Strategies for Gene Discovery and Mechanistic Insight Using Pleiotropy and Induced Mutagenesis
Akle Serrano, Sebastian
MetadataShow full item record
CitationAkle Serrano, Sebastian. 2020. Strategies for Gene Discovery and Mechanistic Insight Using Pleiotropy and Induced Mutagenesis. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractLarge-scale projects geared towards uncovering the genetic basis for complex traits in both human and non-human species have generated extensive data on a multitude of phenotypes. In order to interpret these data, testable mechanistic hypotheses explaining genetic findings need to be generated. Additionally, studies of many traits remain underpowered, lacking the sample sizes needed to discover most genetic associations.
In this thesis we use two different approaches to tackle those challenges. First, we use model organisms to find and prioritize genes and pathways involved in the aetiology of a given trait, in a context where further targeted experimentation is feasible and ethical. Secondly, for an independent approach, we stay within the boundaries of human GWAS and use pleiotropic signals from correlated traits to both bolster statistical power and generate biological hypotheses behind genetic associations.
In chapter one we propose a strategy to increase power to detect genetic associations in small genome wide association studies (GWAS) in humans, using obstructive sleep apnea (OSA) as a test case. This strategy leverages signals from pleiotropic loci with associations to both OSA and traits correlated to OSA in well-powered GWAS. We show that this strategy also provides mechanistic hypotheses behind those genetic associations. Among other findings, we show links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of desmoplakin (DSP) in lung tissue.
In chapter two, we carry out a large quantitative trait locus (QTL) mapping experiment aided by mutagenesis in zebrafish. We find two previously unknown genetic associations to sex, and one to length. By mapping the few induced mutations in the locus, we are able to propose a short list of candidate causal variants for experimental follow up.
In chapter three we describe a novel imputation and phasing algorithm for next generation sequencing data from samples with low coverage that is robust to high sequencing error rates. We use this algorithm to impute single nucleotide polymorphisms (SNPs) in the zebrafish samples from chapter two.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365143
- FAS Theses and Dissertations