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AbstractCauses of individual differences in happiness, as assessed with the Subjective Happiness Scale, are investigated in a large of sample twins and siblings from the Netherlands Twin Register. Over 12,000 twins and siblings, average age 24.7 years (range 12 to 88), took part in the study. A genetic model with an age by sex design was fitted to the data with structural equation modeling in Mx. The heritability of happiness was estimated at 22% for males and 41% in females. No effect of age was observed. To identify the genomic regions contributing to this heritability, a genome-wide linkage study for happiness was conducted in sibling pairs. A subsample of 1157 offspring from 441 families was genotyped with an average of 371 micro-satellite markers per individual. Phenotype and genotype data were analyzed in MERLIN with multipoint variance component linkage analysis and age and sex as covariates. A linkage signal (logarithm of odds score 2.73, empirical p value 0.095) was obtained at the end of the long arm of chromosome 19 for marker D19S254 at 110 cM. A second suggestive linkage peak was found at the short arm of chromosome 1 (LOD of 2.37) at 153 cM, marker D1S534 (empirical p value of .209). These two regions of interest are not overlapping with the regions found for contrasting phenotypes (such as depression, which is negatively associated with happiness). Further linkage and future association studies are warranted.

International Parkinson's Disease Genomics Consortium (IPDGC). ; [Background] Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome‐wide association studies. The most recent large‐scale PD genome‐wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome‐wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus. ; [Methods] We included all significant genome‐wide signals from multiple recent PD genome‐wide association studies including themost recent PD risk genome‐wide association study, age‐at‐onset genome‐wide association study, progression genome‐wide association study, and Asian population PD risk genome‐wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self‐ranked criteria. Multiple databases were queried for each gene to collect additional causal data. ; [Results] We created a PD genome‐wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow‐up functional studies to identify potential therapeutic targets. ; [Conclusions] Our PD genome‐wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large‐scale PD genome‐wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA. ; This work was supported in part by the Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute on Aging (NIA), and the National Institute of Environmental Health Sciences, both part of the National Institutes of Health, Department of Health and Human Services (project numbers 1ZIA‐NS003154, Z01‐AG000949‐02, and Z01‐ES101986). We thank the research participants and employees of 23andMe for making this work possible. C.W. is supported by the UK Dementia Research Institute funded by the Medical Research Council (MRC), Alzheimer's Society and Alzheimer's Research UK. C.S. is supported by the Ser Cymru II program, which is partly funded by Cardiff University and the European Regional Development Fund through the Welsh Government. Data were generated as part of the PsychENCODE Consortium supported by: U01MH103339, U01MH103365, U01MH103392, U01MH103340, U01MH103346, R01MH105472, R01MH094714, R01MH105898, R21MH102791, R21MH105881, R21MH103877, and P50MH106934 awarded to Schahram Akbarian (Icahn School of Medicine at Mount Sinai), Gregory Crawford (Duke), Stella Dracheva (Icahn School of Medicine at Mount Sinai), Peggy Farnham (USC), Mark Gerstein (Yale), Daniel Geschwind (UCLA), Thomas M. Hyde (LIBD), Andrew Jaffe (LIBD), James A. Knowles (USC), Chunyu Liu (UIC), Dalila Pinto (Icahn School of Medicine at Mount Sinai), Nenad Sestan (Yale), Pamela Sklar (Icahn School of Medicine at Mount Sinai), Matthew State (UCSF), Patrick Sullivan (UNC), Flora Vaccarino (Yale), Sherman Weissman (Yale), Kevin White (UChicago), and Peter Zandi (JHU). The Genotype‐Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this article were obtained from the GTEx Portal on February 12, 2020. Molecular data for the Trans‐Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Genome sequencing for "NHLBI TOPMed: Atherosclerosis Risk in Communities (ARIC)" (phs001211.v2.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1)and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for the "NHLBI TOPMed: Cleveland Clinic Atrial Fibrillation (CCAF) Study" (phs001189.v1.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Trans‐Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study (phs001368.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Partners HealthCare Biobank" (phs001024.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Whole Genome Sequencing of Venous Thromboembolism (WGS of VTE)" (phs001402.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Novel Risk Factors for the Development of Atrial Fibrillation in Women" (phs001040.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: The Genetics and Epidemiology of Asthma in Barbados" (phs001143.v2.p1) was performed by Illumina Genomic Services (3R01HL104608‐04S1). Genome sequencing for "NHLBI TOPMed: The Vanderbilt Genetic Basis of Atrial Fibrillation" (phs001032.v4.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for "NHLBI TOPMed: Heart and Vascular Health Study (HVH)" (phs000993.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1) and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for "NHLBI TOPMed: Genetic Epidemiology of COPD (COPDGene)" (phs000951.v3.p3) was performed at the University of Washington Northwest Genomics Center (3R01HL089856‐08S1) and at the Broad Institute of MIT and Harvard (HHSN268201500014C). Genome sequencing for "NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Ablation Registry" (phs000997.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2, 3U54HG003067‐13S1). Genome sequencing for "NHLBI TOPMed: The Jackson Heart Study" (phs000964.v3.p1) was performed at the University of Washington Northwest Genomics Center (HHSN268201100037C). Genome sequencing for "NHLBI TOPMed: Genetics of Cardiometabolic Health in the Amish" (phs000956.