Analysis Of Genetic Data Research Articles

Metabolic modeling helps interpret transcriptomic changes during malaria

Disease represents a specific case of malfunctioning within a complex system. Whereas it is often feasible to observe and possibly treat the symptoms of a disease, it is much more challenging to identify and characterize its molecular root causes. Even in infectious diseases that are caused by a known parasite, it is often impossible to pinpoint exactly which molecular profiles of components or processes are directly or indirectly altered. However, a deep understanding of such profiles is a prerequisite for rational, efficacious treatments. Modern omics methodologies are permitting large-scale scans of some molecular profiles, but these scans often yield results that are not intuitive and difficult to interpret. For instance, the comparison of healthy and diseased transcriptome profiles may point to certain sets of involved genes, but a host of post-transcriptional processes and regulatory mechanisms renders predictions regarding metabolic or physiological consequences of the observed changes in gene expression unreliable. Here we present proof of concept that dynamic models of metabolic pathway systems may offer a tool for interpreting transcriptomic profiles measured during disease. We illustrate this strategy with the interpretation of expression data of genes coding for enzymes associated with purine metabolism. These data were obtained during infections of rhesus macaques (Macaca mulatta) with the malaria parasite Plasmodium cynomolgi or P. coatneyi. The model-based interpretation reveals clear patterns of flux redistribution within the purine pathway that are consistent between the two malaria pathogens and are even reflected in data from humans infected with P. falciparum. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

A Comparative Analysis of Chinese Historical Sources and y-dna Studies with Regard to the Early and Medieval Turkic Peoples

AbstractIn the past 10 years, geneticists have investigated the genetic variation of modern Turkic populations as well as ancientdnaof the Xiongnu and others. The accumulated findings of these surveys, however, have not been adequately noted by specialists in Inner Asian history. In order to fill this gap, we conducted a comparative analysis of textual information and genetic survey data on the early and medieval Turkic peoples. First, we examined the information on the origins, identity, and physiognomy of the early and medieval Turkic peoples contained in the Chinese Standard Histories (zhengshi正史). We then discussed how the findings of genetic surveys complement the textual information. Both Chinese histories and moderndnastudies indicate that the early and medieval Turkic peoples were made up of heterogeneous populations. The Turkicisation of central and western Eurasia was not the product of migrations involving a homogeneous entity, but that of language diffusion.

Single-Molecule Imaging of Escherichia coli Transmembrane Proteins.

Single-molecule imaging in living cells can provide unique information about biological processes. Bacteria offer some particular challenges for single-molecule imaging due to their small size, only slightly larger than the diffraction limit of visible light. Here, we describe how reliable and reproducible single-molecule data can be obtained for a transmembrane protein in the Gram-negative bacterium Escherichia coli by using live-cell fluorescence microscopy. Fluorescent labeling of a protein by genetic fusion, cell culturing, sample preparation, imaging, and data analysis are discussed.

DnaSP 6: DNA Sequence Polymorphism Analysis of Large Data Sets.

We present version 6 of the DNA Sequence Polymorphism (DnaSP) software, a new version of the popular tool for performing exhaustive population genetic analyses on multiple sequence alignments. This major upgrade incorporates novel functionalities to analyze large data sets, such as those generated by high-throughput sequencing technologies. Among other features, DnaSP 6 implements: 1) modules for reading and analyzing data from genomic partitioning methods, such as RADseq or hybrid enrichment approaches, 2) faster methods scalable for high-throughput sequencing data, and 3) summary statistics for the analysis of multi-locus population genetics data. Furthermore, DnaSP 6 includes novel modules to perform single- and multi-locus coalescent simulations under a wide range of demographic scenarios. The DnaSP 6 program, with extensive documentation, is freely available at http://www.ub.edu/dnasp.

Transmission routes maintaining a viral pathogen of steelhead trout within a complex multi-host assemblage.

