Defined morphological criteria allow reliable diagnosis of colorectal serrated polyps and predict polyp genetics.

Criteria for the diagnosis of serrated colorectal lesions (hyperplastic polyp, sessile serrated adenoma without or with dysplasia--which we called mixed polyp--and traditional serrated adenoma) for which consensus has been reached should be validated for applicability in daily practice in terms of inter-observer reproducibility and their association with clinical features and (epi)genetic events. A study set was created from a consecutive series of colorectal polyps (n = 1,926) by selecting all sessile serrated adenomas, traditional serrated adenomas and mixed polyps. We added consecutive series of hyperplastic polyps, classical adenomas and normal mucosa samples for a total of 200 specimens. With this series, we conducted an inter-observer study, encompassing ten pathologists with gastrointestinal pathology experience from five European countries, in three rounds in which all cases were microscopically evaluated. An assessment of single morphological criteria was included, and these were correlated with clinical parameters and the mutation status of KRAS, BRAF and PIK3CA and the methylation status of MLH1. Gender, age and localisation were significantly associated with certain types of lesions. Kappa statistics revealed moderate to good inter-observer agreement for polyp classification (κ = 0.56 to 0.63), but for single criteria, this varied considerably (κ = 0.06 to 0.82). BRAF mutations were frequently found in hyperplastic polyps (86 %, 62/72) and sessile serrated adenomas (80 %, 41/51). KRAS mutations occurred more frequently in traditional serrated adenomas (78 %, 7/9) and less so in classical adenomas (20 %, 10/51). Single morphological criteria for sessile serrated adenomas showed significant correlation with BRAF mutation (all p ≤ 0.001), and those for classical adenomas or traditional serrated adenoma correlated significantly with KRAS mutation (all p < 0.001). Therefore, single well-defined morphological criteria are predictive for genetic alterations in colorectal polyps.

The origins and progress of genomics research on Tef (Eragrostis tef)

Tef, Eragrostis tef (Zucc.) Trotter, is the most important cereal in Ethiopia. Tef is cultivated by more than five million small-scale farmers annually and constitutes the staple food for more than half of the population of 80 million. The crop is preferred by both farmers and consumers due to its beneficial traits associated with its agronomy and utilization. The genetic and phenotypic diversity of tef in Ethiopia is a national treasure of potentially global importance. In order for this diversity to be effectively conserved and utilized, a better understanding at the genomic level is necessary. In the recent years, tef has become the subject of genomic research in Ethiopia and abroad. Genomic-assisted tef improvement holds tremendous potential for improving productivity, thereby benefiting the smallholder farmers who have cultivated and relied on the crop for thousands of years. It is hoped that such research endeavours will provide solutions to some of the age-old problems of tef's husbandry. In this review, we provide a brief description of the genesis and progress of tef genomic research to date, suggest ways to utilize the genomic tools developed so far, discuss the potential of genomics to enable sustainable conservation and use of tef genetic diversity and suggest opportunities for the future research.

Genomics of Divergence along a Continuum of Parapatric Population Differentiation

The patterns of genomic divergence during ecological speciation are shaped by a combination of evolutionary forces. Processes such as genetic drift, local reduction of gene flow around genes causing reproductive isolation, hitchhiking around selected variants, variation in recombination and mutation rates are all factors that can contribute to the heterogeneity of genomic divergence. On the basis of 60 fully sequenced three-spined stickleback genomes, we explore these different mechanisms explaining the heterogeneity of genomic divergence across five parapatric lake and river population pairs varying in their degree of genetic differentiation. We find that divergent regions of the genome are mostly specific for each population pair, while their size and abundance are not correlated with the extent of genome-wide population differentiation. In each pair-wise comparison, an analysis of allele frequency spectra reveals that 25–55% of the divergent regions are consistent with a local restriction of gene flow. Another large proportion of divergent regions (38–75%) appears to be mainly shaped by hitchhiking effects around positively selected variants. We provide empirical evidence that alternative mechanisms determining the evolution of genomic patterns of divergence are not mutually exclusive, but rather act in concert to shape the genome during population differentiation, a first necessary step towards ecological speciation.

