Physiological and genetic analysis to improve quality and quantity of sunflower seed oil under drought stress

The genetic control of tocopherol, phytosterol, percentage of seed protein, oil and fatty acids content in a population of recombinant inbred lines (RILs) of sunflower under various conditions are studied through QTL analysis using genetic-linkage map based on SSR markers and introducing some important tocopherol and phytosterol pathway-related genes, enzymatic antioxidant-related genes, droughtresponsive family genes and Arabidopsis SEC14 homologue genes. Three important candidate genes (HPPD, VTE2 and VTE4), which encode enzymes involved in tocopherol biosynthesis, are mapped to linkage group 8(LG8) and LG14. One of the most important candidate genes coding for sterol methyltransferase II (SMT2) enzyme is anchored to LG17 by CAPS marker. Four SNPs are identified for PAT2, Arabidopsis Sec14 homologue gene, between two parents (PAC2 and RHA266). PAT2 is assigned to LG2 by CAPS marker. Squalene epoxidase (SQE1) is also assigned to LG15 by InDel marker. Through other candidate genes, POD, CAT and GST encoding enzymatic antioxidants are assigned to LG17, LG8 and LG1, respectively. The major QTL for total tocopherol content on linkage group 8 accounted for 59.5% of the phenotypic variation (6.TTC.8), which is overlapped with the QTL of total phytosterol content (7.TPC.8). Under late-sowing condition, a specific QTL of palmitic acid content on linkage group 6 (PAC-LS.6) is located between ORS1233 and SSL66_1 markers. Common chromosomic regions are observed for percentage of seed oil and stearic acid content on linkage group 10 (PSO-PI.10 and SACWI. 10) and 15 (PSO-PI.15 and SAC-LS.15). Overlapping occurs for QTLs of oleic and linoleic acids content on linkage groups 10, 11 and 16. Seven QTLs associated with palmitic, stearic, oleic and linoleic acids content are identified on linkage group 14. These common QTLs are linked to HPPD homologue, HuCL04260C001. QTLs controlling various traits such as days from sowing to flowering, plant height, yield and leaf-related traits are also identified under well-, partial-irrigated and late-sowing conditions in a population of recombinant inbred lines (RILs). The results do emphasis the importance of the role of linkage group 2, 10 and 13 for studied traits. Genomic regions on the linkage group 9 and 12 are important for QTLs of leaf-related traits in sunflower. We finally identified AFLP markers and some candidate genes linked to seed-quality traits under well-irrigated and water-stressed conditions in gammainduced mutants of sunflower. Two mutant lines, M8-826-2-1 and M8-39-2-1, with significant increased level of oleic acid can be used in breeding programs because of their high oxidative stability and hearthealthy properties. The significant increased level of tocopherol in mutant lines, M8-862-1N1 and M8- 641-2-1, is justified by observed polymorphism for tocopherol pathway-related gene; MCT. The most important marker for total tocopherol content is E33M50_16 which explains 33.9% of phenotypic variance. One of the most important candidate genes involving fatty acid biosynthesis, FAD2 (FAD2-1), is linked to oleic and linoleic acids content and explained more than 52% of phenotypic variance

Analyse physiologique et génétique combinées pour améliorer le contenu en huile et la qualité du tournesol soumis à la sécheresse

Le tocophérol, le phytostérol, le pourcentage de protéines des graines, l'huile et les teneurs en acides gras ont été mesurés dans une population de lignées recombinantes (RILS) de tournesol, cultivées sous conditions de sécheresse, irrigation et semis tardif. Une analyse génétique de QTL a été réalisée à partir de ces mesures, en utilisant une carte génétique basée sur des marques SSR et avec des gènes candidats (1) impliqués dans la voie métabolique de tocophérol et phytostérol, (2) des gènes codant des antioxydants enzymatiques, (3) des gènes liés à la sécheresse et (4) des gènes homologues à SEC14 chez Arabidopsis. Trois gènes candidats importants (VTE4, VTE2 et HPPD), qui codent pour des enzymes impliquées dans la biosynthèse du tocophérol, ont été cartographiés sur les groupes de liaison LG8 et LG14. Quatre