Benchmarking network propagation methods for disease gene identification.

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genes.

Single-cell RNAseq reveals seven classes of colonic sensory neuron.

OBJECTIVE: Integration of nutritional, microbial and inflammatory events along the gut-brain axis can alter bowel physiology and organism behaviour. Colonic sensory neurons activate reflex pathways and give rise to conscious sensation, but the diversity and division of function within these neurons is poorly understood. The identification of signalling pathways contributing to visceral sensation is constrained by a paucity of molecular markers. Here we address this by comprehensive transcriptomic profiling and unsupervised clustering of individual mouse colonic sensory neurons. DESIGN: Unbiased single-cell RNA-sequencing was performed on retrogradely traced mouse colonic sensory neurons isolated from both thoracolumbar (TL) and lumbosacral (LS) dorsal root ganglia associated with lumbar splanchnic and pelvic spinal pathways, respectively. Identified neuronal subtypes were validated by single-cell qRT-PCR, immunohistochemistry (IHC) and Ca2+-imaging. RESULTS: Transcriptomic profiling and unsupervised clustering of 314 colonic sensory neurons revealed seven neuronal subtypes. Of these, five neuronal subtypes accounted for 99% of TL neurons, with LS neurons almost exclusively populating the remaining two subtypes. We identify and classify neurons based on novel subtype-specific marker genes using single-cell qRT-PCR and IHC to validate subtypes derived from RNA-sequencing. Lastly, functional Ca2+-imaging was conducted on colonic sensory neurons to demonstrate subtype-selective differential agonist activation. CONCLUSIONS: We identify seven subtypes of colonic sensory neurons using unbiased single-cell RNA-sequencing and confirm translation of patterning to protein expression, describing sensory diversity encompassing all modalities of colonic neuronal sensitivity. These results provide a pathway to molecular interrogation of colonic sensory innervation in health and disease, together with identifying novel targets for drug development.

GWAS of self-reported mosquito bite size, itch intensity and attractiveness to mosquitoes implicates immune-related predisposition loci.

Understanding the interaction between humans and mosquitoes is a critical area of study due to the phenomenal burdens on public health from mosquito-transmitted diseases. In this study, we conducted the first genome-wide association studies (GWAS) of self-reported mosquito bite reaction size (n = 84,724), itchiness caused by bites (n = 69,057), and perceived attractiveness to mosquitoes (n = 16,576). In total, 15 independent significant (P < 5×10-8) associations were identified. These loci were enriched for immunity-related genes that are involved in multiple cytokine signalling pathways. We also detected suggestive enrichment of these loci in enhancer regions that are active in stimulated T-cells, as well as within loci previously identified as controlling central memory T-cell levels. Egger regression analysis between the traits suggests that perception of itchiness and attractiveness to mosquitoes is driven, at least in part, by the genetic determinants of bite reaction size.Our findings illustrate the complex genetic and immunological landscapes underpinning human interactions with mosquitoes.

Transcriptomic and epigenetic regulation of disuse atrophy and the return to activity in skeletal muscle.

