Academic literature on the topic 'Genotype data'

Effective breeding for disease resistance relies on a thorough understanding of host-by-pathogen relations. Achieving such understanding can be difficult and challenging, particularly for large data sets with complex host genotype-by-pathogen strain interactions. This paper presents a biplot approach that facilitates visual analysis of host-by-pathogen data. A biplot displays both host genotypes and pathogen isolates in a single scatter plot; each genotype or isolate is displayed as a point defined by its scores on the first two principal components derived from subjecting genotype- or strain-centered data to singular value decomposition. From a biplot, clusters of host genotypes and clusters of pathogen strains can be simultaneously visualized. Moreover, the basis for genotype and strain classifications, i.e., interactions between individual genotypes and strains, can be visualized at the same time. A biplot based on genotype-centered data and that based on strain-centered data are appropriate for visual evaluation of susceptibility/resistance of genotypes and virulence/avirulence of strains, respectively. Biplot analysis of genotype-by-strain is illustrated with published response scores of 13 barley line groups to 8 net blotch isolate groups.

Abstract Summary AlphaFamImpute is an imputation package for calling, phasing and imputing genome-wide genotypes in outbred full-sib families from single nucleotide polymorphism (SNP) array and genotype-by-sequencing (GBS) data. GBS data are increasingly being used to genotype individuals, especially when SNP arrays do not exist for a population of interest. Low-coverage GBS produces data with a large number of missing or incorrect naïve genotype calls, which can be improved by identifying shared haplotype segments between full-sib individuals. Here, we present AlphaFamImpute, an algorithm specifically designed to exploit the genetic structure of full-sib families. It performs imputation using a two-step approach. In the first step, it phases and imputes parental genotypes based on the segregation states of their offspring (i.e. which pair of parental haplotypes the offspring inherited). In the second step, it phases and imputes the offspring genotypes by detecting which haplotype segments the offspring inherited from their parents. With a series of simulations, we find that AlphaFamImpute obtains high-accuracy genotypes, even when the parents are not genotyped and individuals are sequenced at <1x coverage. Availability and implementation AlphaFamImpute is available as a Python package from the AlphaGenes website http://www.AlphaGenes.roslin.ed.ac.uk/AlphaFamImpute. Supplementary information Supplementary data are available at Bioinformatics online.

AbstractWith an increase in the number of candidate genes for important traits in livestock, effective strategies for incorporating such genes into selection programmes are increasingly important. Those strategies in part depend on the frequency of a favoured allele in a population. Since comprehensive genotyping of a population is seldom possible, we investigate the consequences of sampling strategies on the reliability of the gene frequency estimate for a bi-allelic locus. Even within a subpopulation or line, often only a proportion of individuals will be genotype tested. However, through segregation analysis, probable genotypes can be assigned to individuals that themselves were not tested, using known genotypes on relatives and a starting (presumed) gene frequency. The value of these probable genotypes in estimation of gene frequency was considered. A subpopulation or line was stochastically simulated and sampled at random, over a cluster of years or by favouring a particular genotype. Line was simulated (replicated) 1000 times. The reliability of gene frequency estimates depended on the sampling strategy used. With random sampling, even when a small proportion of a line was genotyped (0·10), the gene frequency of the population was well estimated from the across-line mean. When information on probable genotypes on untested individuals was combined with known genotypes, the between-line variance in gene frequency was estimated well; including probable genotypes overcame problems of statistical sampling. When the sampling strategy favoured a particular genotype, unsurprisingly the estimate of gene frequency was biased towards the allele favoured. In using probable genotypes the bias was lessened but the estimate of gene frequency still reflected the sampling strategy rather than the true population frequency. When sampling was confined to a few clustered years, the estimation of gene frequency was biased for those generations preceding the sampling event, particularly when the presumed starting gene frequency differed from the true population gene frequency. The potential risks of basing inferences about a population from a potentially biased sample are discussed.

AbstractThe objective of this study was to analyse the associations between ovine prion protein (PrP) genotypes and reproduction traits in three German meat sheep breeds. Reproduction traits were age at first early lambing, age at first late lambing, first lambing interval, second lambing interval and total number of lambs born. The data set included 595 genotyped German Texel sheep among 5225 recorded sheep, 351 genotyped German Black-Headed Mutton among 10 177 sheep and 282 genotyped Suffolk sheep among 2849 sheep. Linear animal models were employed for the analysis of the PrP-genotype effect. The PrP-genotype effect was analysed by comparing the most frequent PrP genotypes ARR/ARR, ARR/ARQ, and ARQ/ARQ. In a more general analysis three PrP genotype classes of animals with two, one or no copy of the ARR allele were compared.In most cases, no significant associations were found between the PrP genotypes and the reproduction traits investigated. Only for the traits age at first early lambing in German Texel and second lambing interval in German Black-Headed Mutton and Suffolk could a significant association with the PrP genotype be observed.

