Biology subjects

Tuuli Lappalainen

Publications and source records attributed to Tuuli Lappalainen.

7 recordsLinked to original sources

bioRxiv2016

Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change

Mapping cis-acting expression quantitative trait loci (cis-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. However, measures used for quantifying the effect size of cis-eQTLs have been inconsistent and poorly defined. In this paper, we describe log allelic fold change (aFC) as a biologically interpretable and mathematically convenient unit that represents the magnitude of expression change associated with a given genetic variant. This measure is mathematically independent from expression level and allele frequency, applicable to multi-allelic variants, and generalizable to multiple independent variants. We provide tools and guidelines for estimating aFC from eQTL and allelic expression data sets, and apply it to GTEx data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx, and show that in nearly all cases these eQTLs are independent in their regulatory activity. In summary, aFC is a solid measure of cis-regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.

Bioinformatics↗

bioRxiv2016

Landscape of X chromosome inactivation across human tissues

X chromosome inactivation (XCI) silences the transcription from one of the two X chromosomes in mammalian female cells to balance expression dosage between XX females and XY males. XCI is, however, characteristically incomplete in humans: up to one third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of \"escape\" from inactivation varying between genes and individuals1,2 (Fig. 1). However, the extent to which XCI is shared between cells and tissues remains poorly characterized3,4, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression5 and phenotypic traits6. Here we report a systematic survey of XCI using a combination of over 5,500 transcriptomes from 449 individuals spanning 29 tissues, and 940 single-cell transcriptomes, integrated with genomic sequence data (Fig. 1). By combining information across these data types we show that XCI at the 683 X-chromosomal genes assessed is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven new escape genes supported by multiple lines of evidence, and demonstrate that escape from XCI results in sex biases in gene expression, thus establishing incomplete XCI as a likely mechanism introducing phenotypic diversity6,7. Overall, this updated catalogue of XCI across human tissues informs our understanding of the extent and impact of the incompleteness in the maintenance of XCI.\n\nO_FIG O_LINKSMALLFIG WIDTH=114 HEIGHT=200 SRC=\"FIGDIR/small/073957_fig1.gif\" ALT=\"Figure 1\">\nView larger version (33K):\norg.highwire.dtl.DTLVardef@1c902f7org.highwire.dtl.DTLVardef@1e1b14dorg.highwire.dtl.DTLVardef@3f56feorg.highwire.dtl.DTLVardef@687c65_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Schematic overview of the study. Previous expression-based surveys of XCI1,2 have established the incomplete and variable nature of XCI, showing that XCI is incomplete for up to 30% of X-chromosomal genes, but these studies have been limited in the tissue types and samples assessed. To investigate the landscape of XCI across human tissues, we combined three approaches: 1) sex-biases in expression using population-level GTEx data across 29 tissue types, 2) allelic expression in 16 tissue samples from a female GTEx donor with fully skewed XCI, and 3) validation using single cell RNA-seq by combining allelic expression and genotype phasing. WGS, whole genome sequencing; WES, whole exome sequencing; scRNA-seq, single-cell RNA-seq.\n\nC_FIG

Genomics↗

bioRxiv2016

Distant regulatory effects of genetic variation in multiple human tissues

Understanding the genetics of gene regulation provides information on the cellular mechanisms through which genetic variation influences complex traits. Expression quantitative trait loci, or eQTLs, are enriched for polymorphisms that have been found to be associated with disease risk. While most analyses of human data has focused on regulation of expression by nearby variants (cis-eQTLs), distal or trans-eQTLs may have broader effects on the transcriptome and important phenotypic consequences, necessitating a comprehensive study of the effects of genetic variants on distal gene transcription levels. In this work, we identify trans-eQTLs in the Genotype Tissue Expression (GTEx) project data1, consisting of 449 individuals with RNA-sequencing data across 44 tissue types. We find 81 genes with a trans-eQTL in at least one tissue, and we demonstrate that trans-eQTLs are more likely than cis-eQTLs to have effects specific to a single tissue. We evaluate the genomic and functional properties of trans-eQTL variants, identifying strong enrichment in enhancer elements and Piwi-interacting RNA clusters. Finally, we describe three tissue-specific regulatory loci underlying relevant disease associations: 9q22 in thyroid that has a role in thyroid cancer, 5q31 in skeletal muscle, and a previously reported master regulator near KLF14 in adipose. These analyses provide a comprehensive characterization of trans-eQTLs across human tissues, which contribute to an improved understanding of the tissue-specific cellular mechanisms of regulatory genetic variation.

Genomics↗

bioRxiv2016

Rare variant phasing and haplotypic expression from RNA-sequencing with phASER

Haplotype phasing of genetic variants is important for clinical interpretation of the genome, population genetic analysis, and functional genomic analysis of allelic activity. Here we present phASER, a fast and accurate approach for phasing variants that are overlapped by sequencing reads, including those from RNA-sequencing (RNA-seq), which often span multiple exons due to splicing. This provides 1) dramatically more accurate phasing of rare and de novo variants compared to population-based phasing; 2) phasing of variants in the same gene up to hundreds of kilobases away which cannot be obtained from DNA-sequencing reads; 3) high confidence measures of haplotypic expression, greatly improving power for allelic expression studies.

Genetics↗

bioRxiv2015

Tools and best practices for allelic expression analysis

Allelic expression (AE) analysis has become an important tool for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. In this paper, we systematically analyze the properties of AE read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting and filtering for such errors, and show that the resulting AE data has extremely low technical noise. Finally, we introduce novel software for high-throughput production of AE data from RNA-sequencing data, implemented in the GATK framework. These improved tools and best practices for AE analysis yield higher quality AE data by reducing technical bias. This provides a practical framework for wider adoption of AE analysis by the genomics community.

Genomics↗

bioRxiv2014

Transcriptome Sequencing Reveals Widespread Gene-Gene and Gene-Environment Interactions

Understanding the genetic architecture of gene expression is an intermediate step to understand the genetic architecture of complex diseases. RNA-seq technologies have improved the quantification of gene expression and allow to measure allelic specific expression (ASE)1-3. ASE is hypothesized to result from the direct effect of cis regulatory variants, but a proper estimation of the causes of ASE has not been performed to date. In this study we take advantage of a sample of twins to measure the relative contribution of genetic and environmental effects on ASE and we found substantial effects of gene x gene (GxG) and gene x environment (GxE) interactions. We propose a model where ASE requires genetic variability in cis, a difference in the sequence of both alleles, but the magnitude of the ASE effect depends on trans genetic and environmental factors that interact with the cis genetic variants. We uncover large GxG and GxE effects on gene expression and likely complex phenotypes that currently remain elusive.

Genomics↗

bioRxiv2014

Assessing allele specific expression across multiple tissues from RNA-seq read data

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 project (GTEx) 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: MAMBA software: http://birch.well.ox.ac.uk/~rivas/mamba/ R source code and data examples: http://www.iki.fi/mpirinen/ Contact: matti.pirinen@helsinki.fi rivas@well.ox.ac.uk

Bioinformatics↗