Caleb Lareau

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.A new method tests whether disease heritability is enriched near genes with high tissue-specific expression. The authors use gene expression data together with GWAS summary statistics for 48 diseases and traits to identify disease-relevant tissues.

Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation

Human hematopoiesis involves cellular differentiation of multipotent cells into progressively more lineage-restricted states. While the chromatin accessibility landscape of this process has been explored in defined populations, single-cell regulatory variation has been hidden by ensemble averaging. We collected single-cell chromatin accessibility profiles across 10 populations of immunophenotypically defined human hematopoietic cell types and constructed a chromatin accessibility landscape of human hematopoiesis to characterize differentiation trajectories. We find variation consistent with lineage bias toward different developmental branches in multipotent cell types. We observe heterogeneity within common myeloid progenitors (CMPs) and granulocyte-macrophage progenitors (GMPs) and develop a strategy to partition GMPs along their differentiation trajectory. Furthermore, we integrated single-cell RNA sequencing (scRNA-seq) data to associate transcription factors to chromatin accessibility changes and regulatory elements to target genes through correlations of expression and regulatory element accessibility. Overall, this work provides a framework for integrative exploration of complex regulatory dynamics in a primary human tissue at single-cell resolution.

Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single-cell resolution using RNA sequencing

Recent advances in single-cell, transcriptomic profiling have provided unprecedented access to investigate cell heterogeneity during tissue and organ development. In this study, we used massively parallel, single-cell RNA sequencing to define cell heterogeneity within the zebrafish kidney marrow, constructing a comprehensive molecular atlas of definitive hematopoiesis and functionally distinct renal cells found in adult zebrafish. Because our method analyzed blood and kidney cells in an unbiased manner, our approach was useful in characterizing immune-cell deficiencies within DNA–protein kinase catalytic subunit (prkdc), interleukin-2 receptor &ggr; a (il2rga), and double-homozygous–mutant fish, identifying blood cell losses in T, B, and natural killer cells within specific genetic mutants. Our analysis also uncovered novel cell types, including two classes of natural killer immune cells, classically defined and erythroid-primed hematopoietic stem and progenitor cells, mucin-secreting kidney cells, and kidney stem/progenitor cells. In total, our work provides the first, comprehensive, single-cell, transcriptomic analysis of kidney and marrow cells in the adult zebrafish.

“Unexpected mutations after CRISPR-Cas9 editing in vivo” are most likely pre-existing sequence variants and not nuclease-induced mutations

Schaefer et al. recently advanced the provocative conclusion that CRISPR-Cas9 nuclease can induce off-target alterations at genomic loci that do not resemble the intended on-target site.1 Using high-coverage whole genome sequencing (WGS), these authors reported finding SNPs and indels in two CRISPR-Cas9-treated mice that were not present in a single untreated control mouse. On the basis of this association, Schaefer et al. concluded that these sequence variants were caused by CRISPR-Cas9. This new proposed CRISPR-Cas9 off-target activity runs contrary to previously published work2–8 and, if the authors are correct, could have profound implications for research and therapeutic applications. Here, we demonstrate that the simplest interpretation of Schaefer et al.’s data is that the two CRISPR-Cas9-treated mice are actually more closely related genetically to each other than to the control mouse. This strongly suggests that the so-called “unexpected mutations” simply represent SNPs and indels shared in common by these mice prior to nuclease treatment. In addition, given the genomic and sequence distribution profiles of these variants, we show that it is challenging to explain how CRISPR-Cas9 might be expected to induce such changes. Finally, we argue that the lack of appropriate controls in Schaefer et al.’s experimental design precludes assignment of causality to CRISPR-Cas9. Given these substantial issues, we urge Schaefer et al. to revise or re-state the original conclusions of their published work so as to avoid leaving misleading and unsupported statements to persist in the literature.

Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo ”

To the Editor: Schaefer et al.1 recently stated that CRISPR–Cas9 nuclease can induce off-target alterations at genomic loci that do not resemble the intended on-target site. This new proposed CRISPR– Cas9 off-target activity runs contrary to previously published work (performed mostly in cells, but also in mice)2–6 and, if the authors are correct, could have profound implications for research and therapeutic applications. However, we here demonstrate that the simplest interpretation of data in Schaefer et al.1 is that the two CRISPR–Cas9treated mice are genetically more closely related to each other than to the control mouse. This strongly suggests that the so-called ‘unexpected mutations’ simply represent shared single-nucleotide polymorphisms (SNPs) and indels that existed before nuclease treatment. The conclusion of Schaefer et al.1 that the sequence variants shared by the genome-edited F03 and F05 mice (and not found in the control untreated FVB mouse) are caused by CRISPR–Cas9 critically depends upon the assumption that all of these mice were initially genetically identical. If this clonality assumption were true, one would expect that all three mice should be nearly identical for common variants found in dbSNP (a hypothetical result represented in Fig. 1a). However, after genotyping these mice with GATK best practices, we identified a total of 31,079 high-quality variants at dbSNP loci that were concordant in two mice but distinct from the third when examining all possible pairwise combinations (Fig. 1b and Supplementary Note 1). Furthermore, 33–46% of these highconfidence-genotyped variants in each mouse are heterozygous (Supplementary Table 1), which the authors have argued should not be the case in highly inbred mice7. Thus, the three mice are neither clonal nor completely isogenic. Even under a more realistic and relaxed equal-distance model that allows rare and private mutations (Fig. 1c), our reanalysis still reveals that the F03 and F05 mice are genetically more closely related to each other than to the control FVB mouse (Fig. 1b,d and Supplementary Note 2). Even if one were to assume that the variants in question were induced by CRISPR–Cas9, it is difficult to reconcile the off-target activity proposed by Schaefer et al.1 with our current understanding of how this nuclease functions. We confirmed the authors’ claim that no DNA sequences resembling the on-target site can be found near the sequence variants that they attribute to CRISPR– Cas9 (Supplementary Figs. 1 and 2; Supplementary Note 3). Additionally, we could not find an alternative consensus DNA motif at or near the locations of these variants that might be recognized by the CRISPR–Cas9 nuclease (Supplementary Fig. 3 and Supplementary Note 4). This makes it hard to envision any reasonable mechanism for how CRISPR–Cas9 could direct alterations to the same genomic loci in the two mice. Furthermore, given the well-established variability of indel mutations induced by CRISPR–Cas9 at any given cleavage site8, we calculate that the probability that these proposed Cas9-induced changes would be exactly the same at a large number of loci (as observed in the data of Schaefer et al.1; Supplementary Fig. 4) is less than 1 in 1012 under even the most generous assumptions (binomial test; Supplementary Fig. 5 and Supplementary Note 5). Based on the analyses described above and further common variant analyses (Supplementary Figs. 6 and 7; Supplementary Note 6), the simplest explanation of the results in Schaefer et al.1 is that the CRISPR-treated F03 and F05 embryos already harbored these shared private SNPs and indels before nuclease treatment, whereas the control mouse did not. This alternative explanation avoids the need to postulate a new CRISPR–Cas9 activity that has not been previously observed and that is inconsistent with previously reported observations about how it functions. Schaefer et al.1 mistakenly assumed that association meant causality, but this can lead to erroneous conclusions. For example, our analysis shows an equally high percentage of heterozygous variants in the control mouse that are not present in the two nuclease-treated mice, but we would certainly not attribute these to mutations induced by the lack of CRISPR–Cas9 treatment in the control mouse. In summary, our analyses of the primary data demonstrate that the original conclusions by Schaefer et al.1 are not supported by their existing data. In addition, given our current understanding of CRISPR–Cas9 function based on the published literature, it seems exceedingly unlikely that the new activities proposed by Schaefer et al.1 would be proven true even if one were to perform additional WGS experiments with appropriate and important controls missing from their original study.

