Probe Lasso: a novel method to rope in differentially methylated regions with 450K DNA methylation data.

The speed and resolution at which we can scour the genome for DNA methylation changes has improved immeasurably in the last 10years and the advent of the Illumina 450K BeadChip has made epigenome-wide association studies (EWAS) a reality. The resulting datasets are conveniently formatted to allow easy alignment of significant hits to genes and genetic features, however; methods that parse significant hits into discreet differentially methylated regions (DMRs) remain a challenge to implement. In this paper we present details of a novel DMR caller, the Probe Lasso: a flexible window based approach that gathers neighbouring significant-signals to define clear DMR boundaries for subsequent in-depth analysis. The method is implemented in the R package ChAMP (Morris et al., 2014) and returns sets of DMRs according to user-tuned levels of probe filtering (e.g., inclusion of sex chromosomes, polymorphisms) and probe-lasso size distribution. Using a sub-sample of colon cancer- and healthy colon-samples from TCGA we show that Probe Lasso shifts DMR calling away from just probe-dense regions, and calls a range of DMR sizes ranging from tens-of-bases to tens-of-kilobases in scale. Moreover, using TCGA data we show that Probe Lasso leverages more information from the array and highlights a potential role of hypomethylated transcription factor binding motifs not discoverable using a basic, fixed-window approach.

ChAMP: 450k Chip Analysis Methylation Pipeline.

UNLABELLED: The Illumina Infinium HumanMethylation450 BeadChip is a new platform for high-throughput DNA methylation analysis. Several methods for normalization and processing of these data have been published recently. Here we present an integrated analysis pipeline offering a choice of the most popular normalization methods while also introducing new methods for calling differentially methylated regions and detecting copy number aberrations. AVAILABILITY AND IMPLEMENTATION: ChAMP is implemented as a Bioconductor package in R. The package and the vignette can be downloaded at bioconductor.org

AutoMeDIP-seq: a high-throughput, whole genome, DNA methylation assay.

DNA methylation is an epigenetic mark linking DNA sequence and transcription regulation, and therefore plays an important role in phenotypic plasticity. The ideal whole genome methylation (methylome) assay should be accurate, affordable, high-throughput and agnostic with respect to genomic features. To this end, the methylated DNA immunoprecipitation (MeDIP) assay provides a good balance of these criteria. In this Methods paper, we present AutoMeDIP-seq, a technique that combines an automated MeDIP protocol with library preparation steps for subsequent second-generation sequencing. We assessed recovery of DNA sequences covering a range of CpG densities using in vitro methylated λ-DNA fragments (and their unmethylated counterparts) spiked-in against a background of human genomic DNA. We show that AutoMeDIP is more reliable than manual protocols, shows a linear recovery profile of fragments related to CpG density (R(2)=0.86), and that it is highly specific (>99%). AutoMeDIP-seq offers a competitive approach to high-throughput methylome analysis of medium to large cohorts.

Whole genome DNA methylation analysis based on high throughput sequencing technology.

There are numerous approaches to decipher a whole genome DNA methylation profile ("methylome"), each varying in cost, throughput and resolution. The gold standard of these methods, whole genome bisulfite-sequencing (BS-seq), involves treatment of DNA with sodium bisulfite combined with subsequent high throughput sequencing. Using BS-seq, we generated a single-base-resolution methylome in human peripheral blood mononuclear cells (in press). This BS-seq map was then used as the reference methylome to compare two alternative sequencing-based methylome assays (performed on the same donor of PBMCs): methylated DNA immunoprecipitation (MeDIP-seq) and methyl-binding protein (MBD-seq). In our analysis, we found that MeDIP-seq and MBD-seq are complementary strategies, with MeDIP-seq more sensitive to highly methylated, high-CpG densities and MDB-seq more sensitive to highly methylated, moderate-CpG densities. Taking into account the size of a mammalian genome and the current expense of sequencing, we feel 3gigabases (Gbp) 45bp paired-end MeDIP-seq or MBD-seq uniquely mapped reads is the minimum requirement and cost-effective strategy for methylome pattern analysis.

