Indiana University-Purdue University Indianapolis (IUPUI) / Indoleamine 2,3-dioxygenase (IDO) is a rate limiting enzyme in tryptophan catabolism that has been implicated in the pathogenesis of a number of diseases. Large interindividual variability in IDO activity in the absence of stimuli and as the result of therapy induced changes has been reported. This variability has the potential to contribute to susceptibility to disease and to interindividual variability in therapeutic response. To identify genetic variations that might contribute to interindividual variability in IDO activity, we resequenced the exons, intron/exon borders and 1.3 kb of the 5’-flanking region of the INDO gene in 48 African-American (AA) and 48 Caucasian-American (CA) subjects from the Coriell DNA Repository. A total of 24 INDO variants were identified. Seventeen of these were in exons, introns, or exon/intron boundries, while seven were within 1.3 kb upstream of the translation start site. Seventeen are novel and 7 were previously identified. When transiently expressed in COS-7 or HEK293 cells the amino acid sequence change in Arg77His resulted in significant decrease in activity, and it reduced the Vmax of IDO. The Arg77His variant and the 9 bp deletion resulted in nearly complete loss of enzyme activity and a lack of detectable protein expression. The function of the Arg77His variant IDO was restored in a dose dependent manner by the heme analog hemin; but there was no associated increase in IDO protein. Cellular heme concentration was higher in cells transfected with the wild type and Ala4Thr variant constructs, but not in cells transfected with the Arg77His variant. The heme synthesis inhibitor, succinylacetone (SA), blocked IDO activity in cells transfected with Arg77His. We identified 22 putative transcription binding sites within the 1.3 kb upstream of the translation start site. Two of the SNPs were located in GATA3 and FOXC1 sites. A specific 3-SNP haplotype reduced promoter activity when transiently transfected into 2 different cell lines. We conclude that there are naturally occurring genetic variants in the INDO gene which affect both expression and activity. These results make clear that interindividual variability in IDO activity at baseline or in response to therapy may be in part due to inherited genetic variability.

Microorganisms are an indispensable part of our ecosystem, yet the natural metabolic and ecological diversity of these organisms is poorly understood due to a historical reliance of microbiology on laboratory grown cultures. The awareness that this diversity cannot be studied by laboratory isolation, together with recent advances in low cost scalable sequencing technology, have enabled the foundation of culture-independent microbiology, or metagenomics. The study of environmental microbial samples with metagenomics has led to many advances, but a number of technological and methodological challenges still remain. A potentially diverse set of taxa may be represented in anyone environmental sample. Existing tools for representing the genetic composition of such samples sequenced with short-read data, and tools for identifying variation amongst them, are still in their infancy. This thesis makes the case that a new framework based on a joint-genome graph can constitute a powerful tool for representing and manipulating the joint genomes of population samples. I present the development of a collection of methods, called SCRAPS, to construct these efficient graphs in small communities without the availability or bias of a reference genome. A key novelty is that genetic variation is identified from the data structure using a probabilistic algorithm that can provide a measure of the confidence in each call. SCRAPS is first tested on simulated short read data for accuracy and efficiency. At least 95% of non-repetitive small-scale genetic variation with a minor allele read depth greater than 10x is correctly identified; the number false positives per conserved nucleotide is consistently better than 1 part in 333 x 103. SCRAPS is then applied to artificially pooled experimental datasets. As part of this study, SCRAPS is used to identify genetic variation in an epidemiological 11 sample Neisseria meningitidis dataset collected from the African meningitis belt". In total 14,000 sites of genetic variation are identified from 48 million Illumina/Solexa reads. The results clearly show the genetic differences between two waves of infection that has plagued northern Ghana and Burkina Faso.

Mutations cause genetic variations among cells within an individual as well as variations between individuals within a species. It is the fuel for evolution and contributes to most human diseases. Despite its importance, it still remains elusive how mutagenesis and repair shape the mutation pattern in the human genome and how to interpret the impact of a mutation with respect to its ability to cause disease (referred to as pathogenicity). The availability of large-scale genomic data provides us an opportunity to use machine learning methods to answer these questions. This thesis is composed of two parts. In the first part, a single statistical model is applied to both mutations in germline and soma to compare the determinant factors that influence local mutation. Notably, our model revealed that one determinant, expression level, has an opposite effect on mutation rate in the two types of tissues. More specifically, somatic mutation rates decrease with expression levels and, in sharp contrast, germline mutation rates increase with expression levels, indicating that the DNA damage or repair processes during transcription differ between them. In the second part, we developed a new neural-network-based machine learning method to predict the pathogenicity of missense variants. Besides predictors commonly used in previous methods, we included additional predictors at the variant-level such as the probability of being in protein-protein interaction interface and gene-level such as dosage sensitivity and protein complex formation probability. To benchmark real-world performance, we compiled somatic mutation data in cancer and germline de novo mutation data in developmental disorders. Our model achieved better performance in prioritizing pathogenic missense variants than previously published methods.