This is the first comprehensive region wide, spatially explicit epidemiologic analysis of surveillance data of the aquatic viral pathogen infectious hematopoietic necrosis virus (IHNV) infecting native salmonid fish. The pathogen has been documented in the freshwater ecosystem of the Pacific Northwest of North America since the 1950s, and the current report describes the disease ecology of IHNV during 2000–2012. Prevalence of IHNV infection in monitored salmonid host cohorts ranged from 8% to 30%, with the highest levels observed in juvenile steelhead trout. The spatial distribution of all IHNV‐infected cohorts was concentrated in two sub‐regions of the study area, where historic burden of the viral disease has been high. During the study period, prevalence levels fluctuated with a temporal peak in 2002. Virologic and genetic surveillance data were analyzed for evidence of three separate but not mutually exclusive transmission routes hypothesized to be maintaining IHNV in the freshwater ecosystem. Transmission between year classes of juvenile fish at individual sites (route 1) was supported at varying levels of certainty in 10%–55% of candidate cases, transmission between neighboring juvenile cohorts (route 2) was supported in 31%–78% of candidate cases, and transmission from adult fish returning to the same site as an infected juvenile cohort was supported in 26%–74% of candidate cases. The results of this study indicate that multiple specific transmission routes are acting to maintain IHNV in juvenile fish, providing concrete evidence that can be used to improve resource management. Furthermore, these results demonstrate that more sophisticated analysis of available spatio‐temporal and genetic data is likely to yield greater insight in future studies.

Gene Ontology-Based Analysis of Zebrafish Omics Data Using the Web Tool Comparative Gene Ontology.

Gene Ontology (GO) analysis is a powerful tool in systems biology, which uses a defined nomenclature to annotate genes/proteins within three categories: "Molecular Function," "Biological Process," and "Cellular Component." GO analysis can assist in revealing functional mechanisms underlying observed patterns in transcriptomic, genomic, and proteomic data. The already extensive and increasing use of zebrafish for modeling genetic and other diseases highlights the need to develop a GO analytical tool for this organism. The web tool Comparative GO was originally developed for GO analysis of bacterial data in 2013 ( www.comparativego.com ). We have now upgraded and elaborated this web tool for analysis of zebrafish genetic data using GOs and annotations from the Gene Ontology Consortium.

Description of a new subfamily of casebearer moths, Omphalopodinae subfam. n. (Lepidoptera, Coleophoridae), based on morphological and molecular genetic characters

Based on a complex analysis of both morphological and molecular genetic data, a new subfamily of casebearer moths, Omphalopodinae subfam. n., is allocated. Omphalopoda Falkovitsh, 1987, the type genus of this subfamily, is well distinguished by a set of apomorphic morphological characters and is clearly clustered between two genetically well differentiated groups of the family Coleophoridae: Tolleophorinae and Coleophorinae + Augasminae + Ischnophaninae + Metriotinae.

Conditional asymptotic inference for the kernel association test.

The kernel association test (KAT) is popular in biological studies for its ability to combine weak effects potentially of opposite direction. Its P-value is typically assessed via its (unconditional) asymptotic distribution. However, such an asymptotic distribution is known only for continuous traits and for dichotomous traits. Furthermore, the derived P-values are known to be conservative when sample size is small, especially for the important case of dichotomous traits. One alternative is the permutation test, a widely accepted approximation to the exact finite sample conditional inference. But it is time-consuming to use in practice due to stringent significance criteria commonly seen in these analyses. Based on a previous theoretical result a conditional asymptotic distribution for the KAT is introduced. This distribution provides an alternative approximation to the exact distribution of the KAT. An explicit expression of this distribution is provided from which P-values can be easily computed. This method applies to any type of traits. The usefulness of this approach is demonstrated via extensive simulation studies using real genotype data and an analysis of genetic data from the Ocular Hypertension Treatment Study. Numerical results showed that the new method can control the type I error rate and is a bit conservative when compared to the permutation method. Nevertheless the proposed method may be used as a fast screening method. A time-consuming permutation procedure may be conducted at locations that show signals of association. An implementation of the proposed method is provided in the R package iGasso. kai-wang@uiowa.edu.

Copy number variants analysis in a cohort of isolated and syndromic developmental delay/intellectual disability reveals novel genomic disorders, position effects and candidate disease genes.