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL121007‐01S1). Genome sequencing for "NHLBI TOPMed: Massachusetts General Hospital Atrial Fibrillation (MGH AF) Study" (phs001062.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1, 3U54HG003067‐12S2, 3U54HG003067‐13S1, 3UM1HG008895‐01S2). Genome sequencing for "NHLBI TOPMed: The Framingham Heart Study" (phs000974.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample‐identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL‐120393; U01HL‐120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institute of Health, Department of Health and Human Services, under contract numbers (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I). The authors thank the staff and participants of the ARIC study for their important contributions. The research reported in this article was supported by grants from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute grants R01 HL090620 and R01 HL111314, the NIH National Center for Research Resources for Case Western Reserve University and Cleveland Clinic Clinical and Translational Science Award (CTSA) UL1‐RR024989, the Department of Cardiovascular Medicine philanthropic research fund, Heart and Vascular Institute, Cleveland Clinic, the Fondation Leducq grant 07‐CVD 03, and the Atrial Fibrillation Innovation Center, state of Ohio. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01‐HC85079, N01‐HC‐85080, N01‐HC‐85081, N01‐HC‐85082, N01‐HC‐85083, N01‐HC‐85084, N01‐HC‐85085, N01‐HC‐85086, N01‐HC‐35129, N01‐HC‐15103, N01‐HC‐55222, N01‐HC‐75150, N01‐HC‐45133, and N01‐ HC‐85239; grant numbers U01 HL080295 and U01 HL130014 from the National Heart, Lung, and Blood Institute, and R01 AG023629 from the National Institute on Aging, with additional contribution from the National Institute of Neurological Disorders and Stroke. A full list of principal CHS investigators and institutions can be found at https://chs-nhlbi.org/pi. This article was not prepared in collaboration with CHS investigators and does not necessarily reflect the opinions or views of CHS or the NHLBI. We thank the Broad Institute for generating high‐quality sequence data supported by NHLBI grant 3R01HL092577‐06S1 to Dr. Patrick Ellinor. Funded in part by grants from the National Institutes of Health, National Heart, Lung, and Blood Institute (HL66216 and HL83141), and the National Human Genome Research Institute (HG04735). The Women's Genome Health Study (WGHS) is supported by HL 043851 and HL099355 from the National Heart, Lung, and Blood Institute and CA 047988 from the National Cancer Institute, the Donald W. Reynolds Foundation with collaborative scientific support and funding for genotyping provided by Amgen. AF end‐point confirmation was supported by HL‐093613 and a grant from the Harris Family Foundation and Watkin's Foundation. The Genetics and Epidemiology of Asthma in Barbados is supported by National Institutes of Health (NIH) National Heart, Lung, and Blood Institute TOPMed (R01 HL104608‐S1), and R01 AI20059, K23 HL076322, and RC2 HL101651. The research reported in this article was supported by grants from the American Heart Association to Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (HL092217), and Dr. Roden (U19 HL65962, and UL1 RR024975). This project was also supported by a CTSA award (UL1TR000445) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences of the NIH. The research reported in this article was supported by grants HL068986, HL085251, HL095080, and HL073410 from the National Heart, Lung, and Blood Institute. This article was not prepared in collaboration with Heart and Vascular Health (HVH) Study investigators and does not necessarily reflect the opinions or views of the HVH Study or the NHLBI. This research used data generated by the COPDGene study, which was supported by NIH grants U01 HL089856 and U01 HL089897. The COPDGene project is also supported by the COPD Foundation through contributions made by an Industry Advisory Board composed of Pfizer, AstraZeneca, Boehringer Ingelheim, Novartis, and Sunovion. Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample‐identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL‐120393‐02S1; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. This study is part of the Centers for Common Disease Genomics (CCDG) program, a large‐scale genome sequencing effort to identify rare risk and protective alleles that contribute to a range of common disease phenotypes. The CCDG program is funded by the National Human Genome Research Institute (NHGRI) and the National Heart, Lung, and Blood Institute (NHLBI). Sequencing was completed at the Human Genome Sequencing Center at Baylor College of Medicine under NHGRI grant UM1 HG008898. The research reported in this article was supported by grants from the American Heart Association to Dr. Shoemaker (11CRP742009) and Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (R01 HL092217) and Dr. Roden (U19 HL65962 and UL1 RR024975). The project was also supported by a CTSA award (UL1 TR00045) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the NIH. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I/HHSN26800001), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The authors also thank the staffs and participants of the JHS. The Amish studies on which these data are based were supported by NIH grants R01 AG18728, U01 HL072515, R01 HL088119, R01 HL121007, and P30 DK072488. See publication PMID: 18440328. The research reported in this article was supported by NIH grants K23HL071632, K23HL114724, R21DA027021, R01HL092577, R01HL092577S1, R01HL104156, K24HL105780, and U01HL65962. The research has also been supported by an Established Investigator Award from the American Heart Association (13EIA14220013) and by support from the Fondation Leducq (14CVD01). This article was not prepared in collaboration with MGH AF Study investigators and does not necessarily reflect the opinions or views of the MGH AF Study investigators or the NHLBI. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (contract nos. N01‐HC‐25195, HHSN268201500001I, and 75N92019D00031). This article was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.

