Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics

Abstract

Summary Knockoff-based methods have become increasingly popular due to their enhanced power for locus discovery and their ability to prioritize putative causal variants in a genome-wide analysis. However, because of the substantial computational cost for generating knockoffs, existing knockoff approaches cannot analyze millions of rare genetic variants in biobank-scale whole-genome sequencing and whole-genome imputed datasets. We propose a scalable knockoff-based method for the analysis of common and rare variants across the genome, KnockoffScreen-AL , that is applicable to biobank-scale studies with hundreds of thousands of samples and millions of genetic variants. The application of KnockoffScreen-AL to the analysis of Alzheimer disease (AD) in 388,051 WG-imputed samples from the UK Biobank resulted in 31 significant loci, including 14 loci that are missed by conventional association tests on these data. We perform replication studies in an independent meta-analysis of clinically diagnosed AD with 94,437 samples, and additionally leverage single-cell RNA-sequencing data with 143,793 single-nucleus transcriptomes from 17 control subjects and AD-affected individuals, and proteomics data from 735 control subjects and affected indviduals with AD and related disorders to validate the genes at these significant loci. These multi-omics analyses show that 79.1% of the proximal genes at these loci and 76.2% of the genes at loci identified only by KnockoffScreen-AL exhibit at least suggestive signal (p < 0.05) in the scRNA-seq or proteomics analyses. We highlight a potentially causal gene in AD progression, EGFR , that shows significant differences in expression and protein levels between AD-affected individuals and healthy control subjects.

Leveraging Electronic Medical Records and Knowledge Networks to Predict Disease Onset and Gain Biological Insight Into Alzheimer’s Disease

“…For example, we were able to identify known genetic associations with dementia based upon symptoms from the rest of the body, such as through identification of known autosomal dominant early AD genes such as APP and PSEN 1/2 41 . Other genes identified with possible associations with AD include APOE, HFE, and HSPG2 variants that impact AD risk [42][43][44][45][46] . While these associations are included in the SPOKE network due to evidence in literature, the association of these genes with other early clinical predictors is less established, and thus this analysis allowed us to identify a novel constellation of phenotypes observable in a clinical setting that can lead a clinician to suspect future AD risk.…”

“…Coloc analysis tables between the gene, molecular QTLs, and phenotypes were extracted, with protein QTLs for APOE specifically identified based on blood plasma data from Sun et al 74 and Suhre et al 75 Similarly for osteoporosis and AD, we utilized the Open Genetics platform to identify shared locus between heel bone mineral density (proxy for osteoporosis) and Family History of AD or AD. To further investigate the locus, we extracted GWAS summary statistics from Jansen et al 43 for AD and sex-stratified GWAS summary statistics for heel bone mineral density (HBMD) from Neale's Lab GWAS round 2, Phenotype Code:3148, based on data from the UK Biobank (www.nealelab.is/uk-biobank/) 76 . We then conducted colocalization analysis using the coloc method described in Giambartolomei et al 77 , from R package coloc 5.1.0.…”

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

“…The knockoff methodology was designed to test precisely the conditional independence hypothesis 𝐻 # $%& : 𝐺 ' ⊥ 𝑌|𝑮 (𝒋 . [11][12][13][14][15]18 Here, we leverage several recent advances in the model-X knockoffs framework.…”

“…The knockoffs aid in the selection of significant genetic variants and help mitigate the confounding effect of linkage disequilibrium (LD). [13][14][15] Several knockoffs-based methodologies have been proposed for genetic research, including those by Candès et 11,12,14,16,17 Motivated by the frequent unavailability of individual-level data in large meta-analyses of GWAS, He et al (2022) introduced GhostKnockoffs, which can take as input summary statistics readily available from conventional GWAS, enabling a knockoff approach without access to individual genotypes. 18 Chen et al (2024) paired GhostKnockoffs with modern regression statistics (such as the Lasso or other types of penalized regression methods) to improve statistical power.…”

BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data

“…One commonly used strategy to improve the computational efficiency when dealing with large-scale datasets is sampling. Here, to reduce the computational time and make the test scalable to biobank sized datasets, we employ the shrinkage leveraging (SL) algorithm [ 9 , 66 ]. The SL algorithm is a sampling technique based on the empirical statistical leverage scores as an importance sampling distribution.…”