Lee M. Butcher

Methylome analysis using MeDIP-seq with low DNA concentrations

DNA methylation is an epigenetic mark that has a crucial role in many biological processes. To understand the functional consequences of DNA methylation on phenotypic plasticity, a genome-wide analysis should be embraced. This in turn requires a technique that balances accuracy, genome coverage, resolution and cost, yet is low in DNA input in order to minimize the drain on precious samples. Methylated DNA immunoprecipitation-sequencing (MeDIP-seq) fulfils these criteria, combining MeDIP with massively parallel DNA sequencing. Here we report an improved protocol using 100-fold less genomic DNA than that commonly used. We show comparable results for specificity (>97%) and enrichment (>100-fold) over a wide range of DNA concentrations (5,000–50 ng) and demonstrate the utility of the protocol for the generation of methylomes from rare bone marrow cells using 160–300 ng of starting DNA. The protocol described here, i.e., DNA extraction to generation of MeDIP-seq library, can be completed within 3–5 d.

Genome‐wide quantitative trait locus association scan of general cognitive ability using pooled DNA and 500K single nucleotide polymorphism microarrays

General cognitive ability (g), which refers to what cognitive abilities have in common, is an important target for molecular genetic research because multivariate quantitative genetic analyses have shown that the same set of genes affects diverse cognitive abilities as well as learning disabilities. In this first autosomal genome‐wide association scan of g, we used a two‐stage quantitative trait locus (QTL) design with pooled DNA to screen more than 500 000 single nucleotide polymorphisms (SNPs) on microarrays, selecting from a sample of 7000 7‐year‐old children. In stage 1, we screened for allele frequency differences between groups pooled for low and high g. In stage 2, 47 SNPs nominated in stage 1 were tested by individually genotyping an independent sample of 3195 individuals, representative of the entire distribution of g scores in the full 7000 7‐year‐old children. Six SNPs yielded significant associations across the normal distribution of g, although only one SNP remained significant after a false discovery rate of 0.05 was imposed. However, none of these SNPs accounted for more than 0.4% of the variance of g, despite 95% power to detect associations of that size. It is likely that QTL effect sizes, even for highly heritable traits such as cognitive abilities and disabilities, are much smaller than previously assumed. Nonetheless, an aggregated ‘SNP set’ of the six SNPs correlated 0.11 (P < 0.00000003) with g. This shows that future SNP sets that will incorporate many more SNPs could be useful for predicting genetic risk and for investigating functional systems of effects from genes to brain to behavior.

Genotyping pooled DNA using 100K SNP microarrays: a step towards genomewide association scans

The identification of quantitative trait loci (QTLs) of small effect size that underlie complex traits poses a particular challenge for geneticists due to the large sample sizes and large numbers of genetic markers required for genomewide association scans. An efficient solution for screening purposes is to combine single nucleotide polymorphism (SNP) microarrays and DNA pooling (SNP-MaP), an approach that has been shown to be valid, reliable and accurate in deriving relative allele frequency estimates from pooled DNA for groups such as cases and controls for 10K SNP microarrays. However, in order to conduct a genomewide association study many more SNP markers are needed. To this end, we assessed the validity and reliability of the SNP-MaP method using Affymetrix GeneChip® Mapping 100K Array set. Interpretable results emerged for 95% of the SNPs (nearly 110 000 SNPs). We found that SNP-MaP allele frequency estimates correlated 0.939 with allele frequencies for 97 605 SNPs that were genotyped individually in an independent population; the correlation was 0.971 for 26 SNPs that were genotyped individually for the 1028 individuals used to construct the DNA pools. We conclude that extending the SNP-MaP method to the Affymetrix GeneChip® Mapping 100K Array set provides a useful screen of >100 000 SNP markers for QTL association scans.

