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Few studies have been conducted in low-selenium areas of China to assess the relationships between dietary intake of selenium and zinc and the risk of squamous cell carcinoma of the esophagus (SCCE). We studied dietary mineral and trace element intake and risk of SCCE in a population- based, case-control study in Taixing, China, in 2000. A total of 218 SCCE patients and 415 population healthy controls were interviewed using a standard dietary and health questionnaire. The median and quartiles were calculated to represent the average level and distribution of selected dietary minerals and trace elements estimated by the Chinese Standard Tables of Food Composition. The adjusted odds ratios (ORs) comparing the highest with the lowest quartiles were 0.30 (95% confidence intervals, CIs = 0.13-0.67) for selenium intake and 0.28 (95% CI = 0.11-0.70) for zinc intake with obvious dose-dependent patterns (P values for trend = 0.01). The adjusted OR for the combined effect of selenium and zinc intake was 0.53 (95% CI = 0.29-0.96) after controlling for potential confounding factors, including age, gender, educational level, body mass index, and total energy intake. Our results suggested that the potential joint effect of zinc and selenium might contribute to SCCE risk. Increased dietary intake of selenium and zinc may decrease the risk of SCCE in a low-selenium area of China. [ABSTRACT FROM AUTHOR]

Effective intracellular delivery of therapeutic proteins can potentially treat a wide array of diseases. However, efficient delivery of functional proteins across the cell membrane remains challenging. Exosomes are nanosized vesicles naturally secreted by various types of cells and may serve as promising nanocarriers for therapeutic biomolecules. Here, we engineered exosomes equipped with a photoinducible cargo protein release system, termed mMaple3-mediated protein loading into and release from exosome (MAPLEX), in which cargo proteins can be loaded into the exosomes by fusing them with photocleavable protein (mMaple3)-conjugated exosomal membrane markers and subsequently released from the exosomal membrane by inducing photocleavage with blue light illumination. Using this system, we first induced transcriptional regulation by delivering octamer-binding transcription factor 4 and SRY-box transcription factor 2 to fibroblasts in vitro. Second, we induced in vivo gene recombination in Cre reporter mice by delivering Cre recombinase. Last, we achieved targeted epigenome editing in the brains of 5xFAD and 3xTg-AD mice, two models of Alzheimer's disease. Administration of MAPLEXs loaded with β-site amyloid precursor protein cleaving enzyme 1 ( Bace1 )-targeting single guide RNA-incorporated dCas9 ribonucleoprotein complexes, coupled with the catalytic domain of DNA methyltransferase 3A, resulted in successful methylation of the targeted CpG sites within the Bace1 promoter. This approach led to a significant reduction in Bace1 expression, improved recognition memory impairment, and reduced amyloid pathology in 5xFAD and 3xTg-AD mice. These results suggest that MAPLEX is an efficient intracellular protein delivery system that can deliver diverse therapeutic proteins for multiple diseases.

Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS is restricted by an inability to distinguish confounding due to linkage disequilibrium (LD) from true pleiotropy. Here we describe CoPheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systematic exploration of causal associations while simultaneously addressing LD confounding. We demonstrate its performance through simulation, showing considerably better control of false positive rates than a conventional approach not accounting for LD. We used CoPheScan to perform PheWAS of protein-truncating variants and fine-mapped variants from disease and pQTL studies, in 2275 disease phenotypes from the UK Biobank. Our results identify the complexity of known pleiotropic genes such as APOE, and suggest a new causal role for TGM3 in skin cancer., (© 2024. The Author(s).)

Systemic sclerosis is an autoimmune disease characterized by inflammatory reactions and fibrosis. Myofibroblasts are considered therapeutic targets for preventing and reversing the pathogenesis of fibrosis in systemic sclerosis. Although the mechanisms that differentiate into myofibroblasts are diverse, transforming growth factor β (TGF-β) is known to be a key mediator of fibrosis in systemic sclerosis. This study investigated the effects of extracellular vesicles derived from human adipose stem cells (ASC-EVs) in an in vivo systemic sclerosis model and in vitro TGF-β1-induced dermal fibroblasts. The therapeutic effects of ASC-EVs on the in vivo systemic sclerosis model were evaluated based on dermal thickness and the number of α-smooth muscle actin (α-SMA)-expressing cells using hematoxylin and eosin staining and immunohistochemistry. Administration of ASC-EVs decreased both the dermal thickness and α-SMA expressing cell number as well as the mRNA levels of fibrotic genes, such as Acta2, Ccn2, Col1a1 and Comp. Additionally, we discovered that ASC-EVs can decrease the expression of α-SMA and CTGF and suppress the TGF-β pathway by inhibiting the activation of SMAD2 in dermal fibroblasts induced by TGF-β1. Finally, TGF-β1-induced dermal fibroblasts underwent selective death through ASC-EVs treatment. These results indicate that ASC-EVs could provide a therapeutic approach for preventing and reversing systemic sclerosis.

