MR-SPLIT: A novel method to address selection and weak instrument bias in one-sample Mendelian randomization studies.

Mendelian Randomization (MR) is a widely embraced approach to assess causality in epidemiological studies. Two-stage least squares (2SLS) method is a predominant technique in MR analysis. However, it can lead to biased estimates when instrumental variables (IVs) are weak. Moreover, the issue of the winner's curse could emerge when utilizing the same dataset for both IV selection and causal effect estimation, leading to biased estimates of causal effects and high false positives. Focusing on one-sample MR analysis, this paper introduces a novel method termed Mendelian Randomization with adaptive Sample-sPLitting with cross-fitting InstrumenTs (MR-SPLIT), designed to address bias issues due to IV selection and weak IVs, under the 2SLS IV regression framework. We show that the MR-SPLIT estimator is more efficient than its counterpart cross-fitting MR (CFMR) estimator. Additionally, we introduce a multiple sample-splitting technique to enhance the robustness of the method. We conduct extensive simulation studies to compare the performance of our method with its counterparts. The results underscored its superiority in bias reduction, effective type I error control, and increased power. We further demonstrate its utility through the application of a real-world dataset. Our study underscores the importance of addressing bias issues due to IV selection and weak IVs in one-sample MR analyses and provides a robust solution to the challenge.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

A high-dimensional omnibus test for set-based association analysis.

Set-based association analysis is a valuable tool in studying the etiology of complex diseases in genome-wide association studies, as it allows for the joint testing of variants in a region or group. Two common types of single nucleotide polymorphism (SNP)-disease functional models are recognized when evaluating the joint function of a set of SNP: the cumulative weak signal model, in which multiple functional variants with small effects contribute to disease risk, and the dominating strong signal model, in which a few functional variants with large effects contribute to disease risk. However, existing methods have two main limitations that reduce their power. Firstly, they typically only consider one disease-SNP association model, which can result in significant power loss if the model is misspecified. Secondly, they do not account for the high-dimensional nature of SNPs, leading to low power or high false positives. In this study, we propose a solution to these challenges by using a high-dimensional inference procedure that involves simultaneously fitting many SNPs in a regression model. We also propose an omnibus testing procedure that employs a robust and powerful P-value combination method to enhance the power of SNP-set association. Our results from extensive simulation studies and a real data analysis demonstrate that our set-based high-dimensional inference strategy is both flexible and computationally efficient and can substantially improve the power of SNP-set association analysis. Application to a real dataset further demonstrates the utility of the testing strategy.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis.

MOTIVATION: Genes function in networks are typically correlated due to their functional connectivity. Variable selection methods have been developed to select important genes associated with a trait while incorporating network graphical information. However, no method has been proposed to quantify the uncertainty of individual genes under such settings. RESULTS: In this paper, we construct confidence intervals and provide p-values for parameters of a high-dimensional linear model incorporating graphical structures where the number of variables p diverges with the number of observations. For combining the graphical information, we propose a graph-constrained desparsified LASSO (GCDL) estimator, which reduces dramatically the influence of high correlation of predictors and enjoys the advantage of faster computation and higher accuracy compared with the desparsified LASSO. Theoretical results show that the GCDL estimator achieves asymptotic normality. The asymptotic property of the uniform convergence is established, with which an explicit expression of the uniform confidence interval can be derived. Extensive numerical results indicate that the GCDL estimator and its (uniform) confidence interval performs well even when predictors are highly correlated. AVAILABILITY AND IMPLEMENTATION: An R package implementing the proposed method is available at https://github.com/XiaoZhangryy/gcdl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.

BACKGROUND: Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping. METHODS: We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision. RESULTS: wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes. CONCLUSIONS: The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

Placebo and Nocebo Responses in Pharmacological Trials of Tic Disorders: A Meta-Analysis.

