Hailiang Mei

Whole-genome sequence variation, population structure and demographic history of the Dutch population

Whole-genome sequencing enables complete characterization of genetic variation, but geographic clustering of rare alleles demands many diverse populations be studied. Here we describe the Genome of the Netherlands (GoNL) Project, in which we sequenced the whole genomes of 250 Dutch parent-offspring families and constructed a haplotype map of 20.4 million single-nucleotide variants and 1.2 million insertions and deletions. The intermediate coverage (∼13×) and trio design enabled extensive characterization of structural variation, including midsize events (30–500 bp) previously poorly catalogued and de novo mutations. We demonstrate that the quality of the haplotypes boosts imputation accuracy in independent samples, especially for lower frequency alleles. Population genetic analyses demonstrate fine-scale structure across the country and support multiple ancient migrations, consistent with historical changes in sea level and flooding. The GoNL Project illustrates how single-population whole-genome sequencing can provide detailed characterization of genetic variation and may guide the design of future population studies.

Disease variants alter transcription factor levels and methylation of their binding sites

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.

Identification of context-dependent expression quantitative trait loci in whole blood

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA–seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.

Blood lipids influence DNA methylation in circulating cells

BackgroundCells can be primed by external stimuli to obtain a long-term epigenetic memory. We hypothesize that long-term exposure to elevated blood lipids can prime circulating immune cells through changes in DNA methylation, a process that may contribute to the development of atherosclerosis. To interrogate the causal relationship between triglyceride, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol levels and genome-wide DNA methylation while excluding confounding and pleiotropy, we perform a stepwise Mendelian randomization analysis in whole blood of 3296 individuals.ResultsThis analysis shows that differential methylation is the consequence of inter-individual variation in blood lipid levels and not vice versa. Specifically, we observe an effect of triglycerides on DNA methylation at three CpGs, of LDL cholesterol at one CpG, and of HDL cholesterol at two CpGs using multivariable Mendelian randomization. Using RNA-seq data available for a large subset of individuals (N = 2044), DNA methylation of these six CpGs is associated with the expression of CPT1A and SREBF1 (for triglycerides), DHCR24 (for LDL cholesterol) and ABCG1 (for HDL cholesterol), which are all key regulators of lipid metabolism.ConclusionsOur analysis suggests a role for epigenetic priming in end-product feedback control of lipid metabolism and highlights Mendelian randomization as an effective tool to infer causal relationships in integrative genomics data.

Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

BackgroundEpigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages.ResultsHere, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related gain of methylation at CpG islands marked by PRC2 or a loss of methylation at enhancers. This distinct pattern extends to other tissues and multiple cancer types. Finally, genes associated with aVMPs in trans whose expression is variably upregulated with age (733 genes) play a key role in DNA repair and apoptosis, whereas downregulated aVMP-associated genes (121 genes) are mapped to defined pathways in cellular metabolism.ConclusionsOur results link age-related changes in DNA methylation to fundamental mechanisms that are thought to drive human ageing.

A high-quality human reference panel reveals the complexity and distribution of genomic structural variants

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100 bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals.

A framework for the detection of de novo mutations in family-based sequencing data

Germline mutation detection from human DNA sequence data is challenging due to the rarity of such events relative to the intrinsic error rates of sequencing technologies and the uneven coverage across the genome. We developed PhaseByTransmission (PBT) to identify de novo single nucleotide variants and short insertions and deletions (indels) from sequence data collected in parent-offspring trios. We compute the joint probability of the data given the genotype likelihoods in the individual family members, the known familial relationships and a prior probability for the mutation rate. Candidate de novo mutations (DNMs) are reported along with their posterior probability, providing a systematic way to prioritize them for validation. Our tool is integrated in the Genome Analysis Toolkit and can be used together with the ReadBackedPhasing module to infer the parental origin of DNMs based on phase-informative reads. Using simulated data, we show that PBT outperforms existing tools, especially in low coverage data and on the X chromosome. We further show that PBT displays high validation rates on empirical parent-offspring sequencing data for whole-exome data from 104 trios and X-chromosome data from 249 parent-offspring families. Finally, we demonstrate an association between father’s age at conception and the number of DNMs in female offspring’s X chromosome, consistent with previous literature reports.

A SNP panel for identification of DNA and RNA specimens.

BackgroundSNP panels that uniquely identify an individual are useful for genetic and forensic research. Previously recommended SNP panels are based on DNA profiles and mostly contain intragenic SNPs. With the increasing interest in RNA expression profiles, we aimed for establishing a SNP panel for both DNA and RNA-based genotyping.ResultsTo determine a small set of SNPs with maximally discriminative power, genotype calls were obtained from DNA and blood-derived RNA sequencing data belonging to healthy, geographically dispersed, Dutch individuals. SNPs were selected based on different criteria like genotype call rate, minor allele frequency, Hardy–Weinberg equilibrium and linkage disequilibrium. A panel of 50 SNPs was sufficient to identify an individual uniquely: the probability of identity was 6.9 × 10− 20 when assuming no family relations and 1.2 × 10− 10 when accounting for the presence of full sibs. The ability of the SNP panel to uniquely identify individuals on DNA and RNA level was validated in an independent population dataset. The panel is applicable to individuals from European descent, with slightly lower power in non-Europeans. Whereas most of the genes containing the 50 SNPs are expressed in various tissues, our SNP panel needs optimization for other tissues than blood.ConclusionsThis first DNA/RNA SNP panel will be useful to identify sample mix-ups in biomedical research and for assigning DNA and RNA stains in crime scenes to unique individuals.

SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines

BackgroundMany tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included:a)Creating an automated, standardized pipeline for SV prediction.b)Identifying the best tool(s) for SV prediction through benchmarking.c)Providing a statistically sound method for merging SV calls.ResultsThe SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%.ConclusionSV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers.

Genes associated with venous thromboembolism in colorectal cancer patients

Essentials The underlying pathophysiological mechanisms behind cancer‐associated thrombosis are unknown. We compared expression profiles in tumor cells from patients with and without thrombosis. Tumors from patients with thrombosis showed significant differential gene expression profiles. Patients with thrombosis had a proinflammatory status and increased fibrin levels in the tumor.