Cardiopulmonary resuscitation (CPR) is a life-saving technique used in emergencies when the heart stops beating, typically involving chest compressions and ventilation. Current adult CPR guidelines do not differentiate based on age beyond infancy and childhood. This oversight increases the risk of fatigue fractures in the elderly due to decreased bone density and changes in thoracic structure. Therefore, this study aimed to investigate the correlation and impact of factors influencing rib fatigue fractures for safer out-of-hospital manual cardiopulmonary resuscitation (OHMCPR) application. Using the finite element analysis (FEA) method, we performed fatigue analysis on rib cage models incorporating chest compression conditions and age-specific trabecular bone properties. Fatigue life analyses were conducted on three age-specific rib cage models, each differentiated by trabecular bone properties, to determine the influence of four explanatory variables (the properties of the trabecular bone (a surrogate for the age of the subject), the site of application of the compression force on the breastbone, the magnitude of applied compression force, and the rate of application of the compression force) on the fatigue life of the model. Additionally, considering the complex interaction of chest compression conditions during actual CPR, we aimed to predict rib fatigue fractures under conditions simulating real-life scenarios by analyzing the sensitivity and interrelation of chest compression conditions on the model's fatigue life. Time constraints led to the selection of optimal analysis conditions through the use of design of experiments (DOE), specifically orthogonal array testing, followed by the construction of a deep learning-based metamodel. The predicted fatigue life values of the rib cage model, obtained from the metamodel, showed the influence of the four explanatory variables on fatigue life. These results may be used to devise safer CPR guidelines, particularly for the elderly at a high risk of acute cardiac arrest, safeguarding against potential complications like fatigue fractures.

Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen., Competing Interests: B.H. is the CTO of Genealogy Inc., (© 2023 The Authors.)

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored 1,2 . FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10 -11 ) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants., (© 2023. The Author(s).)

Background: Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative-an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736)., Methods: We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes., Results: We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals' SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10 -16 , EAA p-value=6.73×10 -11 ). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group., Conclusions: Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping., (© 2022. The Author(s).)

Congenital heart disease (CHD) is a rare structural defect that occurs in ∼1% of live births. Studies on CHD genetic architecture have identified pathogenic single-gene mutations in less than 30% of cases. Single-gene mutations often show incomplete penetrance and variable expressivity. Therefore, we hypothesize that genetic background may play a role in modulating disease expression. Polygenic risk scores (PRSs) aggregate effects of common genetic variants to investigate whether, cumulatively, these variants are associated with disease penetrance or severity. However, the major limitations in this field have been in generating sufficient sample sizes for these studies. Here we used CHD-phenotype matched genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) as our base study and whole-genome sequencing data from the CHD cohort (n 1  = 711 trios, n 2  = 362 European trios) of the Gabriella Miller Kids First dataset as our target study to develop PRSs for CHD. PRSs estimated using a GWAS for heart valve problems and heart murmur explain 2.5% of the variance in case-control status of CHD (all SNVs, p = 7.90 × 10 -3 ; fetal cardiac SNVs, p = 8.00 × 10 -3 ) and 1.8% of the variance in severity of CHD (fetal cardiac SNVs, p = 6.20 × 10 -3 ; all SNVs, p = 0.015). These results show that common variants captured in CHD phenotype-matched GWASs have a modest but significant contribution to phenotypic expression of CHD. Further exploration of the cumulative effect of common variants is necessary for understanding the complex genetic etiology of CHD and other rare diseases., Competing Interests: Dr. Sul is an employee of Merck as of April 2021., (© 2022 The Author(s).)

Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes-microbial interactions in the gut contribute to human diseases including AD. We sought to develop an accurate and automated pipeline for AD diagnosis based on transcriptome and microbiota data. Using these data of 161 subjects including AD patients and healthy controls, we trained a machine learning classifier to predict the risk of AD. We found that the classifier could accurately differentiate subjects with AD and healthy individuals based on the omics data with an average F1-score of 0.84. With this classifier, we also identified a set of 35 genes and 50 microbiota features that are predictive for AD. Among the selected features, we discovered at least three genes and three microorganisms directly or indirectly associated with AD. Although further replications in other cohorts are needed, our findings suggest that these genes and microbiota features may provide novel biological insights and may be developed into useful biomarkers of AD prediction., (© 2022. The Author(s).)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry is mediated by the interaction of the viral spike (S) protein with angiotensin-converting enzyme 2 (ACE2) on the host cell surface. Although a clinical trial testing soluble ACE2 (sACE2) for COVID-19 is currently ongoing, our understanding of the delivery of sACE2 via small extracellular vesicles (sEVs) is still rudimentary. With excellent biocompatibility allowing for the effective delivery of molecular cargos, sEVs are broadly studied as nanoscale protein carriers. In order to exploit the potential of sEVs, we design truncated CD9 scaffolds to display sACE2 on the sEV surface as a decoy receptor for the S protein of SARS-CoV-2. Moreover, to enhance the sACE2-S binding interaction, we employ sACE2 variants. sACE2-loaded sEVs exhibit typical sEVs characteristics and bind to the S protein. Furthermore, engineered sEVs inhibit the entry of wild-type (WT), the globally dominant D614G variant, Beta (K417N-E484K-N501Y) variant, and Delta (L452R-T478K-D614G) variant SARS-CoV-2 pseudovirus, and protect against authentic SARS-CoV-2 and Delta variant infection. Of note, sACE2 variants harbouring sEVs show superior antiviral efficacy than WT sACE2 loaded sEVs. Therapeutic efficacy of the engineered sEVs against SARS-CoV-2 challenge was confirmed using K18-hACE2 mice. The current findings provide opportunities for the development of new sEVs-based antiviral therapeutics., (© 2022 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.)

