Most traits and disorders have a multifactorial background indicating that they are controlled by environmental factors as well as an unknown number of quantitative trait loci (QTLs). The identification of mutations underlying QTLs is a challenge because each locus explains only a fraction of the phenotypic variation. A paternally expressed QTL affecting muscle growth, fat deposition and size of the heart in pigs maps to the IGF2 (insulin-like growth factor 2) region. Here we show that this QTL is caused by a nucleotide substitution in intron 3 of IGF2. The mutation occurs in an evolutionarily conserved CpG island that is hypomethylated in skeletal muscle. The mutation abrogates in vitro interaction with a nuclear factor, probably a repressor, and pigs inheriting the mutation from their sire have a threefold increase in IGF2 messenger RNA expression in postnatal muscle. Our study establishes a causal relationship between a single-base-pair substitution in a non-coding region and a QTL effect. The result supports the long-held view that regulatory mutations are important for controlling phenotypic variation.

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With several hundred genetic diseases and an advantageous genome structure, dogs are ideal for mapping genes that cause disease. Here we report the development of a genotyping array with approximately 27,000 SNPs and show that genome-wide association mapping of mendelian traits in dog breeds can be achieved with only approximately 20 dogs. Specifically, we map two traits with mendelian inheritance: the major white spotting (S) locus and the hair ridge in Rhodesian ridgebacks. For both traits, we map the loci to discrete regions of <1 Mb. Fine-mapping of the S locus in two breeds refines the localization to a region of approximately 100 kb contained within the pigmentation-related gene MITF. Complete sequencing of the white and solid haplotypes identifies candidate regulatory mutations in the melanocyte-specific promoter of MITF. Our results show that genome-wide association mapping within dog breeds, followed by fine-mapping across multiple breeds, will be highly efficient and generally applicable to trait mapping, providing insights into canine and human health.

Internet interventions, and in particular Internet-delivered cognitive behaviour therapy (ICBT), have existed for at least 20 years. Here we review the treatment approach and the evidence base, arguing that ICBT can be viewed as a vehicle for innovation. ICBT has been developed and tested for several psychiatric and somatic conditions, and direct comparative studies suggest that therapist-guided ICBT is more effective than a waiting list for anxiety disorders and depression, and tends to be as effective as face-to-face CBT. Studies on the possible harmful effects of ICBT are also reviewed: a significant minority of people do experience negative effects, although rates of deterioration appear similar to those reported for face-to-face treatments and lower than for control conditions. We further review studies on change mechanisms and conclude that few, if any, consistent moderators and mediators of change have been identified. A recent trend to focus on knowledge acquisition is considered, and a discussion on the possibilities and hurdles of implementing ICBT is presented. The latter includes findings suggesting that attitudes toward ICBT may not be as positive as when using modern information technology as an adjunct to face-to-face therapy (i.e., blended treatment). Finally, we discuss future directions, including the role played by technology and machine learning, blended treatment, adaptation of treatment for minorities and non-Western settings, other therapeutic approaches than ICBT (including Internet-delivered psychodynamic and interpersonal psychotherapy as well as acceptance and commitment therapy), emerging regulations, and the importance of reporting failed trials.

Fully automated self-help interventions can serve as highly cost-effective mental health promotion tools for massive amounts of people. However, these interventions are often characterised by poor adherence. One way to address this problem is to mimic therapy support by a conversational agent. The objectives of this study were to assess the effectiveness and adherence of a smartphone app, delivering strategies used in positive psychology and CBT interventions via an automated chatbot (Shim) for a non-clinical population — as well as to explore participants' views and experiences of interacting with this chatbot. A total of 28 participants were randomized to either receive the chatbot intervention (n = 14) or to a wait-list control group (n = 14). Findings revealed that participants who adhered to the intervention (n = 13) showed significant interaction effects of group and time on psychological well-being (FS) and perceived stress (PSS-10) compared to the wait-list control group, with small to large between effect sizes (Cohen's d range 0.14–1.06). Also, the participants showed high engagement during the 2-week long intervention, with an average open app ratio of 17.71 times for the whole period. This is higher compared to other studies on fully automated interventions claiming to be highly engaging, such as Woebot and the Panoply app. The qualitative data revealed sub-themes which, to our knowledge, have not been found previously, such as the moderating format of the chatbot. The results of this study, in particular the good adherence rate, validated the usefulness of replicating this study in the future with a larger sample size and an active control group. This is important, as the search for fully automated, yet highly engaging and effective digital self-help interventions for promoting mental health is crucial for the public health.

