scholarly journals Sharing information between related diseases using Bayesian joint fine mapping increases accuracy and identifies novel associations in six immune mediated diseases

2019 ◽  
Author(s):  
Jennifer L Asimit ◽  
Daniel B Rainbow ◽  
Mary D Fortune ◽  
Nastasiya F Grinberg ◽  
Linda S Wicker ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Disease Risk ◽  
Computationally Efficient ◽  
Specific Expression ◽  
Immune Mediated ◽  
Allele Specific ◽  
Causal Variants ◽  
Molecular Phenotypes ◽  
Disease Analysis ◽  
Individual Disease

AbstractThousands of genetic variants have been associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. We show that stepwise regression, and, to a lesser extent, stochastic search fine mapping can mis-identify as causal, SNPs which jointly tag distinct causal variants. Frequent sharing of causal variants between immune-mediated diseases (IMD) motivated us to develop a computationally efficient multinomial fine-mapping (MFM) approach that borrows information between diseases in a Bayesian framework. We show that MFM has greater accuracy than single disease analysis when shared causal variants exist, and negligible loss of precision otherwise. Applying MFM to data from six IMD revealed causal variants undetected in individual disease analysis, including in IL2RA where we confirm functional effects of multiple causal variants using allele-specific expression in sorted CD4+ T cells from genotype-selected individuals. MFM has the potential to increase fine-mapping resolution in related diseases enabling the identification of associated cellular and molecular phenotypes.

scholarly journals Leveraging allele-specific expression to refine fine-mapping for eQTL studies

2018 ◽  
Author(s):  
Jennifer Zou ◽  
Farhad Hormozdiari ◽  
Brandon Jew ◽  
Jason Ernst ◽  
Jae Hoon Sul ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Reduction Rate ◽  
Genome Wide Association Studies ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific ◽  
Causal Variants

AbstractMany disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. It is hypothesized that these variants affect complex traits by acting as expression quantitative trait loci (eQTLs) that influence expression of nearby genes. This indicates that many causal variants for complex traits are likely to be causal variants for gene expression. Hence, identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allele-specific expression (ASE) that measures imbalance in gene expression due to different alleles. In this work, we develop a novel statistical method that leverages both ASE and eQTL information to detect causal variants that regulate gene expression. We illustrate through simulations and application to the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. In the GTEx dataset, our method achieves the median reduction rate of 11% in the number of putative causal [email protected], [email protected]

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

2020 ◽  
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.

scholarly journals A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yanyu Liang ◽  
François Aguet ◽  
Alvaro N. Barbeira ◽  
Kristin Ardlie ◽  
Hae Kyung Im
Keyword(s):  
Fine Mapping ◽  
Regulatory Elements ◽  
Read Coverage ◽  
Unified Framework ◽  
Specific Expression ◽  
Genetic Studies ◽  
Allele Specific ◽  
Qtl Fine Mapping ◽  
And Performance ◽  
Transcriptomic Studies

AbstractGenetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a unified framework that addresses these needs and is scalable to thousands of samples. Using simulations and data from GTEx, we demonstrate its calibration and performance. For example, mixQTL shows a power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. To showcase the potential of mixQTL, we apply it to 49 GTEx tissues and find 20% additional eQTLs (FDR < 0.05, per tissue) that are significantly more enriched among trait associated variants and candidate cis-regulatory elements comparing to the standard approach.

scholarly journals Fine-mapping of Parkinson’s disease susceptibility loci identifies putative causal variants

2020 ◽  
Author(s):  
Brian M. Schilder ◽  
Towfique Raj
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fine Mapping ◽  
Disease Susceptibility ◽  
Disease Risk ◽  
Association Studies ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Causal Variants ◽  
Underlying Mechanisms

