Fine mapping of QTL and genomic prediction using allele-specific expression SNPs demonstrates that the complex trait of genetic resistance to Marek’s disease is predominantly determined by transcriptional regulation

Abstract

BackgroundMarek’s disease (MD) is a lymphoproliferative disease of poultry induced by Marek’s disease virus (MDV), a highly oncogenic alphaherpesvirus. Identifying the underlying genes conferring MD genetic resistance is desired for more efficacious control measures including genomic selection, which requires accurately identified genetic markers throughout the chicken genome.MethodsHypothesizing that variants located in transcriptional regulatory regions are the main mechanism underlying this complex trait, a genome-wide association study was conducted by genotyping a ~1,000 bird MD resource population derived from experimental inbred layers with SNPs containing 1,824 previously identified allele-specific expression (ASE) SNPs in response to MDV infection as well as 3,097 random SNPs equally spaced throughout the chicken genome. Based on the calculated associations, genomic predictions were determined for 200 roosters and selected sires had their progeny tested for Marek’s disease incidence.ResultsOur analyses indicate that these ASE SNPs account for more than 83 % of the genetic variance and exhibit nearly all the highest associations. To validate these findings, 200 roosters had their genetic merit predicted from the ASE SNPs only, and the top 30 and bottom 30 ranked roosters were reciprocally mated to random hens. The resulting progeny showed that after only one generation of bidirectional selection, there was a 22 % difference in MD incidence and this approach gave a 125 % increase in accuracy compared to current pedigree-based estimates.ConclusionsWe conclude that variation in transcriptional regulation is the major driving cause for genetic resistance to MD, and ASE SNPs identify the underlying genes and are sufficiently linked to the causative polymorphisms that they can be used for accurate genomic prediction as well as help define the underlying molecular basis. Furthermore, this approach should be applicable to other complex traits.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2016-0) contains supplementary material, which is available to authorized users.

Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens: A review

“…Even though broilers and layers showed substantial differences in enriched pathways after Marek virus infection, toll-like receptor and janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathways were activated in both the genotypes when responding to the infection [ 117 ]. It has also been reported that 1,824 allele-specific expression SNP account for more than 83% of the genetic variance in resistance against MD in an experimental inbred layer population [ 118 ]. According to these results, the genetic merit of 200 rosters was predicted only using the allele-specific expression SNP and a progeny test was carried out.…”

“…The progeny showed a reduction of 22% in the incidence of MD after only one generation of bidirectional selection based on those SNP. Moreover, the accuracy of the estimated breeding value was increased by 125% using the allele-specific expression SNP compared to the traditional pedigree-based method [ 118 ]. In order to better understand the genetic basis of the resistance against MD, other brilliant genome-wide [ 119 , 120 ] and transcriptomic [ 121 , 122 ] studies have been performed.…”

Vaccination and Host Marek's Disease-Resistance Genotype Significantly Reduce Oncogenic <b><i>Gallid alphaherpesvirus 2</i></b> Telomere Integration in Host Birds

“…For example, Avian Disease and Oncology Laboratory (ADOL) experimental inbred lines 6 3 and 7 2 chickens share the same MHC haplotype, yet differ greatly in resistance to MD. As these non-MHC loci are smaller in genetic effect, identifying them has been more challenging though recent efforts based on allele-specific expression in response to MDV challenge have been able to identify a large collection that accounts for over 80% of the genetic variance [Cheng et al, 2015].…”

Cancer Detection Using Co-Training of SNP/Gene/MiRNA Expressions Classifiers

“…They use the publicly available unlabeled sets to enrich the training data of the classifiers using self-learning and co-training to combine both miRNA and gene expression sets. Other techniques were proposed that perform mapping between different biological features, as [9,10].…”