BACKGROUND: Clinical trials of new drugs for tic disorders (TD) often fail to yield positive results. Placebo and nocebo responses play a vital role in interpreting the outcomes of randomized controlled trials (RCTs), yet these responses in RCTs of TD remain unexplored. OBJECTIVE: The aim was to assess the magnitude of placebo and nocebo responses in RCTs of pharmacological interventions for TD and identify influencing factors. METHODS: A systematic search of the Embase, Medline, Cochrane Central Register of Controlled Trials, and PsycINFO databases was conducted. Eligible studies were RCTs that compared active pharmacological agents with placebos. Placebo response was defined as the change from baseline in TD symptom severity in the placebo group, and nocebo response as the proportion experiencing adverse events (AEs) in this group. Subgroup analysis and meta-regression were performed to explore modifying factors. RESULTS: Twenty-four trials involving 2222 participants were included in this study. A substantial placebo response in TD symptom severity was identified, with a pooled effect size of -0.79 (95% confidence interval [CI] -0.99 to -0.59; I2 = 67%). Forty-four percent (95% CI 27% to 63%; I2 = 92%) of patients experienced AEs while taking inert pills. Sample size, study design, and randomization ratio were correlated with changes in placebo and nocebo responses. CONCLUSION: There were considerable placebo and nocebo responses in TD clinical trials. These results are of great relevance for the design of future trials and for clinical practice in TD. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration ID CRD42023388397. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Novel binary regulated silicon-carbon materials as high-performance anodes for lithium-ion batteries.

The massive volume dilation, unsteady solid electrolyte interphase, and weak conductivity about Si have failed to bring it to practical applications, although its potential capacity is up to 4200 mAh g-1. For solving these problems, novel binary regulated silicon-carbon materials (Si/BPC) were done by a sol-gel procedure combined with single carbonization. Analytical techniques were systematically utilized to examine the effects of element doping at several gradients on morphology, structure and electrochemical properties of composites, thus the optimal content was identified. Si/BPC preserves a discharge specific capacity of 1021.6 mAh g-1with a coulomb efficiency of 99.27% after 180 cycles at 1000 mA g-1, within the upgrade than single-doped and undoped. In rate test, it has a specific capacity of 1003.2 mAh g-1at a high current density of 5000 mA g-1, quickly back towards 2838.6 mAh g-1at 200 mA g-1. The inclusion of B and P elements is linked to the electrochemical characteristics. In the co-doped carbon layers, the synergistic impact of doping B and P accelerates the diffusion kinetics of lithium ions, boosts diffusion rate of Li+, offers low electrochemical impedance (45.75 Ω). This brings more defects to provide transport carriers and induces a substantial amount of electrochemically active sites, which fosters the storage of Li+, thus making silicon material electrochemically more active and potential.

Gene-based mediation analysis in epigenetic studies.

Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.

Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach.

Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene-gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene-gene interactions associated with complex traits.

Gene set analysis with graph-embedded kernel association test.

MOTIVATION: Kernel-based association test (KAT) has been a popular approach to evaluate the association of expressions of a gene set (e.g. pathway) with a phenotypic trait. KATs rely on kernel functions which capture the sample similarity across multiple features, to capture potential linear or non-linear relationship among features in a gene set. When calculating the kernel functions, no network graphical information about the features is considered. While genes in a functional group (e.g. a pathway) are not independent in general due to regulatory interactions, incorporating regulatory network (or graph) information can potentially increase the power of KAT. In this work, we propose a graph-embedded kernel association test, termed gKAT. gKAT incorporates prior pathway knowledge when constructing a kernel function into hypothesis testing. RESULTS: We apply a diffusion kernel to capture any graph structures in a gene set, then incorporate such information to build a kernel function for further association test. We illustrate the geometric meaning of the approach. Through extensive simulation studies, we show that the proposed gKAT algorithm can improve testing power compared to the one without considering graph structures. Application to a real dataset further demonstrate the utility of the method. AVAILABILITY AND IMPLEMENTATION: The R code used for the analysis can be accessed at https://github.com/JialinQu/gKAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Disrupted Timing of MET Signaling Derails the Developmental Maturation of Cortical Circuits and Leads to Altered Behavior in Mice.

