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Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals

There is growing recognition that mammalian cells produce many thousands of large intergenic transcripts. However, the functional significance of these transcripts has been particularly controversial. Although there are some well-characterized examples, most (>95%) show little evidence of evolutionary conservation and have been suggested to represent transcriptional noise. Here we report a new approach to identifying large non-coding RNAs using chromatin-state maps to discover discrete transcriptional units intervening known protein-coding loci. Our approach identified ∼1,600 large multi-exonic RNAs across four mouse cell types. In sharp contrast to previous collections, these large intervening non-coding RNAs (lincRNAs) show strong purifying selection in their genomic loci, exonic sequences and promoter regions, with greater than 95% showing clear evolutionary conservation. We also developed a functional genomics approach that assigns putative functions to each lincRNA, demonstrating a diverse range of roles for lincRNAs in processes from embryonic stem cell pluripotency to cell proliferation. We obtained independent functional validation for the predictions for over 100 lincRNAs, using cell-based assays. In particular, we demonstrate that specific lincRNAs are transcriptionally regulated by key transcription factors in these processes such as p53, NFκB, Sox2, Oct4 (also known as Pou5f1) and Nanog. Together, these results define a unique collection of functional lincRNAs that are highly conserved and implicated in diverse biological processes.

The mystery of missing heritability: Genetic interactions create phantom heritability

Human genetics has been haunted by the mystery of “missing heritability” of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator—that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating “phantom heritability.” Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohns disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.

A high-resolution map of human evolutionary constraint using 29 mammals

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ∼4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ∼60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease.

Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses

Peeking at Pathogen Response Networks Networks controlling gene expression serve as key decision-making circuits in cells, but the regulatory networks that control dynamic and specific gene expression responses to stimuli are often not well understood. This is particularly true for immune dendritic cells (DCs), which respond to pathogens by mounting elaborate transcriptional responses, and are centrally involved in infectious diseases, autoimmunity, and vaccines. Amit et al. (p. 257, published online 3 September) explored the transcriptional response of dendritic cells to specific classes of pathogens. The transcriptional subnetworks responsible for mammalian dendritic cell responses to different pathogens were identified, and the function of 100 regulators clarified. Inflammatory and antiviral programs in dendritic cells are controlled and tuned by a network of regulators. Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data but have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We applied this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins, which enabled the construction of a network model consisting of 24 core regulators and 76 fine-tuners that help to explain how pathogen-sensing pathways achieve specificity. This study establishes a broadly applicable, comprehensive, and unbiased approach to reveal the wiring and functions of a regulatory network controlling a major transcriptional response in primary mammalian cells.

A Composite of Multiple Signals Distinguishes Causal Variants in Regions of Positive Selection

Pinpointing Genetic Selection The human genome contains hundreds of regions with evidence of recent positive natural selection, yet, for all but a handful of cases, the underlying advantageous mutation remains unknown. Current methods to detect the signal of selection often results in the identification of a broad genomic region containing many candidate regions that vary among individuals. By combining existing statistical methods, Grossman et al. (p. 883, published online 7 January) developed a method, termed Composite of Multiple Signals, which can increase the ability to pinpoint the specific variant under selection. Several candidate regions under selection in human populations were identified. Combining statistical methods detects signals of selection with increased sensitivity and a lower false-positive rate. The human genome contains hundreds of regions whose patterns of genetic variation indicate recent positive natural selection, yet for most the underlying gene and the advantageous mutation remain unknown. We developed a method, composite of multiple signals (CMS), that combines tests for multiple signals of selection and increases resolution by up to 100-fold. By applying CMS to candidate regions from the International Haplotype Map, we localized population-specific selective signals to 55 kilobases (median), identifying known and novel causal variants. CMS can not just identify individual loci but implicates precise variants selected by evolution.

Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction

Summary Myocardial infarction (MI), a leading cause of death around the world, displays a complex pattern of inheritance1,2. When MI occurs early in life, the role of inheritance is substantially greater1. Previously, rare mutations in low-density lipoprotein (LDL) genes have been shown to contribute to MI risk in individual families3–8 whereas common variants at more than 45 loci have been associated with MI risk in the population9–15. Here, we evaluate the contribution of rare mutations to MI risk in the population. We sequenced the protein-coding regions of 9,793 genomes from patients with MI at an early age (≤50 years in males and ≤60 years in females) along with MI-free controls. We identified two genes where rare coding-sequence mutations were more frequent in cases versus controls at exome-wide significance. At low-density lipoprotein receptor (LDLR), carriers of rare, damaging mutations (3.1% of cases versus 1.3% of controls) were at 2.4-fold increased risk for MI; carriers of null alleles at LDLR were at even higher risk (13-fold difference). This sequence-based estimate of the proportion of early MI cases due to LDLR mutations is remarkably similar to an estimate made more than 40 years ago using total cholesterol16. At apolipoprotein A-V (APOA5), carriers of rare nonsynonymous mutations (1.4% of cases versus 0.6% of controls) were at 2.2-fold increased risk for MI. When compared with non-carriers, LDLR mutation carriers had higher plasma LDL cholesterol whereas APOA5 mutation carriers had higher plasma triglycerides. Recent evidence has connected MI risk with coding sequence mutations at two genes functionally related to APOA5, namely lipoprotein lipase15,17 and apolipoprotein C318,19. When combined, these observations suggest that, beyond LDL cholesterol, disordered metabolism of triglyceride-rich lipoproteins contributes to MI risk.