[Motivation] The Burrows-Wheeler transform (BWT) is widely used for the fast alignment of high-throughput sequence data. This method also has potential applications in other areas of bioinformatics, and it can be specially useful for the fast searching of patterns on coverage data from different sources. ; [Results] We present a nucleosome pattern search method that converts levels of nucleosomal occupancy to a sequence-like format to which BWT searches can be applied. The method is embedded in a nucleosome map browser, 'Nucleosee', an interactive visual tool specifically designed to enhance BWT searches, giving them context and making them suitable for visual discourse analysis of the results. The proposed method is fast, flexible and sufficiently generic for the exploration of data in a broad and interactive way. ; [Availability and implementation] The proposed algorithm and visual browser are available for testing at http://cpg3.der.usal.es/nucleosee. The source code and installation packages are also available at https://github.com/rodrigoSantamaria/nucleosee. ; This work was supported by the Government of Spain, Ministerio de Economía y Competitivad [Refs. BFU2017-89622-P and PCIN-2017-064] ; Peer reviewed

AbstractAll etiological studies of complex human traits focus on analyzing the causes of variation. Given this complexity, there is a premium on studying those processes that mediate between gene products and cellular or organismal phenotypes. Studies of levels of gene expression could offer insight into these processes and are likely to be especially useful to the extent that the major sources of their variation are known in normal tissues. The classical study of monozygotic (MZ) and dizygotic (DZ) twins was employed to partition the genetic and environmental influences in gene expression for over 6500 human genes measured using microarrays from lymphoblastoid cell lines. Our results indicate that mean expression levels are correlated about .3 in monozygotic (MZ) and .0 in dizygotic (DZ) twins suggesting an overall epistatic regulation of gene expression. Furthermore, the functions of several of the genes whose expression was most affected by environmental effects, after correction for measurement error, were consistent with their known role in mediating sensitivity to environmental influences.

Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary data are available at Bioinformatics online.

