Browsing by Author "Kimmel, Marek"
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Item A comparison of three methods used to determine functionally important protein residues(2003) Spratt, Heidi Marie; Kimmel, MarekA new method for determining functionally important protein residues is analyzed and compared with two previously existing methods. This thesis presents the analysis of several different protein sequences and shows how the functionally important protein residues compare between the evolutionary trace method, the maximum likelihood method of protein evolution, and the Hidden Markov method of protein evolution. The results are presented graphically as well as structurally since structure information is known about all the protein sequences studied. All three methods produce similar results for most of the proteins and show that the most highly conserved protein residues are detectable by all three methods but that the less conserved important residues may not always be identified by all methods.Item A mathematical model as a tool to identify microRNAs with highest impact on transcriptome changes(BioMed Central, 2019) Mura, Marzena; Jaksik, Roman; Lalik, Anna; Biernacki, Krzysztof; Kimmel, Marek; Rzeszowska-Wolny, Joanna; Fujarewicz, KrzysztofBackground: Rapid changes in the expression of many messenger RNA (mRNA) species follow exposure of cells to ionizing radiation. One of the hypothetical mechanisms of this response may include microRNA (miRNA) regulation, since the amounts of miRNAs in cells also vary upon irradiation. To address this possibility, we designed experiments using cancer-derived cell lines transfected with luciferase reporter gene containing sequences targeted by different miRNA species in its 3′- untranslated region. We focus on the early time-course response (1 h past irradiation) to eliminate secondary mRNA expression waves. Results: Experiments revealed that the irradiation-induced changes in the mRNA expression depend on the miRNAs which interact with mRNA. To identify the strongest interactions, we propose a mathematical model which predicts the mRNA fold expression changes, caused by perturbation of microRNA-mRNA interactions. Model was applied to experimental data including various cell lines, irradiation doses and observation times, both ours and literature-based. Comparison of modelled and experimental mRNA expression levels given miRNA level changes allows estimating how many and which miRNAs play a significant role in transcriptome response to stress conditions in different cell types. As an example, in the human melanoma cell line the comparison suggests that, globally, a major part of the irradiation-induced changes of mRNA expression can be explained by perturbed miRNA-mRNA interactions. A subset of about 30 out of a few hundred miRNAs expressed in these cells appears to account for the changes. These miRNAs play crucial roles in regulatory mechanisms observed after irradiation. In addition, these miRNAs have a higher average content of GC and a higher number of targeted transcripts, and many have been reported to play a role in the development of cancer. Conclusions: Our proposed mathematical modeling approach may be used to identify miRNAs which participate in responses of cells to ionizing radiation, and other stress factors such as extremes of temperature, exposure to toxins, and drugs.Item A microsatellite-based statistic for inferring patterns of population growth: Sampling properties and hypothesis testing(2000) King, J. Patrick; Kimmel, MarekDNA sequences sampled from a genetic locus within a population are related by a genealogy. If there is no recombination within the locus, each pair of sequences is descended from some ancestral sequence, one of which is the most recent common ancestor of the entire sample. Past demography shapes this genealogy since the branch lengths depend on the size history of the population. For this reason, observed distributions of allelic types carry information about the population's demographic history. Because of their abundance and relative ease of typing, microsatellites, or short tandem repeats, represent a useful class of loci for the study of demography. This thesis investigates the properties of the imbalance index beta, a microsatellite-based statistic constructed for demographic inference. Simulated data sets are used to explore the sampling properties of beta and to compare its performance to that of other statistics available in the literature. Tests based on these statistics are applied to samples of microsatellite loci from human populations, and the results are interpreted in light of recent hypotheses concerning the evolution of modern humans.Item A Simulation-based Approach to Study Rare Variant Associations Across the Disease Spectrum(2013-09-16) Banuelos, Rosa; Kimmel, Marek; Leal, Suzanne; Thompson, James