Population genetics of VNTR loci and their applications in evolutionary studies

Variable number of tandem repeats (VNTR) are genetic loci at which short sequence motifs are found repeated different numbers of times among chromosomes. To explore the potential utility of VNTR loci in evolutionary studies, I have conducted a series of studies to address the following questions: (1) What are the population genetic properties of these loci? (2) What are the mutational mechanisms of repeat number change at these loci? (3) Can DNA profiles be used to measure the relatedness between a pair of individuals? (4) Can DNA fingerprint be used to measure the relatedness between populations in evolutionary studies? (5) Can microsatellite and short tandem repeat (STR) loci which mutate stepwisely be used in evolutionary analyses?^ A large number of VNTR loci typed in many populations were studied by means of statistical methods developed recently. The results of this work indicate that there is no significant departure from Hardy-Weinberg expectation (HWE) at VNTR loci in most of the human populations examined, and the departure from HWE in some VNTR loci are not solely caused by the presence of population sub-structure.^ A statistical procedure is developed to investigate the mutational mechanisms of VNTR loci by studying the allele frequency distributions of these loci. Comparisons of frequency distribution data on several hundreds VNTR loci with the predictions of two mutation models demonstrated that there are differences among VNTR loci grouped by repeat unit sizes.^ By extending the ITO method, I derived the distribution of the number of shared bands between individuals with any kinship relationship. A maximum likelihood estimation procedure is proposed to estimate the relatedness between individuals from the observed number of shared bands between them.^ It was believed that classical measures of genetic distance are not applicable to analysis of DNA fingerprints which reveal many minisatellite loci simultaneously in the genome, because the information regarding underlying alleles and loci is not available. I proposed a new measure of genetic distance based on band sharing between individuals that is applicable to DNA fingerprint data.^ To address the concern that microsatellite and STR loci may not be useful for evolutionary studies because of the convergent nature of their mutation mechanisms, by a theoretical study as well as by computer simulation, I conclude that the possible bias caused by the convergent mutations can be corrected, and a novel measure of genetic distance that makes the correction is suggested. In summary, I conclude that hypervariable VNTR loci are useful in evolutionary studies of closely related populations or species, especially in the study of human evolution and the history of geographic dispersal of Homo sapiens. (Abstract shortened by UMI.) ^

Cerebellar cortex involvement in classical eyelid conditioning

A model for cerebellar involvement in motor learning was tested using classical eyelid conditioning in the rabbit. Briefly, we assume that modifications of the strength of granule cell synapses at Purkinje cells in the cerebellar cortex and mossy fiber (MF) synapses at cerebellar interpositus nuclei are responsible for the acquisition, adaptively-timed expression, and extinction of conditioned eyelid responses (CRs). A corollary of these assumptions is that the cerebellar cortex is necessary for acquisition and extinction. This model also suggests a mechanism whereby the cerebellar cortex can discriminate different times during a conditioned stimulus (CS) and thus mediate the learned timing of CRs. Therefore, experiments were done to determine the role of the cerebellar cortex in the timing, extinction, and acquisition of CRs. Lesions of the cerebellar cortex that included the anterior lobe disrupted the learned timing of CRs such that they occurred at extremely short latencies. Stimulation of MFs in the middle cerebellar peduncle as the CS could support differently timed CRs in the same animal. These data indicate that synaptic plasticity in the cerebellar cortex mediates the learned timing of CRs. These short-latency CRs which resulted from anterior lobe damage did not extinguish, while CRs in animals receiving lesions which did not include the anterior lobe extinguished normally. Preliminary data suggests that lesions of the anterior lobe which produce short-latency responses prevent the acquisition of CRs to a novel CS. These findings indicate that the anterior lobe of cerebellar cortex is necessary for eyelid conditioning. The involvement of the anterior lobe in eyelid conditioning has not been previously reported, however, the anterior lobe has generally been spared in lesion studies examining cerebellar cortex involvement in eyelid conditioning due to its relatively inaccessible location. The observation that the anterior lobe of the cerebellar cortex is not always required for the basic expression of CRs, but is necessary for response timing, extinction, and acquisition, is consistent with the hypothesis that eyelid conditioning can involve plasticity in both the cerebellar cortex and interpositus nucleus and that plasticity in the nucleus is controlled by Purkinje cell activity. ^

Behavioral and cellular analysis of classical conditioning of feeding behavior in Aplysia

In classical conditioning, an associative form of learning, animals learn to associate two stimuli. Cellular and molecular mechanisms for the induction and consolidation of associative learning and memory at the level of single cells and synaptic connections have been studied in both vertebrate and invertebrate animals. The majority of studies, however, relied on aversive stimuli to induce learning. This bias may limit the extent to which identified mechanisms generalize to other forms of associative learning and memory, such as appetitive forms. The goal of the present study was to develop a classical conditioning procedure for the marine mollusk Aplysia californica using appetitive reinforcement, and to analyze associative learning using behavioral and electrophysiological techniques. ^ Using tactile stimulation of the lips as the conditional stimulus (CS) and food as the unconditional stimulus (US) a training protocol was developed that reliably induced classical conditioning of feeding behavior. Memory persisted for at least 24 hours. The gross organization of reinforcement-mediating pathways was analyzed in additional behavioral experiments. Moreover, neurophysiological correlates of classical conditioning were identified and characterized in an in vitro preparation containing the circuitry for feeding behavior. In vitro stimulation of a nerve (AT4) that may mediate the CS during training, resulted in a greater number of buccal motor patterns (BMPs) in brains from conditioned animals, as compared to control animals. The majority of these BMPs were ingestion-like, consistent with the increased number of bites in response to the CS after classical conditioning. Moreover, classical conditioning correlated with increased excitatory synaptic input to BMP-initiating neuron B31/32, in response to stimulation of AT 4, as compared to controls. The expression of the correlates of classical conditioning identified in this study was specific to stimulation of AT 4, which is consistent the stimulus specificity that is characteristic for classical conditioning. ^ The identification of cellular correlates of classical conditioning documented here provides the basis for future, more detailed analyses of an appetitive form of associative learning and memory, that may extend the working knowledge of the cellular and molecular mechanisms for associative plasticity in general. ^

Cellular mechanisms of operant and classical conditioning

The ability to associate a predictive stimulus with a subsequent salient event (i.e., classical conditioning) and the ability to associate an expressed behavior with the consequences (i.e., operant conditioning) allow for a predictive understanding of a changing environment. Although they are operationally distinct, there has been considerable debate whether at some fundamental level classical and operant conditioning are mechanistically distinct or similar. Feeding behavior of Aplysia (i.e., biting) was chosen as the model system and was successfully conditioned with appetitive forms of both operant and classical conditioning. The neuronal circuitry responsible for feeding is well understood and is suitable for cellular analyses, thus providing for a mechanistic comparison between these two forms of associative learning. ^ Neuron B51 is part of the feeding circuitry of Aplysia and is critical for the expression of ingestive behaviors. B51 also is a locus of plasticity following both operant and classical conditioning. Both in vivo and in vitro operant conditioning increased the input resistance and the excitability of B51. No pairing-specific changes in the input resistance were observed following both in vivo and in vitro classical conditioning. However, classical conditioning decreased the excitability of B51. Thus, both operant and classical conditioning modified the threshold level for activation of neuron B51, but in opposite directions, revealing key differences in the cellular mechanisms underlying these two forms of associative learning. ^ Next, the cellular mechanisms underlying operant conditioning were investigated in more detail using a single-cell analogue. The single-cell analogue successfully recapitulated the previous in vivo and in vitro operant conditioning results by increasing the input resistance and the excitability of B51. Both PKA and PKC were necessary for operant conditioning. Dopamine appears to be the transmitter mediating the reinforcement signal in this form of conditioning. A D1 dopamine receptor antibody revealed that the D1receptor localizes to the axon hillock, which is also the region that gives the strongest response when iontophoresing dopamine. ^ The studies presented herein, thus, provide for a greater understanding of the mechanisms underlying both of these forms of associative learning and demonstrate that they likely operate through distinct cellular mechanisms. ^

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^