Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function

AbstractFor most complex traits, known genetic associations only explain a small fraction of the narrow sense heritability prompting intense debate on the genetic basis of complex traits. Joint analysis of all common variants together explains much of this missing heritability and reveals that large numbers of weakly associated loci are enriched in regulatory regions, but fails to identify specific regions or biological pathways. Here, we use epigenomic annotations across 127 tissues and cell types to investigate weak regulatory associations, the specific enhancers they reside in, their downstream target genes, their upstream regulators, and the biological pathways they disrupt in eight common diseases. We show weak associations are significantly enriched in disease-relevant regulatory regions across thousands of independent loci. We develop methods to control for LD between weak associations and overlap between annotations. We show that weak non-coding associations are additionally enriched in relevant biological pathways implicating additional downstream target genes and upstream disease-specific master regulators. Our results can help guide the discovery of biologically meaningful, but currently undetectable regulatory loci underlying a number of common diseases.

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Molecular Stratification of Chronic Kidney Disease

10.1101/2021.09.09.21263234 ◽

2021 ◽

Author(s):

Anna Reznichenko ◽

Viji Nair ◽

Sean Eddy ◽

Mark Tomilo ◽

Timothy Slidel ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Cell Types ◽

Biological Pathways ◽

Self Organizing Maps ◽

Molecular Stratification ◽

Indirect Measures ◽

Genome Wide ◽

Neural Network Algorithm

Current classification of chronic kidney disease (CKD) into stages based on the indirect measures of kidney functional state, estimated glomerular filtration rate and albuminuria, is agnostic to the heterogeneity of underlying etiologies, histopathology, and molecular processes. We used genome-wide transcriptomics from patients kidney biopsies, directly reflecting kidney biological processes, to stratify patients from three independent CKD cohorts. Unsupervised Self-Organizing Maps (SOM), an artificial neural network algorithm, assembled CKD patients into four novel subgroups, molecular categories, based on the similarity of their kidney transcriptomics profiles. The unbiased, molecular categories were present across CKD stages and histopathological diagnoses, highlighting heterogeneity of conventional clinical subgroups at the molecular level. CKD molecular categories were distinct in terms of biological pathways, transcriptional regulation and associated kidney cell types, indicating that the molecular categorization is founded on biologically meaningful mechanisms. Importantly, our results revealed that not all biological pathways are equally activated in all patients; instead, different pathways could be more dominant in different subgroups and thereby differentially influencing disease progression and outcomes. This first kidney-centric unbiased categorization of CKD paves the way to an integrated clinical, morphological and molecular diagnosis. This is a key step towards enabling precision medicine for this heterogeneous condition with the potential to advance biological understanding, clinical management, and drug development, as well as establish a roadmap for molecular reclassification of CKD and other complex diseases.

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Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

10.1101/2021.07.05.450786 ◽

2021 ◽

Author(s):

Milton Pividori ◽

Sumei Lu ◽

Binglan Li ◽

Chun Su ◽

Matthew E. Johnson ◽

...

Keyword(s):

Gene Expression ◽

Association Studies ◽

Expression Patterns ◽

Cell Types ◽

Joint Analysis ◽

Genetic Associations ◽

Disease Etiology ◽

Mediating Role ◽

Gene Modules ◽

Trait Associations

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

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Integration of GWAS Summary Statistics and Gene Expression Reveals Target Cell Types Underlying Kidney Function Traits

Journal of the American Society of Nephrology ◽

10.1681/asn.2020010051 ◽

2020 ◽

Vol 31 (10) ◽

pp. 2326-2340 ◽

Cited By ~ 2

Author(s):

Yong Li ◽

Stefan Haug ◽

Pascal Schlosser ◽

Alexander Teumer ◽

Adrienne Tin ◽

...