AbstractRecent genome-wide association studies have identified 78 loci associated with Parkinson’s Disease susceptibility but the underlying mechanisms remain largely unclear. To identify variants likely causal for disease risk, we fine-mapped these Parkinson’s-associated loci using four different statistical and functional fine-mapping methods. We then integrated multi-assay cell-type-specific epigenomic profiles to pinpoint the likely mechanism of action of each variant, allowing us to identify Consensus SNPs that disrupt LRRK2 and FCGR2A regulatory elements in microglia, MBNL2 enhancers in oligodendrocytes, and DYRK1A enhancers in neurons. Finally, we confirmed the functional relevance of fine-mapped SNPs using a suite of in silico validation approaches. Together, these results provide a robust list of likely causal variants underlying Parkinson’s Disease risk for further mechanistic studies.

scholarly journals Allele-Specific QTL Fine-Mapping with PLASMA

2019 ◽  
Author(s):  
Austin T. Wang ◽  
Anamay Shetty ◽  
Edward O’Connor ◽  
Connor Bell ◽  
Mark M. Pomerantz ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Small Samples ◽  
Open Chromatin ◽  
Rna Seq ◽  
Fold Reduction ◽  
Wide Range ◽  
Allele Specific ◽  
Qtl Fine Mapping ◽  
Causal Variants ◽  
Credible Set

AbstractAlthough quantitative trait locus (QTL) associations have been identified for many molecular traits such as gene expression, it remains challenging to distinguish the causal nucleotide from nearby variants. In addition to traditional QTLs by association, allele-specific (AS) QTLs are a powerful measure of cis-regulation that are largely concordant with traditional QTLs, and can be less susceptible to technical/environmental noise. However, existing asQTL analysis methods do not produce probabilities of causality for each marker, and do not take into account correlations among markers at a locus in linkage disequilibrium (LD). We introduce PLASMA (PopuLation Allele-Specific MApping), a novel, LD-aware method that integrates QTL and asQTL information to fine-map causal regulatory variants while drawing power from both the number of individuals and the number of allelic reads per individual. We demonstrate through simulations that PLASMA successfully detects causal variants over a wide range of genetic architectures. We apply PLASMA to RNA-Seq data from 524 kidney tumor samples and show that over 17 percent of loci can be fine-mapped to within 5 causal variants, compared less than 2 percent of loci using existing QTL-based fine-mapping. PLASMA furthermore achieves a greater power at 50 samples than conventional QTL fine-mapping does at over 500 samples. Overall, PLASMA achieves a 6.9-fold reduction in median 95% credible set size compared to existing QTL-based fine-mapping. We additionally apply PLASMA to H3K27AC ChIP-Seq from 28 prostate tumor/normal samples and demonstrate that PLASMA is able to prioritize markers even at small samples, with PLASMA achieving a 1.3-fold reduction in median 95% credible set sizes over existing QTL-based fine-mapping. Variants in the PLASMA credible sets for RNA-Seq and ChIP-Seq were enriched for open chromatin and chromatin looping (respectively) at a comparable or greater degree than credible variants from existing methods, while containing far fewer markers. Our results demonstrate how integrating AS activity can substantially improve the detection of causal variants from existing molecular data and at low sample size.

scholarly journals Flashfm: A Flexible and Shared Information Fine-mapping Approach for Multiple Quantitative Traits

2021 ◽  
Author(s):  
Nicolas Hernandez ◽  
Jana Soenksen ◽  
Paul Newcombe ◽  
Manj Sandhu ◽  
Ines Barroso ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Quantitative Traits ◽  
Causal Variant ◽  
Summary Statistics ◽  
Computationally Efficient ◽  
Multiple Traits ◽  
Improve Accuracy ◽  
Shared Information ◽  
Causal Variants ◽  
Related Individuals

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (FLexible And SHared information Fine-Mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.

scholarly journals HLA imputation and its application to genetic and molecular fine-mapping of the MHC region in autoimmune diseases

2021 ◽  
Author(s):  
Tatsuhiko Naito ◽  
Yukinori Okada
Keyword(s):  
Autoimmune Diseases ◽  
Fine Mapping ◽  
Disease Risk ◽  
Human Leukocyte ◽  
Nucleotide Polymorphisms ◽  
Imputation Methods ◽  
Leukocyte Antigen ◽  
Sequencing Technologies ◽  
Computational Performance ◽  
Causal Variants

