Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Real-world consumers, though, are motivated by more than incentives and information (Michalek et al. 2015). These are innovative policy tools that are designed with a specific focus on behavioral factors 1 Most economist readers can be assumed to be familiar with the key insights of Behavioral Economics; those interested in the details may be referred to the excellent surveys by Camerer and Loewenstein (2004) and Della Vigna (2009). Van den Bergh et al. (2000) give an outline of behavioral economics insights with a focus on their relevance for environmental economics prior to Nudge. The contributions by Norton et al. (1998) and Söderbaum (1994) are particularly interesting as precursors of the nudge agenda, as they argue that environmental policies should try to exploit endogenous preference change. 2 An important exception to that rule was Jack Knetsch, see, e.g. Knetsch and Sinden (1984), Knetsch (1989 and structured. To nudge someone is to deliberately intervene in a given CA. Nudges are widely regarded as potential complements to more traditional incentive-based regulation; the hope is that adding them to the policy mix may be both more effective and more popular among the general public than relying on traditional regulatory tools alone (Thaler and Sunstein 2008: ch. 12). 6 As Cass Sunstein (2014: 13) puts it, the general aim is to develop "sensible, low-cost policies with close reference to how human beings actually think and behave." There is an important caveat, though: Due to their unclear welfare foundations and the potentially paternalistic and manipulative way in which these tools shape human behavior (viz., by addressing and exploiting cognitive biases), nudges raise complex ethical questions. Terms of use: Documents in EconStor mayThe exact definition of nudges is a matter of some controversy. What's typically offered as a back-of-the-envelope definition -"interventions that influence people's behavior without significantly changing their monetary incentives or coercing them" -is unhelpful, as it also lets the mere provision of information count as a nudge. We rather suggest to follow Hansen (2016) by supplementing this shorthand with the notion, originally advanced by Thaler and Sunstein (2008: 8) To illustrate, the subsidy and tax schemes discussed by Allcott and Taubinsky (2015) in the context of energy efficiency policy qualify as BEP in our sense of the term. 5 See in particular (ibid.: ch. 12) on green nudging, and Sunstein (201...

Neuropsychiatric symptoms like mood changes and depression are common in patients with chronic inflammatory disorders such as infections, autoimmune diseases or cancer. The pathogenesis of these symptoms is still unclear. Pro-inflammatory stimuli interfere not only with the neural circuits and neurotransmitters of the serotonergic, but also with those of the adrenergic system. The pro-inflammatory cytokine interferon-gamma stimulates the biosynthesis of 5,6,7,8-tetrahydrobiopterin (BH4), which is cofactor for several aromatic amino acid monooxygenases and thus is strongly involved in the biosynthesis of the neurotransmitter serotonin and the catecholamines dopamine, epinephrine (adrenaline) and norepinephrine (noradrenaline). In macrophages, interferon-gamma also triggers the high output of reactive oxygen species, which can destroy the oxidation-labile BH4. Recent data suggest that oxidative loss of BH4 in chronic inflammatory conditions can reduce the biosynthesis of catecholamines, which may relate to disturbed adrenergic neurotransmitter pathways in patients.

SummaryDoublecortin (Dcx) defines a growing family of microtubule (MT)-associated proteins (MAPs) involved in neuronal migration and process outgrowth. We show that Dcx is essential for the function of Kif1a, a kinesin-3 motor protein that traffics synaptic vesicles. Neurons lacking Dcx and/or its structurally conserved paralogue, doublecortin-like kinase 1 (Dclk1), show impaired Kif1a-mediated transport of Vamp2, a cargo of Kif1a, with decreased run length. Human disease-associated mutations in Dcx's linker sequence (e.g., W146C, K174E) alter Kif1a/Vamp2 transport by disrupting Dcx/Kif1a interactions without affecting Dcx MT binding. Dcx specifically enhances binding of the ADP-bound Kif1a motor domain to MTs. Cryo-electron microscopy and subnanometer-resolution image reconstruction reveal the kinesin-dependent conformational variability of MT-bound Dcx and suggest a model for MAP-motor crosstalk on MTs. Alteration of kinesin run length by MAPs represents a previously undiscovered mode of control of kinesin transport and provides a mechanism for regulation of MT-based transport by local signals.

This study examined chronic and short-term stress effects on heart rate variability (HRV), comparing time, frequency and phase domain (complexity) measures in 50 healthy adults. The hassles frequency subscale of the combined hassles and uplifts scale (CHUS) was used to measure chronic stress. Shortterm stressor reactivity was assessed with a speech task. HRV measures were determined via surface electrocardiogram (ECG). Because respiration rate decreased during the speech task (p < .001), this study assessed the influence of respiration rate changes on the effects of interest. A series of repeatedmeasures analyses of covariance (ANCOVA) with Bonferroni adjustment revealed that short-term stress decreased HR D2 (calculated via the pointwise correlation dimension PD2) (p < .001), but increased HR mean (p < .001), standard deviation of R-R (SD RR ) intervals (p < .001), low (LF) (p < .001) and high frequency band power (HF) (p = .009). Respiratory sinus arrhythmia (RSA) and LF/HF ratio did not change under short-term stress. Partial correlation adjusting for respiration rate showed that HR D2 was associated with chronic stress (r = −.35, p = .019). Differential effects of chronic and short-term stress were observed on several HRV measures. HR D2 decreased under both stress conditions reflecting lowered functionality of the cardiac pacemaker. The results confirm the importance of complexity metrics in modern stress research on HRV.

Although autism has a clear genetic component, the high genetic heterogeneity of the disorder has been a challenge for the identification of causative genes. We used homozygosity analysis to identify probands from nonconsanguineous families that showed evidence of distant shared ancestry, suggesting potentially recessive mutations. Whole-exome sequencing of 16 probands revealed validated homozygous, potentially pathogenic recessive mutations that segregated perfectly with disease in 4/16 families. The candidate genes (UBE3B, CLTCL1, NCKAP5L, ZNF18) encode proteins involved in proteolysis, GTPase-mediated signaling, cytoskeletal organization, and other pathways. Furthermore, neuronal depolarization regulated the transcription of these genes, suggesting potential activity-dependent roles in neurons. We present a multidimensional strategy for filtering whole-exome sequence data to find candidate recessive mutations in autism, which may have broader applicability to other complex, heterogeneous disorders.

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