The purpose of this study was to analyze the frequency distributions of alleles at the 3$ sp prime$HVR (hypervariable region) and 5$ sp prime$HVR, two highly polymorphic regions on chromosome 16p. About 300 DNA samples from individuals of East Asian, French Canadian, Greek, Italian, Jewish and Middle Eastern origin were analyzed by hybridization to probes for the 3$ sp prime$HVR and 5$ sp prime$HVR. / The distributions of alleles at both loci are skewed with the long tail towards the larger alleles. The observed heterozygosity at the 3$ sp prime$HVR locus for 281 individuals was 0.91, ranging from 0.85 in the Jewish group to 1.00 among French Canadians and East Asians. Statistical analysis demonstrated significant variation among some of the ethnic groups. / The observed heterozygosity at the 5$ sp prime$HVR locus in 225 individuals was 0.75. Heterozygosity varied from 0.91 in East Asians to 0.61 of Middle Eastern samples studied. 28% of samples also display a RsaI site polymorphism near the 5$ sp prime$HVR locus. / Genetic distance analysis demonstrated that the largest distance at these two loci exists between the Jews and East Asians (D = 0.119). / Both the 3$ sp prime$HVR and the 5$ sp prime$HVR are extremely variable in all the populations studied, and thus will serve as informative markers for chromosome 16p for clinical as well as population studies.

No description available.

Assessing the functional impact of genetic variants across the human genome is essential for understanding the molecular mechanisms underlying complex traits and disease risk. Genetic variation that causes changes in gene expression can be analyzed through parallel genotyping and functional genomics assays across sets of individuals. In particular, regulatory variants may impact transcription factor regulation. In this thesis, to map variants that impact the expression of many genes simultaneously through a shared transcription factor (TF), we use an approach in which the protein-level regulatory activity of the TF is inferred from genome-wide expression data and then genetically mapped as a quantitative trait. In Chapter 2, we developed a generalized linear model (GLM) to estimate TF activity levels in an individual-specific manner, and used it to analyze RNA-seq profiles from the Genotype-Tissue Expression (GTEx) project. A key feature is that we fit a beta-binomial GLM at the level of pairs of neighboring genes in order to control for variation in local chromatin structure along the genome and other confounding effects. As a predictor in our model, we use differential gene expression signatures from TF perturbation experiments. After estimating genotype-specific activities for 55 TFs across 49 tissues, in Chapter 3, we performed genome-wide association analysis on the virtual TF activity trait. This revealed hundreds of TF activity quantitative trait loci, or aQTLs, highlighting the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omic approach. Lastly, in Chapter 4, we studied the direct impact of genetic variants on TF binding by predicting genetic effects on TF binding affinity. Specifically, we predicted binding affinity on allele-specific binding data using TF binding models derived by the ProBound recently developed by our laboratory, and constructed a likelihood model to assess the performances across different binding models. This indicates that ProBound provides a promising tool for the prediction of genetic effects on in vivo TF binding.

Indiana University-Purdue University Indianapolis (IUPUI) / The information gained from sequencing of the human genome has begun to transform human biology and genetic medicine. The discovery of functionally important genetic variation lies at the heart of these endeavors, and there has been substantial progress in understanding the common patterns of single-nucleotide polymorphism (SNP) in humans- the most frequent type of variation in humans. Although more than 99% of human DNA sequences are the same across the population, variations in DNA sequence have a major impact on how we humans respond to disease; to environmental entities such as bacteria, viruses, toxins, and chemicals; and drugs and other therapies and thus studying differences between our genomes is vital. This makes SNPs as well other genetic variation data of great value for biomedical research and for developing pharmaceutical products or medical diagnostics. The goal of the project is to link genetic variation data to biological pathways and networks data, and also to clinical data for creating a framework for translational and systems biology studies. The study of the interactions between the components of biological systems and biological pathways has become increasingly important. It is known and accepted by scientists that it as important to study different biological entities as interacting systems, as in isolation. This project has ideas rooted in this thinking aiming at the integration of a genetic variation dataset with biological pathways dataset. Annotating genetic variation data with standardized disease notation is a very difficult yet important endeavor. One of the goals of this research is to identify whether informatics approaches can be applied to automatically annotate genetic variation data with a classification of diseases.

