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Most neurons that make up the human brain are postmitotic, living and functioning for a very long time without renewal (see the Perspective by Lee). Bae et al. examined the genomes of single neurons from the prenatal developing human brain. Both the type of mutation and the rates of accumulation changed between gastrulation and neurogenesis. These early mutations could be generating useful neuronal diversity or could predispose individuals to later dysfunction. Lodato et al. also found that neurons take on somatic mutations as they age by sequencing single neurons from subjects aged 4 months to 82 years. Somatic mutations accumulated with increasing age and accumulated faster in individuals affected by inborn errors in DNA repair. Postmitotic mutations might only affect one neuron, but the accumulated divergence of genomes across the brain could affect function. Science , this issue p. 550 , p. 555 ; see also p. 521 Hundreds of oxidative damage–related somatic mutations per cell accumulate during early brain development. Somatic mosaicism in the human brain may alter function of individual neurons. We analyzed genomes of single cells from the forebrains of three human fetuses (15 to 21 weeks postconception) using clonal cell populations. We detected 200 to 400 single-nucleotide variations (SNVs) per cell. SNV patterns resembled those found in cancer cell genomes, indicating a role of background mutagenesis in cancer. SNVs with a frequency of >2% in brain were also present in the spleen, revealing a pregastrulation origin. We reconstructed cell lineages for the first five postzygotic cleavages and calculated a mutation rate of ~1.3 mutations per division per cell. Later in development, during neurogenesis, the mutation spectrum shifted toward oxidative damage, and the mutation rate increased. Both neurogenesis and early embryogenesis exhibit substantially more mutagenesis than adulthood.

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies. Sharing of whole genome sequencing (WGS) data improves study scale and power, but data from different groups are often incompatible. Here, US genome centers and NIH programs define WGS data processing standards and a flexible validation method, facilitating collaboration in human genetics research.

The mechanisms underlying retinal development have not been completely elucidated. Extracellular vesicles (EVs) are novel essential mediators of cell-to-cell communication with emerging roles in developmental processes. Nevertheless, the identification of EVs in human retinal tissue, characterization of their cargo, and analysis of their potential role in retina development has not been accomplished. Three-dimensional retinal tissue derived from human induced pluripotent stem cells (hiPSC) provide an ideal developmental system to achieve this goal. Here we report that hiPSC-derived retinal organoids release exosomes and microvesicles with small noncoding RNA cargo. EV miRNA cargo-predicted targetome correlates with Gene Ontology (GO) pathways involved in mechanisms of retinogenesis relevant to specific developmental stages corresponding to hallmarks of native human retina development. Furthermore, uptake of EVs by human retinal progenitor cells leads to changes in gene expression correlated with EV miRNA cargo predicted gene targets, and mechanisms involved in retinal development, ganglion cell and photoreceptor differentiation and function.

Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression), an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should-and should not-be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.

Understanding the effect of genetic sequence variation on phenotype is a major challenge that lies at the heart of genetics. We developed GOLPH (GenOmic Linkage to PHenotype), a statistical method to identify genetic interactions, and used it to characterize the landscape of genetic interactions between gene expression quantitative trait loci. Our results reveal that allele-specific interactions, in which a gene only exerts an influence on the phenotype in the presence of a particular allele at the primary locus, are widespread and that genetic interactions are predominantly nonadditive. The data portray a complex picture in which interacting loci influence the expression of modules of coexpressed genes involved in coherent biological processes and pathways. We show that genetic variation at a single gene can have a major impact on the global transcriptional response, altering interactions between genes through shutdown or activation of pathways. Thus, different cellular states occur not only in response to the external environment but also result from intrinsic genetic variation.

