Into the night : evaluating sleep as a measure of animal welfare

There is a need for a simple non-invasive measure of animal welfare. In humans, sleep quality correlates strongly with a person’s wellbeing; this suggests that sleep may also prove to be a suitable tool to measure mammals’ welfare. Studies in humans have shown that lack of sleep compromises the health of individuals, causing heart attacks, strokes, diabetes and even cancer. Likewise, research conducted with shift workers demonstrated they are more likely to develop such diseases but are also more susceptible to psychological conditions such as depression. Considering humans and mammals have similar physiology and sleeping patterns, disturbances in mammals’ natural sleeping cycles could have similar outcomes. Our model system to examine this is the domestic dog. This system was chosen because dogs are a well-studied species regarding their physiology and have been used as a model in human sleep studies. They also co-exist with humans which gives us insight on their environment. This thesis presents the results of a multidisciplinary approach to evaluate sleep as a measure of animal welfare in domestic dogs. Firstly, trough behavioural observations, the sleep structure of kenneled dogs was investigated and after finding the dogs had an altered sleep architecture and highly fragmented sleep in the surveyed environment, we then verified the impact of sleep loss in other behaviours. Secondly, using glucocorticoids levels and assessing environmental variables such as temperature, light and sound levels, we evaluated how the environment along with stress responses can further compromise sleep and found important correlations between these measures. Thirdly, using wearable technology, dogs sleep, activity and health parameters (heart rate and respiration rate) were measured and results compared which sleep parameters, demonstrating remote sensing is a reliable technology and can provide further information on the effects of sleep loss in dogs. Lastly, an autonomous system was developed which combines deep leaning techniques (convolutional neural networks) with classical data processing methods to automatically detect and quantify dogs’ sleeping patterns and the results demonstrated it is an efficient tool to measure sleep and a practical solution to common problems associated with welfare research.Keywords: animal welfare, sleep behaviour, sleep quality, domestic dogs

The cyclic interaction between daytime behavior and the sleep behavior of laboratory dogs

Sleep deprivation has been found to negatively affect an individual´s physical and psychological health. Sleep loss affects activity patterns, increases anxiety-like behaviors, decreases cognitive performance and is associated with depressive states. The activity/rest cycle of dogs has been investigated before, but little is known about the effects of sleep loss on the behavior of the species. Dogs are polyphasic sleepers, meaning the behavior is most observed at night, but bouts are also present during the day. However, sleep can vary with ecological and biological factors, such as age, sex, fitness, and even human presence. In this study, kennelled laboratory adult dogs’ sleep and diurnal behavior were recorded during 24-h, five-day assessment periods to investigate sleep quality and its effect on daily behavior. In total, 1560 h of data were analyzed, and sleep metrics and diurnal behavior were quantified. The relationship between sleeping patterns and behavior and the effect of age and sex were evaluated using non-parametric statistical tests and GLMM modelling. Dogs in our study slept substantially less than previously reported and presented a modified sleep architecture with fewer awakenings during the night and almost no sleep during the day. Sleep loss increased inactivity, decreased play and alert behaviors, while increased time spent eating during the day. Males appeared to be more affected by sleep fragmentation than females. Different age groups also experienced different effects of sleep loss. Overall, dogs appear to compensate for the lack of sleep during the night by remaining inactive during the day. With further investigations, the relationship between sleep loss and behavior has the potential to be used as a measure of animal welfare

Identification of shared genetic variants between schizophrenia and lung cancer.

Epidemiology studies suggest associations between schizophrenia and cancer. However, the underlying genetic mechanisms are not well understood, and difficult to identify from epidemiological data. We investigated if there is a shared genetic architecture between schizophrenia and cancer, with the aim to identify specific overlapping genetic loci. First, we performed genome-wide enrichment analysis and second, we analyzed specific loci jointly associated with schizophrenia and cancer by the conjunction false discovery rate. We analyzed the largest genome-wide association studies of schizophrenia and lung, breast, prostate, ovary, and colon-rectum cancer including more than 220,000 subjects, and included genetic association with smoking behavior. Polygenic enrichment of associations with lung cancer was observed in schizophrenia, and weak enrichment for the remaining cancer sites. After excluding the major histocompatibility complex region, we identified three independent loci jointly associated with schizophrenia and lung cancer. The strongest association included nicotinic acetylcholine receptors and is an established pleiotropic locus shared between lung cancer and smoking. The two other loci were independent of genetic association with smoking. Functional analysis identified downstream pleiotropic effects on epigenetics and gene-expression in lung and brain tissue. These findings suggest that genetic factors may explain partly the observed epidemiological association of lung cancer and schizophrenia

