7 research outputs found
Crop Updates 2007 - Lupins, Pulses and Oilseeds
This session covers forty eight papers from different authors:
2006 REGIONAL ROUNDUP
1. South east agricultural region, Mark Seymour1 and Jacinta Falconer2, 1Department of Agriculture and Food, 2Cooperative Bulk Handling Group
2. Central agricultural region, Ian Pritchard, Department of Agriculture and Food
3. Great Southern and Lakes region, Rodger Beermier, Department of Agriculture and Food
4. Northern agricultural region, Wayne Parker and Martin Harries, Department of Agriculture and Food
LUPINS
5. Development of anthracnose resistant and early flowering albus lupins (Lupinus albus L) in Western Australia, Kedar Adhikari and Geoff Thomas, Department of Agriculture and Food
6. New lupins adapted to the south coast, Peter White, Bevan Buirchell and Mike Baker, Department of Agriculture and Food
7. Lupin species and row spacing interactions by environment, Martin Harries, Peter White, Bob French, Jo Walker, Mike Baker and Laurie Maiolo, Department of Agriculture and Food
8. The interaction of lupin species row spacing and soil type, Martin Harries, Bob French, Laurie Maiolo and Jo Walker, Department of Agriculture and Food
9. The effects of row spacing and crop density on competitiveness of lupins with wild radish, Bob French and Laurie Maiolo, Department of Agriculture and Food
10. The effect of time of sowing and radish weed density on lupin yield, Martin Harries and Jo Walker, Department of Agriculture and Food
11. Interaction of time of sowing and weed management in lupins, Martin Harries and Jo Walker, Department of Agriculture and Food
12. Delayed sowing as a strategy to manage annual ryegrass, Bob French and Laurie Maiolo, Department of Agriculture and Food
13. Is delayed sowing a good strategy for weed management in lupins? Bob French, Department of Agriculture and Food
14. Lupins aren’t lupins when it comes to simazine, Peter White and Leigh Smith, Department of Agriculture and Food
15. Seed yield and anthracnose resistance of Tanjil mutants tolerant to metribuzin, Ping Si1, Bevan Buirchell1,2 and Mark Sweetingham1,2, 1Centre for Legumes in Mediterranean Agriculture, Australia; 2Department of Agriculture and Food
16. The effect of herbicides on nodulation in lupins, Lorne Mills1, Harmohinder Dhammu2 and Beng Tan1, 1Curtin University of Technology and 2Department of Agriculture and Food
17. Effect of fertiliser placements and watering regimes on lupin growth and seed yield in the central grain belt of Western Australia, Qifu Ma1, Zed Rengel1, Bill Bowden2, Ross Brennan2, Reg Lunt2 and Tim Hilder2, 1Soil Science & Plant Nutrition UWA, 2Department of Agriculture and Food
18. Development of a forecasting model for Bean Yellow Mosaic Virus in lupins, T. Maling1,2, A. Diggle1, D. Thackray1,2, R.A.C. Jones2, and K.H.M. Siddique1, 1Centre for Legumes in Mediterranean Agriculture, The University of Western Australia; 2Department of Agriculture and Food
19. Manufacturing of lupin tempe,Vijay Jayasena1,4, Leonardus Kardono2,4, Ken Quail3,4 and Ranil Coorey1,4, 1Curtin University of Technology, Perth, Australia, 2Indonesian Institute of Sciences (LIPI), Indonesia, 3BRI Australia Ltd, Sydney, Australia, 4Grain Foods CRC, Sydney, Australia
20. The impact of lupin based ingredients in ice-cream, Hannah Williams, Lee Sheer Yap and Vijay Jayasena, Curtin University of Technology, Perth WA
21. The acceptability of muffins substituted with varying concentrations of lupin flour, Anthony James, Don Elani Jayawardena and Vijay Jayasena, Curtin University of Technology, PerthWA
PULSES
22. Chickpea variety evaluation, Kerry Regan1, Rod Hunter1, Tanveer Khan1,2and Jenny Garlinge1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia
23. Advanced breeding trials of desi chickpea, Khan, T.N.1, Siddique, K.H.M.3, Clarke, H.2, Turner, N.C.2, MacLeod, W.1, Morgan, S.1, and Harris, A.1, 1Department of Agriculture and Food, 2Centre for Legumes in Mediterranean Agriculture, 3TheUniversity of Western Australia
24. Ascochyta resistance in chickpea lines in Crop Variety Testing (CVT) of 2006, Tanveer Khan1 2, Bill MacLeod1, Alan Harris1, Stuart Morgan1and Kerry Regan1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia
25. Yield evaluation of ascochyta blight resistant Kabuli chickpeas, Kerry Regan1and Kadambot Siddique2, 1Department of Agriculture and Food, 2Institute of Agriculture, The University of Western Australia
26. Pulse WA Chickpea Industry Survey 2006, Mark Seymour1, Ian Pritchard1, Wayne Parker1and Alan Meldrum2, 1Department of Agriculture and Food, 2Pulse Australia
27. Genes from the wild as a valuable genetic resource for chickpea improvement, Heather Clarke1, Helen Bowers1and Kadambot Siddique2, 1Centre for Legumes in Mediterranean Agriculture, 2Institute of Agriculture, The University of Western Australia
28. International screening of chickpea for resistance to Botrytis grey mould, B. MacLeod1, Dr T. Khan1, Prof. K.H.M. Siddique2and Dr A. Bakr3, 1Department of Agriculture and Food, 2The University of Western Australia, 3Bangladesh Agricultural Research Institute
29. Balance® in chickpea is safest applied post sowing to a level seed bed, Wayne Parker, Department of Agriculture and Food,
30. Demonstrations of Genesis 510 chickpea, Wayne Parker, Department of Agriculture and Food