The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient's profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments. ; L.A. was supported by grant BES-2017-081635. This publication is part of R&D and Innovation grant BES-2017-081635 funded by MCIN and by "FSE Investing in your future"I.M. was supported by grant FJCI-2017-31878. This publication is part of R&D and Innovation grant FJCI-2017-31878 funded by MCIN. C.S. received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. ; Peer Reviewed ; Postprint (published version)

Dissertação de Mestrado em Biotecnologia e Qualidade Alimentar ; O SO2 tem sido amplamente utilizado na indústria vínica, não apenas como agente antioxidante e antioxidásico, mas mais importante, devido às suas propriedades antimicrobianas. A ocorrência de leveduras de contaminação que podem tolerar altas concentrações de SO2 tem levado ao uso de níveis perto do limite legal estabelecido pela legislação da União Europeia, constituindo uma ameaça à saúde humana. Assim, existe um grande interesse na procura de outros conservantes mais seguros para substituir ou apenas reduzir a utilização de SO2 como um agente antimicrobiano. A quitosana, um biopolímero natural não-tóxico, tem sido sugerida como um potencial agente útil na conservação de alimentos, devido às suas propriedades biológicas, tais como a sua atividade antimicrobiana. A adição de quitosana, até a um limite de 0.1 g/L foi aceite desde julho de 2009 pelo Codex Enológico Internacional como uma nova prática enológica, como agente de clarificação do vinho. Além disso, é também reconhecida a sua eficácia para eliminar microrganismos de contaminação, tais como leveduras, nomeadamente Dekkera/ Brettanomyces spp. Tanto o modo de ação da quitosana como os mecanismos de resistência da levedura são ainda pouco compreendidos e sujeitos a debate. Na tentativa de contribuir para o esclarecimento destas questões, neste trabalho foi utilizada a levedura Saccharomyces cerevisiae como modelo. Assim, a coleção de mutantes haploides de S. cerevisiae deletados de genes individuais foi utilizada para identificar novos genes/vias importantes envolvidas na resistência da levedura a este antimicrobiano. Neste estudo, a concentração máxima de quitosana permitida para uso enológico não teve um efeito fungicida sobre a estirpe parental Saccharomyces cerevisiae BY4741. A utilização de uma gama de concentrações de quitosana (0.25 - 1.0 g/L) permitiu a dentificação de 252 genes cuja deleção conduz a um fenótipo de hipersensibilidade à quitosana e 207 genes cuja deleção conferiu resistência, dos quais 29 mutantes foram classificados como hiper-resistentes. A distribuição funcional dos genes cuja deleção conferiu hipersensibilidade à quitosana, inclui Proteínas ribossomais, Ciclo celular e processamento de DNA, Regulação do metabolismo de compostos de carbono e aminoácidos, Parede celular, Metabolismo dos fosfolípidos, Transporte vacuolar/lisossomal e Transcrição. Por outro lado, categorias funcionais como Transporte intracelular (Peroxissoma, Retículo endolasmatico e Golgi), Homeostasia iónica, Modificação de proteínas e Envelhecimento celular foram as mais representativas entre os genes cuja ausência conduziu a um fenótipo de resistência. Estas descobertas elucidam a base molecular da toxicidade da quitosana e serão úteis para pesquisas futuras sobre a aplicação da quitosana como um agente antimicrobiano eficaz e seguro não só no vinho, mas também noutras indústrias de alimentos. ; In wine industry SO2 has been widely used not only as an antioxidant and antioxidasic agent, but most importantly, due to its antimicrobial proprieties. The occurrence of spoilage yeasts that can tolerate high concentrations of SO2 requires the use of levels near the European Union legal limit, constituting a threat to human health. Thus, there has been a great interest in looking for safer preservatives to replace or at least reduce the use of SO2 as an antimicrobial agent. Chitosan, a natural nontoxic biopolymer, has been proposed as potential useful agent in food preservation due to their biological activities, such as antimicrobial activity. Chitosan addition, up to 0.1 g/L, has been accepted as a new oenological practice since July 2009 by the International Oenological Codex as a fining agent of wine. Additionally, it has been recognized its effectiveness in the control of the spoilage yeast such as Dekkera/Brettanomyces spp. Both the mode of action of chitosan and mechanisms of resistance in yeast are still poorly understood and subject to debate. In an effort to contribute to the elucidation of these questions, in this work we used the yeast Saccharomyces cerevisiae as a model. In this way, a genome-wide screen for altered susceptibility to chitosan was performed using the EUROSCARF haploid yeast deletion collection in order to identify new genes/pathways relevant in yeast resistance to this antimicrobial agent. In this study, we found that the maximum permissible concentration of chitosan for oenological use had no fungicide effect on the parental strain Saccharomyces cerevisiae BY4741. The use of a range of chitosan concentrations (0.25 - 1.0 g/L) allowed the identification of 252 genes whose deletion caused hypersensitivity to chitosan and 207 genes whose deletion conferred chitosan resistance, of which 29 mutants were classified as hyper resistant. Functional categories overrepresented with genes whose absence renders cells hypersensitivity to chitosan mainly include ribosomal proteins, cell cycle and DNA processing, regulation of C-compound and amino acid metabolism, cell wall, phospholipids metabolism, vacuolar/lysosomal transport and transcription. On the other hand, functional categories, such as intracellular transport routes (peroxisome, endoplasmatic reticulum and Golgi), ionic homeostasis, protein modification and cell aging, were overrepresented among the genes whose absence rendered mutants resistance. These findings shed light on the molecular basis of chitosan toxicity and will be helpful for future research on the application of chitosan as an effective and safer antimicrobial agent not only in wine, but also in other food industries. ; Fundos FEDER, FCT – Fundação para a Ciência e a Tecnologia