The advent of cost‐effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug‐free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene–drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug. Synopsis We want to understand how differences in genotype account for the wide range of phenotypic diversity between individuals. Most traits are determined by multiple genes; so the challenge of predicting an individual's phenome (i.e., spectrum of traits) from its genome requires both identification of the genes that influence the trait and models that describe how they interact to determine the trait (Gabriel et al , 2002 ; Maller et al , 2006 ). We developed a computational framework, Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), which combines genotype and gene expression data to associate genetic factors with phenotype. Our premise is that gene expression is useful because it integrates information from multiple loci that are individually too weak to detect, but which, in combination, contribute significantly to the phenotype. We applied Camelot to a data set containing genotype, gene expression (profiled in the absence of drug) and phenotype (growth in the presence of drug) data from segregants obtained from a cross between two diverse strains of S. cerevisiae (BY and RM; Brem and Kruglyak, 2005 ; Perlstein et al , 2007 ). The genetic diversity between strains manifests in extensive phenotypic diversity. We obtain genotype and gene expression data for each segregant grown in the absence of drug, to derive a quantitative prediction of the strain's phenome. Camelot identifies a small set of features: markers at a genetic locus, or transcripts, that actively influence growth in the presence of each drug and explain the observed differences between segregants. Camelot uses these features to accurately predict the growth of ‘unseen’ segregants that have an entirely different genotype. Camelot accurately predicted the response to a drug, for 87/94 drugs. Gene expression data measured under an unrelated condition (no drug) significantly contributes to the accuracy of prediction and to the ability of Camelot to detect causal genes involved in this response. Two statistical methods, the triangle test and zoom‐in score, use gene expression to identify genes that actively influence the phenotype. The triangle test is used when the selected feature is a transcript, whereas the zoom‐in score pinpoints causal variants within large linked regions (when the selected feature is a marker). Experimental validation demonstrates the outstanding performance of these two methods, with 7/9 predictions validated for the triangle test and 18/18 predictions validated for the zoom‐in score. Camelot's triangle test predicted DHH1 as a gene that actively influences growth in the presence of each of six drugs, including hydrogen peroxide ( Figure 3A, B and D ). We tested the prediction by measuring the growth yield of wild‐type and dhh1 Δ strains in hydrogen peroxide. As predicted, the dhh1 Δ strain grew better than the wild type, confirming that DHH1 negatively influences drug resistance (Figure 3C ). We subsequently validated the role of DHH1 in resistance to three additional drugs (Figure 3D ). Although the drugs linked to DHH1 are diverse and include an antibiotic and an antipsychotic drug, they all affect mitochondrial function (Ni colson et al , 1999 ; Evans et al , 2000 ; Nulton‐Persson and Szweda, 2001 ; Lee et al , 2005 ; Lee et al , 2008 ; Safiulina et al , 2006 ; Yip et al , 2006 ; Sancho et al , 2007 ). Dhh1 post‐transcriptionally regulates genes involved in mitochondrial biogenesis (Lee et al , 2009 ), suggesting that mitochondrial function is important in the response to these drugs. The zoom‐in score identified GPB2 , a gene not previously implicated in the phenotypic differences of the parental strains. We validated Camelot's prediction that GPB2 plays a causal role in the response to three drugs (Figure 5A ). The zoom‐in score also identified PHO84 as a causal variant for multiple drugs. We used the zoom‐in score to distinguish which drugs are causally influenced by PHO84 and validated these predictions by growing wild‐type BY and the allele‐swapped (AS) strain (BY PHO84‐ RM) in the presence of one of nine drugs. The AS and RM strains behaved as Camelot predicted 9 out of 9 times. Both PHO84 and GPB2 were identified as causal genes for the variation of growth in haloperidol, and strains carrying both RM‐ PHO84 and BY‐ GPB2 grow better than strains with other combinations of the two alleles, suggesting that PHO84 and GPB2 may function through a common pathway (Figure 5B and C ). Both genes are involved in the cAMP/PKA pathway, which suggests a possible mechanism of action for haloperidol. To better understand why gene expression helps us identify causal variants, we monitored PHO84 abundance in BY, RM and AS (BY PHO84‐RM ) strains. Although the AS strain contains cis ‐ and trans ‐regulatory factors from BY, the presence of the RM coding region brought the expression of PHO84 down to that of the RM strain. It is likely that the difference in expression results from negative feedback that acts through the Pho84 protein (Wykoff et al , 2007 ). Additional data suggest that negative feedback is stronger in the RM and AS strains. We propose that variation in gene expression serves as an indicator of the variation in protein function, and that the difference in protein function is responsible for the observed differences in drug sensitivity between strains. In conclusion, we systematically demonstrate Camelot's performance in predicting phenotypes and in identifying genes responsible for the variation in the growth in the presence of drug. It is intriguing that a gene expression profile measured in the absence of drugs empowers the prediction of traits under novel conditions (+drugs). Camelot provides another step towards the realization of personalized medicine and highlights the power to be gained by exploiting gene expression data for this application. Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits) is a method that integrates genotype, gene expression and phenotype data from individuals and uses it to build models that predict complex quantitative phenotypes and identify genes that actively influence them. Gene expression data measured in a reference condition (drug‐free) empowers prediction of the drug response and identification of genes that actively influence this response. We applied our method to segregants obtained from a cross between two diverse strains of Saccharomyces cerevisiae . Camelot accurately predicts the response of the segregants to 87/94 drugs and identifies genes (both inside and outside linked regions) that actively influence the phenotype. 25/27 of the gene‐drug interactions predicted were confirmed. The integration of gene expression data was critical for achieving the performance reported. In addition to identifying linked regions Camelot pinpoints the causal gene within the region. For example Camelot identified GPB2 as the gene responsible for linkage to 3 drugs within the Chromosome I:1‐55329 region. GPB2's influence on drug resistance was subsequently validated for all 3 drugs. While the mechanism of action of these drugs is unknown, identification of GPB2 suggests that the PKA pathway is likely involved in the response to these drugs. PHO84 was identified and validated as a causal gene for 5 drug responses. BY strains containing only a single coding nucleotide polymorphism from RM grew at a similar rate to RM strains in the presence of drug and expressed PHO84 at a similar level likely due to negative feedback. We believe that variation in gene expression serves as an indicator of variation in protein function and thus explains why expression helps identify the role of the gene in the response to drug.

Background Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. Methods A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. Results WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. Conclusion These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.

Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.