New determinations of gamma-ray line intensities of the Ep = 550 keV and Ep = 1747 keV resonances of the 13-C(p,gamma)14-N reaction

Gamma-ray angular distributions for the resonances at Ep = 550 keV and 1747 keV of the radiative capture reaction 13-C(p,g)14-N have been measured, using intense proton beams on isotopically pure 13-C targets. Relative intensities for the strongest transitions were extracted with an accuracy of typically five per cent, making these resonances new useful gamma-ray standards for efficiency calibration in the energy range Egamma = 1.6 to 9 MeV.Comment: 17 pages, 6 figures, Nuclear Instruments and Methods, Sec. A, accepte

Anatomical study of serotonergic innervation and 5-HT1A receptor in the human spinal cord

Serotonergic innervation of the spinal cord in mammals has multiple roles in the control of motor, sensory and visceral functions. In rats, functional consequences of spinal cord injury at thoracic level can be improved by a substitutive transplantation of serotonin (5-HT) neurons or regeneration under the trophic influence of grafted stem cells. Translation to either pharmacological and/or cellular therapies in humans requires the mapping of the spinal cord 5-HT innervation and its receptors to determine their involvement in specific functions. Here, we have performed a preliminary mapping of serotonergic processes and serotonin-lA (5-HT1A) receptors in thoracic and lumbar segments of the human spinal cord. As in rodents and non-human primates, 5-HT profiles in human spinal cord are present in the ventral horn, surrounding motoneurons, and also contact their presumptive dendrites at lumbar level. 5-HT1A receptors are present in the same area, but are more densely expressed at lumbar level. 5-HT profiles are also present in the intermediolateral region, where 5-HT1A receptors are absent. Finally, we observed numerous serotonergic profiles in the superficial part (equivalent of Rexed lamina II) of the dorsal horn, which also displayed high levels of 5-HT1A receptors. These findings pave the way for local specific therapies involving cellular and/or pharmacological tools targeting the serotonergic system

The Role of Inbreeding in the Extinction of a European Royal Dynasty

The kings of the Spanish Habsburg dynasty (1516–1700) frequently married close relatives in such a way that uncle-niece, first cousins and other consanguineous unions were prevalent in that dynasty. In the historical literature, it has been suggested that inbreeding was a major cause responsible for the extinction of the dynasty when the king Charles II, physically and mentally disabled, died in 1700 and no children were born from his two marriages, but this hypothesis has not been examined from a genetic perspective. In this article, this hypothesis is checked by computing the inbreeding coefficient (F) of the Spanish Habsburg kings from an extended pedigree up to 16 generations in depth and involving more than 3,000 individuals. The inbreeding coefficient of the Spanish Habsburg kings increased strongly along generations from 0.025 for king Philip I, the founder of the dynasty, to 0.254 for Charles II and several members of the dynasty had inbreeding coefficients higher than 0.20. In addition to inbreeding due to unions between close relatives, ancestral inbreeding from multiple remote ancestors makes a substantial contribution to the inbreeding coefficient of most kings. A statistically significant inbreeding depression for survival to 10 years is detected in the progenies of the Spanish Habsburg kings. The results indicate that inbreeding at the level of first cousin (F = 0.0625) exerted an adverse effect on survival of 17.8%±12.3. It is speculated that the simultaneous occurrence in Charles II (F = 0.254) of two different genetic disorders: combined pituitary hormone deficiency and distal renal tubular acidosis, determined by recessive alleles at two unlinked loci, could explain most of the complex clinical profile of this king, including his impotence/infertility which in last instance led to the extinction of the dynasty

AWclust: point-and-click software for non-parametric population structure analysis

<p>Abstract</p> <p>Background</p> <p>Population structure analysis is important to genetic association studies and evolutionary investigations. Parametric approaches, e.g. STRUCTURE and L-POP, usually assume Hardy-Weinberg equilibrium (HWE) and linkage equilibrium among loci in sample population individuals. However, the assumptions may not hold and allele frequency estimation may not be accurate in some data sets. The improved version of STRUCTURE (version 2.1) can incorporate linkage information among loci but is still sensitive to high background linkage disequilibrium. Nowadays, large-scale single nucleotide polymorphisms (SNPs) are becoming popular in genetic studies. Therefore, it is imperative to have software that makes full use of these genetic data to generate inference even when model assumptions do not hold or allele frequency estimation suffers from high variation.</p> <p>Results</p> <p>We have developed point-and-click software for non-parametric population structure analysis distributed as an R package. The software takes advantage of the large number of SNPs available to categorize individuals into ethnically similar clusters and it does not require assumptions about population models. Nor does it estimate allele frequencies. Moreover, this software can also infer the optimal number of populations.</p> <p>Conclusion</p> <p>Our software tool employs non-parametric approaches to assign individuals to clusters using SNPs. It provides efficient computation and an intuitive way for researchers to explore ethnic relationships among individuals. It can be complementary to parametric approaches in population structure analysis.</p

