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Microarrays are revolutionizing genetics by making it possible to genotype hundreds of thousands of DNA markers and to assess the expression (RNA transcripts) of all of the genes in the genome. Microarrays are slides the size of a postage stamp that contain millions of DNA sequences to which single-stranded DNA or RNA can hybridize. This miniaturization requires little DNA or RNA and makes the method fast and inexpensive; multiple assays of each target make the method highly accurate. DNA microarrays with hundreds of thousands of DNA markers have made it possible to conduct systematic scans of the entire genome to identify genetic associations with complex disorders or dimensions likely to be influenced by many genes of small effect size. RNA microarrays can provide snapshots of gene expression across all of the genes in the genome at any time in any tissue, which has far-reaching applications such as structural and functional "genetic neuroimaging" and providing a biological basis for understanding environmental influence.

Motivation Data normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes; thus, it poses a significant challenge to normalize sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalize sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately. Results Here, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalization methods. By this new strategy, the autosomal CpGs are first normalized independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome-linked CpGs are estimated as the weighted average of their nearest neighbors on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalization methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalization effect. Availability and implementation The proposed methods are available by calling the function ' adjustedDasen ' or ' adjustedFunnorm ' in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A genome-wide survey investigates the level of allelic skewing of DNA methylation in order to identify previously unknown differentially methylated regions (DMRs) mainly associated with DNA sequence variation acting in cis or genomic imprinting. A quantitative assessment of allele-specific DNA methylation (ASM) using SNP microarrays reveals that allelic skewing of DNA methylation is widespread across the genome, with likely heterogeneity between individuals and across tissues.

A P1 genomic library of 17-fold coverage and a cosmid library of eight genome equivalents were ordered by hybridizing genetic markers and individual clones from the two libraries. Both were gridded on high density filters and subdivided by yeast artificial chromosome clones covering the entire genome. Further data was provided by the hybridization of short oligonucleotides and DNA pools made from randomly selected cosmids. This approach of using parallel libraries greatly enhances the mapping and resolution of the clone order, although further improvements can be made in speed and accuracy.

Abstract Background Insulin-like growth factor 2 (Igf2) is a paternally expressed imprinted gene regulating fetal growth, playing an integral role in the development of many tissues including the brain. The parent-of-origin specific expression of Igf2 is largely controlled by allele-specific DNA methylation at CTCF-binding sites in the imprinting control region (ICR), located immediately upstream of the neighboring H19 gene. Previously we reported evidence of a negative correlation between DNA methylation in this region and cerebellum weight in humans. Results We quantified cerebellar DNA methylation across all four CTCF binding sites spanning the murine Igf2/H19 ICR in an outbred population of Heterogeneous Stock (HS) mice (n = 48). We observe that DNA methylation at the second and third CTCF binding sites in the Igf2/H19 ICR shows a negative relationship with cerebellar mass, reflecting the association observed in human post-mortem cerebellum tissue. Conclusions Given the important role of the cerebellum in motor control and cognition, and the link between structural cerebellar abnormalities and neuropsychiatric phenotypes, the identification of epigenetic factors associated with cerebellum growth and development may provide important insights about the etiology of psychiatric disorders.

Abstract Background miRNAs are short single-stranded non-coding RNAs involved in post-transcriptional gene regulation that play a major role in normal biological functions and diseases. Little is currently known about how expression of miRNAs is regulated. We surveyed variation in miRNA abundance in the hippocampus of mouse inbred strains, allowing us to take a genetic approach to the study of miRNA regulation, which is novel for miRNAs. The BXD recombinant inbred panel is a very well characterized genetic reference panel which allows quantitative trait locus (QTL) analysis of miRNA abundance and detection of correlates in a large store of brain and behavioural phenotypes. Results We found five suggestive trans QTLs for the regulation of miRNAs investigated. Further analysis of these QTLs revealed two genes, Tnik and Phf17, under the miR-212 regulatory QTLs, whose expression levels were significantly correlated with miR-212 expression. We found that miR-212 expression is correlated with cocaine-related behaviour, consistent with a reported role for this miRNA in the control of cocaine consumption. miR-31 is correlated with anxiety and alcohol related behaviours. KEGG pathway analysis of each miRNA’s expression correlates revealed enrichment of pathways including MAP kinase, cancer, long-term potentiation, axonal guidance and WNT signalling. Conclusions The BXD reference panel allowed us to establish genetic regulation and characterize biological function of specific miRNAs. QTL analysis enabled detection of genetic loci that regulate the expression of these miRNAs. eQTLs that regulate miRNA abundance are a new mechanism by which genetic variation influences brain and behaviour. Analysis of one of these QTLs revealed a gene, Tnik, which may regulate the expression of a miRNA, a molecular pathway and a behavioural phenotype. Evidence of genetic covariation of miR-212 abundance and cocaine related behaviours is strongly supported by previous functional studies, demonstrating the value of this approach for discovery of new functional roles and downstream processes regulated by miRNA.

Abstract Background We report an attempt to extend the previously successful approach of combining SNP (single nucleotide polymorphism) microarrays and DNA pooling (SNP-MaP) employing high-density microarrays. Whereas earlier studies employed a range of Affymetrix SNP microarrays comprising from 10 K to 500 K SNPs, this most recent investigation used the 6.0 chip which displays 906,600 SNP probes and 946,000 probes for the interrogation of CNVs (copy number variations). The genotyping assay using the Affymetrix SNP 6.0 array is highly demanding on sample quality due to the small feature size, low redundancy, and lack of mismatch probes. Findings In the first study published so far using this microarray on pooled DNA, we found that pooled cheek swab DNA could not accurately predict real allele frequencies of the samples that comprised the pools. In contrast, the allele frequency estimates using blood DNA pools were reasonable, although inferior compared to those obtained with previously employed Affymetrix microarrays. However, it might be possible to improve performance by developing improved analysis methods. Conclusions Despite the decreasing costs of genome-wide individual genotyping, the pooling approach may have applications in very large-scale case-control association studies. In such cases, our study suggests that high-quality DNA preparations and lower density platforms should be preferred.

Abstract 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.

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts. [ABSTRACT FROM AUTHOR]