FAPI: Fast and accurate P-value Imputation for genome-wide association study

Kwan, Johnny S. H.; Li, Miao-Xin; Deng, Jiaen; Sham, Pak C.

doi:10.1038/ejhg.2015.190

Cited by 12 publications

(15 citation statements)

References 19 publications

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“…Next, we estimated inversion gene-expression effects in other tissues through the LD between inversion alleles and eQTLs in GTEx V7 release (GTEx Consortium 2013, 2017) using FAPI v0.1 (Kwan et al 2016). First, we randomly took three samples of 30 experimentally genotyped individuals following ethnic proportions of GTEx donors (25 individuals from CEU, 4 YRI and 1 EAS) per inversion-gene pair and tissue to calculate LD patterns between each inversion and neighbouring SNPs, which were subsequently used to impute the corresponding inversion association P values from GTEx eQTL P values.…”

Section: Gene Expression Analysismentioning

confidence: 99%

Determining the impact of uncharacterized inversions in the human genome by droplet digital PCR

Puig

Lerga-Jaso

Giner-Delgado

et al. 2019

Preprint

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Despite the interest in characterizing all genomic variation, the presence of large repeats at the breakpoints of many structural variants hinders their analysis. This is especially problematic in the case of inversions, since they are balanced changes without gain or loss of DNA. Here we tested novel linkage-based droplet digital PCR (ddPCR) assays on 20 inversions ranging from 3.1 to 742 kb and flanked by long inverted repeats (IRs) of up to 134 kb. Among these, we validated 13 inversions predicted by different genome-wide techniques. In addition, we have generated new experimental human population information across 95 African, European and East-Asian individuals for 16 of them, including four already known inversions for which there were no high-throughput methods to determine directly the orientation, like the well-characterized 17q21 inversion. Through comparison with previous data, independent replicates and both inversion breakpoints, we have demonstrated that the technique is highly accurate and reproducible. Most of the studied inversions are frequent and widespread across continents, showing a negative correlation with genetic length. Moreover, all except two show clear signs of being recurrent, and the new data allowed us to define more clearly the factors affecting recurrence levels and estimate the inversion rate across the genome. Finally, thanks to the generated genotypes, we have been able to check inversion functional effects in multiple tissues, validating gene expression differences reported before for two inversions and finding new candidate associations. Our work therefore provides a tool to screen these and other complex genomic variants quickly in a large number of samples for the first time, highlighting the importance of direct genotyping to assess their potential consequences and clinical implications.

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Section: Gene Expression Analysismentioning

confidence: 99%

Determining the impact of uncharacterized inversions in the human genome by droplet digital PCR

Puig

Lerga-Jaso

Giner-Delgado

et al. 2019

Preprint

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“…The nine diseases are: Autoimmune Thyroid Disease (ATD), Celiac Disease (CEL), Juvenile Idiopathic Arthritis (JIA), Multiple Sclerosis (MS), Narcolepsy (NAR), Primary Biliary Cirrhosis (PBC), Psoriasis (PSO), Rheumatoid Arthritis (RA), Type 1 Diabetes (T1D). We imputed the p-values of un-genotyped SNPs using FAPI and 1000 Genome European data (Phase 1 version 3) [22]. We then obtained the p-values of SNPs that fall within the pre-defined risk loci available from ImmunoBase for each of the 9 immune traits.…”

Section: Methodsmentioning

confidence: 99%

RiVIERA-beta: Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases

Kellis

2016

Preprint

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Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA-beta (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants by modelling summary statistics p-values in Beta density function across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA-beta promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power. We applied RiVIERA-beta to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA-beta identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the 9 autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models.

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“…The association p-values of untyped SNPs were imputed directly by the tool FAPI (http://grass.cgs.hku.hk/limx/fapi/) [12] with default settings. The p-values of the two GWASs were then combined by Stouffer's Z-score method for meta-analysis on FAPI as well: (see Equation 1 in the Supplementary Files) in which N is the number of GWASs, z i is the individual z-score of the i th GWAS study, and n i is the sample size of the i th study.…”

Section: Meta-analysis Of Variantsmentioning

confidence: 99%

“…Association p-values were imputed based on the linkage disequilibrium (LD) pattern in the Eastern Asian Panel from the 1000 Genomes Project. A genome-wide meta-analysis was then performed with SNP p-values from two existing Chinese HCC GWASs using the tool FAPI [12]. After quality control (QC), 5,375,073 meta-analysis p-values of SNPs were obtained.…”

Section: Genome-wide Meta-analysis Of Two Hbv-related Hcc Gwass In Chmentioning

confidence: 99%

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma

Jiang

Deng

Chen

et al. 2020

Preprint

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Background: Recent genome-wide association studies (GWASs) have suggested several susceptibility loci of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) by statistical analysis at individual single-nucleotide polymorphisms (SNPs). However, these loci only explain a small fraction of HBV-related HCC heritability. In the present study, we aimed to identify additional susceptibility loci of HBV-related HCC using advanced knowledge-based analysis.Methods: We performed knowledge-based analysis (including gene- and gene-set-based association tests) on variant-level association p-values from two existing GWASs of HBV-related HCC. Five different types of gene-sets were collected for the association analysis. A number of SNPs within the gene prioritized by the knowledge-based association tests were selected to replicate genetic associations in an independent sample of 965 cases and 923 controls.Results: The gene-based association analysis detected four genes significantly or suggestively associated with HBV-related HCC risk: SLC39A8, GOLGA8M, SMIM31, and WHAMMP2. The gene-set-based association analysis prioritized two promising gene set for HCC, cell cycle G1/S transition and NOTCH1 intracellular domain regulates transcription. Within the gene sets, three promising candidate genes (CDC45, NCOR1 and KAT2A) were further prioritized for HCC. Among genes of liver-specific expression, multiple genes previously implicated in HCC were also highlighted. However, probably due to small sample size, none of the genes prioritized by the knowledge-based association analyses were successfully replicated in the independent sample. Conclusions: This comprehensive knowledge-based association mining study suggested several promising genes and gene-sets associated with HBV-related HCC risks, which would facilitate follow-up functional studies on the pathogenic mechanism of HCC.

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FAPI: Fast and accurate P-value Imputation for genome-wide association study

Cited by 12 publications

References 19 publications

Determining the impact of uncharacterized inversions in the human genome by droplet digital PCR

Determining the impact of uncharacterized inversions in the human genome by droplet digital PCR

RiVIERA-beta: Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma

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