Genome-wide linkage analysis of 972 bipolar pedigrees using single-nucleotide polymorphisms.

Because of the high costs associated with ascertainment of families, most linkage studies of Bipolar I disorder (BPI) have used relatively small samples. Moreover, the genetic information content reported in most studies has been less than 0.6. Although microsatellite markers spaced every 10 cM typically extract most of the genetic information content for larger multiplex families, they can be less informative for smaller pedigrees especially for affected sib pair kindreds. For these reasons we collaborated to pool family resources and carried out higher density genotyping. Approximately 1100 pedigrees of European ancestry were initially selected for study and were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel 12 set of 6090 single-nucleotide polymorphisms. Of the ~1100 families, 972 were informative for further analyses, and mean information content was 0.86 after pruning for linkage disequilibrium. The 972 kindreds include 2284 cases of BPI disorder, 498 individuals with bipolar II disorder (BPII) and 702 subjects with recurrent major depression. Three affection status models (ASMs) were considered: ASM1 (BPI and schizoaffective disorder, BP cases (SABP) only), ASM2 (ASM1 cases plus BPII) and ASM3 (ASM2 cases plus recurrent major depression). Both parametric and non-parametric linkage methods were carried out. The strongest findings occurred at 6q21 (non-parametric pairs LOD 3.4 for rs1046943 at 119 cM) and 9q21 (non-parametric pairs logarithm of odds (LOD) 3.4 for rs722642 at 78 cM) using only BPI and schizoaffective (SA), BP cases. Both results met genome-wide significant criteria, although neither was significant after correction for multiple analyses. We also inspected parametric scores for the larger multiplex families to identify possible rare susceptibility loci. In this analysis, we observed 59 parametric LODs of 2 or greater, many of which are likely to be close to maximum possible scores. Although some linkage findings may be false positives, the results could help prioritize the search for rare variants using whole exome or genome sequencing

A meta-analytic review of stand-alone interventions to improve body image

Objective Numerous stand-alone interventions to improve body image have been developed. The present review used meta-analysis to estimate the effectiveness of such interventions, and to identify the specific change techniques that lead to improvement in body image. Methods The inclusion criteria were that (a) the intervention was stand-alone (i.e., solely focused on improving body image), (b) a control group was used, (c) participants were randomly assigned to conditions, and (d) at least one pretest and one posttest measure of body image was taken. Effect sizes were meta-analysed and moderator analyses were conducted. A taxonomy of 48 change techniques used in interventions targeted at body image was developed; all interventions were coded using this taxonomy. Results The literature search identified 62 tests of interventions (N = 3,846). Interventions produced a small-to-medium improvement in body image (d+ = 0.38), a small-to-medium reduction in beauty ideal internalisation (d+ = -0.37), and a large reduction in social comparison tendencies (d+ = -0.72). However, the effect size for body image was inflated by bias both within and across studies, and was reliable but of small magnitude once corrections for bias were applied. Effect sizes for the other outcomes were no longer reliable once corrections for bias were applied. Several features of the sample, intervention, and methodology moderated intervention effects. Twelve change techniques were associated with improvements in body image, and three techniques were contra-indicated. Conclusions The findings show that interventions engender only small improvements in body image, and underline the need for large-scale, high-quality trials in this area. The review identifies effective techniques that could be deployed in future interventions

A Polymorphism in a Gene Encoding Perilipin 4 Is Associated with Height but not with Bone Measures in Individuals from the Framingham Osteoporosis Study

There is increasing interest in identifying new pathways and candidate genes that confer susceptibility to osteoporosis. There is evidence that adipogenesis and osteogenesis may be related, including a common bone marrow progenitor cell for both adipocytes and osteoblasts. Perilipin 1 (PLIN1) and Perilipin 4 (PLIN4) are members of the PATS family of genes and are involved in lipolysis of intracellular lipid deposits. A previous study reported gender-specific associations between one polymorphism of PLIN1 and bone mineral density (BMD) in a Japanese population. We hypothesized that polymorphisms in PLIN1 and PLIN4 would be associated with bone measures in adult Caucasian participants of the Framingham Osteoporosis Study (FOS). We genotyped 1,206 male and 1,445 female participants of the FOS for four single-nucleotide polymorphism (SNPs) in PLIN1 and seven SNPs in PLIN4 and tested for associations with measures of BMD, bone ultrasound, hip geometry, and height. We found several gender-specific significant associations with the measured traits. The association of PLIN4 SNP rs8887, G>A with height in females trended toward significance after simulation testing (adjusted P = 0.07) and remained significant after simulation testing in the combined-sex model (adjusted P = 0.033). In a large study sample of men and women, we found a significant association between one SNP in PLIN4 and height but not with bone traits, suggesting that PATS family genes are not important in the regulation of bone. Identification of genes that influence human height may lead to a better understanding of the processes involved in growth and development

