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1. Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration

Author: Joana A. Revez, Tian Lin, Zhen Qiao, Angli Xue, Yan Holtz, Zhihong Zhu, Jian Zeng, Huanwei Wang, Julia Sidorenko, Kathryn E. Kemper, Anna A. E. Vinkhuyzen, Julanne Frater, Darryl Eyles, Thomas H. J. Burne, Brittany Mitchell, Nicholas G. Martin, Gu Zhu, Peter M. Visscher, Jian Yang, Naomi R. Wray, and John J. McGrath
Subjects: Science
Abstract: Vitamin D is a precursor of the steroid hormone 1,25-dihydroxyvitamin D3, and its deficiency is associated with many adverse health outcomes. Here, Revez et al. perform a genome-wide association study for circulating 25-hydroxyvitamin D in 417,580 individuals and test for potential causal relationships with other traits using Mendelian randomization.
Published: 2020
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2. Efficient algorithms for Longest Common Subsequence of two bucket orders to speed up pairwise genetic map comparison.

Author: Lisa De Mattéo, Yan Holtz, Vincent Ranwez, and Sèverine Bérard
Subjects: Medicine, Science
Abstract: Genetic maps order genetic markers along chromosomes. They are, for instance, extensively used in marker-assisted selection to accelerate breeding programs. Even for the same species, people often have to deal with several alternative maps obtained using different ordering methods or different datasets, e.g. resulting from different segregating populations. Having efficient tools to identify the consistency and discrepancy of alternative maps is thus essential to facilitate genetic map comparisons. We propose to encode genetic maps by bucket order, a kind of order, which takes into account the blurred parts of the marker order while being an efficient data structure to achieve low complexity algorithms. The main result of this paper is an O(n log(n)) procedure to identify the largest agreements between two bucket orders of n elements, their Longest Common Subsequence (LCS), providing an efficient solution to highlight discrepancies between two genetic maps. The LCS of two maps, being the largest set of their collinear markers, is used as a building block to compute pairwise map congruence, to visually emphasize maker collinearity and in some scaffolding methods relying on genetic maps to improve genome assembly. As the LCS computation is a key subroutine of all these genetic map related tools, replacing the current LCS subroutine of those methods by ours -to do the exact same work but faster- could significantly speed up those methods without changing their accuracy. To ease such transition we provide all required algorithmic details in this self contained paper as well as an R package implementing them, named LCSLCIS, which is freely available at: https://github.com/holtzy/LCSLCIS.
Published: 2018
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3. Evolutionary forces affecting synonymous variations in plant genomes.

Author: Yves Clément, Gautier Sarah, Yan Holtz, Felix Homa, Stéphanie Pointet, Sandy Contreras, Benoit Nabholz, François Sabot, Laure Sauné, Morgane Ardisson, Roberto Bacilieri, Guillaume Besnard, Angélique Berger, Céline Cardi, Fabien De Bellis, Olivier Fouet, Cyril Jourda, Bouchaib Khadari, Claire Lanaud, Thierry Leroy, David Pot, Christopher Sauvage, Nora Scarcelli, James Tregear, Yves Vigouroux, Nabila Yahiaoui, Manuel Ruiz, Sylvain Santoni, Jean-Pierre Labouisse, Jean-Louis Pham, Jacques David, and Sylvain Glémin
Subjects: Genetics, QH426-470
Abstract: Base composition is highly variable among and within plant genomes, especially at third codon positions, ranging from GC-poor and homogeneous species to GC-rich and highly heterogeneous ones (particularly Monocots). Consequently, synonymous codon usage is biased in most species, even when base composition is relatively homogeneous. The causes of these variations are still under debate, with three main forces being possibly involved: mutational bias, selection and GC-biased gene conversion (gBGC). So far, both selection and gBGC have been detected in some species but how their relative strength varies among and within species remains unclear. Population genetics approaches allow to jointly estimating the intensity of selection, gBGC and mutational bias. We extended a recently developed method and applied it to a large population genomic dataset based on transcriptome sequencing of 11 angiosperm species spread across the phylogeny. We found that at synonymous positions, base composition is far from mutation-drift equilibrium in most genomes and that gBGC is a widespread and stronger process than selection. gBGC could strongly contribute to base composition variation among plant species, implying that it should be taken into account in plant genome analyses, especially for GC-rich ones.
Published: 2017
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4. Genotyping by Sequencing Using Specific Allelic Capture to Build a High-Density Genetic Map of Durum Wheat.

Author: Yan Holtz, Morgane Ardisson, Vincent Ranwez, Alban Besnard, Philippe Leroy, Gérard Poux, Pierre Roumet, Véronique Viader, Sylvain Santoni, and Jacques David
Subjects: Medicine, Science
Abstract: Targeted sequence capture is a promising technology which helps reduce costs for sequencing and genotyping numerous genomic regions in large sets of individuals. Bait sequences are designed to capture specific alleles previously discovered in parents or reference populations. We studied a set of 135 RILs originating from a cross between an emmer cultivar (Dic2) and a recent durum elite cultivar (Silur). Six thousand sequence baits were designed to target Dic2 vs. Silur polymorphisms discovered in a previous RNAseq study. These baits were exposed to genomic DNA of the RIL population. Eighty percent of the targeted SNPs were recovered, 65% of which were of high quality and coverage. The final high density genetic map consisted of more than 3,000 markers, whose genetic and physical mapping were consistent with those obtained with large arrays.
Published: 2016
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5. The genetic map comparator: a user-friendly application to display and compare genetic maps.

Author: Yan Holtz, Jacques David, and Vincent Ranwez
Published: 2017
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6. Exploring Comorbidity Within Mental Disorders Among a Danish National Population

