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Soumya Raychaudhuri and colleagues report a broadly applicable method that uses chromatin marks, specifically H3K4me3, to identify critical cell types to fine map complex trait variants.
Peter Donnelly and colleagues report an analysis considering the inclusion of non-confounding covariates within genome-wide association studies and provide software that can be used to assess the impact on power within a particular study. They find that, when the disease prevalence is low, including known covariates, such as sex or established genetic associations, can reduce the power to detect new associations.
Bogdan Pasaniuc, David Reich, Alkes Price and colleagues report analyses considering the potential of genome-wide association studies (GWAS) based on extremely low-coverage sequence data sets combined with imputation using data sets from the 1000 Genomes Project. They show with simulations and real exome-sequencing data that low-coverage sequencing can increase power for GWAS relative to genotyping arrays.
Eli Stahl, Robert Plenge and colleagues report the application of a polygenic analysis, using a Bayesian inference framework, to rheumatoid arthritis GWAS datasets. They find that polygenic risk scores are associated with rheumatoid arthritis case-control status and estimate the total variance explained by common variants in these GWAS. They show comparable estimates for applications to GWAS for celiac disease, myocardial infarction and coronary artery disease and type 2 diabetes.
Peter Visscher and colleagues report a new method for approximate conditional and joint association analysis that makes use of summary statistics from meta-analysis of GWAS. They apply this to meta-analysis summary data for height, body mass index and type 2 diabetes.
Naomi Wray, Peter Visscher and colleagues report analyses of the common variation that contributes to schizophrenia risk within three independent case-control datasets from the Psychiatric GWAS Consortium for schizophrenia. They estimate that 23% of the variation in liability to schizophrenia is captured by SNPs on current platforms.
Gil McVean and Iain Mathieson report an analysis of the differential effects of population stratification on rare and common variants within association studies. They find that rare variants may show stronger stratification in some situations and that this is not corrected for by current structure methods, suggesting the need for the development of new statistical methods.
Brian Oliver, Jason Lieb, Christine Disteche and colleagues present an analysis of expression data in mammals, C. elegans and Drosophila. They conclude that dosage compensation corrects the imbalance in the number of X chromosomes relative to autosomes by upregulating X-linked genes in both males and females.
Amos Tanay and Eitan Yaffe report methods to correct biases in the Hi-C method for mapping chromosomal contacts on a genome-wide scale. Their analysis of Hi-C data shows interchromosomal aggregation of hypersensitive sites, transcriptionally active foci and other epigenetic markers of active chromatin.
Peter Visscher and colleagues report an analysis to partition the genetic variation for several complex traits onto chromosome segments and find that the variation explained is approximately proportional to the total length of genes included within a chromosome segment. They estimate that ~45%, ~17%, ~25% and ~21% of the phenotypic variation, respectively, for height, body mass index, von Willebrand factor and QT interval is tagged by common SNPs, and they partition this variation by chromosome and chromosome segments.
George Patrinos and colleagues report the first implementation of the microattribution approach to systematically document genetic variation associated with a disease, applied here to hemoglobinopathies and thalassemias. They developed a series of connected locus-specific databases that document genotype and phenotype information for genetic variation in 37 globin and erythroid protein genes in individuals with globin disorders, with reciprocal attribution to data contributors.
Johan Paulsson and Dann Huh report a mathematical modeling analysis proposing that stochastic partitioning errors during cell division contribute to non-genetic heterogeneity between cells in a population. They find that fluctuations arising from such partitioning errors are difficult to suppress and can mimic noise in gene expression.
Jianzhi Zhang and Xionglei He report analyses of published RNA sequencing data examining relative expression levels between genes located on the X chromosome and genes located on autosomes. Unlike previous reports of dosage compensation between the X chromosome and autosomes, their analyses detect an X:autosome expression ratio of ∼0.5.
Nilanjan Chatterjee and colleagues report an analysis of the number and effect size distribution of susceptibility variants identified from current genome-wide association studies. They estimate the number of susceptibility loci expected to be discovered by GWAS over a range of sample sizes and compare to recent findings from GWAS for height, Crohn's disease and several cancers.
Peter Visscher and colleagues report an analysis of the heritability explained by common variants identified through genome-wide association studies. They find that 45% of the variance for height can be explained by using a linear model to simultaneously consider the combined effect of common SNPs.
Four teams of analysts attempted exact reproduction of results of 18 microarray experiments published in the journal in 2005–2006 using the data and analytical methods detailed in the original publications. In addition to MIAME criteria, the authors recommend publication of an explicit record of the analytical protocols used.
Lars Bertram and colleagues report the creation of an online database, SzGene, containing all published genetic association studies for schizophrenia. A series of meta-analyses reveals 24 variants in 16 genes to be associated with the disease with nominal significance, and four of these have strong epidemiological support.