Genetics of 35 blood and urine biomarkers in the UK Biobank. - Sinnott-Armstrong, Nasa; Tanigawa, Yosuke; Amar, David; Mars, Nina; Benner, Christian; Aguirre, Matthew; Venkataraman, Guhan Ram; Wainberg, Michael; Ollila, Hanna M; Kiiskinen, Tuomo; Havulinna, Aki S; Pirruccello, James P; et al. (Nat Genet, 2021)
Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.
Keywords: Biological Specimen Banks; Biomarkers; Cardiovascular Diseases; Diabetes Mellitus, Type 2; DNA Copy Number Variations; Genetic Pleiotropy; HLA Antigens; Humans; Linkage Disequilibrium; Liver-Specific Organic Anion Transporter 1; Mendelian Randomization Analysis; Polymorphism, Single Nucleotide; Proteins; Renal Insufficiency, Chronic; Serine Endopeptidases; United Kingdom

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks. - Amar, David; Sinnott-Armstrong, Nasa; Ashley, Euan A; Rivas, Manuel A (Nat Commun, 2021)
Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.
Keywords: Biological Specimen Banks; Cardiovascular Diseases; Causality; Computer Simulation; Gene Regulatory Networks; Genetic Pleiotropy; Genetic Variation; Genome-Wide Association Study; Genotype; Humans; Mendelian Randomization Analysis; Models, Theoretical; Multifactorial Inheritance; Phenotype; Risk Factors

Opportunities and challenges for transcriptome-wide association studies. - Wainberg, Michael; Sinnott-Armstrong, Nasa; Mancuso, Nicholas; Barbeira, Alvaro N; Knowles, David A; Golan, David; Ermel, Raili; Ruusalepp, Arno; Quertermous, Thomas; Hao, Ke; Björkegren, Johan L M; Im, Hae Kyung; et al. (Nat Genet, 2019)
Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn's disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.
Keywords: Crohn Disease; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; Humans; Lipoproteins, LDL; Quantitative Trait Loci; Schizophrenia; Transcriptome