Invited review: Bioinformatic methods to discover the likely causal variant of a new autosomal recessive genetic condition using genome-wide data

In animals, new autosomal recessive genetic diseases (ARGD) arise all the time due to the regular, random mutations that occur during meiosis. In order to reduce the effect of any damaging new variant, it is necessary to find its cause. To evaluate the best way of doing this, 34 papers which found the exact location of a new genetic disease in livestock were reviewed and found to require at least two stages. In the initial stage the commonly used χ2 method, applied in a case-control association analysis with single nucleotide polymorphism (SNP)-chip data, was found to have limitations and was almost always used in conjunction with a second method to locate the target region on the genome containing the variant. The commonly used methods had their drawbacks; so a new method was devised based on long runs of homozygosity, a common feature of new ARGD. This ‘autozygosity by difference’ method was found to be as good as, or better than, all the reviewed methods tested based on its ability to unambiguously find the shortest known target region in an already analysed data set. Mean target region length was found to be 4.6 megabases in the published reports. Success did not depend on the size of commercial SNP-chip used, and studies with as few as three cases and four controls were large enough to find the target region. The final stage relied on either sequencing the candidate genes found in the target region or using whole genome sequencing (WGS) on a small number of cases. Sometimes this latter method was used in conjunction with WGS on a number of control animals or resources such as the 1000 bull genomes data. Calculations showed that, in cattle, less than 15 animals would be needed in order to locate the new variant when using WGS data. This could be any combination of cases plus parents or other unrelated animals in the breed. Using WGS data, it would be necessary to search the three billion bases of the cattle genome for base positions which were homozygous for the same allele in all cases and heterozygous for that allele in parents, or not containing that homozygote in unrelated controls. This site could be confirmed on other healthy animals using much cheaper methods, and then a genetic test could be devised for that variant in order to screen the whole population and to devise a breeding programme to eliminate the disorder from the population

Association between Single Nucleotide Polymorphism in RelA with Somatic Cell Count and Longevity Supports Importance of NF-¦ÊB Signalling in Cattle Health

Mastitis reduces milk production and causes culling. The NF-κB transcription factor RelA plays a central regulatory role in innate immunity. This study used a candidate gene approach to investigate associations between the synonymous C/G SNP rs48035703 in RELA with somatic cell count (SCC) and survival time. Blood samples were collected from 337 Holstein-Friesian heifers on 19 farms and genotyped by PCR-restriction fragment length polymorphism. Animals were monitored from 6 months until 2340 d of age. Pedigree, milk production and disease records were obtained. Genotype frequencies were CC 0.63, CG 0.30 and GG 0.06. The C allele had a favourable additive effect on survival: average longevities from birth were CC, 1872 d; CG, 1745 d and GG 1596 d (P < 0.003). Log transformed first lactation somatic cell count (SCC)data showed a significant association with this SNP using an allele substitution model (mean residuals ± SD: GG 0.30 ± 1.263; CG 0.22 ± 0.994, CC −0.04 ± 0.803, P < 0.05). More CC cows than expected were classified as intermediate and fewer as mastitic (30.4% v 45.9%) with respect to SCC class when categorised as 0 (unaffected), 1 (intermediate) and 2 (mastitic), whereas for CG heterozygotes fewer were intermediate and more were mastitic (12.1% v 60.3%) (p = 0.05). RELA rs48035703 CC genotype cows were therefore less likely to experience a high SCC and survived longer. These results support a role for RelA in combating mammary gland infection and warrant further studies in additional populations

Environmental factors affecting lactation curve parameters in the United Kingdom’s commercial dairy herds

Environmental factors affecting lactation curve parameters in the United Kingdom&apos;s commercial dairy herds Factores ambientales que determinan los parámetros de la curva de lactación usando un modelo biológico en rebaños lecheros comerciales en el Reino Unido RESUMEN El objetivo del trabajo fue determinar los factores ambientales que determinan los parámetros de la curva de lactación utilizando un modelo biológico de ajuste de curva. El modelo propuesto ajusta dos curvas logísticas que simulan el incremento inicial en el número de células secretoras de leche en la lactación temprana, y la progresión de la apoptosis en la lactación tardía. Se analizaron lactaciones de 182.987 vacas Holstein-Friesian. Los factores vaca, rebaño y número de lactación explican el 74% de la suma total de cuadrados (P &lt; 0,001). La edad promedio a primer parto fue de 28 meses, teniendo un efecto significativo sobre la mayoría de los parámetros de la curva. Incrementos en la edad a primer de parto (20-40 meses) fueron asociados con incrementos lineales en los rendimientos totales de leche. Los parámetros tasa máxima de secreción y máximo de lactación estuvieron altamente correlacionados entre sí, indicando que son virtualmente los mismos. Adicionalmente, altos valores de estos dos parámetros indican altos rendimientos totales de leche. El día del máximo de lactación se correlacionó negativamente (0,64) con persistencia de la lactación. Los factores vaca, rebaño número de lactación y edad a primer parto fueron los factores más determinantes sobre los parámetros de la curva de lactación de vacas de primera lactancia así como de lactaciones múltiples