R.; Nakhleh, Luay K.Although complete understanding of the mechanisms of rare genetic variants in disease continues to elude us, Next Generation Sequencing (NGS) has facilitated significant gene discoveries across the disease spectrum. However, the cost of NGS hinders its use for identifying rare variants in common diseases that require large samples. To circumvent the need for larger samples, designing efficient sampling studies is crucial in order to detect potential associations. This research therefore evaluates sampling designs for rare variant - quantitative trait association studies and assesses the effect on power that freely available public cohort data can have in the design. Performing simulations and evaluating common and unconventional sampling schemes results in several noteworthy findings. Specifically, the extreme-trait design is the most powerful design for analyzing quantitative traits. This research also shows that sampling more individuals from the extreme of clinical interest does not increase power. Variant filtering has served as a "proof-of-concept" approach for the discovery of disease-causing genes in Mendelian traits and formal statistical methods have been lacking in this area. However, combining variant filtering schemes with existing rare variant association tests is a practical alternative. Thus, this thesis also compares the robustness of six burden-based rare variant association tests for Mendelian traits after a variant filtering step in the presence of genetic heterogeneity and genotyping errors. This research shows that with low locus heterogeneity, these tests are powerful for testing association. With the exception of the weighted sum statistic (WSS), the remaining tests were very conservative in preserving the type I error when the number of affected and unaffected individuals was unequal. The WSS, on the other hand, had inflated type I error as the number of unaffected individuals increased. The framework presented can serve as a catalyst to improve sampling design and to develop robust statistical methods for association testing.Item Analysis of regulatory mechanisms of genes controlled by the transcription factor NF-kappaB(2005) Rajan, Deepa; Kimmel, MarekNF-kappaB transcription factors are central to the regulation of many vital processes including immune response. It is known from microarray measurements and clustering methods that NF-kappaB dependent genes in humans are expressed in functionally distinct "waves". This research helps determine how the expression of these sequences is a consequence of their structure, molecular constitution and evolution. This thesis identifies location of TF-binding sites and consensus regions in the DNA sequences that are upregulated by NF-kappaB and examines their structure. This project uses a variety of tools and databases available for sequence analysis including Blast, BLAT, MATCH, GeneBee, BioProspector and MEME. This analysis is one aspect of the larger 'Investigation of NF-kappaB Pathways' project underway at UTMB, Galveston and Rice University. Future promoter analysis of these results will verify the location of functional regulatory sites, thereby enabling us to postulate and verify a model governing expression of NF-kappaB dependent genes.Item Application of the Moran Model in Estimating Selection Coefficient of Mutated CSF3R Clones in the Evolution of Severe Congenital Neutropenia to Myeloid Neoplasia(Frontiers, 2020) Dinh, Khanh N.; Corey, Seth J.; Kimmel, MarekBone marrow failure (BMF) syndromes, such as severe congenital neutropenia (SCN) are leukemia predisposition syndromes. We focus here on the transition from SCN to pre-leukemic myelodysplastic syndrome (MDS). Stochastic mathematical models have been conceived that attempt to explain the transition of SCN to MDS, in the most parsimonious way, using extensions of standard processes of population genetics and population dynamics, such as the branching and the Moran processes. We previously presented a hypothesis of the SCN to MDS transition, which involves directional selection and recurrent mutation, to explain the distribution of ages at onset of MDS or AML. Based on experimental and clinical data and a model of human hematopoiesis, a range of probable values of the selection coefficient s and mutation rate μ have been determined. These estimates lead to predictions of the age at onset of MDS or AML, which are consistent with the clinical data. In the current paper, based on data extracted from published literature, we seek to provide an independent validation of these estimates. We proceed with two purposes in mind: (i) to determine the ballpark estimates of the selection coefficients and verify their consistency with those previously obtained and (ii) to provide possible insight into