Most disease associated genetic risk factors are non-coding, making it challenging to design experiments to understand their functional consequences1,2. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer downstream effects of disease variants but the large majority remains unexplained.3,4. The analysis of DNA methylation, a key component of the epigenome5, offers highly complementary data on the regulatory potential of genomic regions6,7. However, a large-scale, combined analysis of methylome and transcriptome data to infer downstream effects of disease variants is lacking. Here, we show that disease variants have wide-spread effects on DNA methylation in trans that likely reflect the downstream effects on binding sites of cis-regulated transcription factors. Using data on 3,841 Dutch samples, we detected 272,037 independent cis-meQTLs (FDR < 0.05) and identified 1,907 trait-associated SNPs that affect methylation levels of 10,141 different CpG sites in trans (FDR < 0.05), an eight-fold increase in the number downstream effects that was known from trans-eQTL studies3,8,9. Trans-meQTL CpG sites are enriched for active regulatory regions, being correlated with gene expression and overlap with Hi-C determined interchromosomal contacts10,11. We detected many trans-meQTL SNPs that affect expression levels of nearby transcription factors (including NFKB1, CTCF and NKX2-3), while the corresponding trans-meQTL CpG sites frequently coincide with its respective binding site. Trans-meQTL mapping therefore provides a strategy for identifying and better understanding downstream functional effects of many disease-associated variants.

In eukaryotes, cytosine methylation is a primary heritable epigenetic modification of the genome that regulates many cellular processes. While the whole-genome methylation pattern has been generally conserved in different eukaryotic groups, invertebrates and vertebrates exhibit two distinct patterns. Whereas almost all CpG sites are methylated in most vertebrates, with the exception of short unmethylated regions call CpG islands, the most frequent pattern in invertebrate animals is ‘mosaic methylation’, comprising domains of heavily methylated DNA interspersed with domains that are methylation free. The mechanism by which the genome methylation pattern transited from a mosaic to a global pattern and the role of the one or two-round whole-genome duplication in this transition remain largely elusive, partly owing to the lack of methylome data from early vertebrates. In this study, we used the whole-genome bisulfite-sequencing technology to investigate the genome-wide methylation in three tissues (heart, muscle, and sperm) from the sea lamprey, an extant Agarthan vertebrate. Analyses of methylation level and the extent of CpG dinucleotide depletion of gene-encoding, intergenic and promoter regions revealed a gradual increase in the methylation level from invertebrates to vertebrates, with the sea lamprey exhibiting an intermediate position. In addition, the methylation level of the majority of CpGs was intermediate in each sea lamprey tissue, indicating a high level of heterogeneity of methylation status between individual cells. In this regard, we defined the genomic methylation pattern of sea lamprey as ???global genomic DNA intermediate methylation???. The methylation features in different genomic regions, such as the transcription start site (TSS) region of the gene body, exon-intron boundaries, transposons, as well as genes grouping with different expression levels, supported the gradual methylation transition hypothesis. We further discussed that the copy number difference in DNA methylation transferases and the loss of the PWWP domain and/or DNTase domain in DNMT3 sub-family enzymes may have contributed to the methylation pattern transition in early vertebrates. These findings demonstrate an intermediate genomic methylation pattern between invertebrates and jawed vertebrates, providing evidence that supports the hypothesis that methylation patterns underwent a gradual transition from invertebrates (mosaic) to vertebrates (global).

Genetic risk factors often localize in non-coding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying the association of genetic risk factors with disease. More mechanistic insights can be derived from knowledge of the context, such as cell type or the activity of signaling pathways, influencing the nature and strength of eQTLs. Here, we generated peripheral blood RNA-seq data from 2,116 unrelated Dutch individuals and systematically identified these context-dependent eQTLs using a hypothesis-free strategy that does not require prior knowledge on the identity of the modifiers. Out of the 23,060 significant cis-regulated genes (false discovery rate ≤ 0.05), 2,743 genes (12%) show context-dependent eQTL effects. The majority of those were influenced by cell type composition, revealing eQTLs that are particularly strong in cell types such as CD4+ T-cells, erythrocytes, and even lowly abundant eosinophils. A set of 145 cis-eQTLs were influenced by the activity of the type I interferon signaling pathway and we identified several cis-eQTLs that are modulated by specific transcription factors that bind to the eQTL SNPs. This demonstrates that large-scale eQTL studies in unchallenged individuals can complement perturbation experiments to gain better insight in regulatory networks and their stimuli.

