conducting GWAS and/or GP in multiple breeds which adds power in finding and utilizing variants segregating in multiple breeds. Therefore, we proposed the topic of “Multi-layered genome-wide association/prediction in livestock and animal species” to examine to what extent the additional information beyond SNPs described above can improve GWAS and GP.
We welcome manuscript formats including Mini-Reviews, full-length Reviews, and Original Research. Our topic welcomes, but is not necessarily limited to:
· GWAS with functional information from the transcriptome, epigenome, metabolome, or any functional annotation of the genome
· GWAS with more than one trait, either fitting multiple traits in one model or using meta-analysis to combine results from single-trait studies
· GWAS with more than one breed, either using multi-breed regression or using meta-analysis to combine results from single-breed analyses
· Genomic prediction with functional information of the genome
· Genomic prediction with multiple traits, either using multi-variable training/prediction or using post hoc methods to combine results from single-trait analyses
· Genomic prediction with multiple breeds, either using multiple breeds in the training/prediction or using post hoc methods to combine results from single-breed analyses
· GWAS and/or genomic prediction that cover more than one of above-described areas, e.g., GWAS + multi-omics + multi-breeds