Hepatitis B virus (HBV) is divided into nine genotypes, A to I. Currently, it remains unclear how the individual genotypes contribute to the estimated 250 million chronic HBV infections. We performed a literature search on HBV genotyping data throughout the world. Over 900 publications were assessed and data were extracted from 213 records covering 125 countries. Using previously published HBV prevalence, and population data, we approximated the number of infections with each HBV genotype per country and the genotype distribution among global chronic HBV infections. We estimated that 96% of chronic HBV infections worldwide are caused by five of the nine genotypes: genotype C is most common (26%), followed by genotype D (22%), E (18%), A (17%) and B (14%). Genotypes F to I together cause less than 2% of global chronic HBV infections. Our work provides an up-to-date analysis of global HBV genotyping data and an initial approach to estimate how genotypes contribute to the global burden of chronic HBV infection. Results highlight the need to provide HBV cell culture and animal models that cover at least genotypes A to E and represent the vast majority of global HBV infections to test novel treatment strategies.

Introduction: HCV infections are an important public health problem due to their high chronicity, cirrhosis and advanced complications like hepatocellular carcinoma. In HCV infections, it is important and necessary to determine genotypes before treatment because treatment times and protocols vary according to genotypes. The aim of this study was to determine the distribution of HCV genotypes in HCV-RNA positive individuals in Giresun province and to investigate whether there was a statistically significant difference between genotypes and patient ages. Materials and Methods: HCV-RNA level and HCV genotype of 147 patients were included in the study. HCV RNA levels of the samples were studied by QIAsymphony SP (Qiagen) automated device, genotype determination by Rotor Gene Q (Qiagen) automated device by “real-time polymerase chain reaction (PCR)” method. Each sample was tested for HCV genotypes 1a, 1b, 2, 3, 4, 5a and 6. Results: Of the 147 patients genotyped; 128 (87%) genotype 1b, 9 (6%) genotype 1a, 8 (6%) genotype 3, 2 (1%) genotype 2 were detected. Genotypes 4, 5a and 6 were not detected. According to the HCV genotype distribution, median age and range (min.-max.) of the patients weren50 (30-83) for 1a, 70 (22-87) for 1b, 48.5 (36-61) for 2 and 33 (25-52) for 3. HCV RNA levels were between 37 and 12.630.170 IU/mL. Conclusion: The predominant genotype in the world and Turkey is genotype 1b. In our study, the most common genotype was also genotype 1b (87%), consistent with other studies in our country. This study contributed to HCV genotype distribution data in our region and country.

Abstract Elimination of genotypes or alleles for each individual or meiosis, which are inconsistent with observed genotypes, is a component of various genetic analyses of complex pedigrees. Computational efficiency of the elimination algorithm is critical in some applications such as genotype sampling via descent graph Markov chains. We present an allele elimination algorithm and two genotype elimination algorithms for complex pedigrees with incomplete genotype data. We modify all three algorithms to incorporate inheritance restrictions imposed by a complete or incomplete descent graph such that every inconsistent complete descent graph is detected in any pedigree, and every inconsistent incomplete descent graph is detected in any pedigree without loops with the genotype elimination algorithms. Allele elimination requires less CPU time and memory, but does not always eliminate all inconsistent alleles, even in pedigrees without loops. The first genotype algorithm produces genotype lists for each individual, which are identical to those obtained from the Lange-Goradia algorithm, but exploits the half-sib structure of some populations and reduces CPU time. The second genotype elimination algorithm deletes more inconsistent genotypes in pedigrees with loops and detects more illegal, incomplete descent graphs in such pedigrees.

Various study of multi-location test of rice genotypes was strongly influenced by field and environmental factors. This study aimed to implement and to identify wetland rice genotypes. Multi-location test was conducted on some rice genotypes in Southeast Sulawesi in 2010 at various locations. Randomized block design was used with three replications in different locations. Data were analyzed using AMMI model. Results showed that rice production had the main effect with the genotype of KUI 1 and KUI 2. Moreover, BIPLOT test found that the genotype 2 of S3393-2F-17-1-1 was the only one of unstable genotype, and the rest were stable genotype. Not surprisingly, genotype (G) 4 (S3382-2D-PN-4-1), G 5 (S3382-2D-PN-6-3-3), G 7 (S3382-2D-PN-2D-1-1), G 11 (S3382-2D-1-1), G13 (Ciherang) and G 14 (Cisantana) were relatively stable. Those genotypes could be recommended as the high potential yield due to higher average production compared with general average. This study also indicated that G 3 (S3381-2D-PN-27-2), G 9 (S4359-E-11-2), and G 12 (IR64) are very suitable for growing in location A at Ladongi. Moreover, in location B study suggested that G 11 (OBS-9595) should be recommended at Wundulako. Finally, in Lambuya, particularly in location C, study indicated that some genotypes can grow well, viz. G 6 (S3382-2D-PN-17-3), G 8 (S4690 G-KN-4-3) and G 14 (Cisantana).

Accessibility of high-throughput genotyping technology makes possible genome-wide association studies for common complex diseases. When dealing with common diseases, it is necessary to search and analyze multiple independent causes resulted from interactions of multiple genes scattered over the entire genome. The optimization formulations for searching disease-associated risk/resistant factors and predicting disease susceptibility for given case-control study have been introduced. Several discrete methods for disease association search exploiting greedy strategy and topological properties of case-control studies have been developed. New disease susceptibility prediction methods based on the developed search methods have been validated on datasets from case-control studies for several common diseases. Our experiments compare favorably the proposed algorithms with the existing association search and susceptibility prediction methods.

