Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

Ma, Xiaochun; Wang, Peilan; Xu, Guobing; Yu, Fang; Ma, Yunlong

doi:10.1186/s12920-020-00768-z

Cited by 19 publications

(21 citation statements)

References 85 publications

(88 reference statements)

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“…Studies have found TAP2 to be associated with various immune-related disorders, including autoimmune thyroiditis and type 1 diabetes [ 80 , 81 ], and pulmonary tuberculosis in Iranian populations [ 82 ]. Recently, Ma and colleagues [ 83 ] identified three cis-regulatory eSNPS for TAP2 as candidates for childhood-onset asthma risk (rs9267798, rs4148882 and rs241456). One of these (rs4148882) is present in the asthma GWAS data set used for our analysis ( FDR = 0.12) and is in weak LD with rs4148869 ( r 2 = 0.4).…”

Section: Resultsmentioning

confidence: 99%

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Hutchinson

Reales²,

Willis³

et al. 2021

PLoS Genet

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Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions (“Flexible cFDR”). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.

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Section: Resultsmentioning

confidence: 99%

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Hutchinson

Reales²,

Willis³

et al. 2021

PLoS Genet

View full text Add to dashboard Cite

show abstract

“…Studies have found TAP2 to be associated with various immune-related disorders, including autoimmune thyroiditis and type 1 diabetes 75,76 , and pulmonary tuberculosis in Iranian populations 77 . Recently, Ma and colleagues 78 identified three cis-regulatory eSNPS for TAP2 as candidates for childhood-onset asthma risk (rs9267798, rs4148882 and rs241456). One of these (rs4148882) is present in the asthma GWAS data set used for our analysis ( FDR = 0.12) and is in weak LD with rs4148869 ( r 2 = 0.4).…”

Section: Resultsmentioning

confidence: 99%

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Hutchinson

Reales

Wallace

2020

Preprint

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1.AbstractGenome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary data has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate (FDR) can be a more powerful approach as sample sizes increase and many associations are expected in each study. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions. We relax the distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary data from any continuous distribution (“Flexible cFDR”). Our method is iterative, whereby additional layers of auxiliary data can be leveraged in turn. Through simulations we show that flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional data to find additional genetic associations for asthma, which we validated in the larger UK Biobank data resource.

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“…To highlight the functional risk genes whose expressions were significantly associated with PBC, we leveraged both S-PrediXcan 26 and S-MultiXcan 27 to combine GWAS summary statistics on PBC with GTEx eQTL data 28 (Supplemental Methods S5). Furthermore, We referenced the method used in previous studies 11,24,29,30 to perform an in silico permutation analysis of 100,000 times of random selections (N Total ) for validating the consistency of results from S-MultiXcan analysis with other results from three distinct analyses: MAGMA analysis, and S-PrediXcan on liver and blood (Supplemental Methods S6). We also performed a gene-property analysis using the FUMA tool 31 to integrate gene-level association signals from GWAS summary statistics on PBC with scRNA-seq data based on liver tissue from the Mouse Cell Atlas 32 .…”

Section: Methodsmentioning

confidence: 99%

Combination of single cell sequencing data and GWAS summary statistics reveals genetically-influenced liver cell types for primary biliary cholangitis

Xiang

Deng

et al. 2021

Preprint

Self Cite

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Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with primary biliary cholangitis (PBC). However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. We constructed a powerful computational framework to integrate a large-scale GWAS summary statistics (N = 13,239) with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. We found that 29 genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. Gene-property analysis revealed that four immune cell types including Cst3+ dendritic cell, Chil3+ macrophage, Trbc2+ T cell, and Gzma+ T cell were significantly enriched by PBC-risk genes. By combining GWAS summary statistics with scRNA-seq data, we identified that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals. The ORMDL3 gene showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). Compared with ORMDL3+ cholangiocytes, we identified that ORMDL3- cholangiocytes predispose to play important immune-modulatory roles in the etiology of PBC. To the best of our knowledge, this is the first study to integrate human genetic information with single cell sequencing data for parsing genetics-influenced liver cells and its immune microenvironment for PBC risk.

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Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

Cited by 19 publications

References 85 publications

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Combination of single cell sequencing data and GWAS summary statistics reveals genetically-influenced liver cell types for primary biliary cholangitis

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