Ancestral Informative Marker Selection and Population Structure Visualization Using Sparse Laplacian Eigenfunctions

Identification of a small panel of population structure informative markers can reduce genotyping cost and is useful in various applications, such as ancestry inference in association mapping, forensics and evolutionary theory in population genetics. Traditional methods to ascertain ancestral informative markers usually require the prior knowledge of individual ancestry and have difficulty for admixed populations. Recently Principal Components Analysis (PCA) has been employed with success to select SNPs which are highly correlated with top significant principal components (PCs) without use of individual ancestral information. The approach is also applicable to admixed populations. Here we propose a novel approach based on our recent result on summarizing population structure by graph Laplacian eigenfunctions, which differs from PCA in that it is geometric and robust to outliers. Our approach also takes advantage of the priori sparseness of informative markers in the genome. Through simulation of a ring population and the real global population sample HGDP of 650K SNPs genotyped in 940 unrelated individuals, we validate the proposed algorithm at selecting most informative markers, a small fraction of which can recover the similar underlying population structure efficiently. Employing a standard Support Vector Machine (SVM) to predict individuals' continental memberships on HGDP dataset of seven continents, we demonstrate that the selected SNPs by our method are more informative but less redundant than those selected by PCA. Our algorithm is a promising tool in genome-wide association studies and population genetics, facilitating the selection of structure informative markers, efficient detection of population substructure and ancestral inference

North African Influences and Potential Bias in Case-Control Association Studies in the Spanish Population

BACKGROUND: Despite the limited genetic heterogeneity of Spanish populations, substantial evidences support that historical African influences have not affected them uniformly. Accounting for such population differences might be essential to reduce spurious results in association studies of genetic factors with disease. Using ancestry informative markers (AIMs), we aimed to measure the African influences in Spanish populations and to explore whether these might introduce statistical bias in population-based association studies. METHODOLOGY/PRINCIPAL FINDINGS: We genotyped 93 AIMs in Spanish (from the Canary Islands and the Iberian Peninsula) and Northwest Africans, and conducted population and individual-based clustering analyses along with reference data from the HapMap, HGDP-CEPH, and other sources. We found significant differences for the Northwest African influence among Spanish populations from as low as ≈ 5% in Spanish from the Iberian Peninsula to as much as ≈ 17% in Canary Islanders, whereas the sub-Saharan African influence was negligible. Strikingly, the Northwest African ancestry showed a wide inter-individual variation in Canary Islanders ranging from 0% to 96%, reflecting the violent way the Islands were conquered and colonized by the Spanish in the XV century. As a consequence, a comparison of allele frequencies between Spanish samples from the Iberian Peninsula and the Canary Islands evidenced an excess of markers with significant differences. However, the inflation of p-values for the differences was adequately controlled by correcting for genetic ancestry estimates derived from a reduced number of AIMs. CONCLUSIONS/SIGNIFICANCE: Although the African influences estimated might be biased due to marker ascertainment, these results confirm that Northwest African genetic footprints are recognizable nowadays in the Spanish populations, particularly in Canary Islanders, and that the uneven African influences existing in these populations might increase the risk for false positives in association studies. Adjusting for population stratification assessed with a few dozen AIMs would be sufficient to control this effect

Compound Evolutionary History of the Rhesus Macaque Mhc Class I B Region Revealed by Microsatellite Analysis and Localization of Retroviral Sequences

