©2009 Macmillan Publishers Limited. All rights reservedCorrespondence and requests for materials should be addressed to J.L.R. (jrinn@broad.mit.edu). * These authors contributed equally to this work. Author Contributions J.L.R., E.S.L., A.R. and M. Guttman conceived and designed experiments.

Summary Recently, more than a thousand large intergenic non-coding RNAs (lincRNAs) have been reported. These RNAs are evolutionarily conserved in mammalian genomes and thus presumably function in diverse biological processes. Here, we report the identification of lincRNAs that are regulated by p53. One of these lincRNAs (lincRNA-p21) serves as a repressor in p53-dependent transcriptional responses. Inhibition of lincRNA-p21 affects the expression of hundreds of gene targets enriched for genes normally repressed by p53. The observed transcriptional repression by lincRNA-p21 is mediated through the physical association with hnRNP-K. This interaction is required for proper genomic localization of hnRNP-K at repressed genes and regulation of p53 mediated apoptosis. We propose a model whereby transcription factors activate lincRNAs that serve as key repressors by physically associating with repressive complexes and modulating their localization to sets of previously active genes.

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 Crohn's 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.

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 report constrained elements covering ~4.2% of the genome. We use evolutionary signatures and comparison with experimental datasets 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 ~1,000 primate- and human-accelerated elements. Overlap with disease-associated variants suggests our findings will be relevant for studies of human biology and health.

Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data, yet have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We apply this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells (DCs) to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins and constructed a network model consisting of two dozen core regulators and 76 fine-tuners that help 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.