Physical inactivity and disuse are major contributors to age-related muscle loss. Denervation of skeletal muscle has been previously used as a model with which to investigate muscle atrophy following disuse. Although gene regulatory networks that control skeletal muscle atrophy after denervation have been established, the transcriptome in response to the recovery of muscle after disuse and the associated epigenetic mechanisms that may function to modulate gene expression during skeletal muscle atrophy or recovery have yet to be investigated. We report that silencing the tibialis anterior muscle in rats with tetrodotoxin (TTX)-administered to the common peroneal nerve-resulted in reductions in muscle mass of 7, 29, and 51% with corresponding reductions in muscle fiber cross-sectional area of 18, 42, and 69% after 3, 7, and 14 d of TTX, respectively. Of importance, 7 d of recovery, during which rodents resumed habitual physical activity, restored muscle mass from a reduction of 51% after 14 d TTX to a reduction of only 24% compared with sham control. Returning muscle mass to levels observed at 7 d TTX administration (29% reduction). Transcriptome-wide analysis demonstrated that 3714 genes were differentially expressed across all conditions at a significance of P ≤ 0.001 after disuse-induced atrophy. Of interest, after 7 d of recovery, the expression of genes that were most changed during TTX had returned to that of the sham control. The 20 most differentially expressed genes after microarray analysis were identified across all conditions and were cross-referenced with the most frequently occurring differentially expressed genes between conditions. This gene subset included myogenin (MyoG), Hdac4, Ampd3, Trim63 (MuRF1), and acetylcholine receptor subunit α1 (Chrna1). Transcript expression of these genes and Fboxo32 (MAFbx), because of its previously identified role in disuse atrophy together with Trim63 (MuRF1), were confirmed by real-time quantitative RT-PCR, and DNA methylation of their promoter regions was analyzed by PCR and pyrosequencing. MyoG, Trim63 (MuRF1), Fbxo32 (MAFbx), and Chrna1 demonstrated significantly decreased DNA methylation at key time points after disuse-induced atrophy that corresponded with significantly increased gene expression. Of importance, after TTX cessation and 7 d of recovery, there was a marked increase in the DNA methylation profiles of Trim63 (MuRF1) and Chrna1 back to control levels. This also corresponded with the return of gene expression in the recovery group back to baseline expression observed in sham-surgery controls. To our knowledge, this is the first study to demonstrate that skeletal muscle atrophy in response to disuse is accompanied by dynamic epigenetic modifications that are associated with alterations in gene expression, and that these epigenetic modifications and gene expression profiles are reversible after skeletal muscle returns to normal activity.-Fisher, A. G., Seaborne, R. A., Hughes, T. M., Gutteridge, A., Stewart, C., Coulson, J. M., Sharples, A. P., Jarvis, J. C. Transcriptomic and epigenetic regulation of disuse atrophy and the return to activity in skeletal muscle.

Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks.

BACKGROUND: Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks. RESULTS: In this article, we propose a new statistical method that will infer likely upstream regulators based on observed patterns of up- and down-regulated transcripts. The method is suitable for use with public interaction networks with a mix of signed and unsigned causal edges. It subsumes and extends two previously published approaches and we provide a novel algorithmic method for efficient statistical inference. Notably, we demonstrate the feasibility of using the approach to generate biological insights given current public networks in the context of controlled in-vitro overexpression experiments, stem-cell differentiation data and animal disease models. We also provide an efficient implementation of our method in the R package QuaternaryProd available to download from Bioconductor. CONCLUSIONS: In this work, we have closed an important gap in utilizing causal networks to analyze differentially expressed genes. Our proposed Quaternary test statistic incorporates all available evidence on the potential relevance of an upstream regulator. The new approach broadens the use of these types of statistics for highly curated signed networks in which ambiguities arise but also enables the use of networks with unsigned edges. We design and implement a novel computational method that can efficiently estimate p-values for upstream regulators in current biological settings. We demonstrate the ready applicability of the implemented method to analyze differentially expressed genes using the publicly available networks.

Molecular Mechanisms Mediating Retinal Reactive Gliosis Following Bone Marrow Mesenchymal Stem Cell Transplantation.

A variety of diseases lead to degeneration of retinal ganglion cells (RGCs) and their axons within the optic nerve resulting in loss of visual function. Although current therapies may delay RGC loss, they do not restore visual function or completely halt disease progression. Regenerative medicine has recently focused on stem cell therapy for both neuroprotective and regenerative purposes. However, significant problems remain to be addressed, such as the long-term impact of reactive gliosis occurring in the host retina in response to transplanted stem cells. The aim of this work was to investigate retinal glial responses to intravitreally transplanted bone marrow mesenchymal stem cells (BM-MSCs) to help identify factors able to modulate graft-induced reactive gliosis. We found in vivo that intravitreal BM-MSC transplantation is associated with gliosis-mediated retinal folding, upregulation of intermediate filaments, and recruitment of macrophages. These responses were accompanied by significant JAK/STAT3 and MAPK (ERK1/2 and JNK) cascade activation in retinal Muller glia. Lipocalin-2 (Lcn-2) was identified as a potential new indicator of graft-induced reactive gliosis. Pharmacological inhibition of STAT3 in BM-MSC cocultured retinal explants successfully reduced glial fibrillary acidic protein expression in retinal Muller glia and increased BM-MSC retinal engraftment. Inhibition of stem cell-induced reactive gliosis is critical for successful transplantation-based strategies for neuroprotection, replacement, and regeneration of the optic nerve.