Process evaluation for complex interventions in health services research: Analysing context, text trajectories and disruptions

Background: Process evaluations assess the implementation and sustainability of complex healthcare interventions within clinical trials, with well-established theoretical models available for evaluating intervention delivery within specific contexts. However, there is a need to translate conceptualisations of context into analytical tools which enable the dynamic relationship between context and intervention implementation to be captured and understood. Methods: In this paper I propose an alternative approach to the design, implementation and analysis of process evaluations for complex health interventions through a consideration of trial protocols as textual documents, distributed and enacted at multiple contextual levels. As an example, I conduct retrospective analysis of a sample of field notes and transcripts collected during the ESTEEM study - a cluster randomised controlled trial of primary care telephone triage. I draw on theoretical perspectives associated with Linguistic Ethnography to examine the delivery of ESTEEM through staff orientation to different texts. In doing so I consider what can be learned from examining the flow and enactment of protocols for notions of implementation and theoretical fidelity (i.e. intervention delivered as intended and whether congruent with the intervention theory). Results: Implementation of the triage intervention required staff to integrate essential elements of the protocol within everyday practice, seen through the adoption and use of different texts that were distributed across staff and within specific events. Staff were observed deploying texts in diverse ways (e.g. reinterpreting scripts, deviating from standard operating procedures, difficulty completing decision support software), providing numerous instances of disruption to maintaining intervention fidelity. Such observations exposed tensions between different contextual features in which the trial was implemented, offering theoretical explanations for the main trial findings. Conclusions: The value of following how trial protocols produce new texts is that we can observe the flow of 'the intervention as intended' across a series of events which are enacted to meet specific demands of intervention delivery. Such observations are not solely premised on identifying routines or practices of implementation, but where 'protocols as intended' breaks down. In doing so, I discuss whether it is here where we might expose the 'active ingredients' of interventions in action

Evidence For Genetic Heterogeneity Between Clinical Subtypes of Bipolar Disorder

We performed a genome-wide association study of 6447 bipolar disorder (BD) cases and 12 639 controls from the International Cohort Collection for Bipolar Disorder (ICCBD). Meta-analysis was performed with prior results from the Psychiatric Genomics Consortium Bipolar Group for a combined sample of 13 902 cases and 19 279 controls. We identified eight genome-wide significant, associated regions, including a novel associated region on chromosome 10 (rs10884920; P = 3.28 × 10 − 8) that includes the brain-enriched cytoskeleton protein adducin 3 (ADD3), a non-coding RNA, and a neuropeptide-specific aminopeptidase P (XPNPEP1). Our large sample size allowed us to test the heritability and genetic correlation of BD subtypes and investigate their genetic overlap with schizophrenia (SCZ) and major depressive disorder. We found a significant difference in heritability of the two most common forms of BD (BD I h2 = 0.35; BD II h2 = 0.25; P = 0.02) with a genetic correlation between BD I and BD II of 0.78,compared with a genetic correlation of 0.97 when BD cohorts containing both types were compared. In addition, we demonstrated a significantly greater load of polygenic risk alleles for SCZ and BD in patients with BD I compared with patients with BD II, and a greater load of SCZ risk alleles in the bipolar type of schizoaffective disorder (SAB) compared with both other BD subtypes. These results point to a partial difference in genetic architecture of BD subtypes, and are suggestive of a molecular correlate for the clinical division of BD into subtypes

Serious adverse events reported in placebo randomised controlled trials of oral naltrexone: a systematic review and meta-analysis

Background Naltrexone is an opioid antagonist used in many different conditions, both licensed and unlicensed. It is used at widely varying doses from 3 - 250 mg. The aim of this review was to evaluate the safety of oral naltrexone by examining the risk of serious adverse events (SAEs) in randomised controlled trials (RCTs) of naltrexone compared to placebo. Methods A systematic search of Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, other databases and clinical trials registries was undertaken up to March 2018. Parallel placebo-controlled RCTs longer than 4 weeks published after 1/1/2001, of oral naltrexone at any dose were selected. Any condition and age group were included, excluding only studies for opioid or ex-opioid users, due to possible opioid/opioid antagonist interactions. The systematic review used the guidance of the Cochrane Handbook throughout. Numerical data was independently extracted by two people and cross-checked. Risk of bias was assessed with the Cochrane Risk of Bias Tool. Meta-analyses were performed using Stata 15 and R, using random and fixed effects models throughout. Results Eighty-nine RCTs with 11194 participants were found, studying alcohol use disorders, various psychiatric disorders, impulse control disorders, other addictions, obesity, Crohn’s disease, fibromyalgia and cancers. Twenty-six studies (4,960 participants) recorded SAEs occurring by arm of study. There was no evidence of increased risk of SAEs for naltrexone compared to placebo, relative risk (RR) 0.84 (95% CI: 0.66 to 1.06). Sensitivity analyses pooling risk differences supported this conclusion (RD = -0.01 (-0.02, 0.00)) and subgroup analyses showed that results were consistent across different doses and disease groups. The quality of evidence for this outcome was judged high using the GRADE criteria. Conclusions Naltrexone does not appear to increase the risk of SAEs over placebo. These findings confirm the safety of naltrexone when used in licensed indications and encourage investments to undertake efficacy studies in unlicensed indications