The application of a mechanistic model to analyze the factors that affect the lactation curve parameters of dairy sheep in Mexico

Pollott́s mechanistic model has been designed to describe lactation curve parameters based on the known biology of milk production and can be useful for analyzing the factors that affect this process. A total of 553 lactations (9956 weekly test-day records) of crossbred dairy sheep from four commercial farms located in Mexico, were analyzed to investigate environmental factors that influenced lactation curve parameters, using Pollott’s 5-parameter additive model. This model was fitted to each lactation using an iterative nonlinear procedure. The estimated parameters were maximum milk secretion potential (MSmax), relative rate of increase in cell differentiation (GR), maximum secretion loss (MSLmax), relative rate of decline in cell numbers (DR) and the proportion of parenchyma cells dead at parturition. A general linear model procedure was used to determine the effect of type of lambing, lambing number, flock and lambing season on total lactation milk yield (TMY), lactation length and estimated parameters of the Pollott model. Ewes had an average milk yield of 74.4 L with an average lactation length of 140 days. Flock had a significant (P < 0.05) effect on most of the analyzed traits, which can be explained by the different farmś management practices. The TMY were significantly (P = 0.005) higher for twin-lambing than single-lambing lactations. Sheep in their first lambing had lower TMY than those in their fourth lambing (P = 0.01), possibly explained by the lower values of MSmax (2.85 vs. 5.3 L) and the decrease in DR throughout life (P = 0.03). However, the relative GR was greatest (P = 0.04) during first lambing and then decreased as lambing number increased. Both lambing number and type of lambing also affected milk yield. The parameters of the Pollott model can be useful to explain, with a biological approximation, the dynamics of differentiation, secretion and death of mammary cells in dairy sheep

Prediction of metabolic clusters in early lactation dairy cows using models based on 2 milk biomarkers

The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and beta-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immuno-gamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the "balanced" group (n = 43) and were compared with cows in what was referred to as the "other balanced" group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the "imbalanced" group (n = 19) and compared with cows in what was referred to as the "other imbalanced" group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids arid BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-beta-D-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield featurs) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows

Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep

An (Awassi × Merino) × Merino backcross family of 172 ewes was used to map quantitative trait loci (QTL) for different milk production traits on a framework map of 200 loci across all autosomes. From five previously proposed mathematical models describing lactation curves, the Wood model was considered the most appropriate due to its simplicity and its ability to determine ovine lactation curve characteristics. Derived milk traits for milk, fat, protein and lactose yield, as well as percentage composition and somatic cell score were used for single and two-QTL approaches using maximum likelihood estimation and regression analysis. A total of 15 significant (P < 0.01) and additional 25 suggestive (P < 0.05) QTL were detected across both single QTL methods and all traits. In preparation of a meta-analysis, all QTL results were compared with a meta-assembly of QTL for milk production traits in dairy ewes from various public domain sources and can be found on the ReproGen ovine gbrowser http://crcidp.vetsci.usyd.edu.au/cgi-bin/gbrowse/oaries_genome/. Many of the QTL for milk production traits have been reported on chromosomes 1, 3, 6, 16 and 20. Those on chromosomes 3 and 20 are in strong agreement with the results reported here. In addition, novel QTL were found on chromosomes 7, 8, 9, 14, 22 and 24. In a cross-species comparison, we extended the meta-assembly by comparing QTL regions of sheep and cattle, which provided strong evidence for synteny conservation of QTL regions for milk, fat, protein and somatic cell score data between cattle and sheep

Genetic parameters for growth, reproductive and maternal traits in a multibreed meat sheep population

