Large-scale genomic inference of multiple phenotypes

Many human diseases and other observable non-disease traits are multifactorial. Some of them have shared genetic bases, yet a systematic analysis of such components across human phenome has been challenging, limiting our understanding of the shared genetic factors across traits and their influences on disease. The emergence of genotyped cohorts with dense phenotypic information and catalogs of molecular profiles provides unprecedented opportunities. The statement of the dissertation is that given a sufficiently large genomic dataset with multiple phenotypes, such as population-based genotyped cohorts with dense phenotypic information or large-scale functional genomic datasets with rich molecular phenotypes, we can typically learn the genetics of diseases better by jointly analyzing multiple traits. This dissertation consists of 6 chapters. In chapter 1, I introduce some of the key concepts and methods commonly used in large-scale genomic inference, provide an overview of the rest of the thesis, and summarize my other research contributions during my graduate studies. In chapters 2, 3, and 4, I introduce methods to analyze large-scale population-based genotyped cohorts, such as UK Biobank. Specifically, in chapter 2, I introduce Decomposition of Genetic Associations (DeGAs), a method that characterizes the latent genetic components from genome- and phenome-wide association summary statistics. As I describe in the chapter, I analyzed genetic associations across more than 2,000 phenotypes in UK Biobank, systematically characterized latent components and their functional enrichment, and demonstrated its application to guide functional follow-up experiments in adipocytes. This work was published in Nature Communications (Tanigawa*, Li* et al., 2019). Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. In chapter 3, I propose an approach to scan for such protein-altering variants. Using datasets consists of more than 514,000 individuals with European ancestries in two population cohorts in the UK and Finland, as well as multiple phenotyping endpoints, such as intraocular pressure and glaucoma, I report allelic series of rare protein-altering variants in ANGPTL7 show protection against glaucoma. This work was published in PLOS Genetics (Tanigawa et al., 2020). Laboratory tests are often used in clinical practice to guide diagnosis and treatment plans. In chapter 4, I present a comprehensive genetic analysis of 35 blood and urine biomarkers in UK Biobank. I characterized the genetic basis of serum and urine laboratory tests and demonstrated their influences on disease. This work was published in Nature Genetics (Sinnott-Armstrong*, Tanigawa* et al., 2021). In chapter 6, I consider an analysis of molecular phenotypes, specifically focusing on transcription factors (TFs) with an aid of large-scale gene-to-phenotype information characterized from mouse knockout experiments. TFs regulate cellular context-specific functions of the genome, yet finding TFs with cell-type-specific functional importance is challenging. I co-developed WhichTF, a new method designed to address this problem. The method takes an experimentally characterized open chromatin regions as input and returns a ranked list of TFs with an integrative analysis of functional annotation in Mammalian phenotype ontology, sequence conservation, and gene regulatory domain models. A manuscript describing this work (Tanigawa*, Dyer*, and Bejerano. 2019) and its revision have been submitted to a journal. I will conclude in chapter 6, where I summarize the advantages of multi-trait analysis in large-scale human genetic studies and delineate the future prospects

Advisor: Bejerano Gill
Rivas Manuel (Manuel A.) 
Hastie Trevor