GMMAT is an R package for performing genetic association tests in genome-wide association studies (GWAS) and sequencing association studies, for outcomes with distribution in the exponential family (e.g. binary outcomes) based on generalized linear mixed models (GLMMs). It can be used to analyze genetic data from individuals with population structure and relatedness. GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT), including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets. See user manual here.References:
School Models - rare sets!
Evaluation of surveillance systems for early detection of outbreaks is particularly challenging when the systems are designed to detect events for which minimal or no historic examples exist (1). Although infection by biologic agents is rare, exceptions have occurred. For example, in 1979, persons living in Sverdlovsk in the former Soviet Union were exposed to Bacillus anthracis during an unintentional release from a weapons plant (2), and a limited number of persons were exposed in Florida, New York, and the District of Columbia during 2001 when B. anthracis spores were released through the mail (3). However, absent sufficient real outbreak data, measuring a system's detection performance requires simulation. Simulated outbreaks must reflect the diversity of threats, both natural and man-made, that a surveillance system might reasonably be expected to encounter and detect. This paper describes a flexible approach to generating standardized simulated data sets for benchmarking surveillance systems and provides examples of its application. Rather than model all possible conditions and factors, the approach relies on simulated outbreaks characterized by a controlled feature set that systematically defines the magnitude, temporal progression, duration, and spatial characteristics of the simulated outbreaks on the basis of variable parameters.Stages of Outbreak Detection
Performance of outbreak-detection models can be measured by using authentic data, synthetic data, or combinations of the two (Table). Two kinds of purely authentic data sets are possible. One is genuine syndromic data contemporaneous with either a known large-scale local outbreak (e.g., a winter influenza surge) (11) or a more circumscribed event (e.g., a diarrheal outbreak) (12). The data set would contain the background of ordinary disease or symptom occurrence and the signal of the actual outbreak. A second type of authentic data set is a hybrid containing background from a regional surveillance system spiked with cases from a known outbreak. This approach was taken when over-the-counter medication-sales data were spiked with an outbreak based on the Sverdlosk incident (13). Alternatively, a hypothetical baseline can be constructed, and actual or simulated signals can be imposed and injected. Although this approach is valid, limited need exists to simulate background activity, given the abundance of readily available real-signal streams from surveillance systems.
The approach described in this paper superimposes a simulated signal onto an authentic baseline, permitting exploration of the effects of controlled variations of signal characteristics. Two main approaches can be taken to creating this simulated signal: 1) using multistage, multivariate mathematical models to produce the signal or 2) defining a series of parameters that enable generation of a controlled feature set simulated signal. For example, a complex mathematical model (14) might be based on a scenario in which a particular form of aerosolized B. anthracis is dispersed under a certain set of atmospheric conditions over a specific geographic region with a well-characterized population demographic. The number of susceptible persons might be estimated and their subsequent behaviors modeled. The resulting effect on the syndromic surveillance data set (e.g., retail sales, primary care visits, or ED visits) could be projected. However, this approach for evaluating outbreak-detection performance is labor-intensive, and the models are based on multiple assumptions. A more flexible approach is to use a set of variable parameters describing a particular outbreak. Defining feature sets of outbreaks (e.g., magnitude, shape, and duration) allows rapid determination of the limits of a system's ability to detect an outbreak under varying conditions.Using Parameters To Specify Outbreak Characteristics
In the last set of experiments, the optimal method for integrating data from multiple regional EDs was determined (21). In one simulation, the synthetic outbreak was introduced evenly into both hospital data sets (aggregate model). In the second, the outbreak was introduced into only one or the other of the hospital data sets (local model). The aggregate model had a higher sensitivity for detecting outbreaks that were evenly distributed between the hospitals. However, for outbreaks that were localized to one facility, maintaining individual models for each location proved to be better. Given the complementary benefits offered by both approaches, the results suggested building a hybrid system that includes both individual models for each location and an aggregate model that combines all the data.Limitations
And the benefits do not stop with childhood. Federal law must be updated to account for the large number of children with autism transitioning to adulthood. The last time Congress revised the Individuals with Disabilities Education Act, in 2004, autism was still relatively rare, and only one state, Indiana, mandated that health insurance cover autism. Now, as a sizeable number of children with severe forms of autism outgrow the public schools, it makes sense to extend the age of eligibility for special education services from 21 to 26, with an emphasis placed on independent living and employment.
Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at The stand-alone program is also available at -Li2019/MetaRNN.
Because experimentally validating the effects of these variants is highly time-consuming and costly, computational approaches have been developed for this purpose [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. These methods can be loosely categorized into three groups: functional prediction methods, which model the functional importance of the variants; conservation-based methods, which use evolutionary data to identify functional regions and variants; and ensemble methods, which combine multiple individual prediction tools into a single more powerful predictor. While these methods have been widely used to predict potentially pathogenic variants, there are still two significant limitations in their application to whole-exome sequencing studies. First, most of these methods either deployed models trained with rare pathogenic and common benign variants or ignored the importance of observed allele frequencies as features, leading to less optimized performance for separating rare pathogenic and rare benign variants. Second, most methods provide prediction scores for only nsSNVs or incomparable scores for nsSNVs and nfINDELs separately, making it infeasible to use these scores as weights in an integrated (nsSNV+nfINDELs) burden test for genotype-phenotype association analysis.
This study developed the MetaRNN and MetaRNN-indel models to overcome these limitations, enabling users to easily annotate and score both nsSNVs and nfINDELs. As predictive features, our classifiers combine recently developed independent prediction algorithms, conservation scores, and allele frequency information from the 1000 Genomes Project (1000GP) [19], ExAC [20], and gnomAD [21]. Annotations from flanking 1 codon of nucleotides around the target variants were extracted by bidirectional gated recurrent units [22] (GRUs). We trained our recurrent neural network (RNN) model with 26,517 nsSNVs (absent from at least one of the three population datasets, namely gnomAD, ExAC, and 1000GP) and 1981 nfINDELs reported in ClinVar [23] on or before 20190102. To evaluate the performance of the proposed models, we compared multiple state-of-the-art computational methods using independent test sets constructed from well-known variation-disease association databases, i.e., ClinVar [23] and HGMD [24], a TP53 functional mutation dataset [25], and a dataset of potential cancer driver variants [26]. Our results suggest that utilizing flanking region annotations helps boost model performance for separating rare pathogenic variants versus rare (and common) benign variants. In addition, we provide pre-computed MetaRNN scores for all possible human nsSNVs available at [27, 28]. A GitHub page for a stand-alone annotation software package for both nsSNVs and nfINDELs is available at -Li2019/MetaRNN [29]. 2ff7e9595c
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