Author: Anders Prior, Ole Mors, Sukanta Saha, Aja Greve, Merete Nordentoft, Carsten Bøcker Pedersen, Brian K. Lee, Ronald C. Kessler, Harvey Whiteford, Preben Bo Mortensen, Chun Chieh Fan, Holger J. Sørensen, Kate M. Scott, Lars Vedel Kessing, Terry Stedman, Andrew J. Schork, Kim Moesgaard Iburg, Louisa Degenhardt, Carmen C.W. Lim, Yan Holtz, John J. McGrath, Jane Gunn, Annelieke M. Roest, Michael E. Benros, Peter de Jonge, Esben Agerbo, Andrea Ganna, Thomas Werge, Oleguer Plana-Ripoll, James Scott, Søren Dalsgaard, Thomas Munk Laursen, APH - Mental Health, Developmental Psychology, Life Course Epidemiology (LCE), and Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE)
Subjects: Male, Time Factors, Denmark, Comorbidity, Cohort Studies, 0302 clinical medicine, SCHIZOPHRENIA, Registries, Original Investigation, Psychiatry, RISK, education.field_of_study, DSM-IV DISORDERS, Mental Disorders, LIFETIME PREVALENCE, Hazard ratio, Age Factors, HEALTH SURVEY, 3. Good health, Psychiatry and Mental health, Eating disorders, Schizophrenia, RELIABILITY, Female, Coronavirus Infections, Cohort study, Adult, Population, Pneumonia, Viral, DIAGNOSES, CO-MORBIDITY, 03 medical and health sciences, Betacoronavirus, Young Adult, Sex Factors, medicine, International Statistical Classification of Diseases and Related Health Problems, Humans, VALIDITY, education, Pandemics, business.industry, SARS-CoV-2, COVID-19, medicine.disease, 030227 psychiatry, Mood disorders, business, 030217 neurology & neurosurgery, Demography
Abstract: IMPORTANCE Individuals with mental disorders often develop comorbidityover time. Past studies of comorbidity have often restricted analyses to a subset of disorders and few studies have provided absolute risks of later comorbidity.OBJECTIVES To undertake a comprehensive study of comorbidity within mental disorders, by providing temporally ordered age-and sex-specific pairwise estimates between the major groups of mental disorders, and to develop an interactive website to visualize all results and guide future research and clinical practice.DESIGN, SETTING. AND PARTICIPANTS This population-based cohort study included all individuals born in Denmark between January 1, 190 0, and December 31, 2015, and living in the country between January 1, 20 0 0, and December 31, 2016. The analyses were conducted between June 2017 and May 2018.MAIN OUTCOMES AND MEASURES Danish health registers were used to identify mental disorders, which were examined within the broad 10-level International Statistical Classification of Diseases and Related Health Problems, lOth Revision, subchapter groups (eg, codes F00-F09 and F10-F19). For each temporally ordered pair of disorders, overall and lagged hazard ratios and 95% Cls were calculated using Cox proportional hazards regress on models. Absolute risks were estimated using competing risks survival analyses. Estimates for each sex were generated.RESULTS A total of 5 940 778 persons were Included in this study (2 958 293 men and 2 982 485 women; mean [SD] age at beginning of follow-up, 32.1[25.4] years). They were followed up for 83.9 million person-years. All mental disorders were associated with an increased risk of all other mental disorders when adjusting for sex, age, and calendar time (hazard ratios ranging from 2.0 [95% CI, 1.7-2.4] for prior intellectual disabilities and later eating disorders to 48.6 [95% Cl, 46.6-50.7] for prior developmental disorders and later intellectual disabilities). The hazard ratios were temporally patterned, with higher estimates during the first year after the onset of the first disorder, but with persistently elevated rates during the entire observation period. Some disorders were associated with substantial absolute risks of developing specific later disorders (eg, 30.6% [95% CI, 29.3%-32.0%] of men and 38.4% [95% CI, 37.5%-39.4%] of women with a diagnosis of mood disorders before age 20 years developed neurotic disorders within the following 5 years).CONCLUSIONS AND RELEVANCE Comorbidity within mental disorders is pervasive, and the risk persists over time. This study provides disorder-, sex-, and age-specific relative and absolute risks of the comorbidity of mental disorders. Web-based interactive data visualization tools are provided for clinical utility.
Published: 2019
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7. Comorbidity within mental disorders

Author: Oleguer Plana-Ripoll, Jibril Abdulmalik, F. Navarro-Mateu, Ronald C. Kessler, Carmen C.W. Lim, Yan Holtz, Yolanda Torres, Ronny Bruffaerts, Dan J. Stein, Evelyn J. Bromet, Natalie C. Momen, Sergio Aguilar-Gaxiola, Brendan Bunting, P. de Jonge, J M de Almeida, Chiyi Hu, Josep Maria Haro, Ali Al-Hamzawi, Norito Kawakami, Meredith Harris, J. Posada-Villa, Ricardo Orozco, Yuval Ziv, Sing Lee, Y. de Vries, Oye Gureje, Maria Carmen Viana, Zeina Mneimneh, Esben Agerbo, Carsten Bøcker Pedersen, G. de Girolamo, Juan Carlos Stagnaro, Annelieke M. Roest, Kate M. Scott, V. Kovess-Masfety, Jordi Alonso, Sukanta Saha, Preben Bo Mortensen, Andrzej Kiejna, John J. McGrath, Elie G. Karam, S. Florescu, University of Queensland, St Lucia QLD 4072, Australia, Aarhus University [Aarhus], University College Hospital [Ibadan, Nigeria], AL-Qadisiyah University, CIBER de Epidemiología y Salud Pública (CIBERESP), Stony Brook University [SUNY] (SBU), State University of New York (SUNY), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), University of Ulster, Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Istituto Centro San Giovanni di Dio Fatebenefratelli, Partenaires INRAE, University of Groningen [Groningen], National School of Public Health, Management and Development, King Saud University [Riyadh] (KSU), Universitat de Barcelona (UB), University of Queensland [Herston], Shenzhen Institute of Mental Health & Shenzhen Kangning Hospital, University of Balamand - UOB (LIBAN), The University of Tokyo (UTokyo), Wroclaw Medical University [Wrocław, Pologne], École des Hautes Études en Santé Publique [EHESP] (EHESP), The Chinese University of Hong Kong [Hong Kong], University of Michigan [Ann Arbor], University of Michigan System, National Institute of Psychiatry Ramón de la Fuente Muñiz [Mexico City] (INPRF), Cundinamarca University, University of Otago [Dunedin, Nouvelle-Zélande], Universidad de Buenos Aires [Buenos Aires] (UBA), University of Cape Town, CES University, Federal University of Espírito Santo, Israeli Ministry of Health, Harvard Medical School [Boston] (HMS), APH - Mental Health, Psychiatry, Developmental Psychology, and Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE)
Subjects: Adult, Male, Adolescent, Cross-sectional study, Epidemiology, Population, Comorbidity, Population survey, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Interquartile range, Prevalence, Humans, Medicine, Risk factor, education, Aged, Proportional Hazards Models, Retrospective Studies, education.field_of_study, Proportional hazards model, business.industry, Mental Disorders, Hazard ratio, Public Health, Environmental and Occupational Health, diagnosis and classification, Original Articles, Middle Aged, medicine.disease, Health Surveys, Mental health, population survey, 3. Good health, 030227 psychiatry, Diagnostic and Statistical Manual of Mental Disorders, Psychiatry and Mental health, Cross-Sectional Studies, Psychotic Disorders, Female, epidemiology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, 030217 neurology & neurosurgery, Demography
Abstract: Aims Epidemiological studies indicate that individuals with one type of mental disorder have an increased risk of subsequently developing other types of mental disorders. This study aimed to undertake a comprehensive analysis of pair-wise lifetime comorbidity across a range of common mental disorders based on a diverse range of population-based surveys. Methods The WHO World Mental Health (WMH) surveys assessed 145 990 adult respondents from 27 countries. Based on retrospectively-reported age-of-onset for 24 DSM-IV mental disorders, associations were examined between all 548 logically possible temporally-ordered disorder pairs. Overall and time-dependent hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards models. Absolute risks were estimated using the product-limit method. Estimates were generated separately for men and women. Results Each prior lifetime mental disorder was associated with an increased risk of subsequent first onset of each other disorder. The median HR was 12.1 (mean = 14.4; range 5.2–110.8, interquartile range = 6.0–19.4). The HRs were most prominent between closely-related mental disorder types and in the first 1–2 years after the onset of the prior disorder. Although HRs declined with time since prior disorder, significantly elevated risk of subsequent comorbidity persisted for at least 15 years. Appreciable absolute risks of secondary disorders were found over time for many pairs. Conclusions Survey data from a range of sites confirms that comorbidity between mental disorders is common. Understanding the risks of temporally secondary disorders may help design practical programs for primary prevention of secondary disorders.
Published: 2020
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8. A unified framework for association and prediction from vertex-wise grey-matter structure