Keyword(s):

Gene Expression ◽

Kidney Function ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Rna Seq ◽

Cell Type ◽

Gene Set Enrichment ◽

Gene Set

BackgroundGenetic variants identified in genome-wide association studies (GWAS) are often not specific enough to reveal complex underlying physiology. By integrating RNA-seq data and GWAS summary statistics, novel computational methods allow unbiased identification of trait-relevant tissues and cell types.MethodsThe CKDGen consortium provided GWAS summary data for eGFR, urinary albumin-creatinine ratio (UACR), BUN, and serum urate. Genotype-Tissue Expression Project (GTEx) RNA-seq data were used to construct the top 10% specifically expressed genes for each of 53 tissues followed by linkage disequilibrium (LD) score–based enrichment testing for each trait. Similar procedures were performed for five kidney single-cell RNA-seq datasets from humans and mice and for a microdissected tubule RNA-seq dataset from rat. Gene set enrichment analyses were also conducted for genes implicated in Mendelian kidney diseases.ResultsAcross 53 tissues, genes in kidney function–associated GWAS loci were enriched in kidney (P=9.1E-8 for eGFR; P=1.2E-5 for urate) and liver (P=6.8·10-5 for eGFR). In the kidney, proximal tubule was enriched in humans (P=8.5E-5 for eGFR; P=7.8E-6 for urate) and mice (P=0.0003 for eGFR; P=0.0002 for urate) and confirmed as the primary cell type in microdissected tubules and organoids. Gene set enrichment analysis supported this and showed enrichment of genes implicated in monogenic glomerular diseases in podocytes. A systematic approach generated a comprehensive list of GWAS genes prioritized by cell type–specific expression.ConclusionsIntegration of GWAS statistics of kidney function traits and gene expression data identified relevant tissues and cell types, as a basis for further mechanistic studies to understand GWAS loci.

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Uterine sarcomas, insight into its risk factors: A systematic review.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e23540 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e23540-e23540

Author(s):

Caroline Hana ◽

Gina Z. D'Amato

Keyword(s):

Risk Factors ◽

Malignant Tumors ◽

Cell Types ◽

Age At Menarche ◽

Clear Correlation ◽

Genetic Associations ◽

Case Control Studies ◽

Retrospective Case ◽

Uterine Sarcomas ◽

Mesh Terms

e23540 Background: Uterine sarcomas are rare and aggressive malignant tumors that arise from the smooth muscle or connective tissue cells of the uterus. They account for approximately 1% of the female tract malignancies and 3% of all malignant tumors of the uterus. The risk factors that would predispose to uterine sarcomas have always been unknown, with no clear correlation with specific etiologies. This review is to investigate the risk factors that have been looked at by prior studies to have a better understanding of the population at higher risk for uterine sarcomas. Methods: A total of 88 studies were obtained via Pubmed search. The search terms included: Uterus, sarcoma, and risk factors. We also included reviews with the following mesh terms: Uterus, Sarcoma, Etiology and factors. We reviewed studies in English (n = 75) and French (n = 4) languages, human studies and comprising adults. We included only studies with electronic copies. Retrospective, case-control studies and systematic reviews were included. Results: Of the 88 studies, 18 mentioned risk factors and demographic characteristics of the population with uterine sarcomas. Some studies have shown an association with the tumor size – especially more than or equal to 8cm-, uterine weight, patient’s age being > 45-50 years old, age at menarche, obesity, tamoxifen use, previous pelvic irradiation, as well as genetic associations. Mixed evidence has been found regarding the role of the ethnicity, age at menopause, age at first live birth and exogenous estrogen use. Breast cancer has been linked to more aggressive histologic cell types. Conclusions: Given its relatively rare incidence, uterine sarcoma has been mysterious in regards to its risk factors. The results of this review highlight the need for further studies to investigate the risk factors and exposures that may lead to development of uterine sarcomas, as well as linking them to the different histologic types.

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Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for twelve immune-mediated diseases

10.1101/2020.01.15.907436 ◽

2020 ◽

Cited By ~ 4

Author(s):

Kousik Kundu ◽

Alice L. Mann ◽

Manuel Tardaguila ◽

Stephen Watt ◽

Hannes Ponstingl ◽

...

Keyword(s):

Fine Mapping ◽

Molecular Mechanisms ◽

Immune Cell ◽

Meta Analysis ◽

Cell Types ◽

Genetic Associations ◽

Sequencing Data ◽

Immune Mediated ◽

Causal Genes ◽

Causal Variants

AbstractThe identification of causal genetic variants for common diseases improves understanding of disease biology. Here we use data from the BLUEPRINT project to identify regulatory quantitative trait loci (QTL) for three primary human immune cell types and use these to fine-map putative causal variants for twelve immune-mediated diseases. We identify 340 unique, non major histocompatibility complex (MHC) disease loci that colocalise with high (>98%) posterior probability with regulatory QTLs, and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping applied to regulatory QTLs yields smaller credible set sizes and higher posterior probabilities for candidate causal variants compared to disease summary statistics. We also describe a systematic under-representation of insertion/deletion (INDEL) polymorphisms in credible sets derived from publicly available disease meta-analysis when compared to QTLs based on genome-sequencing data. Overall, our findings suggest that fine-mapping applied to disease-colocalising regulatory QTLs can enhance the discovery of putative causal disease variants and provide insights into the underlying causal genes and molecular mechanisms.

