Overview
Why do we study gene expression as a quantitative trait?
- Because the road from genotype to phenotype runs through gene expression
- Because we know a lot about simple phenotypes and their causes, but very little of the genetic basis of complex and quantitative traits including most common diseases
The Problem
- Complex and quantitative traits are poorly defined. We don’t know:
- the number of loci underlying variation in heritable phenotypes
- the distribution of effect sizes
- the mechanism of action or interaction
- how the environment influences quantitative traits
Expression quantitative trait (eQTL) analysis
- Genome-wide gene expression is heritable in most organisms
- Insight into heritability, effect sizes, mechanisms, and environmental influences
- can be studied in fields of biomedicine, agriculture and evolutionary biology
- same approach as with phenotypes like cholesterol, plant yield or bird egg size
- Abundance of transcript is a quantitative trait like these others
- can be described with linkage and association mapping like these others
Introduction
Genome variability influences disease risk
- our task - identify effects of genome variants to understand disease biology or organismal phenotype
Simple Mendelian traits demonstrate direct route from geno to expression to pheno
- CF involves variants of a single gene that result in misshapen Cl+ channel protein
Complex & quantitative traits not so directly understood
- includes common human diseases - asthma, Alzheimer’s, most cancers & others
- complex interplay between not one but many genes & the environment
- GWAS associate genetic variants with complex traits, but most variants located in non-coding regions
- likely involved in gene regulation, which controls quantity, timing & locale of gene expression
eQTL analysis studies effects of genetic variants on cell or tissue gene expression
- eQTL is locus associated with expression of gene(s)
- explains some of variation in gene expression
The promise
- reveal architecture of quant traits
- connect DNA sequence variation to phenotypic variation
- shed light on transcriptional regulation & regulatory variation
- traditional linkage & association mapping applied to thousand of GE transcripts
- much like mapping physiological phenotypes like cholesterol or blood pressure
- combine GE and physiological phenos with genetic variation to find genes affecting disease phenos
We’ll look at an eQTL study of type-2 diabetes-associated traits in DO mice as example
Genetic Drivers of Pancreatic Islet Function
T2D is a complex disease influenced by many genes interacting with the environment
- environment changed drastically as we moved away from farming into factories
- Industrial agriculture drives a food pipeline of soft drinks, burgers & cheap foods
- U.S. farms produce 3900 cals per citizen, twice what we need and up 700 cals from 1980
- Food industry found a way to get extra calories into us even if we didn’t want them
- Larger packaging, supersizing - we are willing subjects in this experiment
- Prevalence of T2D has expanded rapidly as has obesity
Susceptibility to T2D increases with obesity such that T2D loci operate mainly under obesity
- Most of 100 loci associated with T2D affect pancreatic islet function
- produce insulin for regulating blood glucose
- like most GWAS loci, T2D loci have very small effect sizes and odds ratios ~ 1
Genetic Drivers studied islet gene expression
- hypothesis: gene expression changes in response to dietary challenge would reveal signaling pathways involved in stress responses
- expression of many genes often map to same locus, so genes are regulated in common
- if their mRNAs encode proteins with common functions, function of driver gene revealed
- variation in expression of driver gene, rather than genetic variant, is immediate cause of disease-related pheno
DO mice on high-fat high-sugar diet as stressor sensitizing them to develop diabetic traits
- body weight, plasma glucose, insulin, triglyceride
- glucose tolerance test with measure of dynamic glucose and insulin changes over time
- area under curve for glucose and insulin
- Islet cells isolated from pancreas for RNA extraction & GE measurements