the role of recurrent mutations of the G-CSF receptor in the SCN to MDS transition.Item Approximate dynamic factor models for mixed frequency data(2015-10-15) Zhao, Xin; Ensor, Katherine; Kimmel, Marek; Sizova, NataliaTime series observed at different temporal scales cannot be simultaneously analyzed by traditional multivariate time series methods. Adjustments must be made to address issues of asynchronous observations. For example, many macroeconomic time series are published quarterly and other price series are published monthly or daily. Common solutions to the analysis of asynchronous time series include data aggregation, mixed frequency vector autoregressive models, and factor models. In this research, I set up a systematic approach to the analysis of asynchronous multivariate time series based on an approximate dynamic factor model. The methodology treats observations of various temporal frequencies as contemporaneous series. A two-step model estimation and identification scheme is proposed. This method allows explicit structural restrictions that account for appropriate temporal ordering of the mixed frequency data. The methodology consistently estimates the dynamic factors, however, no prior knowledge on the factors is required. To ensure a computationally efficient robust algorithm and model specification, I make use of modern penalized likelihood methodologies. The fitted model captures the effects of temporal relationships across the asynchronous time series in an interpretable manner. The methodology is studied through simulation and applied to several examples. The simulations and examples demonstrate good performance in model specification, estimation and out-of-sample forecasting.Item Branching processes with biological applications(2010) Wu, Xiaowei; Kimmel, MarekBranching processes play an important role in models of genetics, molecular biology, microbiology, ecology and evolutionary theory. This thesis explores three aspects of branching processes with biological applications. The first part of the thesis focuses on fluctuation analysis, with the main purpose to estimate mutation rates in microbial populations. We propose a novel estimator of mutation rates, and apply it to a number of Luria-Delbruck type fluctuation experiments in Saccharomyces cerevisiae. Second, we study the extinction of Markov branching processes, and derived theorems for the path to extinction in the critical case, as an extension to Jagers' theory. The third part of the thesis introduces infinite-allele Markov branching processes. As an important non-trivial example, the limiting frequency spectrum for the birth-death process has been derived. Potential application of modeling the proliferation and mutation of human Alu sequences is also discussed.Item Cell fate in antiviral response arises in the crosstalk of IRF, NF-κB and JAK/STAT pathways(Springer Nature, 2018) Czerkies, Maciej; Korwek, Zbigniew; Prus, Wiktor; Kochańczyk, Marek; Jaruszewicz-Błońska, Joanna; Tudelska, Karolina; Błoński, Sławomir; Kimmel, Marek; Brasier, Allan R.; Lipniacki, TomaszThe innate immune system processes pathogen-induced signals into cell fate decisions. How information is turned to decision remains unknown. By combining stochastic mathematical modelling and experimentation, we demonstrate that feedback interactions between the IRF3, NF-κB and STAT pathways lead to switch-like responses to a viral analogue, poly(I:C), in contrast to pulse-like responses to bacterial LPS. Poly(I:C) activates both IRF3 and NF-κB, a requirement for induction of IFNβ expression. Autocrine IFNβ initiates a JAK/STAT-mediated positive-feedback stabilising nuclear IRF3 and NF-κB in first responder cells. Paracrine IFNβ, in turn, sensitises second responder cells through a JAK/STAT-mediated positive feedforward pathway that upregulates the positive-feedback components: RIG-I, PKR and OAS1A. In these sensitised cells, the ‘live-or-die’ decision phase following poly(I:C) exposure is shorter—they rapidly produce antiviral responses and commit to apoptosis. The interlinked positive feedback and feedforward signalling is key for coordinating cell fate decisions in cellular populations restricting pathogen spread.Item Embargo Characterization of cancer development and recurrence through mathematical and statistical modeling(2024-04-10) Nguyen, Hoai Nam; Wang, Wenyi; Kimmel, MarekLi-Fraumeni syndrome (LFS) is a genetic disorder characterized by deleterious germline mutations in the TP53 tumor suppressor gene. Due to the compromised DNA repair mechanisms, patients with LFS are significantly more likely to develop a spectrum of cancer types. Furthermore, it is not uncommon for LFS patients to develop multiple primary cancers. Two risk prediction models were developed for LFS: (i) a cancer-specific model that predicts cancer-specific