Author: Peter M. Visscher, Baptiste Couvy-Duchesne, Olivier Colliot, Kathryn E. Kemper, Jian Yang, Zhili Zheng, Margaret J. Wright, Loic Yengo, Futao Zhang, Naomi R. Wray, Lachlan T. Strike, Yan Holtz, Institute for Molecular Bioscience, University of Queensland [Brisbane], Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Queensland Brain Institute, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Institut National de la Santé et de la Recherche Médicale (INSERM), National Council for Scientific Research = Conseil national de la recherche scientifique du Liban [Lebanon] (CNRS-L), Institute for Advanced Research (IFAR), Wenzhou Medical University [Wenzhou, China] (WMU), This research was supported by the Australian National Health and Medical Research Council (1078037, 1078901, 1113400, 1161356 and 1107258), the Australian Research Council (FT180100186 and FL180100072), the Sylvia & Charles Viertel Charitable Foundation, as well as the Agence Nationale de la Recherche as part of the 'Investissements d'avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-2910-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Male, Databases, Factual, [SDV]Life Sciences [q-bio], Body fat percentage, Correlation, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics, Body Size, Gray Matter, Mixed models, Research Articles, 2. Zero hunger, Aged, 80 and over, [STAT.AP]Statistics [stat]/Applications [stat.AP], Human Connectome Project, Radiological and Ultrasound Technology, 05 social sciences, Age Factors, Middle Aged, Random effects model, Magnetic Resonance Imaging, [STAT]Statistics [stat], Phenotype, Neurology, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Anatomy, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Research Article, Mixed model, Neuroimaging, Bivariate analysis, Grey-matter correlation, Biology, 050105 experimental psychology, Association, 03 medical and health sciences, Sex Factors, Region of interest, Connectome, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Morphometricity, Aged, grey‐matter correlation, Brain MRI, Neurology (clinical), Prediction, Body mass index, 030217 neurology & neurosurgery
Abstract: The recent availability of large‐scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex‐wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N = 9,497 for discovery, N = 4,323 for replication) and the Human Connectome Project (N = 1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP. We then extended the model for a bivariate analysis to estimate grey‐matter correlation between phenotypes, which revealed that body size (i.e., height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the morphometricity (confirmed using a conditional analysis), providing possible insight into previous MRI case–control results for psychiatric disorders where case status is associated with body mass index. Our LMM framework also allowed to predict some of the associated phenotypes from the vertex‐wise measures, in two independent samples. Finally, we demonstrated additional new applications of our approach (a) region of interest (ROI) analysis that retain the vertex‐wise complexity; (b) comparison of the information retained by different MRI processings., This manuscript introduces a set of analyses, that rely on linear mixed models to perform association and prediction, while being suited to tackle the challenges of big‐data in neuroimaging. Our framework allows estimating new sample characteristics such as the total association (morphometricity) between a phenotype and vertex‐wise brain data or grey‐matter correlations that quantify how much phenotypes may be similarly associated with grey‐matter. In addition, it offers to build performant brain‐based predictors that do not require hyper‐parameter estimation.
Published: 2020
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9. Genetic correlates of social stratification in Great Britain

Author: Michel G. Nivard, Kathryn E. Kemper, Karin J. H. Verweij, Laura Veul, Timothy M. Frayling, Yan Holtz, Loic Yengo, David Hugh-Jones, Jian Yang, Naomi R. Wray, Peter M. Visscher, Brendan P. Zietsch, Abdel Abdellaoui, Biological Psychology, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Adult Psychiatry, and APH - Mental Health
Subjects: Social Psychology, Health Status, Geographic Mapping, Experimental and Cognitive Psychology, Social class, Polymorphism, Single Nucleotide, Body Mass Index, 03 medical and health sciences, Behavioral Neuroscience, 0302 clinical medicine, Cluster Analysis, Humans, Allele, European Continental Ancestry Group/genetics, Polymorphism, Cluster analysis, Socioeconomic status, Allele frequency, Multifactorial Inheritance/genetics, Alleles, 030304 developmental biology, 0303 health sciences, Body Height/genetics, Single Nucleotide, SDG 10 - Reduced Inequalities, Emigration and Immigration, Social stratification, Educational attainment, United Kingdom, Geography, Phenotype, Adipose Tissue, Social Class, Educational Status, 030217 neurology & neurosurgery, Demography
Abstract: Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a sample of ~450,000 individuals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene-environment correlations.
Published: 2019
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10. A comprehensive analysis of mortality-related health metrics associated with mental disorders: a nationwide, register-based cohort study

Author: Nanna Weye, James Scott, Harvey Whiteford, Preben Bo Mortensen, Alize J. Ferrari, Merete Nordentoft, Vladimir Canudas-Romo, Maria K. Christensen, Thomas Munk Laursen, Kim Moesgaard Iburg, John J. McGrath, Holly E. Erskine, Per Kragh Andersen, Esben Agerbo, Yan Holtz, Oleguer Plana-Ripoll, Natalie C. Momen, Damian Santomauro, Fiona J Charlson, Carsten Bøcker Pedersen, and Annette Erlangsen
Subjects: Adult, Male, Adolescent, Substance-Related Disorders, Denmark, Population, Substance-Related Disorders/mortality, 030204 cardiovascular system & hematology, Cohort Studies, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Cause of Death, medicine, Humans, Mental Disorders/mortality, 030212 general & internal medicine, Registries, education, Cause of death, Aged, Quality Indicators, Health Care, Aged, 80 and over, education.field_of_study, Mood Disorders, business.industry, Mortality, Premature, Mental Disorders, Mortality rate, Suicide/statistics & numerical data, General Medicine, Middle Aged, medicine.disease, Denmark/epidemiology, 3. Good health, Suicide, Mood disorders, Relative risk, Life expectancy, Female, Age of onset, business, Mood Disorders/mortality, Demography, Cohort study
Abstract: BACKGROUND: Systematic reviews have consistently shown that individuals with mental disorders have an increased risk of premature mortality. Traditionally, this evidence has been based on relative risks or crude estimates of reduced life expectancy. The aim of this study was to compile a comprehensive analysis of mortality-related health metrics associated with mental disorders, including sex-specific and age-specific mortality rate ratios (MRRs) and life-years lost (LYLs), a measure that takes into account age of onset of the disorder.METHODS: In this population-based cohort study, we included all people younger than 95 years of age who lived in Denmark at some point between Jan 1, 1995, and Dec 31, 2015. Information on mental disorders was obtained from the Danish Psychiatric Central Research Register and the date and cause of death was obtained from the Danish Register of Causes of Death. We classified mental disorders into ten groups and causes of death into 11 groups, which were further categorised into natural causes (deaths from diseases and medical conditions) and external causes (suicide, homicide, and accidents). For each specific mental disorder, we estimated MRRs using Poisson regression models, adjusting for sex, age, and calendar time, and excess LYLs (ie, difference in LYLs between people with a mental disorder and the general population) for all-cause mortality and for each specific cause of death.FINDINGS: 7 369 926 people were included in our analysis. We found that mortality rates were higher for people with a diagnosis of a mental disorder than for the general Danish population (28·70 deaths [95% CI 28·57-28·82] vs 12·95 deaths [12·93-12·98] per 1000 person-years). Additionally, all types of disorders were associated with higher mortality rates, with MRRs ranging from 1·92 (95% CI 1·91-1·94) for mood disorders to 3·91 (3·87-3·94) for substance use disorders. All types of mental disorders were associated with shorter life expectancies, with excess LYLs ranging from 5·42 years (95% CI 5·36-5·48) for organic disorders in females to 14·84 years (14·70-14·99) for substance use disorders in males. When we examined specific causes of death, we found that males with any type of mental disorder lost fewer years due to neoplasm-related deaths compared with the general population, although their cancer mortality rates were higher.INTERPRETATION: Mental disorders are associated with premature mortality. We provide a comprehensive analysis of mortality by different types of disorders, presenting both MRRs and premature mortality based on LYLs, displayed by age, sex, and cause of death. By providing accurate estimates of premature mortality, we reveal previously underappreciated features related to competing risks and specific causes of death.FUNDING: Danish National Research Foundation.
Published: 2019
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11. Evolutionary transcriptomics reveals the origins of olives and the genomic changes associated with their domestication