Array-comparative genomic hybridization (array-CGH) is a widely used technique to detect copy number variants (CNVs) associated with developmental delay/intellectual disability (DD/ID). Identification of genomic disorders in DD/ID. We performed a comprehensive array-CGH investigation of 1,015 consecutive cases with DD/ID and combined literature mining, genetic evidence, evolutionary constraint scores, and functional information in order to assess the pathogenicity of the CNVs. We identified non-benign CNVs in 29% of patients. Amongst the pathogenic variants (11%), detected with a yield consistent with the literature, we found rare genomic disorders and CNVs spanning known disease genes. We further identified and discussed 51 cases with likely pathogenic CNVs spanning novel candidate genes, including genes encoding synaptic components and/or proteins involved in corticogenesis. Additionally, we identified two deletions spanning potential Topological Associated Domain (TAD) boundaries probably affecting the regulatory landscape. We show how phenotypic and genetic analyses of array-CGH data allow unraveling complex cases, identifying rare disease genes, and revealing unexpected position effects.

Variations in biofilm formation, desiccation resistance and Benzalkonium chloride susceptibility among Listeria monocytogenes strains isolated in Canada

Listeria monocytogenes is a pathogenic foodborne microorganism noted for its ability to survive in the environment and food processing facilities. Survival may be related to the phenotype of individual strains including the ability to form biofilms and resist desiccation and/or sanitizer exposure. The objectives of this research were to compare 14 L. monocytogenes strains isolated from blood (3), food (6) and water (5) with respect to their benzalkonium chloride (BAC) sensitivity, desiccation resistance, and ability to form biofilm. Correlations were tested between those responses, and the presence of the SSI-1 (Stress Survival Islet) and LGI1/CC8 (Listeria Genomic Island 1 in a clonal complex 8 background) genetic markers. Genetic sequences from four strains representing different phenotypes were also probed for predicted amino acid differences in biofilm, desiccation, and membrane related genes. The water isolates were among the most desiccation susceptible strains, while strains exhibiting desiccation resistance harboured SSI-1 or both the SSI-1 and LGI1/CC8 markers. BAC resistance was greatest in planktonic LGI1/CC8 cells (relative to non-LGI1/CC8 cells), and higher BAC concentrations were also needed to inhibit the formation of biofilm by LGI1/CC8 strains during incubation for 48h and 6days compared to other strains. Formation of biofilm on stainless steel was not significantly (p>0.05) different among the strains. Analysis of genetic sequence data from desiccation and BAC sensitive (CP4 5-1, CP5 2-3, both from water), intermediate (Lm568, food) and desiccation and BAC resistant (08 5578, blood, human outbreak) strains led to the finding of amino acid differences in predicted functional protein domains in several biofilm, desiccation and peptidoglycan related genes (e.g., lmo0263, lmo0433, lmo0434, lmo0771, lmo0973, lmo1080, lmo1224, lmo1370, lmo1744, and lmo2558). Notably, the LGI1/CC8 strain 08-5578 had a frameshift mutation in lmo1370, a gene previously associated with desiccation resistance. In conclusion, the more desiccation and BAC resistant LGI1/CC8 isolates may pose a challenge for sanitation efforts.

Genetically contextual effects of smoking on genome wide DNA methylation.

Smoking is the leading cause of death in the United States. It exerts its effects by increasing susceptibility to a variety of complex disorders among those who smoke, and if pregnant, to their unborn children. In prior efforts to understand the epigenetic mechanisms through which this increased vulnerability is conveyed, a number of investigators have conducted genome wide methylation analyses. Unfortunately, secondary to methodological limitations, these studies were unable to examine methylation in gene regions with significant amounts of genetic variation. Using genome wide genetic and epigenetic data from the Framingham Heart Study, we re-examined the relationship of smoking status to genome wide methylation status. When only methylation status is considered, smoking was significantly associated with differential methylation in 310 genes that map to a variety of biological process and cellular differentiation pathways. However, when SNP effects on the magnitude of smoking associated methylation changes are also considered, cis and trans-interaction effects were noted at a total of 266 and 4353 genes with no marked enrichment for any biological pathways. Furthermore, the SNP variation participating in the significant interaction effects is enriched for loci previously associated with complex medical illnesses. The enlarged scope of the methylome shown to be affected by smoking may better explicate the mediational pathways linking smoking with a myriad of smoking related complex syndromes. Additionally, these results strongly suggest that combined epigenetic and genetic data analyses may be critical for a more complete understanding of the relationship between environmental variables, such as smoking, and pathophysiological outcomes.

Modelling Mixed Types of Outcomes in Additive Genetic Models.

We present a general statistical modelling framework for handling multivariate mixed types of outcomes in the context of quantitative genetic analysis. The models are based on the multivariate covariance generalized linear models, where the matrix linear predictor is composed of an identity matrix combined with a relatedness matrix defined by a pedigree, representing the environmental and genetic components, respectively. We also propose a new index of heritability for non-Gaussian data. A case study on house sparrow (Passer domesticus) population with continuous, binomial and count outcomes is employed to motivate the new model. Simulation of multivariate marginal models is not trivial, thus we adapt the NORTA (Normal to anything) algorithm for simulation of multivariate covariance generalized linear models in the context of genetic data analysis. A simulation study is presented to assess the asymptotic properties of the estimating function estimators for the correlation between outcomes and the new heritability index parameters. The data set and R code are available in the supplementary material.

TRAK2, a novel regulator of ABCA1 expression, cholesterol efflux and HDL biogenesis.

The recent failures of HDL-raising therapies have underscored our incomplete understanding of HDL biology. Therefore there is an urgent need to comprehensively investigate HDL metabolism to enable the development of effective HDL-centric therapies. To identify novel regulators of HDL metabolism, we performed a joint analysis of human genetic, transcriptomic, and plasma HDL-cholesterol (HDL-C) concentration data and identified a novel association between trafficking protein, kinesin binding 2 (TRAK2) and HDL-C concentration. Here we characterize the molecular basis of the novel association between TRAK2 and HDL-cholesterol concentration. Analysis of lymphocyte transcriptomic data together with plasma HDL from the San Antonio Family Heart Study (n = 1240) revealed a significant negative correlation between TRAK2 mRNA levels and HDL-C concentration, HDL particle diameter and HDL subspecies heterogeneity. TRAK2 siRNA-mediated knockdown significantly increased cholesterol efflux to apolipoprotein A-I and isolated HDL from human macrophage (THP-1) and liver (HepG2) cells by increasing the mRNA and protein expression of the cholesterol transporter ATP-binding cassette, sub-family A member 1 (ABCA1). The effect of TRAK2 knockdown on cholesterol efflux was abolished in the absence of ABCA1, indicating that TRAK2 functions in an ABCA1-dependent efflux pathway. TRAK2 knockdown significantly increased liver X receptor (LXR) binding at the ABCA1 promoter, establishing TRAK2 as a regulator of LXR-mediated transcription of ABCA1. We show, for the first time, that TRAK2 is a novel regulator of LXR-mediated ABCA1 expression, cholesterol efflux, and HDL biogenesis. TRAK2 may therefore be an important target in the development of anti-atherosclerotic therapies.

Predicting the development of cardiac arrhythmias and cardiac conduction disorders in patients

The article is devoted to descriftion of the new method for diagnostics and predicting of the development of risk for development of cardiac arrhythmias and cardiac conduction disorders in patients by using of the computer system. For oftimization of the diagnostic and prevention of these pathologies in different health facilities (outpatient clinics, in-patient clinics)was developed a technology for the estimation of genetic risk and predicting in families by analysis of clinical, instrumental and genetic data. The process is automated by using the analytical software complex (computer system). The basis for the technology is the results of our multiyear researches to identify associations between the genetic polymorphisms and different cardiac conduction disorders. The article presents the possibilities of using the developed technology in clinical practice, the basic principles of work with the computer system.

Sparse redundancy analysis of high-dimensional genetic and genomic data.