Predicting the effect of missense variants is critically important in population and medical genetics. It is essential to interpret genetic variation in population screening and clinical diagnostic sequencing, to reach optimal statistical power of risk gene discovery in genetic studies of diseases and traits. A quantitative analysis of the fitness effect of all possible missense variants can provide a foundation for understanding how proteins evolve in humans and other species. In this thesis, I describe new methods to infer the effect of missense variants using various machine learning techniques. First, I worked on a ResNet-based supervised model to predict pathogenicity trained on curated databases. The curated clinical databases have uneven quality and uncertain bias across genes. To address this issue, I developed a new method, MisFit, to separately model the molecular effect and population fitness effect of missense variants, and to estimate them jointly using a probabilistic graphical model. The architecture of MisFit follows the biological causality of the variant effect, that is, for a missense variant, the protein sequence and structure context determine its molecular effect, which in turn determines its fitness effect given how the protein is involved in various conditions and traits. The latter is a latent factor encapsulated in a sigmoid-shaped function with gene-specific parameters. The fitness effect determines the expected allele counts in human populations. This model can be trained using large-scale population genome data without known pathogenicity labels. I investigated how informative allele counts are for inferring fitness effect using simulations with realistic demographic parameters. To take advantage of the latest deep learning techniques and large population genome data sets, I use a Poisson-Inverse-Gaussian distribution, which is differentiable, to approximate the probability of allele counts given fitness effect and sample size. We show that MisFit estimated heterozygous selection coefficient of missense variants is consistent with ratio of de novo mutations among observed variants in a population with child-parents trio data. Furthermore, de novo missense variants with selection coefficient >0.01 are significantly enriched in neurodevelopmental disorders cases, achieving the best performance in prioritization of de novos for new risk gene discovery compared to previous methods. We also show that the estimated molecular effect reached the state-of-the-art performance in the classification of damaging variants in deep mutational scanning assays, with improved consistency of the score scale across genes. Finally, I analyzed the transmission disequilibrium of inherited variants in autism using a new empirical Bayesian method to identify risk genes, which models relative risk as a continuous function of variant effect in each gene.

published_or_final_version / abstract / Pathology / Master / Master of Philosophy

El trabajo realizado trata de estudiar la diversidad del genoma mitocondrial humano en poblaciones humanas de diferentes áreas geográficas que habían sido hasta ahora poco o nada estudiadas. Los grupos de poblaciones humanos estudiados en este trabajo esta formado por las poblaciones del oeste del Mediterráneo, de l'África sub-Sahariana, de la Isla de La Reunión, y de l'Asia. Cada una de estas poblaciones pertenecen a un entorno geográfico diferente y han padecido diferentes y numerosos movimientos de poblaciones que han modulado su composición genética. El análisis de diferentes polimorfismos del genoma mitocondrial han permitido entender los factores poblacionales, tal como la migración, la mezcla genética, la deriva genética, los efectos fundadores, y inferir la historia d la poblaciones bajo estudio, La metodología utilizada incluye diferentes tipos de técnicas adaptadas a los diferentes tipos de polimorfismos estudiados. La técnica aplicadas fueron la secuenciación, el análisis de fragmentos y la técnica de SNaPshot. Los resultados obtenidos han aportado un conocimiento nuevo de las poblaciones que han modulado la diversidad genética de los grandes grupos humanos a nivel continental pero también a un nivel mas regional.

Las enfermedades genéticas se clasifican típicamente en dos grandes grupos: las enfermedades mendelianas y las enfermedades complejas. Mientras que las enfermedades mendelianas se caracterizan por ser de baja frecuencia en la población y estar causadas por mutaciones en un gen particular, las enfermedades complejas son el principal problema sanitario en los países desarrollados y se encuentran producidas por la interacción de factores ambientales y factores genéticos. En este caso no se puede hablar de mutación en un determinado gen, sino de polimorfismo que incrementa en una pequeña fracción el riesgo a padecer la enfermedad. En la presente tesis se ha estudiado la distribución espacial de la variabilidad genética tanto en enfermedades mendelianas (en concreto la fibrosis quística, la fenilcetonuria y la b-talasemia) como en una enfermedad compleja (la enfermedad coronaria) en poblaciones europeas y de todo el mundo. Los resultados obtenidos sugieren que la distribución geográfica de la variabilidad genética de las enfermedades mendelianas depende principalmente de factores demográficos y de la historia de las poblaciones. Ahora bien, este efecto no es independiente de factores selectivos. En particular, fenómenos de selección equilibradora pueden incrementar o disminuir la variabilidad genética en una población dependiendo de el momento en el que se dio el evento selectivo. En el caso de la enfermedad compleja estudiada, la enfermedad coronaria, nuestros resultados indican que la distribución espacial de los polimorfismos de riesgo en poblaciones europeas depende, al igual que sucede con otros marcadores genéticos, principalmente de la historia de poblaciones, especialmente del poblamiento del continente europeo, la posterior reexpansión después del último periodo glacial y de las gran expansión poblacional de los agricultores durante el neolítico.