The genetic parameters for growth, reproductive and maternal traits in a multibreed meat sheep population were estimated by applying the Average Information Restricted Maximum Likelihood method to an animal model. Data from a flock supported by the Programa de Melhoramento Genético de Caprinos e Ovinos de Corte (GENECOC) were used. The traits studied included birth weight (BW), weaning weight (WW), slaughter weight (SW), yearling weight (YW), weight gain from birth to weaning (GBW), weight gain from weaning to slaughter (GWS), weight gain from weaning to yearling (GWY), age at first lambing (AFL), lambing interval (LI), gestation length (GL), lambing date (LD - number of days between the start of breeding season and lambing), litter weight at birth (LWB) and litter weight at weaning (LWW). The direct heritabilities were 0.35, 0.81, 0.65, 0.49, 0.20, 0.15 and 0.39 for BW, WW, SW, YW, GBW, GWS and GWY, respectively, and 0.04, 0.06, 0.10, 0.05, 0.15 and 0.11 for AFL, LI, GL, LD, LWB and LWW, respectively. Positive genetic correlations were observed among body weights. In contrast, there was a negative genetic correlation between GBW and GWS (-0.49) and GBW and GWY (-0.56). Positive genetic correlations were observed between AFL and LI, LI and GL, and LWB and LWW. These results indicate a strong maternal influence in this herd and the presence of sufficient genetic variation to allow mass selection for growth traits. Additive effects were of little importance for reproductive traits, and other strategies are necessary to improve the performance of these animals

Agribusiness Sheep Updates - 2004 part 2

Precision Pastures Using Species Diversity to Improve Pasture Performance Anyou Liu and Clinton Revell, Department of Agriculture, Western Australia New Annual Pasture Legumes for Sheep Graziers Phil Nichols, Angelo Loi, Brad Nutt and Darryl McClements Department of Agriculture Western Australia Pastures from Space – Can Satellite Estimates of Pasture Growth Rate be used to Increase Farm Profit? Lucy Anderton, Stephen Gherardi and Chris Oldham Department of Agriculture Western Australia Summer-active Perennial Grasses for Profitable Sheep Production Paul Sanford and John Gladman, Department of Agriculture, Western Australia Pastures From Space – Validation Of Predictions Of Pasture Growth Rates DONALD, G.E.A, EDIRISINGHE, A.A, HENRY, D.A.A, MATA, G.A, GHERARDI, S.G.B, OLDHAM, C.M.B, GITTINS, S.P.B AND SMITH, R. C. G.C ACSIRO, Livestock Industries, PMB 5, Wembley, WA, 6913. BDepartment of Agriculture Western Australia, Bentley, WA, 6983. C Department of Land Information Western Australia, Floreat, WA, 6214. Production and Management of Biserrula Pasture - Managing the Risk of Photosensitivity Dr Clinton Revell and Roy Butler, Department of Agriculture Western Australia Meat Quality of Sheep Grazed on a Saltbush-based Pasture Kelly Pearce1,2, David Masters1, David Pethick2, 1 CSIRO LIVESTOCK INDUSTRIES, WEMBLEY, WA 2 SCHOOL OF VETERINARY AND BIOMEDICAL SCIENCE, MURDOCH UNIVERSITY, MURDOCH, WA Precision Sheep Lifetime Wool – Carryover Effects on Subsequent Reproduction of the Ewe Flock Chris Oldham, Department of Agriculture Western Australia Andrew Thompson, Primary Industries Research Victoria (PIRVic), Dept of Primary Industries, Hamilton, Vic Ewe Productivity Trials - a Linked Analysis Ken Hart, Johan Greeff, Department of Agriculture Western Australia, Beth Paganoni, School of Animal Biology, Faculty of Natural and Agricultural Sciences, University of Western Australia. Grain Finishing Systems For Prime Lambs Rachel Kirby, Matt Ryan, Kira Buttler, Department of Agriculture, Western Australia The Effects of Nutrition and Genotype on the Growth and Development, Muscle Biochemistry and Consumer Response to Lamb Meat David Pethick, Department of Veterinary Science, Murdoch University, WA, Roger Heggarty and David Hopkins, New South Wales Agriculture ‘Lifetime Wool’ - Effects of Nutrition During Pregnancy and Lactation on Mortality of Progeny to Hogget Shearing Samantha Giles, Beth Paganoni and Tom Plaisted, Department of Agriculture Western Australia, Mark Ferguson and Darren Gordon, Primary Industries Research Victoria (PIRVic), Dept of Primary Industries, Hamilton, Vic Lifetime Wool - Target Liveweights for the Ewe Flock J. Young, Farming Systems Analysis Service, Kojonup, C. Oldham, Department of Agriculture Western Australia, A. Thompson, Primary Industries Research Victoria (PIRVic), Hamilton, VIC Lifetime Wool - Effects of Nutrition During Pregnancy and Lactation on the Growth and Wool Production of their Progeny at Hogget Shearing B. Paganoni, University of Western Australia, Nedlands WA, C. Oldham, Department of Agriculture Western Australia, M. Ferguson, A. Thompson, Primary Industries Research Victoria (PIRVic), Hamilton, VIC RFID Technology – Esperance Experiences Sandra Brown, Department of Agriculture Western Australia The Role of Radio Frequency Identification (RFID) Technology in Prime Lamb Production - a Case Study. Ian McFarland, Department of Agriculture, Western Australia. John Archer, Producer, Narrogin, Western Australia Win with Twins from Merinos John Milton, Rob Davidson, Graeme Martin and David Lindsay The University of Western Australia Precision Sheep Need Precision Wool Harvesters Jonathan England, Castle Carrock Merinos, Kingston SE, South Australia Business EBVs and Indexes – Genetic Tools for your Toolbox Sandra Brown, Department of Agriculture Western Australia Green Feed Budget Paddock Calculator Mandy Curnow, Department of Agriculture Western Australia Minimising the Impact of Drought - Evaluating Flock Recovery Options using the ImPack Model Karina P. Wood, Ashley K. White, B. Lloyd Davies, Paul M. Carberry, NSW Department of Primary Industries (NSW DPI), Lifetime Wool - Modifying GrazFeed® for WA Mike Hyder, Department of Agriculture Western Australia , Mike Freer, CSIRO Plant Industry, Canberra, A.C.T. , Andrew van Burgel, and Kazue Tanaka, Department of Agriculture Western Australia Profile Calculator – A Way to Manage Fibre Diameter Throughout the Year to Maximise Returns Andrew Peterson, Department of Agriculture, Western Australia Pasture Watch - a Farmer Friendly Tool for Downloading and Analysing Pastures from Space Data Roger Wiese,Fairport Technologies International, South Perth, WA, Stephen Gherardi, BDepartment of Agriculture Western Australia, Gonzalo Mata, CCSIRO, Livestock Industries, Wembley, Western Australia, and Chris Oldham, Department of Agriculture Western Australia Sy Sheep Cropping Systems An Analysis of a Cropping System Containing Sheep in a Low Rainfall Livestock System. Evan Burt, Amanda Miller, Anne Bennett, Department of Agriculture, Western Australia Lucerne-based Pasture for the Central Wheatbelt – is it Good Economics? Felicity FluggeA, Amir AbadiA,B and Perry DollingA,B,A CRC for Plant-based Management of Dryland Salinity: BDept. of Agriculture, WA Sheep and Biserrula can Control Annual Ryegrass Dean Thomas, John Milton, Mike Ewing and David Lindsay, The University of WA, Clinton Revell, Department of Agriculture, Western Australia Sustainable Management Pasture Utilisation, Fleece Weight and Weaning Rate are Integral to the Profitability of Dohnes and SAMMs. Emma Kopke,Department of Agriculture Western Australia, John Young, Farming Systems Analysis Service Environmental Impact of Sheep Confinement Feeding Systems E A Dowling and E K Crossley, Department of Agriculture, Western Australia Smart Grazing Management for Production and Environmental Outcomes Dr Brien E (Ben) Norton, Centre for the Management of Arid Environments, Curtin University of Technology, WA Common Causes of Plant Poisoning in the Eastern Wheatbelt of Western Australia. Roy Butler, Department of Agriculture, Western Australia Selecting Sheep for Resistance to Worms and Production Trait Responses John Karlsson, Johan Greeff, Department of Agriculture, Western Australia, Geoff Pollott, Imperial College, London UK Production and Water Use of Lucerne and French Serradella in Four Soil Types, Diana Fedorenko1,4, Darryl McClements2,4 and Robert Beard3,4, 12Department of Agriculture, Western Australia; 3Farmer, Meckering; 4CRC for Plant-based Management of Dryland Salinity. Worm Burdens in Sheep at Slaughter Brown Besier, Department of Agriculture Western Australia, Una Ryan, Caroline Bath, Murdoch Universit

Bovine telomere dynamics and the association between telomere length and productive lifespan

Average telomere length (TL) in blood cells has been shown to decline with age in a range of vertebrate species, and there is evidence that TL is a heritable trait associated with late-life health and mortality in humans. In non-human mammals, few studies to date have examined lifelong telomere dynamics and no study has estimated the heritability of TL, despite these being important steps towards assessing the potential of TL as a biomarker of productive lifespan and health in livestock species. Here we measured relative leukocyte TL (RLTL) in 1,328 samples from 308 Holstein Friesian dairy cows and in 284 samples from 38 female calves. We found that RLTL declines after birth but remains relatively stable in adult life. We also calculated the first heritability estimates of RLTL in a livestock species which were 0.38 (SE = 0.03) and 0.32 (SE = 0.08) for the cow and the calf dataset, respectively. RLTL measured at the ages of one and five years were positively correlated with productive lifespan (p < 0.05). We conclude that bovine RLTL is a heritable trait, and its association with productive lifespan may be used in breeding programmes aiming to enhance cow longevity