Genomics of Rapid Incipient Speciation in Sympatric Threespine Stickleback

Ecological speciation is the process by which reproductively isolated populations emerge as a consequence of divergent natural or ecologically-mediated sexual selection. Most genomic studies of ecological speciation have investigated allopatric populations, making it difficult to infer reproductive isolation. The few studies on sympatric ecotypes have focused on advanced stages of the speciation process after thousands of generations of divergence. As a consequence, we still do not know what genomic signatures of the early onset of ecological speciation look like. Here, we examined genomic differentiation among migratory lake and resident stream ecotypes of threespine stickleback reproducing in sympatry in one stream, and in parapatry in another stream. Importantly, these ecotypes started diverging less than 150 years ago. We obtained 34,756 SNPs with restriction-site associated DNA sequencing and identified genomic islands of differentiation using a Hidden Markov Model approach. Consistent with incipient ecological speciation, we found significant genomic differentiation between ecotypes both in sympatry and parapatry. Of 19 islands of differentiation resisting gene flow in sympatry, all were also differentiated in parapatry and were thus likely driven by divergent selection among habitats. These islands clustered in quantitative trait loci controlling divergent traits among the ecotypes, many of them concentrated in one region with low to intermediate recombination. Our findings suggest that adaptive genomic differentiation at many genetic loci can arise and persist in sympatry at the very early stage of ecotype divergence, and that the genomic architecture of adaptation may facilitate this.

DNA, Red Tide and the Sea: a new exhibit at Mystic Aquarium & IFE

DNA, Red Tide and the Sea is a new exhibit at Mystic Aquarium & IFE. It was developed by UConn Marine Sciences Professor Senjie Lin, and Mystic Aquarium. Children can extract DNA from fruit and learn about genetic codes and red tides in the ocean.

Familial aggregation, candidate genes and genome scans: Analyzing the role of genetics in stroke

The genetic etiology of stroke likely reflects the influence of multiple loci with small effects, each modulating different pathophysiological processes. This research project utilized three analytical strategies to address the paucity of information related to the identification and characterization of genetic variation associated with stroke in the general population. ^ First, the general contribution of familial factors to stroke susceptibility was evaluated in a population-based sample of unrelated individuals. Increased risk of subclinical cerebral infarction was observed among individuals with a positive parental history of stroke. This association did not appear to be mediated by established stroke risk factors, specifically blood pressure levels or hypertension status. ^ The need to identify specific gene variation associated with stroke in the general population was addressed by evaluating seven candidate gene polymorphisms in a population-based sample of unrelated individuals. Three polymorphisms were significantly associated with increased subclinical cerebral infarction or incident clinical ischemic stroke risk. These relationships include the G-protein β3 subunit 825C/T polymorphism and clinical stroke in Whites, the lipoprotein lipase S/X447 polymorphism and subclinical and clinical stroke in men, and the angiotensin I-converting enzyme Ins/Del polymorphism and subclinical stroke in White men. These associations did not appear to be obfuscated by the stroke risk factors adjusted for in the analysis models specifically blood pressure levels or anti-hypertensive medication use. ^ The final research strategy considered, on a genome-wide scale, the idea that genetic variation may contribute to the occurrence of hypertension or stroke through a common etiologic pathway. Genomic regions were identified for which significant evidence of heterogeneity was observed among hypertensive sibpairs stratified by family history of stroke information. Regions identified on chromosome 15 in African Americans, and chromosome 13 in Whites and African Americans, suggest the presence of genes influencing hypertension and stroke susceptibility. ^ Insight into the role of genetics in stroke is useful for the potential early identification of individuals at increased risk for stroke and improved understanding of the etiology of the disease. The ultimate goal of these endeavors is to guide the development of therapeutic intervention and informed prevention to provide a lasting and positive impact on public health. ^