risks for the first primary and (ii) a multiple primary cancer model that predicts the risk of a second primary without distinguishing between cancer types. Although they have been validated on research cohorts, it is essential to show that they perform well on a clinical cohort, which more closely resembles the patient data that are observed in real counseling sessions. In the first project, we validate the models in both discrimination and calibration via the Area Under the Curve (AUC) and Observed/Expected (O/E) ratio, respectively, on a dataset collected from the Clinical Cancer Genetics program at MD Anderson Cancer Center (MDACC). To expedite the dissemination of these models, we further refine the associated software tools, LFSPRO and LFSPROShiny. A major limitation of the previous models is that they do not predict cancer-specific risks beyond the first primary. In statistical survival analysis, multiple primary cancers can be regarded as recurrent events, and different cancer types can be regarded as non-terminal competing risks. Although many models have been proposed to address these two phenomenons separately, a unified statistical framework remains a gap in knowledge. In the second project, we develop a generalized and interpretable Bayesian model that fully accounts for the complex relationships between the recurrent events. We use a non-homogeneous Poisson process to model the occurrence processes of the competing risks, each of which is characterized by a time-dependent intensity function that follows a Cox regression model. For family datasets, we further introduce fraity terms to capture within-family correlations that are induced by the unobserved covariates, and recursively compute the family-wise likelihood via the Elston-Stewart peeling algorithm to account for the dependence of family members through missing genotypes. The model parameters are estimated via a Metropolis-Hastings-within-Gibbs sampling scheme. We train and cross-validate our model on a LFS patient cohort that is prospectively collected at MDACC. In the third project, we perform a much more extensive validation of the model on independent patient cohorts from major cancer institutes across the United States. Stem cells are closely related to cancer. Given their ability to develop into many different cell types, stem cell transplants can be used to replace cells that are damaged by high doses of radiotherapy and chemotherapy, thus accelerating the process of cancer treatment. On the other hand, stem cells survive much longer than ordinary cells, and are thus more likely to accumulate harmful genetic mutations, which have the potential to trigger carcinogenesis. During cell division, a stem cell forms a progenitor cell, which continues to differentiate into the target cell type, and renews itself. In the last project, we mathematically describe this process using a two-type age-dependent branching process. By deriving closed-form expressions of the probability generating functions, we study the behavior of such process in both finite time and large time under different dynamics of the two cell types, which correspond to various biological scenarios.Item Chronic Infection Depletes Hematopoietic Stem Cells through Stress-Induced Terminal Differentiation(Cell Press, 2016) Matatall, Katie A.; Jeong, Mira; Chen, Siyi; Sun, Deqiang; Chen, Fengju; Mo, Qianxing; Kimmel, Marek; King, Katherine Y.Chronic infections affect a third of the world’s population and can cause bone marrow suppression, a severe condition that increases mortality from infection. To uncover the basis for infection-associated bone marrow suppression, we conducted repeated infection of WT mice with Mycobacterium avium. After 4–6 months, mice became pancytopenic. Their hematopoietic stem and progenitor cells (HSPCs) were severely depleted and displayed interferon gamma (IFN-γ) signaling-dependent defects in self-renewal. There was no evidence of increased HSPC mobilization or apoptosis. However, consistent with known effects of IFN-γ, transcriptome analysis pointed toward increased myeloid differentiation of HSPCs and revealed the transcription factor Batf2 as a potential mediator of IFN-γ-induced HSPC differentiation. Gain- and loss-of-function studies uncovered a role for Batf2 in myeloid differentiation in both murine and human systems. We thus demonstrate that chronic infection can deplete HSPCs and identify BATF2 as a mediator of infection-induced HSPC terminal differentiation.Item Coalescence computations for large samples drawn from populations of time-varying sizes(Public Library of Science, 2017) Polanski, Andrzej; Szczesna, Agnieszka; Garbulowski, Mateusz; Kimmel, MarekWe present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset.Item Embargo Computational Analysis of Cancer Genomic Evolution and Human Endogenous