Author: Gautier Sarah, Bouchaib Khadari, Sylvain Santoni, Muriel Gros-Balthazard, Julie Leclercq, Yan Holtz, Daniel Wegmann, Sylvain Glémin, Guillaume Besnard, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Evolution et Diversité Biologique (EDB), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), University of Fribourg, Swiss Institute of Bioinformatics [Genève] (SIB), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Centre National de la Recherche Scientifique (CNRS), Uppsala University, Conservatoire Botanique National Méditerranéen de Porquerolles, OliveMed N° 1403‐026, Labex AGRO (ANR-10LABX-01-01), Agropolis Fondation, Labex CEBA (ANR‐10LABX‐25‐01), Agence Nationale de la Recherche, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université de Fribourg = University of Fribourg (UNIFR), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), ANR-10-LABX-0025,CEBA,CEnter of the study of Biodiversity in Amazonia(2010), ANR-10-LABX-0001,AGRO,Agricultural Sciences for sustainable Development(2010), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: 0106 biological sciences, 0301 basic medicine, approche transcriptomique, Biodiversité et Ecologie, [SDV]Life Sciences [q-bio], Plant Science, RNA‐sequencing, 01 natural sciences, Mediterranean Basin, F30 - Génétique et amélioration des plantes, Evolutionsbiologi, Transcriptome, Domestication, transcriptomics, F01 - Culture des plantes, Gene Expression Regulation, Plant, Olea europaea, ComputingMilieux_MISCELLANEOUS, Artificial selection, Differential expression analysis, Olea europaea (olive tree), Perennial crop, Transcriptomics, 2. Zero hunger, biology, Mediterranean Region, Domestication des plantes, domestication des espèces, F70 - Taxonomie végétale et phytogéographie, Genomics, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, expression différentielle, Olea, differential expression analysis, [SDE]Environmental Sciences, Original Article, Genome, Plant, RNA-sequencing, artificial selection, Sélection artificielle, Séquence d'ARN, Life history theory, Biodiversity and Ecology, [SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, Evolution, Molecular, domestication, 03 medical and health sciences, Variation génétique, Species Specificity, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Genetics, Genetik, Selection, Genetic, Gene, perennial crop, Evolutionary Biology, Genetic diversity, Transcription génique, Sequence Analysis, RNA, Genetic Variation, Cell Biology, Original Articles, 15. Life on land, biology.organism_classification, Olive trees, 030104 developmental biology, Evolutionary biology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, 010606 plant biology & botany
Abstract: Summary The olive (Olea europaea L. subsp. europaea) is one of the oldest and most socio‐economically important cultivated perennial crop in the Mediterranean region. Yet, its origins are still under debate and the genetic bases of the phenotypic changes associated with its domestication are unknown. We generated RNA‐sequencing data for 68 wild and cultivated olive trees to study the genetic diversity and structure both at the transcription and sequence levels. To localize putative genes or expression pathways targeted by artificial selection during domestication, we employed a two‐step approach in which we identified differentially expressed genes and screened the transcriptome for signatures of selection. Our analyses support a major domestication event in the eastern part of the Mediterranean basin followed by dispersion towards the West and subsequent admixture with western wild olives. While we found large changes in gene expression when comparing cultivated and wild olives, we found no major signature of selection on coding variants and weak signals primarily affected transcription factors. Our results indicated that the domestication of olives resulted in only moderate genomic consequences and that the domestication syndrome is mainly related to changes in gene expression, consistent with its evolutionary history and life history traits., Significance Statement We revisited the evolutionary history of the iconic olive tree by reconstructing a model of domestication and inferring the genomic changes associated with its cultivation. Although more challenging than studying annual crops, further studies of the genomic of the domestication process in perennial should broaden our understanding of the genomic bases and dynamics of adaptation.
Published: 2019
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12. Widespread associations between grey matter structure and the human phenome

Author: Jian Yang, Yan Holtz, Margaret J. Wright, Futao Zhang, Zhili Zheng, Baptiste Couvy-Duchesne, Peter M. Visscher, Naomi R. Wray, Kathryn E. Kemper, Olivier Colliot, Lachlan T. Strike, and Loic Yengo
Subjects: 2. Zero hunger, 0303 health sciences, Human Connectome Project, Waist, Cognition, Biology, Phenome, Body fat percentage, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, Region of interest, Body mass index, 030217 neurology & neurosurgery, 030304 developmental biology, Demography
Abstract: The recent availability of large-scale neuroimaging cohorts (here the UK Biobank [UKB] and the Human Connectome Project [HCP]) facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. We tested the association between 654,386 vertex-wise measures of cortical and subcortical morphology (from T1w and T2w MRI images) and behavioural, cognitive, psychiatric and lifestyle data. We found a significant association of grey-matter structure with 58 out of 167 UKB phenotypes spanning substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains (UKB discovery sample: N=9,888). Twenty-three of the 58 associations replicated (UKB replication sample: N=4,561; HCP, N=1,110). In addition, differences in body size (height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the association, providing possible insight into previous MRI case-control studies for psychiatric disorders where case status is associated with body mass index. Using the same linear mixed model, we showed that most of the associated characteristics (e.g. age, sex, body size, diabetes, being a twin, maternal smoking, body size) could be significantly predicted using all the brain measurements in out-of-sample prediction. Finally, we demonstrated other applications of our approach including a Region Of Interest (ROI) analysis that retain the vertex-wise complexity and ranking of the information contained across MRI processing options.HighlightsOur linear mixed model approach unifies association and prediction analyses for highly dimensional vertex-wise MRI dataGrey-matter structure is associated with measures of substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domainsBody size (height, weight, BMI, waist and hip circumference) is an important source of covariation between the phenome and grey-matter structureGrey-matter scores quantify grey-matter based risk for the associated traits and allow to study phenotypes not collectedThe most general cortical processing (“fsaverage” mesh with no smoothing) maximises the brain-morphometricity for all UKB phenotypes
Published: 2019
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13. Comparative Analysis Based on Transcriptomics and Metabolomics Data Reveal Differences between Emmer and Durum Wheat in Response to Nitrogen Starvation

