Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome data.

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.

Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative.

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.

Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease.

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 (n1 = 711 trios, n2 = 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.

Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.

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.

Engineered small extracellular vesicles displaying ACE2 variants on the surface protect against SARS-CoV-2 infection.

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.

Extracellular vesicles from adipose tissue-derived stem cells alleviate osteoporosis through osteoprotegerin and miR-21-5p.

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.

Alzheimer's disease-causing presenilin-1 mutations have deleterious effects on mitochondrial function.

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.

Rare variants in the endocytic pathway are associated with Alzheimer's disease, its related phenotypes, and functional consequences.

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.

Rare variants regulate expression of nearby individual genes in multiple tissues.

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.

Purifying selection on noncoding deletions of human regulatory loci detected using their cellular pleiotropy.

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.

Reactive oxygen species-responsive dendritic cell-derived exosomes for rheumatoid arthritis.

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-ß, 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.

O-GlcNAcylation ameliorates the pathological manifestations of Alzheimer's disease by inhibiting necroptosis.

O-GlcNAcylation (O-linked ß-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, ß-amyloid (Aß) 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 ß-N-acetylglucosaminase. O-GlcNAcylation of RIPK3 suppresses phosphorylation of RIPK3 and its interaction with RIPK1. Moreover, increased O-GlcNAcylation ameliorated AD pathology, including Aß 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 Aß accumulation and neurodegeneration, suggesting O-GlcNAcylation-based treatments as potential interventions for AD.

xGAP: a python based efficient, modular, extensible and fault tolerant genomic analysis pipeline for variant discovery.

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.

Synaptic processes and immune-related pathways implicated in Tourette syndrome.

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.

Publisher Correction: Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

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.

Cell reprogramming using extracellular vesicles from differentiating stem cells into white/beige adipocytes.

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.

Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates.

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.

Leveraging allelic imbalance to refine fine-mapping for eQTL studies.

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.