Osteoporosis is one of the most common skeletal disorders caused by the imbalance between bone formation and resorption, resulting in quantitative loss of bone tissue. Since stem cell-derived extracellular vesicles (EVs) are growing attention as novel cell-free therapeutics that have advantages over parental stem cells, the therapeutic effects of EVs from adipose tissue-derived stem cells (ASC-EVs) on osteoporosis pathogenesis were investigated. ASC-EVs were isolated by a multi-filtration system based on the tangential flow filtration (TFF) system and characterized using transmission electron microscopy, dynamic light scattering, zeta potential, flow cytometry, cytokine arrays, and enzyme-linked immunosorbent assay. EVs are rich in growth factors and cytokines related to bone metabolism and mesenchymal stem cell (MSC) migration. In particular, osteoprotegerin (OPG), a natural inhibitor of receptor activator of nuclear factor-κB ligand (RANKL), was highly enriched in ASC-EVs. We found that the intravenous administration of ASC-EVs attenuated bone loss in osteoporosis mice. Also, ASC-EVs significantly inhibited osteoclast differentiation of macrophages and promoted the migration of bone marrow-derived MSCs (BM-MSCs). However, OPG-depleted ASC-EVs did not show anti-osteoclastogenesis effects, demonstrating that OPG is critical for the therapeutic effects of ASC-EVs. Additionally, small RNA sequencing data were analysed to identify miRNA candidates related to anti-osteoporosis effects. miR-21-5p in ASC-EVs inhibited osteoclast differentiation through Acvr2a down-regulation. Also, let-7b-5p in ASC-EVs significantly reduced the expression of genes related to osteoclastogenesis. Finally, ASC-EVs reached the bone tissue after they were injected intravenously, and they remained longer. OPG, miR-21-5p, and let-7b-5p in ASC-EVs inhibit osteoclast differentiation and reduce gene expression related to bone resorption, suggesting that ASC-EVs are highly promising as cell-free therapeutic agents for osteoporosis treatment., (© 2021 The Authors. Journal of Extracellular Vesicles published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.)

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia causing irreversible brain damage to the elderly and presents a major public health challenge. Clinical research and genome-wide association studies have suggested a potential contribution of the endocytic pathway to AD, with an emphasis on common loci. However, the contribution of rare variants in this pathway to AD has not been thoroughly investigated. In this study, we focused on the effect of rare variants on AD by first applying a rare-variant gene-set burden analysis using genes in the endocytic pathway on over 3,000 individuals with European ancestry from three large whole-genome sequencing (WGS) studies. We identified significant associations of rare-variant burden within the endocytic pathway with AD, which were successfully replicated in independent datasets. We further demonstrated that this endocytic rare-variant enrichment is associated with neurofibrillary tangles (NFTs) and age-related phenotypes, increasing the risk of obtaining severer brain damage, earlier age-at-onset, and earlier age-of-death. Next, by aggregating rare variants within each gene, we sought to identify single endocytic genes associated with AD and NFTs. Careful examination using NFTs revealed one significantly associated gene, ANKRD13D. To identify functional associations, we integrated bulk RNA-Seq data from over 600 brain tissues and found two endocytic expression genes (eGenes), HLA-A and SLC26A7, that displayed significant influences on their gene expressions. Differential expressions between AD patients and controls of these three identified genes were further examined by incorporating scRNA-Seq data from 48 post-mortem brain samples and demonstrated distinct expression patterns across cell types. Taken together, our results demonstrated strong rare-variant effect in the endocytic pathway on AD risk and progression and functional effect of gene expression alteration in both bulk and single-cell resolution, which may bring more insight and serve as valuable resources for future AD genetic studies, clinical research, and therapeutic targeting., Competing Interests: The authors have declared that no competing interests exist.