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Genetic Analyses of Blood Cell Structure for Biological and Pharmacological Inference

10.1101/2020.01.30.927483 ◽

2020 ◽

Author(s):

Parsa Akbari ◽

Dragana Vuckovic ◽

Tao Jiang ◽

Kousik Kundu ◽

Roman Kreuzhuber ◽

...

Keyword(s):

Flow Cytometry ◽

Drug Targets ◽

Complex Disease ◽

Cell Function ◽

Disease Risk ◽

Secretory Granules ◽

Cell Structure ◽

Cell Types ◽

Nucleic Acid Content ◽

Genetic Associations

SUMMARYThousands of genetic associations with phenotypes of blood cells are known, but few are with phenotypes relevant to cell function. We performed GWAS of 63 flow-cytometry phenotypes, including measures of cell granularity, nucleic acid content, and reactivity, in 39,656 participants in the INTERVAL study, identifying 2,172 variant-trait associations. These include associations mediated by functional cellular structures such as secretory granules, implicated in vascular, thrombotic, inflammatory and neoplastic diseases. By integrating our results with epigenetic data and with signals from molecular abundance/disease GWAS, we infer the hematopoietic origins of population phenotypic variation and identify the transcription factor FOG2 as a regulator of platelet α-granularity. We show how flow cytometry genetics can suggest cell types mediating complex disease risk and suggest efficacious drug targets, presenting Daclizumab/Vedolizumab in autoimmune disease as positive controls. Finally, we add to existing evidence supporting IL7/IL7-R as drug targets for multiple sclerosis.

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Large-scale multivariate multi-ancestry Interaction analyses point towards different genetic mechanisms by population and exposure

10.1101/562157 ◽

2019 ◽

Author(s):

Vincent Laville ◽

Timothy Majarian ◽

Yun J Sung ◽

Karen Schwander ◽

Mary F Feitosa ◽

...

Keyword(s):

Complex Traits ◽

Large Scale ◽

Biological Significance ◽

Genetic Effect ◽

Cell Types ◽

Interaction Effects ◽

Phenotypic Variance ◽

Genetic Associations ◽

Genetic Heritability ◽

Meta Analyses

AbstractTheCHARGE Gene-Lifestyle Interactions Working Groupis a unique initiative formed to improve our understanding of the role and biological significance of gene-environment interactions in human traits and diseases. The consortium published several multi-ancestry genome-wide interaction studies (GWIS) involving up to 610,475 individuals for three lipids and four blood pressure traits while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and GxE interactions across phenotype-exposure-population trios, and to derive new insights on the potential mechanistic underlying GxE through in-silico functional analyses. Our comparative analysis shows first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted a negligible correlation between main and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell-types. In these analyses, we found multiple instances of heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work we identified potential biases in methods used to jointly meta-analyses genetic and interaction effects. We performed a series of simulations to characterize these limitations and to provide the community with guideline for future GxE studies.

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scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures

10.1101/2020.01.15.907592 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yan Zhang ◽

Yaru Zhang ◽

Jun Hu ◽

Ji Zhang ◽

Fangjie Guo ◽

...

Keyword(s):

Data Analysis ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Biological Pathways ◽

Rna Seq ◽

Web Tool ◽

Functional Interpretation ◽

Cell Functions ◽

Pathway Activation

ABSTRACTThe most fundamental challenge in current single-cell RNA-seq data analysis is functional interpretation and annotation of cell clusters. The biological pathways in distinct cell types have different activation patterns, which facilitates understanding cell functions in single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptomic data analysis based on prior biological pathway knowledge. Here, we introduce scTPA (http://sctpa.bio-data.cn/sctpa), which is a web-based platform providing pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Together, scTPA is a comprehensive tool to identify pathway activation signatures for dissecting single cell heterogeneity.

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