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Rare gene-disrupting variants and common regulatory variants play key roles in rare and common disease, respectively. These variants are of great interest for investigation into genetic contributions to disease, but experimental methods to validate their impact on gene expression levels are lacking. In this study, we utilized CRISPR/Cas9 genome editing to validate regulatory variants including cis-eQTLs, rare stop-gained variants in healthy and disease cases and one immune-response trans-eQTL master regulator. For investigation into common and rare regulatory variants within transcribed regions, we developed a scalable CRISPR-based polyclonal assay for experimental assessment. First, we applied this assay to nine rare stop-gained variants found in the general population, in GTEx. After editing, the stop-gained variants show a significant allele-specific depletion in transcript abundance, as expected. Next, we utilized the assay to validate 33 common eQTLs found in GTEx. After editing, the eQTL variants show higher variance in effect size than control variants, indicating a regulatory effect. Finally, we applied the polyclonal editing approach to clinical and new stop-gained variants in two disease-associated genes. The results follow the expected trend, with NMD being triggered by variants upstream of the NMD threshold but not by those beyond. This method demonstrates scalable experimental confirmation of putative causal regulatory variants, and improved interpretation of regulatory variation in humans. Next, we sought to experimentally validate an immune-response eQTL for IRF1 in cis and many genes in trans under LPS stimulation. We used CRISPRi to repress the enhancer locus and found that the enhancer is active in our immune cell system. Next, we used CRISPR-Cas9 genome editing and isolation of monoclonal cell lines to target this variant locus. After LPS stimulation, we performed RNA-sequencing on wild type and edited clones, showing that the effect size of the genes which are associated with the trans-eQTL are correlated with differential expression between the edited and wild type cell lines for the same genes. Additionally, we find that the differential expression between edited clones is correlated with CRISPRi repression of the IRF1 promoter and enhancer. In this way, we were able to identify a common genetic variant which modifies the transcriptomic immune response to LPS and validate the trans-eQTL signal.

Bridging the gap between genetic variants and functional relevance is a principal goal of human genetics. Despite centuries of research, interpreting the biological mechanisms that link variants to phenotypes is a continuous challenge. This goal applies to rare and common variants, although the specific challenges vary depending on the variant’s frequency and effect on gene dosage or protein structure. Deciphering these variants’ modes of action is crucial for a more holistic understanding of genome regulation. This dissertation advances interpretation of rare and common variants across the annotation spectrum, by utilizing functional data derived from population scale RNA-sequencing studies. Thus, three main research questions are addressed: (1) How do rare variants affect gene expression, and can these subtle changes be robustly detected? (2) How do common variants that influence pre-mRNA splicing influence protein structure and human traits? (3) Can joint effects between common splice-regulatory and rare loss-of-function variants be detected through the lens of purifying selection? All three chapters build on knowledge acquired through large-scale transcriptomics and open access data. Chapter 1 evaluates the utility of allele specific expression to prioritize variants with functional effects. Chapter 2 involves quantifying splicing using the common Percent Spliced In (PSI) metric, and performing quantitative trait locus (QTL) mapping. Chapter 3 builds on the known phenomenon of modified penetrance, where common regulatory variants reduce the pathogenicity of rare coding variants. Ultimately, these three studies will contribute to our knowledge of genome regulation, which will be crucial in a future of personalized medicine.