AbstractVariations of human leukocyte antigen (HLA) genes in the major histocompatibility complex region (MHC) significantly affect the risk of various diseases, especially autoimmune diseases. Fine-mapping of causal variants in this region was challenging due to the difficulty in sequencing and its inapplicability to large cohorts. Thus, HLA imputation, a method to infer HLA types from regional single nucleotide polymorphisms, has been developed and has successfully contributed to MHC fine-mapping of various diseases. Different HLA imputation methods have been developed, each with its own advantages, and recent methods have been improved in terms of accuracy and computational performance. Additionally, advances in HLA reference panels by next-generation sequencing technologies have enabled higher resolution and a more reliable imputation, allowing a finer-grained evaluation of the association between sequence variations and disease risk. Risk-associated variants in the MHC region would affect disease susceptibility through complicated mechanisms including alterations in peripheral responses and central thymic selection of T cells. The cooperation of reliable HLA imputation methods, informative fine-mapping, and experimental validation of the functional significance of MHC variations would be essential for further understanding of the role of the MHC in the immunopathology of autoimmune diseases.

scholarly journals High throughput characterization of genetic effects on DNA:protein binding and gene transcription

2018 ◽  
Author(s):  
Cynthia A. Kalita ◽  
Christopher D. Brown ◽  
Andrew Freiman ◽  
Jenna Isherwood ◽  
Xiaoquan Wen ◽  
...  
Keyword(s):  
High Throughput ◽  
Complex Traits ◽  
Disease Risk ◽  
Specific Binding ◽  
P Value ◽  
Regulatory Sequences ◽  
Specific Expression ◽  
Pcr Duplicates ◽  
Allele Specific ◽  
Altered Gene

Many variants associated with complex traits are in non-coding regions, and contribute to phenotypes by disrupting regulatory sequences. To characterize these variants, we developed a streamlined protocol for a high-throughput reporter assay, BiT-STARR-seq (Biallelic Targeted STARR-seq), that identifies allele-specific expression (ASE) while accounting for PCR duplicates through unique molecular identifiers. We tested 75,501 oligos (43,500 SNPs) and identified 2,720 SNPs with significant ASE (FDR 10%). To validate disruption of binding as one of the mechanisms underlying ASE, we developed a new high throughput allele specific binding assay for NFKB-p50. We identified 2,951 SNPs with allele-specific binding (ASB) (FDR 10%); 173 of these SNPs also had ASE (OR=1.97, p-value=0.0006). Of variants associated with complex traits, 1,531 resulted in ASE and 1,662 showed ASB. For example, we characterized that the Crohn’s disease risk variant for rs3810936 increases NFKB binding and results in altered gene expression.

scholarly journals Fine-mapping within eQTL credible intervals by expression CROP-seq

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Yidan Pan ◽  
Ruoyu Tian ◽  
Ciaran Lee ◽  
Gang Bao ◽  
Greg Gibson
Keyword(s):  
Single Cell ◽  
Fine Mapping ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Myeloid Cell ◽  
Transcript Abundance ◽  
Chromatin Accessibility ◽  
Genome Wide ◽  
Credible Intervals ◽  
Causal Variants

Abstract The majority of genome-wide association study (GWAS)-identified SNPs are located in noncoding regions of genes and are likely to influence disease risk and phenotypes by affecting gene expression. Since credible intervals responsible for genome-wide associations typically consist of ≥100 variants with similar statistical support, experimental methods are needed to fine map causal variants. We report here a moderate-throughput approach to identifying regulatory GWAS variants, expression CROP-seq, which consists of multiplex CRISPR-Cas9 genome editing combined with single-cell RNAseq to measure perturbation in transcript abundance. Mutations were induced in the HL60/S4 myeloid cell line nearby 57 SNPs in three genes, two of which, rs2251039 and rs35675666, significantly altered CISD1 and PARK7 expression, respectively, with strong replication and validation in single-cell clones. The sites overlap with chromatin accessibility peaks and define causal variants for inflammatory bowel disease at the two loci. This relatively inexpensive approach should be scalable for broad surveys and is also implementable for the fine mapping of individual genes.

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