BackgroundFace-to-face brief interventions for problem drinking are effective, but they have found limited implementation in routine care and the community. Internet-based interventions could overcome this treatment gap. We investigated effectiveness and moderators of treatment outcomes in internet-based interventions for adult problem drinking (iAIs).Methods and findingsSystematic searches were performed in medical and psychological databases to 31 December 2016. A one-stage individual patient data meta-analysis (IPDMA) was conducted with a linear mixed model complete-case approach, using baseline and first follow-up data. The primary outcome measure was mean weekly alcohol consumption in standard units (SUs, 10 grams of ethanol). Secondary outcome was treatment response (TR), defined as less than 14/21 SUs for women/men weekly. Putative participant, intervention, and study moderators were included. Robustness was verified in three sensitivity analyses: a two-stage IPDMA, a one-stage IPDMA using multiple imputation, and a missing-not-at-random (MNAR) analysis. We obtained baseline data for 14,198 adult participants (19 randomised controlled trials [RCTs], mean age 40.7 [SD = 13.2], 47.6% women). Their baseline mean weekly alcohol consumption was 38.1 SUs (SD = 26.9). Most were regular problem drinkers (80.1%, SUs 44.7, SD = 26.4) and 19.9% (SUs 11.9, SD = 4.1) were binge-only drinkers. About one third were heavy drinkers, meaning that women/men consumed, respectively, more than 35/50 SUs of alcohol at baseline (34.2%, SUs 65.9, SD = 27.1). Post-intervention data were available for 8,095 participants. Compared with controls, iAI participants showed a greater mean weekly decrease at follow-up of 5.02 SUs (95% CI −7.57 to −2.48, p < 0.001) and a higher rate of TR (odds ratio [OR] 2.20, 95% CI 1.63–2.95, p < 0.001, number needed to treat [NNT] = 4.15, 95% CI 3.06–6.62). Persons above age 55 showed higher TR than their younger counterparts (OR = 1.66, 95% CI 1.21–2.27, p = 0.002). Drinking profiles were not significantly associated with treatment outcomes. Human-supported interventions were superior to fully automated ones on both outcome measures (comparative reduction: −6.78 SUs, 95% CI −12.11 to −1.45, p = 0.013; TR: OR = 2.23, 95% CI 1.22–4.08, p = 0.009). Participants treated in iAIs based on personalised normative feedback (PNF) alone were significantly less likely to sustain low-risk drinking at follow-up than those in iAIs based on integrated therapeutic principles (OR = 0.52, 95% CI 0.29–0.93, p = 0.029). The use of waitlist control in RCTs was associated with significantly better treatment outcomes than the use of other types of control (comparative reduction: −9.27 SUs, 95% CI −13.97 to −4.57, p < 0.001; TR: OR = 3.74, 95% CI 2.13–6.53, p < 0.001). The overall quality of the RCTs was high; a major limitation included high study dropout (43%). Sensitivity analyses confirmed the robustness of our primary analyses.ConclusionTo our knowledge, this is the first IPDMA on internet-based interventions that has show...

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Little is known about clinically relevant changes in guided Internet-based interventions for depression. Moreover, methodological and power limitations preclude the identification of patients' groups that may benefit more from these interventions. This study aimed to investigate response rates, remission rates, and their moderators in randomized controlled trials (RCTs) comparing the effect of guided Internet-based interventions for adult depression to control groups using an individual patient data meta-analysis approach. Literature searches in PubMed, Embase, PsycINFO and Cochrane Library resulted in 13,384 abstracts from database inception to January 1, 2016. Twenty-four RCTs (4889 participants) comparing a guided Internet-based intervention with a control group contributed data to the analysis. Missing data were multiply imputed. To examine treatment outcome on response and remission, mixed-effects models with participants nested within studies were used. Response and remission rates were calculated using the Reliable Change Index. The intervention group obtained significantly higher response rates (OR = 2.49, 95% CI 2.17-2.85) and remission rates compared to controls (OR = 2.41, 95% CI 2.07-2.79). The moderator analysis indicated that older participants (OR = 1.01) and native-born participants (1.66) were more likely to respond to treatment compared to younger participants and ethnic minorities respectively. Age (OR = 1.01) and ethnicity (1.73) also moderated the effects of treatment on remission.Moreover, adults with more severe depressive symptoms at baseline were more likely to remit after receiving internet-based treatment (OR = 1.19). Guided Internet-based interventions lead to substantial positive treatment effects on treatment response and remission at post-treatment. Thus, such interventions may complement existing services for depression and potentially reduce the gap between the need and provision of evidence-based treatments.