scholarly journals 52. FINE-MAPPING ACROSS DIVERSE ANCESTRIES DRIVES THE DISCOVERY OF PUTATIVE CAUSAL VARIANTS UNDERLYING HUMAN COMPLEX TRAITS AND DISEASES

2021 ◽  
Vol 51 ◽  
pp. e68
Author(s):  
Kai Yuan ◽  
Tzu-Ting Chen ◽  
Shu-Chin Lin ◽  
Ryan Longchamps ◽  
Antonio Pardiñas ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Causal Variants ◽  
Human Complex

scholarly journals SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations

2021 ◽  
Author(s):  
Wenmin Zhang ◽  
Hamed S Najafabadi ◽  
Yue Li
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Association Studies ◽  
Computational Cost ◽  
Mapping Method ◽  
Genome Wide Association Studies ◽  
Functional Annotations ◽  
Genome Wide ◽  
Causal Variants

Identifying causal variants from genome-wide association studies (GWASs) is challenging due to widespread linkage disequilibrium (LD). Functional annotations of the genome may help prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. However, classical fine-mapping methods have a high computational cost, particularly when the underlying genetic architecture and LD patterns are complex. Here, we propose a novel approach, SparsePro, to efficiently conduct functionally informed statistical fine-mapping. Our method enjoys two major innovations: First, by creating a sparse low-dimensional projection of the high-dimensional genotype, we enable a linear search of causal variants instead of an exponential search of causal configurations used in existing methods; Second, we adopt a probabilistic framework with a highly efficient variational expectation-maximization algorithm to integrate statistical associations and functional priors. We evaluate SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved more accurate and well-calibrated posterior inference with greatly reduced computation time. We demonstrate the utility of SparsePro by investigating the genetic architecture of five functional biomarkers of vital organs. We identify potential causal variants contributing to the genetically encoded coordination mechanisms between vital organs and pinpoint target genes with potential pleiotropic effects. In summary, we have developed an efficient genome-wide fine-mapping method with the ability to integrate functional annotations. Our method may have wide utility in understanding the genetics of complex traits as well as in increasing the yield of functional follow-up studies of GWASs.

scholarly journals Identifying causal variants by fine mapping across multiple studies

PLoS Genetics ◽  
2021 ◽  
Vol 17 (9) ◽  
pp. e1009733
Author(s):  
Nathan LaPierre ◽  
Kodi Taraszka ◽  
Helen Huang ◽  
Rosemary He ◽  
Farhad Hormozdiari ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Genome Wide Association Studies ◽  
Multivariate Normal ◽  
Multiple Study ◽  
Genome Wide ◽  
Causal Variants ◽  
Different Populations

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).

scholarly journals Identifying Causal Variants by Fine Mapping Across Multiple Studies

2020 ◽  
Author(s):  
Nathan LaPierre ◽  
Kodi Taraszka ◽  
Helen Huang ◽  
Rosemary He ◽  
Farhad Hormozdiari ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Multiple Study ◽  
Current State ◽  
Genome Wide ◽  
Causal Variants ◽  
Different Populations

AbstractIncreasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. In a trans-ethnic, trans-biobank Type 2 Diabetes analysis, we show that MsCAVIAR returns causal set sizes that are over 20% smaller than those given by current state of the art methods for trans-ethnic fine-mapping.

scholarly journals Predicting trait regulators by identifying co-localization of DNA binding and GWAS variants in regulatory regions

2018 ◽  
Author(s):  
Gerald Quon ◽  
Soheil Feizi ◽  
Daniel Marbach ◽  
Melina Claussnitzer ◽  
Manolis Kellis
Keyword(s):  
Transcription Factor ◽  
Fine Mapping ◽  
Binding Sites ◽  
Complex Traits ◽  
Pancreatic Beta Cells ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Regulatory Regions ◽  
Factor Binding ◽  
Causal Variants

AbstractGenomic regions associated with complex traits and diseases are primarily located in non-coding regions of the genome and have unknown mechanism of action. A critical step to understanding the genetics of complex traits is to fine-map each associated locus; that is, to find the causal variant(s) that underlie genetic associations with a trait. Fine-mapping approaches are currently focused on identifying genomic annotations, such as transcription factor binding sites, which are enriched in direct overlap with candidate causal variants. We introduce CONVERGE, the first computational tool to search for co-localization of GWAS causal variants with transcription factor binding sites in the same regulatory regions, without requiring direct overlap. As a proof of principle, we demonstrate that CONVERGE is able to identify five novel regulators of type 2 diabetes which subsequently validated in knockdown experiments in pancreatic beta cells, while existing fine-mapping methods were unable to find any statistically significant regulators. CONVERGE also recovers more established regulators for total cholesterol compared to other fine-mapping methods. CONVERGE is therefore unique and complementary to existing fine-mapping methods and is useful for exploring the regulatory architecture of complex traits.