Double-stranded RNA(2023-10-10) Chen, Yujie; Kimmel, MarekThe development of next generation sequencing (NGS) technologies has allowed rapid and cost-effective sequencing of large amounts of DNA or RNA, enabling systematic analysis of biological and pathological processes. Interpretation of genomic and transcriptomic data became crucial in both research and clinical settings, particularly for investigating complex human diseases. This thesis employed various computational analysis methods in three projects that utilized different types of NGS data: (1) Constructing genomic trajectory for the progression process of multiple hematopoietic malignancies using panel sequencing and karyotype data; (2) Mathematical modeling of the mutation accumulation history in bladder cancer on a whole-organ level; and (3) Identifying endogenous immunogenetic double-stranded RNA species by classic bioinformatic analysis of RNA-seq data. The research conducted in this thesis revealed genomic evolution dynamics in cancer development providing important reference for cancer progression monitoring and intervention, and shed light on RNA therapeutic targets in autoimmune diseases.Item Computational models of signaling processes in cells with applications: Influence of stochastic and spatial effects(2012) Bertolusso, Roberto; Kimmel, MarekThe usual approach to the study of signaling pathways in biological systems is to assume that high numbers of cells and of perfectly mixed molecules within cells are involved. To study the temporal evolution of the system averaged over the cell population, ordinary differential equations are usually used. However, this approach has been shown to be inadequate if few copies of molecules and/or cells are present. In such situation, a stochastic or a hybrid stochastic/deterministic approach needs to be used. Moreover, considering a perfectly mixed system in cases where spatial effects are present can be an over-simplifying assumption. This can be corrected by adding diffusion terms to the ordinary differential equations describing chemical reactions and proliferation kinetics. However, there exist cases in which both stochastic and spatial effects have to be considered. We study the relevance of differential equations, stochastic Gillespie algorithm, and deterministic and stochastic reaction-diffusion models for the study of important biological processes, such as viral infection and early carcinogenesis. To that end we have developed two optimized libraries of C functions for R (r-project.org) to simulate biological systems using Petri Nets, in a pure deterministic, pure stochastic, or hybrid deterministic/stochastic fashion, with and without spatial effects. We discuss our findings in the terms of specific biological systems including signaling in innate immune response, early carcinogenesis and spatial spread of viral infection.Item Cross talk between cytokine and hyperthermia-induced pathways: identification of different subsets of NF-κ B-dependent genes regulated by TNFα and heat shock(Springer, 2015) Janus, Patryk; Stokowy, Tomasz; Jaksik, Roman; Szoltysek, Katarzyna; Handschuh, Luiza; Podkowinski, Jan; Widlak, Wieslawa; Kimmel, Marek; Widlak, PiotrHeat shock inhibits NF-κB signaling, yet the knowledge about its influence on the regulation of NF-κB-dependent genes is limited. Using genomic approaches, i.e., expression microarrays and ChIP-Seq, we aimed to establish a global picture for heat shock-mediated impact on the expression of genes regulated by TNFα cytokine. We found that 193 genes changed expression in human U-2 osteosarcoma cells stimulated with cytokine (including 77 genes with the κB motif in the proximal promoters). A large overlap between sets of genes modulated by cytokine or by heat shock was revealed (86 genes were similarly affected by both stimuli). Binding sites for heat shock-induced HSF1 were detected in regulatory regions of 1/3 of these genes. Furthermore, pre-treatment with heat shock affected the expression of 2/3 of cytokine-modulated genes. In the largest subset of co-affected genes, heat shock suppressed the cytokine-mediated activation (antagonistic effect, 83 genes), which genes were associated with the canonical functions of NF-κB signaling. However, subsets of co-activated and co-repressed genes were also revealed. Importantly, pre-treatment with heat shock resulted in the suppression of NF-κB binding in the promoters of the cytokine-upregulated genes, either antagonized or co-activated by both stimuli. In conclusion, we confirmed that heat shock inhibited activation of genes involved in the classical cytokine-mediated functions of NF-κB. On the other hand, genes involved in transcription regulation were over-represented in the subset of genes upregulated by both stimuli. This suggests the replacement of NF-κB-mediated regulation