Optimizing the leveraging of real-world data to improve the development and use of medicines.

Health research, including health outcomes and comparative effectiveness research, is on the cusp of a golden era of access to digitized real-world data, catalyzed by the adoption of electronic health records and the integration of clinical and biological information with other data. This era promises more robust insights into what works in health care. Several barriers, however, will need to be addressed if the full potential of these new data are fully realized; these will involve both policy solutions and stakeholder cooperation. Although a number of these issues have been widely discussed, we focus on the one we believe is the most important-the facilitation of greater openness among public and private stakeholders to collaboration, connecting information and data sharing, with the goal of making robust and complete data accessible to all researchers. In this way, we can better understand the consequences of health care delivery, improve the effectiveness and efficiency of health care systems, and develop advancements in health technologies. Early real-world data initiatives illustrate both potential and the need for future progress, as well as the essential role of collaboration and data sharing. Health policies critical to progress will include those that promote open source data standards, expand access to the data, increase data capture and connectivity, and facilitate communication of findings.

Characterizing human stem cell-derived sensory neurons at the single-cell level reveals their ion channel expression and utility in pain research.

The generation of human sensory neurons by directed differentiation of pluripotent stem cells opens new opportunities for investigating the biology of pain. The inability to generate this cell type has meant that up until now their study has been reliant on the use of rodent models. Here, we use a combination of population and single-cell techniques to perform a detailed molecular, electrophysiological, and pharmacological phenotyping of sensory neurons derived from human embryonic stem cells. We describe the evolution of cell populations over 6 weeks of directed differentiation; a process that results in the generation of a largely homogeneous population of neurons that are both molecularly and functionally comparable to human sensory neurons derived from mature dorsal root ganglia. This work opens the prospect of using pluripotent stem-cell-derived sensory neurons to study human neuronal physiology and as in vitro models for drug discovery in pain and sensory disorders.

Molecular causes of transcriptional response: a Bayesian prior knowledge approach.

MOTIVATION: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes. METHODS: We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile. RESULTS: Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated. AVAILABILITY AND IMPLEMENTATION: Source code for the method is available upon request.

Directing differentiation of human embryonic stem cells toward anterior neural ectoderm using small molecules.

Based on knowledge of early embryo development, where anterior neural ectoderm (ANE) development is regulated by native inhibitors of bone morphogenic protein (BMP) and Nodal/Activin signaling, most published protocols of human embryonic stem cell differentiation to ANE have demonstrated a crucial role for Smad signaling in neural induction. The drawbacks of such protocols include the use of an embryoid body culture step and use of polypeptide secreted factors that are both expensive and, when considering clinical applications, have significant challenges in terms of good manufacturing practices compliancy. The use of small molecules to direct differentiation of pluripotent stem cells toward a specified lineage represents a powerful approach to generate specific cell types for further understanding of biological function, for understanding disease processes, for use in drug discovery, and finally for use in regenerative medicine. We therefore aimed to find controlled and reproducible animal-component-free differentiation conditions that would use only small molecules. Here, we demonstrate that pluripotent stem cells can be reproducibly and efficiently differentiated to PAX6(+) (a marker of neuroectoderm) and OCT4(-) (a marker of pluripotent stem cells) cells with the use of potent small inhibitors of the BMP and Activin/Nodal pathways, and in animal-component-free conditions, replacing the frequently used Noggin and SB431542. We also show by transcript analysis, both at the population level and for the first time at the single-cell level, that differentiated cells express genes characteristic for the development of ANE, in particular for the development of the future forebrain.