The molecular regulation of the temporal dynamics of circuit maturation is a key contributor to the emergence of normal structure-function relations. Developmental control of cortical MET receptor tyrosine kinase, expressed early postnatally in subpopulations of excitatory neurons, has a pronounced impact on the timing of glutamatergic synapse maturation and critical period plasticity. Here, we show that using a controllable overexpression (cto-Met) transgenic mouse, extending the duration of MET signaling after endogenous Met is switched off leads to altered molecular constitution of synaptic proteins, persistent activation of small GTPases Cdc42 and Rac1, and sustained inhibitory phosphorylation of cofilin. These molecular changes are accompanied by an increase in the density of immature dendritic spines, impaired cortical circuit maturation of prefrontal cortex layer 5 projection neurons, and altered laminar excitatory connectivity. Two photon in vivo imaging of dendritic spines reveals that cto-Met enhances de novo spine formation while inhibiting spine elimination. Extending MET signaling for two weeks in developing cortical circuits leads to pronounced repetitive activity and impaired social interactions in adult mice. Collectively, our data revealed that temporally controlled MET signaling as a critical mechanism for controlling cortical circuit development and emergence of normal behavior.

Multilevel heterogeneous omics data integration with kernel fusion.

High-throughput omics data are generated almost with no limit nowadays. It becomes increasingly important to integrate different omics data types to disentangle the molecular machinery of complex diseases with the hope for better disease prevention and treatment. Since the relationship among different omics data features are typically unknown, a supervised learning model assuming a particular distribution with a specific structure will not serve the purpose to capture the underlying complex relationship between multiple features and a disease phenotype. In this work, we briefly reviewed methods for kernel fusion (KF) based on support vector machine and kernel partial least squares (KPLS) algorithms. We then proposed a fused KPLS (fKPLS) model for disease classification and prediction with multilevel omics data. The fused kernel can deal with effect heterogeneity in which different omic data types may have different effect contribution to the trait of interest, with the purpose to improve the prediction performance. We proposed to optimize the kernel parameters and kernel weights with the genetic algorithm (GA). The proposed GA-fKPLS model can substantially improve disease classification performance by integrating multiple omics data types, demonstrated via extensive simulations and real data analysis. With properly defined fitness functions during GA optimization, the proposed KF method can be extended to other kernel-based analyses such as in kernel association analysis with common or rare variants.

Time-delimited signaling of MET receptor tyrosine kinase regulates cortical circuit development and critical period plasticity.

Normal development of cortical circuits, including experience-dependent cortical maturation and plasticity, requires precise temporal regulation of gene expression and molecular signaling. Such regulation, and the concomitant impact on plasticity and critical periods, is hypothesized to be disrupted in neurodevelopmental disorders. A protein that may serve such a function is the MET receptor tyrosine kinase, which is tightly regulated developmentally in rodents and primates, and exhibits reduced cortical expression in autism spectrum disorder and Rett Syndrome. We found that the peak of MET expression in developing mouse cortex coincides with the heightened period of synaptogenesis, but is precipitously downregulated prior to extensive synapse pruning and certain peak periods of cortical plasticity. These results reflect a potential on-off regulatory synaptic mechanism for specific glutamatergic cortical circuits in which MET is enriched. In order to address the functional significance of the 'off' component of the proposed mechanism, we created a controllable transgenic mouse line that sustains cortical MET signaling. Continued MET expression in cortical excitatory neurons disrupted synaptic protein profiles, altered neuronal morphology, and impaired visual cortex circuit maturation and connectivity. Remarkably, sustained MET signaling eliminates monocular deprivation-induced ocular dominance plasticity during the normal cortical critical period; while ablating MET signaling leads to early closure of critical period plasticity. The results demonstrate a novel mechanism in which temporal regulation of a pleiotropic signaling protein underlies cortical circuit maturation and timing of cortical critical period, features that may be disrupted in neurodevelopmental disorders.

A conditional autoregressive model for genetic association analysis accounting for genetic heterogeneity.