Robust allele-specific polymerase chain reaction markers developed for single nucleotide polymorphisms in expressed barley sequences

Many methods have been developed to assay for single nucleotide polymorphisms (SNPs), but generally these depend on access to specialised equipment. Allele-specific polymerase chain reaction (AS-PCR) is a method that does not require specialised equipment (other than a thermocycler), but there is a common perception that AS-PCR markers can be unreliable. We have utilised a three primer AS-PCR method comprising of two flanking-primers combined with an internal allele-specific primer. We show here that this method produces a high proportion of robust markers (from candidate allele specific primers). Forty-nine inter-varietal SNP sites in 31 barley (Hordeum vulgare L.) genes were targeted for the development of AS-PCR assays. The SNP sites were found by aligning barley expressed sequence tags from public databases. The targeted genes correspond to cDNAs that have been used as restriction fragment length polymorphic probes for linkage mapping in barley. Two approaches were adopted in developing the markers. In the first approach, designed to maximise the successful development of markers to a SNP site, markers were developed for 18 sites from 19 targeted (95% success rate). With the second approach, designed to maximise the number of markers developed per primer synthesised, markers were developed for 18 SNP sites from 30 that were targeted (a 60% success rate). The robustness of markers was assessed from the range of annealing temperatures over which the PCR assay was allele-specific. The results indicate that this form of AS-PCR is highly successful for the development of robust SNP markers

Pleiotropic analysis of lung cancer and blood triglycerides.

Epidemiologically related traits may share genetic risk factors, and pleiotropic analysis could identify individual loci associated with these traits. Because of their shared epidemiological associations, we conducted pleiotropic analysis of genome-wide association studies of lung cancer (12 160 lung cancer case patients and 16 838 control subjects) and cardiovascular disease risk factors (blood lipids from 188 577 subjects, type 2 diabetes from 148 821 subjects, body mass index from 123 865 subjects, and smoking phenotypes from 74 053 subjects). We found that 6p22.1 (rs6904596, ZNF184) was associated with both lung cancer (P = 5.50x10(-6)) and blood triglycerides (P = 1.39x10(-5)). We replicated the association in 6097 lung cancer case patients and 204 657 control subjects (P = 2.40 × 10(-4)) and in 71 113 subjects with triglycerides data (P = .01). rs6904596 reached genome-wide significance in lung cancer meta-analysis (odds ratio = 1.15, 95% confidence interval = 1.10 to 1.21 ,: Pcombined = 5.20x10(-9)). The large sample size provided by the lipid GWAS data and the shared genetic risk factors between the two traits contributed to the uncovering of a hitherto unidentified genetic locus for lung cancer

Pleiotropic analysis of lung cancer and blood triglycerides

Epidemiologically related traits may share genetic risk factors, and pleiotropic analysis could identify individual loci associated with these traits. Because of their shared epidemiological associations, we conducted pleiotropic analysis of genome-wide association studies of lung cancer (12 160 lung cancer case patients and 16 838 control subjects) and cardiovascular disease risk factors (blood lipids from 188 577 subjects, type 2 diabetes from 148 821 subjects, body mass index from 123 865 subjects, and smoking phenotypes from 74 053 subjects). We found that 6p22.1 (rs6904596, ZNF184) was associated with both lung cancer (P = 5.50x10-6) and blood triglycerides (P = 1.39x10-5). We replicated the association in 6097 lung cancer case patients and 204 657 control subjects (P = 2.40 × 10-4) and in 71 113 subjects with triglycerides data (P = .01). rs6904596 reached genome-wide significance in lung cancer meta-analysis (odds ratio = 1.15, 95% confidence interval = 1.10 to 1.21,Pcombined = 5.20x10-9). The large sample size provided by the lipid GWAS data and the shared genetic risk factors between the two traits contributed to the uncovering of a hitherto unidentified genetic locus for lung cancer