The evolutionary and ecological forces that govern microbial biogeography remain poorly characterized. We examined the biogeography of Streptomyces at regional spatial scales to identify factors that govern extant patterns of microbial diversity within the genus. Streptomyces are spore forming filamentous bacteria that are widespread in soil and are a predominant source of antibiotics. We applied population genetic approaches to analyze geographic and genetic population structure in six phylogroups of Streptomyces identified from a geographically explicit culture collection. Streptomyces strains were isolated from soils associated with perennial grass habitats sampled across a spatial scale of more than 5,000 km. We find that Streptomyces allelic diversity correlates with geographic distance and that gene flow between sites is constrained by latitude. In addition, we find that phylogroup nucleotide diversity is negatively correlated with latitude. These observations are consistent with the hypothesis that historical demographic processes have influenced the contemporary biogeography of Streptomyces.

We have previously established two lines of rat for studying the functional basis of aerobic exercise capacity (AEC) and its impact on metabolic health. The two lines, high capacity runners (HCR) and low capacity runners (LCR), have been selectively bred for high and low intrinsic AEC, respectively. They were started from the same genetically heterogeneous population and have now diverged in both AEC and many other physiological measures, including weight, body composition, blood pressure, body mass index, lung capacity, lipid and glucose metabolism, and natural life span. In order to exploit this rat model to understand the genomic regions under differential selection within the two lines, we used SNP genotype and whole genome pooled sequencing data to identify signatures of selection using three different statistics: runs of homozygosity, fixation index, and aberrant allele frequency spectrum, and developed a composite score that combined the three signals. We found that several pathways (ATP transport and fatty acid metabolism) are enriched in regions under differential selection. The candidate genes and pathways under selection will be integrated with the previous mRNA expression data and future F2 QTL results for a multi-omics approach to understanding the biological basis of AEC and metabolic traits.

Recent advances in Web technology and information sciences, especially the development of knowledge representation systems—ontology languages (Web Ontology Language) and syntaxes (Manchester syntax)—are now infiltrating the world of insect biodiversity research. Data generated from taxonomic revisions, comparative morphology studies, and other enterprises now have the potential to be shared broadly and to be computed across—i.e., they are rendered semantic—in order to address questions relevant to multiple domains in the life sciences. In this chapter we describe the philosophy behind these new tools, the mechanisms by which they operate, and the real and future benefits of the semantic representation of phenotype data (e.g., standardization of terminology). We provide examples using real data and describe some limitations of semantic phenotype annotations.

While identifying routes of transmission during an infectious disease outbreak was traditionally conducted through exhaustive contact tracing efforts, the increasing availability of pathogen sequencing has provided a new resource with which one can identify plausible routes of infection. However, while transmission clusters can be identified using single genome sequences, individual transmission routes remain relatively uncertain. Deep sequence data may provide additional information where single genomes lack sufficient resolution – presence of shared minor variants can suggest epidemiological linkage when observed between multiple hosts. In this study we formalize shared variant methods to reconstruct the transmission tree in an outbreak, and using simulated outbreak data, we quantify the improved accuracy when compared with analogous single genome approaches. Furthermore we propose a hybrid approach, drawing information from both deep sequence and single genome data. Our simulation studies demonstrate the superior performance of transmission tree identification methods using shared variants in most settings. Application of these methods to deep sequence data collected during the 2014 Sierra Leone Ebola epidemic demonstrates the ability to identify plausible transmission routes without any additional data. The methods we describe should become a common step in outbreak investigations and epidemiological analyses once the collection of deep sequence data becomes increasingly widespread.