Converging evidence from genetic studies and population genetics theory suggest that complex diseases are characterized by remarkable genetic heterogeneity, and individual rare mutations with different effects could collectively play an important role in human diseases. Many existing statistical models for association analysis assume homogeneous effects of genetic variants across all individuals, and could be subject to power loss in the presence of genetic heterogeneity. To consider possible heterogeneous genetic effects among individuals, we propose a conditional autoregressive model. In the proposed method, the genetic effect is considered as a random effect and a score test is developed to test the variance component of genetic random effect. Through simulations, we compare the type I error and power performance of the proposed method with those of the generalized genetic random field and the sequence kernel association test methods under different disease scenarios. We find that our method outperforms the other two methods when (i) the rare variants have the major contribution to the disease, or (ii) the genetic effects vary in different individuals or subgroups of individuals. Finally, we illustrate the new method by applying it to the whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative.

Multivariate partial linear varying coefficients model for gene-environment interactions with multiple longitudinal traits.

Correlated phenotypes often share common genetic determinants. Thus, a multi-trait analysis can potentially increase association power and help in understanding pleiotropic effect. When multiple traits are jointly measured over time, the correlation information between multivariate longitudinal responses can help to gain power in association analysis, and the longitudinal traits can provide insights on the dynamic gene effect over time. In this work, we propose a multivariate partially linear varying coefficients model to identify genetic variants with their effects potentially modified by environmental factors. We derive a testing framework to jointly test the association of genetic factors and illustrated with a bivariate phenotypic trait, while taking the time varying genetic effects into account. We extend the quadratic inference functions to deal with the longitudinal correlations and used penalized splines for the approximation of nonparametric coefficient functions. Theoretical results such as consistency and asymptotic normality of the estimates are established. The performance of the testing procedure is evaluated through Monte Carlo simulation studies. The utility of the method is demonstrated with a real data set from the Twin Study of Hormones and Behavior across the menstrual cycle project, in which single nucleotide polymorphisms associated with emotional eating behavior are identified.

Conditional knockout of MET receptor tyrosine kinase in cortical excitatory neurons leads to enhanced learning and memory in young adult mice but early cognitive decline in older adult mice.

Human genetic studies established MET gene as a risk factor for autism spectrum disorders. We have previously shown that signaling mediated by MET receptor tyrosine kinase, expressed in early postnatal developing forebrain circuits, controls glutamatergic neuron morphological development, synapse maturation, and cortical critical period plasticity. Here we investigated how MET signaling affects synaptic plasticity, learning and memory behavior, and whether these effects are age-dependent. We found that in young adult (postnatal 2-3 months) Met conditional knockout (Metfx/fx:emx1cre, cKO) mice, the hippocampus exhibits elevated plasticity, measured by increased magnitude of long-term potentiation (LTP) and depression (LTD) in hippocampal slices. Surprisingly, in older adult cKO mice (10-12 months), LTP and LTD magnitudes were diminished. We further conducted a battery of behavioral tests to assess learning and memory function in cKO mice and littermate controls. Consistent with age-dependent LTP/LTD findings, we observed enhanced spatial memory learning in 2-3 months old young adult mice, assessed by hippocampus-dependent Morris water maze test, but impaired spatial learning in 10-12 months mice. Contextual and cued learning were further assessed using a Pavlovian fear conditioning test, which also revealed enhanced associative fear acquisition and extinction in young adult mice, but impaired fear learning in older adult mice. Lastly, young cKO mice also exhibited enhanced motor learning. Our results suggest that a shift in the window of synaptic plasticity and an age-dependent early cognitive decline may be novel circuit pathophysiology for a well-established autism genetic risk factor.

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data.