This thesis focuses on developing novel statistical tests for rare variants association analysis incorporating both marginal effects and interaction effects among rare variants. Compared with common variants, rare variants have lower minor allele frequencies (typically less than 5%), and hence traditional association tests for common variants will lose power for rare variants. Therefore, there is a pressing need of new analytical tools to tackle the problem of rare variants association with complex human traits. Several collapsing methods have been proposed that aggregate information of rare variants in a region and test them together. They can be divided into burden tests and non-burden tests based on their aggregation strategies. They are all variations of regression-based methods with the assumption that the phenotype is associated with the genotype via a (linear) regression model. Most of these methods consider only marginal effects of rare variants and fail to take into account gene-gene and gene-environmental interactive effects, which are ubiquitous and are of utmost importance in biological systems. In this thesis, we propose a summation of partition approach (SPA) -- a nonparametric strategy for rare variants association analysis. Extensive simulation studies show that SPA is powerful in detecting not only marginal effects but also gene-gene interaction effects of rare variants. Moreover, extensions of SPA are able to detect gene-environment interactions and other interactions existing in complicated biological system as well. We are also able to obtain the asymptotic behavior of the marginal SPA score, which guarantees the power of the proposed method. Inspired by the idea of stepwise variable selection, a significance-based backward dropping algorithm(SDA) is proposed to locate truly influential rare variants in a genetic region that has been identified significant. Unlike traditional backward dropping approaches which remove the least significant variables first, SDA introduces the idea of eliminating the most significant variable at each round. The removed variables are collected and their effects are evaluated by an influence ratio score -- the relative p-value change. Our simulation studies show that SDA is powerful to detect causal variables and SDA has lower false discovery rate than LASSO. We also demonstrate our method using the dataset provided by Genetic Analysis Workshop (GAW) 17 and the results support the superiority of SDA over LASSO. The general partition-retention framework can also be applied to detect gene-environmental interaction effects for common variants. We demonstrate this method using the dataset from Genetic Analysis Workshop (GAW) 18. Our nonparametric approach is able to identify a lot more possible influential gene-environmental pairs than traditional linear regression models. We propose in this thesis a "SPA-SDA" two step approach for rare variants association analysis at genomic scale: first identify significant regions of moderate sizes using SPA, and then apply SDA to the identified regions to pinpoint truly influential variables. This approach is computationally efficient for genomic data and it has the capacity to detect gene-gene and gene-environmental interactions.

Human genomic data sets are now reaching sample sizes on the order of hundreds of thousands and soon exceeding millions, providing unprecedented opportunities to understand human evolution. Most studies of human adaptation so far have focused on selection that has acted over the past million to few thousand years. However, powered by large data sets, it is now feasible to study allele frequency changes that occur within the short timescale of a few generations, directly observing selection acting in contemporary humans. I take this approach in the work presented in Chapter 1 of this thesis, where we performed a genome-wide scan to identify a set of genetic variants that influence age-specific mortality in present-day samples. Our findings include two variants in the APOE and CHRNA3 loci, as well as sets of variants contributing to a number of traits, including coronary artery disease and cholesterol levels, and intriguingly, to timing of puberty and child birth. New research directions have also opened up with the advent of large-scale genome-wide association studies (GWAS), which have begun to uncover genetic variants underlying a number of human traits, ranging from disease susceptibility to social and behavioral traits such as educational attainment and neuroticism. One such direction is the use of polygenic scores (PGS), which aggregate GWAS findings into one score as a measure of genetic propensity for traits, for phenotypic prediction. A major obstacle to this application is that the prediction accuracy of PGS drops in samples that have a different genetic ancestry than the GWAS sample. Our work, presented in Chapter 2, demonstrates that PGS prediction accuracy is also variable within genetic ancestries depending on factors such as age, sex, and socioeconomic status, as well as GWAS study design. These findings have important implications for the increasing use of these measures in diverse disciplines such as social sciences and human genetics.

The adaptability and robustness of the central nervous system is partially explained by the vast diversity of neuronal identities. Molecular mechanisms generating such heterogeneity have evolved through multiple independent pathways. The olfactory sensory system provides a unique and tractable platform for investigating at least two orthogonal gene expression systems that generate neuronal diversity through stochastic promoter choice: olfactory receptor genes and clustered protocadherins. Olfactory sensory neuron identity is defined by the specific olfactory receptor (OR) gene chosen. Greater than 1300 OR genes are scattered throughout the mouse genome, and expression of an OR defines a unique sensory neuron class that responds to a selective set of odorants. This work further delineated an unprecedented network interchromosomal (trans) interactions indispensable for singular OR choice. In a largely orthogonal gene expression system, I sought to understand the molecular mechanisms governing stochastic protocadherin choice. Clustered protocadherins are an evolutionary- conserved system that is involved in cell-cell identification through a series of homo- and heterophilic interactions. This work uncovered a methylation-dependent mechanism for generating stochastic gene expression in the context of cis-regulatory elements. Overall, this work highlighted divergent cis and trans transcriptional regulatory mechanisms for generating stochastic gene expression and neuronal diversity.

No description available.