Inferring Genomic Sequences

Recent advances in next generation sequencing have provided unprecedented opportunities for high-throughput genomic research, inexpensively producing millions of genomic sequences in a single run. Analysis of massive volumes of data results in a more accurate picture of the genome complexity and requires adequate bioinformatics support. We explore computational challenges of applying next generation sequencing to particular applications, focusing on the problem of reconstructing viral quasispecies spectrum from pyrosequencing shotgun reads and problem of inferring informative single nucleotide polymorphisms (SNPs), statistically covering genetic variation of a genome region in genome-wide association studies. The genomic diversity of viral quasispecies is a subject of a great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software cannot be used to simultaneously assemble and estimate the abundance of multiple closely related (but non-identical) quasispecies sequences. Here, we introduce a new Viral Spectrum Assembler (ViSpA) for inferring quasispecies spectrum and compare it with the state-of-the-art ShoRAH tool on both synthetic and real 454 pyrosequencing shotgun reads from HCV and HIV quasispecies. While ShoRAH has an advanced error correction algorithm, ViSpA is better at quasispecies assembling, producing more accurate reconstruction of a viral population. We also foresee ViSpA application to the analysis of high-throughput sequencing data from bacterial metagenomic samples and ecological samples of eukaryote populations. Due to the large data volume in genome-wide association studies, it is desirable to find a small subset of SNPs (tags) that covers the genetic variation of the entire set. We explore the trade-off between the number of tags used per non-tagged SNP and possible overfitting and propose an efficient 2LR-Tagging heuristic

Calibrating the Performance of SNP Arrays for Whole-Genome Association Studies

To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level

Localization of adaptive variants in human genomes using averaged one-dependence estimation.

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios

Meta-GWAS Accuracy and Power (MetaGAP) Calculator Shows that Hiding Heritability Is Partially Due to Imperfect Genetic Correlations across Studies

Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called ‘missing heritability’. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51–62% in the number of genome-wide significant loci and a relative loss in polygenic score R2of 36–38%). Hence, cross-study heterogeneity contributes to the missing heritability

Investigating Genotype-Phenotype relationship extraction from biomedical text

During the last decade biomedicine has developed at a tremendous pace. Every day a lot of biomedical papers are published and a large amount of new information is produced. To help enable automated and human interaction in the multitude of applications of this biomedical data, the need for Natural Language Processing systems to process the vast amount of new information is increasing. Our main purpose in this research project is to extract the relationships between genotypes and phenotypes mentioned in the biomedical publications. Such a system provides important and up-to-date data for database construction and updating, and even text summarization. To achieve this goal we had to solve three main problems: finding genotype names, finding phenotype names, and finally extracting phenotype--genotype interactions. We consider all these required modules in a comprehensive system and propose a promising solution for each of them taking into account available tools and resources. BANNER, an open source biomedical named entity recognition system, which has achieved good results in detecting genotypes, has been used for the genotype name recognition task. We were the first group to start working on phenotype name recognition. We have developed two different systems (rule-based and machine-learning based) for extracting phenotype names from text. These systems incorporated the available knowledge from the Unified Medical Language System metathesaurus and the Human Phenotype Onotolgy (HPO). As there was no available annotated corpus for phenotype names, we created a valuable corpus with annotated phenotype names using information available in HPO and a self-training method which can be used for future research. To solve the final problem of this project i.e. , phenotype--genotype relationship extraction, a machine learning method has been proposed. As there was no corpus available for this task and it was not possible for us to annotate a sufficiently large corpus manually, a semi-automatic approach has been used to annotate a small corpus and a self-training method has been proposed to annotate more sentences and enlarge this corpus. A test set was manually annotated by an expert. In addition to having phenotype-genotype relationships annotated, the test set contains important comments about the nature of these relationships. The evaluation results related to each system demonstrate the significantly good performance of all the proposed methods

Polygenic risk score analysis of pathologically confirmed Alzheimer's disease

Previous estimates of the utility of polygenic risk score analysis for the prediction of Alzheimer’s disease have given Area Under the Curve estimates of <80%. However, these have been based on the genetic analysis of clinical case control series. Here we apply the same analytic approaches to a pathological case control series and show a predictive AUC of 84%. We suggest that this analysis has clinical utility and that there is limited room for further improvement using genetic data

A selection operator for summary association statistics reveals allelic heterogeneity of complex traits

A general objective of genetic studies is to understand the genetic basis of complex traits such as height, body mass index (BMI), disease endpoints, etc. Such researches have been facilitated due to the completion of the human genome project and developments of high-throughput technologies. With the help of high-throughput genotyping and sequencing technologies, the information on millions of genetic markers can be measured for each individual. The most widely used strategy to detect the associations between genetic variants and a complex trait is genome-wide association study (GWAS). Because the genetic architecture of most complex traits is highly polygenic, the signal to noise ratio is usually tiny. Thus, especially in human populations, GWAS often requires large samples to obtain sufficient power. Unfortunately, given the restrictions on sharing individual-level data, it is often not feasible to pool data from different cohorts. Despite that, in each cohort, it is possible to report and share GWAS summary statistics, such as sample sizes, allele frequencies, estimates of genetic effect sizes, and their standard errors for the genetic markers across the genome. Therefore one recent focus in statistical methodology development for genetic studies has been on meta-analysis techniques using summary-level data. The objective of this thesis is to develop novel statistical genetics methods based on GWAS summary statistics and to apply these methods to better understand the genetic architecture underlying complex traits. In Study I, we developed a Selection Operator for JOint analyzing multiple SNPs (SOJO). We mathematically proved and empirically showed that the least absolute shrinkage and selection operator (LASSO) could be achieved using GWAS summary-level data. Compared to the stepwise selection procedures, SOJO performs better in variable selection. SOJO is useful for detecting additional variants with independent effects and assessing the magnitude of allelic heterogeneity within loci. In Study II, we developed a High-Definition Likelihood (HDL) method to improve the accuracy in genetic correlation estimation using GWAS summary statistics. Compared to the stateof- the-art method LD Score regression (LDSC), HDL achieves higher statistical power to detect genetic correlations between phenotypes by fully accounting for linkage disequilibrium (LD) information across the genome. In Study III, we introduced a four-level strategy for replication of loci detected by multi-trait GWAS methods. The four methods provide different degrees of replication strength, useful for providing additional evidence when a locus has been discovered and replicated by multivariate analysis of variance (MANOVA) or other multi-trait methods. The replication methods only require summary association statistics and are straightforward to be applied to multi-trait GWAS analyses. In Study IV, using GWAS summary statistics, we developed a method named Genetic Correlation Contrast for Causality (G3C) as a more robust test for the existence and direction of causal relationships between phenotypes. In contrast to Mendelian Randomization (MR), G3C does not rely on the assumption of no horizontal pleiotropy. G3C takes full advantage of genome-wide genetic association data and account for underlying genetic correlations between complex traits

Genome-wide SNPs resolve spatiotemporal patterns of connectivity within striped marlin (Kajikia audax), a broadly distributed and highly migratory pelagic species

Genomic methodologies offer unprecedented opportunities for statistically robust studies of species broadly distributed in environments conducive to high gene flow, providing valuable information for wildlife conservation and management. Here, we sequence restriction site‐associated DNA to characterize genome‐wide single nucleotide polymorphisms (SNPs) in a broadly distributed and highly migratory large pelagic fish, striped marlin (Kajikia audax). Assessment of over 4,000 SNPs resolved spatiotemporal patterns of genetic connectivity throughout the species range in the Pacific and, for the first time, Indian oceans. Individual‐based cluster analyses identified six genetically distinct populations corresponding with the western Indian, eastern Indian, western South Pacific, and eastern central Pacific oceans, as well as two populations in the North Pacific Ocean (FST = 0.0137–0.0819). FST outlier analyses identified a subset of SNPs (n = 59) putatively under the influence of natural selection and capable of resolving populations separated by comparatively high degrees of genetic differentiation. Temporal collections available for some regions demonstrated the stability of allele frequencies over three to five generations of striped marlin. Relative migration rates reflected lower levels of genetic connectivity between Indian Ocean populations (mR ≤ 0.37) compared with most populations in the Pacific Ocean (mR ≥ 0.57) and highlight the importance of the western South Pacific in facilitating gene flow between ocean basins. Collectively, our results provide novel insights into rangewide population structure for striped marlin and highlight substantial inconsistencies between genetically distinct populations and stocks currently recognized for fisheries management. More broadly, we demonstrate that species capable of long‐distance dispersal in environments lacking obvious physical barriers to movement can display substantial population subdivision that persists over multiple generations and that may be facilitated by both neutral and adaptive processes. Importantly, surveys of genome‐wide markers enable inference of population‐level relationships using sample sizes practical for large pelagic fishes of conservation concern

A Neuro-Evolutionary Corpus-Based Method for Word Sense Disambiguation

International audienceWe propose a supervised approach to Word Sense Disambiguation based on Neural Networks combined with Evolutionary Algorithms. An established method to automatically design the structure and learn the connection weights of Neural Networks by means of an Evolutionary Algorithm is used to evolve a neural-network disambiguator for each polysemous word, against a dataset extracted from an annotated corpus. Two distributed encoding schemes, based on the orthography of words and characterized by different degrees of information compression, have been used to represent the context in which a word occurs. The performance of such encoding schemes has been compared. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. Comparison with the best entry of the Semeval-2007 competition has shown that the proposed approach is almost competitive with state-of-the-art WSD approaches