MOTIVATION: Large genomic datasets combining genotype and sequence data, such as for expression quantitative trait loci (eQTL) detection, require perfect matching between both data types. RESULTS: We described here MBV (Match BAM to VCF); a method to quickly solve sample mislabeling and detect cross-sample contamination and PCR amplification bias. AVAILABILITY AND IMPLEMENTATION: MBV is implemented in C ++ as an independent component of the QTLtools software package, the binary and source codes are freely available at https://qtltools.github.io/qtltools/ . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. RESULTS: We have developed FastQTL, a method that implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted P-values can be estimated at any level of significance with little computational cost. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches. AVAILABILITY AND IMPLEMENTATION: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Motivation: RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data. Results: We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally. Availability and implementation: http://www.well.ox.ac.uk/~rivas/mamba/. R-sources and data examples http://www.iki.fi/mpirinen/ Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: High-throughput sequencing technologies enable the genome-wide analysis of the impact of genetic variation on molecular phenotypes at unprecedented resolution. However, although powerful, these technologies can also introduce unexpected artifacts. RESULTS: We investigated the impact of library amplification bias on the identification of allele-specific (AS) molecular events from high-throughput sequencing data derived from chromatin immunoprecipitation assays (ChIP-seq). Putative AS DNA binding activity for RNA polymerase II was determined using ChIP-seq data derived from lymphoblastoid cell lines of two parent-daughter trios. We found that, at high-sequencing depth, many significant AS binding sites suffered from an amplification bias, as evidenced by a larger number of clonal reads representing one of the two alleles. To alleviate this bias, we devised an amplification bias detection strategy, which filters out sites with low read complexity and sites featuring a significant excess of clonal reads. This method will be useful for AS analyses involving ChIP-seq and other functional sequencing assays. AVAILABILITY: The R package abs filter for library clonality simulations and detection of amplification-biased sites is available from http://updepla1srv1.epfl.ch/waszaks/absfilter

MOTIVATION: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. RESULTS: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants. AVAILABILITY: LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/.

UNLABELLED: Genevar (GENe Expression VARiation) is a database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression. Genevar allows researchers to investigate expression quantitative trait loci (eQTL) associations within a gene locus of interest in real time. The database and application can be installed on a standard computer in database mode and, in addition, on a server to share discoveries among affiliations or the broader community over the Internet via web services protocols. AVAILABILITY: http://www.sanger.ac.uk/resources/software/genevar.

BACKGROUND: High-throughput measurement of allele-specific expression (ASE) is a relatively new and exciting application area for array-based technologies. In this paper, we explore several data sets which make use of Illumina's GoldenGate BeadArray technology to measure ASE. This platform exploits coding SNPs to obtain relative expression measurements for alleles at approximately 1500 positions in the genome. RESULTS: We analyze data from a mixture experiment where genomic DNA samples from pairs of individuals of known genotypes are pooled to create allelic imbalances at varying levels for the majority of SNPs on the array. We observe that GoldenGate has less sensitivity at detecting subtle allelic imbalances (around 1.3 fold) compared to extreme imbalances, and note the benefit of applying local background correction to the data. Analysis of data from a dye-swap control experiment allowed us to quantify dye-bias, which can be reduced considerably by careful normalization. The need to filter the data before carrying out further downstream analysis to remove non-responding probes, which show either weak, or non-specific signal for each allele, was also demonstrated. Throughout this paper, we find that a linear model analysis of the data from each SNP is a flexible modelling strategy that allows for testing of allelic imbalances in each sample when replicate hybridizations are available. CONCLUSIONS: Our analysis shows that local background correction carried out by Illumina's software, together with quantile normalization of the red and green channels within each array, provides optimal performance in terms of false positive rates. In addition, we strongly encourage intensity-based filtering to remove SNPs which only measure non-specific signal. We anticipate that a similar analysis strategy will prove useful when quantifying ASE on Illumina's higher density Infinium BeadChips.