SNPs sont identifiés pour PAT2, le gène homologue chez Arabidopsis SEC14, entre les deux parents (PAC2 et RHA266) et un SNP, identifié par alignement de séquences est converti en marqueur CAPS pour permettre l'analyse génotypique des RIL. Les gènes homologues à SFH3, HPPD, CAT et CYP51G1 ont été cartographiés grâce à la mise au point de marqueurs dominants, tandis que des marqueurs co-dominants ont permis la cartographie des gènes homologues à SEC14-1, VTE4, DROU1, POD, SEC14-2 et AQUA. Les gènes POD, CAT et GST, codant pour des antioxydants enzymatiques, ont également été cartographiés sur les groupes de liaison 17, 8 et 1, respectivement. Le QTL majeur pour la teneur en tocophérol a été identifié sur le groupe de liaison 8, qui explique 59,5% de la variation phénotypique (6.TTC.8). Il colocalsie également avec le QTL identifié pour la teneur en phytostérol (7.TPC.8). Sous condition de semis tardif, un QTL spécifique de la teneur en acide palmitique a été identifié sur le groupe de liaison 6 (PAC-LS.6). Il est situé entre les marqueurs ORS1233 et SSL66_1. Les QTLs pour le pourcentage d'huile de graines et la teneur en acide stéarique colocalisent sur les groupes de liaison 10 (PSO-PI.10 et SAC-WI.10) et 15 (PSO-PI.15 et SAC-LS.15). Sept QTLs associés à teneur en acides palmitique, stéarique, oléique et linoléique sont identifiés sur le groupe de liaison 14. Ils sont liés à l’homologue du gène HPPD. Par ailleurs, les caractères agronomiques tels que les jours du semis à la floraison, la hauteur des plantes, le rendement et la morphologie foliaire ont été étudiés. Des analyses association génétique ont permis d’identifier des QTLs intérêts sur les groupes de liaison 2, 10 et 13 pour les caractères étudiés, d’autres QTLs identifies sur les groupes de liaison 9 et 12 mettent en avant l'importance de ces régions génomiques pour les caractères de morphologie foliaire. Nous avons finalement identifié des marqueurs AFLP et quelques gènes candidats liés aux caractères impliqués dans la qualité des graines sous conditions irriguée et stress hydrique chez une population de mutants (M8). Deux lignées mutantes, M8-826-2-1 et M8-39-2-1, produisent un niveau significativement élevé d'acide oléique peuvent être utilisées dans les programmes de sélection en raison de la haute stabilité à l'oxydation et des propriétés cardiovasculaire apportés par l’acide oléique qu’elles produisent. L'augmentation du niveau de tocophérol dans les lignées mutantes, M8-862-1N1 et M8-641-2-1, est justifiée par le polymorphisme observé pour le gène, MCT, impliqué dans la voie métabolique du tocophérol. Le marqueur le plus important pour le contenu en tocophérol total est E33M50_16 qui explique 33,9% de la variation phénotypique. Un des gènes candidats les plus importants concernant la biosynthèse des acides gras, FAD2 (FAD2-1), est lié à la teneur en acides oléique et linoléique. Il explique plus de 52% de la variation phénotypique. ABSTRACT : The genetic control of tocopherol, phytosterol, percentage of seed protein, oil and fatty acids content in a population of recombinant inbred lines (RILs) of sunflower under various conditions are studied through QTL analysis using genetic-linkage map based on SSR markers and introducing some important tocopherol and phytosterol pathway-related genes, enzymatic antioxidant-related genes, droughtresponsive family genes and Arabidopsis SEC14 homologue genes. Three important candidate genes (HPPD, VTE2 and VTE4), which encode enzymes involved in tocopherol biosynthesis, are mapped to linkage group 8(LG8) and LG14. One of the most important candidate genes coding for sterol methyltransferase II (SMT2) enzyme is anchored to LG17 by CAPS marker. Four SNPs are identified for PAT2, Arabidopsis Sec14 homologue gene, between two parents (PAC2 and RHA266). PAT2 is assigned to LG2 by CAPS marker. Squalene epoxidase (SQE1) is also assigned to LG15 by InDel marker. Through other candidate genes, POD, CAT and GST encoding enzymatic antioxidants are assigned to LG17, LG8 and LG1, respectively. The major QTL for total tocopherol content on linkage group 8 accounted for 59.5% of the phenotypic variation (6.TTC.8), which is overlapped with the QTL of total phytosterol content (7.TPC.8). Under late-sowing condition, a specific QTL of palmitic acid content on linkage group 6 (PAC-LS.6) is located between ORS1233 and SSL66_1 markers. Common chromosomic regions are observed for percentage of seed oil and stearic acid content on linkage group 10 (PSO-PI.10 and SACWI. 10) and 15 (PSO-PI.15 and SAC-LS.15). Overlapping occurs for QTLs of oleic and linoleic acids content on linkage groups 10, 11 and 16. Seven QTLs associated with palmitic, stearic, oleic and linoleic acids content are identified on linkage group 14. These common QTLs are linked to HPPD homologue, HuCL04260C001. QTLs controlling various traits such as days from sowing to flowering, plant height, yield and leaf-related traits are also identified under well-, partial-irrigated and late-sowing conditions in a population of recombinant inbred lines (RILs). The results do emphasis the importance of the role of linkage group 2, 10 and 13 for studied traits. Genomic regions on the linkage group 9 and 12 are important for QTLs of leaf-related traits in sunflower. We finally identified AFLP markers and some candidate genes linked to seed-quality traits under well-irrigated and water-stressed conditions in gammainduced mutants of sunflower. Two mutant lines, M8-826-2-1 and M8-39-2-1, with significant increased level of oleic acid can be used in breeding programs because of their high oxidative stability and hearthealthy properties. The significant increased level of tocopherol in mutant lines, M8-862-1N1 and M8- 641-2-1, is justified by observed polymorphism for tocopherol pathway-related gene; MCT. The most important marker for total tocopherol content is E33M50_16 which explains 33.9% of phenotypic variance. One of the most important candidate genes involving fatty acid biosynthesis, FAD2 (FAD2-1), is linked to oleic and linoleic acids content and explained more than 52% of phenotypic variance

Genomic evidence for genes encoding leucine-rich repeat receptors linked to resistance against the eukaryotic extra- and intracellular Brassica napus pathogens Leptosphaeria maculans and Plasmodiophora brassicae

© 2018 Stotz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Genes coding for nucleotide-binding leucine-rich repeat (LRR) receptors (NLRs) control resistance against intracellular (cell-penetrating) pathogens. However, evidence for a role of genes coding for proteins with LRR domains in resistance against extracellular (apoplastic) fungal pathogens is limited. Here, the distribution of genes coding for proteins with eLRR domains but lacking kinase domains was determined for the Brassica napus genome. Predictions of signal peptide and transmembrane regions divided these genes into 184 coding for receptor-like proteins (RLPs) and 121 coding for secreted proteins (SPs). Together with previously annotated NLRs, a total of 720 LRR genes were found. Leptosphaeria maculans-induced expression during a compatible interaction with cultivar Topas differed between RLP, SP and NLR gene families; NLR genes were induced relatively late, during the necrotrophic phase of pathogen colonization. Seven RLP, one SP and two NLR genes were found in Rlm1 and Rlm3/Rlm4/Rlm7/Rlm9 loci for resistance against L. maculans on chromosome A07 of B. napus. One NLR gene at the Rlm9 locus was positively selected, as was the RLP gene on chromosome A10 with LepR3 and Rlm2 alleles conferring resistance against L. maculans races with corresponding effectors AvrLm1 and AvrLm2, respectively. Known loci for resistance against L. maculans (extracellular hemi-biotrophic fungus), Sclerotinia sclerotiorum (necrotrophic fungus) and Plasmodiophora brassicae (intracellular, obligate biotrophic protist) were examined for presence of RLPs, SPs and NLRs in these regions. Whereas loci for resistance against P. brassicae were enriched for NLRs, no such signature was observed for the other pathogens. These findings demonstrate involvement of (i) NLR genes in resistance against the intracellular pathogen P. brassicae and a putative NLR gene in Rlm9-mediated resistance against the extracellular pathogen L. maculans.Peer reviewe

Two independent approaches converge to the cloning of a new Leptosphaeria maculans avirulence effector gene, AvrLmS-Lep2.

