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Kaur Alasoo

Bio: Kaur Alasoo is an academic researcher from University of Tartu. The author has contributed to research in topics: Biology & Induced pluripotent stem cell. The author has an hindex of 12, co-authored 25 publications receiving 1043 citations. Previous affiliations of Kaur Alasoo include Instituto Superior Técnico & Wellcome Trust Sanger Institute.

Papers
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Journal ArticleDOI
15 Jun 2017-Nature
TL;DR: This study outlines the major sources of genetic and phenotypic variation in iPS cells and establishes their suitability as models of complex human traits and cancer.
Abstract: Technology utilizing human induced pluripotent stem cells (iPS cells) has enormous potential to provide improved cellular models of human disease. However, variable genetic and phenotypic characterization of many existing iPS cell lines limits their potential use for research and therapy. Here we describe the systematic generation, genotyping and phenotyping of 711 iPS cell lines derived from 301 healthy individuals by the Human Induced Pluripotent Stem Cells Initiative. Our study outlines the major sources of genetic and phenotypic variation in iPS cells and establishes their suitability as models of complex human traits and cancer. Through genome-wide profiling we find that 5-46% of the variation in different iPS cell phenotypes, including differentiation capacity and cellular morphology, arises from differences between individuals. Additionally, we assess the phenotypic consequences of genomic copy-number alterations that are repeatedly observed in iPS cells. In addition, we present a comprehensive map of common regulatory variants affecting the transcriptome of human pluripotent cells.

462 citations

Journal ArticleDOI
TL;DR: Analysis of chromatin accessibility and expression quantitative trait loci in stimulated or naïve macrophages identifies loci that constitutively alter chromatin but affect expression only after stimulation, thus indicating an effect on enhancer priming.
Abstract: Regulatory variants are often context specific, modulating gene expression in a subset of possible cellular states. Although these genetic effects can play important roles in disease, the molecular mechanisms underlying context specificity are poorly understood. Here, we identified shared quantitative trait loci (QTLs) for chromatin accessibility and gene expression in human macrophages exposed to IFNγ, Salmonella and IFNγ plus Salmonella. We observed that ~60% of stimulus-specific expression QTLs with a detectable effect on chromatin altered the chromatin accessibility in naive cells, thus suggesting that they perturb enhancer priming. Such variants probably influence binding of cell-type-specific transcription factors, such as PU.1, which can then indirectly alter the binding of stimulus-specific transcription factors, such as NF-κB or STAT2. Thus, although chromatin accessibility assays are powerful for fine-mapping causal regulatory variants, detecting their downstream effects on gene expression will be challenging, requiring profiling of large numbers of stimulated cellular states and time points.

236 citations

Posted ContentDOI
06 Mar 2022-medRxiv
TL;DR: The power of bottlenecked populations to find unique entry points into the biology of common diseases through low-frequency, high impact variants is demonstrated.
Abstract: Population isolates such as Finland provide benefits in genetic studies because the allelic spectrum of damaging alleles in any gene is often concentrated on a small number of low-frequency variants (0.1<=minor allele frequency<5%), which survived the founding bottleneck, as opposed to being distributed over a much larger number of ultra-rare variants. While this advantage is well-established in Mendelian genetics, its value in common disease genetics has been less explored. FinnGen aims to study the genome and national health register data of 500,000 Finns, already reaching 224,737 genotyped and phenotyped participants. Given the relatively high median age of participants (63 years) and dominance of hospital-based recruitment, FinnGen is enriched for many disease endpoints often underrepresented in population-based studies (e.g. rarer immune-mediated diseases and late onset degenerative and ophthalmologic endpoints). We report here a genome-wide association study (GWAS) of 1,932 clinical endpoints defined from nationwide health registries. We identify genome-wide significant associations at 2,491 independent loci. Among these, finemapping implicates 148 putatively causal coding variants associated with 202 endpoints, 104 with low allele frequency (AF<10%) of which 62 were over two-fold enriched in Finland. We studied a benchmark set of 15 diseases that had previously been investigated in large genome-wide association studies. FinnGen discovery analyses were meta-analysed in Estonian and UK biobanks. We identify 30 novel associations, primarily low-frequency variants strongly enriched, in or specific to, the Finnish population and Uralic language family neighbors in Estonia and Russia. These findings demonstrate the power of bottlenecked populations to find unique entry points into the biology of common diseases through low-frequency, high impact variants. Such high impact variants have a potential to contribute to medical translation including drug discovery.

