Fast and efficient QTL mapper for thousands of molecular phenotypes

Ongen, Halit; Buil, Alfonso; Brown, Andrew Anand; Dermitzakis, Emmanouil T.; Delaneau, Olivier

doi:10.1093/bioinformatics/btv722

Cited by 456 publications

(444 citation statements)

References 22 publications

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“…Within each tissue, cis -eQTLs were identified by linear regression, as implemented in FastQTL 71 , adjusting for PEER factors, sex, genotyping platform, and three genotype-based principal components (PCs). We restricted our search to variants within 1 Mb of the TSS of each gene and, in the tissue of analysis, minor allele frequencies ≥0.01 with the minor allele observed in at least 10 samples.…”

Section: Methodsmentioning

confidence: 99%

Genetic effects on gene expression across human tissues

groups

Fund

Nhgri³

et al. 2017

Nature

3,571

2,412

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Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

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Section: Methodsmentioning

confidence: 99%

Genetic effects on gene expression across human tissues

groups

Fund

Nhgri³

et al. 2017

Nature

3,571

2,412

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show abstract

“…To alleviate the effect of gene dosage, we divided each gene’s expression by the tumor/normal gene copy-number ratio (derived from ACEseq), following log 2 -transformation. We then related breakpoint presence/absence patterns with gene expression values using the FastQTL (v2.1) algorithm 56 , by employing a 2 Mb cis -window centered on the TAD’s midpoint. We performed 1,000 permutations with FastQTL for statistical inference 56 .…”

Section: Methodsmentioning

confidence: 99%

The whole-genome landscape of medulloblastoma subtypes

Northcott¹,

Buchhalter²,

Morrissy³

et al. 2017

Nature

843

1,161

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Summary Current therapies for medulloblastoma (MB), a highly malignant childhood brain tumor, impose debilitating effects on the developing child, warranting deployment of molecularly targeted treatments with reduced toxicities. Prior studies failed to disclose the full spectrum of driver genes and molecular processes operative in MB subgroups. Herein, we detail the somatic landscape across 491 sequenced MBs and molecular heterogeneity amongst 1,256 epigenetically analyzed cases, identifying subgroup-specific driver alterations including previously unappreciated actionable targets. Driver mutations explained the majority of Group 3 and Group 4 patients, remarkably enhancing previous knowledge. Novel molecular subtypes were differentially enriched for specific driver events, including hotspot in-frame insertions targeting KBTBD4 and ‘enhancer hijacking’ driving PRDM6 activation. Thus, application of integrative genomics to an unprecedented cohort of clinical samples derived from a single childhood cancer entity disclosed a series of new cancer genes and biologically relevant subtype diversity that represent attractive therapeutic targets for treating MB patients.

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“…In sQTL analyses, this burden is even larger since there are many SNPs tested for each gene. There, the issue of multiple comparisons is usually accounted for by applying a permutation scheme in combination with the false discovery rate (FDR) estimation 27, 32, 35, 46, 48– 50 .…”

Section: Approaches To Ds and Sqtl Analysesmentioning

confidence: 99%

“…sQTLseekeR tests for the association between genotype and transcript composition, using an approach similar to a multivariate analysis of variance (MANOVA) without assuming any probabilistic distribution and Hellinger distance as a dissimilarity measure between transcript ratios. Very recently, LeafCutter 52 gives intron usage quantifications that can be used for both DS analyses (also using the DM model) and sQTL analyses via a correlation-based approach with FastQTL 50 .…”

Section: Approaches To Ds and Sqtl Analysesmentioning

confidence: 99%

“…Additional challenges to be handled in tuQTL analyses include a large number of tests per gene with highly variable allele frequencies (models) and linkage disequilibrium, which can be accounted for in the multiple testing corrections. As in other sQTL studies 35, 49, 50 , we apply a permutation approach to empirically assess the null distribution of associations and use it for the adjustment of nominal p-values (see Supplementary Note 2 in Supplementary File). For computational efficiency, SNPs within a given gene that exhibit the same genotypes are grouped into blocks.…”

Section: Detecting Dtu and Tuqtls With The Dirichlet-multinomial Modelmentioning

confidence: 99%

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DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

2016

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There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.

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Fast and efficient QTL mapper for thousands of molecular phenotypes

Cited by 456 publications

References 22 publications

Genetic effects on gene expression across human tissues

Genetic effects on gene expression across human tissues

The whole-genome landscape of medulloblastoma subtypes

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

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