Expression quantitative trait loci in human brain tissues

To what extent genetic variability influences gene expression in human primary tissues is a critical question in molecular genetics. Work investigating this phenomenon is not only interesting biologically, but also has the potential to provide mechanistic insight into traits, including disease. The past decade has seen tremendous progress in this field, and this thesis includes a description of work that spanned from the relatively early stages of this type of work, to current, more refined efforts. This work sought to ask three questions: first, are eQTL detectable in brain tissues using whole genome methods; second, are eQTL measurably different in different parts of the brain; and third, does the investigation of eQTL in a particular neuronal cell type offer significant advantages over similar studies in tissue with a mixed cellular composition. In the first part of this work, I present a pilot study aimed at assessing the feasibility of eQTL detection in brain tissue. This study showed that the use of genome wide genotyping and expression arrays revealed a number of significant eQTL, and that in general, when genetic variability was associated with expression, the genetic locus and the expressed transcript were physically close. This work was then expanded to assess eQTL in multiple brain regions, with an attempt to assess whether eQTL were measurably different between distinct brain regions. In this work, tissue from cerebral frontal cortex, cerebral temporal cortex, caudal pons, and cerebellum was used. The analysis showed that there are region-specific eQTL, but that many of the strongest eQTL were present in multiple tissues. Lastly, I show using data from laser capture microdissected Purkinje cells that additional cell-type specific eQTL may be found that are not revealed when performing eQTL in heterogeneous tissue containing this cell type. In summary this work initially revealed the feasibility of eQTL work in human brain, showed that eQTL were measurably different, but generally similar across varied brain tissues, and showed that there are likely several advantages in pursuing single cell type work in tandem with whole tissue efforts

A genetic analysis of molecular traits in skeletal muscle

Genome Wide Association Studies (GWASs) have identified variants associated with disease that promise to deliver insights into disease aetiology. However, because many GWAS variants lie in non-coding genomic regions, it is difficult to define the genes and pathways underlying a GWAS signal. The possibility of linking GWAS variants to molecular traits, combined with the development of high throughput assays, has motivated the mapping of molecular quantitative trait loci (QTLs), genetic associations with molecular traits such as gene expression (eQTLs) and DNA methylation (mQTLs). The Finland-United States Investigation of NIDDM (FUSION) tissue biopsy study is motivated by the desire to understand the molecular pathogenesis of Type 2 diabetes (T2D), a complex disease where the vast majority of the ~100 independent GWAS loci occur in non-coding regions. To elucidate the molecular mechanisms underlying these signals, we collected skeletal muscle biopsies, a T2D-relevant tissue, from 318 extensively phenotyped individuals who exhibit a range of glucose tolerance levels. From these biopsies, we generated genotype, gene expression, and DNA methylation information, enabling us to directly measure the effects of T2D on molecular traits, and to link non-coding T2D GWAS loci to candidate molecular targets. In this thesis, I present a catalogue of genetic effects on gene expression and DNA methylation. I use this catalogue firstly, to reveal basic biology of the genetic regulators of skeletal muscle molecular traits, and secondly, to identify molecular traits that are relevant to T2D, glycemic, and other complex traits. In regards to basic biology, I characterise the broader genomic context of QTLs by calculating the enrichment of QTLs in chromatin states across a diverse panel of cell/tissue types. I also identify key skeletal muscle transcription factors (TFs) and classify them as activators or repressors by aggregating the effects of QTLs predicted to perturb TF binding sites. In addition, I characterise the properties of methylation sites associated with gene expression and use inference models to dissect these methylation-expression relationships, classifying cases where the genetic effect is mediated by methylation, expression, or is independent. I also integrate molecular trait genetics with complex traits. First, I perform a conditional analysis, mapping GWAS variants for T2D and glycemic traits to molecular traits, prioritising disease relevant skeletal muscle molecular traits. Second, recognising QTLs may also be specific to a disease state or environmental context, I leverage the rich phenotyping of participants to map genotype by environment (GxE) effects on gene expression—eQTLs that exhibit effects specific to an environmental context. Altogether, these analyses form a thorough survey of the genetic regulators of skeletal muscle expression and DNA methylation, and provide an important resource for interpreting complex diseases.National Institutes of Health - Cambridge scholars progra