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Kohlschütter-Tönz syndrome (KTS) manifests as neurological dysfunctions, including early-onset seizures. Mutations in the citrate transporter SLC13A5 are associated with KTS, yet their underlying mechanisms remain elusive. Here, we report that a Drosophila SLC13A5 homolog, I’m not dead yet (Indy), constitutes a neurometabolic pathway that suppresses seizure. Loss of Indy function in glutamatergic neurons caused “bang-induced” seizure-like behaviors. In fact, glutamate biosynthesis from the citric acid cycle was limiting in Indy mutants for seizure-suppressing glutamate transmission. Oral administration of the rate-limiting α-ketoglutarate in the metabolic pathway rescued low glutamate levels in Indy mutants and ameliorated their seizure-like behaviors. This metabolic control of the seizure susceptibility was mapped to a pair of glutamatergic neurons, reversible by optogenetic controls of their activity, and further relayed onto fan-shaped body neurons via the ionotropic glutamate receptors. Accordingly, our findings reveal a micro-circuit that links neural metabolism to seizure, providing important clues to KTS-associated neurodevelopmental deficits., Author summary Kohlschütter-Tönz syndrome (KTS) is a neurodevelopmental disorder linked to two distinct genomic loci encoding the citrate transporter SLC13A5 and synaptic protein ROGDI, respectively. An early-onset seizure is the most prominent neurological symptom in KTS patients, yet how these genes contribute to the control of seizure susceptibility remains poorly understood. Our study establishes behavioral models of seizure in Drosophila mutants of KTS-associated genes and demonstrates a genetic, metabolic, and neural pathway of seizure suppression. We discover that the metabolic flux of the Krebs cycle to glutamate biosynthesis plays a critical role in scaling seizure-relevant glutamate transmission. We further map this seizure-suppressing pathway to a surprisingly small number of glutamatergic neurons and their ionotropic glutamate transmission onto a key sleep-promoting locus in the adult fly brain. Given that the excitatory amino acid glutamate is considered a general seizure-promoting neurotransmitter, our findings illustrate how glutamatergic transmission can have opposing effects on seizure susceptibility in the context of a micro-neural circuit, possibly explaining drug-resistant epilepsy. This seizure-suppressing locus in the Drosophila brain is also implicated in metabolism, circadian rhythms, and sleep, revealing the conserved neural principles of their intimate interaction with epilepsy across species.

Recent advances in consortium-scale genome-wide association studies (GWAS) have highlighted the involvement of common genetic variants in autism spectrum disorder (ASD), but our understanding of their etiologic roles, especially the interplay with rare variants, is incomplete. In this work, we introduce an analytical framework to quantify the transmission disequilibrium of genetically regulated gene expression from parents to offspring. We applied this framework to conduct a transcriptome-wide association study (TWAS) on 7,805 ASD proband-parent trios, and replicated our findings using 35,740 independent samples. We identified 31 associations at the transcriptome-wide significance level. In particular, we identified POU3F2 (p = 2.1E-7), a transcription factor mainly expressed in developmental brain. Gene targets regulated by POU3F2 showed a 2.7-fold enrichment for known ASD genes (p = 2.0E-5) and a 2.7-fold enrichment for loss-of-function de novo mutations in ASD probands (p = 7.1E-5). These results provide a novel connection between rare and common variants, whereby ASD genes affected by very rare mutations are regulated by an unlinked transcription factor affected by common genetic variations., Author summary Autism spectrum disorder is a neurodevelopmental disorder with complex genetic etiology. Mutational variant studies link damaging and typically rare variants in protein-coding genes with disease outcomes, while genome-wide association studies identify genetic variations that are common in the human population associated with autism risk. Interestingly, studies targeting common and rare variants have implicated distinct risk pathways for autism. Here, we introduce a novel statistical framework for risk gene mapping, i.e., TITANS, to better analyze common genetic variants from parent-proband trios. TITANS integrates transmission disequilibrium information with tissue-specific regulatory annotations of multiple linked variants to infer risk genes. We pinpoint a novel autism gene POU3F2, which encodes a key transcription factor regulating multiple autism risk genes implicated in exome sequencing studies. Our findings provide a novel connection between rare and common variants, whereby autism genes affected by rare mutations are regulated by an unlinked transcription factor affected by common genetic variations.

Starvation caused by adverse feeding stresses or food shortages has been reported to result in sleep loss in animals. However, how the starvation signal interacts with the central nervous system is still unknown. Here, the adipokinetic hormone (AKH)—Fork head Box-O (FOXO) pathway is shown to respond to energy change and adjust the sleep of Drosophila through remodeling of the s-LNv (small ventral lateral neurons) dorsal projections. Our results show that starvation prevents flies from going to sleep after the first light-dark transition. The LNvs are required for starvation-induced sleep loss through extension of the pigment dispersing factor (PDF)-containing s-LNv dorsal projections. Further studies reveal that loss of AKH or AKHR (akh receptor) function blocks starvation-induced extension of s-LNv dorsal projections and rescues sleep suppression during food deprivation. FOXO, which has been reported to regulate synapse plasticity of neurons, acts as starvation response factor downstream of AKH, and down regulation of FOXO level considerably alleviates the influence of starvation on s-LNv dorsal projections and sleep. Taking together, our results outline the transduction pathways between starvation signal and sleep, and reveal a novel functional site for sleep regulation., Author summary Sleep of Drosophila is regulated by circadian rhythm and homeostasis on the normal condition, in the presence of cellular stress or injury, the central nervous system neurons adjust sleep on the basis of impulse, which is transmitted by neuropeptides generated by cytokines from either peripheral tissues or nervous system cells. Metabolism and sleep are inextricably linked, energy deficiency has an enormous impact on the sleep, starvation induced sleep loss in the Drosophila has been reported and many genes play roles in this process. However, how the starvation signals transmit to nervous system and then make the sleep loss are still no clear. In this study we find the starvation prevent flies going to sleep, PDF/PDFR and LNvs are required for starvation induced sleep loss, during starvation flies show more expanded s-LNvs dorsal projection compared to flies on the normal condition. LNvs are vital awakening neurons, they transmit arousal signal to downstream neurons through its dorsal projections, more patulous s-LNvs dorsal projections accelerate the transmission of awakening signals, can prevent flies from going to sleep. Flies losing AKH/AKHR/FOXO function limit starvation induced sleep loss. Flies with blocked AKH-FOXO pathway also limit starvation induced s-LNvs dorsal projections opening, indicating that the s-LNvs dorsal projections are function sites of starvation signal on sleep regulation.

As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation, which we improved to accommodate variable sample size across SNVs. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, genotype imputation boasts a 3- to 5-fold lower root-mean-square error, and better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded a decrease in statistical power by 9, 43 and 35%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression., Author summary Genome-wide association studies (GWASs) quantify the effect of genetic variants and traits, such as height. Such estimates are called association summary statistics and are typically publicly shared through publication. Typically, GWASs are carried out by genotyping ∼ 500′000 SNVs for each individual which are then combined with sequenced reference panels to infer untyped SNVs in each’ individuals genome. This process of genotype imputation is resource intensive and can therefore be a limitation when combining many GWASs. An alternative approach is to bypass the use of individual data and directly impute summary statistics. In our work we compare the performance of summary statistics imputation to genotype imputation. We observe that genotype imputation shows a 3- to 5-fold lower RMSE compared to summary statistics imputation, as well as a better capability to distinguish true associations from null results. Furthermore, we demonstrate the potential of summary statistics imputation by presenting 34 novel height-associated loci, 19 of which were confirmed in UK Biobank. Our study demonstrates that given current reference panels, summary statistics imputation is a very efficient and cost-effective way to identify common or low-frequency trait-associated loci.

Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality., Author summary Understanding the causal relationships between pairs of traits is crucial for unravelling the causes of disease. To this end, results from genome-wide association studies are valuable because if a trait is known to be influenced by a genetic variant then this knowledge can be used to test the trait’s causal influences on other traits and diseases. Here we discuss scenarios where the nature of the genetic association with the causal trait can lead existing causal inference methods to give the wrong direction of causality. We introduce a new method that can be applied to summary level data and is potentially less susceptible to problems such as measurement error, and apply it to evaluate the causal relationships between DNA methylation levels and gene expression. While our results show that DNA methylation is more likely to be the causal factor, we point out that is it crucial to acknowledge that systematic differences in measurement error between the platforms could influence such conclusions.