Recent technological developments have enabled the possibility of genetic and genomic integrated data analysis approaches, where multiple omics datasets from various biological levels are combined and used to describe (disease) phenotypic variations. The main goal is to explain and ultimately predict phenotypic variations by understanding their genetic basis and the interaction of the associated genetic factors. Therefore, understanding the underlying genetic mechanisms of phenotypic variations is an ever increasing research interest in biomedical sciences. In many situations, we have a set of variables that can be considered to be the outcome variables and a set that can be considered to be explanatory variables. Redundancy analysis (RDA) is an analytic method to deal with this type of directionality. Unfortunately, current implementations of RDA cannot deal optimally with the high dimensionality of omics data (p≫n). The existing theoretical framework, based on Ridge penalization, is suboptimal, since it includes all variables in the analysis. As a solution, we propose to use Elastic Net penalization in an iterative RDA framework to obtain a sparse solution. We proposed sparse redundancy analysis (sRDA) for high dimensional omics data analysis. We conducted simulation studies with our software implementation of sRDA to assess the reliability of sRDA. Both the analysis of simulated data, and the analysis of 485 512 methylation markers and 18,424 gene-expression values measured in a set of 55 patients with Marfan syndrome show that sRDA is able to deal with the usual high dimensionality of omics data. http://uva.csala.me/rda. a.csala@amc.uva.nl. Supplementary data are available at Bioinformatics online.

Correlates and catalysts of hominin evolution in Africa.

Hominin evolution in the African Pliocene and Pleistocene was accompanied and mediated by changes in the abiotic and biotic spheres. It has been hypothesized that such environmental changes were catalysts of hominin morphological evolution and speciations. Whereas there is little doubt that ecological changes were relevant to shaping the trajectories of mammalian evolution, testing specific hypotheses with data from the fossil record has yielded ambiguous results regarding environmental disruption as a primary catalyst. Proposed mechanisms for abiotic and biotic causes of evolution are not always consistent with the timing and trends exhibited by the African fossil record of hominins and other mammals. Analyses of fossil and genetic data suggest that much of hominin evolution, and by extension mammalian evolution, was autocatalytic, driven by feedback loops within a species or lineage, irrespective of changes in the external environment.

Cytological, genetic and morphological variation in mixed stands of dogroses (Rosa section Caninae; Rosaceae) in Germany with a focus on the hybridogenic R. micrantha

Polyploid, hemisexual dogroses are known for their complicated reticulate evolutionary history and their ongoing hybridization events that challenge taxonomic concepts. We investigated how spontaneous hybrids between subsections Rubigineae and Caninae can be identified by cytological, genetic and morphological analyses and how they fit into the current taxonomic treatment. We sampled plants from mixed stands of both subsections in Germany and performed morphological measurements. We compared the morphological data with data from flow cytometry and microsatellite analyses compiled from a previous study based on a much wider sampling. Most of the investigated plants were pentaploid, but some species were composed of tetra- and hexaploid individuals co-occurring with the pentaploids. In Germany, all hexaploid plants were of hybridogenic origin and they were more frequently found in south-western Germany. Genetic and morphological data differentiated clearly between subsections and between informal aggregates of subsection Rubigineae. However, the currently accepted microspecies could be neither genetically nor morphologically unambiguously recognized. In contrast, multivariate analyses of genetic and morphological data distinguished between hexaploid and pentaploid cytotypes of the R. rubiginosa aggregate. The hexaploid hybridogenic individuals, mostly identified as R. micrantha, were characterized by more hairs and fewer glands on the leaflets and a smaller angle of the lamina base, characters typical for the paternal parent of subsection Caninae.

Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA.

Patterns of local adaptation at fine spatial scales are central to understanding how evolution proceeds, and are essential to the effective management of economically and ecologically important forest tree species. Here, we employ single and multilocus analyses of genetic data (n=116231 SNPs) to describe signatures of fine-scale adaptation within eight whitebark pine (Pinus albicaulis Engelm.) populations across the local extent of the environmentally heterogeneous Lake Tahoe Basin, USA. We show that despite highly shared genetic variation (FST =0.0069), there is strong evidence for adaptation to the rain shadow experienced across the eastern Sierra Nevada. Specifically, we build upon evidence from a common garden study and find that allele frequencies of loci associated with four phenotypes (mean=236 SNPs), 18 environmental variables (mean=99 SNPs), and those detected through genetic differentiation (n=110 SNPs) exhibit significantly higher signals of selection (covariance of allele frequencies) than could be expected to arise, given the data. We also provide evidence that this covariance tracks environmental measures related to soil water availability through subtle allele frequency shifts across populations. Our results replicate empirical support for theoretical expectations of local adaptation for populations exhibiting strong gene flow and high selective pressures and suggest that ongoing adaptation of many P.albicaulis populations within the Lake Tahoe Basin will not be constrained by the lack of genetic variation. Even so, some populations exhibit low levels of heritability for the traits presumed to be related to fitness. These instances could be used to prioritize management to maintain adaptive potential. Overall, we suggest that established practices regarding whitebark pine conservation be maintained, with the additional context of fine-scale adaptation.