by heat shock-mediated regulation in the latter subset of genes, which may contribute to the robust response of cells to both stress conditions.Item Detection and characterization of constitutive replication origins defined by DNA polymerase epsilon(Springer Nature, 2023) Jaksik, Roman; Wheeler, David A.; Kimmel, MarekDespite the process of DNA replication being mechanistically highly conserved, the location of origins of replication (ORI) may vary from one tissue to the next, or between rounds of replication in eukaryotes, suggesting flexibility in the choice of locations to initiate replication. Lists of human ORI therefore vary widely in number and location, and there are currently no methods available to compare them. Here, we propose a method of detection of ORI based on somatic mutation patterns generated by the mutator phenotype of damaged DNA polymerase epsilon (POLE).Item Dynamic Cross Talk Model of the Epithelial Innate Immune Response to Double-Stranded RNA Stimulation: Coordinated Dynamics Emerging from Cell-Level Noise(Public Library of Science, 2014) Bertolusso, Roberto; Tian, Bing; Zhao, Yingxin; Vergara, Leoncio; Sabree, Aqeeb; Iwanaszko, Marta; Lipniacki, Tomasz; Brasier, Allan R.; Kimmel, MarekWe present an integrated dynamical cross-talk model of the epithelial innate immune reponse (IIR) incorporating RIG-I and TLR3 as the two major pattern recognition receptors (PRR) converging on the RelA and IRF3 transcriptional effectors. bioPN simulations reproduce biologically relevant gene-and protein abundance measurements in response to time course, gene silencing and dose-response perturbations both at the population and single cell level. Our computational predictions suggest that RelA and IRF3 are under auto- and cross-regulation. We predict, and confirm experimentally, that RIG-I mRNA expression is controlled by IRF7. We also predict the existence of a TLR3-dependent, IRF3-independent transcription factor (or factors) that control(s) expression of MAVS, IRF3 and members of the IKK family. Our model confirms the observed dsRNA dose-dependence of oscillatory patterns in single cells, with periods of 1-3 hr. Model fitting to time series, matched by knockdown data suggests that the NF-kB module operates in a different regime (with different coefficient values) than in the TNFa-stimulation experiments. In future studies, this model will serve as a foundation for identification of virus-encoded IIR antagonists and examination of stochastic effects of viral replication. Our model generates simulated time series, which reproduce the noisy oscillatory patterns of activity (with 1-3 hour period) observed in individual cells. Our work supports the hypothesis that the IIR is a phenomenon that emerged by evolution despite highly variable responses at an individual cell level.Item Effects of Gene Interactions on Polymorphism and Divergence(2014-05-20) Shih, Ching-Hua; Kohn, Michael H.; Nakhleh, Luay K.; Putnam, Nicholas H.; Kimmel, MarekPatterns of interactions could influence the biological systems at various levels and potentially affect the evolutionary history. Gene interactions could affect the relation among genotypes and their phenotypes. Polymorphisms of genes potentially alter interactions among genes, and hence, affect the fitness of individuals. Certain combinations of polymorphisms among genes can be maintained by selection. The main question of this thesis regards the effects of interactions in biological systems. Reproductive isolation arises as a by-product of different combinations of substitutions between divergent populations. Bateson-Dobzhansky-Muller (BDM) model states fitness changes due to incompatible combinations of loci. Nonlinear rates of accumulation of incompatibilities have been proposed considering interactions among multiple loci. However, the effects of topologies of gene interaction networks (GINs) altering the rates of accumulation of incompatibilities have not been investigated. The third topic revolves around effects of gene interactions in hybridizing species. Gene flow homogenizes the gene pool of incipient species and impedes divergence. This process can take place because incipient species either remain in spatial contact or have secondary contact through range shifts. The porous intrinsic reproductive barriers between species for loci post various properties contributing to success to move between species. We utilized human GINs combined with single nucleotide polymorphisms (SNPs) from human HapMap to investigate the correlations between interactions and interlocus nonrandom associations of polymorphisms. To investigate the effects of gene interactions between species, we modified the “snowball effect” and simulated the rates of accumulation of incompatibilities by introducing the structure information of GINs. To profile the