FZD6, MATN2 AND SLC25A32, POSSIBLE CANDIDATE GENES IN NONSYNDROMIC CLEFT LIP AND PALATE

Nonsyndromic cleft lip with or without cleft palate (NSCLP) is a common birth defect with a multifactorial etiology. Despite decades of research, the genetic underpinnings of NSCLP still remain largely unexplained. A genome wide association study (GWAS) of a large NSCLP African American family with seven affected individuals across three generations found evidence for linkage at 8q21.3-24.12 (LOD = 2.98). This region contained three biologically relevant candidate genes: Frizzled-6 (FZD6) (LOD = 2.8), Matrilin-2 (MATN2) (LOD = 2.3), and Solute Carrier Family 25, Member 32 (SLC26A32) (LOD = 1.6). Sequencing of the coding regions and the 5’ and 3’ UTRs of these genes in two affected family members identified a rare intronic variant, rs138557689 (c.-153+432A>C), in FZD6. The rs138557689/C allele segregated with the NSCLP phenotype; in silico analysis predicted and EMSA analysis showed that the 138557689/C allele creates new DNA binding sites. FZD6 is part of the WNT pathway, which is involved in craniofacial development, including midface development and upper lip fusion. Our novel findings suggest that an alteration in FZD6 gene regulation may perturb this tightly controlled biological pathway and in turn contribute to the development of NSCLP in this family. Studies are underway to further define how the rs138557689/C variant affects expression of FZD6.

MOLECULAR AND GENOMIC BASED INSIGHTS INTO THE EVOLUTION OF ENTEROCOCCUS FAECIUM FROM COMMENSAL TO HOSPITAL-ADAPTED PATHOGEN

The basis for the recent transition of Enterococcus faecium from a primarily commensal organism to one of the leading causes of hospital-acquired infections in the United States is not yet understood. To address this, the first part of my project assessed isolates from early outbreaks in the USA and South America using sequence analysis, colony hybridizations, and minimal inhibitory concentrations (MICs) which showed clinical isolates possess virulence and antibiotic resistance determinants that are less abundant or lacking in community isolates. I also revealed that the level of ampicillin resistance increased over time in clinical strains. By sequencing the pbp5 gene, I demonstrated an ~5% difference in the pbp5 gene between strains with MICs <4ug/ml and those with MICs >4µg/ml, but no specific sequence changes correlated with increases in MICs within the latter group. A 3-10% nucleotide difference was also seen in three other genes analyzed, which suggested the existence of two distinct subpopulations of E. faecium. This led to the second part of my project analyzing concatenated core gene sequences, SNPs, the 16S rRNA, and phylogenetics of 21 E. faecium genomes confirming two distinct clades; a community-associated (CA) clade and hospital-associated (HA) clade. Molecular clock calculations indicate that these two clades likely diverged ~ 300,000 to > 1 million years ago, long before the modern antibiotic era. Genomic analysis also showed that, in addition to core genomic differences, HA E. faecium harbor specific accessory genetic elements that may confer selection advantages over CA E. faecium. The third part of my project discovered 6 E. faecium genes with the newly identified “WxL” domain. My analyses, using RT-PCR, western blots, patient sera, whole-cell ELISA, and immunogold electron microscopy, indicated that E. faecium WxL genes exist in operons, encode bacterial cell surface localized proteins, that WxL proteins are antigenic in humans, and are more exposed on the surface of clinical isolates versus community isolates (even though they are ubiquitous in both clades). ELISAs and BIAcore analyses also showed that proteins encoded by these operons bind several different host extracellular matrix proteins, as well as to each other, suggesting a novel cell-surface complex. In summary, my studies provide new insights into the evolution of E. faecium by showing that there are two distantly related clades; one being more successful in the hospital setting. My studies also identified operons encoding WxL proteins whose characteristics could also contribute to colonization and virulence within this species.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Molecular and evolutionary genetics of the X -linked visual pigment genes in humans and New World monkeys

