scholarly journals Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

2021 ◽  
Author(s):  
Steven Gazal ◽  
Omer Weissbrod ◽  
Farhad Hormozdiari ◽  
Kushal Dey ◽  
Joseph Nasser ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Common Disease ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Functional Interpretation ◽  
Genome Wide

Although genome-wide association studies (GWAS) have identified thousands of disease-associated common SNPs, these SNPs generally do not implicate the underlying target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis, but it is unclear how these strategies should be applied in the context of interpreting common disease risk variants. We developed a framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk, leveraging polygenic analyses of disease heritability to define and estimate their precision and recall. We applied our framework to GWAS summary statistics for 63 diseases and complex traits (average N=314K), evaluating 50 S2G strategies. Our optimal combined S2G strategy (cS2G) included 7 constituent S2G strategies (Exon, Promoter, 2 fine-mapped cis-eQTL strategies, EpiMap enhancer-gene linking, Activity-By-Contact (ABC), and Cicero), and achieved a precision of 0.75 and a recall of 0.33, more than doubling the precision and/or recall of any individual strategy; this implies that 33% of SNP-heritability can be linked to causal genes with 75% confidence. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 7,111 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. Finally, we applied cS2G to genome-wide fine-mapping results for these traits (not restricted to GWAS loci) to rank genes by the heritability linked to each gene, providing an empirical assessment of disease omnigenicity; averaging across traits, we determined that the top 200 (1%) of ranked genes explained roughly half of the heritability linked to all genes. Our results highlight the benefits of our cS2G strategy in providing functional interpretation of GWAS findings; we anticipate that precision and recall will increase further under our framework as improved functional assays lead to improved S2G strategies. 

scholarly journals Challenges and progress in interpretation of non-coding genetic variants associated with human disease

2017 ◽  
Vol 242 (13) ◽  
pp. 1325-1334 ◽  
Author(s):  
Yizhou Zhu ◽  
Cagdas Tazearslan ◽  
Yousin Suh
Keyword(s):  
Molecular Mechanisms ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Functional Interpretation ◽  
Genome Wide ◽  
Coding Variants

Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.

scholarly journals Genome-wide maps of enhancer regulation connect risk variants to disease genes

2020 ◽  
Author(s):  
Joseph Nasser ◽  
Drew T. Bergman ◽  
Charles P. Fulco ◽  
Philine Guckelberger ◽  
Benjamin R. Doughty ◽  
...  
Keyword(s):  
Complex Traits ◽  
Disease Risk ◽  
Association Studies ◽  
Cell Types ◽  
Common Disease ◽  
Genome Wide Association Studies ◽  
Abc Model ◽  
Risk Variants ◽  
Genome Wide ◽  
Causal Variants

AbstractGenome-wide association studies have now identified tens of thousands of noncoding loci associated with human diseases and complex traits, each of which could reveal insights into biological mechanisms of disease. Many of the underlying causal variants are thought to affect enhancers, but we have lacked genome-wide maps of enhancer-gene regulation to interpret such variants. We previously developed the Activity-by-Contact (ABC) Model to predict enhancer-gene connections and demonstrated that it can accurately predict the results of CRISPR perturbations across several cell types. Here, we apply this ABC Model to create enhancer-gene maps in 131 cell types and tissues, and use these maps to interpret the functions of fine-mapped GWAS variants. For inflammatory bowel disease (IBD), causal variants are >20-fold enriched in enhancers in particular cell types, and ABC outperforms other regulatory methods at connecting noncoding variants to target genes. Across 72 diseases and complex traits, ABC links 5,036 GWAS signals to 2,249 unique genes, including a class of 577 genes that appear to influence multiple phenotypes via variants in enhancers that act in different cell types. Guided by these variant-to-function maps, we show that an enhancer containing an IBD risk variant regulates the expression of PPIF to tune mitochondrial membrane potential. Together, our study reveals insights into principles of genome regulation, illuminates mechanisms that influence IBD, and demonstrates a generalizable strategy to connect common disease risk variants to their molecular and cellular functions.