Author: Franca Nigro, P. De Vita, Nooshin Omranian, Ulrich Schurr, Zoran Nikoloski, Nicola Pecchioni, Romina Beleggia, Jacques David, Tania Gioia, Yan Holtz, Roberto Papa, and Fabio Fiorani
Subjects: 0106 biological sciences, 0301 basic medicine, Subspecies, Triticum turgidum, 01 natural sciences, Transcriptome, transcriptomics, stress, GABA, 0302 clinical medicine, Gene Expression Regulation, Plant, Genotype, Biology (General), Plant nutrition, Triticum, Spectroscopy, 2. Zero hunger, 0303 health sciences, food and beverages, General Medicine, metabolomics, Computer Science Applications, Chemistry, 030220 oncology & carcinogenesis, ddc:540, Shoot, Metabolome, Glutamate, Metabolomics, Stress, Transcriptomics, Nitrogen, Seedlings, Tetraploidy, QH301-705.5, plant nutrition, glutamate, Biology, Article, Catalysis, Inorganic Chemistry, 03 medical and health sciences, Botany, Physical and Theoretical Chemistry, QD1-999, Molecular Biology, Gene, 030304 developmental biology, Organic Chemistry, Primary metabolite, Plant, Metabolism, 030104 developmental biology, Gene Expression Regulation, 010606 plant biology & botany
Abstract: SummaryMounting evidence indicates the key role of Nitrogen (N) on diverse processes in plant, including not only yield but also development and defense. Using a combined transcriptomics and metabolomics approach, we studied the response of seedlings to N starvation of two different tetraploid wheat genotypes from the two main domesticated subspecies, emmer (Triticum turgidum ssp. dicoccum) and durum wheat (Triticum turgidum ssp. durum). We found that durum wheat exhibits broader and stronger response in comparison to emmer as evidenced by the analysis of the differential expression pattern of both genes and metabolites and gene enrichment analysis. Emmer and durum wheat showed major differences in the responses to N starvation for transcription factor families. While emmer showed differential reduction in the levels of primary metabolites to N starvation, durum wheat exhibited increased levels of most metabolites, including GABA as an indicator of metabolic imbalance. The correlation-based networks including the differentially expressed genes and metabolites revealed tighter regulation of metabolism in durum wheat in comparison to emmer, as evidenced by the larger number of significant correlations. We also found that glutamate and GABA had highest values of centrality in the metabolic correlation network, suggesting their critical role in the genotype-specific response to N starvation of emmer and durum wheat, respectively. Moreover, this finding indicates that there might be contrasting strategies associated to GABA and Glutamate signaling modulating shoot vs root growth in the two different wheat subspecies.
Published: 2021
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14. Disentangling homeologous contigs in allo-tetraploid assembly: application to durum wheat.

Author: Vincent Ranwez, Yan Holtz, Gautier Sarah, Morgane Ardisson, Sylvain Santoni, Sylvain Glemin, Muriel Tavaud-Pirra, and Jacques David
Published: 2013
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15. Genetic correlates of social stratification in Great Britain

Author: Abdel, Abdellaoui, David, Hugh-Jones, Loic, Yengo, Kathryn E, Kemper, Michel G, Nivard, Laura, Veul, Yan, Holtz, Brendan P, Zietsch, Timothy M, Frayling, Naomi R, Wray, Jian, Yang, Karin J H, Verweij, and Peter M, Visscher
Subjects: Multifactorial Inheritance, Health Status, Geographic Mapping, Emigration and Immigration, Polymorphism, Single Nucleotide, Body Height, United Kingdom, White People, Body Mass Index, Phenotype, Adipose Tissue, Social Class, Cluster Analysis, Educational Status, Humans, Alleles
Abstract: Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a sample of ~450,000 individuals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene-environment correlations.
Published: 2018

16. Genetic Consequences of Social Stratification in Great Britain

Author: Abdel Abdellaoui, Kathryn E. Kemper, Yan Holtz, Karin J. H. Verweij, Jian Yang, Naomi R. Wray, Brendan P. Zietsch, Timothy M. Frayling, Michel G. Nivard, Peter M. Visscher, Loic Yengo, Laura Veul, and David Hugh-Jones
Subjects: 0303 health sciences, Social stratification, Educational attainment, 03 medical and health sciences, 0302 clinical medicine, Variation (linguistics), Geography, Brexit, Trait, Allele, Cluster analysis, 030217 neurology & neurosurgery, 030304 developmental biology, Demography, Genetic association
Abstract: Human DNA varies across geographic regions, with most variation observed so far reflecting distant ancestry differences. Here, we investigate the geographic clustering of genetic variants that influence complex traits and disease risk in a sample of ~450,000 individuals from Great Britain. Out of 30 traits analyzed, 16 show significant geographic clustering at the genetic level after controlling for ancestry, likely reflecting recent migration driven by socio-economic status (SES). Alleles associated with educational attainment (EA) show most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals that leave coal mining areas carry more EA-increasing alleles on average than the rest of Great Britain. In addition, we leveraged the geographic clustering of complex trait variation to further disentangle regional differences in socio-economic and cultural outcomes through genome-wide association studies on publicly available regional measures, namely coal mining, religiousness, 1970/2015 general election outcomes, and Brexit referendum results.
Published: 2018
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17. How to optimize the precision of allele and haplotype frequency estimates using pooled-sequencing data

Author: Nicolas O. Rode, Yan Holtz, Karine Loridon, Sylvain Santoni, Joëlle Ronfort, Laurène Gay, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Agence Nationale de la Recherche : ANR SEAD - ANR-13-ADAP-0011, ANR-13-ADAP-0011,SEAD,Comment l'autofécondation affecte-t-elle l'adaptation : Conséquences génétiques et démographiques(2013), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), and Rode, Nicolas O.
Subjects: 0301 basic medicine, population genomics, Coefficient of variation, Biology, DNA sequencing, Deep sequencing, Population genomics, 03 medical and health sciences, Gene Frequency, Medicago truncatula, Statistics, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, experimental evolution, Allele, génomique des populations, Allele frequency, Ecology, Evolution, Behavior and Systematics, Allele frequency estimation, coverage depth, fitness, haplotype frequency estimation, Vegetal Biology, estimation, Haplotype, Computational Biology, Sequence Analysis, DNA, DNA extraction, Genetics, Population, 030104 developmental biology, Haplotypes, Biologie végétale, Biotechnology
Abstract: Agap : équipe GE2pop et ID; International audience; Sequencing pools of individuals rather than individuals separately reduces the costs of estimating allele frequencies at many loci in many populations. Theoretical and empirical studies show that pool-sequencing a limited number of individuals (typically fewer than 50) provides reliable allele frequency estimates, provided that the DNA pooling and DNA sequencing steps are carefully controlled. Unequal contributions of different individuals to the DNA pool and the mean and variance in sequencing depth both can affect the standard error of allele frequency estimates. To our knowledge, no study separately investigated the effect of these two factors on allele frequency estimates; so that there is currently no method to a priori estimate the relative importance of unequal individual DNA contributions independently of sequencing depth. We develop a new analytical model for allele frequency estimation that explicitly distinguishes these two effects. Our model shows that the DNA pooling variance in a pool-sequencing experiment depends solely on two factors: the number of individuals within the pool and the coefficient of variation of individual DNA contributions to the pool. We present a new method to experimentally estimate this coefficient of variation when planning a pool-sequencing design where samples are either pooled before or after DNA extraction. Using this analytical and experimental framework, we provide guidelines to optimize the design of pool-sequencing experiments. Finally, we sequence replicated pools of inbred lines of the plant Medicago truncatula and show that the predictions from our model generally hold true when estimating the frequency of known multi-locus haplotypes using pool-sequencing.
Published: 2018
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18. SYNTHESIS OF NEW INTERSPECIFIC TRIPLOID HYBRIDS FROM NATURAL AB GERMPLASM IN BANANA (MUSA SP.)