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 Insights from complex trait fine-mapping across diverse populations

2021 ◽  
Author(s):  
Masahiro Kanai ◽  
Jacob C Ulirsch ◽  
Juha Karjalainen ◽  
Mitja Kurki ◽  
Konrad J Karczewski ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Large Scale ◽  
Association Studies ◽  
Great Success ◽  
Genome Wide Association Studies ◽  
Diverse Populations ◽  
High Confidence ◽  
Causal Variants ◽  
Coding Variants

AbstractDespite the great success of genome-wide association studies (GWAS) in identifying genetic loci significantly associated with diseases, the vast majority of causal variants underlying disease-associated loci have not been identified1–3. To create an atlas of causal variants, we performed and integrated fine-mapping across 148 complex traits in three large-scale biobanks (BioBank Japan4,5, FinnGen6, and UK Biobank7,8; total n = 811,261), resulting in 4,518 variant-trait pairs with high posterior probability (> 0.9) of causality. Of these, we found 285 high-confidence variant-trait pairs replicated across multiple populations, and we characterized multiple contributors to the surprising lack of overlap among fine-mapping results from different biobanks. By studying the bottlenecked Finnish and Japanese populations, we identified 21 and 26 putative causal coding variants with extreme allele frequency enrichment (> 10-fold) in these two populations, respectively. Aggregating data across populations enabled identification of 1,492 unique fine-mapped coding variants and 176 genes in which multiple independent coding variants influence the same trait (i.e., with an allelic series of coding variants). Our results demonstrate that fine-mapping in diverse populations enables novel insights into the biology of complex traits by pinpointing high-confidence causal variants for further characterization.

scholarly journals Fine-mapping and QTL tissue-sharing information improve causal gene identification and transcriptome prediction performance

2020 ◽  
Author(s):  
Alvaro N. Barbeira ◽  
Yanyu Liang ◽  
Rodrigo Bonazzola ◽  
Gao Wang ◽  
Heather E. Wheeler ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Prediction Models ◽  
Association Studies ◽  
False Positives ◽  
Prediction Performance ◽  
Genome Wide Association Studies ◽  
Causal Gene ◽  
Genome Wide ◽  
Causal Variants

AbstractThe integration of transcriptomic studies and GWAS (genome-wide association studies) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA-sequencing samples from 948 post-mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine-mapping (dap-g) and borrowing information across tissues (mashr) lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes identified as indicated by precision-recall and ROC (Receiver Operating Characteristic) curves. All prediction models are made publicly available at predictdb.org.Author summaryIntegrating molecular biology information with genome-wide association studies (GWAS) sheds light on the mechanisms tying genetic variation to complex traits. However, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium of distinct causal variants. By integrating fine-mapping information into the models, and leveraging the widespread tissue-sharing of eQTLs, we improve the proportion of likely causal genes among significant gene-trait associations, as well as the prediction of “ground truth” genes.

scholarly journals SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations

2022 ◽  
Author(s):  
Wenmin Zhang ◽  
Hamed Najafabadi ◽  
Yue Li
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Association Studies ◽  
Computational Cost ◽  
Mapping Method ◽  
Genome Wide Association Studies ◽  
Functional Annotations ◽  
Genome Wide ◽  
Causal Variants

Abstract Identifying causal variants from genome-wide association studies (GWASs) is challenging due to widespread linkage disequilibrium (LD). Functional annotations of the genome may help prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. However, classical fine-mapping methods have a high computational cost, particularly when the underlying genetic architecture and LD patterns are complex. Here, we propose a novel approach, SparsePro, to efficiently conduct genome-wide fine-mapping. Our method enjoys two major innovations: First, by creating a sparse low-dimensional projection of the high-dimensional genotype data, we enable a linear search of causal variants instead of a combinatorial search of causal configurations used in most existing methods; Second, we adopt a probabilistic framework with a highly efficient variational expectation-maximization algorithm to integrate statistical associations and functional priors. We evaluate SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved more accurate and well-calibrated posterior inference with greatly reduced computation time. We demonstrate the utility of SparsePro by investigating the genetic architecture of five functional biomarkers of vital organs. We show that, compared to other methods, the causal variants identified by SparsePro are highly enriched for expression quantitative trait loci and explain a larger proportion of trait heritability. We also identify potential causal variants contributing to the genetically encoded coordination mechanisms between vital organs, and pinpoint target genes with potential pleiotropic effects. In summary, we have developed an efficient genome-wide fine-mapping method with the ability to integrate functional annotations. Our method may have wide utility in understanding the genetics of complex traits as well as in increasing the yield of functional follow-up studies of GWASs. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro.

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