Immune activation is generally acknowledged to be costly. These costs are frequently assumed to result from trade-offs arising due to the reallocation of resources from other life-history traits to be invested in immunity. Here, I investigated the energetic basis of the costs associated with immune activation in Drosophila melanogaster. I found that immune activation significantly reduced fly fecundity (45%) and also caused a decline in metabolic rate (6%) but had no effect on body weight. To understand the factors behind reduced fecundity and metabolic rate I measured feeding and found that food intake was reduced by almost 31% in immune-challenged D. melanogaster. These findings suggest that fecundity costs of immune activation result not from the commonly accepted resource reallocation hypothesis but probably because resource acquisition is impaired during immune responses. The individuals of any animal population generally vary greatly in their ability to resist infectious disease. This variation arises due to both environmental heterogeneity and genetic diversity. Genetic variation in disease susceptibility has generally been considered to lie in the nuclear genome. Here, for the first time, I explored the influence of mitochondrial genetic (mtDNA) variation on disease susceptibility. I crossed 22 mitochondrial haplotypes onto a single nuclear genome and also studied epistasis interactions between mitochondrial and nuclear genomes (mitonuclear epistasis) by crossing five haplotypes onto five different genetic backgrounds. I found that fly susceptibility to Serratia marcescens was influenced significantly by mtDNA allelic variation. Furthermore, the effect of mitonuclear epistasis on fly susceptibility to S. marcescens was twice as great as the individual effects of either mitochondrial or nuclear genome. However, susceptibility to Beauveria bassiana was not affected by mtDNA allelic variation. These findings suggest the mitochondrial genome may play an important role in host-parasite coevolution. The Mother’s Curse hypothesis suggests that sex-specific selection due to maternal mitochondrial inheritance means that mitochondria are poorly adapted to function in males, resulting in impaired male fitness. Mother’s Curse effects have previously only been studied for two phenotypic traits (sperm-infertility and ageing) and their generality for broader life-history has not been explored. I investigated the impact of mtDNA allelic variation on 10 phenotypic traits and tested whether the patterns of phenotypic variation in males and females conformed to the expectations of the Mother’s Curse hypothesis. I found that seven of the 10 traits were significantly influenced by mtDNA allelic variation. However, there was no evidence that the effects of this variation differed between males and females. I therefore concluded that Mother’s Curse is unlikely to be a general phenomenon, nor to provide a general explanation for sexual dimorphism in life-history traits. Overall, this thesis explored the impacts of immunity costs, mitochondrial genetic variation, mitonuclear epistasis and sex-specific mitochondrial selection on D. melanogaster life-history.

Indiana University-Purdue University Indianapolis (IUPUI) / The time course and biological mechanisms by which breast cancer (BC) and/or alterations in estrogen status lead to cognitive and brain changes remain unclear. The studies presented here use neuroimaging, cognitive testing, genetics, and biomarkers to investigate how post-chemotherapy interval (PCI), chemotherapy-induced amenorrhea (CIA), and genetic variation in the estrogen pathway affect the brain. Chapter 1 examines the association of post-chemotherapy interval (PCI) with gray matter density (GMD) and working memory-related brain activation in BC survivors (mean PCI 6.4, range 3-10 years). PCI was positively associated with GMD and activation in the right frontal lobe, and GMD in this region was correlated with global neuropsychological function. In regions where BC survivors showed decreased GMD compared to controls, this was inversely related to oxidative DNA damage and learning and memory scores. This is the first study to show neural effects of PCI and relate DNA damage to brain alterations in BC survivors. Chapter 2 demonstrates prospectively, in an independent cohort, decreased combined magnitudes of brain activation and deactivation from pre-to post-chemotherapy in patients undergoing CIA compared to both postmenopausal BC patients undergoing chemotherapy and healthy controls. CIA’s change in activity magnitude was strongly correlated with change in processing speed, suggesting this activity increase reflects effective cognitive compensation. These results demonstrate that the pattern of change in brain activity from pre- to post-chemotherapy varies according to pre-treatment menopausal status. Chapter 3 presents the effects of variation in ESR1, the gene that codes for estrogen receptor-α, on brain structure in healthy older adults. ESR1 variation was associated with hippocampus and amygdala volumes, particularly in females. Single nucleotide polymorphism (SNP) rs9340799 influenced cortical GMD and thickness differentially by gender. Apolipoprotein E (APOE)-ε4 carrier status modulated the effect of SNP rs2234693 on amygdala volumes in women. This study showed that genetic variation in estrogen relates to brain morphology in ways that differ by sex, brain region and APOE-ε4 carrier status. The three studies presented here explore the interplay of BC, estrogen, and cognition, showing that PCI, CIA, and ESR1 genotype influence brain phenotypes. Cognitive correlates of neuroimaging findings indicate potential clinical significance of these results.