Exploring The Genetic Architecture Of Complex Diseases
| dc.contributor.author | Chang, Diana | en_US |
| dc.contributor.chair | Keinan, Alon | en_US |
| dc.contributor.committeeMember | Clark, Andrew | en_US |
| dc.contributor.committeeMember | Mezey, Jason G. | en_US |
| dc.contributor.committeeMember | Altan-Bonnett, Gregoire | en_US |
| dc.date.accessioned | 2015-01-07T20:57:10Z | |
| dc.date.available | 2019-08-19T06:01:41Z | |
| dc.date.issued | 2014-08-18 | en_US |
| dc.description.abstract | Over the past decade, the number of genome-wide association studies (GWAS) carried out has increased exponentially. These studies, mostly by investigating single nucleotide polymorphisms (SNPs), have discovered thousands of new loci associated to numerous complex diseases and traits, such as Crohn's Disease, Type-1 and Type-2 diabetes, height and body mass index. Unfortunately, there are several limitations to current GWAS. Firstly these newly discovered associations fail to explain all of the observed phenotypic variability attributed to genetic sources. This issue of missing heritability can be attributed to multiple sources such as rare variants, epigenetics and gene-gene interactions. Secondly, the majority of GWAS have not investigated the contribution of the sex chromosomes to complex disease. And thirdly, though comorbidity studies have well-established the overlap between some diseases, many initial GWAS focused on single phenotypes, and are only recently investigating the genetic overlap between various complex diseases (and traits). Here, we investigate and extend various aspects of GWAS to address these issues. First, we investigate the implication of rare or low frequency causal variants (SNPs with a minor allele frequency <5%) for GWAS and find that when diseases are caused by (unassayed) rare variants, the associated SNPs tend to lie further away than expected when diseases are caused by common variants. Second, we investigate the role of chromosome X in complex disease. The X chromosome was routinely ignored and mishandled in many GWAS, thus possibly explaining the lack of X-linked associations. Hence, we developed an X-tailored pipeline and applied it to 16 datasets of autoimmune and immune-mediated disorders. We found several genes implicated in disease risk, some of which have sexdifferentiated function. Finally, we developed a novel method, disPCA, that uses principal component analysis to investigate the shared genetics between various complex diseases and traits. Applying disPCA to 31 GWAS datasets, we found several pathways that may underlie shared pathogenesis between distinct diseases and traits. Though genotyping-based GWAS are being quickly replaced with sequencing-based association studies, the conclusions and tools developed here can also be applied to this new generation of data. | en_US |
| dc.identifier.other | bibid: 8793286 | |
| dc.identifier.uri | https://hdl.handle.net/1813/38789 | |
| dc.language.iso | en_US | en_US |
| dc.subject | GWAS | en_US |
| dc.subject | Complex diseases | en_US |
| dc.title | Exploring The Genetic Architecture Of Complex Diseases | en_US |
| dc.type | dissertation or thesis | en_US |
| thesis.degree.discipline | Computational Biology | |
| thesis.degree.grantor | Cornell University | en_US |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph. D., Computational Biology |
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