A genome-wide association study of social and non-social autistic-like traits in the general population using pooled DNA, 500 K SNP microarrays and both community and diagnosed autism replication samples.

Two separate genome-wide association studies were conducted to identify single nucleotide polymorphisms (SNPs) associated with social and nonsocial autistic-like traits. We predicted that we would find SNPs associated with social and non-social autistic-like traits and that different SNPs would be associated with social and nonsocial. In Stage 1, each study screened for allele frequency differences in approximately 430,000 autosomal SNPs using pooled DNA on microarrays in high-scoring versus low-scoring boys from a general population sample (N = approximately 400/group). In Stage 2, 22 and 20 SNPs in the social and non-social studies, respectively, were tested for QTL association by individually genotyping an independent community sample of 1,400 boys. One SNP (rs11894053) was nominally associated (P < .05, uncorrected for multiple testing) with social autistic-like traits. When the sample was increased by adding females, 2 additional SNPs were nominally significant (P < .05). These 3 SNPs, however, showed no significant association in transmission disequilibrium analyses of diagnosed ASD families.

A three-stage genome-wide association study of general cognitive ability: hunting the small effects.

Childhood general cognitive ability (g) is important for a wide range of outcomes in later life, from school achievement to occupational success and life expectancy. Large-scale association studies will be essential in the quest to identify variants that make up the substantial genetic component implicated by quantitative genetic studies. We conducted a three-stage genome-wide association study for general cognitive ability using over 350,000 single nucleotide polymorphisms (SNPs) in the quantitative extremes of a population sample of 7,900 7-year-old children from the UK Twins Early Development Study. Using two DNA pooling stages to enrich true positives, each of around 1,000 children selected from the extremes of the distribution, and a third individual genotyping stage of over 3,000 children to test for quantitative associations across the normal range, we aimed to home in on genes of small effect. Genome-wide results suggested that our approach was successful in enriching true associations and 28 SNPs were taken forward to individual genotyping in an unselected population sample. However, although we found an enrichment of low P values and identified nine SNPs nominally associated with g (P < 0.05) that show interesting characteristics for follow-up, further replication will be necessary to meet rigorous standards of association. These replications may take advantage of SNP sets to overcome limitations of statistical power. Despite our large sample size and three-stage design, the genes associated with childhood g remain tantalizingly beyond our current reach, providing further evidence for the small effect sizes of individual loci. Larger samples, denser arrays and multiple replications will be necessary in the hunt for the genetic variants that influence human cognitive ability.

Testing replication of a 5-SNP set for general cognitive ability in six population samples.

A 5-single nucleotide polymorphism (SNP) set has been associated with general cognitive ability in 5000 7-year-old children from the Twins Early Development Study (TEDS). Four of these SNPs were identified through a 10 K microarray analysis and one was identified through a targeted analysis of brain-expressed genes. The present study tested this association with general cognitive ability in six population samples of varying size and age from Australia, the UK (Scotland and England) and the Netherlands. Results from the largest sample (N=1310) approached significance (P=0.06) in the direction of the original finding, but results from the other samples (N=205-758) were mixed. A meta-analysis of the results--allowing for effect size heterogeneity between samples--yielded a non-significant correlation (r=-0.01, P=0.57), indicating that this SNP set was not associated with general cognitive ability in the populations studied.

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.

No evidence for association between BMI and 10 candidate genes at ages 4, 7 and 10 in a large UK sample of twins.

