Guadalupe pluton–Mariposa Formation age relationships in the southern Sierran Foothills: Onset of Mesozoic subduction in northern California?

We report a new 153 ± 2 Ma SIMS U-Pb date for zircons from the hypabyssal Guadalupe pluton which crosscuts and contact metamorphoses upper crustal Mariposa slates in the southern Sierra. A ~950 m thick section of dark metashales lies below sandstones from which clastic zircons were analyzed at 152 ± 2 Ma. Assuming a compacted depositional rate of ~120 m/Myr, accumulation of Mariposa volcanogenic sediments, which overlie previously stranded Middle Jurassic and older ophiolite + chert-argillite belts in the Sierran Foothills, began no later than ~160 Ma. Correlative Oxfordian-Kimmeridgian strata of the Galice Formation occupy a similar position in the Klamath Mountains. We speculate that the Late Jurassic was a time of transition from (1) a mid-Paleozoic–Middle Jurassic interval of mainly but not exclusively strike-slip and episodic docking of oceanic terranes; (2) to transpressive plate underflow, producing calcalkaline igneous arc rocks ± outboard blueschists at ~170–150 Ma, whose erosion promoted accumulation of the Mariposa-Galice overlap strata; (3) continued transpressive underflow attending ~200 km left-lateral displacement of the Klamath salient relative to the Sierran arc at ~150–140 Ma and development of the apparent polar wander path cusps for North and South America; and (4) then nearly orthogonal mid and Late Cretaceous convergence commencing at ~125–120 Ma, during reversal in tangential motion of the Pacific plate. After ~120 Ma, nearly head-on subduction involving minor dextral transpression gave rise to voluminous continent-building juvenile and recycled magmas of the Sierran arc, providing the erosional debris to the Great Valley fore arc and Franciscan trench

ECONOMIC GROWTH AND EVOLUTION: PARENTAL PREFERENCE FOR QUALITY AND QUANTITY OF OFFSPRING

This paper presents a quantitative analysis of the model developed in Galor and Moav, Natural Selection and the Origin of Economic Growth (2002), in which agents vary genetically in their preference for quality and quantity of children. We simulate a parametric form of the model, enabling examination of the transition from Malthusian stagnation to modern rates of economic growth. The simulations allow an assessment of the strength of the biological foundations of the model and demonstrate the susceptibility of the modern high-growth state to invasion by cheaters. Extending the model from two to three genotypes suggests the possibility of a return to Malthusian conditions rather than a permanent state of modern growth.

ChromHMM: automating chromatin-state discovery and characterization

To the Editor: Chromatin-state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type–specific activity patterns and for interpreting disease-association studies1, 2, 3, 4, 5. However, the computational challenge of learning chromatin-state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise. To address this challenge, we developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets and visualizing the resulting genome-wide maps of chromatin-state annotations.Massachusetts Institute of Technology. Computational and Systems Biology InitiativeNational Science Foundation (U.S.) (postdoctoral fellowship 0905968)National Institutes of Health (U.S.) (1-RC1- HG005334)National Institutes of Health (U.S.) (1 U54 HG004570

Interplay between chromatin state, regulator binding, and regulatory motifs in six human cell types

The regions bound by sequence-specific transcription factors can be highly variable across different cell types despite the static nature of the underlying genome sequence. This has been partly attributed to changes in chromatin accessibility, but a systematic picture has been hindered by the lack of large-scale data sets. Here, we use 456 binding experiments for 119 regulators and 84 chromatin maps generated by the ENCODE in six human cell types, and relate those to a global map of regulatory motif instances for these factors. We find specific and robust chromatin state preferences for each regulator beyond the previously reported open-chromatin association, suggesting a much richer chromatin landscape beyond simple accessibility. The preferentially bound chromatin states of regulators were enriched for sequence motifs of regulators relative to all states, suggesting that these preferences are at least partly encoded by the genomic sequence. Relative to all regions bound by a regulator, however, regulatory motifs were surprisingly depleted in the regulator's preferentially bound states, suggesting additional non-sequence-specific binding beyond the level predicted by the regulatory motifs. Such permissive binding was largely restricted to open-chromatin regions showing histone modification marks characteristic of active enhancer and promoter regions, whereas open-chromatin regions lacking such marks did not show permissive binding. Lastly, the vast majority of cobinding of regulator pairs is predicted by the chromatin state preferences of individual regulators. Overall, our results suggest a joint role of sequence motifs and specific chromatin states beyond mere accessibility in mediating regulator binding dynamics across different cell types.National Institutes of Health (U.S.) (Grant R01HG004037)National Institutes of Health (U.S.) (Grant RC1HG005334

STEM: a tool for the analysis of short time series gene expression data

BACKGROUND: Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. RESULTS: We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. CONCLUSION: The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at

Using genomic annotations increases statistical power to detect eGenes.

MotivationExpression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power.ResultsWe applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation [email protected] or [email protected]

Temperamental factors in remitted depression: The role of effortful control and attentional mechanisms

Temperamental effortful control and attentional networks are increasingly viewed as important underlying processes in depression and anxiety. However, it is still unknown whether these factors facilitate depressive and anxiety symptoms in the general population and, more specifically, in remitted depressed individuals. We investigated to what extent effortful control and attentional networks (i.e., Attention Network Task) explain concurrent depressive and anxious symptoms in healthy individuals (n\u202f=\u202f270) and remitted depressed individuals (n\u202f=\u202f90). Both samples were highly representative of the US population. Increased effortful control predicted a substantial decrease in symptoms of both depression and anxiety in the whole sample, whereas decreased efficiency of executive attention predicted a modest increase in depressive symptoms. Remitted depressed individuals did not show less effortful control nor less efficient attentional networks than healthy individuals. Moreover, clinical status did not moderate the relationship between temperamental factors and either depressive or anxiety symptoms. Limitations include the cross-sectional nature of the study. Our study shows that temperamental effortful control represents an important transdiagnostic process for depressive and anxiety symptoms in adults