In humans, the single polymorphic B locus of the major histocompatibility complex is linked to the microsatellite MIB. In rhesus macaques, however, haplotypes are characterized by the presence of unique combinations of multiple B genes, which may display different levels of polymorphism. The aim of the study was to shed light on the evolutionary history of this highly complex region. First, the robustness of the microsatellite MIB-linked to almost half of the B genes in rhesus macaques (Mamu-B)–for accurate B haplotyping was studied. Based on the physical map of an established haplotype comprising 7 MIB loci, each located next to a certain Mamu-B gene, two MIB loci, MIB1 and MIB6, were investigated in a panel of MHC homozygous monkeys. MIB1 revealed a complex genotyping pattern, whereas MIB6 analysis resulted in the detection of one or no amplicon. Both patterns are specific for a given B haplotype, show Mendelian segregation, and even allow a more precise haplotype definition than do traditional typing methods. Second, a search was performed for retroelements that may have played a role in duplication processes as observed in the macaque B region. This resulted in the description of two types of duplicons. One basic unit comprises an expressed Mamu-B gene, adjacent to an HERV16 copy closely linked to MIB. The second type of duplicon comprises a Mamu-B (pseudo)gene, linked to a truncated HERV16 structure lacking its MIB segment. Such truncation seems to coincide with the loss of B gene transcription. Subsequent to the duplication processes, recombination between MIB and Mamu-B loci appears to have occurred, resulting in a hyperplastic B region. Thus, analysis of MIB in addition to B loci allows deciphering of the compound evolutionary history of the class I B region in Old World monkeys

Accurate Inference of Subtle Population Structure (and Other Genetic Discontinuities) Using Principal Coordinates

Accurate inference of genetic discontinuities between populations is an essential component of intraspecific biodiversity and evolution studies, as well as associative genetics. The most widely-used methods to infer population structure are model-based, Bayesian MCMC procedures that minimize Hardy-Weinberg and linkage disequilibrium within subpopulations. These methods are useful, but suffer from large computational requirements and a dependence on modeling assumptions that may not be met in real data sets. Here we describe the development of a new approach, PCO-MC, which couples principal coordinate analysis to a clustering procedure for the inference of population structure from multilocus genotype data.PCO-MC uses data from all principal coordinate axes simultaneously to calculate a multidimensional "density landscape", from which the number of subpopulations, and the membership within subpopulations, is determined using a valley-seeking algorithm. Using extensive simulations, we show that this approach outperforms a Bayesian MCMC procedure when many loci (e.g. 100) are sampled, but that the Bayesian procedure is marginally superior with few loci (e.g. 10). When presented with sufficient data, PCO-MC accurately delineated subpopulations with population F(st) values as low as 0.03 (G'(st)>0.2), whereas the limit of resolution of the Bayesian approach was F(st) = 0.05 (G'(st)>0.35).We draw a distinction between population structure inference for describing biodiversity as opposed to Type I error control in associative genetics. We suggest that discrete assignments, like those produced by PCO-MC, are appropriate for circumscribing units of biodiversity whereas expression of population structure as a continuous variable is more useful for case-control correction in structured association studies

Identifying Signatures of Natural Selection in Tibetan and Andean Populations Using Dense Genome Scan Data

High-altitude hypoxia (reduced inspired oxygen tension due to decreased barometric pressure) exerts severe physiological stress on the human body. Two high-altitude regions where humans have lived for millennia are the Andean Altiplano and the Tibetan Plateau. Populations living in these regions exhibit unique circulatory, respiratory, and hematological adaptations to life at high altitude. Although these responses have been well characterized physiologically, their underlying genetic basis remains unknown. We performed a genome scan to identify genes showing evidence of adaptation to hypoxia. We looked across each chromosome to identify genomic regions with previously unknown function with respect to altitude phenotypes. In addition, groups of genes functioning in oxygen metabolism and sensing were examined to test the hypothesis that particular pathways have been involved in genetic adaptation to altitude. Applying four population genetic statistics commonly used for detecting signatures of natural selection, we identified selection-nominated candidate genes and gene regions in these two populations (Andeans and Tibetans) separately. The Tibetan and Andean patterns of genetic adaptation are largely distinct from one another, with both populations showing evidence of positive natural selection in different genes or gene regions. Interestingly, one gene previously known to be important in cellular oxygen sensing, EGLN1 (also known as PHD2), shows evidence of positive selection in both Tibetans and Andeans. However, the pattern of variation for this gene differs between the two populations. Our results indicate that several key HIF-regulatory and targeted genes are responsible for adaptation to high altitude in Andeans and Tibetans, and several different chromosomal regions are implicated in the putative response to selection. These data suggest a genetic role in high-altitude adaption and provide a basis for future genotype/phenotype association studies necessary to confirm the role of selection-nominated candidate genes and gene regions in adaptation to altitude