31. Field pea 2006, Ian Pritchard, Department of Agriculture and Food
32. Field pea variety evaluation, Kerry Regan1, Rod Hunter1, Tanveer Khan1,2 and Jenny Garlinge1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia
33. Breeding highlights of the Australian Field Pea Improvement Program (AFPIP),Kerry Regan1, Tanveer Khan1,2, Phillip Chambers1, Chris Veitch1, Stuart Morgan1 , Alan Harris1and Tony Leonforte3, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia, 3Department of Primary Industries, Victoria
34. Field pea germplasm enhancement for black spot resistance, Tanveer Khan, Kerry Regan, Stuart Morgan, Alan Harris and Phillip Chambers, Department of Agriculture and Food
35. Validation of Blackspot spore release model and testing moderately resistant field pea line, Mark Seymour, Ian Pritchard, Rodger Beermier, Pam Burgess and Leanne Young, Department of Agriculture and Food
36. Yield losses from sowing field pea seed infected with Pea Seed-borne Mosaic Virus, Brenda Coutts, Donna O’Keefe, Rhonda Pearce, Monica Kehoe and Roger Jones, Department of Agriculture and Food
37. Faba bean in 2006, Mark Seymour, Department of Agriculture and Food
38. Germplasm evaluation – faba bean, Mark Seymour1, Terri Jasper1, Ian Pritchard1, Mike Baker1 and Tim Pope1,2, 1Department of Agriculture and Food, , 2CLIMA, The University of Western Australia
39. Breeding highlights of the Coordinated Improvement Program for Australian Lentils (CIPAL), Kerry Regan1, Chris Veitch1, Phillip Chambers1 and Michael Materne2, 1Department of Agriculture and Food, 2Department of Primary Industries, Victoria
40. Screening pulse lentil germplasm for tolerance to alternate herbicides, Ping Si1, Mike Walsh2 and Mark Sweetingham1,3, 1Centre for Legumes in Mediterranean Agriculture, 2West Australian Herbicide Resistance Initiative, 3Department of Agriculture and Food
41. Genomic synteny in legumes: Application to crop breeding, Phan, H.T.T.1, Ellwood, S.R.1, Hane, J.1, Williams, A.1, Ford, R.2, Thomas, S.3 and Oliver R1, 1Australian Centre of Necrotrophic Plant Pathogens, Murdoch University, 2BioMarka, University of Melbourne, 3NSW Department of Primary Industries
42. Tolerance of lupins, chickpeas and canola to Balanceâ(Isoxaflutole) and Galleryâ (Isoxaben), Leigh Smith and Peter White, Department of Agriculture and Food
CANOLA AND OILSEEDS
43. The performance of TT Canola varieties in the National Variety Test (NVT),WA,2006,Katie Robinson, Research Agronomist, Agritech Crop Research
44. Evaluation of Brassica crops for biodiesel in Western Australia, Mohammad Amjad, Graham Walton, Pat Fels and Andy Sutherland, Department of Agriculture and Food
45. Production risk of canola in different rainfall zones in Western Australia, Imma Farré1, Michael Robertson2 and Senthold Asseng3, 1Department of Agriculture and Food, 2CSIRO Sustainable Ecosystems, 3CSIRO Plant Industry
46. Future directions of blackleg management – dynamics of blackleg susceptibility in canola varieties, Ravjit Khangura, Moin Salam and Bill MacLeod, Department of Agriculture and Food
47. Appendix 1: Contributors
48. Appendix 2: List of common acronym
Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure
Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies
Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis
Funding Information: GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file : Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe
Integrative prognostic risk score in acute myeloid leukemia with normal karyotype
To integrate available clinical and molecular information for cytogenetically normal acute myeloid leukemia (CN-AML) patients into one risk score, 275 CN-AML patients from multicenter treatment trials AML SHG Hannover 0199 and 0295 and 131 patients from HOVON/SAKK protocols as external controls were evaluated for mutations/polymorphisms in NPM1, FLT3, CEBPA, MLL, NRAS, IDH1/2, and WT1. Transcript levels were quantified for BAALC, ERG, EVI1, ID1, MN1, PRAME, and WT1. Integrative prognostic risk score (IPRS) was modeled in 181 patients based on age, white blood cell count, mutation status of NPM1, FLT3-ITD, CEBPA, single nucleotide polymorphism rs16754, and expression levels of BAALC, ERG, MN1, and WT1 to represent low, intermediate, and high risk of death. Complete remission (P = .005), relapse-free survival (RFS, P < .001), and overall survival (OS, P < .001) were significantly different for the 3 risk groups. In 2 independent validation cohorts of 94 and 131 patients, the IPRS predicted different OS (P < .001) and RFS (P < .001). High-risk patients with related donors had longer OS (P = .016) and RFS (P = .026) compared with patients without related donors. In contrast, intermediate-risk group patients with related donors had shorter OS (P=.003) and RFS(P=.05). Donor availability had no impact on outcome of patients in the low-risk group. Thus, the IPRS may improve consolidation treatment stratification in CN-AML patients. Study registered at www.clinicaltrials.gov as #NCT00209833
Rare and low-frequency coding variants alter human adult height
Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of
Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis
Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk
Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis
Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe