Statistical Methods for Characterizing Genomic Heterogeneity in Mixed Samples

Recently, sequencing technologies have generated massive and heterogeneous data sets. However, interpretation of these data sets is a major barrier to understand genomic heterogeneity in complex diseases. In this dissertation, we develop a Bayesian statistical method for single nucleotide level analysis and a global optimization method for gene expression level analysis to characterize genomic heterogeneity in mixed samples. The detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various computational algorithms have been proposed to detect variants at the single nucleotide level in mixed samples. Yet, the noise inherent in the biological processes involved in NGS technology necessitates the development of statistically accurate methods to identify true rare variants. At the single nucleotide level, we propose a Bayesian probabilistic model and a variational expectation maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations. We demonstrate that our variational EM algorithm has comparable sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficient on tests of relatively low coverage (27x and 298x) data. Furthermore, we show that our model with a variational EM inference algorithm has higher specificity than many state-of-the-art algorithms. In an analysis of a directed evolution longitudinal yeast data set, we are able to identify a time-series trend in non-reference allele frequency and detect novel variants that have not yet been reported. Our model also detects the emergence of a beneficial variant earlier than was previously shown, and a pair of concomitant variants. Characterization of heterogeneity in gene expression data is a critical challenge for personalized treatment and drug resistance due to intra-tumor heterogeneity. Mixed membership factorization has become popular for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. At the gene expression level, we derive a global optimization (GOP) algorithm that provides a guaranteed epsilon-global optimum for a sparse mixed membership matrix factorization problem for molecular subtype classification. We test the algorithm on simulated data and find the algorithm always bounds the global optimum across random initializations and explores multiple modes efficiently. The GOP algorithm is well-suited for parallel computations in the key optimization steps

Haplotype estimation in polyploids using DNA sequence data

Polyploid organisms possess more than two copies of their core genome and therefore contain k>2 haplotypes for each set of ordered genomic variants. Polyploidy occurs often within the plant kingdom, among others in important corps such as potato (k=4) and wheat (k=6). Current sequencing technologies enable us to read the DNA and detect genomic variants, but cannot distinguish between the copies of the genome, each inherited from one of the parents. To detect inheritance patterns in populations, it is necessary to know the haplotypes, as alleles that are in linkage over the same chromosome tend to be inherited together. In this work, we develop mathematical optimisation algorithms to indirectly estimate haplotypes by looking into overlaps between the sequence reads of an individual, as well as into the expected inheritance of the alleles in a population. These algorithm deal with sequencing errors and random variations in the counts of reads observed from each haplotype. These methods are therefore of high importance for studying the genetics of polyploid crops. </p

A Comprehensive Map of Mobile Element Insertion Polymorphisms in Humans

As a consequence of the accumulation of insertion events over evolutionary time, mobile elements now comprise nearly half of the human genome. The Alu, L1, and SVA mobile element families are still duplicating, generating variation between individual genomes. Mobile element insertions (MEI) have been identified as causes for genetic diseases, including hemophilia, neurofibromatosis, and various cancers. Here we present a comprehensive map of 7,380 MEI polymorphisms from the 1000 Genomes Project whole-genome sequencing data of 185 samples in three major populations detected with two detection methods. This catalog enables us to systematically study mutation rates, population segregation, genomic distribution, and functional properties of MEI polymorphisms and to compare MEI to SNP variation from the same individuals. Population allele frequencies of MEI and SNPs are described, broadly, by the same neutral ancestral processes despite vastly different mutation mechanisms and rates, except in coding regions where MEI are virtually absent, presumably due to strong negative selection. A direct comparison of MEI and SNP diversity levels suggests a differential mobile element insertion rate among populations

Application of Pool-seq for variation detection and proteogenomic database creation in β-hemolytic streptococci.

Proteogenomics is an emerging field that combines genomic (transcriptomic) and proteomic data with the aim of improving gene models and identification of proteins. Technological advances in each domain increase the potential of the field in fostering further understanding of organisms. For instance, the current low cost and fast sequencing technologies have made it possible to sequence multiple representative samples of organisms thus improving the comprehensiveness of the organisms’ reference proteomes. At the same time, improvements in mass spectrometry techniques have led to an increase in the quality and quantity of proteomics data produced, which are utilized to update the annotation of coding sequences in genomes. Sequencing of pooled individual DNAs (Pool-seq) is one method for sequencing large numbers of samples cost effectively. It is a robust method that can accurately identify variations that exist between samples. Similar to other proteogenomics methods such as the sample specific databases derived from RNA-seq data, the variants from Pool-seq experiments can be utilized to create variant protein databases and improve the completeness of protein reference databases used in mass spectrometry (MS)-based proteomics analysis. In this thesis work, the efficiency of Pool-seq in identifying variants and estimating allele frequencies from strains of three β-hemolytic bacteria (GAS, GGS and GBS) is investigated. Moreover, in this work a novel Python package (‘PoolSeqProGen’) for creating variant protein databases from the Pool-seq experiments was developed. To our knowledge, this was the first work to use Pool-seq for sequencing large numbers of β-hemolytic bacteria and assess its efficiency on such genetically polymorphic bacteria. The ‘PoolSeqProGen’ tool is also the first and only tool available to create proteogenomic databases from Pool-seq data. For organisms such as the β-hemolytic bacteria GAS, GBS and GGS that have open pangenomes, the sequencing and annotation of multiple representative strains is paramount in advancing our understanding of these human pathogens and in developing mass spectrometry databases. Due to the increasing use of MS in diagnostics of infectious diseases, this in turn translates to better diagnosis and treatment of the diseases caused by the pathogens and alleviating their devastating burdens on the human population. In this thesis, it is demonstrated that Pool-seq can be used to cost effectively and accurately identify variations that exist among strains of these polymorphic bacteria. In addition, the utility of the tool developed to extend single genome based databases and thereby improve the completeness of the databases and peptide/protein identifications by using variants identified from Pool-seq experiments is illustrated.Proteogenomiikka on kehittyvä tieteenala, joka yhdistää genomiikkaa ja proteomiikkaa geenimallien parantamiseksi ja proteiinien tunnistamiseksi. Molempien alojen tekninen kehitys lisää tämän yhdistetyn tieteenalan mahdollisuuksia eri eliöiden toimintojen ymmärtämiseksi. Esimerkiksi nykyiset edulliset ja nopeat sekvensointitekniikat ovat mahdollistaneet useiden eri organismien kattavan sekvensoinnin, mikä luonnollisesti parantaa myös näiden organismien vertailuproteomien kattavuutta. Samanaikaisesti massaspektrometriatekniikan kehitys on johtanut proteomiikka-analyysien laadun paranemiseen ja syvyyden lisääntymiseen. Tämä mahdollistaa ennustettujen sekvenssialueiden (esim. uusien geenien) validoinnin. Yhdistettyjen yksittäisten DNA-näytteiden sekvensointi (Pool-sekvensointi) mahdollistaa suurten näytemäärien sekvensoinnin erittäin kustannustehokkaasti. Se on luotettava menetelmä, jolla voidaan tunnistaa tarkasti eri näytteiden väliset vaihtelut. Pool-sekvensointikokeiden muunnelmia voidaan käyttää luomaan variantti-proteiinitietokantoja ja parantamaan massaspektrometriaan perustuvien proteiinitietokantojen kattavuutta. Tässä väitöskirjassa tutkittiin Pool-sekvensoinnin tehokkuutta eri varianttien tunnistamisessa ja alleelitaajuuksien arvioimisessa kolmen β-hemolyyttisen streptokokki-bakteerin (GAS, GGS ja GBS) kannoista. Lisäksi työssä kehitettiin uusi Python-ohjelmointikielellä kirjoitettu ohjelmisto (‘PoolSeqProGen’) proteiinivariantitietokantojen luomiseksi Pool-sekvensointi -kokeista. Tämä on ensimmäinen työ, jossa Pool-sekvensointia käytettiin sekvensoimaan suuri määrä streptokokkeja ja arvioimaan menetelmän tehokkuutta geneettisesti polymorfisissa bakteereissa. ”PoolSeqProGen” -työkalu on myös ensimmäinen ja ainoa saatavilla oleva työkalu proteogenomisten tietokantojen luomiseen Pool-sekvensoinnilla tuotetusta datasta. Kehitettäessä massaspektrometria tietokantoja avoimiin pangenomeihin perustuville organismeille, kutenβ-hemolyyttisille streptokokeille GAS, GBS ja GGS, useiden edustavien kantojen sekvensointi ja annotointi on ensiarvoisen tärkeää. Massaspekrometrian lisääntynyt käyttö tartuntatautien diagnosoinnissa parantaa näiden mikrobien aiheuttamien sairauksien diagnosointia ja mahdollistaa siten myös hoidon paremman kohdentamisen. Tässä väitöskirjatyössä osoitetaan, että Pool-sekvensointia voi käyttää kustannustehokkaasti ja tarkasti polymorfisten bakteerikantojen välillä esiintyvien variaatioiden tunnistamiseen. Lisäksi havainnollistamme yhteen genomiin pohjautuvien tietokantojen laajentamiseksi kehitetyn työkalun hyödyllisyyttä, jolla voidaan parantaa tietokantojen kattavuutta ja peptidi- ja proteiinitunnistusta käyttämällä Pool-sekvensointikokeissa tunnistettuja variantteja

GBStools: A Statistical Method for Estimating Allelic Dropout in Reduced Representation Sequencing Data

Reduced representation sequencing methods such as genotyping-by-sequencing (GBS) enable low-cost measurement of genetic variation without the need for a reference genome assembly. These methods are widely used in genetic mapping and population genetics studies, especially with non-model organisms. Variant calling error rates, however, are higher in GBS than in standard sequencing, in particular due to restriction site polymorphisms, and few computational tools exist that specifically model and correct these errors. We developed a statistical method to remove errors caused by restriction site polymorphisms, implemented in the software package GBStools. We evaluated it in several simulated data sets, varying in number of samples, mean coverage and population mutation rate, and in two empirical human data sets (N = 8 and N = 63 samples). In our simulations, GBStools improved genotype accuracy more than commonly used filters such as Hardy-Weinberg equilibrium p-values. GBStools is most effective at removing genotype errors in data sets over 100 samples when coverage is 40X or higher, and the improvement is most pronounced in species with high genomic diversity. We also demonstrate the utility of GBS and GBStools for human population genetic inference in Argentine populations and reveal widely varying individual ancestry proportions and an excess of singletons, consistent with recent population growth.Facultad de Ciencias Naturales y MuseoInstituto Multidisciplinario de Biología Celula

GBStools: A Statistical Method for Estimating Allelic Dropout in Reduced Representation Sequencing Data

Reduced representation sequencing methods such as genotyping-by-sequencing (GBS) enable low-cost measurement of genetic variation without the need for a reference genome assembly. These methods are widely used in genetic mapping and population genetics studies, especially with non-model organisms. Variant calling error rates, however, are higher in GBS than in standard sequencing, in particular due to restriction site polymorphisms, and few computational tools exist that specifically model and correct these errors. We developed a statistical method to remove errors caused by restriction site polymorphisms, implemented in the software package GBStools. We evaluated it in several simulated data sets, varying in number of samples, mean coverage and population mutation rate, and in two empirical human data sets (N = 8 and N = 63 samples). In our simulations, GBStools improved genotype accuracy more than commonly used filters such as Hardy-Weinberg equilibrium p-values. GBStools is most effective at removing genotype errors in data sets over 100 samples when coverage is 40X or higher, and the improvement is most pronounced in species with high genomic diversity. We also demonstrate the utility of GBS and GBStools for human population genetic inference in Argentine populations and reveal widely varying individual ancestry proportions and an excess of singletons, consistent with recent population growth.Facultad de Ciencias Naturales y MuseoInstituto Multidisciplinario de Biología Celula

Pool-seq analysis for the identification of polymorphisms in bacterial strains and utilization of the variants for protein database creation

Pooled sequencing (Pool-seq) is the sequencing of a single library that contains DNA pooled from different samples. It is a cost-effective alternative to individual whole genome sequencing. In this study, we utilized Pool-seq to sequence 100 streptococcus pyogenes strains in two pools to identify polymorphisms and create variant protein databases for shotgun proteomics analysis. We investigated the efficacy of the pooling strategy and the four tools used for variant calling by using individual sequence data of six of the strains in the pools as well as 3407 publicly available strains from the European Nucleotide Archive. Besides the raw sequence data from the public repository, we also extracted polymorphisms from 19 S.pyogenes publicly available complete genomes and compared the variations against our pools. In total 78955 variants (76981 SNPs and 1725 INDELs ) were identified from the two pools. Of these, ∼ 60.5% and 95.7% were discovered in the complete genomes and the European Nucleotide Archive data respectively. Collectively, the four variant calling tools were able to mine majority of the variants, ∼ 96.5%, found from the six individual strains, suggesting Pool-seq is a robust approach for variation discovery. Variants from the pools that fell in coding regions and had non synonymous effects constituted 24% and were used to create variant protein databases for shotgun proteomics analysis. These variant databases improved protein identification in mass spectrometry analysis

Study designs and statistical methods for pharmacogenomics and drug interaction studies

Indiana University-Purdue University Indianapolis (IUPUI)Adverse drug events (ADEs) are injuries resulting from drug-related medical interventions. ADEs can be either induced by a single drug or a drug-drug interaction (DDI). In order to prevent unnecessary ADEs, many regulatory agencies in public health maintain pharmacovigilance databases for detecting novel drug-ADE associations. However, pharmacovigilance databases usually contain a significant portion of false associations due to their nature structure (i.e. false drug-ADE associations caused by co-medications). Besides pharmacovigilance studies, the risks of ADEs can be minimized by understating their mechanisms, which include abnormal pharmacokinetics/pharmacodynamics due to genetic factors and synergistic effects between drugs. During the past decade, pharmacogenomics studies have successfully identified several predictive markers to reduce ADE risks. While, pharmacogenomics studies are usually limited by the sample size and budget. In this dissertation, we develop statistical methods for pharmacovigilance and pharmacogenomics studies. Firstly, we propose an empirical Bayes mixture model to identify significant drug-ADE associations. The proposed approach can be used for both signal generation and ranking. Following this approach, the portion of false associations from the detected signals can be well controlled. Secondly, we propose a mixture dose response model to investigate the functional relationship between increased dimensionality of drug combinations and the ADE risks. Moreover, this approach can be used to identify high-dimensional drug combinations that are associated with escalated ADE risks at a significantly low local false discovery rates. Finally, we proposed a cost-efficient design for pharmacogenomics studies. In order to pursue a further cost-efficiency, the proposed design involves both DNA pooling and two-stage design approach. Compared to traditional design, the cost under the proposed design will be reduced dramatically with an acceptable compromise on statistical power. The proposed methods are examined by extensive simulation studies. Furthermore, the proposed methods to analyze pharmacovigilance databases are applied to the FDA’s Adverse Reporting System database and a local electronic medical record (EMR) database. For different scenarios of pharmacogenomics study, optimized designs to detect a functioning rare allele are given as well