In search of causal variants: refining disease association signals using cross-population contrasts

<p>Abstract</p> <p>Background</p> <p>Genome-wide association (GWA) using large numbers of single nucleotide polymorphisms (SNPs) is now a powerful, state-of-the-art approach to mapping human disease genes. When a GWA study detects association between a SNP and the disease, this signal usually represents association with a set of several highly correlated SNPs in strong linkage disequilibrium. The challenge we address is to distinguish among these correlated loci to highlight potential functional variants and prioritize them for follow-up.</p> <p>Results</p> <p>We implemented a systematic method for testing association across diverse population samples having differing histories and LD patterns, using a logistic regression framework. The hypothesis is that important underlying biological mechanisms are shared across human populations, and we can filter correlated variants by testing for heterogeneity of genetic effects in different population samples. This approach formalizes the descriptive comparison of p-values that has typified similar cross-population fine-mapping studies to date. We applied this method to correlated SNPs in the cholinergic nicotinic receptor gene cluster <it>CHRNA5-CHRNA3-CHRNB4</it>, in a case-control study of cocaine dependence composed of 504 European-American and 583 African-American samples. Of the 10 SNPs genotyped in the r²≥ 0.8 bin for <it>rs16969968</it>, three demonstrated significant cross-population heterogeneity and are filtered from priority follow-up; the remaining SNPs include <it>rs16969968 </it>(heterogeneity p = 0.75). Though the power to filter out rs16969968 is reduced due to the difference in allele frequency in the two groups, the results nevertheless focus attention on a smaller group of SNPs that includes the non-synonymous SNP rs16969968, which retains a similar effect size (odds ratio) across both population samples.</p> <p>Conclusion</p> <p>Filtering out SNPs that demonstrate cross-population heterogeneity enriches for variants more likely to be important and causative. Our approach provides an important and effective tool to help interpret results from the many GWA studies now underway.</p

Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations

BACKGROUND: Meta-analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Meta-analyses are increasingly applied to synthesize data from genome-wide association (GWA) studies and from other teams that try to replicate the genetic variants that emerge from such investigations. Between-study heterogeneity is important to document and may point to interesting leads. METHODOLOGY/PRINCIPAL FINDINGS: To exemplify these issues, we used data from three GWA studies on type 2 diabetes and their replication efforts where meta-analyses of all data using fixed effects methods (not incorporating between-study heterogeneity) have already been published. We considered 11 polymorphisms that at least one of the three teams has suggested as susceptibility loci for type 2 diabetes. The I2 inconsistency metric (measuring the amount of heterogeneity not due to chance) was different from 0 (no detectable heterogeneity) for 6 of the 11 genetic variants; inconsistency was moderate to very large (I2 = 32-77%) for 5 of them. For these 5 polymorphisms, random effects calculations incorporating between-study heterogeneity revealed more conservative p-values for the summary effects compared with the fixed effects calculations. These 5 associations were perused in detail to highlight potential explanations for between-study heterogeneity. These include identification of a marker for a correlated phenotype (e.g. FTO rs8050136 being associated with type 2 diabetes through its effect on obesity); differential linkage disequilibrium across studies of the identified genetic markers with the respective culprit polymorphisms (e.g., possibly the case for CDKAL1 polymorphisms or for rs9300039 and markers in linkage disequilibrium, as shown by additional studies); and potential bias. Results were largely similar, when we treated the discovery and replication data from each GWA investigation as separate studies. SIGNIFICANCE: Between-study heterogeneity is useful to document in the synthesis of data from GWA investigations and can offer valuable insights for further clarification of gene-disease associations

Interpreting Meta-Analyses of Genome-Wide Association Studies

Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect

GAPscreener: An automatic tool for screening human genetic association literature in PubMed using the support vector machine technique

<p>Abstract</p> <p>Background</p> <p>Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies.</p> <p>Results</p> <p>The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy.</p> <p>Conclusion</p> <p>GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.</p