BACKGROUND: Over the last decade, associations between Body Mass Index (BMI) and a variety of candidate genes have been reported, but samples have almost all been limited to adults. The purpose of the present study was to test the developmental origins of some of these associations in a large longitudinal sample of children. METHODS: For 10 single-nucleotide polymorphisms (SNPs) in candidate genes reported to be associated with BMI in adults, we examined associations with BMI in a sample of 5000 children (2500 twin pairs) with BMI data at 4, 7 and 10 years. Association analyses were performed using the Quantitative Transmission Disequilibrium Test and we corrected for multiple testing using the False Discovery Rate. RESULTS: Despite having 80% power to detect associations that account for as little as 0.2% of the variance of BMI, none of the 10 SNPs were significantly associated with BMI at any age, although two SNPs showed trends in the expected direction. CONCLUSION: The lack of association for these ten previously reported associations, despite our large sample size, is typical of associations between candidate genes and complex traits. However, some of the reported SNP associations with BMI might emerge as we continue to follow the sample into adolescence and adulthood. This report highlights the importance of developmentally appropriate candidate genes.

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.

Molecular Signatures of Regression of the Canine Transmissible Venereal Tumor.

The canine transmissible venereal tumor (CTVT) is a clonally transmissible cancer that regresses spontaneously or after treatment with vincristine, but we know little about the regression mechanisms. We performed global transcriptional, methylation, and functional pathway analyses on serial biopsies of vincristine-treated CTVTs and found that regression occurs in sequential steps; activation of the innate immune system and host epithelial tissue remodeling followed by immune infiltration of the tumor, arrest in the cell cycle, and repair of tissue damage. We identified CCL5 as a possible driver of CTVT regression. Changes in gene expression are associated with methylation changes at specific intragenic sites. Our results underscore the critical role of host innate immunity in triggering cancer regression.

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

BACKGROUND: Genetic 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. RESULTS: In 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. CONCLUSION: We 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.

A central resource for accurate allele frequency estimation from pooled DNA genotyped on DNA microarrays.

Analysing pooled DNA on microarrays is an efficient way to genotype hundreds of individuals for thousands of markers for genome-wide association. Although direct comparison of case and control fluorescence scores is possible, correction for differential hybridization of alleles is important, particularly for rare single nucleotide polymorphisms. Such correction relies on heterozygous fluorescence scores and requires the genotyping of hundreds of individuals to obtain sufficient estimates of the correction factor, completely negating any benefit gained by pooling samples. We explore the effect of differential hybridization on test statistics and provide a solution to this problem in the form of a central resource for the accumulation of heterozygous fluorescence scores, allowing accurate allele frequency estimation at no extra cost.

Nano-MeDIP-seq Methylome Analysis Using Low DNA Concentrations.

DNA methylation is an epigenetic mark that is indispensable for mammalian development and occurs at cytosine residues throughout the genome (the "methylome"). Approximately 70 % of all CpG dinucleotides are affected by DNA methylation, which serve to "lock in" chromatin states and thus transcriptional programs. The systemic and pervasive occurrence of DNA methylation throughout the genome defines cellular identity and therefore requires genome-wide assays to fully appreciate and discern differential patterns of methylation that influence aspects of phenotypic plasticity including susceptibility to common complex disease.One method that permits methylome analysis is methylated DNA immunoprecipitation (MeDIP) combined with next-generation sequencing (MeDIP-seq). MeDIP uses an antibody raised against 5-methylcytosine to capture methylated fragments of DNA, which are subsequently sequenced to envisage the methylome landscape. The advantageous cost versus coverage balance of MeDIP-seq has made it the method of choice to replace or complement array-based methods for population epigenetic studies. Here we detail nano-MeDIP-seq, which allows methylome analysis using nanogram quantities of starting material.

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.

Genetic heterogeneity between the three components of the autism spectrum: a twin study.

