Person:

Guo, Michael

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

Genetics is an incredibly powerful tool for interrogating human biology, as it allows us to identify genes influencing biological processes and diseases in a hypothesis-free manner and in the context of the human body. The discovery of these genes can also have diagnostic and prognostic value for patients. The study of human genetics can be applied along the allelic spectrum, from rare variants in Mendelian disorders to common variants in polygenic traits and diseases. Here, I perform genetic studies along the allelic spectrum to uncover underlying biological and disease mechanisms for human growth and development. First, I demonstrated that exome sequencing can be a useful tool for diagnosing rare growth disorders and can lend unsuspected insights into the phenotypic spectrum associated with various genes. I also generated a novel resource for evaluating phase of rare variants, which will aid in the diagnosis of recessive Mendelian conditions. I next applied a segregation-based approach to identify mutations in ACAN as the genetic cause of a short stature syndrome, which generated insights into mechanisms of growth and skeletal maturation. I also applied a gene-based burden testing approach to try to find novel genes for a rare disorder of reproductive timing, and extended existing methods to leverage publicly available controls in a resource-efficient manner. As the gene-based burden testing approach has not been rigorously evaluated in the context of Mendelian disorders, I performed extensive simulations, which revealed components of genetic architecture that affect power of this approach. Finally, I performed genetic and functional studies at the HMGA2 locus to understand how common variants regulating HMGA2 influence height, a classic polygenic trait. These studies demonstrate how analyses of genetic variation along the allelic spectrum can offer complementary and unsuspected insights into biological processes and diseases. This dissertation covers a wide breadth of genetic studies and advances our understanding of the genetic architecture and pathophysiology of disorders of growth and development.