Sex differences in response to Marek’s Disease: mapping quantitative trait loci regions (QTLRs) to the Z chromosome

Marek’s Disease (MD) has a significant impact on both the global poultry economy and animal welfare. The disease pathology can include neurological damage and tumour formation. Sexual dimorphism in immunity and known higher susceptibility of females to MD makes the chicken Z chromosome (GGZ) a particularly attractive target to study the chicken MD response. Previously, we used a Hy-Line F6 population from a full-sib advanced intercross line to map MD QTL regions (QTLRs) on all chicken autosomes. Here, we mapped MD QTLRs on GGZ in the previously utilized F6 population with individual genotypes and phenotypes, and in eight elite commercial egg production lines with daughter-tested sires and selective DNA pooling (SDP). Four MD QTLRs were found from each analysis. Some of these QTLRs overlap regions from previous reports. All QTLRs were tested by individuals from the same eight lines used in the SDP and genotyped with markers located within and around the QTLRs. All QTLRs were confirmed. The results exemplify the complexity of MD resistance in chickens and the complex distribution of p-values and Linkage Disequilibrium (LD) pattern and their effect on localization of the causative elements. Considering the fragments and interdigitated LD blocks while using LD to aid localization of causative elements, one must look beyond the non-significant markers, for possible distant markers and blocks in high LD with the significant block. The QTLRs found here may explain at least part of the gender differences in MD tolerance, and provide targets for mitigating the effects of MD

Mapping QTL associated with resistance to avian oncogenic Marek’s Disease Virus (MDV) reveals major candidate genes and variants

Marek's disease (MD) represents a significant global economic and animal welfare issue. Marek's disease virus (MDV) is a highly contagious oncogenic and highly immune-suppressive α-herpes virus, which infects chickens, causing neurological effects and tumour formation. Though partially controlled by vaccination, MD continues to have a profound impact on animal health and on the poultry industry. Genetic selection provides an alternative and complementary method to vaccination. However, even after years of study, the genetic mechanisms underlying resistance to MDV remain poorly understood. The Major Histocompatability Complex (MHC) is known to play a role in disease resistance, along with a handful of other non-MHC genes. In this study, one of the largest to date, we used a multi-facetted approach to identify QTL regions (QTLR) influencing resistance to MDV, including an F population from a full-sib advanced intercross line (FSIL) between two elite commercial layer lines differing in resistance to MDV, RNA-seq information from virus challenged chicks, and genome wide association study (GWAS) from multiple commercial lines. Candidate genomic elements residing in the QTLR were further tested for association with offspring mortality in the face of MDV challenge in eight pure lines of elite egg-layer birds. Thirty-eight QTLR were found on 19 chicken chromosomes. Candidate genes, miRNAs, lncRNAs and potentially functional mutations were identified in these regions. Association tests were carried out in 26 of the QTLR, using eight pure lines of elite egg-layer birds. Numerous candidate genomic elements were strongly associated with MD resistance. Genomic regions significantly associated with resistance to MDV were mapped and candidate genes identified. Various QTLR elements were shown to have a strong genetic association with resistance. These results provide a large number of significant targets for mitigating the effects of MDV infection on both poultry health and the economy, whether by means of selective breeding, improved vaccine design, or gene-editing technologies

The Use of Kosher Phenotyping for Mapping QTL Affecting Susceptibility to Bovine Respiratory Disease

<div><p>Bovine respiratory disease (BRD) is the leading cause of morbidity and mortality in feedlot cattle, caused by multiple pathogens that become more virulent in response to stress. As clinical signs often go undetected and various preventive strategies failed, identification of genes affecting BRD is essential for selection for resistance. Selective DNA pooling (SDP) was applied in a genome wide association study (GWAS) to map BRD QTLs in Israeli Holstein male calves. Kosher scoring of lung adhesions was used to allocate 122 and 62 animals to High (Glatt Kosher) and Low (Non-Kosher) resistant groups, respectively. Genotyping was performed using the Illumina BovineHD BeadChip according to the Infinium protocol. Moving average of -logP was used to map QTLs and Log drop was used to define their boundaries (QTLRs). The combined procedure was efficient for high resolution mapping. Nineteen QTLRs distributed over 13 autosomes were found, some overlapping previous studies. The QTLRs contain polymorphic functional and expression candidate genes to affect kosher status, with putative immunological and wound healing activities. Kosher phenotyping was shown to be a reliable means to map QTLs affecting BRD morbidity.</p></div

Smooth light curves from a bumpy ride: relativistic blast wave encounters a density jump

Peer reviewe

Examples of chromosomal regions with one QTLR but possibly including two putative QTLRs.

<p>a. QTLR 3 on BTA 1. b. QTLR 4 on BTA 2. c. QTLR 14 on BTA 16. Vertical bars on the X-axis, QTLR markers locations; Blue diamonds, -LogP values of the markers; Yellow squares: X-axis, mean location of the markers in the window; Y-axis, mean -LogP of the window. Three uppermost horizontal bars from top down: significance thresholds for individual markers at PFP = 0.05, 0.10 and 0.20, respectively.</p

Complete list of all genes within the QTLRs or within 0.5 Mb upstream or downstream of the QTLR boundaries.

<p>Complete list of all genes within the QTLRs or within 0.5 Mb upstream or downstream of the QTLR boundaries.</p

Number of individuals in the pools.

<p>Number of individuals in the pools.</p

Critical P-values and number of significant SNPs, by PFP level.

<p>Critical P-values and number of significant SNPs, by PFP level.</p

Effects of QTLR’s genes and pathogens.

<p>Effects of QTLR’s genes and pathogens.</p