Normal humans have one red and at least one green visual pigment genes. These genes are tightly linked as tandem repeats on the X chromosome and each of them has six exons. There is only one X-linked visual pigment gene in New World monkeys (NWMs) but the locus has three polymorphic alleles encoding red, yellow and green visual pigments, respectively. The spectral properties of the squirrel monkey and the marmoset (both NWMs) have been studied and partial sequences of the three alleles are available. To study the evolutionary history of these X-linked opsin genes in humans and NWMs, coding and intron sequences of the three squirrel monkey alleles and the three marmoset alleles were amplified by PCR followed by subcloning and sequencing. Introns 2 and 4 of the human red and green pigment genes were also sequenced. The results obtained are as follows: (1) The sequences of introns 2 and 4 of the human red and green opsin genes are significantly more similar between the two genes than are coding sequences, contrary to the usual situation where coding regions are better conserved in evolution than are introns. The high similarities in the two introns are probably due to recent gene conversion events during evolution of the human lineage. (2) Phylogenetic analysis of both intron and exon sequences indicates that the phylogenetic tree of the available primate opsin genes is the same as the species tree. The two human genes were derived from a gene duplication event after the divergence of the human and NWM lineages. The three alleles in each of the two NWM species diverged after the split of the two NWMs but have persisted in the population for at least 5 million years. (3) Allelic gene conversion might have occurred between the three squirrel monkey alleles. (4) A model of additive effect of hydroxyl-bearing amino acids on spectral tuning is proposed by treating some unknown variables as groups. Under the assumption that some residues have no effect, it is found that at least five amino acid residues, at positions 178 (3 nm), 180 (5 nm), 230 ($-$4 nm), 277 (9 nm) and 285 (13 nm), have linear spectral tuning effects. (5) Adaptive evolution of the opsin genes to different spectral peaks was observed at four residues that are important for spectral tuning. ^

A short critical history of the application of genomics to animal breeding

Two scientific schools have been in coexistence from the beginning of genetics, one of them searching for factors of inheritance and the other one applying biometrical models to study the relationships between relatives. With the development of molecular genetics, the possibilities of detecting genes having a noticeable effect in traits augmented. Some genes with large or medium effects were localized in animals, although the most common result was to detect markers linked to these genes, allowing the possibility of assisting selection programs with markers. When a large amount of simple and inexpensive markers were available, the SNPs, new possibilities were opened since they did not need the presence of genes of large or medium effect controlling a trait, because the whole genome was scanned. Using a large amount of SNPs permits having a prediction of the breeding value at birth accurate enough to be used in some cases, like dairy cattle, to halve its generation interval. In other animal breeding programs, the implementation of genomic selection is less clear and the way in which it can be useful should be carefully studied. The need for large populations for associating phenotypic data and markers, plus the need for repeating the process continuously, complicates its application in some cases. The implementation of the information provided by the SNPs in current genetic programs has led to the development of complex statistical tools, joining the efforts of the two schools, factorial and biometrical, that nowadays work closely related.

Structuring research methods and data with the research object model: genomics workflows as a case study

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”. Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.

Ethnohistory, genetics, and cancer mortality in Europeans

Geographic variation in cancer rates is thought to be the result of two major factors: environmental agents varying spatially and the attributes, genetic or cultural, of the populations inhabiting the areas studied. These attributes in turn result from the history of the populations in question. We had previously constructed an ethnohistorical database for Europe since 2200 B.C., permitting estimates of the ethnic composition of modern European populations. We were able to show that these estimates correlate with genetic distances. In this study, we wanted to see whether they also correlate with cancer rates. We employed two data sets of cancer mortalities from 42 types of cancer for the European Economic Community and for Central Europe. We subjected spatial differences in cancer mortalities, genetic, ethnohistorical, and geographic distances to matrix permutation tests to determine the magnitude and significance of their association. Our findings are that distances in cancer mortalities are correlated more with ethnohistorical distances than with genetic distances. Possibly the cancer rates may be affected by loci other than the genetic systems available to us, and/or by cultural factors mediated by the ethnohistorical differences. We find it remarkable that patterns of frequently ancient ethnic admixture are still reflected in modern cancer mortalities. Partial correlations with geography suggest that local environmental factors affect the mortalities as well.