Identification and functional validation of HPV-mediated hypermethylation in head and neck squamous cell carcinoma

BackgroundHuman papillomavirus-positive (HPV+) head and neck squamous cell carcinoma (HNSCC) represents a distinct clinical and epidemiological condition compared with HPV-negative (HPV-) HNSCC. To test the possible involvement of epigenetic modulation by HPV in HNSCC, we conducted a genome-wide DNA-methylation analysis.MethodsUsing laser-capture microdissection of 42 formalin-fixed paraffin wax-embedded (FFPE) HNSCCs, we generated DNA-methylation profiles of 18 HPV+ and 14 HPV- samples, using Infinium 450 k BeadArray technology. Methylation data were validated in two sets of independent HPV+/HPV- HNSCC samples (fresh-frozen samples and cell lines) using two independent methods (Infinium 450 k and whole-genome methylated DNA immunoprecipitation sequencing (MeDIP-seq)). For the functional analysis, an HPV- HNSCC cell line was transduced with lentiviral constructs containing the two HPV oncogenes (E6 and E7), and effects on methylation were assayed using the Infinium 450 k technology.Results and discussionUnsupervised clustering over the methylation variable positions (MVPs) with greatest variation showed that samples segregated in accordance with HPV status, but also that HPV+ tumors are heterogeneous. MVPs were significantly enriched at transcriptional start sites, leading to the identification of a candidate CpG island methylator phenotype in a sub-group of the HPV+ tumors. Supervised analysis identified a strong preponderance (87%) of MVPs towards hypermethylation in HPV+ HNSCC. Meta-analysis of our HNSCC and publicly available methylation data in cervical and lung cancers confirmed the observed DNA-methylation signature to be HPV-specific and tissue-independent. Grouping of MVPs into functionally more significant differentially methylated regions identified 43 hypermethylated promoter DMRs, including for three cadherins of the Polycomb group target genes. Integration with independent expression data showed strong negative correlation, especially for the cadherin gene-family members. Combinatorial ectopic expression of the two HPV oncogenes (E6 and E7) in an HPV- HNSCC cell line partially phenocopied the hypermethylation signature seen in HPV+ HNSCC tumors, and established E6 as the main viral effector gene.ConclusionsOur data establish that archival FFPE tissue is very suitable for this type of methylome analysis, and suggest that HPV modulates the HNSCC epigenome through hypermethylation of Polycomb repressive complex 2 target genes such as cadherins, which are implicated in tumor progression and metastasis.

Differential methylation of the TRPA1 promoter in pain sensitivity

Chronic pain is a global public health problem, but the underlying molecular mechanisms are not fully understood. Here we examine genome-wide DNA methylation, first in 50 identical twins discordant for heat pain sensitivity and then in 50 further unrelated individuals. Whole-blood DNA methylation was characterized at 5.2 million loci by MeDIP sequencing and assessed longitudinally to identify differentially methylated regions associated with high or low pain sensitivity (pain DMRs). Nine meta-analysis pain DMRs show robust evidence for association (false discovery rate 5%) with the strongest signal in the pain gene TRPA1 (P=1.2 × 10−13). Several pain DMRs show longitudinal stability consistent with susceptibility effects, have similar methylation levels in the brain and altered expression in the skin. Our approach identifies epigenetic changes in both novel and established candidate genes that provide molecular insights into pain and may generalize to other complex traits.

Genotyping DNA pools on microarrays: Tackling the QTL problem of large samples and large numbers of SNPs

BackgroundQuantitative trait locus (QTL) theory predicts that genetic influence on complex traits involves multiple genes of small effect size. To detect QTL associations of small effect size, large samples and systematic screens of thousands of DNA markers are required. An efficient solution is to genotype case and control DNA pools using SNP microarrays. We demonstrate that this is practical using DNA pools of 100 individuals.ResultsUsing standard microarray protocols for the Affymetrix GeneChip® Mapping 10 K Array Xba 131, we show that relative allele signal (RAS) values provide a quantitative index of allele frequencies in pooled DNA that correlate 0.986 with allele frequencies for 104 SNPs that were genotyped individually for 100 individuals. The sensitivity of the assay was demonstrated empirically in a spiking experiment in which 15% and 20% of one individuals DNA was added to a DNA pool.ConclusionWe conclude that this approach, which we call SNP-MaP (SNP m icroarrays a nd p ooling), is rapid, cost effective and promises to be a valuable initial screening method in the hunt for QTLs.