Association of survival and disease progression with chromosomal instability: A genomic exploration of colorectal cancer

During disease progression the cells that comprise solid malignancies undergo significant changes in gene copy number and chromosome structure. Colorectal cancer provides an excellent model to study this process. To indentify and characterize chromosomal abnormalities in colorectal cancer, we performed a statistical analysis of 299 expression and 130 SNP arrays profiled at different stages of the disease, including normal tissue, adenoma, stages 1–4 adenocarcinoma, and metastasis. We identified broad (> 1/2 chromosomal arm) and focal (< 1/2 chromosomal arm) events. Broad amplifications were noted on chromosomes 7, 8q, 13q, 20, and X and broad deletions on chromosomes 4, 8p, 14q, 15q, 17p, 18, 20p, and 22q. Focal events (gains or losses) were identified in regions containing known cancer pathway genes, such as VEGFA, MYC, MET, FGF6, FGF23, LYN, MMP9, MYBL2, AURKA, UBE2C, and PTEN. Other focal events encompassed potential new candidate tumor suppressors (losses) and oncogenes (gains), including CCDC68, CSMD1, POLR1D, and PMEPA1. From the expression data, we identified genes whose expression levels reflected their copy number changes and used this relationship to impute copy number changes to samples without accompanying SNP data. This analysis provided the statistical power to show that deletions of 8p, 4p, and 15q are associated with survival and disease progression, and that samples with simultaneous deletions in 18q, 8p, 4p, and 15q have a particularly poor prognosis. Annotation analysis reveals that the oxidative phosphorylation pathway shows a strong tendency for decreased expression in the samples characterized by poor prognosis.

Conservation and divergence in the transcriptional programs of the human and mouse immune systems

Much of the knowledge about cell differentiation and function in the immune system has come from studies in mice, but the relevance to human immunology, diseases, and therapy has been challenged, perhaps more from anecdotal than comprehensive evidence. To this end, we compare two large compendia of transcriptional profiles of human and mouse immune cell types. Global transcription profiles are conserved between corresponding cell lineages. The expression patterns of most orthologous genes are conserved, particularly for lineage-specific genes. However, several hundred genes show clearly divergent expression across the examined cell lineages, and among them, 169 genes did so even with highly stringent criteria. Finally, regulatory mechanisms—reflected by regulators’ differential expression or enriched cis-elements—are conserved between the species but to a lower degree, suggesting that distinct regulation may underlie some of the conserved transcriptional responses.

Identification of transcriptional regulators in the mouse immune system

The differentiation of hematopoietic stem cells into cells of the immune system has been studied extensively in mammals, but the transcriptional circuitry that controls it is still only partially understood. Here, the Immunological Genome Project gene-expression profiles across mouse immune lineages allowed us to systematically analyze these circuits. To analyze this data set we developed Ontogenet, an algorithm for reconstructing lineage-specific regulation from gene-expression profiles across lineages. Using Ontogenet, we found differentiation stage–specific regulators of mouse hematopoiesis and identified many known hematopoietic regulators and 175 previously unknown candidate regulators, as well as their target genes and the cell types in which they act. Among the previously unknown regulators, we emphasize the role of ETV5 in the differentiation of γδ T cells. As the transcriptional programs of human and mouse cells are highly conserved, it is likely that many lessons learned from the mouse model apply to humans.

Chromatin profiling by directly sequencing small quantities of immunoprecipitated DNA.

Chromatin structure and transcription factor localization can be assayed genome-wide by sequencing genomic DNA fractionated by protein occupancy or other properties, but current technologies involve multiple steps that introduce bias and inefficiency. Here we apply a single-molecule approach to directly sequence chromatin immunoprecipitated DNA with minimal sample manipulation. This method is compatible with just 50 pg of DNA and should thus facilitate charting chromatin maps from limited cell populations.