Dingoes play a strong role in Australia's ecological framework as the apex predator but are under threat from hybridization and agricultural control programs. Government legislation lists the conservation of the dingo as an important aim, yet little is known about the biogeography of this enigmatic canine, making conservation difficult. Mitochondrial and Y chromosome DNA studies show evidence of population structure within the dingo. Here, we present the data from Illumina HD canine chip genotyping for 23 dingoes from five regional populations, and five New Guinea Singing Dogs to further explore patterns of biogeography using genome-wide data. Whole genome single nucleotide polymorphism (SNP) data supported the presence of three distinct dingo populations (or ESUs) subject to geographical subdivision: southeastern (SE), Fraser Island (FI) and northwestern (NW). These ESUs should be managed discretely. The FI dingoes are a known reservoir of pure, genetically distinct dingoes. Elevated inbreeding coefficients identified here suggest this population may be genetically compromised and in need of rescue; current lethal management strategies that do not consider genetic information should be suspended until further data can be gathered. D statistics identify evidence of historical admixture or ancestry sharing between southeastern dingoes and South East Asian village dogs. Conservation efforts on mainland Australia should focus on the SE dingo population that is under pressure from domestic dog hybridization and high levels of lethal control. Further data concerning the genetic health, demographics and prevalence of hybridization in the SE and FI dingo populations is urgently needed to develop evidence based conservation and management strategies.

Genetic technologies used in breeding of small ruminants requires searching for new molecular markers of productive traits. The most effective for this is genome-wide association study (GWAS) of single nucleotide polymorphisms (SNP) with economically valuable traits. The paper presents results of study of associations of the frequency of single nucleotide polymorphisms with a rank assessment according to complex of productive traits (super-elite) in Romanov sheep using DNA biochips Ovine Infinium HD BeadChip 600K. Eleven SNPs have been found having significant correlation with the animals belonging to the "super-elite" group. Five substitutions are located in the genes introns, six are related to intergenic polymorphisms. The highest reliability of association with productivity was observed in substitution rs410516628 (р = 3,14 · 10-9) located on the 3rd chromosome. Substitution rs422028000 on 2nd chromosome differs with the fact that in the "super-elite" group it was found in 90 % of haplotypes. Polymorphisms rs411162754 (1st chromosome) and rs417281100 (10th chromosome) in our study turned out to be the rarest – only in "super-elite" group and only in a quarter of haplotypes. The genes located near the identified SNPs are mainly associated with metabolic and regulatory processes. Our study has identified several new candidate genes with polymorphism probably associated with the ranking in terms of productivity in Romanov sheep: LTBP1, KCNH8, LMX1B, ZBTB43, MSRA, CHPF, PID1 and DNER. The results obtained create a theoretical basis for further study of candidate genes affecting implementation of phenotypic traits in Romanov sheep. The revealed polymorphisms associated with the productive traits of sheep can be used in practical breeding as molecular and genetic markers for selection of parental pairs.

