Graham McVicker

Identification of genetic variants that affect histone modifications in human cells.

DNA Differences The extent to which genetic variation affects an individuals phenotype has been difficult to predict because the majority of variation lies outside the coding regions of genes. Now, three studies examine the extent to which genetic variation affects the chromatin of individuals with diverse ancestry and genetic variation (see the Perspective by Furey and Sethupathy). Kasowski et al. (p. 750, published online 17 October) examined how genetic variation affects differences in chromatin states and their correlation to histone modifications, as well as more general DNA binding factors. Kilpinen et al. (p. 744, published online 17 October) document how genetic variation is linked to allelic specificity in transcription factor binding, histone modifications, and transcription. McVicker et al. (p. 747, published online 17 October) identified how quantitative trait loci affect histone modifications in Yoruban individuals and established which specific transcription factors affect such modifications. Human genetic variation affects transcription factor binding, leading to histone modifications. [Also see Perspective by Furey and Sethupathy] Histone modifications are important markers of function and chromatin state, yet the DNA sequence elements that direct them to specific genomic locations are poorly understood. Here, we identify hundreds of quantitative trait loci, genome-wide, that affect histone modification or RNA polymerase II (Pol II) occupancy in Yoruba lymphoblastoid cell lines (LCLs). In many cases, the same variant is associated with quantitative changes in multiple histone marks and Pol II, as well as in deoxyribonuclease I sensitivity and nucleosome positioning. Transcription factor binding site polymorphisms are correlated overall with differences in local histone modification, and we identify specific transcription factors whose binding leads to histone modification in LCLs. Furthermore, variants that affect chromatin at distal regulatory sites frequently also direct changes in chromatin and gene expression at associated promoters.

Controls of Nucleosome Positioning in the Human Genome

Nucleosomes are important for gene regulation because their arrangement on the genome can control which proteins bind to DNA. Currently, few human nucleosomes are thought to be consistently positioned across cells; however, this has been difficult to assess due to the limited resolution of existing data. We performed paired-end sequencing of micrococcal nuclease-digested chromatin (MNase–seq) from seven lymphoblastoid cell lines and mapped over 3.6 billion MNase–seq fragments to the human genome to create the highest-resolution map of nucleosome occupancy to date in a human cell type. In contrast to previous results, we find that most nucleosomes have more consistent positioning than expected by chance and a substantial fraction (8.7%) of nucleosomes have moderate to strong positioning. In aggregate, nucleosome sequences have 10 bp periodic patterns in dinucleotide frequency and DNase I sensitivity; and, across cells, nucleosomes frequently have translational offsets that are multiples of 10 bp. We estimate that almost half of the genome contains regularly spaced arrays of nucleosomes, which are enriched in active chromatin domains. Single nucleotide polymorphisms that reduce DNase I sensitivity can disrupt the phasing of nucleosome arrays, which indicates that they often result from positioning against a barrier formed by other proteins. However, nucleosome arrays can also be created by DNA sequence alone. The most striking example is an array of over 400 nucleosomes on chromosome 12 that is created by tandem repetition of sequences with strong positioning properties. In summary, a large fraction of nucleosomes are consistently positioned—in some regions because they adopt favored sequence positions, and in other regions because they are forced into specific arrangements by chromatin remodeling or DNA binding proteins.

Genome Variation in Cryptococcus gattii, an Emerging Pathogen of Immunocompetent Hosts