Die Untersuchung des Phänotyps bringt z.B. bei genetischen Krankheiten ein Verständnis der zugrunde liegenden Mechanismen mit sich. Aufgrund dessen wurden neue Technologien wie RNA-Interferenz (RNAi) entwickelt, die Genfunktionen entschlüsseln und mehr phänotypische Daten erzeugen. Interpretation der Ergebnisse solcher Versuche ist insbesondere bei heterogenen Daten eine große Herausforderung. Wenige Ansätze haben bisher Daten über die direkte Verknüpfung von Genotyp und Phänotyp hinaus interpretiert. Diese Dissertation zeigt neue Methoden, die Entdeckungen in Phänotypen über Spezies und Methodik hinweg ermöglichen. Es erfolgt eine Erfassung der verfügbaren Datenbanken und der Ansätze zur Analyse ihres Inhalts. Die Grenzen und Hürden, die noch bewältigt werden müssen, z.B. fehlende Datenintegration, lückenhafte Ontologien und der Mangel an Methoden zur Datenanalyse, werden diskutiert. Der Ansatz zur Integration von Genotyp- und Phänotypdaten, PhenomicDB 2, wird präsentiert. Diese Datenbank assoziiert Gene mit Phänotypen durch Orthologie über Spezies hinweg. Im Fokus sind die Integration von RNAi-Daten und die Einbindung von Ontologien für Phänotypen, Experimentiermethoden und Zelllinien. Ferner wird eine Studie präsentiert, in der Phänotypendaten aus PhenomicDB genutzt werden, um Genfunktionen vorherzusagen. Dazu werden Gene aufgrund ihrer Phänotypen mit Textclustering gruppiert. Die Gruppen zeigen hohe biologische Kohärenz, da sich viele gemeinsame Annotationen aus der Gen-Ontologie und viele Protein-Protein-Interaktionen innerhalb der Gruppen finden, was zur Vorhersage von Genfunktionen durch Übertragung von Annotationen von gut annotierten Genen zu Genen mit weniger Annotationen genutzt wird. Zuletzt wird der Prototyp PhenoMIX präsentiert, in dem Genotypen und Phänotypen mit geclusterten Phänotypen, PPi, Orthologien und weiteren Ähnlichkeitsmaßen integriert und deren Gruppierungen zur Vorhersage von Genfunktionen, sowie von phänotypischen Wörtern genutzt.
In diseases with a genetic component, examination of the phenotype can aid understanding the underlying genetics. Technologies to generate high-throughput phenotypes, such as RNA interference (RNAi), have been developed to decipher functions for genes. This large-scale characterization of genes strongly increases phenotypic information. It is a challenge to interpret results of such functional screens, especially with heterogeneous data sets. Thus, there have been only few efforts to make use of phenotype data beyond the single genotype-phenotype relationship. Here, methods are presented for knowledge discovery in phenotypes across species and screening methods. The available databases and various approaches to analyzing their content are reviewed, including a discussion of hurdles to be overcome, e.g. lack of data integration, inadequate ontologies and shortage of analytical tools. PhenomicDB 2 is an approach to integrate genotype and phenotype data on a large scale, using orthologies for cross-species phenotypes. The focus lies on the uptake of quantitative and descriptive RNAi data and ontologies of phenotypes, assays and cell-lines. Then, the results of a study are presented in which the large set of phenotype data from PhenomicDB is taken to predict gene annotations. Text clustering is utilized to group genes based on their phenotype descriptions. It is shown that these clusters correlate well with indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. The clusters are then used to predict gene function by carrying over annotations from well-annotated genes to less well-characterized genes. Finally, the prototype PhenoMIX is presented, integrating genotype and phenotype data with clustered phenotypes, orthologies, interaction data and other similarity measures. Data grouped by these measures are evaluated for theirnpredictiveness in gene functions and phenotype terms.

Although many genetic factors have been successfully identified for human diseases in genome-wide association studies (GWAS), genes discovered to date only account for a small proportion of overall genetic contributions to many complex traits. Association studies have difficulty in detecting the remaining true genetic variants that are either common variants with weak allelic effects, or rare variants that have strong allelic effects but are weakly associated at the population level. In this work we applied a goodness-of-fit test for detecting sets of common and rare variants associated with quantitative or binary traits by using whole genome sequencing (WGS) data. This test has been proved optimal for detecting weak and sparse signals in the literature, which fits the requirements for targeting the genetic components of missing heritability. Furthermore, this p-value-combining method allows one to incorporate different data and/or research results for meta-analysis. The method was used to simultaneously analyse the WGS and GWAS data of Genetic Analysis Workshop (GAW) 18 for detecting true genetic variants. The results show that goodness-of-fit test is comparable or better than the influential sequence kernel association test in many cases.

