Sheep Updates 2006 - part 2

This session covers six papers from different authors: GENETICS 1. Novel selection traits - what are the possible side effects?, Darryl Smith, Kathryn Kemper, South Australian Research and Development Institute, David Rutley, University of Adelaide. 2. Genetic Changes in the Australian Merino since 1900, Sheep Genetics Australia Technical Committee, R.R. Woolaston Pullenvale, Queensland, D.J. Brown, Animal Genetics and Breeding Unit*, University of New England, K.D. Atkins, A.E. Casey, NSW Department of Primary Industries, A.J. Ball, Meat and Livestock Australia, University of New England 3. Influence of Sire Growth Estimated Breeding Value (EBV0 on Progeny Growth, David Hopkins, David Stanley, Leonie Martin, NSW Department Primary Industries, Centre for Sheep Meat Development, Arthur Gilmour, Remy van de Ven, NSW Department Primary Industries, Orange Agricultural Institute FINISHING 4. Predicting Input Sensitivity on Lamb Feedlot Profitability by Using Feedlot Calculator, David Stanley, NSW Department Primary Industries, Centre for Sheep Meat Development, Geoff Duddy, NSW Department Primary Industries, Yanco Agricultural Institute, Steve Semple, NSW Department Primary Industries, Orange Agricultural Institute, David Hopkins, NSW Department Primary Industries, Centre for Sheep Meat Development 5. Annual ryegrass toxicity (ARGT) in WA - 2006, David Kessell, Meat & Livestock Australia ARGT Project, Northam, WA 6. Poor ewe nutrition during pregnancy increases fatness of their progeny, Andrew Thompson, Department of Primary Industries, Victori

Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants

Spatial trade-offs in the digestive and reproductive systems of grape phylloxera

Trade-offs between reproduction and other energy-requiring activities are present in insects. However, feeding and reproduction are not often thought to be trade-offs, although in small insects space may be limiting for both ingestion of food and egg development. This study characterised the structure of the digestive system of radicicolae Daktulosphaira vitifoliae (Hemiptera: Phylloxeridae) to investigate how feeding and egg development occur in this species. Using light and electron microscopy, the midgut of D. vitifoliae was observed to be composed of anterior and posterior regions, separated by a hindgut connection. The midgut is compressed during the development of parthenogenetically produced eggs in adults; individual eggs are ∼30% of the adult length and in volume internally occupy 3-5% of the body cavity. The midgut posterior chamber is suggested to be essential for the continual supply of energy during periods of reduced food intake. The presence of the hindgut and an anal opening indicated that waste excretion through the anus was physiologically possible, although honeydew excretion was not observed. The structure of the digestive system of radicicolae D. vitifoliae is atypical, containing adaptations that may assist the survival of the monophagous insect during dispersal events to a new Vitis food source

Sheep Updates 2006 - part 2

This session covers six papers from different authors: GENETICS 1. Novel selection traits - what are the possible side effects?, Darryl Smith, Kathryn Kemper, South Australian Research and Development Institute, David Rutley, University of Adelaide. 2. Genetic Changes in the Australian Merino since 1900, Sheep Genetics Australia Technical Committee, R.R. Woolaston Pullenvale, Queensland, D.J. Brown, Animal Genetics and Breeding Unit*, University of New England, K.D. Atkins, A.E. Casey, NSW Department of Primary Industries, A.J. Ball, Meat and Livestock Australia, University of New England 3. Influence of Sire Growth Estimated Breeding Value (EBV0 on Progeny Growth, David Hopkins, David Stanley, Leonie Martin, NSW Department Primary Industries, Centre for Sheep Meat Development, Arthur Gilmour, Remy van de Ven, NSW Department Primary Industries, Orange Agricultural Institute FINISHING 4. Predicting Input Sensitivity on Lamb Feedlot Profitability by Using Feedlot Calculator, David Stanley, NSW Department Primary Industries, Centre for Sheep Meat Development, Geoff Duddy, NSW Department Primary Industries, Yanco Agricultural Institute, Steve Semple, NSW Department Primary Industries, Orange Agricultural Institute, David Hopkins, NSW Department Primary Industries, Centre for Sheep Meat Development 5. Annual ryegrass toxicity (ARGT) in WA - 2006, David Kessell, Meat & Livestock Australia ARGT Project, Northam, WA 6. Poor ewe nutrition during pregnancy increases fatness of their progeny, Andrew Thompson, Department of Primary Industries, Victori

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples (r b = 0.70 for cis-eQTLs and r ^ b = 0.78 for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes. © 2018 The Author(s).Peer reviewe