ABSTRACT Cryptococcus gattii recently emerged as the causative agent of cryptococcosis in healthy individuals in western North America, despite previous characterization of the fungus as a pathogen in tropical or subtropical regions. As a foundation to study the genetics of virulence in this pathogen, we sequenced the genomes of a strain (WM276) representing the predominant global molecular type (VGI) and a clinical strain (R265) of the major genotype (VGIIa) causing disease in North America. We compared these C. gattii genomes with each other and with the genomes of representative strains of the two varieties of Cryptococcus neoformans that generally cause disease in immunocompromised people. Our comparisons included chromosome alignments, analysis of gene content and gene family evolution, and comparative genome hybridization (CGH). These studies revealed that the genomes of the two representative C. gattii strains (genotypes VGI and VGIIa) are colinear for the majority of chromosomes, with some minor rearrangements. However, multiortholog phylogenetic analysis and an evaluation of gene/sequence conservation support the existence of speciation within the C. gattii complex. More extensive chromosome rearrangements were observed upon comparison of the C. gattii and the C. neoformans genomes. Finally, CGH revealed considerable variation in clinical and environmental isolates as well as changes in chromosome copy numbers in C. gattii isolates displaying fluconazole heteroresistance. IMPORTANCE Isolates of Cryptococcus gattii are currently causing an outbreak of cryptococcosis in western North America, and most of the cases occurred in the absence of coinfection with HIV. This pattern is therefore in stark contrast to the current global burden of one million annual cases of cryptococcosis, caused by the related species Cryptococcus neoformans, in the HIV/AIDS population. The genome sequences of two outbreak-associated major genotypes of C. gattii reported here provide insights into genome variation within and between cryptococcal species. These sequences also provide a resource to further evaluate the epidemiology of cryptococcal disease and to evaluate the role of pathogen genes in the differential interactions of C. gattii and C. neoformans with immunocompromised and immunocompetent hosts. Isolates of Cryptococcus gattii are currently causing an outbreak of cryptococcosis in western North America, and most of the cases occurred in the absence of coinfection with HIV. This pattern is therefore in stark contrast to the current global burden of one million annual cases of cryptococcosis, caused by the related species Cryptococcus neoformans, in the HIV/AIDS population. The genome sequences of two outbreak-associated major genotypes of C. gattii reported here provide insights into genome variation within and between cryptococcal species. These sequences also provide a resource to further evaluate the epidemiology of cryptococcal disease and to evaluate the role of pathogen genes in the differential interactions of C. gattii and C. neoformans with immunocompromised and immunocompetent hosts.

Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels.

DNA methylation is an important epigenetic regulator of gene expression. Recent studies have revealed widespread associations between genetic variation and methylation levels. However, the mechanistic links between genetic variation and methylation remain unclear. To begin addressing this gap, we collected methylation data at ∼300,000 loci in lymphoblastoid cell lines (LCLs) from 64 HapMap Yoruba individuals, and genome-wide bisulfite sequence data in ten of these individuals. We identified (at an FDR of 10%) 13,915 cis methylation QTLs (meQTLs)—i.e., CpG sites in which changes in DNA methylation are associated with genetic variation at proximal loci. We found that meQTLs are frequently associated with changes in methylation at multiple CpGs across regions of up to 3 kb. Interestingly, meQTLs are also frequently associated with variation in other properties of gene regulation, including histone modifications, DNase I accessibility, chromatin accessibility, and expression levels of nearby genes. These observations suggest that genetic variants may lead to coordinated molecular changes in all of these regulatory phenotypes. One plausible driver of coordinated changes in different regulatory mechanisms is variation in transcription factor (TF) binding. Indeed, we found that SNPs that change predicted TF binding affinities are significantly enriched for associations with DNA methylation at nearby CpGs.

WASP: allele-specific software for robust molecular quantitative trait locus discovery

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), but they are challenging to analyze and are prone to technical artifacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and chromatin immunoprecipitation sequencing (ChIP-seq) reads, we demonstrate that WASP has a low error rate and is far more powerful than existing QTL-mapping approaches.

A Genomic Map of the Effects of Linked Selection in Drosophila

Natural selection at one site shapes patterns of genetic variation at linked sites. Quantifying the effects of “linked selection” on levels of genetic diversity is key to making reliable inference about demography, building a null model in scans for targets of adaptation, and learning about the dynamics of natural selection. Here, we introduce the first method that jointly infers parameters of distinct modes of linked selection, notably background selection and selective sweeps, from genome-wide diversity data, functional annotations and genetic maps. The central idea is to calculate the probability that a neutral site is polymorphic given local annotations, substitution patterns, and recombination rates. Information is then combined across sites and samples using composite likelihood in order to estimate genome-wide parameters of distinct modes of selection. In addition to parameter estimation, this approach yields a map of the expected neutral diversity levels along the genome. To illustrate the utility of our approach, we apply it to genome-wide resequencing data from 125 lines in Drosophila melanogaster and reliably predict diversity levels at the 1Mb scale. Our results corroborate estimates of a high fraction of beneficial substitutions in proteins and untranslated regions (UTR). They allow us to distinguish between the contribution of sweeps and other modes of selection around amino acid substitutions and to uncover evidence for pervasive sweeps in untranslated regions (UTRs). Our inference further suggests a substantial effect of other modes of linked selection and of adaptation in particular. More generally, we demonstrate that linked selection has had a larger effect in reducing diversity levels and increasing their variance in D. melanogaster than previously appreciated.