Genotype microarrays assay hundreds of thousands of genetic variants on an individual's genome. The availability of this high throughput genotyping capability has transformed the field of genetics over the past decade by enabling thousands of individuals to be rapidly assayed. This has lead to the discovery of hundreds of genetic variants that are associated with disease and other phenotypes in genome wide association studies (GWAS). These data have also brought with them a number of new statistical and computational challenges. This thesis deals with two primary analysis problems involving microarray data; genotype calling and haplotype inference. Genotype calling involves converting the noisy bivariate fluorescent signals generated by microarray data into genotype values for each genetic variant and individual. Poor quality genotype calling can lead to false positives and loss of power in GWAS so this is an important task. We introduce a new genotype calling method that is highly accurate and has the novel capability of fusing microarray data with next-generation sequencing data for greater accuracy and fewer missing values. Our new method compares favourably to other available genotype calling software. Haplotype inference (or phasing) involves deconvolving these genotypes into the two inherited parental chromosomes for an individual. The development of phasing methods has been a fertile field for statistical genetics research for well over ten years. Depending on the demography of a cohort, different phasing methods may be more appropriate than others. We review the popular offerings and introduce a new approach to try and unify two distinct problems; the phasing of extended pedigrees and the phasing of unrelated individuals. We conduct an extensive comparison of phasing methods on real and simulated data. Finally we demonstrate some preliminary results on extending methodology to sample sizes in the tens of thousands.

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Haplotype information has a central role in the understanding and diagnosis of certain illnesses, and also for evolution studies. Since that type of information is hard to obtain directly, computational methods to infer haplotype from genotype data have received great attention from the computational biology community. Unfortunately, haplotype inference is a very hard computational biology problem and the existing methods can only partially identify correct solutions. I present neural network models that use different properties of the data to predict when a method is more prone to make errors. I construct models for three different Haplotype Inference approaches and I show that our models are accurate and statistically relevant. The results of our experiments offer valuable insights on the performance of those methods, opening opportunity for a combination of strategies or improvement of individual approaches. I formally demonstrate that Linkage Disequilibrium (LD) and heterozygosity are very strong indicators of Switch Error tendency for four methods studied, and I delineate scenarios based on LD measures, that reveal a higher or smaller propension of the HI methods to present inference errors, so the correlation between LD and the occurrence of errors varies among regions along the genotypes. I present evidence that considering windows of length 10, immediately to the left of a SNP (upstream region), and eliminating the non-informative SNPs through Fisher’s Test leads to a more suitable correlation between LD and Inference Errors. I apply Multiple Linear Regression to explore the relevance of several biologically meaningful properties of the genotype sequences for the accuracy of the haplotype inference results, developing models for two databases (considering only Humans) and using two error metrics. The accuracy of our results and the stability of our proposed models are supported by statistical evidence.
Haplótipos têm um papel central na compreensão e diagnóstico de determinadas doenças e também para estudos de evolução. Este tipo de informação é difícil de obter diretamente, diante disto, métodos computacionais para inferir haplótipos a partir de dados genotípicos têm recebido grande atenção da comunidade de biologia computacional. Infelizmente, a Inferência de Halótipos é um problema difícil e os métodos existentes só podem predizer parcialmente soluções corretas. Foram desenvolvidos modelos de redes neurais que utilizam diferentes propriedades dos dados para prever quando um método é mais propenso a cometer erros. Foram calibrados modelos para três abordagens de Inferência de Haplótipos diferentes e os resultados validados estatisticamente. Os resultados dos experimentos oferecem informações valiosas sobre o desempenho e comportamento desses métodos, gerando condições para o desenvolvimento de estratégias de combinação de diferentes soluções ou melhoria das abordagens individuais. Foi demonstrado que Desequilíbrio de Ligação (LD) e heterozigosidade são fortes indicadores de tendência de erro, desta forma foram delineados cenários com base em medidas de LD, que revelam quando um método tem maior ou menor propensão de cometer erros. Foi identificado que utilizando janelas de 10 SNPs (polimorfismo de um único nucleotídeo), imediatamente a montante, e eliminando os SNPs não informativos pelo Teste de Fisher leva-se a uma correlação mais adequada entre LD e a ocorrência de erros. Por fim, foi aplicada análise de Regressão Linear para explorar a relevância de várias propriedades biologicamente significativas das sequências de genótipos para a precisão dos resultados de Inferência de Haplótipos, estimou-se modelos para duas bases de dados (considerando apenas humanos) utilizando duas métricas de erro. A precisão dos resultados e a estabilidade dos modelos propostos foram validadas por testes estatísticos.

Genotype data, consisting large numbers of markers, is used as demographic and association studies to determine genes related to specific traits or diseases. Handling of these datasets usually takes a significant amount of time in its application of population structure inference. Therefore, we suggested applying PCA on genotyped data and then clustering algorithms to specify the individuals to their particular subpopulations. We collected both real and simulated datasets in this study. We studied PCA and selected significant features, then applied five different clustering techniques to obtain better results. Furthermore, we studied three different methods for predicting the optimal number of subpopulations in a collected dataset. The results of four different simulated datasets and two real human genotype datasets show that our approach performs well in the inference of population structure. NbClust is more effective to infer subpopulations in the population. In this study, we showed that centroid-based clustering: such as k-means and PAM, performs better than model-based, spectral, and hierarchical clustering algorithms. This approach also has the benefit of being fast and flexible in the inference of population structure.

Målet med projektet är att undersöka om det går att effektivisera hur man undersöker människors gener. Detta görs genom att skapa ett program i Java. Resultatet är ett program som sorterar genotypdata från 1000 Genomes Project och utvärderar nyttan av att undersöka genotyper från flera individer samtidigt.

La present Tesi Doctoral s’emmarca en una línia d’investigació del Departament de Producció Animal de la Universitat de Lleida, dedicada a la millora genètica de la qualitat de la carn en bestiar porcí, en particular del contingut i composició del greix intramuscular. La Tesi es composa de quatre estudis, centrant-se el primer d’ells en el desenvolupament d’un mètode per a determinar el contingut i composició del greix intramuscular a partir de biòpsies i mostres post-mortem petites amb les que després es puguin portar a terme estudis en disseny longitudinal. La metodologia proposada ha resultat útil, demostrant-se que, especialment per al contingut de greix intramuscular, els espècimens petits del múscul objectiu són tan informatius com mostres grans d’altres músculs. En el segon estudi s’ha investigat, mitjançant un experiment amb dades longitudinals obtingudes segons la metodologia descrita anteriorment, l’efecte de l’edat sobre el contingut i composició del greix intramuscular i subcutani al llarg del cicle d’engreix en porcs de raça Duroc. Es conclou que un retard en l’edat de sacrifici implica un augment del contingut de greix intramuscular i d’àcid oleic, tot i que això s’aconsegueix a expenses de disminuir la velocitat de creixement magre. Per altra part es demostra que el greix intramuscular i el greix subcutani tenen comportaments diferents de creixement i composició i que la quantitat de greix per si mateix també influeix en la seva composició. El que un porc sigui més gras de l’esperat a una edat determinada és degut, en el cas del greix intramuscular, a que ha augmentat el contingut de greix monoinsaturat, en especial d’olèic, mentre que, en el del greix subcutani a que s’ha incrementat el contingut de saturat. En els dos últims estudis s’examina si la variació al·lèlica en els gens IGF-1 (insulin-like growth factor-1) i LEP (leptina), així com la concentració de IGF-1 i leptina en plasma, s’associen amb el contingut i la composició del greix intramuscular i, en cas que així fos, si aquesta associació és funció de l’edat. Es posa en evidència que els polimorfismes moleculars estudiats no són neutrals en relació al contingut de greix intramuscular, però també que els seus efectes no són constants al llarg del període de creixement. En aquest sentit, tant l’edat com l’estat d’engrassament poden modificar-los.
La presente Tesis Doctoral se emmarca en una línea de investigación del Departamento de Producción Animal de la Universidad de Lleida dedicada a la mejora genética de la calidad de la carne en porcino, en particular del contenido y la composición de la grasa intramuscular. La Tesis se compone de cuatro estudios, centrándose el primero de ellos en el desarrollo de un método para determinar el contenido y la composición de la grasa intramuscular a partir de biopsias y muestras post-mortem pequeñas con las que luego poder realizar estudios mediante diseños longitudinales. La metodología propuesta ha resultado útil, demostrándose que, especialmente para el contenido de grasa intramuscular, los especímenes pequeños del músculo objetivo son tan informativos como muestras grandes de otros músculos. En el segundo estudio se ha investigado mediante un experimento con datos longitudinales, obtenidos según la metodología descrita anteriormente, el efecto de la edad sobre el contenido y la composición de la grasa intramuscular y subcutánea durante el engorde de cerdos Duroc. Se concluye que un retraso en la edad de sacrificio comporta un aumento del contenido de grasa intramuscular y de ácido oleico, aunque ello se consigue a costa de disminuir la velocidad de crecimiento magro. Por otra parte, se demuestra que la grasa intramuscular y la grasa subcutánea tienen patrones distintos de crecimiento y composición y que la cantidad de grasa por sí misma influye en su composición. El que un cerdo sea más graso de lo esperado a una edad determinada es debido, en el caso de la grasa intramuscular, a que ha aumentado el contenido de grasa monoinsaturada, en especial de oleico, mientras que, en el de la subcutánea, a que se ha incrementado el de la saturada. En los dos últimos estudios se examina si la variación alélica en los genes IGF-1 (insulin-like growth factor-1) y LEP (leptina), así como la concentración de IGF-1 y leptina en plasma, se asocian con el contenido y la composición de la grasa intramuscular y, en caso de que así fuera, si tal asociación es función de la edad. Se constata que los polimorfismos moleculares estudiados no son neutrales respecto al contenido de grasa intramuscular, pero, también, que sus efectos no son constantes a lo largo del crecimiento. En este sentido, tanto la edad como el estado de engrasamiento pueden modificarlos.
This PhD is part of a line of research conducted in the Department of Animal Production of the Universitat de Lleida dedicated to the genetic improvement of pig meat quality, with particular reference to intramuscular fat content and composition. The PhD comprises four studies, with the first one focusing on the development of a method to jointly determine the content and composition of intramuscular fat from biopsies and small post-mortem samples and, in this way, to carry out studies with longitudinal data. It has been found that this particular methodology is useful and, in for intramuscular fat, small specimens of the target muscle are as informative as large samples of other muscles. In the second study the effect of age on the content and composition of the intramuscular and subcutaneous fat in the fattening period in Duroc pigs was investigated by an experiment using longitudinal data obtained following the methodology described above. It was concluded that a delay in the age of slaughter of the pig leads to an increase in intramuscular fat and oleic acid, although this comes at the cost of reducing the rate of lean growth. Moreover, it was proved that intramuscular and subcutaneous fat behaved differently in terms of fat accretion and composition and that the amount of fat itself affected composition. Whereas, for the intramuscular fat, values above the expected at a given age were because of increased monounsaturated fatty acid content, especially oleic acid, for the subcutaneous fat, they were due to the increased saturated fatty acid content. The final two studies considered whether allelic variation at the IGF-1 (insuline-like growth factor-1) and LEP (leptin) genes, as well as the concentration of IGF-1 and leptin in plasma, are associated to intramuscular fat content and composition and, if so, whether this is a function of age. It can be seen that the molecular polymorphisms studied are not neutral with regard to the content of intramuscular fat, but that their effects are not constant throughout the growing period. In this sense, both age and fatness can modify them.