Improvements in immunosuppression have modified short- term survival of deceased- donor allografts, but not their rate of long- term failure. Mismatches between donor and recipient HLA play an important role in the acute and chronic allogeneic immune response against the graft. Perfect matching at clinically relevant HLA loci does not obviate the need for immunosuppression, suggesting that additional genetic variation plays a critical role in both short- and long- term graft outcomes. By combining patient data and samples from supranational cohorts across the United Kingdom and European Union, we performed the first large- scale genome- wide association study analyzing both donor and recipient DNA in 2094 complete renal transplant-pairs with replication in 5866 complete pairs. We studied deceased- donor grafts allocated on the basis of preferential HLA matching, which provided some control for HLA genetic effects. No strong donor or recipient genetic effects contributing to long- or short- term allograft survival were found outside the HLA region. We discuss the implications for future research and clinical application.

Genome-Wide and Mla Locus-Specific Characterisation of Latvian Barley Varieties
Genetic diversity in locally adapted germplasm forms the basis for crop improvement through breeding. While single loci have been routinely used for studies of genetic diversity, the highthroughput genotyping platforms that have recently become available for large genome crop plants offer an unbiased view on genetic diversity on a genome-wide scale. We assessed genetic diversity in Latvian barley varieties and some progenitors using DArT markers and studied the extent of linkage disequilibrium in Latvian germplasm. Further, genetic diversity at three loci flanking the barley powdery mildew Mla locus conferring race-specific resistance was studied in Latvian barley germplasm. The Mla locus encompasses several closely related resistance gene homologues with a complex evolutionary history, which complicates the design of molecular markers for different Mla genes. We observed significant linkage disequilibrium between the single nucleotide polymorphisms (SNPs) at the three loci, 206i20_T7, ABC15612, and 538P8, flanking the Mla locus. SNP haplotypes were largely in agreement with known phenotypic data and, thus, may be potentially diagnostic for Mla resistance genes in hybrids.

[Background]: Pigs were domesticated independently in Eastern and Western Eurasia early during the agricultural revolution, and have since been transported and traded across the globe. Here, we present a worldwide survey on 60K genome-wide single nucleotide polymorphism (SNP) data for 2093 pigs, including 1839 domestic pigs representing 122 local and commercial breeds, 215 wild boars, and 39 out-group suids, from Asia, Europe, America, Oceania and Africa. The aim of this study was to infer global patterns in pig domestication and diversity related to demography, migration, and selection. ; [Results]: A deep phylogeographic division reflects the dichotomy between early domestication centers. In the core Eastern and Western domestication regions, Chinese pigs show differentiation between breeds due to geographic isolation, whereas this is less pronounced in European pigs. The inferred European origin of pigs in the Americas, Africa, and Australia reflects European expansion during the sixteenth to nineteenth centuries. Human-mediated introgression, which is due, in particular, to importing Chinese pigs into the UK during the eighteenth and nineteenth centuries, played an important role in the formation of modern pig breeds. Inbreeding levels vary markedly between populations, from almost no runs of homozygosity (ROH) in a number of Asian wild boar populations, to up to 20% of the genome covered by ROH in a number of Southern European breeds. Commercial populations show moderate ROH statistics. For domesticated pigs and wild boars in Asia and Europe, we identified highly differentiated loci that include candidate genes related to muscle and body development, central nervous system, reproduction, and energy balance, which are putatively under artificial selection. ; [Conclusions]: Key events related to domestication, dispersal, and mixing of pigs from different regions are reflected in the 60K SNP data, including the globalization that has recently become full circle since Chinese pig breeders in the past decades started selecting Western breeds to improve local Chinese pigs. Furthermore, signatures of ongoing and past selection, acting at different times and on different genetic backgrounds, enhance our insight in the mechanism of domestication and selection. The global diversity statistics presented here highlight concerns for maintaining agrodiversity, but also provide a necessary framework for directing genetic conservation. ; This study is supported by National Production Technology System for the Pig Industry in China (nycytx-008) and Outstanding Talents and Innovation Team of Agricultural Science (2011-81) to LH, AGL2010-14822 and AGL2013-41834-R (Ministry of Economy and Science, Spain) to MPE. LI received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 656697. HJM received funding from the IMAGE project (Horizon 2020, No. 677353). ; Peer reviewed

