Summer Students

Research Project at The Churchill Lab

Type II diabetes is a disease that affects over 17.9 million people in the United States alone. The goal of this study is to develop a gene expression model that shows the relationships between specific genes in the liver, and how they affect the phenotypes insulin and glucose. The first part of my project involved analyzing all the transcript data for liver tissue that I was given. I used R, a statistical software, to identify quantitative trait loci, or regions of the genome that have a strong relationship with the clinical trait in question, and correlated the transcripts with insulin and glucose. This produced a gene list that I was then able to use to produce a graphical model showing how these genes interact with each other. I identified chromosomes 1,17, and 6 as chromosomes of interest using QTL analysis of insulin and glucose. I also identified approximately forty genes of interest. The next step in this project is to map these genes to the insulin signaling pathway to determine their effect on insulin resistance, and identify strong candidates that can be linked to the direct cause of insulin resistance and type II diabetes.

Elizabeth investigated hypertension as a complex phenotype and searched for the genetic basis of this widespread disease using quantitative trait loci analysis. In her study she conducted a QTL analysis of systolic blood pressure in F2 males from eight intercrosses comprising fourteen inbred mouse strains. The use of multiple crosses allows for greater precision in narrowing QTL regions through identification of concordant peaks. The search for individual and interacting pairs of loci affecting systolic blood pressure indicated fourteen significant QTL, three epistatic interactions, and linked QTL on Chr 3. Through multiple regression analysis, she developed multiple QTL models for each of the eight intercrosses accounting for as much as 35% of phenotypic variance. These novel loci affecting blood pressure contributed to our understanding of the complex genetic basis of hypertension.

Identification of a Genetic Network Linking Sleep and Adiposity

Obesity is one of the most detrimental health problems of the 21st century. Over the past few decades, the obesity epidemic has become a major public health problem of the 21st century. In 2008, approximately 500 million adults were diagnosed as obese. By 2015, scientists predict that 700 million adults will be diagnosed as obese in America (World Health Organization, 2011). Past studies have shown that there may be a relationship between amount of sleep and adipose tissue, but the intricacies of this relationship have not been thoroughly studied (University of Chicago Medical Center, 2010). Circadian rhythms, controlled by the suprachiasmatic nucleus in the brain, control both the sleep-wake cycle and the feeding cycle and may be the underlying mechanism linking sleep to adipose tissue (Laposky et al, 2007). A method for studying underlying mechanisms, Quantitative Trait Loci analysis , will be conducted on gene expression data from the different regions of the brain and multiple sleep-wake traits from the B6/BALB cross in order to determine the varying relationships between the sleep-wake traits in addition to relationships between both the expression data and the sleep traits. After linkage analysis has been conducted, significant genes identified through the gene expression data as well MGI will be used to create a genetic network linking adiposity and metabolic genes to sleep QTL Preliminary results show that chromosomes 5 and 13 may be chromosomes of interest in the phenotypic data. Using the confidence intervals of chromosome 13 in the phenotypic data, 600 genes have been identified for chromosome 13 using MGI. In the expression data , there were 120 significant genes with 5 of these genes overlapping between the two. Further analysis of the relationship between the genes and sleep-wake traits will be conducted using conditional genome scans. Additionally, we will strategic equation mapping analysis between to the sleep-wake traits to determine any causal, reactive or independent relationships.

Text about the project.Natural variation is an important component underlying multiple traits of medical and commercial relevance. The objective of this study was to use genotype and microarray data to predict unobserved phenotypes. Methodology/Principal Findings: Linear models representing trait networks were built in soybeans (Glycine max) from the training dataset, which consists of genotype data at 941 markers and microarray data for 28,395 genes. The approach used natural genetic variation to infer statistical models of complex networks for genetic and genomic data. Numerous models that incorporate genotype data, microarray data, and a combination of both data sets were estimated to predict complex disease phenotypes related to a major soybean pathogen (Phytophthora sojae). Linear Models were adjusted by two methods: the least squares regression and a regularized multivariate regression approach called Elastic Net. Their predictive ability was assessed by 10-fold cross validation to measure prediction. For genotype data, QTL analysis followed by step-wise variable selection outperformed Elastic Net in terms of the prediction accuracy of the two phenotypes (Err=0.823, 0.670 vs. Err=1.00, 0.982). For microarray data, Elastic Net produced the most predictive model (0.332 and 0.327 vs. 1.210 and 0.980). Combining genotype and microarray data showed that Elastic Net produces the best predictive model for Phenotype 1, while linear modeling produced the best model for Phenotype 2 (Err=0.926, 0.982 vs. Err=1.79, 0.769). Results were consistent for two disease resistance phenotypes analyzed separately. Conclusions: Genome-wide gene expression data best captures information about genetic effects on disease resistance in soybean. The model was not improved by the addition of QTL genotypes in the model.

Identification of QTL and Candidate Genes Underlying Sleep

Sleep, a fundamental behavior that occupies about one-third of the human lifespan, has been linked to various physical and behavioral diseases such as obesity or metabolic syndrome. While there is much known about sleep-wake properties, little is known about the identity or nature individual of genes behind sleep regulation. We have set out to identify individual genes that regulate sleep traits in order to further our understanding of the mechanisms underlying sleep and its effect on our bodies. We performed quantitative trait locus (QTL) mapping using the R statistical programming language in order to determine regions of the genome that influence sleep traits. Results from the analysis revealed a large number of sleep QTLs on chromosome 17. We then set out to find genes that shared a QTL with sleep traits, and to correlate expression data from said genes with sleep traits using R in order to compile a list of genes that regulate the sleep QTLs on chromosome 17. We identified Lta, a gene known to be a cause of abnormal sleep patterns, and Twsg1, which may be further researched as a gene involved in sleep mechanisms.

David investigated gene expression patterns in multi-factorial DNA microarray data. The core of his study was the utilization of ANOVA-based statistical tests to test general and focused research hypotheses through overall F-tests and specific contrasts. Groups of co-expressed genes were resolved through hierarchical and k-means clustering analysis. Important biological processes associated with key factors were determined by statistical tests for association with gene ontology (GO) terms. Finally, he investigated relationships between gene expression levels and phenotypic response patterns.

Luis worked on analyzing, quantifying, and assessing the gene expression in 12 inbred mouse strains of each sex raised on high fat or chow diets using Genechip arrays. Luis assessed probe level quality, normalized probe intensity, adjusted background, and performed graphical diagnostics on microarray images. He then assessed the influence and interactions of strain, sex, and diet on the overall intensity value of each gene on the array. He finally generated lists of genes that will be studied for functional associations using available genomic annotation software tools.