Disentangling effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex trait loci

Gosia Trynka, Harm-Jan Westra, Kamil Slowikowski, Xinli Hu, Han Xu, Barbara E Stranger, Buhm Han, Soumya Raychaudhuri
doi: http://dx.doi.org/10.1101/009258

Identifying genomic annotations that differentiate causal from associated variants is critical to fine-map disease loci. While many studies have identified non-coding annotations overlapping disease variants, these annotations colocalize, complicating fine-mapping efforts. We demonstrate that conventional enrichment tests are inflated and cannot distinguish causal effects from colocalizing annotations. We developed a sensitive and specific statistical approach that is able to identify independent effects from colocalizing annotations. We first confirm that gene regulatory variants map to DNase-I hypersensitive sites (DHS) near transcription start sites. We then show that (1) 15-35% of causal variants within disease loci map to DHS independent of other annotations; (2) breast cancer and rheumatoid arthritis loci harbor potentially causal variants near the summits of histone marks rather than full peak bodies; and (3) variants associated with height are highly enriched for embryonic stem cell DHS sites. We highlight specific loci where we can most effectively prioritize causal variation.

Century-scale methylome stability in a recently diverged Arabidopsis thaliana lineage

Joerg Hagmann, Claude Becker, Jonas Müller, Oliver Stegle, Rhonda C Meyer, Korbinian Schneeberger, Joffrey Fitz, Thomas Altmann, Joy Bergelson, Karsten Borgwardt, Detlef Weigel
doi: http://dx.doi.org/10.1101/009225

There has been much excitement about the possibility that exposure to specific environments can induce an ecological memory in the form of whole-sale, genome-wide epigenetic changes that are maintained over many generations. In the model plant Arabidopsis thaliana, numerous heritable DNA methylation differences have been identified in greenhouse-grown isogenic lines, but it remains unknown how natural, highly variable environments affect the rate and spectrum of such changes. Here we present detailed methylome analyses in a geographically dispersed A. thaliana population that constitutes a collection of near-isogenic lines, diverged for at least a century from a common ancestor. We observed little DNA methylation divergence whole-genome wide. Nonetheless, methylome variation largely reflected genetic distance, and was in many aspects similar to that of lines raised in uniform conditions. Thus, even when plants are grown in varying and diverse natural sites, genome-wide epigenetic variation accumulates in a clock-like manner, and epigenetic divergence thus parallels the pattern of genome-wide DNA sequence divergence.

Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell type-specific expression

Maxwell W Libbrecht, Ferhat Ay, Michael M Hoffman, David M Gilbert, Jeffrey A Bilmes, William Stafford Noble
doi: http://dx.doi.org/10.1101/009209

The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation, in which regions of hundreds or thousands of kilobases known as domains are regulated as a unit. Previous studies using genomics assays such as chromatin immunoprecipitation (ChIP)-seq and chromatin conformation capture (3C)-based assays have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods can incorporate only data sets that can be expressed as a one-dimensional vector over the genome and therefore cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a comprehensive model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly-regulated genes expressed in only a small number of cell types, which we term “specific expression domains.” We additionally found that a subset of domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used for the seemingly unrelated task of transferring information from well-studied cell types to less well characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data.

Methods for Joint Imaging and RNA-seq Data Analysis

Junhai Jiang, Nan Lin, Shicheng Guo, Jinyun Chen, Momiao Xiong
(Submitted on 13 Sep 2014)

Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimension (2DFPCA) for representing imaging data and develop a multiple functional linear model (MFLM) in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA-seq data of ovarian cancer and KIRC studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoform of gene expressions.

Average genome size estimation enables accurate quantification of gene family abundance and sheds light on the functional ecology of the human microbiome

Stephen Nayfach, Katherine S Pollard
doi: http://dx.doi.org/10.1101/009001

Average genome size (AGS) is an important, yet often overlooked property of microbial communities. We developed MicrobeCensus to rapidly and accurately estimate AGS from short-read metagenomics data and applied our tool to over 1,300 human microbiome samples. We found that AGS differs significantly within and between body sites and tracks with major functional and taxonomic differences. For example, in the gut, AGS ranges from 2.5 to 5.8 megabases and is positively correlated with the abundance of Bacteroides and polysaccharide metabolism. Furthermore, we found that AGS variation can bias comparative analyses, and that normalization improves detection of differentially abundant genes.

