GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies

He, Zihuai; Liu, Linxi; Belloy, Michaël E.; Guen, Yann Le; Sossin, Aaron; Liu, Xiaoxia; Qi, Xinran; Ma, Shiyang; Gyawali, Prashnna Kumar; Wyss‐Coray, Tony; Tang, Hua; Sabatti, Chiara; Candès, Emmanuel J.; Greicius, Michael D.; Ionita‐Laza, Iuliana

doi:10.1038/s41467-022-34932-z

Cited by 7 publications

(23 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a salient advantage of our proposed methodology compared to the original GhostKnockoffs method, which conducts similar conditional independence tests with marginal association test statistics. 20 Existing fine-mapping methods calculate relative likelihoods of causality using the PIP. 10 Interpretations of PIP calculated through different methods can be inconsistent, lacking a systematic approach to defining a cutoff for determining causal variants.…”

Section: Discussionmentioning

confidence: 99%

“…Assuming we generate 𝑀 knockoff copies, He et al ( 2022) 20 showed that one can generate knockoff feature importance measure according to a Z-score test by…”

Section: Conditional Independence Test Via Ghostknockoffsmentioning

confidence: 99%

“…[15][16][17] Several knockoffs-based methodologies have been proposed for genetic research. 13,14,16,18,19 Motivated by the frequent unavailability of individual-level data in large meta-analyses of GWAS, He et al (2022) developed GhostKnockoffs, which requires only summary statistics and enables a knockoff approach without access to individual genotypes. 20 The variants identified by the knockoff approach were shown more likely to be causal.…”

Section: Introductionmentioning

confidence: 99%

“…13,14,16,18,19 Motivated by the frequent unavailability of individual-level data in large meta-analyses of GWAS, He et al (2022) developed GhostKnockoffs, which requires only summary statistics and enables a knockoff approach without access to individual genotypes. 20 The variants identified by the knockoff approach were shown more likely to be causal. The connections between conditional testing and causal inference have been further analyzed for genetic trio studies and in studies searching for consistent conditional associations across environments.…”

Section: Introductionmentioning

confidence: 99%

“…To achieve this we rely on several technical advancements, as (1) the ability to use summary statistics (e.g., p-values and direction of effects), from potentially overlapping studies, to jointly assess all genetic variants across the genome via ultrahigh-dimensional sparse regression with FDR control. The new approach is substantially more powerful than the original version of GhostKnockoffs developed by He et al (2022); 24 (2) the optimized construction of group knockoffs to enhance the power to identify tightly linked causal variants. 23 We implement the approach in a user-friendly open-source software, which analyzes genome-wide summary statistics on a single central processing unit (CPU) in less than 15 minutes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics

He,

Chu,

Yang

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Despite the widespread availability of genome-wide data, existing methods to analyze genetic data still primarily focus on marginal association models, which fall short of fully capturing the polygenic nature of complex traits and elucidating biological causal mechanisms. Here we present a computationally efficient causal inference framework for genome-wide detection of putative causal variants underlying genetic associations. Our approach utilizes summary statistics from potentially overlapping studies as input, constructs in silico knockoff copies of summary statistics as negative controls to attenuate confounding effects induced by linkage disequilibrium, and employs efficient ultrahigh-dimensional sparse regression to jointly model all genetic variants across the genome. Our method is computationally efficient, requiring less than 15 minutes on a single CPU to analyze genome-wide summary statistics. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer s disease (AD) we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline via marginal association testing. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of large-scale genome-wide association studies (GWAS) summary statistics from 2013 to 2022. Results reveal the capacity of the method to robustly discover additional loci for polygenic traits beyond conventional GWAS and pinpoint potential causal variants underpinning each locus (on average, 22.7% more loci and 78.7% fewer proxy variants), contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses. We are making the discoveries and software freely available to the community and anticipate that routine end-to-end in silico identification of putative causal genetic variants will become an important tool that will facilitate downstream functional experiments and future research into disease etiology, as well as the exploration of novel therapeutic avenues.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Assuming we generate 𝑀 knockoff copies, He et al ( 2022) 20 showed that one can generate knockoff feature importance measure according to a Z-score test by…”

Section: Conditional Independence Test Via Ghostknockoffsmentioning

confidence: 99%