PopFly: the Drosophila population genomics browser.

The recent compilation of over 1100 worldwide wild-derived Drosophila melanogaster genome sequences reassembled using a standardized pipeline provides a unique resource for population genomic studies (Drosophila Genome Nexus, DGN). A visual display of the estimated metrics describing genome-wide variation and selection patterns would allow gaining a global view and understanding of the evolutionary forces shaping genome variation. Here, we present PopFly, a population genomics-oriented genome browser, based on JBrowse software, that contains a complete inventory of population genomic parameters estimated from DGN data. This browser is designed for the automatic analysis and display of genetic variation data within and between populations along the D. melanogaster genome. PopFly allows the visualization and retrieval of functional annotations, estimates of nucleotide diversity metrics, linkage disequilibrium statistics, recombination rates, a battery of neutrality tests, and population differentiation parameters at different window sizes through the euchromatic chromosomes. PopFly is open and freely available at site http://popfly.uab.cat . sergi.hervas@uab.cat or antonio.barbadilla@uab.cat.

Identification of genes associated with dissociation of cognitive performance and neuropathological burden: Multistep analysis of genetic, epigenetic, and transcriptional data.

IntroductionThe molecular underpinnings of the dissociation of cognitive performance and neuropathological burden are poorly understood, and there are currently no known genetic or epigenetic determinants of the dissociation.Methods and findings“Residual cognition” was quantified by regressing out the effects of cerebral pathologies and demographic characteristics on global cognitive performance proximate to death. To identify genes influencing residual cognition, we leveraged neuropathological, genetic, epigenetic, and transcriptional data available for deceased participants of the Religious Orders Study (n = 492) and the Rush Memory and Aging Project (n = 487). Given that our sample size was underpowered to detect genome-wide significance, we applied a multistep approach to identify genes influencing residual cognition, based on our prior observation that independent genetic and epigenetic risk factors can converge on the same locus. In the first step (n = 979), we performed a genome-wide association study with a predefined suggestive p < 10−5, and nine independent loci met this threshold in eight distinct chromosomal regions. Three of the six genes within 100 kb of the lead SNP are expressed in the dorsolateral prefrontal cortex (DLPFC): UNC5C, ENC1, and TMEM106B. In the second step, in the subset of participants with DLPFC DNA methylation data (n = 648), we found that residual cognition was related to differential DNA methylation of UNC5C and ENC1 (false discovery rate < 0.05). In the third step, in the subset of participants with DLPFC RNA sequencing data (n = 469), brain transcription levels of UNC5C and ENC1 were evaluated for their association with residual cognition: RNA levels of both UNC5C (estimated effect = −0.40, 95% CI −0.69 to −0.10, p = 0.0089) and ENC1 (estimated effect = 0.0064, 95% CI 0.0033 to 0.0096, p = 5.7 × 10−5) were associated with residual cognition. In secondary analyses, we explored the mechanism of these associations and found that ENC1 may be related to the previously documented effect of depression on cognitive decline, while UNC5C may alter the composition of presynaptic terminals. Of note, the TMEM106B allele identified in the first step as being associated with better residual cognition is in strong linkage disequilibrium with rs1990622A (r2 = 0.66), a previously identified protective allele for TDP-43 proteinopathy. Limitations include the small sample size for the genetic analysis, which was underpowered to detect genome-wide significance, the evaluation being limited to a single cortical region for epigenetic and transcriptomic data, and the use of categorical measures for certain non-amyloid-plaque, non-neurofibrillary-tangle neuropathologies.ConclusionsThrough a multistep analysis of cognitive, neuropathological, genomic, epigenomic, and transcriptomic data, we identified ENC1 and UNC5C as genes with convergent genetic, epigenetic, and transcriptomic evidence supporting a potential role in the dissociation of cognition and neuropathology in an aging population, and we expanded our understanding of the TMEM106B haplotype that is protective against TDP-43 proteinopathy.