Nutrient control of eukaryote cell growth: a systems biology study in yeast.

BACKGROUND: To elucidate the biological processes affected by changes in growth rate and nutrient availability, we have performed a comprehensive analysis of the transcriptome, proteome and metabolome responses of chemostat cultures of the yeast, Saccharomyces cerevisiae, growing at a range of growth rates and in four different nutrient-limiting conditions. RESULTS: We find significant changes in expression for many genes in each of the four nutrient-limited conditions tested. We also observe several processes that respond differently to changes in growth rate and are specific to each nutrient-limiting condition. These include carbohydrate storage, mitochondrial function, ribosome synthesis, and phosphate transport. Integrating transcriptome data with proteome measurements allows us to identify previously unrecognized examples of post-transcriptional regulation in response to both nutrient and growth-rate signals. CONCLUSIONS: Our results emphasize the unique properties of carbon metabolism and the carbon substrate, the limitation of which induces significant changes in gene regulation at the transcriptional and post-transcriptional level, as well as altering how many genes respond to growth rate. By comparison, the responses to growth limitation by other nutrients involve a smaller set of genes that participate in specific pathways. See associated commentary http://www.biomedcentral.com/1741-7007/8/62.

Understanding nature's catalytic toolkit.

Enzymes catalyse numerous reactions in nature, often causing spectacular accelerations in the catalysis rate. One aspect of understanding how enzymes achieve these feats is to explore how they use the limited set of residue side chains that form their 'catalytic toolkit'. Combinations of different residues form 'catalytic units' that are found repeatedly in different unrelated enzymes. Most catalytic units facilitate rapid catalysis in the enzyme active site either by providing charged groups to polarize substrates and to stabilize transition states, or by modifying the pKa values of other residues to provide more effective acids and bases. Given recent efforts to design novel enzymes, the rise of structural genomics and subsequent efforts to predict the function of enzymes from their structure, these units provide a simple framework to describe how nature uses the tools at her disposal, and might help to improve techniques for designing and predicting enzyme function.

Redesigning Metal Interference Screws Can Improve Ease of Insertion While Maintaining Fixation of Soft-Tissue Anterior Cruciate Ligament Reconstruction Grafts.

PURPOSE: To compare the fixation strength and loads on insertion of a titanium alloy interference screw with a modified tip against a conventional titanium interference screw. METHODS: Slippage of bovine digital extensor tendons (as substitutes for human tendon grafts) under cyclic loading and interference fixation strength under a pullout test were recorded in 10 cadaveric knees, with 2 tunnels drilled in each femur and tibia to provide pair-wise comparisons between the modified-tip screw (MS) and conventional screw (CS). To analyze screw insertion, 10 surgeons blindly inserted pairs of the MS and CS into bone-substitute blocks (with polyester shoelaces as graft substitutes), with insertion loads measured using a force/torque sensor. RESULTS: No differences were found between the MS and CS either in graft slippage from the femur (P = .661) or tibia (P = .950) or in ultimate load to failure from the femur (P = .952) or tibia (P = .126). On insertion, the MS required less axial force application (78 ± 38 N, P = .001) and fewer attempted turns (2 ± 1, P < .001) to engage with the bone tunnel than the CS (99 ± 43 N and 4 ± 4, respectively). In 90% of the paired insertion tests, the screw identified by the surgeon as being easier to initially insert was the MS. CONCLUSIONS: The MS was found to be easier to engage with the bone tunnel and initially insert than the CS while still achieving similar immediate postsurgical fixation strength. CLINICAL RELEVANCE: The study shows that screw designs can be improved to ease insertion into a bone tunnel, which should reduce any likelihood of ligament reconstruction graft damage.

Network and pathway expansion of genetic disease associations identifies successful drug targets.