Mitochondrial dysfunction and oxidative stress are frequently observed in the early stages of Alzheimer's disease (AD). Studies have shown that presenilin-1 (PS1), the catalytic subunit of γ-secretase whose mutation is linked to familial AD (FAD), localizes to the mitochondrial membrane and regulates its homeostasis. Thus, we investigated how five PS1 mutations (A431E, E280A, H163R, M146V, and Δexon9) observed in FAD affect mitochondrial functions. Methods: We used H4 glioblastoma cell lines genetically engineered to inducibly express either the wild-type PS1 or one of the five PS1 mutants in order to examine mitochondrial morphology, dynamics, membrane potential, ATP production, mitochondria-associated endoplasmic reticulum (ER) membranes (MAMs), oxidative stress, and bioenergetics. Furthermore, we used brains of PS1M146V knock-in mice, 3xTg-AD mice, and human AD patients in order to investigate the role of PS1 in regulating MAMs formation. Results: Each PS1 mutant exhibited slightly different mitochondrial dysfunction. Δexon9 mutant induced mitochondrial fragmentation while A431E, E280A, H163R, and M146V mutants increased MAMs formation. A431E, E280A, M146V, and Δexon9 mutants also induced mitochondrial ROS production. A431E mutant impaired both complex I and peroxidase activity while M146V mutant only impaired peroxidase activity. All PS1 mutants compromised mitochondrial membrane potential and cellular ATP levels were reduced by A431E, M146V, and Δexon9 mutants. Through comparative profiling of hippocampal gene expression in PS1M146V knock-in mice, we found that PS1M146V upregulates Atlastin 2 (ATL2) expression level, which increases ER-mitochondria contacts. Down-regulation of ATL2 after PS1 mutant induction rescued abnormally elevated ER-mitochondria interactions back to the normal level. Moreover, ATL2 expression levels were significantly elevated in the brains of 3xTg-AD mice and AD patients. Conclusions: Overall, our findings suggest that each of the five FAD-linked PS1 mutations has a deleterious effect on mitochondrial functions in a variety of ways. The adverse effects of PS1 mutations on mitochondria may contribute to MAMs formation and oxidative stress resulting in an accelerated age of disease onset in people harboring mutant PS1., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Although tolerogenic dendritic cell-derived exosomes (TolDex) have emerged as promising therapeutics for rheumatoid arthritis (RA), their clinical applications have been hampered by their poor in vivo disposition after systemic administration. Herein, we report the development of stimuli-responsive TolDex that induces lesion-specific immunoregulation in RA. Responsiveness to reactive oxygen species (ROS), a physiological stimulus in the RA microenvironment, was conferred on TolDex by introducing a thioketal (TK) linker-embedded poly(ethylene glycol) (PEG) on TolDex surface via hydrophobic insertion. The detachment of PEG following overproduction of ROS facilitates the cellular uptake of ROS-responsive TolDex (TKDex) into activated immune cells. Notably, TolDex and TKDex downregulated CD40 in mature dendritic cells (mDCs) and regulated secretion of pro-inflammatory cytokines, including tumor necrosis factor (TNF)-α and interleukin-6 (IL-6) at the cellular level. In the collagen-induced arthritis (CIA) mouse model, PEG prolonged the blood circulation of TKDex following intravenous administration and enhanced their accumulation in the joints. In addition, TKDex decreased IL-6, increased transforming growth factor-β, and induced the CD4 + CD25 + Foxp3 + regulatory T cells in CIA mice. Overall, ROS-responsive TolDex might have potential as therapeutic agents for RA. STATEMENT OF SIGNIFICANCE: Tolerogenic dendritic cell-derived exosomes (TolDex) are emerging immunoregulators of autoimmune diseases, including rheumatoid arthritis (RA). However, their lack of long-term stability and low targetability are still challenging. To overcome these issues, we developed reactive oxygen species (ROS)-responsive TolDex (TKDex) by incorporating the ROS-sensitive functional group-embedded poly(ethylene glycol) linker into the exosomal membrane of TolDex. Surface-engineered TKDex were internalized in mature DCs because of high ROS-sensitivity and enhanced accumulation in the inflamed joint in vivo. Further, for the first time, we investigated the potential mechanism of action of TolDex relevant to CD40 downregulation and attenuation of tumor necrosis factor (TNF)-α secretion. Our strategy highlighted the promising nanotherapeutic effects of stimuli-sensitive TolDex, which induces immunoregulation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier Ltd.)

The rapid decrease in sequencing cost has enabled genetic studies to discover rare variants associated with complex diseases and traits. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases. Similar to the hypothesis of common variants, rare variants may affect diseases by regulating gene expression, and recently, several studies have identified the effects of rare variants on gene expression using heritability and expression outlier analyses. However, identifying individual genes whose expression is regulated by rare variants has been challenging due to the relatively small sample size of expression quantitative trait loci studies and statistical approaches not optimized to detect the effects of rare variants. In this study, we analyze whole-genome sequencing and RNA-seq data of 681 European individuals collected for the Genotype-Tissue Expression (GTEx) project (v8) to identify individual genes in 49 human tissues whose expression is regulated by rare variants. To improve statistical power, we develop an approach based on a likelihood ratio test that combines effects of multiple rare variants in a nonlinear manner and has higher power than previous approaches. Using GTEx data, we identify many genes regulated by rare variants, and some of them are only regulated by rare variants and not by common variants. We also find that genes regulated by rare variants are enriched for expression outliers and disease-causing genes. These results suggest the regulatory effects of rare variants, which would be important in interpreting associations of rare variants with complex traits., Competing Interests: The authors have declared that no competing interests exist.

Genomic deletions provide a powerful loss-of-function model in noncoding regions to assess the role of purifying selection on genetic variation. Regulatory element function is characterized by nonuniform tissue and cell type activity, necessarily linking the study of fitness consequences from regulatory variants to their corresponding cellular activity. We generated a callset of deletions from genomes in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and used deletions from The 1000 Genomes Project Consortium (1000GP) in order to examine whether purifying selection preserves noncoding sites of chromatin accessibility marked by DNase I hypersensitivity (DHS), histone modification (enhancer, transcribed, Polycomb-repressed, heterochromatin), and chromatin loop anchors. To examine this in a cellular activity-aware manner, we developed a statistical method, pleiotropy ratio score (PlyRS), which calculates a correlation-adjusted count of "cellular pleiotropy" for each noncoding base pair by analyzing shared regulatory annotations across tissues and cell types. By comparing real deletion PlyRS values to simulations in a length-matched framework and by using genomic covariates in analyses, we found that purifying selection acts to preserve both DHS and enhancer noncoding sites. However, we did not find evidence of purifying selection for noncoding transcribed, Polycomb-repressed, or heterochromatin sites beyond that of the noncoding background. Additionally, we found evidence that purifying selection is acting on chromatin loop integrity by preserving colocalized CTCF binding sites. At regions of DHS, enhancer, and CTCF within chromatin loop anchors, we found evidence that both sites of activity specific to a particular tissue or cell type and sites of cellularly pleiotropic activity are preserved by selection., (© 2021 Radke et al.; Published by Cold Spring Harbor Laboratory Press.)

