Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

• Published in: Nature communications 2021-11, Vol.12 (1), p.6972-16, Article 6972
• Main Authors: Patxot, Marion, Banos, Daniel Trejo, Kousathanas, Athanasios, Orliac, Etienne J., Ojavee, Sven E., Moser, Gerhard, Holloway, Alexander, Sidorenko, Julia, Kutalik, Zoltan, Mägi, Reedik, Visscher, Peter M., Rönnegård, Lars, Robinson, Matthew R.
• Format: Article
• Language: English
• Subjects: 631/114/2415; 631/114/794; 631/208/205/2138; Bayes Theorem; Bayesian analysis; Biobanks; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi); Body Height; Body mass; Body Mass Index; Body size; Cardiovascular Diseases; Chromosomes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2; Electronic health records; Electronic medical records; Gene mapping; Genetic diversity; Genetic Techniques; Genetic variance; Genetic Variation; Genetics; Genetik; Genome-Wide Association Study; Genomics; Genotype; Heritability; Humanities and Social Sciences; Humans; Introns; Iterative methods; Models, Statistical; multidisciplinary; Multifactorial Inheritance - genetics; Open Reading Frames; Phenotype; Probabilistic inference; Regulatory sequences; Science; Science (multidisciplinary); Single-nucleotide polymorphism; Software; Source code; Statistical analysis
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-27258-9

We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. Improving inference in large-scale genetic data linked to electronic medical record data requires the development of novel computationally efficient regression methods. Here, the authors develop a Bayesian approach for association analyses to improve SNP-heritability estimation, discovery, fine-mapping and genomic prediction.