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand-receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein-protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.

MOTIVATION: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently. RESULTS: In this article, we characterize four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug-target interaction network topology. We then develop new statistical methods to predict unknown drug-target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug-target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call 'pharmacological space'. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug-target interaction networks. Our comprehensively predicted drug-target interaction networks enable us to suggest many potential drug-target interactions and to increase research productivity toward genomic drug discovery. AVAILABILITY: Softwares are available upon request. SUPPLEMENTARY INFORMATION: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.

Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.

In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach modifies the dataset of interest while incorporating key information from the larger reference dataset via Non-negative Matrix Factorization (NMF). The modified dataset is subsequently provided to a clustering algorithm. We empirically evaluate the benefits of our approach on simulated scRNA-Seq data as well as on publicly available datasets. Finally, we present results for the analysis of a recently published small dataset and find improved clustering when transferring knowledge from a large reference dataset. Implementations of the method are available at https://github.com/nicococo/scRNA.

Publisher Correction: Molecular and functional variation in iPSC-derived sensory neurons.

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

GI-530159, a novel, selective, mechanosensitive two-pore-domain potassium (K2P ) channel opener, reduces rat dorsal root ganglion neuron excitability.

BACKGROUND AND PURPOSE: TREK two-pore-domain potassium (K2P ) channels play a critical role in regulating the excitability of somatosensory nociceptive neurons and are important mediators of pain perception. An understanding of the roles of TREK channels in pain perception and, indeed, in other pathophysiological conditions, has been severely hampered by the lack of potent and/or selective activators and inhibitors. In this study, we describe a new, selective opener of TREK channels, GI-530159. EXPERIMENTAL APPROACH: The effect of GI-530159 on TREK channels was demonstrated using 86 Rb efflux assays, whole-cell and single-channel patch-clamp recordings from recombinant TREK channels. The expression of K2P 2.1 (TREK1), K2P 10.1 (TREK2) and K2P 4.1 (TRAAK) channels was determined using transcriptome analysis from single dorsal root ganglion (DRG) cells. Current-clamp recordings from cultured rat DRG neurons were used to measure the effect of GI-530159 on neuronal excitability. KEY RESULTS: For recombinant human TREK1 channels, GI-530159 had similar low EC50 values in Rb efflux experiments and electrophysiological recordings. It activated TREK2 channels, but it had no detectable action on TRAAK channels nor any significant effect on other K channels tested. Current-clamp recordings from cultured rat DRG neurones showed that application of GI-530159 at 1 µM resulted in a significant reduction in firing frequency and a small hyperpolarization of resting membrane potential. CONCLUSIONS AND IMPLICATIONS: This study provides pharmacological evidence for the presence of mechanosensitive TREK K2P channels in sensory neurones and suggests that development of selective K2P channel openers like GI-530159 could aid in the development of novel analgesic agents. LINKED ARTICLES: This article is part of a themed section on Recent Advances in Targeting Ion Channels to Treat Chronic Pain. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.12/issuetoc.

Molecular and functional variation in iPSC-derived sensory neurons.

Induced pluripotent stem cells (iPSCs), and cells derived from them, have become key tools for modeling biological processes, particularly in cell types that are difficult to obtain from living donors. Here we present a map of regulatory variants in iPSC-derived neurons, based on 123 differentiations of iPSCs to a sensory neuronal fate. Gene expression was more variable across cultures than in primary dorsal root ganglion, particularly for genes related to nervous system development. Using single-cell RNA-sequencing, we found that the number of neuronal versus contaminating cells was influenced by iPSC culture conditions before differentiation. Despite high differentiation-induced variability, our allele-specific method detected thousands of quantitative trait loci (QTLs) that influenced gene expression, chromatin accessibility, and RNA splicing. On the basis of these detected QTLs, we estimate that recall-by-genotype studies that use iPSC-derived cells will require cells from at least 20-80 individuals to detect the effects of regulatory variants with moderately large effect sizes.