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restrict our ability to detect associations. Here we propose an efficient statistical method (MultiXcan) that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes. MultiXcan integrates evidence across multiple panels using multivariate regression, which naturally takes into account the correlation structure. We apply our method to simulated and real traits from the UK Biobank and show that, in realistic settings, we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed a summary result-based extension called S-MultiXcan, which we show yields highly concordant results with the individual level version when LD is well matched. Our multivariate model-based approach allowed us to use the individual level results as a gold standard to calibrate and develop a robust implementation of the summary-based extension. Results from our analysis as well as software and necessary resources to apply our method are publicly available., Author summary We develop a new method, MultiXcan, to test the mediating role of gene expression variation on complex traits, integrating information available across multiple tissue studies. We show this approach has higher power than traditional single-tissue methods. We extend this method to use only summary-statistics from public GWAS. We apply these methods to 222 complex traits available in the UK Biobank cohort, and 109 complex traits from public GWAS and discuss the findings.

The human face is a complex assemblage of highly variable yet clearly heritable anatomic structures that together make each of us unique, distinguishable, and recognizable. Relatively little is known about the genetic underpinnings of normal human facial variation. To address this, we carried out a large genomewide association study and two independent replication studies of Bantu African children and adolescents from Mwanza, Tanzania, a region that is both genetically and environmentally relatively homogeneous. We tested for genetic association of facial shape and size phenotypes derived from 3D imaging and automated landmarking of standard facial morphometric points. SNPs within genes SCHIP1 and PDE8A were associated with measures of facial size in both the GWAS and replication cohorts and passed a stringent genomewide significance threshold adjusted for multiple testing of 34 correlated traits. For both SCHIP1 and PDE8A, we demonstrated clear expression in the developing mouse face by both whole-mount in situ hybridization and RNA-seq, supporting their involvement in facial morphogenesis. Ten additional loci demonstrated suggestive association with various measures of facial shape. Our findings, which differ from those in previous studies of European-derived whites, augment understanding of the genetic basis of normal facial development, and provide insights relevant to both human disease and forensics., Author Summary The human face is made up of distinct yet related anatomic structures that together make both individuals and families recognizable. It is clear there is a strong genetic component to the human face, and though the genetics of the face have been studied for several years, there are relatively few genes known to impact normal human facial development and facial shape. We report here a large-scale human genetic study in which we successfully identify and replicate genetic markers associated with normal facial variation using advanced 3D facial imaging in African children. We identified two significant replicated genes associated with measures of human facial size, SCHIP1 and PDE8A, demonstrated their clear expression in the developing face in the mouse, and identified 10 additional candidate genetic loci for human facial shape. Gene discovery for human facial development is an important first step for both diagnosing and treating craniofacial syndromes and for developing forensic modeling of the human face.

A primary goal of the recent investment in sequencing is to detect novel genetic associations in health and disease improving the development of treatments and playing a critical role in precision medicine. While this investment has resulted in an enormous total number of sequenced genomes, individual studies of complex traits and diseases are often smaller and underpowered to detect rare variant genetic associations. Existing genetic resources such as the Exome Aggregation Consortium (>60,000 exomes) and the Genome Aggregation Database (~140,000 sequenced samples) have the potential to be used as controls in these studies. Fully utilizing these and other existing sequencing resources may increase power and could be especially useful in studies where resources to sequence additional samples are limited. However, to date, these large, publicly available genetic resources remain underutilized, or even misused, in large part due to the lack of statistical methods that can appropriately use this summary level data. Here, we present a new method to incorporate external controls in case-control analysis called ProxECAT (Proxy External Controls Association Test). ProxECAT estimates enrichment of rare variants within a gene region using internally sequenced cases and external controls. We evaluated ProxECAT in simulations and empirical analyses of obesity cases using both low-depth of coverage (7x) whole-genome sequenced controls and ExAC as controls. We find that ProxECAT maintains the expected type I error rate with increased power as the number of external controls increases. With an accompanying R package, ProxECAT enables the use of publicly available allele frequencies as external controls in case-control analysis., Author summary Recent investments have produced sequence data on millions of people with the number of sequenced individuals continuing to grow. Although large sequencing studies exist, most sequencing data is gathered and processed in much smaller units of hundreds to thousands of samples. These silos of data result in underpowered studies for rare-variant association of complex diseases. Existing genetic resources such as the Exome Aggregation Consortium (>60,000 exomes) and the Genome Aggregation Database (~140,000 sequenced samples) have the potential to be used as controls in rare variant studies of complex diseases and traits. However, to date, these large, publicly available genetic resources remain underutilized, or even misused, in part due to the high potential for bias caused by differences in sequencing technology and processing. Here we present a new method, Proxy External Controls Association Test (ProxECAT), to integrate sequencing data from different, previously incompatible sources. ProxECAT provides a robust approach to using publicly available sequencing data enabling case-control analysis when no or limited internal controls exist. Further, ProxECAT’s motivating insight, that readily available but often discarded information can be used as a proxy to adjust for differences in data generation, may motivate further method development in other big data technologies and platforms.