OBJECTIVE: This study investigated the etiology of autistic-like traits in the general population and the etiological overlap between the three aspects of the triad of impairments (social impairments, communication impairments, restricted repetitive behaviors and interests) that together define autism spectrum disorders. METHOD: Parents of 3,400 8-year-old twin pairs from the Twins Early Development Study completed the Childhood Asperger Syndrome Test, a screening instrument for autism spectrum symptoms in mainstream samples. Genetic model-fitting of categorical and continuous data is reported. RESULTS: High heritability was found for extreme autistic-like traits (0.64-0.92 for various cutoffs) and autistic-like traits as measured on a continuum (0.78-0.81), with no significant shared environmental influences. All three subscales were highly heritable but showed low covariation. In the genetic modeling, distinct genetic influences were identified for the three components. CONCLUSIONS: These results suggest the triad of impairments that define autism spectrum disorders is heterogeneous genetically. Molecular genetic research examining the three components separately may identify different causal pathways for the three components. The analyses give no indication that different genetic processes affect extreme autistic impairments and autistic impairments as measured on a continuum, but this can only be directly tested once genes are identified.

Non-CG DNA methylation is a biomarker for assessing endodermal differentiation capacity in pluripotent stem cells.

Non-CG methylation is an unexplored epigenetic hallmark of pluripotent stem cells. Here we report that a reduction in non-CG methylation is associated with impaired differentiation capacity into endodermal lineages. Genome-wide analysis of 2,670 non-CG sites in a discovery cohort of 25 phenotyped human induced pluripotent stem cell (hiPSC) lines revealed unidirectional loss (Δß=13%, P<7.4 × 10(-4)) of non-CG methylation that correctly identifies endodermal differentiation capacity in 23 out of 25 (92%) hiPSC lines. Translation into a simplified assay of only nine non-CG sites maintains predictive power in the discovery cohort (Δß=23%, P<9.1 × 10(-6)) and correctly identifies endodermal differentiation capacity in nine out of ten pluripotent stem cell lines in an independent replication cohort consisting of hiPSCs reprogrammed from different cell types and different delivery systems, as well as human embryonic stem cell (hESC) lines. This finding infers non-CG methylation at these sites as a biomarker when assessing endodermal differentiation capacity as a readout.

eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.

Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology.

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

BACKGROUND: Quantitative 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. RESULTS: Using 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 individual's DNA was added to a DNA pool. CONCLUSION: We conclude that this approach, which we call SNP-MaP (SNP microarrays and pooling), is rapid, cost effective and promises to be a valuable initial screening method in the hunt for QTLs.

Comparative methylome analysis identifies new tumour subtypes and biomarkers for transformation of nephrogenic rests into Wilms tumour.

BACKGROUND: Wilms tumours (WTs) are characterised by several hallmarks that suggest epimutations such as aberrant DNA methylation are involved in tumour progression: loss of imprinting at 11p15, lack of recurrent mutations and formation of nephrogenic rests (NRs), which are lesions of retained undifferentiated embryonic tissue that can give rise to WTs. METHODS: To identify such epimutations, we performed a comprehensive methylome analysis on 20 matched trios of micro-dissected WTs, NRs and surrounding normal kidneys (NKs) using Illumina Infinium HumanMethylation450 Bead Chips and functionally validated findings using RNA sequencing. RESULTS: Comparison of NRs with NK revealed prominent tissue biomarkers: 629 differentially methylated regions, of which 55% were hypermethylated and enriched for domains that are bivalent in embryonic stem cells and for genes expressed during development (P = 2.49 × 10(-5)). Comparison of WTs with NRs revealed two WT subgroups; group-2 WTs and NRs were epigenetically indistinguishable whereas group-1 WTs showed an increase in methylation variability, hypomethylation of renal development genes, hypermethylation and relative loss of expression of cell adhesion genes and known and potential new WT tumour suppressor genes (CASP8, H19, MIR195, RB1 and TSPAN32) and was strongly associated with bilateral disease (P = 0.032). Comparison of WTs and NRs to embryonic kidney highlighted the significance of polycomb target methylation in Wilms tumourigenesis. CONCLUSIONS: Methylation levels vary during cancer evolution. We have described biomarkers related to WT evolution from its precursor NRs which may be useful to differentiate between these tissues for patients with bilateral disease.