ZnO and Aluminum doped ZnO nanoparticles (Al/ZnO NPs) were successfully synthesized by the sol-gel method. Together with the effect of calcination temperatures (200, 300 and 400 °C) and Al dosage (1%, 3%, 5% and 10%) on structural, morphological and optical properties of Al/ZnO NPs, their photocatalytic degradation of methyl orange (MO) dye was investigated. The calcination temperatures at 200, 300 and 400 °C in forming structure of ZnO NPs led to spherical nanoparticle, nanorod and nanoflake structures with a well-crystalline hexagonal wurtzite, respectively. The ZnO NPs calcined at 200 °C exhibited the highest specific surface area and light absorption property, leading to the MO removal efficiency of 80% after 4 h under the Ultraviolet (UV) light irradiation. The MO removal efficiency was approximately two times higher than the nanoparticles calcined at 400 °C. Furthermore, the 5% Al/ZnO NPs exhibited superior MO removal efficiency of 99% in only 40 min which was approximately 20 times enhancement in photocatalytic activity compared to pristine ZnO under the visible light irradiation. This high degradation performance was attributed to the extended light absorption, narrowed band gap and effective suppression of electron-hole recombination through an addition of Al metal.

Studies in rats, monkeys, and humans have found action-value signals in multiple regions of the brain. These findings suggest that action-value signals encoded in these brain structures bias choices toward higher expected rewards. However, previous estimates of action-value signals might have been inflated by serial correlations in neural activity and also by activity related to other decision variables. Here, we applied several statistical tests based on permutation and surrogate data to analyze neural activity recorded from the striatum, frontal cortex, and hippocampus. The results show that previously identified action-value signals in these brain areas cannot be entirely accounted for by concurrent serial correlations in neural activity and action value. We also found that neural activity related to action value is intermixed with signals related to other decision variables. Our findings provide strong evidence for broadly distributed neural signals related to action value throughout the brain., Competing Interests: ES, YJ, SK, HK, XC, HL, JS, SL, YC, MJ No competing interests declared, DL Reviewing editor, eLife, (© 2021, Shin et al.)

Motivation: Since the first human genome was sequenced in 2001, there has been a rapid growth in the number of bioinformatic methods to process and analyze next-generation sequencing (NGS) data for research and clinical studies that aim to identify genetic variants influencing diseases and traits. To achieve this goal, one first needs to call genetic variants from NGS data, which requires multiple computationally intensive analysis steps. Unfortunately, there is a lack of an open-source pipeline that can perform all these steps on NGS data in a manner, which is fully automated, efficient, rapid, scalable, modular, user-friendly and fault tolerant. To address this, we introduce xGAP, an extensible Genome Analysis Pipeline, which implements modified GATK best practice to analyze DNA-seq data with the aforementioned functionalities., Results: xGAP implements massive parallelization of the modified GATK best practice pipeline by splitting a genome into many smaller regions with efficient load-balancing to achieve high scalability. It can process 30× coverage whole-genome sequencing (WGS) data in ∼90 min. In terms of accuracy of discovered variants, xGAP achieves average F1 scores of 99.37% for single nucleotide variants and 99.20% for insertion/deletions across seven benchmark WGS datasets. We achieve highly consistent results across multiple on-premises (SGE & SLURM) high-performance clusters. Compared to the Churchill pipeline, with similar parallelization, xGAP is 20% faster when analyzing 50× coverage WGS on Amazon Web Service. Finally, xGAP is user-friendly and fault tolerant where it can automatically re-initiate failed processes to minimize required user intervention., Availability and Implementation: xGAP is available at https://github.com/Adigorla/xgap., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS.

O -GlcNAcylation ( O -linked β- N -acetylglucosaminylation) is notably decreased in Alzheimer's disease (AD) brain. Necroptosis is activated in AD brain and is positively correlated with neuroinflammation and tau pathology. However, the links among altered O -GlcNAcylation, β-amyloid (Aβ) accumulation, and necroptosis are unclear. Here, we found that O -GlcNAcylation plays a protective role in AD by inhibiting necroptosis. Necroptosis was increased in AD patients and AD mouse model compared with controls; however, decreased necroptosis due to O -GlcNAcylation of RIPK3 (receptor-interacting serine/threonine protein kinase 3) was observed in 5xFAD mice with insufficient O -linked β- N -acetylglucosaminase. O -GlcNAcylation of RIPK3 suppresses phosphorylation of RIPK3 and its interaction with RIPK1. Moreover, increased O -GlcNAcylation ameliorated AD pathology, including Aβ burden, neuronal loss, neuroinflammation, and damaged mitochondria and recovered the M2 phenotype and phagocytic activity of microglia. Thus, our data establish the influence of O -GlcNAcylation on Aβ accumulation and neurodegeneration, suggesting O -GlcNAcylation-based treatments as potential interventions for AD., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

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We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.

