iGWAS: Integrative Genome‐Wide Association Studies of Genetic and Genomic Data for Disease Susceptibility Using Mediation Analysis

Huang, Yen‐Tsung; Liang, Liming; Moffatt, Miriam F.; Cookson, William; Lin, Xihong

doi:10.1002/gepi.21905

Cited by 46 publications

(37 citation statements)

References 49 publications

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“…This motivates the paradigm of transcriptome-wide association studies (TWAS) , which evaluate the association between the expression of each gene and a complex trait of interest. Due to the limited availability of very large samples with measured gene expression and trait values, initial TWAS approaches integrated eQTL and GWAS to identify susceptibility genes either via matching the association signals 25–27 , via mediation analyses 28 , or via assessing whether the same causal variant impacts both gene expression and trait under a single causal variant model 29–31 .…”

Section: Gene-based Association Testsmentioning

confidence: 99%

Dissecting the genetics of complex traits using summary association statistics

2016

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During the past decade, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyze summary association statistics. Here we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

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Section: Gene-based Association Testsmentioning

confidence: 99%

Dissecting the genetics of complex traits using summary association statistics

2016

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“…In the latter case, measuring the intermediate phenotype (gene expression) can be useful in elucidating the mechanism by which genetic variation causes phenotypic variation. More specifically, cases with overlap between expression QTL (eQTL) and drug-response QTL suggest that a common variant could underlie both traits / and provide evidence in support of causality for the candidate gene in question (Sasaki, Frommlet, and Nordborg 2018;Huang et al 2015) .…”

Section: Expression Qtl Mapping Identifies a Hotspot On The Center Ofmentioning

confidence: 99%

A single locus underlies variation inCaenorhabditis eleganschemotherapeutic responses

Evans¹,

Andersen

2020

Preprint

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Pleiotropy, the concept that a single gene controls multiple distinct traits, is prevalent in most organisms and has broad implications for medicine and agriculture. Identifying the molecular mechanisms underlying pleiotropy has the power to unveil previously unknown biological connections between seemingly unrelated traits. Additionally, the discovery of pleiotropic genes increases our understanding of both genetic and phenotypic complexity by characterizing novel gene functions. Quantitative trait locus (QTL) mapping has been used to identify several pleiotropic regions in many organisms. However, gene knockout studies are needed to eliminate the possibility of tightly linked, non-pleiotropic loci. Here, we use a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to identify a single large-effect QTL on the center of chromosome V associated with variation in responses to eight chemotherapeutics. We validate this QTL with near-isogenic lines and pair genome-wide gene expression data with drug response traits to perform mediation analysis, leading to the identification of a pleiotropic candidate gene, scb-1 . Using deletion strains created by genome editing, we show that scb-1 , which was previously implicated in response to bleomycin, also underlies responses to other double-strand DNA break-inducing chemotherapeutics. This finding provides new evidence for the role of scb-1 in the nematode drug response and highlights the power of mediation analysis to identify causal genes. : bioRxiv preprint / Here, we use linkage mapping to identify a single overlapping QTL on chromosome V that influences the responses to eight chemotherapeutic compounds. We show that these drug-response QTL also overlap with an expression QTL hotspot that contains the gene scb-1 , previously implicated in bleomycin response (Brady et al. 2019) . Although the exact mechanism of scb-1 is yet unknown, it is hypothesized to act in response to stress (Riedel et al. 2013) and has weak homology to a viral hydrolase (Zhang et al. 2018;Kelley et al. 2015) . Together, these data suggest that the importance of scb-1 expression might extend beyond bleomycin response. We validated the QTL using near-isogenic lines (NILs) and performed mediation analysis to predict that scb-1 expression explains the observed QTL for five of the eight drugs. Finally, we directly tested the effect of scb-1 loss of function on chemotherapeutic responses. We discovered that expression of scb-1 underlies differential responses to several chemotherapeutics that cause double-strand DNA breaks, not just bleomycin. This discovery of pleiotropy helps to further define the role of scb-1 by expanding its known functions and provides insights into the molecular mechanisms underlying the nematode drug response. MATERIALS AND METHODS StrainsAnimals were grown at 20°C on modified nematode growth media (NGMA) containing 1% agar and 0.7% agarose to prevent burrowing and fed OP50 (Ghosh et al. 2012) . The two parental str...

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“…Genes expression difference identified by TWAS may be causally associated with the phenotype of interest, but also can be due to variants LD or co-expressions (Huang et al 2015;Hormozdiari et al 2016). To pinpoint causal relationship between the target gene of an eQTL and the complex trait, we performed colocalization analysis by using COLOC method; see Methods section.…”

Section: Twas Identified Candidate Genes For Opmentioning

confidence: 99%

Integrating genome-wide association and transcriptome predicted model identify novel target genes with osteoporosis

Yin

Zhu

et al. 2019

Preprint

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Osteoporosis (OP) is a highly polygenetic disease which is usually characterized by low bone mineral density. Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with bone mineral density. However, the biological mechanisms of these loci remain elusive. To identify potential causal genes of the associated loci, we detected trait-gene expression associations by transcriptome-wide association study (TWAS) method. It directly imputes gene expression effects from GWAS data, using a statistical prediction model trained on GTEx reference transcriptome data, with blood and skeletal tissues data. Then we performed a colocalization analysis to evaluate the posterior probability of biological patterns: association characterized by a single shared causal variant or two distinct causal variants. The ultimate analysis identified 276 candidate genes, including 3 novel loci, 204 novel candidate genes and 69 replicated from GWAS. The 3 novel loci located at chr6: 72417543, chr15: 69601206, chr21: 30530692, mapping to gene RIMS1, SPESP1, MAP3K7CL. The results of colocalization analysis indicated that 142 of them showing strong evidence of a single shared causal variant and 134 of them showing evidence of joint causal variants. Their biological function was directly or indirectly associated with the occurrence of OP validated by VarElect tool. Several important OP-associated pathways were detected by protein-protein interaction and pathway enrichment analysis. Target genes were further enriched for differential expression genes in osteoblasts expression profiles, e.g. IBSP, affecting calcium and hydroxyapatite binding, and CD44, regulating alternative splicing of gene transcription. Transcriptome fine-mapping identifies more disease-related genes and provide additional insight into the development of novel targeted therapeutics to treat OP.

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iGWAS: Integrative Genome‐Wide Association Studies of Genetic and Genomic Data for Disease Susceptibility Using Mediation Analysis

Cited by 46 publications

References 49 publications

Dissecting the genetics of complex traits using summary association statistics

Dissecting the genetics of complex traits using summary association statistics

A single locus underlies variation inCaenorhabditis eleganschemotherapeutic responses

Integrating genome-wide association and transcriptome predicted model identify novel target genes with osteoporosis

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