While classical quantitative genetics usually assumes that all genotypes have the same environmental variance (the assumption of homoscedasticity), in reality, genotypes can show heteroscedasticity in the environmental variance. When such variation is heritable (i.e., has an additive variance in an outbred population), then the environmental variance can change under selection. This can either be due to an indirect response (such as during directional selection on a trait), or through direct selection to increase the homogeneity of a trait (such as for increased uniformity during harvesting). This chapter reviews the existing data on the heritability of the environmental variance and examines several different genetic models for predicting its response.

This chapter reviews medications currently available for PTSD in the context of their mechanisms of action, pathophysiological relevance, and clinical efficacy data. It systematically reviews aminergic mechanisms in PTSD pharmacology, including commonly used serotonin and norepinephrine agents, selective reuptake inhibitors and receptors drugs, as well as dopaminergic agents and psychostimulants. It also discusses the use of anticonvusants and antianxiety agents that modulate GABAergic and glutamatergic signaling, such as carbamazepine, VPA, benzodiazepines, gabapentine, and others. It also reviews other clinically available agents as well as HPA axis-modulating compounds, both for treatment and secondary prevention of PTSD. It concludes with the suggestion that clinical selection of one or more of these medications for PTSD should be based on individual patient considerations, including target symptoms, PTSD subtype, post-traumatic interval, comorbidities, genotypes for CYP450 enzymes, and genetic polymorphisms of clinical relevance.

Thrombotic disorders of the circulatory system represent the leading cause of morbidity, motality, and health care expenditure in the United States. Fibrinolytic and Antithrombotic Therapy provides a practical, evidence-based approach to the management of thrombotic disorders for all clinicians involved in the care of patients with these disorders. It provides not only vital conceptual information on fibrinolytic and antithrombotic therapy, but also the means to apply it to everyday decision making and patient care. Focusing on managment guidelines and critical pathways, the text stresses practicality and usability. It will be a valuable resource for the wide range of clinicians involved in the care of patients with these disorders, including cardiologists, emergency physicians, primary care physicians, hematologists, neurologists, intensivists, pharmacists, and nurse practitioners. The origins of mammalian blood coagulation can be traced back over 400 million years. Despite its long history, it is only within the past century that this complex and pivotal teleologic system has begun to be understood. Most recently, the intricacies of hemostasis and pahtologic thrombosis have come to light, leading the way toward new, more effective, and safer treatment modalities. The Second Edition of Fibrinolytic and Antithrombotic Therapy, even more concise and clinically relevant than the First, provides vital, evidence-based information on management of patients with arterial and venous thrombotic disorders. Since the First Edition, the text has been expanded to cover the evolving topics of atherothrombosis, thrombocardiology, hematologic/thrombophilic conditions, and vascular medicine. It includes up-to-date guidelines for antithrombotic and fibrinolytic therapy, and offers concise summaries of current "standards of care." Chapters are dedicated to discussions of patient-specific therapeutics and to the importance of genomics, proteomics, and metabolomics in defining genotype-phenotype relationships, while throughout the book coagulation, inflammation, and vascular medicine are newly examined as elements in an intricatley-linked triad of biochemical and cellular based phenomenology.