Stem cell-derived extracellular vesicles (EVs) offer alternative approaches to stem cell-based therapy for regenerative medicine. In this study, stem cell EVs derived during differentiation are developed to use as cell-free therapeutic systems by inducing tissue-specific differentiation. EVs are isolated from human adipose-derived stem cells (HASCs) during white and beige adipogenic differentiation (D-EV and BD-EV, respectively) via tangential flow filtration. D-EV and BD-EV can successfully differentiate HASCs into white and beige adipocytes, respectively. D-EV are transplanted with collagen/methylcellulose hydrogels on the backs of BALB/c mice, and they produce numerous lipid droplets in injected sites. Treatments of BD-EV attenuate diet-induced obesity through browning of adipose tissue in mice. Furthermore, high-fat diet-induced hepatic steatosis and glucose tolerance are improved by BD-EV treatment. miRNAs are responsible for the observed effects of BD-EV. These results reveal that secreted EVs during stem cell differentiation into white adipocytes or beige adipocytes can promote cell reprogramming., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To identify roles of rare and common variants on BP, we conducted genetic analyses in 26 Colombia and Costa Rica pedigrees ascertained for bipolar disorder 1 (BP1), the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 838 individuals and high-coverage whole-genome sequencing of 449 individuals. We compared polygenic risk scores (PRS), estimated using the latest BP1 genome-wide association study (GWAS) summary statistics, between BP1 individuals and related controls. We also evaluated whether BP1 individuals had a higher burden of rare deleterious single-nucleotide variants (SNVs) and rare copy number variants (CNVs) in a set of genes related to BP1. We found that compared with unaffected relatives, BP1 individuals had higher PRS estimated from BP1 GWAS statistics (P = 0.001 ~ 0.007) and displayed modest increase in burdens of rare deleterious SNVs (P = 0.047) and rare CNVs (P = 0.002 ~ 0.033) in genes related to BP1. We did not observe rare variants segregating in the pedigrees. These results suggest that small-to-moderate effect rare and common variants are more likely to contribute to BP1 risk in these extended pedigrees than a few large-effect rare variants.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data., Competing Interests: The authors have declared that no competing interests exist.

Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity., Competing Interests: The authors have declared that no competing interests exist.

Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC., (© 2019 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology.)

BACE1 is the rate-limiting enzyme for amyloid-β peptides (Aβ) generation, a key event in the pathogenesis of Alzheimer's disease (AD). By an unknown mechanism, levels of BACE1 and a BACE1 mRNA-stabilizing antisense RNA ( BACE1-AS ) are elevated in the brains of AD patients, implicating that dysregulation of BACE1 expression plays an important role in AD pathogenesis. We found that nuclear factor erythroid-derived 2-related factor 2 (NRF2/NFE2L2) represses the expression of BACE1 and BACE1-AS through binding to antioxidant response elements (AREs) in their promoters of mouse and human. NRF2-mediated inhibition of BACE1 and BACE1-AS expression is independent of redox regulation. NRF2 activation decreases production of BACE1 and BACE1-AS transcripts and Aβ production and ameliorates cognitive deficits in animal models of AD. Depletion of NRF2 increases BACE1 and BACE1-AS expression and Aβ production and worsens cognitive deficits. Our findings suggest that activation of NRF2 can prevent a key early pathogenic process in AD., Competing Interests: The authors declare no conflict of interest.

Objective: Tourette's syndrome is polygenic and highly heritable. Genome-wide association study (GWAS) approaches are useful for interrogating the genetic architecture and determinants of Tourette's syndrome and other tic disorders. The authors conducted a GWAS meta-analysis and probed aggregated Tourette's syndrome polygenic risk to test whether Tourette's and related tic disorders have an underlying shared genetic etiology and whether Tourette's polygenic risk scores correlate with worst-ever tic severity and may represent a potential predictor of disease severity., Methods: GWAS meta-analysis, gene-based association, and genetic enrichment analyses were conducted in 4,819 Tourette's syndrome case subjects and 9,488 control subjects. Replication of top loci was conducted in an independent population-based sample (706 case subjects, 6,068 control subjects). Relationships between Tourette's polygenic risk scores (PRSs), other tic disorders, ascertainment, and tic severity were examined., Results: GWAS and gene-based analyses identified one genome-wide significant locus within FLT3 on chromosome 13, rs2504235, although this association was not replicated in the population-based sample. Genetic variants spanning evolutionarily conserved regions significantly explained 92.4% of Tourette's syndrome heritability. Tourette's-associated genes were significantly preferentially expressed in dorsolateral prefrontal cortex. Tourette's PRS significantly predicted both Tourette's syndrome and tic spectrum disorders status in the population-based sample. Tourette's PRS also significantly correlated with worst-ever tic severity and was higher in case subjects with a family history of tics than in simplex case subjects., Conclusions: Modulation of gene expression through noncoding variants, particularly within cortico-striatal circuits, is implicated as a fundamental mechanism in Tourette's syndrome pathogenesis. At a genetic level, tic disorders represent a continuous spectrum of disease, supporting the unification of Tourette's syndrome and other tic disorders in future diagnostic schemata. Tourette's PRSs derived from sufficiently large samples may be useful in the future for predicting conversion of transient tics to chronic tic disorders, as well as tic persistence and lifetime tic severity.

A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to accurately test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors., Competing Interests: The authors have declared that no competing interests exist.

Supercapacitors are irreplaceable energy-storage devices for high power output and rapid charge/discharge of electrical energy. In this study, the laser-based fabrication of reduced graphene oxide (rGO) electrodes for supercapacitors is demonstrated with several new features of laser irradiation. A conventional CO 2 laser irradiation system is equipped with (1) a nitrogen blower to avoid combustion of the GO paper, (2) a cylindrical lens for producing a wide line beam, and (3) an optical chopper system for generating an intensity-modulated laser beam. Scanning of the intensity-modulated line beam transforms an extended area of GO into chemically reduced and physically porous graphene. The effects of the laser beam modifications and scanning parameters on the electrochemical performance of the rGO electrode are investigated. The rGO electrode exhibits a high specific capacitance (up to ∼130 F/g) at a current density of 1 A/g. This work can serve as a reference for the process optimization of laser-induced GO reduction.