Brassica napus (oilseed rape, canola) seedling resistance to Leptosphaeria maculans, the causal agent of blackleg (stem canker) disease, follows a gene-for-gene relationship. The avirulence genes AvrLmS and AvrLep2 were described to be perceived by the resistance genes RlmS and LepR2, respectively, present in B. napus 'Surpass 400'. Here we report cloning of AvrLmS and AvrLep2 using two independent methods. AvrLmS was cloned using combined in vitro crossing between avirulent and virulent isolates with sequencing of DNA bulks from avirulent or virulent progeny (bulked segregant sequencing). AvrLep2 was cloned using a biparental cross of avirulent and virulent L. maculans isolates and a classical map-based cloning approach. Taking these two approaches independently, we found that AvrLmS and AvrLep2 are the same gene. Complementation of virulent isolates with this gene confirmed its role in inducing resistance on Surpass 400, Topas-LepR2, and an RlmS-line. The gene, renamed AvrLmS-Lep2, encodes a small cysteine-rich protein of unknown function with an N-terminal secretory signal peptide, which is a common feature of the majority of effectors from extracellular fungal plant pathogens. The AvrLmS-Lep2/LepR2 interaction phenotype was found to vary from a typical hypersensitive response through intermediate resistance sometimes towards susceptibility, depending on the inoculation conditions. AvrLmS-Lep2 was nevertheless sufficient to significantly slow the systemic growth of the pathogen and reduce the stem lesion size on plant genotypes with LepR2, indicating the potential efficiency of this resistance to control the disease in the field

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. Methods: The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. Findings: Global DALYs increased from 2·63 billion (95% UI 2·44–2·85) in 2010 to 2·88 billion (2·64–3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7–17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8–6·3) in 2020 and 7·2% (4·7–10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0–234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7–198·3]), neonatal disorders (186·3 million [162·3–214·9]), and stroke (160·4 million [148·0–171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3–51·7) and for diarrhoeal diseases decreased by 47·0% (39·9–52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54–1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5–9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0–19·8]), depressive disorders (16·4% [11·9–21·3]), and diabetes (14·0% [10·0–17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7–27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6–63·6) in 2010 to 62·2 years (59·4–64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6–2·9) between 2019 and 2021. Interpretation: Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. Funding: Bill & Melinda Gates Foundation

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Physiological and genetic analysis to improve quality and quantity of sunflower seed oil under drought stress

Le tocophérol, le phytostérol, le pourcentage de protéines des graines, l'huile et les teneurs en acides gras ont été mesurés dans une population de lignées recombinantes (RILS) de tournesol, cultivées sous conditions de sécheresse, irrigation et semis tardif. Une analyse génétique de QTL a été réalisée à partir de ces mesures, en utilisant une carte génétique basée sur des marques SSR et avec des gènes candidats (1) impliqués dans la voie métabolique de tocophérol et phytostérol, (2) des gènes codant des antioxydants enzymatiques, (3) des gènes liés à la sécheresse et (4) des gènes homologues à SEC14 chez Arabidopsis. Trois gènes candidats importants (VTE4, VTE2 et HPPD), qui codent pour des enzymes impliquées dans la biosynthèse du tocophérol, ont été cartographiés sur les groupes de liaison LG8 et LG14. Quatre SNPs sont identifiés pour PAT2, le gène homologue chez Arabidopsis SEC14, entre les deux parents (PAC2 et RHA266) et un SNP, identifié par alignement de séquences est converti en marqueur CAPS pour permettre l'analyse génotypique des RIL. Les gènes homologues à SFH3, HPPD, CAT et CYP51G1 ont été cartographiés grâce à la mise au point de marqueurs dominants, tandis que