Bones are multifunctional passive organs of movement that supports soft tissue and directly attached muscles. They also protect internal organs and are a reserve of calcium, phosphorus and magnesium. Each bone is covered with periosteum, and the adjacent bone surfaces are covered by articular cartilage. Histologically, the bone is an organ composed of many different tissues. The main component is bone tissue (cortical and spongy) composed of a set of bone cells and intercellular substance (mineral and organic), it also contains fat, hematopoietic (bone marrow) and cartilaginous tissue. Bones are a tissue that even in adult life retains the ability to change shape and structure depending on changes in their mechanical and hormonal environment, as well as self-renewal and repair capabilities. This process is called bone turnover. The basic processes of bone turnover are: • bone modeling (incessantly changes in bone shape during individual growth) following resorption and tissue formation at various locations (e.g. bone marrow formation) to increase mass and skeletal morphology. This process occurs in the bones of growing individuals and stops after reaching puberty • bone remodeling (processes involve in maintaining bone tissue by resorbing and replacing old bone tissue with new tissue in the same place, e.g. repairing micro fractures). It is a process involving the removal and internal remodeling of existing bone and is responsible for maintaining tissue mass and architecture of mature bones. Bone turnover is regulated by two types of transformation: • osteoclastogenesis, i.e. formation of cells responsible for bone resorption • osteoblastogenesis, i.e. formation of cells responsible for bone formation (bone matrix synthesis and mineralization) Bone maturity can be defined as the completion of basic structural development and mineralization leading to maximum mass and optimal mechanical strength. The highest rate of increase in pig bone mass is observed in the first twelve weeks after birth. This period of growth is considered crucial for optimizing the growth of the skeleton of pigs, because the degree of bone mineralization in later life stages (adulthood) depends largely on the amount of bone minerals accumulated in the early stages of their growth. The development of the technique allows to determine the condition of the skeletal system (or individual bones) in living animals by methods used in human medicine, or after their slaughter. For in vivo determination of bone properties, Abstract 10 double energy X-ray absorptiometry or computed tomography scanning techniques are used. Both methods allow the quantification of mineral content and bone mineral density. The most important property from a practical point of view is the bone’s bending strength, which is directly determined by the maximum bending force. The most important factors affecting bone strength are: • age (growth period), • gender and the associated hormonal balance, • genotype and modification of genes responsible for bone growth • chemical composition of the body (protein and fat content, and the proportion between these components), • physical activity and related bone load, • nutritional factors: – protein intake influencing synthesis of organic matrix of bone, – content of minerals in the feed (CA, P, Zn, Ca/P, Mg, Mn, Na, Cl, K, Cu ratio) influencing synthesis of the inorganic matrix of bone, – mineral/protein ratio in the diet (Ca/protein, P/protein, Zn/protein) – feed energy concentration, – energy source (content of saturated fatty acids - SFA, content of polyun saturated fatty acids - PUFA, in particular ALA, EPA, DPA, DHA), – feed additives, in particular: enzymes (e.g. phytase releasing of minerals bounded in phytin complexes), probiotics and prebiotics (e.g. inulin improving the function of the digestive tract by increasing absorption of nutrients), – vitamin content that regulate metabolism and biochemical changes occurring in bone tissue (e.g. vitamin D3, B6, C and K). This study was based on the results of research experiments from available literature, and studies on growing pigs carried out at the Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences. The tests were performed in total on 300 pigs of Duroc, Pietrain, Puławska breeds, line 990 and hybrids (Great White × Duroc, Great White × Landrace), PIC pigs, slaughtered at different body weight during the growth period from 15 to 130 kg. Bones for biomechanical tests were collected after slaughter from each pig. Their length, mass and volume were determined. Based on these measurements, the specific weight (density, g/cm3) was calculated. Then each bone was cut in the middle of the shaft and the outer and inner diameters were measured both horizontally and vertically. Based on these measurements, the following indicators were calculated: • cortical thickness, • cortical surface, • cortical index. Abstract 11 Bone strength was tested by a three-point bending test. The obtained data enabled the determination of: • bending force (the magnitude of the maximum force at which disintegration and disruption of bone structure occurs), • strength (the amount of maximum force needed to break/crack of bone), • stiffness (quotient of the force acting on the bone and the amount of displacement occurring under the influence of this force). Investigation of changes in physical and biomechanical features of bones during growth was performed on pigs of the synthetic 990 line growing from 15 to 130 kg body weight. The animals were slaughtered successively at a body weight of 15, 30, 40, 50, 70, 90, 110 and 130 kg. After slaughter, the following bones were separated from the right half-carcass: humerus, 3rd and 4th metatarsal bone, femur, tibia and fibula as well as 3rd and 4th metatarsal bone. The features of bones were determined using methods described in the methodology. Describing bone growth with the Gompertz equation, it was found that the earliest slowdown of bone growth curve was observed for metacarpal and metatarsal bones. This means that these bones matured the most quickly. The established data also indicate that the rib is the slowest maturing bone. The femur, humerus, tibia and fibula were between the values of these features for the metatarsal, metacarpal and rib bones. The rate of increase in bone mass and length differed significantly between