Identifying Causal Variants at Loci with Multiple Signals of Association

By Farhad Hormozdiari, Emrah Kostem, E. Kang, Bogdan Pasaniuc, Eleazar Eskin. Genetics, 2014

468 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b3aae0ce0606743f619986499e65742f81347d76

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In Genetics

Citations in the corpus

The GTEx Consortium atlas of genetic regulatory effects across human tissues (2019, methodology): “We applied three different statistical fine-mapping methods — CaVEMaN (30), CAVIAR (31), and dap-g (32) — to infer likely causal variants of cis-eQTLs in each tissue (Fig.”
Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies (2014, methodology): “Finally, we note that iterative conditioning, a method typically used to detect multiple independent signals, performs worse than the prioritization strategies described here (see Figure S1) [7].”
A more accurate method for colocalisation analysis allowing for multiple causal variants (2021, methodology): “A suite of Bayesian ﬁne-mapping methods have been developed recently which calculate posterior probabilities of sets of causal variants for a given trait[5, 6, 7].”
The genetic architecture of the human cerebral cortex (2020, methodology): “To identify putatively causal variants, we performed fine-mapping with CAVIAR (68).”
Colocalization of GWAS and eQTL Signals Detects Target Genes (2016, background): “2.(18,27) The diagonal elements of S ðpÞ c are set to 1 or 0 such that for variants selected as causal in C, their corresponding diagonal elements in S ðpÞ c are set to 1; otherwise, we set them to 0.”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, methodology): “, [7, 8, 12,16]) are based on the following model: ẑ | z, R̂ ∼ NJ(R̂z, R̂), (20) where z = (z1, .”
Genetic effects on gene expression across human tissues (2017, unknown): “1 ×10−3 for (75,100]% vs [0,25]%, (75,100 vs (25,50]% and (75,100]% vs (50,75]%, respectively).”
Probabilistic fine-mapping of transcriptome-wide association studies (2017, methodology): “We use PIPs to compute ρ-credible gene-sets that contain the causal gene 82 with probability ρ.(14) To account for missing causal mechanisms either due to unpredicted expression or 83 other latent functional mechanisms, we include the null model as a possible outcome in the credible set 84 (see Meth”
FINEMAP: efficient variable selection using summary data from genome-wide association studies (2015, methodology): “Dashed lines correspond respectively to a single-SNP Bayes factor of 100 and P-value of 5 × 10−8.”