ABSTRACT High blood pressure (BP) represents a major cause of life‐threatening cardiovascular conditions. GWAS have identified >2000 BP‐associated signals. BP‐GWAS usually analyze each BP trait individually. However, multi‐trait GWAS may yield novel loci and improve polygenic risk scores (PRS) prediction. We explored whether multi‐trait BP‐GWAS can: (i) find novel loci undetected by single‐trait GWAS; (ii) increase heritability estimates; (iii) improve PRS prediction of hypertension within the Lifelines Cohort Study. We investigate the utility of multi‐trait GWAS approaches of correlated BP traits (systolic BP, diastolic BP, pulse pressure). We tested two multi‐trait methods inputting results from the latest N > 1 million single‐trait BP‐GWAS: (a) Multi‐Trait Analysis of GWAS (MTAG); (b) genomic structural equation modeling (GenomicSEM); and compared results with single‐trait results. We only identified one novel locus from multi‐trait GWAS: ZNF268 . Neither heritability estimates nor PRS performance improved using multi‐trait GWAS. In this scenario of complete sample overlap across BP‐GWAS, we observed little gain in results from multi‐trait BP‐GWAS over single‐trait BP‐GWAS. Our findings quantify the limited benefit of multi‐trait methods under this specific setting. Future BP genetic discoveries are more likely to come from larger GWAS adding new cohort data. However, conclusions may differ for other traits, depending on trait‐specific genetic architecture.
ABSTRACT Imputation can cost‐effectively generate genotypes for millions of variants. This study evaluated the power of performing rare‐variant aggregate association tests using imputed genotypes. UK Biobank exome‐sequence data and variants obtained via imputation using the HRC (r1.1) ( N = 32,488 samples) or TOPMed (r3) ( N = 133,597 samples) reference panels were analyzed. Simulations were used to compare the power of performing rare‐variant aggregate association analysis using either sequence or imputed data. The number of genes available for analysis, that is, those with ≥ 2 rare variants, was approximately the same for exome‐sequence and TOPMed imputed data, but HRC imputed data had ~10% fewer genes. For imputed data, using a less stringent R 2 threshold (i.e., > 0.3 vs. > 0.8) led to greater power to detect aggregate associations due to additional rare variants included in the analysis. Exome‐sequence data provided the highest power to detect rare‐variant aggregate associations. TOPMed imputed variants usually had < 20% reduction in power in comparison to exome‐sequence data, whereas HRC imputed variants provided substantially less power. Rare‐variant aggregate association analyses using exome‐sequence data, and imputed variants for PCSK9 and low‐density lipoprotein and APOC3 and triglyceride levels were also performed. For these analyses, even when only ultra‐rare variants (minor allele frequency < 0.001) were analyzed, significant aggregate associations could be detected for all data types. Although there is a decrease in power for rare‐variant aggregate association tests when analyzing imputed variants compared to sequence data due to missing variants and uncertainty in genotypes, analysis of imputed data is a viable approach to detect rare‐variant aggregate associations when sequence data are unavailable.
ABSTRACT The transcriptome‐wide association study (TWAS) is a powerful approach to identifying novel genes associated with complex phenotypes. Standard TWAS approaches build a prediction model for the genetic component of expression based on reference transcriptome data. Next, an outcome is regressed on the predicted expression in separate GWAS data. The traditional TWAS approach disregards the uncertainty of predicted expression, which can lead to unreliable inference on gene‐phenotype associations. We propose a novel approach that adjusts for the uncertainty of predicted expression in TWAS. We adapt techniques from measurement error theory and implement bootstrapping algorithms for penalized regression to explicitly obtain an adjustment factor to be incorporated into the unadjusted TWAS. We base the framework on adaptive Lasso. Using extensive simulations, we show that our approach produces more accurate estimates of the gene's effect size than a traditional TWAS approach. Traditional TWAS marginally inflates the type I error rate, whereas the adjusted TWAS adequately controls it. At the expense of a modestly inflated false positive rate, an unadjusted TWAS offers a limited increase in power compared to the adjusted TWAS. Simulations show that some other unadjusted and unified TWAS approaches can also inflate the type I error rate, particularly when using summary‐level data. We demonstrate the merits of the proposed adjusted approach by conducting TWAS for height and lipid phenotypes, LDL and HDL cholesterol, and triglycerides while integrating the Geuvadis transcriptome and UK Biobank GWAS data.
Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.
Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex traits. Recent studies show that knockoff-based methods can identify variants with unique, potentially causal effects on phenotypes. However, their statistical validity and effectiveness in studies with related individuals, such as the UK Biobank, remain unexplored. In this paper, we extensively evaluate a simple and effective analytical strategy that integrates GhostKnockoffs and state-of-the-art marginal association tests. We show that this approach is robust to arbitrary relatedness structure as long as the input Z-scores are derived from valid generalized linear mixed models. This robustness also extends GhostKnockoffs to other GWASs settings, including meta-analysis of studies with sample overlap when the input score test Z-scores are properly calibrated, and association test statistics beyond score tests in independent sample settings. We demonstrate the method's validity and practical utility using simulation studies and a meta-analysis of nine European ancestral genome-wide association studies and whole exome/genome sequencing studies for the Alzheimer's disease.