BACKGROUND: Previous studies have reported that labeling errors are not uncommon in omics data. Potential outliers may severely undermine the correct classification of patients and the identification of reliable biomarkers for a particular disease. Three methods have been proposed to address the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the least trimmed square (enetLTS), and Ensemble. Ensemble is an ensembled classification based on distinct feature selection and modeling strategies. The accuracy of biomarker selection and outlier detection of these methods needs to be evaluated and compared so that the appropriate method can be chosen. RESULTS: The accuracy of variable selection, outlier identification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated and an RNA-seq dataset. On simulated datasets, Ensemble had the highest variable selection accuracy, as measured by a comprehensive index, and lowest false discovery rate among the three methods. When the sample size was large and the proportion of outliers was ≤5%, the positive selection rate of Ensemble was similar to that of enetLTS. However, when the proportion of outliers was 10% or 15%, Ensemble missed some variables that affected the response variables. Overall, enetLTS had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and enetLTS still performed well when the proportion of outliers was relatively large. With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but with higher proportions of outliers Ensemble missed many mislabeled samples. Rlogreg and Ensemble were less accurate in identifying outliers than enetLTS. The prediction accuracy of enetLTS was better than that of Rlogreg. Running Ensemble on a subset of data after removing the outliers identified by enetLTS improved the variable selection accuracy of Ensemble. CONCLUSIONS: When the proportion of outliers is ≤5%, Ensemble can be used for variable selection. When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset after removing outliers identified by enetLTS. For outlier identification, enetLTS is the recommended method. In practice, the proportion of outliers can be estimated according to the inaccuracy of the diagnostic methods used.

An optimal kernel-based U-statistic method for quantitative gene-set association analysis.

Single-variant-based genome-wide association studies have successfully detected many genetic variants that are associated with a number of complex traits. However, their power is limited due to weak marginal signals and ignoring potential complex interactions among genetic variants. The set-based strategy was proposed to provide a remedy where multiple genetic variants in a given set (e.g., gene or pathway) are jointly evaluated, so that the systematic effect of the set is considered. Among many, the kernel-based testing (KBT) framework is one of the most popular and powerful methods in set-based association studies. Given a set of candidate kernels, the method has been proposed to choose the one with the smallest p-value. Such a method, however, can yield inflated Type 1 error, especially when the number of variants in a set is large. Alternatively one can get p values by permutations which, however, could be very time-consuming. In this study, we proposed an efficient testing procedure that cannot only control Type 1 error rate but also have power close to the one obtained under the optimal kernel in the candidate kernel set, for quantitative trait association studies. Our method, a maximum kernel-based U-statistic method, is built upon the KBT framework and is based on asymptotic results under a high-dimensional setting. Hence it can efficiently deal with the case where the number of variants in a set is much larger than the sample size. Both simulation and real data analysis demonstrate the advantages of the method compared with its counterparts.

Depletion of microglia in developing cortical circuits reveals its critical role in glutamatergic synapse development, functional connectivity, and critical period plasticity.

Microglia populate the early developing brain and mediate pruning of the central synapses. Yet, little is known on their functional significance in shaping the developing cortical circuits. We hypothesize that the developing cortical circuits require microglia for proper circuit maturation and connectivity, and as such, ablation of microglia during the cortical critical period may result in a long-lasting circuit abnormality. We administered PLX3397, a colony-stimulating factor 1 receptor inhibitor, to mice starting at postnatal day 14 and through P28, which depletes >75% of microglia in the visual cortex (VC). This treatment largely covers the critical period (P19-32) of VC maturation and plasticity. Patch clamp recording in VC layer 2/3 (L2/3) and L5 neurons revealed increased mEPSC frequency and reduced amplitude, and decreased AMPA/NMDA current ratio, indicative of altered synapse maturation. Increased spine density was observed in these neurons, potentially reflecting impaired synapse pruning. In addition, VC intracortical circuit functional connectivity, assessed by laser scanning photostimulation combined with glutamate uncaging, was dramatically altered. Using two photon longitudinal dendritic spine imaging, we confirmed that spine elimination/pruning was diminished during VC critical period when microglia were depleted. Reduced spine pruning thus may account for increased spine density and disrupted connectivity of VC circuits. Lastly, using single-unit recording combined with monocular deprivation, we found that ocular dominance plasticity in the VC was obliterated during the critical period as a result of microglia depletion. These data establish a critical role of microglia in developmental cortical synapse pruning, maturation, functional connectivity, and critical period plasticity.