PUBLISHED ; The Drosophila genome contains >13000 protein-coding genes, the majority of which remain poorly investigated. Important reasons include the lack of antibodies or reporter constructs to visualise these proteins. Here, we present a genome-wide fosmid library of 10000 GFP-tagged clones, comprising tagged genes and most of their regulatory information. For 880 tagged proteins, we created transgenic lines, and for a total of 207 lines, we assessed protein expression and localisation in ovaries, embryos, pupae or adults by stainings and live imaging approaches. Importantly, we visualised many proteins at endogenous expression levels and found a large fraction of them localising to subcellular compartments. By applying genetic complementation tests, we estimate that about two-thirds of the tagged proteins are functional. Moreover, these tagged proteins enable interaction proteomics from developing pupae and adult flies. Taken together, this resource will boost systematic analysis of protein expression and localisation in various cellular and developmental contexts. ; Stocks obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537) were used in this study. We are grateful to Belinda Bullard, Anne Ephrussi, Judith Saide, J?rgen Knoblich, Stefan Baumgartner, Kathleen Clark, Jim Vigoreaux and the DSHB for generously sharing antibodies and fly lines. We thank Franziska Friedrich for drawing the ovariole scheme used for Figure 3A. We thank Andreas Dahl from the Deep Sequencing Group at CRTD/BIOTECH, Dresden for the NGS library preparation and sequencing. We also thank the light microscopy facility and the computer department for assistance with imaging and data processing. We are grateful to Sandra Lemke, Aynur Kaya-Copur and Xu Zhang for help with the genetic rescue experiments and to Cornelia Sch?nbauer, Beatrice Laudenbach and Caroline Sonsteby for assistance during some of the adult and pupal imaging experiments. We thank the entire Schnorrer lab for helpful comments on this manuscript. We are particularly grateful to Reinhard F?ssler and Herbert J?ckle for continuous support of this work. This work was funded by the EMBO Young Investigator Program (FS), the European Research Council under the European Union?s Seventh Framework Programme (FP/2007-2013)/ERC Grant 310939 (FS), the European Research Council under the European Union?s Seventh Framework Programme (FP/2007-2013)/ERC Grant 260746 (PT), European Commission GENCODYS (PT and HJ), Human Frontier Science Program (HFSP) RGY0093/2012 (PT and CS) and the Max Planck Society (PT, FS, MS, MM and EK).

Mendizabal, Isabel et al. ; The Romani, the largest European minority group with approximately 11 million people [1], constitute a mosaic of languages, religions, and lifestyles while sharing a distinct social heritage. Linguistic [2] and genetic [3-8] studies have located the Romani origins in the Indian subcontinent. However, a genome-wide perspective on Romani origins and population substructure, as well as a detailed reconstruction of their demographic history, has yet to be provided. Our analyses based on genome-wide data from 13 Romani groups collected across Europe suggest that the Romani diaspora constitutes a single initial founder population that originated in north/northwestern India ∿1.5 thousand years ago (kya). Our results further indicate that after a rapid migration with moderate gene flow from the Near or Middle East, the European spread of the Romani people was via the Balkans starting ∿0.9 kya. The strong population substructure and high levels of homozygosity we found in the European Romani are in line with genetic isolation as well as differential gene flow in time and space with non-Romani Europeans. Overall, our genome-wide study sheds new light on the origins and demographic history of European Romani. © 2012 Elsevier Ltd. ; I.M. was supported by a PhD grant by the Basque Government (Hezkuntza, Unibertsitate eta Ikerketa Saila, Eusko Jaurlaritza, BFI107.4). O.L., A.W., and M.K. were supported by the Erasmus MC University Medical Center Rotterdam. U.M.M. was supported by a PhD grant by Universitat Pompeu Fabra. M.G.N. was supported by a Vici grant of the Netherlands Organization of Scientific Research. L.G. was supported by an Invited Professor grant from CAPES/Brazil. A.M. was supported by the Estonian Government grant SF0180142s08. This study was supported in parts by Spanish Government MCINN grant CGL2010-14944/BOS to D.C., Czech Republic Ministry of Health grants CZ.2.16/3.1.00/24022 and 00064203 to M.M., Republic of Serbia Ministry of Education and Science grant ON173008 to D.R., Belgium University of Antwerp grant IWS BOFUA 2008/23064 to A.J., and Portuguese Foundation for Science and Technology (FCT) project grant PTDC/ANT/70413/2006 to L.G. IPATIMUP is an Associate Laboratory of the Portuguese Ministry of Education and Science and is partially supported by the FCT. ; Peer Reviewed