functional characteristics of introgressed genes, we adopted the maximum likelihood method for public genomic resources focusing on a primate hybrid zone of cynomolgus monkey (Macaca fascicularis) and rhesus monkey (M. mulatta). Our results suggest that GINs enable global scale studies and provide polygenic insight of complex traits between and within species. Application of gene interactions ranges from enhancement of genome-wide association studies, identification of interacting polymorphisms to biomedical researches. Gene interactions also provide a platform of understanding hybridization and the dynamics of speciation.Item Estimation of uncertainty in genetic linkage data for human pedigrees(1996) Ehm, Margaret Elizabeth Gelder; Kimmel, MarekGenetic linkage analysis entails estimating the distance between two genes on a chromosome using genotype information from a sample of individuals. For human pedigree data counting the number of meiotic crossovers or recombination events is impossible due to the lack of complete information. Consequently maximum likelihood methods are used to estimate the recombination frequency in these cases. Since the advent of high resolution genetic maps, errors in genetic linkage data have become more of a problem. Errors can introduce spurious recombinations which increase the map distance and distort linkage maps reducing the power to locate genetic diseases. A general method for detecting errors in pedigree genotype data is presented. Its performance is evaluated with power studies using Monte Carlo methods on simulated data with pedigree structures similar to the CEPH pedigrees and a larger disease pedigree used in the study of idiopathic dilated cardiomyopathy. An investigation of the effect that errors have on the power of locating a disease gene in a proposed linkage study is also presented. The study's results can be used to plan linkage studies which account for error thereby increasing their probability of success. The error detection method and power study results are important tools for performing linkage studies now and in the future which require high resolution maps.Item Evolution of Altruism and Eusociality: Toward a Cost/Benefit Analysis of Fitness and Genetic Relatedness(2014-06-13) Liao, Xiaoyun; Kohn, Michael H; Nakhleh, Luay K; Kimmel, Marek; Putnam, Nicholas HAltruism is a behavior that benefits others at a cost to one’s own ability of survival and/or reproduction; that is, individual fitness. Thus, altruism poses great challenges to Darwin’s theory of evolution by natural selection on individual fitness. Altruistic behaviors are commonly performed in eusocial animals, such as nearly all hymenoptera (including bees, wasps, and ants), termites, ambrosia beetles, and so on. Inclusive fitness theory predicts that altruistic behavior can evolve when sufficient fitness benefits are given to relatives even though individual fitness is reduced. A different modeling approach has led to a challenge to this theory. The modelers claim that relatedness is not causal, that eusocial behavior is very hard to evolve requiring more workers before the queen increased fitness, and that there is no conflict involved. Here I showed that, even within the terms of this modeling framework, inclusive fitness thinking leads to insights that completely change these conclusions. I showed that relatedness and inclusive fitness indeed are causal and that eusociality does evolve more readily. With regard to the latter this means eusociality can be favored under a lower benefits threshold. I concluded that multiple modeling approaches are useful and that efforts to synthesize them are better than asserting that one is universally better than the other. Moreover, either greenbeard effects or genetic kin recognition requires genetic polymorphisms as cues on which recognition is based. Previous models showed that selection eliminates rare cue alleles and a common allele gets fixed, i.e. altruism cannot persist. So it is unclear how genetic recognition for altruism persists under a Darwinian selection framework. Here, I designed a novel model with three types of genetic components (production, perception, and action). I analyzed my recognition model theoretically toward a cost/benefit analysis of fitness and genetic relatedness. I predicted the stability of recognition for altruism based on my model. Furthermore I tested my recognition model through various computational and biological simulations. My simulation results consistently showed altruism could maintain multiple recognition cues and be evolutionarily stable; given the assumptions of my model. I concluded that cost/benefit of fitness and genetic relatedness both play critical roles in the evolution of altruism and eusociality, and therefore can maintain the stability of recognition for altruism.