des marqueurs co-dominants ont permis la cartographie des gènes homologues à SEC14-1, VTE4, DROU1, POD, SEC14-2 et AQUA. Les gènes POD, CAT et GST, codant pour des antioxydants enzymatiques, ont également été cartographiés sur les groupes de liaison 17, 8 et 1, respectivement. Le QTL majeur pour la teneur en tocophérol a été identifié sur le groupe de liaison 8, qui explique 59,5% de la variation phénotypique (6.TTC.8). Il colocalsie également avec le QTL identifié pour la teneur en phytostérol (7.TPC.8). Sous condition de semis tardif, un QTL spécifique de la teneur en acide palmitique a été identifié sur le groupe de liaison 6 (PAC-LS.6). Il est situé entre les marqueurs ORS1233 et SSL66_1. Les QTLs pour le pourcentage d'huile de graines et la teneur en acide stéarique colocalisent sur les groupes de liaison 10 (PSO-PI.10 et SAC-WI.10) et 15 (PSO-PI.15 et SAC-LS.15). Sept QTLs associés à teneur en acides palmitique, stéarique, oléique et linoléique sont identifiés sur le groupe de liaison 14. Ils sont liés à l’homologue du gène HPPD. Par ailleurs, les caractères agronomiques tels que les jours du semis à la floraison, la hauteur des plantes, le rendement et la morphologie foliaire ont été étudiés. Des analyses association génétique ont permis d’identifier des QTLs intérêts sur les groupes de liaison 2, 10 et 13 pour les caractères étudiés, d’autres QTLs identifies sur les groupes de liaison 9 et 12 mettent en avant l'importance de ces régions génomiques pour les caractères de morphologie foliaire. Nous avons finalement identifié des marqueurs AFLP et quelques gènes candidats liés aux caractères impliqués dans la qualité des graines sous conditions irriguée et stress hydrique chez une population de mutants (M8). Deux lignées mutantes, M8-826-2-1 et M8-39-2-1, produisent un niveau significativement élevé d'acide oléique peuvent être utilisées dans les programmes de sélection en raison de la haute stabilité à l'oxydation et des propriétés cardiovasculaire apportés par l’acide oléique qu’elles produisent. L'augmentation du niveau de tocophérol dans les lignées mutantes, M8-862-1N1 et M8-641-2-1, est justifiée par le polymorphisme observé pour le gène, MCT, impliqué dans la voie métabolique du tocophérol. Le marqueur le plus important pour le contenu en tocophérol total est E33M50_16 qui explique 33,9% de la variation phénotypique. Un des gènes candidats les plus importants concernant la biosynthèse des acides gras, FAD2 (FAD2-1), est lié à la teneur en acides oléique et linoléique. Il explique plus de 52% de la variation phénotypique.The genetic control of tocopherol, phytosterol, percentage of seed protein, oil and fatty acids content in a population of recombinant inbred lines (RILs) of sunflower under various conditions are studied through QTL analysis using genetic-linkage map based on SSR markers and introducing some important tocopherol and phytosterol pathway-related genes, enzymatic antioxidant-related genes, droughtresponsive family genes and Arabidopsis SEC14 homologue genes. Three important candidate genes (HPPD, VTE2 and VTE4), which encode enzymes involved in tocopherol biosynthesis, are mapped to linkage group 8(LG8) and LG14. One of the most important candidate genes coding for sterol methyltransferase II (SMT2) enzyme is anchored to LG17 by CAPS marker. Four SNPs are identified for PAT2, Arabidopsis Sec14 homologue gene, between two parents (PAC2 and RHA266). PAT2 is assigned to LG2 by CAPS marker. Squalene epoxidase (SQE1) is also assigned to LG15 by InDel marker. Through other candidate genes, POD, CAT and GST encoding enzymatic antioxidants are assigned to LG17, LG8 and LG1, respectively. The major QTL for total tocopherol content on linkage group 8 accounted for 59.5% of the phenotypic variation (6.TTC.8), which is overlapped with the QTL of total phytosterol content (7.TPC.8). Under late-sowing condition, a specific QTL of palmitic acid content on linkage group 6 (PAC-LS.6) is located between ORS1233 and SSL66_1 markers. Common chromosomic regions are observed for percentage of seed oil and stearic acid content on linkage group 10 (PSO-PI.10 and SACWI. 