Author: Frédéric Bakry, Christophe Jenny, Jean-Pierre Horry, and Yan Holtz
Subjects: Germplasm, Mycosphaerella musicola, Horticulture, Biology, Plant disease resistance, F30 - Génétique et amélioration des plantes, Fusarium, medicine, Leaf spot, Cultivar, H20 - Maladies des plantes, Hybrid, F63 - Physiologie végétale - Reproduction, food and beverages, Musa, Interspecific competition, Résistance aux maladies, biology.organism_classification, Fusarium wilt, medicine.anatomical_structure, Hybridation interspécifique, Gamete, Triploïdie
Abstract: The release of new sweet-acid banana cultivars resistant to black leaf streak, Sigatoka leaf spot and Fusarium wilt is important for domestic markets in tropical and subtropical countries. Common current breeding strategies consist of selecting tetraploid AAAB hybrids directly from crosses between AAB sweet-acid cultivars pollinated with AA clones carrying disease-resistance genes. However, this crossing pathway is hampered by low gamete fertility and the rare occurrence of desired 2N gametes on the AAB female side (N=3X=33 chromosomes). We propose an alternative pathway which aims to create new triploid hybrids directly from AB landraces. Natural AB clones are sterile but their AABB tetraploid counterparts obtained by colchicine treatment are fertile. This gamete fertility was made profitable in crosses with AA and BB accessions to generate AAB and ABB hybrids. We present here agronomical results of various progenies involving an in vitro synthesized tetraploid 'Kunnan' (AABB) and several AA and BB clones. These first results suggest the very high potential of this new strategy for the release of wellperforming new hybrids combining higher productivity, disease resistance and better fruit quality. Hybrids with a high added-value, produced in this way, could be made available for evaluation within the ProMusa network. (Resume d'auteur)
Published: 2013
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19. Epistatic determinism of durum wheat resistance to the wheat spindle streak mosaic virus

Author: Yan Holtz, Morgane Ardisson, Sylvain Santoni, Nicolas O. Rode, David Gouache, Michel Bonnefoy, Jacques David, Gérard Poux, Véronique Marie-Jeanne, Vincent Ranwez, Pierre Roumet, Véronique Viader, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), ARVALIS - Institut du végétal [Paris], ARVALIS (TRAM project), Agence National de la Recherche (ANR SEAD), Marie Sklodowska-Curie/AgreenSkills Program, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ANR-13-ADAP-0011,SEAD,Comment l'autofécondation affecte-t-elle l'adaptation : Conséquences génétiques et démographiques(2013), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)
Subjects: Genetic Markers, 0106 biological sciences, 0301 basic medicine, résistance aux maladies, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Genotype, Genetic Linkage, Quantitative Trait Loci, Locus (genetics), Plant disease resistance, Quantitative trait locus, 01 natural sciences, maladie de la mosaïque, 03 medical and health sciences, Gene interaction, Gene mapping, Plant virus, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Plant breeding, pathologie végétale, Crosses, Genetic, Triticum, Disease Resistance, Plant Diseases, biology, Chromosome Mapping, food and beverages, Epistasis, Genetic, General Medicine, Potyviridae, biology.organism_classification, resistance to diseases, Phenotype, 030104 developmental biology, Agronomy, blé dur, Wheat spindle streak mosaic virus, maladie virale, hard wheat, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Original Article, Agronomy and Crop Science, 010606 plant biology & botany, Biotechnology
Abstract: Key message The resistance of durum wheat to the Wheat spindle streak mosaic virus (WSSMV) is controlled by two main QTLs on chromosomes 7A and 7B, with a huge epistatic effect. Abstract Wheat spindle streak mosaic virus (WSSMV) is a major disease of durum wheat in Europe and North America. Breeding WSSMV-resistant cultivars is currently the only way to control the virus since no treatment is available. This paper reports studies of the inheritance of WSSMV resistance using two related durum wheat populations obtained by crossing two elite cultivars with a WSSMV-resistant emmer cultivar. In 2012 and 2015, 354 recombinant inbred lines (RIL) were phenotyped using visual notations, ELISA and qPCR and genotyped using locus targeted capture and sequencing. This allowed us to build a consensus genetic map of 8568 markers and identify three chromosomal regions involved in WSSMV resistance. Two major regions (located on chromosomes 7A and 7B) jointly explain, on the basis of epistatic interactions, up to 43% of the phenotypic variation. Flanking sequences of our genetic markers are provided to facilitate future marker-assisted selection of WSSMV-resistant cultivars. Electronic supplementary material The online version of this article (doi:10.1007/s00122-017-2904-6) contains supplementary material, which is available to authorized users.
Published: 2017
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20. Evolutionary forces affecting synonymous variations in plant genomes

Author: Roberto Bacilieri, Jean Louis Pham, Stéphanie Pointet, Christopher Sauvage, Fabien De Bellis, Guillaume Besnard, Yves Vigouroux, Gautier Sarah, Bouchaib Khadari, Jean-Pierre Labouisse, Céline Cardi, Claire Lanaud, Olivier Fouet, Jacques David, Sandy Contreras, Sylvain Santoni, Morgane Ardisson, Thierry Leroy, Manuel Ruiz, Nabila Yahiaoui, Angélique Berger, Sylvain Glémin, Nora Scarcelli, Benoit Nabholz, Cyril Jourda, James Tregear, Felix Homa, Laure Sauné, Yves Clément, David Pot, François Sabot, Yan Holtz, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Sys2Diag-Modélisation et Ingénierie des Systèmes Complexes Biologiques pour le Diagnostic (Sys2Diag), Centre National de la Recherche Scientifique (CNRS)-Alcediag, Genoscreen [Lille], Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Institut de Recherche pour le Développement (IRD [France-Ouest]), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Evolution et Diversité Biologique (EDB), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Peuplements végétaux et bioagresseurs en milieu tropical (UMR PVBMT), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Université de La Réunion (UR), Génétique et Amélioration des Fruits et Légumes (GAFL), Institut National de la Recherche Agronomique (INRA), ISEM 2017–091, ARCAD project W 0900-001, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), PSL Research University (PSL), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Sysdiag-Modélisation et Ingénierie des Systèmes Complexes Biologiques pour le Diagnostic (SysDiag ), BIO-RAD-Centre National de la Recherche Scientifique (CNRS), Cap delta, SouthGreen Platform, Montpellier, SouthGreen Platform, Genoscreen, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Unité de recherche Génétique et amélioration des fruits et légumes (GALF), Université Paris sciences et lettres (PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de La Réunion (UR)-Institut National de la Recherche Agronomique (INRA), and KARLI, Mélanie
Subjects: 0106 biological sciences, [SDV]Life Sciences [q-bio], Population genetics, Gene Expression, Plant Genomes, génome végétal, Plant Science, Plant Genetics, phylogeny, 01 natural sciences, Genome, F30 - Génétique et amélioration des plantes, [SDV.GEN.GPL] Life Sciences [q-bio]/Genetics/Plants genetics, Plant Genomics, ComputingMilieux_MISCELLANEOUS, Flowering Plants, Data Management, Genetics, 0303 health sciences, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Genomics, Plants, Plant genomes, Phylogenetics, Homogeneous, Codon usage bias, [SDE]Environmental Sciences, Transcriptome Analysis, Genome, Plant, Research Article, Biotechnology, Computer and Information Sciences, Substitution Mutation, lcsh:QH426-470, Gene Conversion, Biology, Monocotyledons, Evolution, Molecular, [SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics, 03 medical and health sciences, Magnoliopsida, séquençage, [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], phylogénie, Evolutionary Systematics, Gene conversion, Selection, Genetic, Genetik, analyse du transcriptome, Selection (genetic algorithm), 030304 developmental biology, Taxonomy, Evolutionary Biology, Polymorphism, Genetic, Organisms, Biology and Life Sciences, Computational Biology, Plant Taxonomy, 15. Life on land, Genome Analysis, GC Rich Sequence, lcsh:Genetics, Mutation, Plant Biotechnology, analyse fonctionnelle du génome, 010606 plant biology & botany
Abstract: Base composition is highly variable among and within plant genomes, especially at third codon positions, ranging from GC-poor and homogeneous species to GC-rich and highly heterogeneous ones (particularly Monocots). Consequently, synonymous codon usage is biased in most species, even when base composition is relatively homogeneous. The causes of these variations are still under debate, with three main forces being possibly involved: mutational bias, selection and GC-biased gene conversion (gBGC). So far, both selection and gBGC have been detected in some species but how their relative strength varies among and within species remains unclear. Population genetics approaches allow to jointly estimating the intensity of selection, gBGC and mutational bias. We extended a recently developed method and applied it to a large population genomic dataset based on transcriptome sequencing of 11 angiosperm species spread across the phylogeny. We found that at synonymous positions, base composition is far from mutation-drift equilibrium in most genomes and that gBGC is a widespread and stronger process than selection. gBGC could strongly contribute to base composition variation among plant species, implying that it should be taken into account in plant genome analyses, especially for GC-rich ones., Author summary In protein coding genes, base composition strongly varies within and among plant genomes, especially at positions where changes do not alter the coded protein (synonymous variations). Some species, such as the model plant Arabidopsis thaliana, are relatively GC-poor and homogeneous while others, such as grasses, are highly heterogeneous and GC-rich. The causes of these variations are still debated: are they mainly due to selective or neutral processes? Answering to this question is important to correctly infer whether variations in base composition may have functional roles or not. We extended a population genetics method to jointly estimate the different forces that may affect synonymous variations and applied it to genomic datasets in 11 flowering plant species. We found that GC-biased gene conversion, a neutral process associated with recombination that mimics selection by favouring G and C bases, is a widespread and stronger process than selection and that it could explain the large variation in base composition observed in plant genomes. Our results bear implications for analysing plant genomes and for correctly interpreting what could be functional or not.
Published: 2017
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21. The genetic map comparator: a user-friendly application to display and compare genetic maps

Author: Jacques David, Yan Holtz, Vincent Ranwez, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), TRAM project, ARVALIS, Agence Nationale de la Recherche, Ancestrome : ANR-10-BINF-01-02, ANR-10-BINF-0001,ANCESTROME,Approche de phylogénie intégrative pour la reconstruction de génomes ancestraux(2010), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: 0301 basic medicine, Statistics and Probability, carte génétique, Comparator, Computer science, Sequence analysis, Quantitative Trait Loci, Quantitative trait locus, Genes, Plant, Biochemistry, 03 medical and health sciences, Computer graphics (images), Molecular Biology, Gene, bioinformatique, Selection (genetic algorithm), Triticum, Disease Resistance, Plant Diseases, [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS], User Friendly, Information retrieval, logiciel informatique, Genomics, Sequence Analysis, DNA, Computer Science Applications, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Virus Diseases, Key (cryptography), computer software, genetic mapping, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software
Abstract: Motivation Marker-assisted selection strongly relies on genetic maps to accelerate breeding programs. High-density maps are now available for numerous species. Dedicated tools are required to compare several high-density maps on the basis of their key characteristics, while pinpointing their differences and similarities. Results We developed the Genetic Map Comparator—a web-based application for easy comparison of different maps according to their key statistics and the relative positions of common markers. Availability and Implementation The Genetic Map Comparator is available online at: http://bioweb.supagro.inra.fr/geneticMapComparator. The source code is freely available on GitHub under the under the CeCILL general public license: https://github.com/holtzy/GenMap-Comparator.
Published: 2017
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22. La diversité génétique des plantes cultivées à l'heure de l'informatique génétique à haut débit: génomique comparative de la domestication

Author: Gautier Sarah, Nabholtz, B., Francois Sabot, Yan Holtz, Abdel Félix Homa, Stéphanie Pointet, Sandy Contreras, Laure Saune, Morgane Ardisson, Roberto Bacilieri, Bouchaib Khadari, Claire Lanaud, David Pot, Christopher Sauvage, Nora Scarcelli, James Tregear, Yves Vigouroux, Nabila Yahiaoui, Manuel Ruiz, Sylvain Santoni, Jean-Pierre Labouisse, Jean-Louis Pham, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Recherche pour le Développement (IRD [France-Ouest]), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA), Conservatoire Botanique National, Unité de recherche Génétique et amélioration des fruits et légumes (GALF), and Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: molecular marker, sélection génétique, molecular evolution, génomique comparative, [SDV]Life Sciences [q-bio], domesticated plant, polymorphisme, domestication des espèces, polymorphism, plante cultivée, genêtic variation, évolution moléculaire, diversité génétique, RNA-seq, marqueur moléculaire, expression des gènes
Abstract: UMR AGAP - équipe GE²pop - Génomique évolutive et gestion des populations UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières; La diversité génétique des plantes cultivées à l'heure de l'informatique génétique à haut débit: génomique comparative de la domestication
Published: 2016

