A statistical approach to finding overlooked genetic associations

<p>Abstract</p> <p>Background</p> <p>Complexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects.</p> <p>We propose an alternative method to identify eQTLs that builds on traditional approaches. In contrast to genome-wide techniques, our method determines the significance of an association between an expression trait and a locus with respect to the set of all associations to the expression trait. The use of this specific information facilitates identification of expression traits that have an expression profile that is characterized by a single exceptional association to a locus.</p> <p>Our approach identifies expression traits that have exceptional associations regardless of the genome-wide significance of those associations. This property facilitates the identification of possible false negatives for genome-wide significance. Further, our approach has the property of prioritizing expression traits that are affected by few strong associations. Expression traits identified by this method may warrant additional study because their expression level may be affected by targeting genes near a single locus.</p> <p>Results</p> <p>We demonstrate our method by identifying eQTL hotspots in <it>Plasmodium falciparum </it>(malaria) and <it>Saccharomyces cerevisiae </it>(yeast). We demonstrate the prioritization of traits with few strong genetic effects through Gene Ontology (GO) analysis of Yeast. Our results are strongly consistent with results gathered using genome-wide methods and identify additional hotspots and eQTLs.</p> <p>Conclusions</p> <p>New eQTLs and hotspots found with this method may represent regions of the genome or biological processes that are controlled through few relatively strong genetic interactions. These points of interest warrant experimental investigation.</p

A preliminary study of genetic factors that influence susceptibility to bovine tuberculosis in the British cattle herd

Associations between specific host genes and susceptibility to Mycobacterial infections such as tuberculosis have been reported in several species. Bovine tuberculosis (bTB) impacts greatly the UK cattle industry, yet genetic predispositions have yet to be identified. We therefore used a candidate gene approach to study 384 cattle of which 160 had reacted positively to an antigenic skin test (‘reactors’). Our approach was unusual in that it used microsatellite markers, embraced high breed diversity and focused particularly on detecting genes showing heterozygote advantage, a mode of action often overlooked in SNP-based studies. A panel of neutral markers was used to control for population substructure and using a general linear model-based approach we were also able to control for age. We found that substructure was surprisingly weak and identified two genomic regions that were strongly associated with reactor status, identified by markers INRA111 and BMS2753. In general the strength of association detected tended to vary depending on whether age was included in the model. At INRA111 a single genotype appears strongly protective with an overall odds ratio of 2.2, the effect being consistent across nine diverse breeds. Our results suggest that breeding strategies could be devised that would appreciably increase genetic resistance of cattle to bTB (strictly, reduce the frequency of incidence of reactors) with implications for the current debate concerning badger-culling

Defining the cognitive phenotype of autism

Although much progress has been made in determining the cognitive profile of strengths and weaknesses that characterise individuals with autism spectrum disorders (ASDs), there remain a number of outstanding questions. These include how universal strengths and deficits are; whether cognitive subgroups exist; and how cognition is associated with core autistic behaviours, as well as associated psychopathology. Several methodological factors have contributed to these limitations in our knowledge, including: small sample sizes, a focus on single domains of cognition, and an absence of comprehensive behavioural phenotypic information. To attempt to overcome some of these limitations, we assessed a wide range of cognitive domains in a large sample (N = 100) of 14- to 16-year-old adolescents with ASDs who had been rigorously behaviourally characterised. In this review, we will use examples of some initial findings in the domains of perceptual processing, emotion processing and memory, both to outline different approaches we have taken to data analysis and to highlight the considerable challenges to better defining the cognitive phenotype(s) of ASDs. Enhanced knowledge of the cognitive phenotype may contribute to our understanding of the complex links between genes, brain and behaviour, as well as inform approaches to remediation

Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia.

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence &lt; 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients

Reasoning deficits among illicit drug users are associated with aspects of cannabis use

Background. Deficits in deductive reasoning have been observed among ecstasy/polydrug users. The present study seeks to investigate dose-related effects of specific drugs and whether these vary with the cognitive demands of the task. Methods. One hundred and five participants (mean age 21.33, S.D. 3.14; 77 females, 28 males) attempted to generate solutions for eight one-model syllogisms and one syllogism for which there was no valid conclusion (NVC). All of the one model syllogisms generated at least one valid conclusion and six generated two valid conclusions. In these six cases one of the conclusions was classified as common and the other as non-common. Results. The number of valid common inferences was negatively associated with aspects of short term cannabis use and with measures of IQ. The outcomes observed were more than simple post intoxication effects since cannabis use in the 10 days immediately before testing was unrelated to reasoning performance. Following adjustment for multiple comparisons, the number of non-common valid inferences was not significantly associated with any of the drug use measures. Conclusions. Recent cannabis use appears to impair the processes associated with generating valid common inferences while not affecting the production of non-common inferences. It is possible, therefore, that the two types of inference may recruit different executive resources which may differ in their susceptibility to cannabis-related effects

Diverse Convergent Evidence in the Genetic Analysis of Complex Disease: Coordinating Omic, Informatic, and Experimental Evidence to Better Identify and Validate Risk Factors

In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions

Structural Neuroimaging of Anorexia Nervosa: Future Directions in the Quest for Mechanisms Underlying Dynamic Alterations.

Anorexia nervosa (AN) is a serious eating disorder characterized by self-starvation and extreme weight loss. Pseudoatrophic brain changes are often readily visible in individual brain scans, and AN may be a valuable model disorder to study structural neuroplasticity. Structural magnetic resonance imaging studies have found reduced gray matter volume and cortical thinning in acutely underweight patients to normalize following successful treatment. However, some well-controlled studies have found regionally greater gray matter and persistence of structural alterations following long-term recovery. Findings from diffusion tensor imaging studies of white matter integrity and connectivity are also inconsistent. Furthermore, despite the severity of AN, the number of existing structural neuroimaging studies is still relatively low, and our knowledge of the underlying cellular and molecular mechanisms for macrostructural brain changes is rudimentary. We critically review the current state of structural neuroimaging in AN and discuss the potential neurobiological basis of structural brain alterations in the disorder, highlighting impediments to progress, recent developments, and promising future directions. In particular, we argue for the utility of more standardized data collection, adopting a connectomics approach to understanding brain network architecture, employing advanced magnetic resonance imaging methods that quantify biomarkers of brain tissue microstructure, integrating data from multiple imaging modalities, strategic longitudinal observation during weight restoration, and large-scale data pooling. Our overarching objective is to motivate carefully controlled research of brain structure in eating disorders, which will ultimately help predict therapeutic response and improve treatment

Handgun carrying among youth in the United States

Despite a wealth of research finding that adolescents who carry handguns are involved in risky behaviors, there has been little exploration into the heterogeneity of this behavior. Using a pooled sample of 12- to 17-year-olds from the National Study on Drug Use and Health who report past-year handgun carrying (N = 7,872), this study identified four subgroups of handgun carriers: low risk (n = 3,831; 47.93%), alcohol and marijuana users (n = 1,591; 20.16%), fighters (n = 1,430; 19.40%), and severe externalizers (n = 1,020, 12.51%). These subgroups differed on demographic, behavioral, and psychosocial characteristics. Findings are discussed in light of prevention and focused deterrence