scholarly journals Causal Haplotype Block Identification in Plant Genome-Wide Association Studies

2021 ◽  
Author(s):  
Xing Wu ◽  
Wei Jiang ◽  
Christopher Fragoso ◽  
Jing Huang ◽  
Geyu Zhou ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Haplotype Block ◽  
Association Studies ◽  
Crop Improvement ◽  
Plant Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Haplotype Blocks ◽  
Genome Wide

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) used in many GWAS that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects. In plants, the relatively small population size in GWAS and the high genetic diversity found many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to infer the causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, and GMMAT in both simulation and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in simulations with high polygenicity. Moreover, it resulted in higher mapping resolution, especially in regions of high LD, by identifying small causal blocks in the larger haplotype block. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA results, and its average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved mapping resolution to facilitate crop improvement.

scholarly journals Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants

PLoS Genetics ◽  
2021 ◽  
Vol 17 (11) ◽  
pp. e1009918
Author(s):  
Bernard Ng ◽  
William Casazza ◽  
Nam Hee Kim ◽  
Chendi Wang ◽  
Farnush Farhadi ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Target Genes ◽  
Association Studies ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Cascading Effects ◽  
Risk Alleles ◽  
Gene Sets ◽  
Genome Wide ◽  
Regulatory Landscape

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

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 On the Use of Z-Scores for Fine-Mapping with Related Individuals

2021 ◽  
Author(s):  
Jicai Jiang
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Statistical Framework ◽  
Genome Wide ◽  
Z Scores ◽  
Related Individuals ◽  
Mapping Complex Traits

Using summary statistics from genome-wide association studies (GWAS) has been widely used for fine-mapping complex traits in humans. The statistical framework was largely developed for unrelated samples. Though it is possible to apply the framework to fine-mapping with related individuals, extensive modifications are needed. Unfortunately, this has often been ignored in summary-statistics-based fine-mapping with related individuals. In this paper, we show in theory and simulation what modifications are necessary to extend the use of summary statistics to related individuals. The analysis also demonstrates that though existing summary-statistics-based fine-mapping methods can be adapted for related individuals, they appear to have no computational advantage over individual-data-based methods.

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 Genome-wide meta-analysis, fine-mapping, and integrative prioritization identify new Alzheimer’s disease risk genes

2020 ◽  
Author(s):  
Jeremy Schwartzentruber ◽  
Sarah Cooper ◽  
Jimmy Z Liu ◽  
Inigo Barrio-Hernandez ◽  
Erica Bello ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fine Mapping ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Specific Expression ◽  
Genome Wide ◽  
Causal Genes

AbstractGenome-wide association studies (GWAS) have discovered numerous genomic loci associated with Alzheimer’s disease (AD), yet the causal genes and variants remain incompletely identified. We performed an updated genome-wide AD meta-analysis, which identified 37 risk loci, including novel associations near genes CCDC6, TSPAN14, NCK2, and SPRED2. Using three SNP-level fine-mapping methods, we identified 21 SNPs with greater than 50% probability each of being causally involved in AD risk, and others strongly suggested by functional annotation. We followed this with colocalisation analyses across 109 gene expression quantitative trait loci (eQTL) datasets, and prioritization of genes using protein interaction networks and tissue-specific expression. Combining this information into a quantitative score, we find that evidence converges on likely causal genes, including the above four genes, and those at previously discovered AD loci including BIN1, APH1B, PTK2B, PILRA, and CASS4.

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 A practical view of fine-mapping and gene prioritization in the post-genome-wide association era

Open Biology ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 190221 ◽  
Author(s):  
R. V. Broekema ◽  
O. B. Bakker ◽  
I. H. Jonkers
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Population Based ◽  
Gene Prioritization ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Underlying Mechanisms ◽  
The Impact

Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

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