Most population isolates examined to date were founded from a single ancestral population. Consequently, there is limited knowledge about the demographic history of admixed population isolates. Here we investigate genomic diversity of recently admixed population isolates from Costa Rica and Colombia and compare their diversity to a benchmark population isolate, the Finnish. These Latin American isolates originated during the 16 th century from admixture between a few hundred European males and Amerindian females, with a limited contribution from African founders. We examine whole-genome sequence data from 449 individuals, ascertained as families to build mutigenerational pedigrees, with a mean sequencing depth of coverage of approximately 36×. We find that Latin American isolates have increased genetic diversity relative to the Finnish. However, there is an increase in the amount of identity by descent (IBD) segments in the Latin American isolates relative to the Finnish. The increase in IBD segments is likely a consequence of a very recent and severe population bottleneck during the founding of the admixed population isolates. Furthermore, the proportion of the genome that falls within a long run of homozygosity (ROH) in Costa Rican and Colombian individuals is significantly greater than that in the Finnish, suggesting more recent consanguinity in the Latin American isolates relative to that seen in the Finnish. Lastly, we find that recent consanguinity increased the number of deleterious variants found in the homozygous state, which is relevant if deleterious variants are recessive. Our study suggests that there is no single genetic signature of a population isolate., (Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Inhibition of Notch signalling has shown anti-inflammatory properties in vivo and in vitro models of rheumatoid arthritis (RA). The objective of this study was to determine whether Notch1 might play a role in regulating T-regulatory cells (Tregs) in animal models of RA. Methods: Collagen-induced arthritis (CIA) and collagen antibody-induced arthritis (CAIA) were induced in C57BL/6, Notch1 antisense transgenic (NAS) or DBA1/J mice. We examined whether pharmacological inhibitors of γ-secretase (an enzyme required for Notch1 activation) and antisense-mediated knockdown of Notch1 could attenuate the severity of inflammatory arthritis in CIA and CAIA mice. Proportions of CD4 + CD25 + Foxp3 + Treg cells were measured by flow cytometry. To assess the suppressive capacity of Treg toward responder cells, CFSE-based suppression assay of Treg was performed. Results: γ-secretase inhibitors and antisense-mediated knockdown of Notch1 reduced the severity of inflammatory arthritis in both CIA and CAIA mice. Pharmacological and genetic inhibition of Notch1 signalling induced significant elevation of Treg cell population in CIA and CAIA mice. We also demonstrated that inhibition of Notch signalling suppressed the progression of inflammatory arthritis through modulating the expansion and suppressive function of regulatory T (Treg) cells. Conclusion: Pharmacological and genetic inhibition of Notch1 signalling suppresses the progression of inflammatory arthritis through modulating the population and suppressive function of Treg cells in animal models of RA., Competing Interests: Competing Interests: The authors have declared that no competing interest exists.

Obstructive sleep apnea (OSA) is a common heritable disorder displaying marked sexual dimorphism in disease prevalence and progression. Previous genetic association studies have identified a few genetic loci associated with OSA and related quantitative traits, but they have only focused on single ethnic groups, and a large proportion of the heritability remains unexplained. The apnea-hypopnea index (AHI) is a commonly used quantitative measure characterizing OSA severity. Because OSA differs by sex, and the pathophysiology of obstructive events differ in rapid eye movement (REM) and non-REM (NREM) sleep, we hypothesized that additional genetic association signals would be identified by analyzing the NREM/REM-specific AHI and by conducting sex-specific analyses in multiethnic samples. We performed genome-wide association tests for up to 19,733 participants of African, Asian, European, and Hispanic/Latino American ancestry in 7 studies. We identified rs12936587 on chromosome 17 as a possible quantitative trait locus for NREM AHI in men (N = 6,737; P = 1.7 × 10 -8 ) but not in women (P = 0.77). The association with NREM AHI was replicated in a physiological research study (N = 67; P = 0.047). This locus overlapping the RAI1 gene and encompassing genes PEMT1, SREBF1, and RASD1 was previously reported to be associated with coronary artery disease, lipid metabolism, and implicated in Potocki-Lupski syndrome and Smith-Magenis syndrome, which are characterized by abnormal sleep phenotypes. We also identified gene-by-sex interactions in suggestive association regions, suggesting that genetic variants for AHI appear to vary by sex, consistent with the clinical observations of strong sexual dimorphism.