Applicability of DNA pools on 500 K SNP microarrays for cost-effective initial screens in genomewide association studies

BackgroundGenetic influences underpinning complex traits are thought to involve multiple quantitative trait loci (QTLs) of small effect size. Detection of such QTL associations requires systematic screening of large numbers of DNA markers within large sample populations. Using pooled DNA on SNP microarrays to screen for allelic frequency differences between groups such as cases and controls (called SNP Microarray and Pooling, or SNP-MaP) has been validated as an efficient solution on both 10 k and 100 k platforms. We demonstrate that this approach can be effectively applied to the truly genomewide Affymetrix GeneChip® Mapping 500 K Array.ResultsIn comparisons between five independent DNA pools (N ~200 per pool) on separate Affymetrix GeneChip® Mapping 500 K Array sets, we show that, for SNPs with minor allele frequencies > 0.05, the reliability of the rank order of estimated allele frequencies, assessed as the average correlation between allele frequency estimates across the DNA pools, was 0.948 (average mean difference across the five pools = 0.069). Similarly, validity of the SNP-MaP approach was demonstrated by a rank-order correlation of 0.937 (average mean difference = 0.095) between the average DNA pool allele frequency estimates and the allele frequencies of an independent (CEPH) sample of 60 unrelated individually genotyped subjects.ConclusionWe conclude that SNP-MaP can be extended for use on the Affymetrix GeneChip® Mapping 500 K Array, providing a cost-effective, reliable and valid initial screen of 500 K SNP microarrays in genomewide association scans.

Association analysis of mild mental impairment using DNA pooling to screen 432 brain-expressed single-nucleotide polymorphisms

We hypothesize that mild mental impairment (MMI) represents the low extreme of the same quantitative trait loci (QTLs) that operate throughout the distribution of intelligence. To detect QTLs of small effect size, we employed a direct association strategy by genotyping 432 presumably functional nonsynonymous single-nucleotide polymorphisms (nsSNPs) identified from public databases on DNA pools of 288 cases and 1025 controls. In total, 288 MMI cases were identified by in-home administration of McCarthy Scales of Childrens Abilities to 836 twin pairs selected from a community sample of more than 14 000 children previously screened for nonverbal cognitive delay using parentally administered tests. Controls were selected from the community sample representing the full range of nonverbal intelligence. SNPs showing at least 7% allele frequency differences between case and control DNA pools were tested for their association with the full range of nonverbal intelligence using five DNA subpools, each representing quintiles of the normal quantitative trait scores from the 1025 controls. SNPs showing linear associations in the expected direction across quintiles using pooled DNA were individually genotyped for the 288 cases and 1025 controls and analyzed using standard statistical methods. One SNP (rs1136141) in HSPA8 met these criteria, yielding a significant (P=0.036) allelic frequency difference between cases and controls for individual genotyping and a significant (P=0.013) correlation within the control group that accounts for 0.5% of the variance. The present SNP strategy combined with DNA pooling and large samples represents a step towards identifying QTLs of small effect size associated with complex traits in the postgenomic era when all functional polymorphisms will be known.

Generalist genes and cognitive neuroscience

Multivariate genetic research suggests that a single set of genes affects most cognitive abilities and disabilities. This finding already has far-reaching implications for cognitive neuroscience, and will become even more revealing when this - presumably large - set of generalist genes is identified. Similar to other complex disorders and dimensions, molecular genetic research on cognitive abilities and disabilities is adopting genome-wide association strategies. These strategies involve very large samples to detect DNA associations of small effect size using microarrays that simultaneously assess hundreds of thousands of DNA markers. When this set of generalist genes is identified, it can be used to provide solid footholds in the climb towards a systems-level understanding of how genetically driven brain processes work together to affect diverse cognitive abilities and disabilities.

Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors

Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I–IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab.