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Background The rapid development of next-generation sequencing technologies allow people to analyze human complex diseases at the molecular level. It has been shown that rare variants play important roles for human diseases besides common variants. Thus, effective statistical methods need to be proposed to test for the associations between traits (e.g., diseases) and rare variants. Currently, more and more rare genetic variants are being detected throughout the human genome, which demonstrates the possibility to study rare variants. Yet complex diseases are usually measured as a variety of forms, such as binary, ordinal, quantitative, or some mixture of them. Therefore, the genetic mapping problem can be attributable to the association detection between multiple traits and multiple loci, with sufficiently considering the correlated structure among multiple traits. Methods In this article, we construct a new non-parametric statistic by the generalized Kendall's [tau] theory based on family data. The new test statistic has an asymptotic distribution, it can be used to study the associations between multiple traits and rare variants, which broadens the way to identify genetic factors of human complex diseases. Results We apply our method (called Nonp-FAM) to analyze simulated data and GAW17 data, and conduct comprehensive comparison with some existing methods. Experimental results show that the proposed family-based method is powerful and robust for testing associations between multiple traits and rare variants, even if the data has some population stratification effect.

BackgroundInsomnia disorder (ID) is one of the most common mental disorders. Research on ID focuses on exploring its mechanism of disease, novel treatments and treatment outcome prediction. An emerging technique in this field is the use of electroencephalography (EEG) microstates, which offer a new method of EEG feature extraction that incorporates information from both temporal and spatial dimensions.AimsTo explore the electrophysiological mechanisms of repetitive transcranial magnetic stimulation (rTMS) for ID treatment and use baseline microstate metrics for the prediction of its efficacy.MethodsThis study included 60 patients with ID and 40 age-matched and gender-matched good sleep controls (GSC). Their resting-state EEG microstates were analysed, and the Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) were collected to assess sleep quality. The 60 patients with ID were equally divided into active and sham groups to receive rTMS for 20 days to test whether rTMS had a moderating effect on abnormal microstates in patients with ID. Furthermore, in an independent group of 90 patients with ID who received rTMS treatment, patients were divided into optimal and suboptimal groups based on their median PSQI reduction rate. Baseline EEG microstates were used to build a machine-learning predictive model for the effects of rTMS treatment.ResultsThe class D microstate was less frequent and contribute in patients with ID, and these abnormalities were associated with sleep onset latency as measured by PSG. Additionally, the abnormalities were partially reversed to the levels observed in the GSC group following rTMS treatment. The baseline microstate characteristics could predict the therapeutic effect of ID after 20 days of rTMS, with an accuracy of 80.13%.ConclusionsOur study highlights the value of EEG microstates as functional biomarkers of ID and provides a new perspective for studying the neurophysiological mechanisms of ID. In addition, we predicted the therapeutic effect of rTMS on ID based on the baseline microstates of patients with ID. This finding carries great practical significance for the selection of therapeutic options for patients with ID.

Identifying gene×environment (G×E) interactions, especially when rare variants are included in genome-wide association studies, is a major challenge in statistical genetics. However, the detection of G×E interactions is very important for understanding the etiology of complex diseases. Although currently some statistical methods have been developed to detect the interactions between genes and environment, the detection of the interactions for the case of rare variants is still limited. Therefore, it is particularly important to develop a new method to detect the interactions between genes and environment for rare variants. In this study, we extend an existing method of adaptive combination of P-values (ADA) and design a novel strategy (called iSADA) for testing the effects of G×E interactions for rare variants. We propose a new two-stage test to detect the interactions between genes and environment in a certain region of a chromosome or even for the whole genome. First, the score statistic is used to test the associations between trait value and the interaction terms of genes and environment and obtain the original P-values. Then, based on the idea of the ADA method, we further construct a full test statistic via the P-values of the preliminary tests in the first stage, so that we can comprehensively test the interactions between genes and environment in the considered genome region. Simulation studies are conducted to compare our proposed method with other existing methods. The results show that the iSADA has higher power than other methods in each case. A GAW17 data set is also applied to illustrate the applicability of the new method.

Currently, tACS has been showing results for initial sleep regulation and insomnia treatment.7 8 Since the role of the theta rhythm as a marker of sleepiness during wakefulness and early phases of spontaneous sleep onset is well known,9 the theta band could potentially serve as a target for modulation. [...]abnormalities in neural oscillatory activity in the theta band of the brain in patients with insomnia have been observed.10 11 More importantly, previous studies found that 5 Hz tACS of the DLPFC before bed in healthy subjects induced an accelerated effect on the sleep onset process,12 and theta-tACS during wakefulness resulted in increased sleep pressure and greater slow-wave activity in sleep after the stimulation.13 Therefore, frequency-specific modulation of theta activity might have a therapeutic effect on patients with insomnia disorder. [...]few studies have compared the efficacy of the two NIBS techniques for insomnia disorder. [...]the current study investigated the efficacy of low-frequency rTMS and theta-tACS for insomnia disorder, and a further comparison was performed. [...]another experiment was conducted to investigate whether a personalised frequency of theta-tACS could obtain better efficacy for patients with insomnia disorder. (C) Patients with insomnia disorder treated with a personalised frequency had a significant decrease in PSQI scores at the 1-month follow-up period compared with post-treatment scores (Wilcoxon matched-pairs signed-rank test, p=0.001), with no significant change in ISI scores.

Exclusion criteria for both groups are as follows: other comorbid mental disorders; serious neurological or medical conditions; other sleep disorders; body mass index score >30; frequent jet lag; contraindications for 3 Tesla magnetic resonance imaging (MRI) or transcranial magnetic stimulation. 60 patients with insomnia were enrolled (38 were female), alongside 30 age-matched, gender-matched and education-matched healthy controls. For each subject, a T1-weighted sequence was acquired with the parameters as follows: repetition time (TR)=6.3 ms; echo time (TE)=2.8 ms; inversion time=844.2 ms; data matrix=256×256; slices=176; field of view (FOV)=256×256 mm2; slice thickness=1 mm. The resting-state functional images were acquired with a single echo using the following parameters: TR=2000 ms; TE=30 ms; flip angle=90°; FOV=240×240 mm2; slice thickness=5 mm; slices=30; matrix size=64×64 and total volumes=185. The hyper-RSFC within the left amygdala–hippocampus circuit (p<0.05, family-wise error corrected) in patients with insomnia was restored after active rTMS treatment (figure 1B). [...]the RSFC changes were significantly correlated with the improvement of REM duration (r=0.46, p=0.024) and BDI scores (r=0.70, p=0.004) (figure 1C). Table 1 Demographic and clinical characteristics of the participants Characteristics Active rTMS (n=30) Sham rTMS (n=30) HC (n=30) P value Mean (SD) Mean (SD) Mean (SD) Active-sham Active-HC Sham-HC Age (years） 43.31 (8.98) 43.15 (10.20) 41.50 (11.25) ns ns ns Gender (M/F) 12/18 10/20 12/18 ns ns ns Education (years) 13.25 (3.85) 12.61 (3.88) 13.79 (4.22) ns ns ns PSQI 13.17 (2.78) 13.77 (3.23) 3.23 (1.46) ns <0.001 <0.001 ISI 16.53 (3.81) 17.31 (5.84) 1.40 (1.16) ns <0.001 <0.001 BDI 13.12 (2.62) 16.40 (5.14) 3.50 (4.11) ns <0.001 <0.001 BAI 35.22 (8.28) 36.72 (6.23) 26.10 (2.26) ns <0.001 <0.001 REM duration (min) 75.10 (36.58) 66.23 (21.92) 50.05 (25.72) ns 0.005 0.021 RSFC (amy–hippo) 0.14 (0.11) 0.16 (0.10) 0.03 (0.03) ns <0.001 <0.001 amy-hippo, amygdala-hippocampus; BAI, Beck Anxiety Inventory; BDI, Beck Depression Inventory; HC, healthy control; ISI, Insomnia Severity Index; ns, not significant; PSQI, Pittsburgh Sleep Quality Index; REM, rapid eye movement; RSFC,

This paper studies the main effects and interactive effects between genes on immunosuppression susceptibility caused by ultraviolet radiation in population of mice. We present a two-step strategy, i.e., we first establish one full linear model based on all main effects and interactive effects, and use the Dantzig selector method to screen the genotype effects preliminary; then via the idea of stepwise regression, under the other model we further detect the significant main effects and interactive effects for the UV-induced immunosuppression susceptibility. The most significant main effect site that we identified is D10Mit170, and the most significant interactive sites are D6Mit389 and D16Mit131.

Despite burgeoning evidence for cortical hyperarousal in insomnia disorder, the existing results on electroencephalography spectral features are highly heterogeneous. Phase‐amplitude coupling, which refers to the modulation of the low‐frequency phase to a high‐frequency amplitude, is probably a more sensitive quantitative measure for characterizing abnormal neural oscillations and explaining the therapeutic effect of repetitive transcranial magnetic stimulation in the treatment of patients with insomnia disorder. Sixty insomnia disorder patients were randomly divided into the active and sham treatment groups to receive 4 weeks of repetitive transcranial magnetic stimulation treatment. Behavioral assessments, resting‐state electroencephalography recordings, and sleep polysomnography recordings were performed before and after repetitive transcranial magnetic stimulation treatment. Forty good sleeper controls underwent the same assessment. We demonstrated that phase‐amplitude coupling values in the frontal and temporal lobes were weaker in Insomnia disorder patients than in those with good sleeper controls at baseline and that phase‐amplitude coupling values near the intervention area were significantly enhanced after active repetitive transcranial magnetic stimulation treatment. Furthermore, the enhancement of phase‐amplitude coupling values was significantly correlated with the improvement of sleep quality. This study revealed the potential of phase‐amplitude coupling in assessing the severity of insomnia disorder and the efficacy of repetitive transcranial magnetic stimulation treatment, providing new insights on the abnormal physiological mechanisms and future treatments for insomnia disorder. In this work, we found that phase‐amplitude coupling values in the frontal and temporal lobes were weaker in insomnia disorder patients. RTMS treatment significantly improved phase‐amplitude coupling values near the intervention target. Elevated phase‐amplitude coupling values were correlated with improved sleep quality.

Inflammatory damage aggravates the progression of Alzheimer’s disease (AD) and the mechanism of inflammatory damage may provide a new therapeutic window for the treatment of AD. Toll-like receptor 4 (TLR4)-mediated signaling can regulate the inflammatory process. However, changes in TLR4 signaling pathway induced by beta-amyloid (Aβ) have not been well characterized in brain, especially in the hippocampus. In the present study, we explored the changes of TLR4 signaling pathway induced by Aβ in the hippocampus and the role of atorvastatin in modulating this signal pathway and neurotoxicity induced by Aβ. Experimental AD rats were induced by intrahippocampal injection of Aβ1–42, and the rats were treated with atorvastatin by oral gavage from 3 weeks before to 6 days after injections of Aβ1–42. To determine the spatial learning and memory ability of rats in the AD models, Morris water maze (MWM) was performed. The expression of the glial fibrillary acidic protein (GFAP), ionized calcium binding adapter molecule-1 (Iba-1), TLR4, tumor necrosis factor receptor-associated factor 6 (TRAF6), and nuclear transcription factor (NF)-κB (NF-κB) protein in the hippocampus was detected by immunohistochemistry and Western blot. Compared to the control group, increased expression of TLR4, TRAF6, and NF-κB was observed in the hippocampus at 7 days post-injection of Aβ (P < 0.01). Furthermore, atorvastatin treatment significantly ameliorated cognitive deficits of rats, attenuated microglia and astrocyte activation, inhibited apoptosis, and down-regulated the expression of TLR4, TRAF6, and NF-κB, both at the mRNA and protein levels (P < 0.01). TLR4 signaling pathway is thus actively involved in Aβ-induced neuroinflammation and atorvastatin treatment can exert the therapeutic benefits for AD via the TLR4 signaling pathway.

Gene mapping study is very important task in current biostatistics and real life science. Most biological traits are mainly controlled by the genetic factors, i.e., the function of genes in the genome, and also affected by some environment factors. Reasonable statistical theory and methods should be used in the gene mapping study, so that the gene effects can be inferred precisely and reliable evidence can be further provided to practical domains of genetics and medicine. In this paper, we compare two statistical methods (conventional LSE method and the Dantzig Selector method) on estimating gene effects in the framework of linear model, and make some comparison on them. The two methods are illustrated by applying to a real data set. The analyzing results show that the Dantzig Selector method has some advantagement although the two methods give similar parameter estimates.

Linkage analysis is now being widely used to map markers on each chromosome in the human genome, to map genetic diseases, and to identify genetic forms of common diseases. Two-locus linkage analysis and multi-locus analysis have been investigated comprehensively, and many computer programs have been developed to perform linkage analysis. Yet there exists a shortcoming in traditional methods, i.e., the parameter space of two-locus recombination fractions has not been emphasized sufficiently in the usual analyses. In this paper, we propose a new strategy for estimating the two-locus recombination fractions based on data of backcross family in the framework of some natural and necessary parameter restrictions. The new strategy is based on a restricted projection algorithm, which can provide fast reasonable estimates of recombination fraction, and can therefore serve as a superior alternative algorithm. Results obtained from both real and simulated data indicate that the new algorithm performs well in the estimation of recombination fractions and outperforms current methods.