@article {114, title = {Genetics of 35 blood and urine biomarkers in the UK Biobank.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 02}, pages = {185-194}, abstract = {

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 {\textquoteright}multi-PRS{\textquoteright} 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}, issn = {1546-1718}, doi = {10.1038/s41588-020-00757-z}, author = {Sinnott-Armstrong, Nasa and Tanigawa, Yosuke and Amar, David and Mars, Nina and Benner, Christian and Aguirre, Matthew and Venkataraman, Guhan Ram and Wainberg, Michael and Ollila, Hanna M and Kiiskinen, Tuomo and Havulinna, Aki S and Pirruccello, James P and Qian, Junyang and Shcherbina, Anna and Rodriguez, Fatima and Assimes, Themistocles L and Agarwala, Vineeta and Tibshirani, Robert and Hastie, Trevor and Ripatti, Samuli and Pritchard, Jonathan K and Daly, Mark J and Rivas, Manuel A} } @article {131, title = {Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 01 13}, pages = {350}, abstract = {

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}, issn = {2041-1723}, doi = {10.1038/s41467-020-20516-2}, author = {Amar, David and Sinnott-Armstrong, Nasa and Ashley, Euan A and Rivas, Manuel A} } @article {139, title = {GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background.}, journal = {Elife}, volume = {10}, year = {2021}, month = {2021 Feb 15}, abstract = {

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits-urate, IGF-1, and testosterone-with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

}, issn = {2050-084X}, doi = {10.7554/eLife.58615}, author = {Sinnott-Armstrong, Nasa and Naqvi, Sahin and Rivas, Manuel and Pritchard, Jonathan K} }