Fine mapping of chromosome 15q25 implicates ZNF592 in neurosarcoidosis patients

Neurosarcoidosis is a clinical subtype of sarcoidosis characterized by the presence of granulomas in the nervous system. Here, we report a highly significant association with a variant (rs75652600, P = 3.12 × 10−8, odds ratios = 4.34) within a zinc finger gene, ZNF592, from an imputation‐based fine‐mapping study of the chromosomal region 15q25 in African‐Americans with neurosarcoidosis. We validate the association with ZNF592, a gene previously shown to cause cerebellar ataxia, in a cohort of European‐Americans with neurosarcoidosis by uncovering low‐frequency variants with a similar risk effect size (chr15:85309284, P = 0.0021, odds ratios = 5.36).

Common genes associated with antidepressant response in mouse and man identify key role of glucocorticoid receptor sensitivity.

Response to antidepressant treatment in major depressive disorder (MDD) cannot be predicted currently, leading to uncertainty in medication selection, increasing costs, and prolonged suffering for many patients. Despite tremendous efforts in identifying response-associated genes in large genome-wide association studies, the results have been fairly modest, underlining the need to establish conceptually novel strategies. For the identification of transcriptome signatures that can distinguish between treatment responders and nonresponders, we herein submit a novel animal experimental approach focusing on extreme phenotypes. We utilized the large variance in response to antidepressant treatment occurring in DBA/2J mice, enabling sample stratification into subpopulations of good and poor treatment responders to delineate response-associated signature transcript profiles in peripheral blood samples. As a proof of concept, we translated our murine data to the transcriptome data of a clinically relevant human cohort. A cluster of 259 differentially regulated genes was identified when peripheral transcriptome profiles of good and poor treatment responders were compared in the murine model. Differences in expression profiles from baseline to week 12 of the human orthologues selected on the basis of the murine transcript signature allowed prediction of response status with an accuracy of 76% in the patient population. Finally, we show that glucocorticoid receptor (GR)-regulated genes are significantly enriched in this cluster of antidepressant-response genes. Our findings point to the involvement of GR sensitivity as a potential key mechanism shaping response to antidepressant treatment and support the hypothesis that antidepressants could stimulate resilience-promoting molecular mechanisms. Our data highlight the suitability of an appropriate animal experimental approach for the discovery of treatment response-associated pathways across species.

hichipper: a preprocessing pipeline for calling DNA loops from HiChIP data

loop calling. Further, we have found that taking the location of restriction enzyme cut sites into account also boosts sensitivity by increasing the fraction of reads that can be assigned to loops. hichipper outperforms existing non-HiChIP-specific tools in terms of identifying long-range (>100-kb) loops with typical anchors resolved at a 2.5-kb resolution (see Supplementary Methods). Additionally, hichipper produces a QC report that allows evaluation of library preparation, including factors such as the efficiency of proximity ligation and chromatin immunoprecipitation. We provide example QC reports from both successful and unsuccessful HiChIP experiments to guide new users in assessing their own library quality. The hichipper package, documentation, and QC report examples are available online at http://aryee.mgh.harvard.edu/hichipper. Details of the background model and implementation are available in the Supplementary Methods.

Confounding in ex vivo models of Diamond-Blackfan anemia.

To the editor: In a recent issue of Blood , O’Brien et al[1][1] describe the development of an ex vivo model of Diamond-Blackfan anemia (DBA) using cultured peripheral blood CD34+ progenitor cells obtained from DBA patients. Although many useful models of DBA exist (including animal models,[2][2

diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data

Abstract Summary The 3D architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in DNA looping structure are associated with variation in gene expression and cell state. To systematically assess changes in DNA looping architecture between samples, we introduce diffloop, an R/Bioconductor package that provides a suite of functions for the quality control, statistical testing, annotation, and visualization of DNA loops. We demonstrate this functionality by detecting differences between ENCODE ChIA-PET samples and relate looping to variability in epigenetic state. Availability and implementation Diffloop is implemented as an R/Bioconductor package available at https://bioconductor.org/packages/release/bioc/html/diffloop.html Supplementary information Supplementary data are available at Bioinformatics online.