GeneComber: combining outputs of gene prediction programs for improved results

UNLABELLED We recently demonstrated that combining the output from Genscan and HMMgene can provide increased accuracy of gene predictions. We have created a robust software system that runs algorithms previously described on DNA sequences and provides a public web interface to the system for use by the biological community worldwide. The GeneComber system performs ab initio gene prediction by first taking a user inputted DNA sequence and running Genscan and HMMgene. The outputs of Genscan and HMMgene are then integrated using the EUI, GI and EUI_frame algorithms. All results are then stored into a relational database management system (RDBMS) and can then be retrieved through a web interface. The web interface provides a unified view of the GeneComber predictions by graphically overlaying outputs from Genscan, HMMgene, EUI, GI and EUI_frame. Outputs can also be retrieved in general feature format (GFF) or FASTA format. The software is written in the Perl programming language and is both dependent on and interoperable with the Bioperl toolkit. It includes high-level application programming interfaces (APIs) to run Genscan, HMMgene and a database API to insert prediction results into an RDBMS. The APIs are assembled into the genecomber script which is executed by the web interface or can be run directly from the Unix command line. The web interface is written in PHP and is structured so as to be easily modified for viewing data from any database that stores gene structures. AVAILABILITY The GeneComber public web interface and supplementary information is located at http://bioinformatics.ubc.ca/genecomber The source code is released under the GNU General Public License and is available at ftp://ftp.bioinformatics.ubc.ca/pub/genecomber/software.

Genomic signatures of germline gene expression

Transcribed regions in the human genome differ from adjacent intergenic regions in transposable element density, crossover rates, and asymmetric substitution and sequence composition patterns. We tested whether these differences reflect selection or are instead a byproduct of germline transcription, using publicly available gene expression data from a variety of germline and somatic tissues. Crossover rate shows a strong negative correlation with gene expression in meiotic tissues, suggesting that crossover is inhibited by transcription. Strand-biased composition (G+T content) and A → G versus T → C substitution asymmetry are both positively correlated with germline gene expression. We find no evidence for a strand bias in allele frequency data, implying that the substitution asymmetry reflects a mutation rather than a fixation bias. The density of transposable elements is positively correlated with germline expression, suggesting that such elements preferentially insert into regions that are actively transcribed. For each of the features examined, our analyses favor a nonselective explanation for the observed trends and point to the role of germline gene expression in shaping the mammalian genome.

The chromatin architectural proteins HMGD1 and H1 bind reciprocally and have opposite effects on chromatin structure and gene regulation

BackgroundChromatin architectural proteins interact with nucleosomes to modulate chromatin accessibility and higher-order chromatin structure. While these proteins are almost certainly important for gene regulation they have been studied far less than the core histone proteins.ResultsHere we describe the genomic distributions and functional roles of two chromatin architectural proteins: histone H1 and the high mobility group protein HMGD1 in Drosophila S2 cells. Using ChIP-seq, biochemical and gene specific approaches, we find that HMGD1 binds to highly accessible regulatory chromatin and active promoters. In contrast, H1 is primarily associated with heterochromatic regions marked with repressive histone marks. We find that the ratio of HMGD1 to H1 binding is a better predictor of gene activity than either protein by itself, which suggests that reciprocal binding between these proteins is important for gene regulation. Using knockdown experiments, we show that HMGD1 and H1 affect the occupancy of the other protein, change nucleosome repeat length and modulate gene expression.ConclusionCollectively, our data suggest that dynamic and mutually exclusive binding of H1 and HMGD1 to nucleosomes and their linker sequences may control the fluid chromatin structure that is required for transcriptional regulation. This study provides a framework to further study the interplay between chromatin architectural proteins and epigenetics in gene regulation.

WASP: allele-specific software for robust discovery of molecular quantitative trait loci

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that our approach has a low error rate and is far more powerful than existing QTL mapping approaches.