23. Genotyping by sequencing transcriptomes in an evolutionary pre-breeding durum wheat population

Author: Yan Holtz, Gautier Sarah, Frédéric Choulet, Morgane Ardisson, Clémence Genthon, Jacques David, Muriel Tavaud-Pirra, Pierre Roumet, Sylvain Santoni, Gérard Poux, Vincent Ranwez, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), Institut de Génomique Fonctionnelle - Montpellier GenomiX (IGF MGX), Institut de Génomique Fonctionnelle (IGF), Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), flagship project Agropolis Resource Center for Crop Conservation, Adaptation and Diversity (ARCAD) - Agropolis Fondation 0900-001, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, dispensable, Population, SNP, Plant Science, Biology, 01 natural sciences, DNA sequencing, diversity, 03 medical and health sciences, Genetics, genotyping by sequencing, repeatability, education, Molecular Biology, Genotyping, 030304 developmental biology, Genetic association, Synteny, 2. Zero hunger, 0303 health sciences, Genetic diversity, education.field_of_study, food and beverages, durum wheat, Marker-assisted selection, RNAseq, SNP genotyping, Agronomy and Crop Science, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, 010606 plant biology & botany, Biotechnology
Abstract: The genetic diversity in durum wheat, Triticum turgidum durum, has been strongly reduced since the domestication of the wild Triticum turgidum dicoccoides. Monitoring durum wheat composite crosses incorporating related tetraploid taxa, such as wild and domesticated emmer wheat, is a suitable evolutionary pre-breeding method. Transcriptome sequencing paves the way for a genomic survey of single nucleotide polymorphisms (SNPs) segregating in such populations, offering the possibility of genotyping by sequencing to use these resources in genome-wide association studies (GWAS) and genomic selection (GS) programs. Evolutionary Pre-breeding pOpulation (EPO) is an evolutionary durum wheat pre-breeding population. Sequencing the transcriptome of 179 durum wheat lines (175 from EPO) led to the detection of 103,262 SNPs on two reference transcriptomes: one from the International Wheat Genome Sequencing Consortium and one assembled de novo on durum wheat. Using strict filtering to remove dubious heterozygous SNPs, EPO genetic diversity was eventually described with 76,188 high-confidence SNPs. The percentage of missing genotyping data depended on the expression level, and 88 individuals out of 175 were genotyped per SNP on average. Using the 3B pseudo-molecule of bread wheat, the transcription and diversity levels were shown to be higher in distal regions than in proximal regions, but SNPs were available throughout the chromosomes. Assuming good synteny with Hordeum, the trend was similar on the 14 chromosomes of the durum wheat genome. EPO hosts a high level of diversity, has a number of SNPs in low linkage disequilibrium (< 40 Mb) and would be well suited for GWAS and GS programs.
Published: 2014
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24. New insights for estimating the genetic value of segregating apple progenies for irregular bearing during the first years of tree production

Author: Catherine Trottier, Jean-Baptiste Durand, Yan Holtz, Baptiste Guitton, Yann Guédon, Evelyne Costes, and Jean Peyhardi
Subjects: 0106 biological sciences, Physiology, Sélection, Plant Science, Breeding, 01 natural sciences, F30 - Génétique et amélioration des plantes, Floraison, Statistics, Cultivar, Facteur anthropogène, 2. Zero hunger, 0303 health sciences, education.field_of_study, Inflorescence, Malus, Arbre fruitier, Phénologie, Genotype, F60 - Physiologie et biochimie végétale, Quantitative Trait Loci, Population, Quantitative trait locus, Biology, Essai de provenances, Generalized linear mixed model, 03 medical and health sciences, Genetic variation, Botany, Variété, education, 030304 developmental biology, amélioration génétique, Biennial bearing, Genetic Variation, biology.organism_classification, Fruit, Choix des espèces, production, 010606 plant biology & botany
Abstract: Because irregular bearing generates major agronomic issues in fruit-tree species, particularly in apple, the selection of regular cultivars is desirable. Here, we aimed to define methods and descriptors allowing a diagnostic for bearing behaviour during the first years of tree maturity, when tree production is increasing. Flowering occurrences were collected at whole-tree and (annual) shoot scales on a segregating apple population. At both scales, the number of inflorescences over the years was modelled. Two descriptors were derived from model residuals: a new biennial bearing index, based on deviation around yield trend over years and an autoregressive coefficient, which represents dependency between consecutive yields. At the shoot scale, entropy was also considered to represent the within-tree flowering synchronicity. Clusters of genotypes with similar bearing behaviours were built. Both descriptors at the whole-tree and shoot scales were consistent for most genotypes and were used to discriminate regular from biennial and irregular genotypes. Quantitative trait loci were detected for the new biennial bearing index at both scales. Combining descriptors at a local scale with entropy showed that regular bearing at the tree scale may result from different strategies of synchronization in flowering at the local scale. The proposed methods and indices open an avenue to quantify bearing behaviour during the first years of tree maturity and to capture genetic variations. Their extension to other progenies and species, possible variants of descriptors, and their use in breeding programmes considering a limited number of years or fruit yields are discussed.
Published: 2013

25. Estimating the genetic value of F1 apple progenies for irregular bearing during first years of production

Author: Jean-Baptiste Durand, Baptiste Guitton, Jean Peyhardi, Yan Holtz, Yann Guédon, Catherine Trottier, Evelyne Costes, Modelling and Inference of Complex and Structured Stochastic Systems (MISTIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut de Mathématiques et de Modélisation de Montpellier (I3M), Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM), Risto Sievänen and Eero Nikinmaa and Christophe Godin and Anna Lintunen and Pekka Nygren, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université Paul-Valéry - Montpellier 3 (UM3), Modelling and Inference of Complex and Structured Stochastic Systems ( MISTIS ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire Jean Kuntzmann ( LJK ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Institut National Polytechnique de Grenoble ( INPG ), Modeling plant morphogenesis at different scales, from genes to phenotype ( VIRTUAL PLANTS ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de la Recherche Agronomique ( INRA ) -Centre de coopération internationale en recherche agronomique pour le développement [CIRAD] : UMR51, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Institut de Mathématiques et de Modélisation de Montpellier ( I3M ), Université Montpellier 2 - Sciences et Techniques ( UM2 ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Université Paul-Valéry - Montpellier 3 ( UM3 ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Inria Grenoble - Rhône-Alpes, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)
Subjects: alternance de production, pommier, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, modèle linéaire mixte, F30 - Génétique et amélioration des plantes, biennial bearing, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], alternation indices, [ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST], variabilité genotypique, Malus x domestica, malus domestica, Vegetal Biology, architecture de l'arbre, floraison, U10 - Informatique, mathématiques et statistiques, fungi, food and beverages, analyse de qtl, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH], Alternation indices, Biennial bearing, Breeding, Linear mixed model, QTL detection, ComputingMethodologies_PATTERNRECOGNITION, breeding, linear mixed model, Biologie végétale
Abstract: UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières; International audience; Flowering regularity in apple trees during the beginning of their mature phase was assessed using new descriptors based on annual yields. These descriptors were approximated using subsamples of annual shoot sequences at axis scale to allow genotype evaluation at reasonable sampling costs. The approximation provided a good discrimination between regular and alternate bearing genotypes. QTLs were detected for some descriptors.
Published: 2013

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25 results on '"Yan Holtz"'

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22. La diversité génétique des plantes cultivées à l'heure de l'informatique génétique à haut débit: génomique comparative de la domestication

23. Genotyping by sequencing transcriptomes in an evolutionary pre-breeding durum wheat population

24. New insights for estimating the genetic value of segregating apple progenies for irregular bearing during the first years of tree production

25. Estimating the genetic value of F1 apple progenies for irregular bearing during first years of production

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