198 citations

Journal ArticleDOI
TL;DR: It is estimated that recall-by-genotype studies that use iPSC-derived cells will require cells from at least 20–80 individuals to detect the effects of regulatory variants with moderately large effect sizes, despite high differentiation-induced variability.
Abstract: Induced pluripotent stem cells (iPSCs), and cells derived from them, have become key tools for modeling biological processes, particularly in cell types that are difficult to obtain from living donors. Here we present a map of regulatory variants in iPSC-derived neurons, based on 123 differentiations of iPSCs to a sensory neuronal fate. Gene expression was more variable across cultures than in primary dorsal root ganglion, particularly for genes related to nervous system development. Using single-cell RNA-sequencing, we found that the number of neuronal versus contaminating cells was influenced by iPSC culture conditions before differentiation. Despite high differentiation-induced variability, our allele-specific method detected thousands of quantitative trait loci (QTLs) that influenced gene expression, chromatin accessibility, and RNA splicing. On the basis of these detected QTLs, we estimate that recall-by-genotype studies that use iPSC-derived cells will require cells from at least 20-80 individuals to detect the effects of regulatory variants with moderately large effect sizes.

163 citations

Journal ArticleDOI
TL;DR: The eQTL Catalogue as discussed by the authors is a set of gene expression quantitative trait locus (eQTL) studies published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization.
Abstract: Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue ( https://www.ebi.ac.uk/eqtl ), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

122 citations


Cited by
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01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

Journal ArticleDOI
15 Jun 2017-Cell
TL;DR: It is proposed that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways.

2,257 citations

Journal ArticleDOI
TL;DR: This fundamental discrepancy explains why most surface markers identified on in vitro generated macrophages do not translate to the in vivo situation and is justified by comparing the gene lists positively or negatively correlated with the ratio of IL-12 and arginase 1 in transcriptomes of LPS-treated peritoneal macrophades.
Abstract: Macrophages are found in tissues, body cavities, and mucosal surfaces. Most tissue macrophages are seeded in the early embryo before definitive hematopoiesis is established. Others are derived from blood monocytes. The macrophage lineage diversification and plasticity are key aspects of their functionality. Macrophages can also be generated from monocytes in vitro and undergo classical (LPS+IFN-γ) or alternative (IL-4) activation. In vivo, macrophages with different polarization and different activation markers coexist in tissues. Certain mouse strains preferentially promote T-helper-1 (Th1) responses and other Th2 responses. Their macrophages preferentially induce iNOS or arginase and have been called M1 and M2, respectively. In many publications, M1 and classically activated and M2 and alternatively activated are used interchangeably. We tested whether this is justified by comparing the gene lists positively [M1(=LPS+)] or negatively [M2(=LPS-)] correlated with the ratio of IL-12 and arginase 1 in transcriptomes of LPS-treated peritoneal macrophages with in vitro classically (LPS, IFN-γ) versus alternatively activated (IL-4) bone marrow-derived macrophages, both from published datasets. Although there is some overlap between in vivo M1(=LPS+) and in vitro classically activated (LPS+IFNγ) and in vivo M2(=LPS-) and in vitro alternatively activated macrophages, many more genes are regulated in opposite or unrelated ways. Thus, M1(=LPS+) macrophages are not equivalent to classically activated, and M2(=LPS-) macrophages are not equivalent to alternatively activated macrophages. This fundamental discrepancy explains why most surface markers identified on in vitro generated macrophages do not translate to the in vivo situation. Valid in vivo M1/M2 surface markers remain to be discovered.

977 citations

19 Apr 2011
TL;DR: Administration of spermidine markedly extended the lifespan of yeast, flies and worms, and human immune cells and inhibited oxidative stress in ageing mice, and found that enhanced autophagy is crucial for polyamine-induced suppression of necrosis and enhanced longevity.
Abstract: Ageing results from complex genetically and epigenetically programmed processes that are elicited in part by noxious or stressful events that cause programmed cell death Here, we report that administration of spermidine, a natural polyamine whose intracellular concentration declines during human ageing, markedly extended the lifespan of yeast, flies and worms, and human immune cells In addition, spermidine administration potently inhibited oxidative stress in ageing mice In ageing yeast, spermidine treatment triggered epigenetic deacetylation of histone H3 through inhibition of histone acetyltransferases (HAT), suppressing oxidative stress and necrosis Conversely, depletion of endogenous polyamines led to hyperacetylation, generation of reactive oxygen species, early necrotic death and decreased lifespan The altered acetylation status of the chromatin led to significant upregulation of various autophagy-related transcripts, triggering autophagy in yeast, flies, worms and human cells Finally, we found that enhanced autophagy is crucial for polyamine-induced suppression of necrosis and enhanced longevity

974 citations

Journal ArticleDOI
TL;DR: This Review describes different deep learning techniques and how they can be applied to extract biologically relevant information from large, complex genomic data sets.
Abstract: As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing. This Review describes different deep learning techniques and how they can be applied to extract biologically relevant information from large, complex genomic data sets.

685 citations