10) and 15 (PSO-PI.15 and SAC-LS.15). Overlapping occurs for QTLs of oleic and linoleic acids content on linkage groups 10, 11 and 16. Seven QTLs associated with palmitic, stearic, oleic and linoleic acids content are identified on linkage group 14. These common QTLs are linked to HPPD homologue, HuCL04260C001. QTLs controlling various traits such as days from sowing to flowering, plant height, yield and leaf-related traits are also identified under well-, partial-irrigated and late-sowing conditions in a population of recombinant inbred lines (RILs). The results do emphasis the importance of the role of linkage group 2, 10 and 13 for studied traits. Genomic regions on the linkage group 9 and 12 are important for QTLs of leaf-related traits in sunflower. We finally identified AFLP markers and some candidate genes linked to seed-quality traits under well-irrigated and water-stressed conditions in gammainduced mutants of sunflower. Two mutant lines, M8-826-2-1 and M8-39-2-1, with significant increased level of oleic acid can be used in breeding programs because of their high oxidative stability and hearthealthy properties. The significant increased level of tocopherol in mutant lines, M8-862-1N1 and M8- 641-2-1, is justified by observed polymorphism for tocopherol pathway-related gene; MCT. The most important marker for total tocopherol content is E33M50_16 which explains 33.9% of phenotypic variance. One of the most important candidate genes involving fatty acid biosynthesis, FAD2 (FAD2-1), is linked to oleic and linoleic acids content and explained more than 52% of phenotypic variance

Analyse physiologique et génétique combinées pour améliorer le contenu en huile et la qualité du tournesol soumis à la sécheresse

Le tocophérol, le phytostérol, le pourcentage de protéines des graines, l'huile et les teneurs en acides gras ont été mesurés dans une population de lignées recombinantes (RILS) de tournesol, cultivées sous conditions de sécheresse, irrigation et semis tardif. Une analyse génétique de QTL a été réalisée à partir de ces mesures, en utilisant une carte génétique basée sur des marques SSR et avec des gènes candidats (1) impliqués dans la voie métabolique de tocophérol et phytostérol, (2) des gènes codant des antioxydants enzymatiques, (3) des gènes liés à la sécheresse et (4) des gènes homologues à SEC14 chez Arabidopsis. Trois gènes candidats importants (VTE4, VTE2 et HPPD), qui codent pour des enzymes impliquées dans la biosynthèse du tocophérol, ont été cartographiés sur les groupes de liaison LG8 et LG14. Quatre SNPs sont identifiés pour PAT2, le gène homologue chez Arabidopsis SEC14, entre les deux parents (PAC2 et RHA266) et un SNP, identifié par alignement de séquences est converti en marqueur CAPS pour permettre l'analyse génotypique des RIL. Les gènes homologues à SFH3, HPPD, CAT et CYP51G1 ont été cartographiés grâce à la mise au point de marqueurs dominants, tandis que des marqueurs co-dominants ont permis la cartographie des gènes homologues à SEC14-1, VTE4, DROU1, POD, SEC14-2 et AQUA. Les gènes POD, CAT et GST, codant pour des antioxydants enzymatiques, ont également été cartographiés sur les groupes de liaison 17, 8 et 1, respectivement. Le QTL majeur pour la teneur en tocophérol a été identifié sur le groupe de liaison 8, qui explique 59,5% de la variation phénotypique (6.TTC.8). Il colocalsie également avec le QTL identifié pour la teneur en phytostérol (7.TPC.8). Sous condition de semis tardif, un QTL spécifique de la teneur en acide palmitique a été identifié sur le groupe de liaison 6 (PAC-LS.6). Il est situé entre les marqueurs ORS1233 et SSL66_1. Les QTLs pour le pourcentage d'huile de graines et la teneur en acide stéarique colocalisent sur les groupes de liaison 10 (PSO-PI.10 et SAC-WI.10) et 15 (PSO-PI.15 et SAC-LS.15). Sept QTLs associés à teneur en acides palmitique, stéarique, oléique et linoléique sont identifiés sur le groupe de liaison 14. Ils sont liés à l homologue du gène HPPD. Par ailleurs, les caractères agronomiques tels que les jours du semis à la floraison, la hauteur des plantes, le rendement et la morphologie foliaire ont été étudiés. Des analyses association génétique ont permis d identifier des QTLs intérêts sur les groupes de liaison 2, 10 et 13 pour les caractères étudiés, d autres QTLs identifies sur les groupes de liaison 9 et 12 mettent en avant l'importance de ces régions génomiques pour les caractères de