the examined bones, but in all cases it was lower (coefficient b <1) than the growth rate of the whole body of the animal. The fastest growth rate was estimated for the rib mass (coefficient b = 0.93). Among the long bones, the humerus (coefficient b = 0.81) was characterized by the fastest rate of weight gain, however femur the smallest (coefficient b = 0.71). The lowest rate of bone mass increase was observed in the foot bones, with the metacarpal bones having a slightly higher value of coefficient b than the metatarsal bones (0.67 vs 0.62). The third bone had a lower growth rate than the fourth bone, regardless of whether they were metatarsal or metacarpal. The value of the bending force increased as the animals grew. Regardless of the growth point tested, the highest values were observed for the humerus, tibia and femur, smaller for the metatarsal and metacarpal bone, and the lowest for the fibula and rib. The rate of change in the value of this indicator increased at a similar rate as the body weight changes of the animals in the case of the fibula and the fourth metacarpal bone (b value = 0.98), and more slowly in the case of the metatarsal bone, the third metacarpal bone, and the tibia bone (values of the b ratio 0.81–0.85), and the slowest femur, humerus and rib (value of b = 0.60–0.66). Bone stiffness increased as animals grew. Regardless of the growth point tested, the highest values were observed for the humerus, tibia and femur, smaller for the metatarsal and metacarpal bone, and the lowest for the fibula and rib. Abstract 12 The rate of change in the value of this indicator changed at a faster rate than the increase in weight of pigs in the case of metacarpal and metatarsal bones (coefficient b = 1.01–1.22), slightly slower in the case of fibula (coefficient b = 0.92), definitely slower in the case of the tibia (b = 0.73), ribs (b = 0.66), femur (b = 0.59) and humerus (b = 0.50). Bone strength increased as animals grew. Regardless of the growth point tested, bone strength was as follows femur > tibia > humerus > 4 metacarpal> 3 metacarpal> 3 metatarsal > 4 metatarsal > rib> fibula. The rate of increase in strength of all examined bones was greater than the rate of weight gain of pigs (value of the coefficient b = 2.04–3.26). As the animals grew, the bone density increased. However, the growth rate of this indicator for the majority of bones was slower than the rate of weight gain (the value of the coefficient b ranged from 0.37 – humerus to 0.84 – fibula). The exception was the rib, whose density increased at a similar pace increasing the body weight of animals (value of the coefficient b = 0.97). The study on the influence of the breed and the feeding intensity on bone characteristics (physical and biomechanical) was performed on pigs of the breeds Duroc, Pietrain, and synthetic 990 during a growth period of 15 to 70 kg body weight. Animals were fed ad libitum or dosed system. After slaughter at a body weight of 70 kg, three bones were taken from the right half-carcass: femur, three metatarsal, and three metacarpal and subjected to the determinations described in the methodology. The weight of bones of animals fed aa libitum was significantly lower than in pigs fed restrictively All bones of Duroc breed were significantly heavier and longer than Pietrain and 990 pig bones. The average values of bending force for the examined bones took the following order: III metatarsal bone (63.5 kg) <III metacarpal bone (77.9 kg) <femur (271.5 kg). The feeding system and breed of pigs had no significant effect on the value of this indicator. The average values of the bones strength took the following order: III metatarsal bone (92.6 kg) <III metacarpal (107.2 kg) <femur (353.1 kg). Feeding intensity and breed of animals had no significant effect on the value of this feature of the bones tested. The average bone density took the following order: femur (1.23 g/cm3) <III metatarsal bone (1.26 g/cm3) <III metacarpal bone (1.34 g / cm3). The density of bones of animals fed aa libitum was higher (P<0.01) than in animals fed with a dosing system. The density of examined bones within the breeds took the following order: Pietrain race> line 990> Duroc race. The differences between the “extreme” breeds were: 7.2% (III metatarsal bone), 8.3% (III metacarpal bone), 8.4% (femur). Abstract 13 The average bone stiffness took the following order: III metatarsal bone (35.1 kg/mm) <III metacarpus (41.5 kg/mm) <femur (60.5 kg/mm). This indicator did not differ between the groups of pigs fed at different intensity, except for the metacarpal bone, which was more stiffer in pigs fed aa libitum (P<0.05). The femur of animals fed ad libitum showed a tendency (P<0.09) to be more stiffer and a force of 4.5 kg required for its displacement by 1 mm. Breed differences in stiffness were found for the femur (P <0.05) and III metacarpal bone (P <0.05). For femur, the highest value of this indicator was found in Pietrain pigs (64.5 kg/mm), lower in pigs of 990 line (61.6 kg/mm) and the lowest in Duroc pigs (55.3 kg/mm). In turn, the 3rd metacarpal bone of Duroc and Pietrain pigs had similar stiffness (39.0 and 40.0 kg/mm respectively) and was smaller than that of line 990 pigs (45.4 kg/mm). The thickness of the cortical bone layer took the following order: III metatarsal bone (2.25 mm) <III metacarpal bone (2.41 mm) <femur (5.12 mm). The feeding system did not affect this indicator. Breed differences (P <0.05) for this trait were found only for the femur bone: Duroc (5.42 mm)> line 990 (5.13 mm)> Pietrain (4.81 mm). The cross sectional area of the examined bones was arranged in the following order: III metatarsal bone (84 mm2) <III metacarpal bone (90 mm2) <femur (286 mm2). The feeding system had no effect on the value of this bone trait, with the exception of the femur, which in animals fed the dosing system was 4.7% higher (P<0.05) than in pigs fed ad libitum. Breed differences (P<0.01) in the coross sectional area were found only in femur and III metatarsal bone. The value of this indicator was the highest in Duroc pigs, lower in 990 animals and the lowest in Pietrain pigs. The cortical index of individual bones was in the following order: III metatarsal bone (31.86) <III metacarpal bone (33.86) <femur (44.75). However, its value did not significantly depend on the intensity of feeding or the breed of pigs.