Motivation: There is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS hits and important insights into the regulatory pathways involved in many diseases. When a significant variant or a candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study this variant or gene. Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. Unfortunately, these datasets often have small sample sizes in some tissues. For this reason, there have been many meta-analysis methods designed to combine gene expression data across many tissues to increase power for finding eQTLs and eGenes. However, these existing techniques are not scalable to datasets containing many tissues, like the GTEx data. Furthermore, these methods ignore a biological insight that the same variant may be associated with the same gene across similar tissues., Results: We introduce a meta-analysis model that addresses these problems in existing methods. We focus on the problem of finding eGenes in gene expression data from many tissues, and show that our model is better than other types of meta-analyses., Availability and Implementation: Source code is at https://github.com/datduong/RECOV ., Contact: eeskin@cs.ucla.edu or datdb@cs.ucla.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Tourette syndrome (TS) is a model neuropsychiatric disorder thought to arise from abnormal development and/or maintenance of cortico-striato-thalamo-cortical circuits. TS is highly heritable, but its underlying genetic causes are still elusive, and no genome-wide significant loci have been discovered to date. We analyzed a European ancestry sample of 2,434 TS cases and 4,093 ancestry-matched controls for rare (< 1% frequency) copy-number variants (CNVs) using SNP microarray data. We observed an enrichment of global CNV burden that was prominent for large (> 1 Mb), singleton events (OR = 2.28, 95% CI [1.39-3.79], p = 1.2 × 10 -3 ) and known, pathogenic CNVs (OR = 3.03 [1.85-5.07], p = 1.5 × 10 -5 ). We also identified two individual, genome-wide significant loci, each conferring a substantial increase in TS risk (NRXN1 deletions, OR = 20.3, 95% CI [2.6-156.2]; CNTN6 duplications, OR = 10.1, 95% CI [2.3-45.4]). Approximately 1% of TS cases carry one of these CNVs, indicating that rare structural variation contributes significantly to the genetic architecture of TS., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Negative selection against deleterious alleles produced by mutation influences within-population variation as the most pervasive form of natural selection. However, it is not known whether deleterious alleles affect fitness independently, so that cumulative fitness loss depends exponentially on the number of deleterious alleles, or synergistically, so that each additional deleterious allele results in a larger decrease in relative fitness. Negative selection with synergistic epistasis should produce negative linkage disequilibrium between deleterious alleles and, therefore, an underdispersed distribution of the number of deleterious alleles in the genome. Indeed, we detected underdispersion of the number of rare loss-of-function alleles in eight independent data sets from human and fly populations. Thus, selection against rare protein-disrupting alleles is characterized by synergistic epistasis, which may explain how human and fly populations persist despite high genomic mutation rates., (Copyright © 2017, American Association for the Advancement of Science.)

Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale.

The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci., (Copyright © 2016 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Hirschsprung disease (HSCR) is a neurocristopathy characterized by the absence of intramural ganglion cells along variable lengths of the gastrointestinal tract. Although the RET proto-oncogene is considered to be the main risk factor for HSCR, only about 30% of the HSCR cases can be explained by variations in previously known genes including RET. Recently, copy number variation (CNV) and loss of heterozygosity (LOH) have emerged as new ways to understand human genomic variation. The goal of this present study is to identify new HSCR genetic factors related to CNV in Korean patients. In the genome-wide genotyping, using Illumina's HumanOmni1-Quad BeadChip (1,140,419 markers), of 123 HSCR patients and 432 unaffected subjects (total n = 555), a total of 8,188 CNVs (1 kb ∼ 1 mb) were identified by CNVpartition. As a result, 16 CNV regions and 13 LOH regions were identified as associated with HSCR (minimum P = 0.0005). Two top CNV regions (deletions at chr6:32675155-32680480 and chr22:20733495-21607293) were successfully validated by additional real-time quantitative polymerase chain reaction analysis. In addition, 2 CNV regions (6p21.32 and 22q11.21) and 2 LOH regions (3p22.2 and 14q23.3) were discovered to be unique to the HSCR patients group. Regarding the large-scale chromosomal aberrations (>1 mb), 11 large aberrations in the HSCR patients group were identified, which suggests that they may be a risk factor for HSCR. Although further replication in a larger cohort is needed, our findings may contribute to the understanding of the etiology of HSCR., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Recently, multiple studies have performed whole-exome or whole-genome sequencing to identify groups of rare variants associated with complex traits and diseases. They have primarily utilized case-control study designs that often require thousands of individuals to reach acceptable statistical power. Family-based studies can be more powerful because a rare variant can be enriched in an extended pedigree and segregate with the phenotype. Although many methods have been proposed for using family data to discover rare variants involved in a disease, a majority of them focus on a specific pedigree structure and are designed to analyze either binary or continuously measured outcomes. In this article, we propose RareIBD, a general and powerful approach to identifying rare variants involved in disease susceptibility. Our method can be applied to large extended families of arbitrary structure, including pedigrees with only affected individuals. The method accommodates both binary and quantitative traits. A series of simulation experiments suggest that RareIBD is a powerful test that outperforms existing approaches. In addition, our method accounts for individuals in top generations, which are not usually genotyped in extended families. In contrast to available statistical tests, RareIBD generates accurate p values even when genetic data from these individuals are missing. We applied RareIBD, as well as other methods, to two extended family datasets generated by different genotyping technologies and representing different ethnicities. The analysis of real data confirmed that RareIBD is the only method that properly controls type I error., (Copyright © 2016. Published by Elsevier Inc.)

Motivation: Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power., Results: We applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation method., Contact: buhm.han@amc.seoul.kr or eeskin@cs.ucla.edu., (© The Author 2016. Published by Oxford University Press.)

Meta-analysis strategies have become critical to augment power of genome-wide association studies (GWAS). To reduce genotyping or sequencing cost, many studies today utilize shared controls, and these individuals can inadvertently overlap among multiple studies. If these overlapping individuals are not taken into account in meta-analysis, they can induce spurious associations. In this article, we propose a general framework for adjusting association statistics to account for overlapping subjects within a meta-analysis. The key idea of our method is to transform the covariance structure of the data, so it can be used in downstream analyses. As a result, the strategy is very flexible and allows a wide range of meta-analysis methods, such as the random effects model, to account for overlapping subjects. Using simulations and real datasets, we demonstrate that our method has utility in meta-analyses of GWAS, as well as in a multi-tissue mouse expression quantitative trait loci (eQTL) study where our method increases the number of discovered eQTL by up to 19% compared with existing methods., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.