Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia.

Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large datasets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at ADH1B. The coding variant rs1229984(∗)T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.

Integrative regulatory mapping indicates that the RNA-binding protein HuR couples pre-mRNA processing and mRNA stability.

RNA-binding proteins coordinate the fates of multiple RNAs, but the principles underlying these global interactions remain poorly understood. We elucidated regulatory mechanisms of the RNA-binding protein HuR, by integrating data from diverse high-throughput targeting technologies, specifically PAR-CLIP, RIP-chip, and whole-transcript expression profiling. The number of binding sites per transcript, degree of HuR association, and degree of HuR-dependent RNA stabilization were positively correlated. Pre-mRNA and mature mRNA containing both intronic and 3' UTR binding sites were more highly stabilized than transcripts with only 3' UTR or only intronic binding sites, suggesting that HuR couples pre-mRNA processing with mature mRNA stability. We also observed HuR-dependent splicing changes and substantial binding of HuR in polypyrimidine tracts of pre-mRNAs. Comparison of the spatial patterns surrounding HuR and miRNA binding sites provided functional evidence for HuR-dependent antagonism of proximal miRNA-mediated repression. We conclude that HuR coordinates gene expression outcomes at multiple interconnected steps of RNA processing.

The protein expression landscape of the Arabidopsis root.

Because proteins are the major functional components of cells, knowledge of their cellular localization is crucial to gaining an understanding of the biology of multicellular organisms. We have generated a protein expression map of the Arabidopsis root providing the identity and cell type-specific localization of nearly 2,000 proteins. Grouping proteins into functional categories revealed unique cellular functions and identified cell type-specific biomarkers. Cellular colocalization provided support for numerous protein-protein interactions. With a binary comparison, we found that RNA and protein expression profiles are weakly correlated. We then performed peak integration at cell type-specific resolution and found an improved correlation with transcriptome data using continuous values. We performed GeLC-MS/MS (in-gel tryptic digestion followed by liquid chromatography-tandem mass spectrometry) proteomic experiments on mutants with ectopic and no root hairs, providing complementary proteomic data. Finally, among our root hair-specific proteins we identified two unique regulators of root hair development.

Abundant contribution of short tandem repeats to gene expression variation in humans.

The contribution of repetitive elements to quantitative human traits is largely unknown. Here we report a genome-wide survey of the contribution of short tandem repeats (STRs), which constitute one of the most polymorphic and abundant repeat classes, to gene expression in humans. Our survey identified 2,060 significant expression STRs (eSTRs). These eSTRs were replicable in orthogonal populations and expression assays. We used variance partitioning to disentangle the contribution of eSTRs from that of linked SNPs and indels and found that eSTRs contribute 10-15% of the cis heritability mediated by all common variants. Further functional genomic analyses showed that eSTRs are enriched in conserved regions, colocalize with regulatory elements and may modulate certain histone modifications. By analyzing known genome-wide association study (GWAS) signals and searching for new associations in 1,685 whole genomes from deeply phenotyped individuals, we found that eSTRs are enriched in various clinically relevant conditions. These results highlight the contribution of STRs to the genetic architecture of quantitative human traits.

Chromatin accessibility reveals insights into androgen receptor activation and transcriptional specificity.

BACKGROUND: Epigenetic mechanisms such as chromatin accessibility impact transcription factor binding to DNA and transcriptional specificity. The androgen receptor (AR), a master regulator of the male phenotype and prostate cancer pathogenesis, acts primarily through ligand-activated transcription of target genes. Although several determinants of AR transcriptional specificity have been elucidated, our understanding of the interplay between chromatin accessibility and AR function remains incomplete. RESULTS: We used deep sequencing to assess chromatin structure via DNase I hypersensitivity and mRNA abundance, and paired these datasets with three independent AR ChIP-seq datasets. Our analysis revealed qualitative and quantitative differences in chromatin accessibility that corresponded to both AR binding and an enrichment of motifs for potential collaborating factors, one of which was identified as SP1. These quantitative differences were significantly associated with AR-regulated mRNA transcription across the genome. Base-pair resolution of the DNase I cleavage profile revealed three distinct footprinting patterns associated with the AR-DNA interaction, suggesting multiple modes of AR interaction with the genome. CONCLUSIONS: In contrast with other DNA-binding factors, AR binding to the genome does not only target regions that are accessible to DNase I cleavage prior to hormone induction. AR binding is invariably associated with an increase in chromatin accessibility and, consequently, changes in gene expression. Furthermore, we present the first in vivo evidence that a significant fraction of AR binds only to half of the full AR DNA motif. These findings indicate a dynamic quantitative relationship between chromatin structure and AR-DNA binding that impacts AR transcriptional specificity.

PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data.

Crosslinking and immunoprecipitation (CLIP) protocols have made it possible to identify transcriptome-wide RNA-protein interaction sites. In particular, PAR-CLIP utilizes a photoactivatable nucleoside for more efficient crosslinking. We present an approach, centered on the novel PARalyzer tool, for mapping high-confidence sites from PAR-CLIP deep-sequencing data. We show that PARalyzer delineates sites with a high signal-to-noise ratio. Motif finding identifies the sequence preferences of RNA-binding proteins, as well as seed-matches for highly expressed microRNAs when profiling Argonaute proteins. Our study describes tailored analytical methods and provides guidelines for future efforts to utilize high-throughput sequencing in RNA biology. PARalyzer is available at http://www.genome.duke.edu/labs/ohler/research/PARalyzer/.

Evidence-ranked motif identification.

cERMIT is a computationally efficient motif discovery tool based on analyzing genome-wide quantitative regulatory evidence. Instead of pre-selecting promising candidate sequences, it utilizes information across all sequence regions to search for high-scoring motifs. We apply cERMIT on a range of direct binding and overexpression datasets; it substantially outperforms state-of-the-art approaches on curated ChIP-chip datasets, and easily scales to current mammalian ChIP-seq experiments with data on thousands of non-coding regions.