morphologie foliaire. Nous avons finalement identifié des marqueurs AFLP et quelques gènes candidats liés aux caractères impliqués dans la qualité des graines sous conditions irriguée et stress hydrique chez une population de mutants (M8). Deux lignées mutantes, M8-826-2-1 et M8-39-2-1, produisent un niveau significativement élevé d'acide oléique peuvent être utilisées dans les programmes de sélection en raison de la haute stabilité à l'oxydation et des propriétés cardiovasculaire apportés par l acide oléique qu elles produisent. L'augmentation du niveau de tocophérol dans les lignées mutantes, M8-862-1N1 et M8-641-2-1, est justifiée par le polymorphisme observé pour le gène, MCT, impliqué dans la voie métabolique du tocophérol. Le marqueur le plus important pour le contenu en tocophérol total est E33M50_16 qui explique 33,9% de la variation phénotypique. Un des gènes candidats les plus importants concernant la biosynthèse des acides gras, FAD2 (FAD2-1), est lié à la teneur en acides oléique et linoléique. Il explique plus de 52% de la variation phénotypique.The genetic control of tocopherol, phytosterol, percentage of seed protein, oil and fatty acids content in a population of recombinant inbred lines (RILs) of sunflower under various conditions are studied through QTL analysis using genetic-linkage map based on SSR markers and introducing some important tocopherol and phytosterol pathway-related genes, enzymatic antioxidant-related genes, droughtresponsive family genes and Arabidopsis SEC14 homologue genes. Three important candidate genes (HPPD, VTE2 and VTE4), which encode enzymes involved in tocopherol biosynthesis, are mapped to linkage group 8(LG8) and LG14. One of the most important candidate genes coding for sterol methyltransferase II (SMT2) enzyme is anchored to LG17 by CAPS marker. Four SNPs are identified for PAT2, Arabidopsis Sec14 homologue gene, between two parents (PAC2 and RHA266). PAT2 is assigned to LG2 by CAPS marker. Squalene epoxidase (SQE1) is also assigned to LG15 by InDel marker. Through other candidate genes, POD, CAT and GST encoding enzymatic antioxidants are assigned to LG17, LG8 and LG1, respectively. The major QTL for total tocopherol content on linkage group 8 accounted for 59.5% of the phenotypic variation (6.TTC.8), which is overlapped with the QTL of total phytosterol content (7.TPC.8). Under late-sowing condition, a specific QTL of palmitic acid content on linkage group 6 (PAC-LS.6) is located between ORS1233 and SSL66_1 markers. Common chromosomic regions are observed for percentage of seed oil and stearic acid content on linkage group 10 (PSO-PI.10 and SACWI. 10) and 15 (PSO-PI.15 and SAC-LS.15). Overlapping occurs for QTLs of oleic and linoleic acids content on linkage groups 10, 11 and 16. Seven QTLs associated with palmitic, stearic, oleic and linoleic acids content are identified on linkage group 14. These common QTLs are linked to HPPD homologue, HuCL04260C001. QTLs controlling various traits such as days from sowing to flowering, plant height, yield and leaf-related traits are also identified under well-, partial-irrigated and late-sowing conditions in a population of recombinant inbred lines (RILs). The results do emphasis the importance of the role of linkage group 2, 10 and 13 for studied traits. Genomic regions on the linkage group 9 and 12 are important for QTLs of leaf-related traits in sunflower. We finally identified AFLP markers and some candidate genes linked to seed-quality traits under well-irrigated and water-stressed conditions in gammainduced mutants of sunflower. Two mutant lines, M8-826-2-1 and M8-39-2-1, with significant increased level of oleic acid can be used in breeding programs because of their high oxidative stability and hearthealthy properties. The significant increased level of tocopherol in mutant lines, M8-862-1N1 and M8- 641-2-1, is justified by observed polymorphism for tocopherol pathway-related gene; MCT. The most important marker for total tocopherol content is E33M50_16 which explains 33.9% of phenotypic variance. One of the most important candidate genes involving fatty acid biosynthesis, FAD2 (FAD2-1), is linked to oleic and linoleic acids content and explained more than 52% of phenotypic variance.TOULOUSE-INP (315552154) / SudocSudocFranceF