Hepatitis B virus (HBV) infection is a common problem in the world, especially in China. More than 60–80% of hepatocellular carcinoma (HCC) cases can be attributed to HBV infection in high HBV prevalent regions. Although traditional Sanger sequencing has been extensively used to investigate HBV sequences, NGS is becoming more commonly used. Further, it is unknown whether word pattern frequencies of HBV reads by Next Generation Sequencing (NGS) can be used to investigate HBV genotypes and predict HCC status. In this study, we used NGS to sequence the pre-S region of the HBV sequence of 94 HCC patients and 45 chronic HBV (CHB) infected individuals. Word pattern frequencies among the sequence data of all individuals were calculated and compared using the Manhattan distance. The individuals were grouped using principal coordinate analysis (PCoA) and hierarchical clustering. Word pattern frequencies were also used to build prediction models for HCC status using both K-nearest neighbors (KNN) and support vector machine (SVM). We showed the extremely high power of analyzing HBV sequences using word patterns. Our key findings include that the first principal coordinate of the PCoA analysis was highly associated with the fraction of genotype B (or C) sequences and the second principal coordinate was significantly associated with the probability of having HCC. Hierarchical clustering first groups the individuals according to their major genotypes followed by their HCC status. Using cross-validation, high area under the receiver operational characteristic curve (AUC) of around 0.88 for KNN and 0.92 for SVM were obtained. In the independent data set of 46 HCC patients and 31 CHB individuals, a good AUC score of 0.77 was obtained using SVM. It was further shown that 3000 reads for each individual can yield stable prediction results for SVM. Thus, another key finding is that word patterns can be used to predict HCC status with high accuracy. Therefore, our study shows clearly that word pattern frequencies of HBV sequences contain much information about the composition of different HBV genotypes and the HCC status of an individual., Author summary HBV infection can lead to many liver complications including hepatocellular carcinoma (HCC), one of the most common liver cancers in China. High-throughput sequencing technologies have recently been used to study the genotype sequence compositions of HBV infected individuals and to distinguish chronic HBV (CHB) infection from HCC. We used NGS to sequence the pre-S region of a large number of CHB and HCC individuals and designed novel word pattern based approaches to analyze the data. We have several surprising key findings. First, most HBV infected individuals contained mixtures of genotypes B and C sequences. Second, multi-dimensional scaling (MDS) analysis of the data showed that the first principal coordinate was closely associated with the fraction of genotype B (or C) sequences and the second principal coordinate was highly associated with the probability of HCC. Third, we also designed K-nearest neighbors (KNN) and support vector machine (SVM) based classifiers for CHB and HCC with high prediction accuracy. The results were validated in an independent data set.

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study., Author summary Identifying trait-relevant tissues is an important step towards understanding disease etiology. Computational methods have been recently developed to integrate SNP functional annotations generated from omics studies to genome-wide association studies (GWASs) to infer trait-relevant tissues. However, two important questions remain to be answered. First, with the increasing number and types of functional annotations nowadays, how do we integrate multiple annotations jointly into GWASs in a trait-specific fashion? Doing so would allow us to take advantage of the complementary information contained in these annotations to optimize the performance of trait-relevant tissue inference. Second, what to do with the inferred trait-relevant tissues? Here, we develop a new statistical method and software to make progress on both fronts. For the first question, we extend the commonly used linear mixed model, with new algorithms and inference strategies, to incorporate multiple annotations in a trait-specific fashion to improve trait-relevant tissue inference accuracy. For the second question, we rely on the close relationship between our proposed method and the widely-used sequence kernel association test, and use the inferred trait-relevant tissues, for the first time, to construct more powerful association tests. We illustrate the benefits of our method through extensive simulations and applications to a wide range of real data sets.