Scalable Genomics with R and Bioconductor
Michael Lawrence, Martin Morgan
Journal-ref: Statistical Science 2014, Vol. 29, No. 2, 214-226
Subjects: Genomics (q-bio.GN); Distributed, Parallel, and Cluster Computing (cs.DC)

This paper reviews strategies for solving problems encountered when analyzing large genomic data sets and describes the implementation of those strategies in R by packages from the Bioconductor project. We treat the scalable processing, summarization and visualization of big genomic data. The general ideas are well established and include restrictive queries, compression, iteration and parallel computing. We demonstrate the strategies by applying Bioconductor packages to the detection and analysis of genetic variants from a whole genome sequencing experiment.

Accurate Liability Estimation Substantially Improves Power in Ascertained Case Control Studies

Omer Weissbrod, Christoph Lippert, Dan Geiger, David Heckerman
(Submitted on 8 Sep 2014)

Future genome wide association studies (GWAS) of diseases will include hundreds of thousands of individuals in order to detect risk variants with small effect sizes. Such samples are susceptible to confounding, which can lead to spurious results. Recently, linear mixed models (LMMs) have emerged as the method of choice for GWAS, due to their robustness to confounding. However, the performance of LMMs in case-control studies deteriorates with increasing sample size, resulting in reduced power. This loss of power can be remedied by transforming observed case-control status to liability space, wherein each individual is assigned a score corresponding to severity of phenotype. We propose a novel method for estimating liabilities, and demonstrate that testing for associations with estimated liabilities by way of an LMM leads to a substantial power increase. The proposed framework enables testing for association in ascertained case-control studies, without suffering from reduced power, while remaining resilient to confounding. Extensive experiments on synthetic and real data demonstrate that the proposed framework can lead to an average increase of over 20 percent for test statistics of causal variants, thus dramatically improving GWAS power.

Mixed Model with Correction for Case-Control Ascertainment Increases Association Power

tristan hayeck, Noah Zaitlen, Po-Ru Loh, Bjarni Vilhjalmsson, Samuela Pollack, Alexander Gusev, Jian Yang, Guo-Bo Chen, Michael E. Goddard, Peter M. Visscher, Nick Patterson, Alkes Price
doi: http://dx.doi.org/10.1101/008755

We introduce a Liability Threshold Mixed Linear Model (LTMLM) association statistic for ascertained case-control studies that increases power vs. existing mixed model methods, with a well-controlled false-positive rate. Recent work has shown that existing mixed model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem using a chi-square score statistic computed from posterior mean liabilities (PML) under the liability threshold model. Each individual’s PML is conditional not only on that individual’s case-control status, but also on every individual’s case-control status and on the genetic relationship matrix obtained from the data. The PML are estimated using a multivariate Gibbs sampler, with the liability-scale phenotypic covariance matrix based on the genetic relationship matrix (GRM) and a heritability parameter estimated via Haseman-Elston regression on case-control phenotypes followed by transformation to liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed model methods in all scenarios tested, with the magnitude of the improvement depending on sample size and severity of case-control ascertainment. In a WTCCC2 multiple sclerosis data set with >10,000 samples, LTMLM was correctly calibrated and attained a 4.1% improvement (P=0.007) in chi-square statistics (vs. existing mixed model methods) at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, an increase in power over existing mixed model methods is available for ascertained case-control studies of diseases with low prevalence.

MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

Mark D Robinson, Abdullah Kahraman, Charity W Law, Helen Lindsay, Malgorzata Nowicka, Lukas M Weber, Xiaobei Zhou
doi: http://dx.doi.org/10.1101/007120

DNA methylation, and specifically the reversible addition of methyl groups at CpG dinucleotides genome-wide, represents an important layer that is associated with the regulation of gene expression. In particular, aberrations in the methylation status have been noted across a diverse set of pathological states, including cancer. With the rapid development and uptake of large scale sequencing of short DNA fragments, there has been an explosion of data analytic methods for processing and discovering changes in DNA methylation across diverse data types. In this mini-review, we aim to condense many of the salient challenges, such as experimental design, statistical methods for differential methylation detection and critical considerations such as cell type composition and the potential confounding that can arise from batch effects, into a compact and accessible format. Our main interests, from a statistical perspective, include the practical use of empirical Bayes or hierarchical models, which have been shown to be immensely powerful and flexible in genomics and the procedures by which control of false discoveries are made. Of course, there are many critical platform-specific data preprocessing aspects that we do not discuss here. In addition, we do not make formal performance comparisons of the methods, but rather describe the commonly used statistical models and many of the pertinent issues; we make some recommendations for further study.

Determination of Nonlinear Genetic Architecture using Compressed Sensing

Chiu Man Ho, Stephen D.H. Hsu
(Submitted on 27 Aug 2014)

We introduce a statistical method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. The computational and data resource requirements are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. Our method uses a generalization of compressed sensing (L1-penalized regression) applied to nonlinear functions of the sensing matrix. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using both real and simulated human genomes.