Fine‐Mapping the Results From Genome‐Wide Association Studies of Primary Biliary Cholangitis Using Susie and h2‐D2 DOI Creative Commons

Aida Gjoka,

Heather J. Cordell

Genetic Epidemiology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 6, 2024

The main goal of fine-mapping is the identification relevant genetic variants that have a causal effect on some trait interest, such as presence disease. From statistical point view, fine mapping can be seen variable selection problem. Fine-mapping methods are often challenging to apply because linkage disequilibrium (LD), is, regions genome where interrogated high correlation. Several been proposed address this issue. Here we explore 'Sum Single Effects' (SuSiE) method, applied real data (summary statistics) from genome-wide meta-analysis autoimmune liver disease primary biliary cholangitis (PBC). in set was previously performed using FINEMAP program; compare these previous results with those obtained SuSiE, which provides an arguably more convenient and principled way generating 'credible sets', predictors correlated response variable. This allows us appropriately acknowledge uncertainty when selecting effects for trait. We focus SuSiE-RSS, fits SuSiE model summary statistics, z-scores, along correlation matrix. also recently developed h2-D2, uses same inputs. Overall, find SuSiE-RSS and, lesser extent, quite concordant FINEMAP. resulting genes biological pathways implicated therefore similar obtained, providing valuable confirmation reported results. Detailed examination credible sets identified suggests that, although majority loci (33 out 56) seem most plausible, there (5 56 loci) h2-D2 compelling. Computer simulations suggest overall, generally has slightly higher power, better precision, ability identify true number region than scenarios power higher. Thus, analysis, use complementary approaches both potentially warranted.

Language: Английский

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias DOI Creative Commons
Mingxuan Cai, Zhiwei Wang,

Jiashun Xiao

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Oct. 28, 2023

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among can limit statistical power and resolution of fine-mapping. Second, it is computationally expensive simultaneously search multiple causal variants. Third, confounding bias hidden in GWAS summary statistics produce spurious signals. To address these challenges, we develop method cross-population (XMAP) leveraging genetic diversity accounting bias. By using from global biobanks genomic consortia, show that XMAP achieve greater power, better control false positive rate, substantially higher computational efficiency identifying signals, compared Importantly, output be integrated with single-cell datasets, which greatly improves interpretation putative their cellular context at resolution.

Language: Английский

Citations

14
MAAT: a new nonparametric Bayesian framework for incorporating multiple functional annotations in transcriptome-wide association studies DOI Creative Commons
Han Wang, Xiang Li, Teng Li

et al.

Genome biology, Journal Year: 2025, Volume and Issue: 26(1)

Published: Feb. 4, 2025

Language: Английский

Citations

0
TWAS-GKF: A Novel Method for Causal Gene Identification in Transcriptome-wide Association Studies with Knockoff Inference DOI Creative Commons
Anqi Wang, Peixin Tian, Yan Zhang

et al.

Bioinformatics, Journal Year: 2024, Volume and Issue: 40(8)

Published: Aug. 1, 2024

Transcriptome-wide association study (TWAS) aims to identify trait-associated genes regulated by significant variants explore the underlying biological mechanisms at a tissue-specific level. Despite advancement of current TWAS methods cover diverse traits, traditional approaches still face two main challenges: (i) lack that can guarantee finite-sample false discovery rate (FDR) control in identifying genes; and (ii) requirement for individual-level data, which is often inaccessible.

Language: Английский

Citations

1
Fine‐Mapping the Results From Genome‐Wide Association Studies of Primary Biliary Cholangitis Using Susie and h2‐D2 DOI Creative Commons

Aida Gjoka,

Heather J. Cordell

Genetic Epidemiology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 6, 2024

The main goal of fine-mapping is the identification relevant genetic variants that have a causal effect on some trait interest, such as presence disease. From statistical point view, fine mapping can be seen variable selection problem. Fine-mapping methods are often challenging to apply because linkage disequilibrium (LD), is, regions genome where interrogated high correlation. Several been proposed address this issue. Here we explore 'Sum Single Effects' (SuSiE) method, applied real data (summary statistics) from genome-wide meta-analysis autoimmune liver disease primary biliary cholangitis (PBC). in set was previously performed using FINEMAP program; compare these previous results with those obtained SuSiE, which provides an arguably more convenient and principled way generating 'credible sets', predictors correlated response variable. This allows us appropriately acknowledge uncertainty when selecting effects for trait. We focus SuSiE-RSS, fits SuSiE model summary statistics, z-scores, along correlation matrix. also recently developed h2-D2, uses same inputs. Overall, find SuSiE-RSS and, lesser extent, quite concordant FINEMAP. resulting genes biological pathways implicated therefore similar obtained, providing valuable confirmation reported results. Detailed examination credible sets identified suggests that, although majority loci (33 out 56) seem most plausible, there (5 56 loci) h2-D2 compelling. Computer simulations suggest overall, generally has slightly higher power, better precision, ability identify true number region than scenarios power higher. Thus, analysis, use complementary approaches both potentially warranted.

Language: Английский

Citations

0